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Open Access Publications from the University of California

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The annual meeting of the Cognitive Science Society is aimed at basic and applied cognitive science research. The conference hosts the latest theories and data from the world's best cognitive science researchers. Each year, in addition to submitted papers, researchers are invited to highlight some aspect of cognitive science.

4. Papers with Poster Presentation

LSTMs Can Learn Basic Wh- and Relative Clause Dependencies in Norwegian

One of the key features of natural languages is that they exhibit long-distance filler-gap dependencies (FGDs): In the sentence `What do you think the pilot sent __?' the wh-filler what is interpreted as the object of the verb sent across multiple words. The ability to establish FGDs is thought to require hierarchical syntactic structure. However, recent research suggests that recurrent neural networks (RNNs) without specific hierarchical bias can learn complex generalizations about wh-questions in English from raw text data (Wilcox et al. 2018; 2019). Across two experiments, we probe the generality of this result by testing whether a long short-term memory (LSTM) RNN model can learn basic generalizations about FGDs in Norwegian. Testing Norwegian allows us to assess whether previous results were due to distributional statistics of the English input or whether models can extract similar generalizations in languages with different syntactic distributions. We also test the model's performance on two different FGDs: wh-questions and relative clauses, allowing us to determine if the model learns abstract generalizations about FGDs that extend beyond a single construction type. Results from Experiment 1 suggest that the model expects fillers to be paired with gaps and that this expectation generalizes across different syntactic positions. Results from Experiment 2 suggest that the model's expectations are largely unaffected by the increased linear distance between the filler and the gap. Our findings provide support for the conclusion that LSTM RNN's ability to learn basic generalizations about FGDs is robust across dependency type and language.

Activation of biographical information via picture of cultural figures during comprehension: evidence from eye-tracking during reading

In an eye-tracking during reading experiment, we examined how rapidly two types of photo-sentence relations impact sentence comprehension. The relation between a photo of a cultural figure and a year in an ensuing sentence (either the cultural figure in the photo was either alive in that year or not) was contrasted with relations between the same photo and achievements or facts about the cultural figure (e.g., a film they had vs. had not starred in). Longer reading times were observed at the regions containing mismatching year or fact, with more robust effects when data were filtered to include only trials in which participants had accurate prior knowledge about the cultural figure, their accomplishments, and their lifetime. These findings indicate that specific long-term knowledge is activated by a picture of a known figure and is rapidly available during language processing.

Zero in on This: Children are Exposed to Various Concepts of “Zero” Prior to Age Six

Math talk has implications for the development of numerical concepts. Research suggests that when caregivers talk about natural numbers (1, 2, 3…), it may enhance children’s later math knowledge. Natural numbers have physical quantities that children can observe, yet abstract numerical concepts do not have such observable quantities. In this analysis, we examined how zero occurs in math talk. Using the CHILDES American English corpora (MacWhinney, 2000), we examined the frequency and nature of math talk about zero in naturalistic interactions between 2- to 6-year-olds and other speakers. Input from other speakers increased in frequency and complexity across development. Input with zero in symbolic sentential contexts (e.g., “one and zero make ten”) and cardinal sentential contexts (e.g., “zero means nothing”) increased with development. Children’s production of zero did not change in frequency or context. These results have implications for the concepts about zero children may bring to formal education.

Judgmental Time Series Forecasting: A systematic analysis of graph format and trend type

In many areas like economics, finance, and health, people make judgmental forecasts looking at previous time series data. In such efforts, either tabular presentations or graphs are utilized, where graphs can be in different formats like bars, lines or points. Different presentations may cause certain biases stemming from bottom-up processing. To delineate such perceptually driven biases in judgmental forecasting, we investigated the effect of graph format (line, bar, point) and trend type (upwards, downwards, flat) on judgmental point forecasts when no domain information was provided. Bringing together perspectives from graph processing, visualization and forecasting literatures, our major goals were to determine which graph formats lead to more accurate forecasts and whether bar graphs lead to mean reversion bias or within-the-bar bias in forecasts. Additionally, we wanted to determine whether asymmetric damping observed in sales forecasts of downward vs. upward trended series were confounded by graph characteristics. We found that forecasts in line and point graphs were less biased than those in bar graphs; forecasts based on bar graphs depicting trended data exhibited mean reversion bias. We also observed a general positivity bias in forecasts for all trend types in line and point graphs. This implied trend following forecasts in upward trends and mean reverting forecasts in downward trends revealing an asymmetricity in the absence of context as well.

Memory without Imagery: No Evidence of Visual Working Memory Impairment in People with Aphantasia

Visual working memory and visual mental imagery both involve the use of internal visual representations, and they likely have overlapping neural substrates. However, research on people with “aphantasia,” or a lack of visual imagery, has not found any evidence that aphantasics are impaired on visual working memory tasks, possibly because they can use non-visual strategies. We designed a task intended to prevent compensatory strategies, and also to explore what happens when aphantasics are required to shift the focus of attention between items in working memory. We found that aphantasics were not significantly different from controls, either when maintaining or shifting the focus of attention. Explanations include non-visual memory strategies, but also the possibility that aphantasics can store information in visual working memory without conscious awareness. Future research should combine behavioral methods with neuroimaging to investigate how aphantasics encode working memory representations.

The shape of option generation in open-ended decision problems

There has been a small but now growing interest in studying decision making in real-world contexts where part of the problem faced by decision makers is to generate candidate options they will actually decide between. While some of this work has employed large decision spaces where options are discrete and valuation is computationally tractable (e.g., chess), very little work has focused on genuinely open-ended decision contexts that more closely mirror mundane real-world decisions. This paper leverages large language models to investigate how people generate options when facing genuinely open-ended problems. Across three experiments, we apply semantic similarity and sentiment analyses to the options that participants sequentially generate for real-world decision problems. We find that the first options generated tend to be sampled from a relatively local region of semantic space and are typically of high value. As additional options are generated, they become increasingly dissimilar and are of lower value. These patterns held both at the level of individual option generation trajectories within a given participant and at the level of individual differences across participants.

Functional Connectivity Differences between Trilinguals and Bilinguals: The Role of Orthographic Depth

Orthographic depth, the consistency of grapheme-phoneme correspondence, influences brain activation in multilinguals’ first (L1) and second language (L2). The intrinsic functional connectivity of cross-language transfer was investigated between two groups of multilinguals, those whose L2 orthography is deeper than their L1 (S-to-D group) and those whose L2 orthography is shallower than their L1 (D-to-S group). We focused on two seed regions: the Visual Word Form Area (VWFA) and the left posterior supramarginal gyrus (pSMG). stronger connectivity was found between the left pSMG and the right precuneus in multilinguals who spoke at least three languages (trilinguals) compared to those who only spoke two languages (bilinguals). Follow-up analyses revealed that this difference was driven by stronger intrinsic connectivity in D-to-S trilinguals compared to the S-to-D trilinguals. Multilinguals’ intrinsic functional connectivity is shaped by the orthographic distance between L1 and L2, as well as differences between bilingualism and trilingualism.

Events and Objects Are Similar Cognitive Entities

Logico-semantic theories have long noted parallels between the linguistic representation of temporal entities (events) and spatial entities (objects): bounded (or telic) predicates such as fix a car resemble count nouns such as sandcastle because they are “atoms” with well-defined boundaries. By contrast, unbounded (or atelic) phrases such as drive a car resemble mass nouns such as sand in that they are unspecified for atomic features. Here, we show for the first time that there are similarities in the perceptual-cognitive representation of events and objects in non-linguistic tasks. Specifically, after viewers form a bounded or an unbounded event category, they can extend the category to objects or substances respectively (Experiment 1). Furthermore, viewers can intuitively make event-to-object mappings that respect atomicity (Experiment 2). These striking similarities between the mental representation of events and objects have implications for current theories of event cognition, as well as the relationship between language and thought.

When close isn’t enough: Semantic similarity does not facilitate cross-situational word-learning

Infants’ earliest words are learned by observation of the referent world, but substantial research suggests such learning is highly error-prone. However, recent work suggests that even learners’ incorrect guesses may fall within the correct meaning’s semantic neighborhood—enabling learners to converge on the correct meaning across exposures. Here, we evaluate the semantic similarity of adults’ hypothesized word meanings in a cross-situational word-learning task. We find evidence for a weak semantic neighborhood effect: incorrect guesses are judged as similar to correct meanings (Study 1). However, this effect is not associated with successful word-learning. While learners tend to provide similar, internally consistent guesses across exposures, their accurate guesses are not similar to their previous guesses (Study 2). Moreover, incorrect guesses similar to the target do not increase accuracy on the subsequent exposure (Study 3). These results suggest early word-learning is driven by cues available in-the-moment, not by gradual exploration of semantic space.

Categorizing perceived causal events

Over the last few decades, Causal Model Theory (CMT) has become a dominant framework for human causal-based reasoning, including categorization and inference. CMT prescribes how people should reason about probabilistic events in terms of causal models. In typical causal-based categorization experiments, subjects are provided with verbal descriptions of causally linked features, generally including probabilistic information. Another line of research focuses on perceived or experienced causal events, rather than on verbal descriptions. In this work we asked whether effects which are consistent with CMT, and that have been obtained with verbal descriptions, generalize to visually perceived events. In two experiments, we presented subjects with videos of a 3D A→B causal event rather than verbal descriptions. In Exp. 1, we found that subjects who saw the causal event did not show the coherence effect in categorization (i.e., subjects tend to rate the null ¬A¬B event as a category member). However, subjects who did see the null event during training did show the effect. In Exp. 2, we ruled out the possibility that Exp. 1’s results were simply an effect of how frequently events were experienced during training. We conclude that a one-shot perceived causal event is not sufficient for people to show causal-based reasoning as CMT predicts.

Meta-Learning of Dynamic Policy Adjustments in Inhibitory Control Tasks

Simple perceptual decision-making tasks such as the Stroop and flanker tasks are popular as a method of measuring individual variation in the processing of conflicting visual stimuli--for instance, the difference in accuracy on stimuli with and without conflict. A major challenge in applying these tasks, for instance, to compare two different populations of subjects, is the low reliability of the nonparametric measures of performance in the tasks. Here, we model dynamic adjustments in decision policies often seen in human behavior, thereby capturing trial-by-trial variation in decision policies, in addition to the classically used average statistics. We propose a recurrent network model to capture behavioral strategies in the task in a model-agnostic manner, and to overcome small-sample learning challenges by pooling across subjects. We show that by splitting the learning into a complex, shared meta-model and simple subject-specific parameters, we learn significantly better predictive models, and also identify latent dimensions indexing the decision policy that may serve as a better measure of individual differences in the task.

Why are reckless socks not (more of) a thing? Towards an empirical classification of evaluative concepts.

This paper proposes new empirical classifiers for evaluative concepts, including thin concepts like 'good' or 'bad' and thick concepts such as 'honest' or 'disgusting', based on quantitative corpus linguistics. Prior work in experimental philosophy has shown that sentiment analysis can be used to track differences between concept classes. Building on this, Task 1 investigates whether the relationship between sentiment and evaluativeness is parabolic rather than linear. Task 2 extends this question to the differences between evaluative and non-evaluative concept classes. The results of both Tasks show that the linear and the parabolic logistic regression classifiers perform equally well. Interestingly, this study also finds that adjectives attributed to animate entities (e.g. "generous customer") generally have a higher probability to be evaluative concepts than those attributed to inanimate entities (e.g."dry soil").

Individual Differences in a Pragmatic Reference Game

While population-level models often provide a good fit to the data, they may mask meaningful individual differences. Exploring individual differences can also be beneficial for gaining a better understanding of the processes that underlie pragmatic phenomena. In this study, we investigate whether the substantial differences in performance on a pragmatic reference game can be traced back to cognitive or socio-pragmatic traits. We observe a significant effect of the ability to inhibit an intuitive response and of abstract reasoning ability. In contrast, we do not find evidence that socio-pragmatic abilities or working memory capacity influence pragmatic responding.

Left to the Reader: Abstracting Solutions in Mathematical Reasoning

Formal mathematical reasoning is unique in its precision: any valid conclusion can be justified by a sequence of base axioms. But human-written proofs or solutions rarely operate at that level. Instead, obvious steps are skipped to provide a simple, lucid argument. This is especially important in an educational setting, where too many details in an example solution, or too few, can confuse a student. What are the key steps for humans in a given formal solution? We investigate several computational hypotheses in the context of equation solving. Specifically, we take a reinforcement learning agent that solves equations using low-level axioms, and propose a series of methods for abstracting its solutions by selecting key steps. We consider methods based on the semantic distance between subsequent steps, based on the steps with the highest uncertainty for the agent, and based on transitions between latent "high-level skills" learned from a large number of agent-produced solutions. In a human evaluation we find that skill-base simplifications were judged most useful. These results suggest new directions for understanding human mathematical reasoning.

Selecting between visuomotor lotteries to measure mental effort in risky decisions

It is intuitive to believe that humans take considerations of mental effort into account when making decisions. However, it has proved difficult to differentiate theories of mental effort in the absence of direct measurements of this psychological construct. Existing measurements of mental effort using response times and revealed preferences have low reliability. In this paper, we present a new experimental task - selecting between visuomotor lotteries using eye-tracking for sampling lotteries - that enables direct measurement of mental effort. Unlike response time-based measures, effort measurements in this task are not confounded by actual effort allocation. Unlike revealed preference-based measures, effort measurements in this task are acquired on a natural scale unitized by automatic visual selection processes. We also report results from a simple experiment conducted using this task, which reproduce existing findings of costly effort-aversion, and also demonstrate adaptive adjustment of mental effort.

Impact of Semantic Representations on Analogical Mapping with Transitive Relations

Analogy problems involving multiple ordered relations of the same type create mapping ambiguity, requiring some mechanism for relational integration to achieve mapping accuracy. We address the question of whether the integration of ordered relations depends on their logical form alone, or on semantic representations that differ across relation types. We developed a triplet mapping task that provides a basic paradigm to investigate analogical reasoning with simple relational structures. Experimental results showed that mapping performance differed across orderings based on category, linear order, and causal relations, providing evidence that each transitive relation has its own semantic representation. Hence, human analogical mapping of ordered relations does not depend solely on their formal property of transitivity. Instead, human ability to solve mapping problems by integrating relations relies on the semantics of relation representations. We also compared human performance to the performance of several vector-based computational models of analogy. These models performed above chance but fell short of human performance for some relations, highlighting the need for further model development.

Modelling Competitive Human Action using Dynamical Motor Primitives for the Development of Human-Like Artificial Agents

With artificial intelligence technologies becoming commonplace today, enhancing the efficiency of human-artificial agent (AA) interactions has become increasingly important. A growing body of research has revealed how dynamic motor primitives (DMPs) of human perceptual-motor behavior can be used to create ‘human-like’ AAs, primarily focusing on cooperative tasks. Using air hockey as a representative task, the current experiment is the first part of a large study aimed at determining the utility of DMP-based models for developing ‘human-like’ competitive AAs. Participants played against a preliminary DMP model and the differences in their behaviors were analyzed. Based on these observed differences, a revised model is proposed, with preliminary results revealing that the new model exhibits behaviors more consistent with those of humans. A major implication of this work is that it presents a framework for creating ‘human-like’ AAs that capture the essential human decision and movement dynamics without requiring large human gameplay datasets.

User-Centric Enhancements to Explainable AI Algorithms for Image Classification

The introduction of deep learning and CNNs to image recognition problems has led to state-of-the-art classification accuracy. However, CNNs exacerbate the issue of algorithm explainability due to deep learning’s black box nature. Numerous explainable AI (XAI) algorithms have been developed that provide developers insight into the operations of deep learning. We aim to make XAI explanations more user-centric by introducing modifications to existing XAI algorithms based on cognitive theory. The goal of this research is to yield intuitive XAI explanations that more closely resemble explanations given by experts in the domain of bird watching. Using an existing base XAI algorithm, we conducted two user studies with expert bird watchers and found that our novel averaged and contrasting XAI algorithms are significantly preferred over the base XAI algorithm for bird identification.

Investigating the Composite Effect in Prototype-Defined Checkerboards vs. Faces

The study reported here examined the role of expertise in the composite face effect which constitutes better recognition of the top half of a face when in composite with a congruent vs. an incongruent (in terms of response required) bottom half. Experiment 1a (n=96) used prototype-defined artificial stimuli (checkerboards) to investigate the composite effect. The advantage of using these stimuli is that expertise can be fully controlled. Experiment 1b (n=96) aimed to replicate the composite effect in face stimuli which served as a control and provided a direct comparison of the composite effect between face and checkerboard stimuli. A full experimental design including congruent/incongruent aligned and misaligned composites was used in both experiments to measure the composite effect. Experiment 1a revealed that the composite effect could not be obtained in checkerboard composites. Experiment 1b confirmed the robust composite face effect. We interpret our results as suggesting that expertise/perceptual learning does not contribute to the composite effect for faces.

How long are real-life events?

Research in event cognition has focused on how people perceive and remember events under experimental conditions. This research study aims to explore the temporal duration of self-reported events from daily life (Sreekumar, et al., 2018; Zhuang, et al., 2012). The small amount of prior work that exists suggests that daily event durations have a Gaussian distribution and that people have prior beliefs that reflect this reality (Griffiths & Tenenbaum, 2006). Forty-eight participants provided activity duration data as they went about their everyday lives for 14 days. Descriptive analyses and activity duration modeling (mixture models of gaussian, gamma, normal and exponential distributions) were used to characterize event durations within activity types. Results show that most of the events present an exponential pattern of durations, while others show a bimodal pattern. Although some preplanned events have a characteristic time, many daily events have a substantial exponential component.

Integration of Event Experiences to Build Relational Memory in the Human Brain

How are experiences of events used to update knowledge of predictive relations in semantic memory? We examined the roles of anterior-lateral entorhinal cortex (alEC), important for encoding recently experienced temporal relations, and middle temporal gyrus (MTG), involved in familiar event concepts. Participants underwent fMRI during exposure to novel event sequences and a memory probe phase (Session 1) and the same process a week later (Session 2). Across distinct sequences, predictive relations among similar events could either be Consistent, or the roles of the events could swap (Inconsistent). We examined the effect of Consistency on the strength of relational memory content. Areas that integrate across diverse experiences should be aided in the Consistent condition. We found that alEC performed this integrative role in Session 1, and at Session 2, similar effects were also observed in MTG. We suggest that these areas both contribute to building relational knowledge from experience.

The Association between Humor Comprehension and Subjective Social Well-being in Non-native English Speakers

The goal of language learning should be to fit in with the language community, and this often requires much more than linguistic knowledge. Although both social wellness in a second language (L2) society and L2 humor comprehension require sophisticated social and cultural knowledge beyond linguistic proficiency, their direct association has not previously been tested. Here we developed a novel method to assess different stages of humor comprehension (i.e., detection and appreciation) and conducted a series of experiments to explore its relationship with subjective social well-being in non-native English speakers. The results revealed significant correlations between language anxiety and social connectedness with both humor detection and humor appreciation in the L2. The findings suggest that the ability of L2 humor detection can be a hallmark of pragmatic proficiency and social wellness in an L2 community.

Temporal Gestures in Different Temporal Perspectives

Temporal perspectives have been studied as a part of spatial thinking of time. They allow us to place ourselves and temporal events on a timeline, making it easier to spatialize time. This study investigates how we adopt temporal perspectives in temporal gestures. We asked participants to retell temporal scenarios written in the Moving-Ego (ME), Moving-Time (MT), and Time-Reference-Point (Time-RP) perspectives in spontaneous and elicited gesture conditions. Participants adopted temporal perspectives similarly regardless of the gesture condition, with few differences. Our results showed that participants’ temporal gestures resonated better with the Ego-Reference-Point versus Time-Reference-Point distinction. Participants produced more ME and Time-RP gestures for the corresponding scenarios and speech, however the MT perspective was not adopted more than the others in any condition. Our findings show that we incorporate temporal perspectives into our temporal gestures to a considerable extent, however, the classical ME and MT classification may not hold for temporal gestures.

Constructing Word Meaning without Latent Representations using Spreading Activation

Models of word meaning, like the Topics model (Griffiths et al., 2007) and word2vec (Mikolov et al., 2013), condense word-by-context co-occurrence statistics to induce representations that organize words along semantically relevant dimensions (e.g., synonymy, antonymy, hyponymy etc.). However, their reliance on latent representations leaves them vulnerable to interference and makes them slow learners. We show how it is possible to construct the meaning of words online during retrieval to avoid these limitations. We implement our spreading activation account of word meaning in an associative net, a one-layer highly recurrent network of associations, called a Dynamic-Eigen-Net, that we developed to address the limitations of earlier variants of associative nets when scaling up to deal with unstructured input domains such as natural language text. After fixing the corpus across models, we show that spreading activation using a Dynamic-Eigen-Net outperforms the Topics model and word2vec in several cases when predicting human free associations and word similarity ratings. We argue in favour of the Dynamic-Eigen-Net as a fast learner that is not subject to catastrophic interference, and present it as an example of delegating the induction of latent relationships to process assumptions instead of assumptions about representation.

The Compelling Complexity of Conspiracy Theories

Causal explanations are important guides to understanding the world. While research suggests people prefer simple explanations, a seeming notable exception exists in the widespread endorsement of conspiracy theories. Researchers have described conspiracy theories as causally complex explanations of world events. We examined whether the lay public agrees with this assessment and sees conspiracy theories as complex explanations, as well as how perceptions of complexity relate to believability of these explanations. We tested publicly available (Experiment 1) and experimenter-generated (Experiment 2) conspiracy theories, alongside fact-based explanations for the same events. We asked participants to rate the complexity of each explanation, along with how believable they find the explanation. Participants across studies rated the conspiracy theory explanations as more complex. Interestingly, complexity was positively correlated with believability of the conspiracy theory, but not fact-based, explanations. We discuss what these findings suggest for the causal explanation field and our understanding of conspiracy theories.

Compositional Generalization in a Graph-based Model of Distributional Semantics

A critical part of language comprehension is inferring omitted but plausible information from linguistic descriptions of events. For instance, the verb phrase ‘preserve vegetable’ implies the instrument vinegar whereas ‘preserve fruit’ implies dehydrator. We studied the ability of distributional semantic models to perform this kind of semantic inference after being trained on an artificial corpus with strictly controlled constraints on which verb phrases occur with which instruments. Importantly, the ability to infer omitted but plausible instruments in our task requires compositional generalization. We found that contemporary neural network models fall short generalizing learned selectional constraints, and that a graph-based distributional semantic model trained on constituency-parsed data and equipped with a spreading-activation procedure for calculating semantic relatedness, achieves perfect performance. Our findings shed light on the mechanisms that give rise to compositional generalization, and using graphs to model semantic memory.

Constructing Individualized Computational Models for Dementia Patients

Dementia is a common and debilitating condition that typically gives rise to increasing language impairment. There is a need to understand the nature of this impairment further so that therapies may be developed, particularly in the case of bilinguals. This paper extends BiLex, an existing computational model of bilingual lexical access, to simulate language decline in dementia. Six lesion types are evaluated for their ability to reproduce the pattern of decline in the semantic variant primary progressive aphasia (svPPA) subtype of dementia. Semantic memory lesions reproduce this pattern of decline best in monolinguals, and further suggest patterns that are likely to be found in longitudinal data from bilingual dementia patients in the future.

Teleological essentialism across development

Do young children have a teleological conception of the essence of natural kinds? We tested this by examining how the preservation or alteration of an animal’s purpose affected children’s persistence judgments (N = 40, ages 4 - 12, Mean Age = 7.04, 61% female). We found that even when surface-level features of an animal (e.g., a bee) were preserved, if the entity’s purpose changed (e.g., the bee now spins webs), children were more likely to categorize the entity as a member of a different natural kind (e.g., a spider) and these effects were similar in magnitude to altering the surface-features of a natural kind. Our results suggest that we might view teleological properties as partially constitutive of the essence of natural kinds.

One strike and you’re a lout: Perceptions of moral character are fugacious, not tenacious

When people who are generally loyal or fair have a momentary lapse in their moral behavior, how does that impact whether we continue to perceive them as loyal or fair people––and how is this shaped by our relative valuation of loyalty and fairness? Reasoning from an error-management perspective suggests the Moral Stringency Hypothesis: Upon witnessing a moral lapse, we should be especially prone to discount category membership for our most deeply valued moral categories, given the potential costs of affiliating with people who do not reliably adhere to our core moral values. However, another line of research on conceptions of the "true self" suggests the opposite. Specifically, the True Self Hypothesis predicts that we should reliably project our most strongly held moral values onto others, even after a lapse. Across two studies (N = 720), we found consistent evidence favoring the Moral Stringency Hypothesis over the True Self Hypothesis.

Visual attention and language exposure during everyday activities: an at-home study of early word learning using wearable eye trackers

Early language learning relies on statistical regularities that exist across timescales in infants’ lives. Two types of these statistical regularities are the routine activities that make up their day, such as mealtime and play, and the real-time repeated behaviors that make up the moment-by-moment dynamics of those routines. These two types of regularities are different in nature and are embedded at two different temporal scales, which led to divergent research in the literature – those who collect long-form recordings and observations of at-home behavior and those who use eye trackers and micro-level analyses to quantify real-time behavior in laboratories. The goal of present paper is to jointly examine and connect the statistical regularities at these two timescales. Towards this goal, we brought wearable eye trackers to English- and Spanish-speaking families’ homes to record parent and toddler visual attention during daily routines. We transcribed parent speech during object play and mealtime and coded toddler visual attention during naming moments. We found that parents and toddlers jointly interacted with the unique vocabularies of the two activities. Although naming and attention were more coordinated during object play, mealtime still afforded opportunities for high-quality naming moments. Our results lay the building blocks for connecting these two lines of research and demonstrate the feasibility of at-home data collection with eye trackers.

Semantic Working Memory Predicts Relative Clause Sentence Comprehension: A Case Series Approach

Sentence comprehension involves simultaneous processes such as maintaining and integrating different types of verbal representations. As such, it has been argued that sentence comprehension relies on working memory (WM). Some findings suggest that semantic (word meaning) WM rather than phonological (speech sound) WM is critical for comprehension. This study took a case-series multiple regression approach to examine the relationship between sentence comprehension and WM for 56 individuals with aphasia. We examined the independent contribution of phonological and semantic WM in predicting comprehension for higher WM target sentences relative to matched lesser WM sentences, while also controlling for single word processing. We found that only semantic WM had a significant contribution to comprehension for three contrasts. However, for the fourth contrast of trials requiring syntactic processing with those requiring only lexical processing, both WM contributions were significant. The possible backup role of phonological WM for comprehension of role reversals is discussed.

Young children’s drawings and descriptions of layouts and objects

Young children tend to prioritize objects over layouts in their drawings, often juxtaposing “floating” objects in the picture plane instead of grounding those objects in drawn representations of the extended layout. In the present study, we explore whether implicitly directing children’s attention to elements of the extended layout through a drawing’s communicative goal—to indicate the location of a hidden target to someone else—might lead children to draw more layout information. By comparing children’s drawings to a different group of children’s verbal descriptions, moreover, we explore how communicative medium affects children’s inclusion of layout and object information. If attention modulates children’s symbolic communication about layouts and objects, then children should both draw and talk about layouts and objects when they are relevant to the communicative task. If there are challenges or advantages specific to either medium, then children might treat layouts and objects differently when drawing versus describing them. We find evidence for both of these possibilities: Attention affects what children include in symbolic communication, like drawings and language, but children are more concise in their inclusion of relevant layout or object information in language versus drawings.

Talker identification as a categorization problem

Learning to identify a person’s voice is a key component of speech perception. In this study, we use a categorization framework to provide insights about the mechanisms supporting talker identification. Native Mandarin Chinese listeners learned to categorize sentences in three tasks with different language contexts – native Mandarin talkers speaking Mandarin, native English talkers speaking English, and native Mandarin talkers speaking English. We compared learning when listeners received fully informative or minimal feedback. Using decision bound models, we examined the strategies participants used in each of the three tasks. Regardless of language context, full feedback was initially better for learning than minimal feedback but was no different after the second block. Across tasks, participants often used strategies based on mean fundamental frequency to separate the talkers. These results demonstrate that talker identification is a categorization problem, which enables leveraging existing category learning frameworks to understand the mechanisms of this important ability.

Does One Size Fit all in Crosslinguistic Dependency Length Minimization?

Previous studies have claimed that language structures tend to minimize the linear distance between syntactic heads and their dependents, a principle known as Dependency Length Minimization (DLM). These studies, however, have largely focused on written modality. In this study we examine the role of dependency length in acceptability ratings of English and Hindi, two typologically distinct languages, using audio stimuli. With double PP constructions as a test case, our results demonstrate no effect of DLM, suggesting the preference for shorter dependencies is different in acceptability and written texts. These findings are further supported with corpus analysis of a total of 10 treebanks for the two languages, which shows additional language-specific differences in the extent of DLM. We discuss the implications of our work and call for more careful consideration of linguistic and modality-specific diversity when it comes to processing-based claims about language typology.

Hering’s opponent-colors theory fails a key test in a non-Western culture

Opponent Colors Theory advances that four colors have special status and are yoked in opponent fashion (yellow-versus-blue, and red-versus-green). Classic hue cancelation studies provide evidence for this theory: people readily pick out colors that are neither red nor green, usually yellow. Here we conducted a version of a hue-cancelation experiment with the Tsimane’ people, a non-industrialized culture in the Amazon. Tsimane’ speakers readily identified reddish and greenish color chips, but they showed idiosyncratic choices when asked to identify a color that is neither reddish nor greenish, unlike English speakers who consistently select focal yellow. The Tsimane’ participants who also spoke Spanish and had a consistent label for English “yellow” (“amarillo”), performed similarly to the Tsimane’ monolinguals, suggesting that simply having a label for “yellow” is not sufficient to explain the consistency of English speakers. The results add to a growing body of evidence that does not support Opponent Colors Theory.

Investigating the real-time effect of register-situation formality congruence versus verb-argument semantic fit during spoken language comprehension

This visual world eye-tracking pilot study investigates the comprehension of register variants (Stelzen_colloquial vs. Beine_standard transl:.‘’legs’) in a German target sentence when this target sentence (mis)matches the formality of a preceding context sentence, given the object argument either matches or mismatches verb meaning constraints (e.g. Ich rasiere bald meine Beine/Stelzen/#Autos/#Karren, transt.: ‘I shave my legs_standard/legs_coll/#cars_standard/#cars_coll’). The aim of this study is to examine whether register congruence rapidly interacts with verb-argument semantic relations. LME results (n=9) show a main effect of verb-argument congruence but no main effect of formality-register congruence at the region between the verb onset and object-argument onset, indicating that verb-argument relations are computed and used rapidly in online language comprehension. These pilot results suggest that situation formality may indeed modulate verb-argument congruency processing, possibly indicating that standard language processing mechanisms interact closely with register representations.

Perceived Agency Changes Performance and Moral Trust in Robots

What is the relationship between trust and perceived agency? The present study experimentally investigated the effect of people’s perception of a robot’s compliance (and resistance) to social norms on their evaluation of a robot’s perceived agency, performance trust, and moral trust. Participants reported a norm-conforming robot to have higher perceived agency and a greater sense of trust than a robot that violated social norms. We also found that perceived agency, regardless of how much a robot followed norms, was correlated with trust. We interpret this finding as evidence that as people see a robot as having agency, they trust it more.

A Model for Optic Flow Integration in Locust Central-Complex Neurons Tuned to Head Direction

Navigation is a fundamental cognitive function of virtually all moving animals. Several navigation strategies require an estimate of the current travelling direction that is updated continuously. In the central complex of the insect brain, multimodal cues are fused into a compass-like head direction representation. Based on the proposed connectivity of columnar neurons in the central complex of the desert locust we designed a computational model to examine how these neurons could maintain a stable representation of heading direction and how shifts occur by optic flow signals when the animal turns. Our proposed model architecture shows that the activity of head direction-encoding CL1a neurons remains stable if the activity of a second class of columnar neurons, CL2, is exactly the same. Shifts occur via modulation of the network connectivity. Our model can be used to deduce testable hypotheses where data are lacking, inspiring new avenues of experimental investigations.

An In Silico Exploration of the Effect of Surprising Information on Hippocampal Representations

Category learning is our ability to generalize across experiences and apply existing knowledge to new situations. Many real-world categories adhere to a “rule-plus-exceptions” structure, wherein most items are rule-followers, but a subset of “exceptions” violate category rules. Rule-plus-exception learning seems tightly coupled with hippocampal function. Though past work has demonstrated that prediction error drives hippocampus to form distinct representations of exceptions, limited work has investigated how this process impacts existing rule-follower representations. Here we use a neural network model of hippocampus to quantify how rule- follower representations are altered by the introduction of exceptions. By recording model representations of rule- followers before and after exceptions are introduced, we computed the shift in rule-follower representation elicited by exceptions. A rule-follower’s similarity to exceptions along category-relevant, but not irrelevant, dimensions predicted its degree of representational shift. This work furthers our understanding of how hippocampus supports the integration of surprising information in dynamic environments.

Of a Different Persuasion: Perception of Minority Status and Persuasive Impact

Racial and gender bias, from advertisement to political rhetoric, is ubiquitous in persuasion. However, the impact of bias on persuasive discourse is often muddied by intent and framing. Reasoners practicing anti-racism may be more likely to scrutinize racially-specific arguments, while arguments made by women may only be diminished when they are emotionally charged. We sought to study how humans evaluate interpretive arguments, what makes certain arguments persuasive, and the impact of bias and emotionality on persuasiveness. We found that shallow heuristics such as argument length and readability are poor indicators for persuasive impact, but reasoners are more likely to be persuaded by arguments made by White people, particularly White women. Further, no difference was observed based on a reasoner's ability to see the arguer's face, implying that judgments are made solely by name recognition. Our focus on written arguments has broad implications for information literacy and racial justice.

Creativity, Compositionality, and Common Sense in Human Goal Generation

Inspired by notions of intrinsic motivation (Schmidhuber, 2010) and play as proposing and solving arbitrary problems (Chu and Schulz, 2020b), we report initial progress toward computational modeling of playful goal generation. We create an embodied, 3D environment resembling a child's room, and ask study participants to play in the environment and then create a scorable game. We propose to model games using a domain-specific language, which represents each game as a computer program. These programs act as reward-generating functions, mapping states visited by an agent as they play a game to the score they should receive. We then analyze our corpus of program representations to highlight four key aspects of human games that would contribute to constructing effective computational models of game generation: creativity, compositionality, common sense, and context sensitivity.

Overloaded Communication as Paternalistic Helping

Even simple, ambiguous signals can have a rich interpretation when viewed in the context of an interaction in a shared environment. We create a model called Paternalistic Communication by combining an existing model of overloaded language -- Rational Speech Acts (RSA) -- with a full agent model of Theory of Mind (ToM). This integration allows signals to be processed in conjunction with common ground in a principled manner dependent on task-dependent action utilities. This modeling perspective treats communication as a way to coordinate diverging perspectives in a cooperative setting. Under Paternalistic Communication, a speaker decides what to say by predicting their partner’s reaction based on the information in common ground and then evaluates those reactions using their own mind which may contain additional information. We demonstrate the flexibility and performance of Paternalistic Communication in a case study with ambiguous signaling through a set of simulations.

Interpreting Logical Metonymy through Dense Paraphrasing

Compositionality has been argued to a necessary component of interpreting language, yet there appear to be many linguistic phenomena that do not overtly exhibit semantic compositional behavior. One of the challenges involves the phenomena of contextual modulations referred to collectively as semantic coercion or logical metonymy. In this paper, we present a computational model that provides the “compositional flexibility” in the interpretation of a verb with its arguments, for such coercive contexts in English. Specifically, we argue that such constructions typically have surface structural correlates in the form of dense paraphrases, and that these forms can be used to model the masked content in the coerced compositional context. We present preliminary results using a transformer architecture on a masked completion task. Our results show that modeling logical metonymy is a challenging task but can be substantially improved by fine-tuning through dense paraphrasing.

Reinforcement Learning, Social Value Orientation, and Decision Making: Computational Models and Empirical Validation

Social environments often impose tradeoffs between pursuing personal goals and maintaining a favorable reputation. We studied how individuals navigate these tradeoffs using Reinforcement Learning (RL), paying particular attention to the role of social value orientation (SVO). We had human participants play an interated Trust Game against various software opponents and analyzed the behaviors. We then incorporated RL into two cognitive models, trained these RL agents against the same software opponents, and performed similar analyses. Our results show that the RL agents reproduce many interesting features in the human data, such as the dynamics of convergence during learning and the tendency to defect once reciprocation becomes impossible. We also endowed some of our agents with SVO by incorporating terms for altruism and inequality aversion into their reward functions. These prosocial agents differed from proself agents in ways that resembled the differences between prosocial and proself participants. This suggests that RL is a useful framework for understanding how people use feedback to make social decisions.

The Principle of Sufficient Reason in ordinary cognition

The Principle of Sufficient Reason (PSR) has been an influential thesis since the earliest stages of western philosophy. According to a simple version of the PSR, for every fact, there must be an explanation of that fact. In the present research, we investigate whether people presuppose a PSR-like principle in judgment. Across four studies (N = 1,007 in total, U.S., Prolific), we find that participants consistently presuppose PSR in judgments about candidate explananda. Such judgments predictably track the metaphysical aspects relevant to the PSR (Study 1) and diverge from related epistemic judgments (Study 2) and value judgments (Study 3). Moreover, we find participants’ PSR-affirming judgments apply to a large set of facts that were sampled from random Wikipedia entries (Studies 4). These findings suggest that certain metaphysical judgments play an important role in our explanatory activities, one that is distinct from the role of the epistemic and value judgments that have been the focus of much recent work in cognitive psychology and philosophy of science.

Intuitions and Perceptual Constraints on Causal Learning from Dynamics

Many of the real world phenomena that cognizers must grapple with are continuous, not only in the values they can take, but also in how these values change over time. The mind must somehow abstract from these inputs to extract useful discrete concepts such as objects, events and causal relationships. We investigate several factors that affect basic inferences about causal relationships between continuous variables based on observations in continuous time. In a novel experiment, we explore the ways in which causal judgments are sensitive to factors that relate to causal inductive biases (e.g. causal lags, the direction of variation) and causal perception (e.g. the range and rapidity of variation). We argue standard statistical time-series models have limited utility in accounting for human sensitivity to these factors. We suggest further work is needed to fully understand the cognitive processes that underlie causal induction from time-series information.

Quantifying the relationships between linguistic experience, general cognitive skills and linguistic processing skills

Humans differ greatly in their ability to use language. Contemporary psycholinguistic theories assume that individual differences in language skills arise from variability in linguistic experience and in general cognitive skills. While much previous research has tested the involvement of select verbal and non-verbal variables in select domains of linguistic processing, comprehensive characterizations of the relationships among the skills underlying language use are rare. We contribute to such a research program by re-analyzing a publicly available set of data from 112 young adults tested on 33 behavioral tests. The tests assessed nine key constructs reflecting linguistic processing skills, linguistic experience and general cognitive skills. Correlation and hierarchical clustering analyses of the test scores showed that most of the tests assumed to measure the same construct correlated moderately to strongly and largely clustered together. Furthermore, the results suggest important roles of processing speed in comprehension, and of linguistic experience in production.

Proto-trust and trust attribution: a theory of intuitive, affective forms of trust and the means by which trust decisions are made

The purpose of this paper is to present a novel conceptualisation of an intuitive, primitive form of trust termed proto-trust. This concept is proposed in order to account for the many different senses, types and domains in which trust has traditionally been defined and theorised. A brief review of the literature on affective and intuitive trust is presented, informing the definition and formalisation of proto-trust. Following this, a preliminary empirical investigation of proto-trust is described, where intuitive trust assessments are compared to analytical trust decisions, under various attribution prompts. Results showed effects of attribution prompts on changes to trust assessments from intuitive to deliberative decisions. In addition, qualitative data are presented for the various reasons participants gave for their trust decisions. One of these reasons (emotional reaction) was found to affect the degree of difference between intuitive and deliberative trust assessments.

Causal invariance guides inference of empirical integration rules

The present paper reports an experiment (N=254) testing two views of how reasoners learn and generalize potentially complex causal knowledge. Previous work has focused on reasoners’ ability to learn rules describing how pre-defined candidate causes combine, potentially interactively, to produce an outcome in a domain. This empirical-function learning view predicts that reasoners would generalize an acquired combination rule based on similarity to stimuli they experienced in the domain. An alternative causal-invariance view goes beyond empirical learning: it allows for the possibility that one’s current representation may not yield useable (i.e., invariant) causal knowledge –– knowledge that holds true when applied. Accordingly, because useable causal knowledge is the evident aspiration of causal induction, this view posits that deviation from causal invariance is a criterion for knowledge revision. The criterion shapes the empirical functions learned and retained. A discriminating test is whether reasoners would re-represent interacting causes as a whole cause that does not interact with other causes, even when in their relevant experience all (pre-defined) causes in the domain interact. Our results favor the causal-invariance view.

Reasoning from Samples to Populations: Children Use Variability Information to Predict Novel Outcomes

The ability to infer general characteristics of populations from specific instances is critical for reasoning. While there is evidence of this capacity in infancy, prior work has not examined children’s ability to use these second-order inferences to make predictions about future outcomes. In the current study, 3-year-olds observed balls drawn at random from two containers. In one sample each ball was a different color. The other sample consisted of balls of only one (Experiment 1) or two (Experiment 2) colors. Children were asked which of the containers was more likely to contain a novel colored ball. A significant majority of children chose the more variable sample’s container. This suggests that 3-year-olds are not only able to make inferences about hidden populations from the variability of observed samples, but also use those inferences to reason beyond their direct experience.

Inhibition and Fraction Arithmetic: Insights from Heat-map Strategy Reports

Proficiency in math is critically important given its implications for education and daily life (e.g., finances, health). However, math is a challenging subject, and proficiency requires a complex interplay of content knowledge and general cognitive processes, including Executive Function (EF). In this exploratory study, we used heat maps to examine whether participants' self-reported attention to strategy-specific components of fraction arithmetic equations (i.e., operations, numerators, denominators) was related to their EF and task performance. Our results indicated that participants with stronger EF (indexed by a numerical stroop task) obtained higher fraction arithmetic scores and were also more likely to attend to strategy-specific components in the fraction problems. Additionally, a positive correlation was found between participants’ selection of strategy-specific components and their fraction arithmetic accuracy. Keywords: Fraction arithmetic; Strategy Reports; Executive Function; Inhibitory Control; Attention

Moving past indirect proxies for language experience: 'Native speaker' and residential history are poor predictors of language behavior

As widely acknowledged in the bilingualism literature, language experience is multifaceted, complex, and dynamic; it cannot be simply reduced to single dimensions or categories. However, cognitive science research outside of bi/multilingualism does not always take into account this fact. Within a population of Hindi-Urdu speakers, we show that proxy categories based on `native speaker' identification or residential history do not neatly map onto patterns of language experience, despite the common assumption that these bring about sufficient homogeneity. Moreover, compared to variables derived from gradient measures of language experience, these proxies do not robustly predict linguistic behavior in the form of acceptability judgments in Hindi-Urdu. In demonstrating alternative approaches to operationalizing language experience, we argue for all language researchers to move past relying on underspecified and ideologically-linked concepts, in favor of more intentional, nuanced, and rigorous testing of experiential factors underlying language processing.

Evidence for Availability Effects on Speaker Choice in the Russian Comparative Alternation

When a language offers multiple options for expressing the same meaning, what principles govern a speaker's choice? Two well-known principles proposed for explaining wide-ranging speaker preference are Uniform Information Density and Availability-Based Production. Here we test the predictions of these theories in a previously uninvestigated case of speaker choice. Russian has two ways of expressing the comparative: an \textsc{explicit} option (\textit{Ona bystree chem ja}/She fast{\sc-comp} than me{\sc-nom}) and a \textsc{genitive} option (\textit{Ona bystree menya/She fast{\sc-comp} me{\sc-gen}}). We lay out several potential predictions of each theory for speaker choice in the Russian comparative construction, including effects of post-comparative word predictability, phrase length, syntactic complexity, and semantic association between the comparative adjective and subsequent noun. In a corpus study, we find that the explicit construction is used preferentially when the post-comparative noun phrase is longer, has a relative clause, and is less semantically associated with the comparative adjective. A follow-up production experiment using visual scene stimuli to elicit comparative sentences replicates the corpus finding that Russian native speakers prefer the explicit form when post-comparative phrases are longer. These findings offer no clear support for the predictions of Uniform Information Density, but are broadly supportive of Availability-Based Production, with the explicit option serving as an unreduced form that eases speakers' planning of complex or low-availability utterances. Code for this study is available at https://github.mit.edu/thclark/russian_uid

Generalization and Transfer Learning in Neural Networks Performing Shape, Size, and Color Classification

We investigated neural networks’ ability to generalize during visual object recognition. In three experiments, we show that while basic multilayer neural networks easily learn to classify the objects on which they are trained, they show serious difficulties transferring that knowledge to novel items. However, our experiments also show that when the previously trained networks are then trained on the novel items, they learn to respond correctly to the novel items much faster than untrained networks. This shows that these networks are learning abstract representations that go beyond the simple items on which they were trained. We argue that this demonstrates that regarding abstract rule learning, the problem with neural networks is not their inability to learn abstractions, but their ability to apply that knowledge when classifying new objects.

Inferring truth from lies

How much information can people gain from being lied to? We propose that people can infer the truth from false messages if two preconditions are met: (1) bigger lies are more costly, and (2) speakers have known, directional deception goals. We tested this with a marble-flipping task in which a judge tried to accurately estimate the number of sampled marbles, while a sender attempted to make the judge over- or underestimate. The sender could produce larger lies about the number of marbles drawn by physically clicking marbles along a lower or higher cost function. We found that judges took into consideration both the senders' goals and costs to correct for bias introduced by senders' lies. Our paradigm allows us to show that a large amount of the variation can be explained by people correcting others' lies based on the lies that they themselves would produce.

Integrating Non-Native Speaker Identity in Semantic and Pragmatic Processing

Little research to date has examined how listeners integrate cues to non-native speaker identity in real time sentence processing. Here, we examine listeners’ interpretation of the semantic and socio-pragmatic content of utterances produced by either a foreign accented speaker or a native speaker. Overall, our findings suggest that processing speed was slower in the presence of foreign accents. However, the extra perceptual demands of processing unfamiliar accents did not translate into listeners’ accuracy rates, and in certain sentence contexts, non-native speakers were also more likely to elicit higher semantic or pragmatic interpretation accuracy. Our findings show that non-native speaker identity plays an important role in listeners’ sentence interpretations.

Exploring Empathy and a Range of Emotions Towards Protest Photographs

Images are a powerful medium known to induce empathy and emotional response in people. In political protests it has the power for a people-initiated policy change and signifies the deep symbolism of a political system. In this study, we aim to quantify the range of emotional connection a person experiences for photographs of a farmers' protest.The protest was the headlines in all media at the time this experiment was conducted and had polarized public opinion. Each photograph is identified to have a set of physical and semantic features. The three selected features were presence of police, gender and close-up (vs.long-shot) in the frame. The intensity on a range of emotions (fear, disgust, anger, sadness, optimism, pessimism, surprise, shock, happiness, and respect) experienced by the viewer for each feature was collected. By statistical and dimensionality analyses, we isolate and identify influencing factors in an image. We found that the presence of police in aggressive actions and close-up shots of had the highest variation in the emotional responses of participants. Interestingly, the gender of the protesters did not show statistically significant effects. The findings from the exploratory investigation highlights the powerful role photographic features have on emotional responses of people, an understudied but critical factor in a world immersed in social media.

Intentional Forgetting of Habits? Combining List-Method Directed Forgetting and Item-Specific Stimulus-Response Priming

Humans are able to intentionally forget declarative memory content as demonstrated in directed-forgetting (DF) experiments. Yet, only few studies assessed whether DF affects associations in procedural memory. We tested how the intention to remember/forget a stimulus affected the formation and/or retrieval of stimulus-response (S-R) associations. To do so, we combined an item-specific priming paradigm with list-method DF. We did not find an impact of the intention to remember/forget on either the retrieval of existing or the formation of new S-R associations: Although participants formed S-R associations (evident in decreasing RTs over stimulis’ prime instances), their persisting activation did not impact on RTs in a subsequent item-recognition-test. Potentially, processes contributing to item recognition impeded S-R retrieval. This finding is informative for future studies aiming to assess how intention differentially affects procedural and declarative memory. We formulate experimental design recommendations for future studies assessing the impact of DF on item-specific S-R associations.

Training flexible categorization to improve arithmetic problem solving: A school-based intervention with 5th graders

Because of its importance in academic achievement, especially in mathematics, training cognitive flexibility at school is a major issue. The present research investigates the effectiveness of a school-based intervention to improve proportion arithmetic problem solving. The study was conducted with 5th graders of 10 classes from 5 high-priority education schools in the Paris region. Students of the control and experimental groups took part in 8 learning sessions about proportion problem solving. The experimental group’s training focused on comparing and flexibly categorizing the problems in the hopes to help students achieve a deeper understanding of proportion problems. Results show that training flexible categorization allowed the experimental group to progress more than the control group, in both categorization and solving tasks. The educational implications of our results are discussed.

Comparing Impact of Time Lag and Item Lag in Relative Judgment of Recency

Many memory models suggest self-terminating backward scanning along a memory representation. In these models, time to retrieve a particular item from memory could depend on how far in the past the item was presented or on the number of items presented since that item. To investigate which of these two types of memory representation is more likely, we designed a relative Judgment of Recency (JOR) task with variable presentation rates. The variable presentation rate deconfounded the age of memory and the number of intervening items. Our results favor the hypothesis that memory representation is temporally organized. This result is important for advancing memory models and for building stronger ties between cognitive and neural models of memory.

The Influence of Mean Product Ratings on Review Judgments and Search

We investigate the way people judge how helpful a review is in informing their decision as to whether to make a purchase. In particular, we are interested in how the summary statistics an individual sees influences judgments of a review’s helpfulness. We find perceived helpfulness of a given review decreases as the star rating of that review gets further from the mean rating. Additionally, participants were more likely to search for reviews close to the mean. Both of these findings are consistent with confirmation bias. We explore, but do not find support for, alternative possible explanations.

Islands effects without extraction: the discourse functions of constructions predicts island status

Each grammatical construction has its own function, and typically multiple constructions are combined to express a message. When the functions of two constructions conflict in a way that cannot be reconciled, their combination is judged ungrammatical. Here we consider one such type of case: “syntactic island violations.” Specifically, we consider combinations of wh-questions with 11 other constructions. Wh-questions request direct information about a particular constituent. Using a new Discourse task, we quantify how directly 11 constructions convey information in simple declarative sentences. Results demonstrate acceptability judgments on the wh-questions correlate with the degree to which the 11 constructions convey information directly. Thus, we argue that degrees of unacceptability of “island violations” result from the extent to which the discourse functions of the constructions involved conflict (N=240).

Deus ex Machina: The Influence of COVID-19 Pandemic on the Young Adults’ Religiosity, Temporal Values, and Time Spatialization across Cultures

We investigated the influence of the COVID-19 pandemic on young people’s value temporal focus, religiosity, and time spatialization. Samples of young participants from eight cultures (Americans, Spaniards, Serbs, Bosniaks, Croats, Moroccans, Turks, and Chinese) collected before the pandemic (N = 497, mean age = 21.09) were matched with samples collected during the first confinement period (N = 497, mean age = 20.96). Our results in study 1 showed that during the pandemic, young adults were less religious, more future-focused, and placed the future in front to of them in a greater extent. In study 2, using the whole sample collected during the pandemic (N = 893, mean age = 21.94), we observed that the more affected the participants were by the pandemic, the greater their future focus, the lower their religiosity, and the greater their tendency to locate the future in front. These pattern of results held in most cultures.

The Pure Poet: How Good is the Subjective Credibility and Stylistic Quality of Literary Short Texts Written with an Artificial Intelligence Tool as Compared to Texts Written by Human Authors?

The application of artificial intelligence (AI) for text generation in creative domains raises questions regarding the credibility of AI-generated content. In two studies, we explored if readers can differentiate between AI-based and human-written texts (generated based on the first line of texts and poems of classic authors) and how the stylistic qualities of these texts are rated. Participants read 9 AI-based continuations and either 9 human-written continuations (Study 1, N=120) or 9 original continuations (Study 2, N=302). Participants' task was to decide whether a continuation was written with an AI-tool or not, to indicate their confidence in each decision, and to assess the stylistic text quality. Results showed that participants generally had low accuracy for differentiating between text types but were overconfident in their decisions. Regarding the assessment of stylistic quality, AI-continuations were perceived as less well-written, inspiring, fascinating, interesting, and aesthetic than both human-written and original continuations. Keywords: Cognition, Artificial Intelligence, Literature, NLP, GPT-2

Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control

Traditionally, theory and practice of Cognitive Control are linked via literature reviews by human domain experts. This approach, however, is inadequate to track the ever-growing literature. It may also be biased, and yield redundancies and confusion. Here we present an alternative approach. We performed automated text analyses on a large body of scientific texts to create a joint representation of tasks and constructs. More specifically, 385,705 scientific abstracts were first mapped into an embedding space using a transformers-based language model. Document embeddings were then used to identify a task-construct graph embedding that grounds constructs on tasks and supports nuanced meaning of the constructs by taking advantage of constrained random walks in the graph. This joint task-construct graph embedding, can be queried to generate task batteries targeting specific constructs, may reveal knowledge gaps in the literature, and inspire new tasks and novel hypotheses.

Young children's reasoning about the epistemic consequences of auditory noise

Prior work suggests children understand how speech conveys information and influences others’ minds. Although these studies have focused on communication under ideal conditions, auditory noise plagues the real world, often corrupting the transmission of information. The current study examines how children reason about the impact of auditory noise on communication. Children (N=72, Age:3;0-5;11) watched scenarios where a teacher tells a learner about two toys, but loud auditory noise masks one of the explanations. When asked which toy the learner wants to hear about again, children were more likely to select the noise-masked toy when the learner knew about neither toy (No Knowledge) than when he already knew about the masked toy (Partial Knowledge). However, their preference for the masked toy also increased with age in both conditions. Overall, these results demonstrate children's developing understanding of when and how communication affects listeners' knowledge and information-seeking behaviors.

Emotion Evaluator: Expanding the Affective Lexicon with Neural Network Model

Measuring the emotion in words is valuable in that it analyzes emotions through language. However, it is difficult to find such measurements in low-resource languages. In this paper, we proposed a method to expand the affective lexicon by utilizing the context of words. The proposed model predicted the Valence and Arousal values of words using their dictionary definitions. In Study 1, we reviewed previous studies about the Korean affective lexicon and integrated data from these studies. The model was trained to minimize the MSE error between the Valence and Arousal values of the words and their predictions. We then checked the distribution of Valence and Arousal values of Korean vocabulary by applying our model to the Korean dictionary. In Study 2, a new affective lexicon was built to empirically validate our model. We found a negatively biased error pattern on model predictions and discussed why it happened.

Bayesian comparators: a probabilistic modeling tool for similarity evaluation between predicted and perceived patterns

A central component of the predictive coding theoretical framework concerns the comparison between predictions and sensory decoding. In the probabilistic setting, this takes the form of assessing the similarity or distance between probability distributions. However, such similarity or distance measures are not associated with explicit probabilistic models, making their assumptions implicit. In this paper, we explore an original variation on probabilistic coherence variables; we define a probabilistic component, that we call a "Bayesian comparator", that mathematically yields a particular similarity measure. A geometrical analogy suggests two variants of this measure. We apply these similarity measures to simulate the comparison of known, predicted patterns to patterns from sensory decoding, first in a simple, illustrative model, and second, in a previous model of visual word recognition. Experimental results suggest that the variant that is scaled by the norms of both predicted and perceived probability distributions yields better robustness and more desirable dynamics.

Developmental changes in the semantic part structure of drawn objects

Children produce increasingly more recognizable drawings of object concepts throughout childhood. What drives this improvement? Here we explore the role of children's ability to include relevant parts of those objects in their drawings. We crowdsourced part tags for every pen stroke in 2,160 drawings of 16 common object categories that had been produced by children between 4 and 8 years old. These part decompositions revealed both substantial variation in the number and kind of parts that children emphasized, as well as a non-monotonic relationship between the number of parts that children drew and how recognizable their drawing was. We plan to publicly release these data to catalyze further investigation of how children's drawings change across development.

Extraordinary entities: Insights into folk ontology from studies of lay people’s beliefs about robots

Robots are extraordinary, category-defying entities. Machines that move autonomously, store and communicate information, display emotions, and cultivate social relationships pose a challenge to our most basic assumptions about what kinds of things exists in the world and how we should reason about them. As such, studies of lay people’s beliefs about robots offer new insights into the ordinary functioning of folk ontologies. In this paper, I propose that there are two ontological questions that human reasoners must grapple with in making sense of robots, or any other entity: Which kind of thing is it? and Which causal forces act on it? Each question highlights a distinct way in which robots are extraordinary—albeit, not exceptional—entities for the human cognitive system. A meditation on the dynamic interplay between these two ontological questions provides a new theoretical framework for understanding conceptual change at both the individual and the cultural-historical level.

Does word boundary information facilitate Chinese sentence reading in children as beginning readers?

Written Chinese sentences consist of a series of characters without word boundary information. Here we examined whether word boundary information facilitated Chinese sentence reading comprehension in children as beginning readers. Primary grade 2-3 children read age-appropriate sentences with either spacing or shading contrast to mark word boundaries and answered related comprehension questions. Compared with regular sentences without word boundary information, spacing significantly impaired comprehension accuracy and reduced eye movement consistency during reading as measured in entropy, and the decrease in accuracy was associated with decrease in consistency of eye gaze transitions during reading. This result suggested that the performance impairment may be related to disturbances to their immature visual routine for reading that may be inconsistent with the provided word boundary information. In contrast, using shading contrast did not change children’s reading performance or eye movement consistency. These findings have important implications for ways to facilitate reading development in children.

Influence of Visual Information on Interpersonal Coordination of Head- and Body- Movement During Dyad Conversations

We investigated the influence of visual information on interpersonal coordination of head- and body- movement during dyadic conversations. Visual information was manipulated by locating a partition at a halfway point between participants. Interpersonal coordination dynamics between head- and body- movement was also compared. To quantify the amount of such movement, human pose estimation software was used. The time series of each body part were submitted to the cross-recurrence quantification analysis to assess the degree of coordination. We hypothesized that unavailability of visual information increase interpersonal bodily coordination and experimental manipulation affects interpersonal coordination during conversation but does differently between head- and body- movement levels. As predicted, results revealed that occlusion of visual information increased head-movement coordination between participants while no significant difference was found in body-movement coordination between conditions. Further investigations on the mechanism of such different influences of perceptual information on coordination dynamics at multiple levels should be pursued.

Verbal Labels Affect Holistic and Analytic Thinking Styles in Native English Speakers

Holistic and analytic thinking styles are well-documented in cultural psychology. However, recent studies suggest that language potentially mediates the influence of culture on thinking styles. The overarching goal of this study is to examine how verbal labels impact people’s thinking styles. Study 1 sought to examine whether thinking styles in a classic triad task could depend on verbal or pictorial formats. Although we observed a significant correlation between performance in verbal and picture triad tasks, more participants were classified as holistic thinkers with a verbal compared to a picture triad task. In Study 2, we examined whether participants could shift their thinking styles in the verbal triad task after being primed to focus on categorical associations. We found that females were influenced by this prime and displayed more analytic thinking. Our results suggest that language can influence thinking styles and that thinking styles are context-dependent.

Do speakers and listeners remember the speech errors or the repairs in communications?

Conversations sometimes include speech errors that are repaired. But what do speakers and listeners remember, the error, the repair, or both? In three experiments, we investigated this question by having speakers give instructions for clicking on pictures (Exp 1) or by having listeners follow those instructions by clicking on the referenced pictures (Exps 2 and 3), followed by a surprise recognition test for the spoken words. Results of Exps 1 and 2 showed that both speakers and listeners have better memory for errors than repairs. Exp 3 managed to reverse this pattern by preventing listeners from clicking on the objects that were the referents of speech errors. Collectively, these results suggest superior memory for errors, not when they are simply perceived, but when they are tied to an action.

From Neurons to Culture: Applying Newell’s Systems Levels to Understanding the Impact of Culture

Allen Newell laid down highly influential principles for the study of cognition with his Systems Levels framework, but it is less well known that this framework also laid down the foundation for understanding social interaction and culture. Although his book Unified Theories of Cognition was focused on the cognitive level, Newell speculated what implications his theory might have for the study of culture. Although these particular ideas did not receive much attention at the time, this paper argues that Newell’s systems levels provide a valuable insight into the connection between brains and culture.

One-second Boosting: A Simple and Cost-effective Intervention that Promotes the Optimal Allocation of Cognitive Resources

Making rational judgments is not always easy. Given that aggregation of the distributed labor force through Internet has become common, a simple and cost-effective solution is needed to improve worker performance. We tested the hypothesis that enforcing a certain decision time boosts job performance by not allowing workers to provide answers within a certain short time after presenting the task. We used the binary judgment tasks, and job performance with various enforced decision times were compared. Two behavioral experiments with physicians (N = 628) demonstrated that job performance was improved by enforcing a one-second decision time; this did not affect the cognitive load of physicians. Furthermore, it was suggested that adding a one-second decision time induced the optimal trade-off between the worker’s performance and cognitive load. Our results show that focusing on resource rationality could lead to simple and cost-effective solutions to real-world problems by boosting workers’ job performance.

Absence Makes the Trust in Causal Models Grow Stronger

People prefer complex explanations for complex phenomena, but make better choices when given only the information required. Thus there is a tension between the information people want, and the information they are able to use effectively. However, little is known about how the specific types of information included in causal models influences how people perceive them. We examine how omitting information influences how people reason about causal models, varying whether commonly known or unexpected information is removed (Experiment 1) or which parts of a causal path are omitted (Experiment 2). We find that omitting causal information participants expect to see lowers ratings of trust and other factors, while omitting less commonly known information improves ratings. However, causal paths can be simplified without harming perceptions of diagrams.

Evaluating unsupervised word segmentation in adults: a meta-analysis

Humans, even from infancy, are capable of unsupervised (“sta- tistical”) learning of linguistic information. However, it re- mains unclear which of the myriad algorithms for unsuper- vised learning captures human abilities. This matters because unsupervised learning algorithms vary greatly in how much can be learned how quickly. Thus, which algorithm(s) humans use may place a strong bound on how much of language can ac- tually be learned in an unsupervised fashion. As a step towards more precisely characterizing human unsupervised learning capabilities, we quantitatively synthesize the literature on adult unsupervised (“statistical”) word segmentation. Unfortunately, most confidence intervals were very large, and few moderators were found to be significant. These findings are consistent with prior work suggesting low power and precision in the litera- ture. Constraining theory will require more, higher-powered studies.

Optimal learning under structural environmental uncertainty reveals inherent learning trade-offs

In some contexts, human learning greatly exceeds what the sparsity of the available data seems to allow, while in others, it can fall short, despite vast amounts of data. This apparent contradiction has led to separate explanations of humans being equipped either with background knowledge that enhances their learning or with suboptimal mechanisms that hinder it. Here, we reconcile these findings by recognising learners can be uncertain about two structural properties of environments: 1) is there only one generative model or are there multiple ones switching across time; 2) how stochastic are the generative models. We show that optimal learning under these conditions of uncertainty results in learning trade-offs: e.g., a prior for determinism fosters fast initial learning but renders learners susceptible to low asymptotic performance, when faced with high model-stochasticity. Our results reveal the existence of optimal-paths-to-not-learning and reconcile within a coherent framework, phenomena previously considered disparate.

Effect of stimuli congruency on gaze behavior and memory

We investigated whether schema congruency differentially affects low level sensory processing (eye gaze) compared to higher-level cognition (memory). Participants performed a two-phase eye tracking task; first a baseline phase with only congruent cartoon events, and subsequently an experimental phase in which the same events were adapted to remain congruent or become incongruent to a theme. Results revealed that participants became quicker in recognizing the congruent cartoon events compared to incongruent in the experimental phase, indicating improved memory for congruent cartoon events. No mean difference in gaze towards congruent versus incongruent events was observed. Surprisingly, a slight bias towards gazing to the left side of the screen in the baseline phase diminished during the experimental phase, indicating that the schema congruency manipulation might affect gaze behavior. Taken together, our results suggest that our schema congruency manipulation affects gaze behavior and memory, but further eye tracking analysis could reveal the dynamic nature of this effect.

Do I need to repeat myself? Getting to the root of the Other Accent Effect

Listeners struggle to identify talkers with a different accent than their own, a phenomenon known as the Other Accent Effect (OAE). But for reasons that are not well understood, the OAE is not consistently observed in all studies. Comprehension-related processing demands offer one explanation, such that other-accented talkers who are more easily understood are also easier to recognize. Here, we test this hypothesis using a forensic-style voice line-up. We examine native English-speaking adults’ ability to recognize talkers from four accent groups, manipulating comprehension-related processing demands by presenting listeners with predictable sentences and subtitles (low-demand condition), or variable sentences without subtitles (high-demand condition). As predicted, the OAE was only observed for talkers with non-native accents. But crucially, our comprehension manipulation had no impact on talker recognition accuracy of any accent type. We conclude that comprehension ease is likely not a key factor driving the OAE. Other possible explanations are discussed.

Neural Language Model-based Readability Assessment of Computer Science Introductory Texts for English-as-a-Second Language Learners

English is the dominant language in computer science. In addition to English-based academic papers, English is frequently the only language provided in introduction sections and manuals of command and software libraries, which are essential aspects of computer programming. Hence, English-as-a-second-language (ESL) learners may have difficulty studying computer science because they must learn this field while also learning English. Despite this problem, few studies have assessed the difficulty level of computer science texts for ESL learners. Ideally, the difficulty levels of texts are assessed by having groups of ESL learners read them. However, owing to the excessive time and financial costs involved, such practices can be impractical. Hence, using two highly accurate automatic readability assessors based on natural language processing (NLP) techniques, we assessed the readability of various computer-science-related texts for ESL learners. The first assessor is based on state-of-the-art deep transfer learning, and the second is based on classical machine learning and applied linguistics. For training the assessors, we used a standard corpus employed in NLP, which was annotated by professional English teachers to evaluate the readability of the texts for ESL learners. To conduct the experiments, we built a collection of computer science texts ranging from academic papers to software manuals (READMEs) crawled from a source-code hosting website, namely GitHub. The experimental results showed that intermediate ESL learners were able to read most of the computer science related texts.

The efficiency of dropping vowels in Romanised Arabic script

When Arabic speakers write in their dialect, they have the choice of using either the standard Arabic script or the non-standard Roman script. Arabizi writing is a new emerging writing system that Arabic speakers use to type their dialects utilizing Roman characters. Although Arabizi is not standardized, people have developed an efficient way to communicate through it. One phenomenon that emerged with this new system is vowel dropping. In this paper, we approach this phenomenon from the perspective of communicative efficiency. We study the informativity of short and long spellings of words and investigate whether the predictability of the word in certain contexts impacts whether the vowel is dropped in that word.

The acquisition of subordinate nouns as pragmatic inference: Semantic alternatives modulate subordinate meanings

Word learning is characterized by a bias for mapping meanings at the “basic”-level such as apple, as opposed to a subordinate-level like red apple (Markman, 1990). The fact that learners nevertheless acquire subordinate nouns has been attributed to properties of the referential world that co-occur with the word (e.g., Xu and Tanenbaum, 2007b; Spencer et al., 2011). However, learners may also make inferences about the informativity of labels as intentional linguistic acts. We investigated whether learners exploit information about semantic contrast to generalize word meanings beyond the basic level. Experiment 1 found that the introduction of a labelled alternative at the subordinate level (green apple) eliminated the basic-level bias. Experiment 2 found that the presence of the alternative exemplar without a label merely suppressed the bias. We propose that the acquisition of subordinate-level meanings is facilitated by expectations of informativity which allow learners to enter the relevant alternatives into consideration.

Posttraumatic stress disorder and differences in eye gaze during a visual search task with cognitive load

Military deployments often expose personnel to highly threatening and stressful circumstances that put them at greater risk for developing Posttraumatic Stress Disorder (PTSD). PTSD may alter internal processes that affect one’s ability to maintain situational awareness (SA). Military personnel conducting patrols must maintain SA to search for threats, with potentially life-threatening consequences if SA drops. Here an exploratory analyses was conducted to determine whether there were differences in performance and eye gaze behavior between those with and without PTSD during a free-viewing visual search task conducted in a virtual desktop environment. Cognitive workload was increased through an additional auditory Math Task. While performance did not differ significantly between the two Groups, key differences in gaze behavior were found. Results showed that those with PTSD viewed significantly more trail markers, had increased duration of individual fixations overall, and decreased fixation and saccade rates during the Math Task. These results appear consistent with previous findings suggesting those with PTSD may have difficulty disengaging from stimuli.

Self-Explanation of Worked Examples Integrated in an Intelligent Tutoring System Enhances Problem Solving and Efficiency in Algebra

One pedagogical technique that promotes conceptual understanding in mathematics learners is self-explanation integrated with worked examples (e.g., Rittle-Johnson et al., 2017). In this work, we implemented self-explanations with worked examples (correct and erroneous) in a software-based Intelligent Tutoring System (ITS) for learning algebra. We developed an approach to eliciting self-explanations in which the ITS guided students to select explanations that were conceptually rich in nature. Students who used the ITS with self-explanations scored higher on a posttest that included items tapping both conceptual and procedural knowledge than did students who used a version of the ITS that included only traditional problem-solving practice. This study replicates previous findings that self-explanation and worked examples in an ITS can foster algebra learning (Booth et al., 2013). Further, this study extends prior work to show that guiding students towards conceptual explanations is beneficial.

A conflict-based model of speech error repairs in humans

Fast and efficient correction of speech errors is essential to effective communication. Yet, despite several accounts of error detection, no computational account exists to explain how humans repair their speech errors. This paper proposes the first such model. We demonstrate that a simple automatic mechanism can form the basis of most repairs. We then demonstrate that augmenting the model with a conflict-based monitoring-control loop allows it to capture more nuanced findings in human speech error repair data.

Maximum Entropy Function Learning

Understanding how people generalize and extrapolate from limited amounts of data remains an outstanding challenge. We study this question in the domain of scalar function learning, and propose a simple model based on the Principle of Maxi- mum Entropy (Jaynes, 1957). Through computational model- ing, we demonstrate that the theory makes two specific predic- tions about peoples’ extrapolation judgments, that we validate through experiments. Moreover, we show that existing Gaus- sian Process models of function learning cannot account for these effects.

A Quantum Walk Model For Emotion Transmission In Serial Reproduction of Narratives

A quantum walk model is developed for emotion transmission in serial reproduction of narratives. The readers' emotions are represented by density operators, and the influences of the narratives on the readers' emotions are modeled by applying the controlled unitary operators to the density operators. The performance of the quantum model is evaluated on a large corpus of narratives, compared to that of the Bayesian Markov chain model. The quantum model not only outperforms the Bayesian model for all five emotion transmissions presented in the corpus but can also account for order effects in serial reproductions. These results suggest a promising first step towards extending quantum-like models to explain group-level cognition.

No agreement attraction facilitation observed in Czech: Not even syncretism helps

Agreement attraction (i.e. facilitatory interference manifested by sped-up reading times) observed in establishing subject-verb number agreement by comprehenders when reading ungrammatical sentences with number-matching attractor nouns, has been long-established and cross-linguistically validated. For languages with rich inflectional morphology, case syncretism has been suggested to play a role in the phenomenon. In the present self-paced reading study on Czech, we show that unlike in other languages, facilitatory interference is not observed and that not even case syncretism is sufficient for its appearance. We put forward several possible explanations for this anomaly exhibited by Czech compared to other languages. We propose that the lack of semantic agreement in the language could be one of these. Finally, we discuss the implications of these results for the models of long-distance dependency resolution in comprehension.

An Alignment of Standards Enhances Metacognitive Judgment Accuracy in Explanatory Knowledge Tasks with Internet Search

Previous research indicates that using the internet in knowledge related tasks increases overestimation. We attempted to replicate this finding and extended previous research by explicitly manipulating the standards that participants used for the explanatory knowledge task in order to reduce the metacognitive bias. We conducted a 2x2 within-subject experiment with N = 166 participants. Replicating previous findings, the results show significantly more overestimation in Internet than in No-Internet conditions. However, with an alignment to external standards participants elicited more accurate metacognitive judgments. We conclude that explicit standards may be an important factor in knowledge-related activities involving the internet because of their effect on metacognitive judgments. On a theoretical level, this has implications for determining the basis of overestimation in knowledge tasks with the internet. On a practical level, providing external standards could be a feasible aid for buffering against this bias, for example in the educational context.

Walking munu and jumping bibi: Sound symbolism in (non)words produced by Turkish speakers

Contrary to the classic idea of arbitrariness in mappings between words and meanings, many languages have words that mimic the sounds of their referents (onomatopoeia) and other subtler sound symbolic associations. However, our knowledge concerning the characteristics of sound-meaning links is still limited. Previous research mostly focused on languages with a large (e.g., Japanese) or limited (e.g., English) inventory of sound symbolic words. We conducted a word-production study with native speakers of Turkish, a language with a moderate amount of sound symbolic words, and examined links between sound properties (e.g., voiced vs. voiceless) and semantic dimensions (e.g., size, speed) in describing motions. Some of the sound-meaning links identified were the links found in Japanese and English samples in previous studies (Saji et al., 2019), whereas many seem to be specific to Turkish. This study provides initial evidence for language-specific sound symbolism in Turkish and links that are consistent across languages.

Learning Through Collaboration: Designing Collaborative Activities to Promote Individual Learning

Knowledge diversity is widely acknowledged to be beneficial for collaborative groups engaged in problem solving. An experiment was conducted to determine whether knowledge diversity and assigned task roles for members in an online virtual collaborative group affect group task performance and individual learning and transfer, and to explore the role of explanations as a mediating variable in these effects. Two control conditions were included that involved individual work, with and without self-explanations. Results showed that the frequency of explanations in dyadic discourse were correlated with individual learning, and that groups with knowledge diversity tend to use more explanations. These findings suggest that discussing explanations is a key feature of successful group work that contributes to determining how much individual learning occurs and how well it transfers from collaborative activities to similar, novel tasks.

Attention Is Not Enough

The human ability to generalize beyond interpolation, often called extrapolation or symbol-binding, is challenging to recreate with computational models. Biologically plausible models incorporating indirection mechanisms have demonstrated strong performance in this regard. Deep learning approaches such as Long Short-Term Memory (LSTM) and Transformers have shown varying degrees of success, but recent work has suggested that Transformers are capable of extrapolation as well. We evaluate the capabilities of the above approaches on a series of increasingly complex sentence-processing tasks to infer the capacity of each individual architecture to extrapolate sentential roles across novel word fillers. We confirm that the Transformer does possess superior abstraction capabilities compared to LSTM. However, what it does not possess is extrapolation capabilities, as evidenced by clear performance disparities on novel filler tasks as compared to working memory-based indirection models.

Can you tell them apart? Using machine learning to classify bilinguals’ and multilinguals’ cognitive and linguistic performance

The debate of whether bilingualism provides a cognitive and or linguistic advantage is a lasting one. Underlying this debate is the idea that an additional language shapes cognition and linguistic processing. The current research analyzes a behavioral dataset containing individuals’ performance in different general cognitive and linguistic tests using a machine learning approach to classify individuals as bilinguals or multilinguals based on their performance. Using an extreme gradient boosting model, we were able to achieve a balanced accuracy of 77%. High scores on a prescriptive grammar test, a verbal fluency test, and a picture naming test were predictive for multilingualism. The implications of the reported results for the field and future research are discussed.

Hierarchical task knowledge constrains and simplifies action understanding

Human social interactions require understanding and predict- ing other people’s behavior. A growing body of work has found that these inferences are structured around an assumption that agents act rationally and efficiently in space. While powerful, this view treats action understanding in a vacuum, ignoring that much social inference happens in the context of familiar, hierarchically structured events (e.g.: buying groceries, ordering in a restaurant). We propose that social and world knowledge is critical for efficiently interpreting behavior and test this idea through a simple block-building paradigm, where participants infer an agent’s sub-task (study 1a), next action (study 1b), and higher-level goal (study 1c), from very sparse observations. We compare these inferences against a Bayesian model of goal inference that exploits task structure to interpret agents’ actions. This model fit participant judgments with high quantitative accuracy, highlighting how world knowledge may help support social inferences in a rich and powerful way. Keywords: Computational modeling; Social cognition

What comes to mind? Samples from relevance-based feature spaces

Recent work in judgment and decision making has focused on which actions people consider when solving open-ended problems and found that the actions that come to mind tend to have particular features, such as having a high historical value. Here, we pursue the idea that the process of generating actions for decision-making tasks may actually reflect more general mechanisms for generating kinds of things. We provide evidence that what comes to mind may simply be a reflection of participants sampling from the most relevant part of the representational space they use to encode the type of thing they are generating. In this paper, we (1) introduce an approach for empirically describing a category in terms of the features that people use to represent category members, and for locating category members within that feature space, (2) show that certain locations in a category's feature space predict an item's likelihood of coming to mind, (3) introduce an approach for understanding the relevance of various features to people's representations of category members, and (4) show that features which are most involved in people's representations of category members are also predictors of what comes to mind within a category. We close by proposing that features that are most relevant to our representations of category members and predict coming to mind are those for which it has been historically useful to have information about during past experiences with the category in question.

Back to the drawing board: Rethinking potential predictors of preschool executive function in low-income South Africa

This study aimed to explore cross-sectional associations between executive function (EF), and community and household factors (household SES, caregiver education, home learning environment, caregiver/child interaction, caregiver wellbeing, and exposure to community violence) in a sample of children from very low-SES settings in Cape Town, South Africa. Results revealed that children exposed to higher levels of violence perform worse on inhibition tasks. No other associations were significant, highlighting the need to reassess how researchers can better understand these settings and the effects on EF development.

Trees neural those: RNNs can learn the hierarchical structure of noun phrases

Humans use both linear and hierarchical representations in language processing, and the exact role of each has been debated. One domain where hierarchical processing is important is noun phrases. English noun phrases have a fixed order of prenominal modifiers: demonstratives - numerals - adjectives (these two green vases). However, when English speakers learn an artificial language with postnominal modifiers, instead of reproducing this linear order they preserve the distance between each modifier and the noun (vases green two these). This has been explained by a hierarchical homomorphism bias. Here, we investigate whether RNNs exhibit this bias. We pre-train one linear and two hierarchical models on English and expose them to a small artificial language. We then test them on noun phrases from a study with humans and find that only the hierarchical models can exhibit the bias, supporting the idea that homomorphic word order preferences arise from hierarchical, and not linear relations.

Frequently produced semantic features reflect principled connections

When people think about the features of common objects, like scissors, they often spontaneously recall a central feature: scissors cut things. They tend not to recall other features of scissors, e.g., that they have handles. The present paper posits a novel explanation for the behavior: the features people recall first and most often reflect semantic generalizations of kinds. A recent taxonomy of such generalizations suggests that people represent privileged links between kinds and their features known as principled connections (Prasada et al., 2013). Several tests diagnose principled connections: for instance, principled connections reflect norms, so one way to diagnose the presence of a principled connection is to test the acceptability of sentences of the form all normal Xs have feature Y, as in all normal cars have four wheels. We tested whether participants accept generalizations about the normality of features produced in a semantic feature production task (Experiments 1 and 2) as well as self-referential generalizations (Experiment 3). The experiments provided participants with generalizations about features listed first and most often as well as features that people list less frequently. They found that people readily accepted generalizations that diagnose the presence of principled connections. The results corroborate the view that principled connections help people recall the features of conceptual categories.

Fact-checking Instruction Strengthens the Association between Attitudes and Use of Lateral Reading Strategies in College Students

In today’s politically polarized environment, college students need strategies to discern trustworthy information. Educational interventions have had modest success in teaching students to fact-check online information using lateral reading, i.e., leaving the original content to investigate information sources and claims. College students (N = 157, M = 20.2 years (SD = 4.0), 61.8% F) completed a semester-long online curriculum teaching fact-checking via lateral reading. Students made gains in their lateral reading attitudes (i.e., preference for fact-checking using lateral reading strategies) and use of lateral reading. Preference predicted use at posttest, but not at pretest. At posttest, preference also partially mediated the effect of reading comprehension on use. The majority of students mentioned cognitive and/or contextual factors when explaining how the Internet contributes to political polarization, though their awareness of such factors did not increase post-intervention.

Reading left-to-right and right-to-left orthographies: Ocular prevalence and hemispheric priority for orthographic conventions

We analyse binocular eye-tracking data from multiline Arabic and Hebrew reading. We describe distributions of small temporal asynchronies between the two eyes as each fixation starts and ends. We test the theory, derived from research on left-to-right orthographies, that these asynchronies reflect ocular prevalence for the left eye in the left hemifield and the right eye in the right hemifield. Ocular prevalence means one eye’s input is prioritised in the fused binocular percept. The overall pattern of asynchronies in Arabic and Hebrew resembles that seen in the left-to-right orthographies, English and Chinese, but with some very specific differences. We discuss the implications of the hemispheric asymmetry in parafoveal lookahead between the two orthographic directions. We consider orthographic conventions associated with reading direction and we conclude that a language tends to get the orthographic conventions that the reading direction and the hemispheres deserve.

Can Adults Revise Their Core Beliefs about Objects?

A set of fundamental principles governs our reasoning about objects since infancy. Studies have shown that adults are surprised when they observe apparent violations of these principles, which might prime them to learn from the violations and update their beliefs. However, little is known about whether these principles can be revised given counterevidence. In the present experiments, we demonstrate that although adults have strong prior beliefs about these principles, they can revise these beliefs in a specific, virtual world, when they observe multiple pieces of counterevidence.

Improving Systematic Generalization Through Modularity and Augmentation

Systematic generalization is the ability to combine known parts into novel meaning; an important aspect of efficient human learning, but a weakness of neural network learning. In this work, we investigate how two well-known modeling principles --- modularity and data augmentation --- affect systematic generalization of neural networks in grounded language learning. We analyze how large the vocabulary needs to be to achieve systematic generalization and how similar the augmented data needs to be to the problem at hand. Our findings show that even in the controlled setting of a synthetic benchmark, achieving systematic generalization remains very difficult. After training on an augmented dataset with almost forty times more adverbs than the original problem, a non-modular baseline is not able to systematically generalize to a novel combination of a known verb and adverb. When separating the task into cognitive processes like perception and navigation, a modular neural network is able to utilize the augmented data and generalize more systematically, achieving 70% and 40% exact match increase over state-of-the-art on two gSCAN tests that have not previously been improved. We hope that this work gives insight into the drivers of systematic generalization, and what we still need to improve for neural networks to learn more like humans do.

Investigating the Presence of NegFirst Biases in Learning and Communication

While an apparent tendency for negative markers to appear before the verb has been observed in typology, language acquisition, and language emergence, it remains uncertain what factors may motivate such a preference. The present study uses an artificial language learning paradigm to test the existence of learning asymmetries consistent with Neg-First preferences in English speakers. The study further incorporates a dyadic interaction task to investigate proposals that the Neg-First tendency is driven by communicative factors. Results show that learners overall produced more preverbal negation than was found in the input language, consistent with a Neg-First bias. However, interaction only induced greater preverbal negation use when preverbal negation was the majority word order in the input language. This does not support the proposal that communication generally promotes a Neg-First bias, but is suggestive of greater regularization when a production bias is aligned with a bias to eliminate variability during communication.

Reverse-engineering the language of thought: a new approach

A foundational hypothesis in cognitive science is that some of human thinking happens in a language of thought (LoT), which is universal across humans (Fodor, 1975). According to this hypothesis, words in different natural languages are labels for primitive concepts or their combinations in LoT. What are LoT's primitives? This is a major challenge because LoT is not directly observable, and thus needs to be inferred or 'reverse-engineered'. We put forward a novel approach to reverse-engineering LoT, capitalizing on the existing knowledge about the optimization of the trade-off between complexity and informativeness in natural languages.

A Resource-Rational Process-Level Account of Violation of Stochastic Dominance

Dominance is widely considered a pillar of rational choice and has played a major role in the history of theorizing and developing models of human decision-making. A wealth of empirical evidence reveals that humans’ violation of dominance is both substantial and systematic. But could violation of dominance be given a rational basis? Specifically, could it be understood in terms of the optimal use of limited cognitive resources? In this work, we present the first resource-rational account of stochastic dominance, the most empirically studied version of dominance. Concretely, we show that a resource-rational process model, sample-based expected utility (SbEU), provides a unified account of a broad range of empirical results on violation of stochastic dominance. We discuss the implications of our work for risky decision-making, and more broadly, human rationality.

A model of path integration that connects neural and symbolic representation

Path integration, the ability to maintain an estimate of one's location by continuously integrating self-motion cues, is a vital component of the brain's navigation system. We present a spiking neural network model of path integration derived from a starting assumption that the brain represents continuous variables, such as spatial coordinates, using Spatial Semantic Pointers (SSPs). SSPs are a representation for encoding continuous variables as high-dimensional vectors, and can also be used to create structured, hierarchical representations for neural cognitive modelling. Path integration can be performed by a recurrently-connected neural network using SSP representations. Unlike past work, we show that our model can be used to continuously update variables of any dimensionality. We demonstrate that symbol-like object representations can be bound to continuous SSP representations. Specifically, we incorporate a simple model of working memory to remember environment maps with such symbol-like representations situated in 2D space.

Modelling Children's Sentence Recall using an Encoder-Decoder Network

Elicited imitation is a widely used method for testing a child's knowledge of a language for scientific or clinical purposes. A child hears an utterance and is asked to repeat what they have heard. While it is assumed that their fluency or speed in doing so is contingent on their linguistic competence, little is known about the cognitive mechanisms and/or representations involved. To explore this, we train an encoder-decoder model, consisting of recurrent neural networks, to encode and reproduce a corpus of child-directed speech and then test its performance on the experimental task of Bannard and Matthews (2008). In that study pre-school children were asked to repeat high- and low-frequency four-word sequences in which the first three words were identical (e.g., sit in your chair and sit in your truck) and the final words and bigrams were closely matched for frequency. We find that like those children our model makes more errors on the initial three words when they are part of a low-frequency than a high-frequency sequence, despite the fact that the words being repeated are identical. We explore why this might be and pinpoint some possible similarities between the model and child language processing.

Consent and the Doctrine of Double Effect

The doctrine of double effect (DDE) explains that it may be permissible to cause harm as a foreseen side-effect of an action that brings about a good result but impermissible to cause harm as a means of bringing about the same good result. The DDE is commonly illustrated with the Trolley Problem, which along with similarly structured examples, have become widely popular as a tool for studying moral psychology and have been taken to demonstrate a universal feature of moral judgment. Across two studies, we investigate how consenting to being harmed interacts with the Doctrine of Double Effect. Specifically, we ask whether (1) harming someone as a means becomes morally acceptable when that person consents to being used as a means, and (2) whether the distinction between harming as a means vs. side-effect persists even when the person being harmed consents. We find that consent significantly interacts with the DDE.

Internalized Beauty Ideals and Sociocultural Pressures Shape How Young Women and Men Perceive Body Attractiveness

This study explored how sociocultural pressures and internalized beauty ideals play a role in how women and men perceive the attractiveness of different body types of the same and opposite gender. Results showed that when judging the attractiveness of bodies of the same gender, internalized beauty ideals have different effects on women and men. Women’s judgments of the attractiveness of female bodies are predicted by the pressure exerted by a thin beauty ideal, while men’s judgments of the attractiveness of male bodies are predicted instead by a muscular beauty ideal. Attractiveness judgments for bodies of the opposite gender are influenced by the pressure to be thin and the perceived influence of significant others. Sociocultural pressures also have a stronger effect on women than men. These findings offer an initial window into the distinct factors that shape body image construction for the digital generation of women and men.

A Bayesian Multilevel Analysis of Belief Alignment Effect Predicting Human Moral Intuitions of Artificial Intelligence Judgements

Despite substantial progress in artificial intelligence (AI), little is known about people’s moral intuitions towards AI systems. Given that politico-moral intuitions often influence judgements in non-rational ways, we investigated participants’ willingness to act on verdicts provided by an expert AI system, trust in AI, and perceived fairness of AI as a function of the AI system’s (dis)agreement with their pre-existing politico-moral beliefs across various morally contentious issues. Results show belief alignment triggered a willingness to act on AI verdicts but did not increase trust or fairness perception of the AI. This result was unaffected by general AI attitudes. Our findings suggest a disassociation between acceptance of AI recommendations and judgements of trust/fairness of the AI, and that such acceptance is partly driven by alignment with pre-existing intuitions.

Modeling Causal Inference from Emotional Displays

Can people learn causal relationships about the world from someone’s emotions? We present a computational model integrating observational causal learning with emotional information, which uses emotional displays to disambiguate the beliefs, desires, and knowledge of other agents, in turn allowing causal inferences about the world. We compared our model predictions to human causal judgements on two observational learning tasks involving multiple possible causes or multiple possible outcomes. Across three studies (N = 129,127,125), emotional displays (compared to actions alone) led people to interpret agents’ beliefs differently, which in some contexts resulted in different causal inferences. Our model closely reflected these patterns of belief and causal inference and revealed new insights on how people learn causal relationships from others’ emotions.

Human-like property induction is a challenge for large language models

The impressive recent performance of large language models such as GPT-3 has led many to wonder to what extent they can serve as models of general intelligence or are similar to human cognition. We address this issue by applying GPT-3 to a classic problem in human inductive reasoning known as property induction. Our results suggest that while GPT-3 can qualitatively mimic human performance for some inductive phenomena (especially those that depend primarily on similarity relationships), it reasons in a qualitatively distinct way on phenomena that require more theoretical understanding. We propose that this emerges due to the reasoning abilities of GPT-3 rather than its underlying representations, and suggest that increasing its scale is unlikely to change this pattern.

How does Sustaining and Interleaving Visual Scaffolding Help Learners? A Classroom Study with an Intelligent Tutoring System

Integrating visual representations in an interactive learning activity effectively scaffolds performance and learning. However, it is unclear whether and how sustaining or interleaving visual scaffolding helps learners solve problems efficiently and learn from problem solving. We conducted a classroom study with 63 middle-school students in which we tested whether sustaining or interleaving a particular form of visual scaffolding, called anticipatory diagrammatic self-explanation in an Intelligent Tutoring System, helps students’ learning and performance in the domain of early algebra. Sustaining visual scaffolding during problem solving helped students solve problems efficiently with no negative effects on learning. However, in-depth log data analyses suggest that interleaving visual scaffolding allowed students to practice important skills that may help them in later phases of algebra learning. This paper extends scientific understanding that sustaining visual scaffold does not over-scaffold student learning in the early phase of skill acquisition in algebra.

The Effects of Reflective Reasoning on Philosophical Dilemmas

Reasoning and reflective thought are critical to the study and practice of philosophy. However, findings from social cognition have challenged the extent to which many decisions are driven by explicit reasoning. We report an experiment that examines how reflective thinking impacts subjects’ judgements on various philosophical topics. Subjects were presented various scenarios on common philosophical topics (e.g., mind-body dualism); each scenario stated a given position. Some subjects were asked to indicate the extent to which they endorsed these positions (control), whereas others were asked to engage in a reflective thinking task before making this choice. Our results revealed that the reflective thinking group was more skeptical of the scenarios’ stated positions than the control group, but this effect depended on the topic of the scenarios. Thus, reflective thinking and reasoning do indeed seem to impact philosophical judgments, but this effect seems to depend on the topic under consideration.

Modeling the Learning and Use of Probability Distributions in Chimpanzees and Humans

We present a neural-network computational model of a recent experiment revealing that chimpanzees show some ability to reason probabilistically. Specifically, we show that the neural probability learner and sampler (NPLS) system can account for both success by chimpanzees and better performance by human controls. NPLS effectively combines learning probability distributions with sampling from those learned distributions to guide action choices. Because NPLS also simulates learning and use of probability distributions by human infants, this brings us closer to a unifying model of probabilistic reasoning, across various age groups and species.

Criticality-Based Advice in Reinforcement Learning

One of the ways to make reinforcement learning (RL) more ef- ficient is by utilizing human advice. Because human advice is expensive, the central question in advice-based reinforcement learning is, how to decide in which states the agent should ask for advice. To approach this challenge, various advice strate- gies have been proposed. Although all of these strategies dis- tribute advice more efficiently than naive strategies (such as choosing random states), they rely solely on the agent’s inter- nal representation of the task (the action-value function, the policy, etc.) and therefore, are rather inefficient when this rep- resentation is not accurate, in particular, in the early stages of the learning process. To address this weakness, we propose an approach to advice-based RL, in which the human’s role is not limited to giving advice in chosen states, but also includes hint- ing apriori (before the learning procedure) which sub-domains of the state space require more advice. Specifically, we sug- gest different ways to improve any given advice strategy by utilizing the concept of critical states: states in which it is very important to choose the correct action. Finally, we present ex- periments in 2 environments that validate the efficiency of our approach.

Name that state: How language affects human reinforcement learning

We describe two experiments designed to test whether the ease with which people can label features of the environment influences human reinforcement learning. The first experiment presents evidence that people are more efficient at learning to discern relevant features of a task when candidate features are easier to name. The second experiment shows that learning what action to take in a given state is easier when states have more readily nameable verbal labels, an effect that was especially pronounced in environments with more states. The interaction between CLIP, a state-of-the-art AI model trained to map images to natural language concepts, and established human RL algorithms, captures the key effects without the need to specify condition-specific parameters. These results suggest a possible role for language information in how humans represent the environment when learning from trial and error.

Test-retest reliability of task-based measures of voluntary persistence

Decision makers face a nontrivial problem when evaluating how much time to invest in an uncertain future prospect. Un- conditional persistence is not always advantageous; rather, different levels of persistence are favored in environments with different temporal statistics. Previous studies using foraging- like decision-making tasks have found that people can rapidly recalibrate their persistence behavior—becoming either more or less willing to tolerate delay—after a short period of direct experience with the temporal statistics of a new environment. Furthermore, substantial individual variation is apparent both in baseline levels of persistence and in the flexibility of re- calibration across environments. However, it is unknown to what degree such variation reflects trait-like individual differences in contrast to session-specific measurement noise. Here we investigated the test-retest reliability of individual variation in behavioral persistence in a computerized decision-making task. We conducted an online experiment in which participants (n=141 after exclusions) performed the task on two occasions separated by a three-week interval. We evaluated the test- retest reliability of several behavior-derived indices, including: a descriptive estimate of overall willingness to wait, a contrast measure reflecting flexibility of recalibration across environments, and individual-level parameter estimates derived from a reinforcement learning model of adaptive persistence. The results showed strong evidence for stable, trait-like individual variation in multiple aspects of persistence-related decision- making behavior. Our findings establish a foundation for future investigations of associations between task-derived parameters of decision behavior and other cognitive and motivational traits.

Making the Question Under Discussion explicit shifts counterfactual interpretation

The comprehension of counterfactual statements (‘If there had been zebras, there would have been lions’) has been subject to much research, but two key questions remain: Can comprehenders interpret counterfactuals without relying on causal inferences? And can comprehenders reach the actual state interpretation relying only on grammatical cues, or is this interpretation triggered by communicative goals? We answer these questions by relying on non-causal counterfactuals, and by manipulating the Question under Discussion between experiments: In Exp. 1, we replicate Orenes et al. (2019), using a web-based eye-tracking paradigm. In Exp. 2, we make the QuD explicit by asking about the actual state of affairs. The results reveal that making a contextually relevant alternative explicit via the QuD shifts counterfactual interpretation, but in general, the suppositional state interpretation is preferred in non-causal counterfactuals. These results imply that the driving forces behind counterfactual processing are pragmatic, not syntactic.

Catastrophic interference in neural network models is mitigated when the training data reflect a power-law environmental structure

Sequential learning in artificial neural networks is known to trigger catastrophic interference (CI), where previously learned skills are forgotten after learning new skills. This is in direct contrast to humans’ ability to learn increasingly complex skills across the lifespan without major instances of CI. The present work builds on techniques for mitigating CI that have been proposed in prior work. Anderson and Schooler (1991) first documented that the memory environment has a lawful structure. Following from their observation, we constructed a training environment where previously mastered tasks (Boolean functions) decrease in frequency over time according to a power law. It was predicted that training in this environment would (1) mitigate CI, (2) replicate human performance in learning curves following a power law of practice, and (3) promote positive transfer of training to new skills, all without the need to posit additional mechanisms. The present results support all three predictions.

Deep in the Trenches: First language performance predicts primacy in statistical learning of two structures

While statistical learning is a well-established language learning mechanism, its usefulness in multiple language contexts is more unknown. A phenomenon known as entrenchment has been proposed, in which learning one language prevents the acquisition of a second language in the same speech stream. The observed L1 advantage or primacy effect has been previously mitigated with various cues to the presence of a second structure (L2). The present study manipulates the number of transitions between L1 and L2 to influence entrenchment. One condition was designed to replicate previous findings of entrenchment and the other was designed to overcome entrenchment. We find that adding more transitions between languages did not increase L2 learning, and second language learning is more dependent on the first learned language than on manipulations of the transitions between languages.

Biologically-Based Neural Representations Enable Fast Online Shallow Reinforcement Learning

Biological brains learn much more quickly than standard deep neural network reinforcement learning algorithms. One reason for this is that the deep neural networks need to learn a representation that is appropriate for the task at hand, whilst biological systems already possess an appropriate representation. Here, we bypass this problem by imposing on the neural network a representation based on what is observed in biology, such as grid cells. This study explores the impact of using a biologically-inspired grid-cell representation vs. a one-hot representation, on the speed at which a Temporal Difference-based Actor-Critic network learns to solve a simple 2D grid-world reinforcement learning task. The results suggest that the use of grid cells does promote faster learning. Furthermore, the grid cells implemented here have the potential for accurately representing unbounded continuous space. Thus, their promising performance on this discrete task acts as a first step in exploring their utility for reinforcement learning in continuous space.

Overlapping semantic representations of sign and speech in novice sign language learners

The presence of semantic information in multivariate patterns of neural activity has been explored as a method of measuring knowledge and learning. Using fMRI, we investigated whether novice learners of American Sign Language (ASL) showed overlapping representations of semantic categories for words presented in a well-known (English) or newly learned (ASL) language. We find evidence of neural patterns that were partially shared between sign and speech in novice participants. This result provides evidence for the influence of even brief learning on neural representations in cross-modality language processing.

Homophily and Incentive Effects in Use of Algorithms

As algorithmic tools increasingly aid experts in making consequential decisions, the need to understand the precise factors that mediate their influence has grown commensurately. In this paper, we present a crowdsourcing vignette study designed to assess the impacts of two plausible factors on AI-informed decision-making. First, we examine homophily---do people defer more to models that tend to agree with them?---by manipulating the agreement during training between participants and the algorithmic tool. Second, we considered incentives---how do people incorporate a (known) cost structure in the hybrid decision-making setting?---by varying rewards associated with true positives vs. true negatives. Surprisingly, we found limited influence of either homophily and no evidence of incentive effects, despite participants performing similarly to previous studies. Higher levels of agreement between the participant and the AI tool yielded more confident predictions, but only when outcome feedback was absent. These results highlight the complexity of characterizing human-algorithm interactions, and suggest that findings from social psychology may require re-examination when humans interact with algorithms.

Exploring the Structure of Predecisional Information Search in Risky Choice

It is commonly assumed that there are qualitatively distinct cognitive strategies that underlie decision making. Because cognitive strategies differ in how information is processed, predecisional information search offers a window onto these strategies. Using a bottom-up approach, we examine whether predecisional information search actually reflects the use of distinct strategies. Specifically, we investigate the extent to which the heterogeneity in people's predecisional information search in a risky choice task reflects qualitatively distinct patterns that should emerge when people use distinct strategies. Our analysis takes into account the distribution of attention across attributes and transitions between attributes. Using cluster analysis, we find just two qualitatively different clusters with low separability: one characterized by balanced attention to all attributes and by transitions occurring mostly within the same option, and one characterized by a focus on outcome information and by frequent attribute-wise transitions. These two clusters were also associated with differences in people's choice behavior. The distribution of these clusters varied considerably across individuals, but less so across choice problems, suggesting that information search is not necessarily guided by features of the choice problem—this result challenges current theories on strategy selection. Our results challenge the common assumption that heterogeneity in predecisional information search is differentiated along clearly distinct information processing policies. Instead, the differentiation seems to fall into just two broad clusters—one resembling rational principles of expectation computation, the other reflecting heuristic principles that neglect probabilities—with considerable variability within each cluster.

Do children interpret costs as signals of commitment to groups?

We explore whether younger children (4- and 5-year-olds) and older children (9- and 10-year-olds) expect a costly signaler (someone who engages in a costly action) to be a more committed group member than someone who engages in a comparatively less costly action. In Experiment 1 (N=173), older children and adults—but not younger children—expect a costly signaler wants to be in a group more than a control, and they give more positive evaluations of the costly signaler than the control. In Experiment 2 (N=84; ongoing), employing a different manipulation of cost both younger and older children infer that a costly signaler wants their goal more than the control, but they make different evaluations of the costly signaler depending on whether they exerted effort on behalf of a group versus an individual. Future research may be needed to rule out alternative explanations.

Beyond financial knowledge and IQ: The effect of temporal values on pension planning and financial wealth of natives and immigrants in the Netherlands

We study pension planning and financial wealth of natives and immigrants (N=1177) in the Netherlands, in relation to their temporal values (past/future-focused), financial knowledge, IQ, and other individual characteristics. We find that, compared to natives, immigrants are less financially literate and rely more on the government for their retirement income, but are more future-focused and think more about their retirement. Second, controlling for financial knowledge, IQ, saving intention, self-control and demographic factors, temporal values help to predict many aspects of pension planning: how much people think about retirement, their desired retirement age, whether they develop a plan to save for retirement, perceived saving adequacy, and home ownership. Furthermore, temporal values predict savings, risky assets and financial wealth in 2016 and 2020, even after controlling for the financial situation in 2016. Our results have strong implications for policies related to pension communication and contribute to the theory on relationships between economic decisions, time and cognition.

The effect of orthographic relationships, lexical status and contextual constraint on visual word recognition: Evidence from event-related potentials

Readers rely on sentence context to generate predictions about the upcoming words so that processing of their visual forms is less necessary. Consequently, processing of an orthographic neighbor of a strongly predicted word is facilitated by that context (as indicated by a reduced N400 ERP amplitude), regardless of the perceived item’s lexicality (i.e., whether it is a real word or a pseudoword). The current study investigated whether lexicality becomes important when the sentence context is less helpful in generating predictions. Our findings indicate that in weakly constraining sentences, the lexical status of a word impacts word recognition processes as indicated by a left anterior negativity, suggesting that readers rely on sublexical properties of words in the absence of strong expectations.

Finding the right words: A computational model of cued lexical retrieval

Failing to come up with a word or name is a fairly common experience that is exacerbated in older adulthood and among populations with language impairments, and yet the mechanisms underlying lexical retrieval remain fairly understudied. In this work, we introduce and evaluate a series of nested computational models of lexical retrieval that combine semantic representations derived from a distributional semantic model with a process model to account for behavioral performance in a primed lexical retrieval task. The models were tested on a behavioral data set where participants attempted to retrieve answers to descriptions of low-frequency words and were provided a semantically and/or phonologically related prime word before the retrieval attempt. Model comparisons indicated that a model that emphasized semantic activations from the description and phonological activations from the prime word best accounted for the overall data. Additionally, incorrect responses and metacognitive judgments indicating that participants had other words in mind were associated with models that instead emphasized semantic activations from the prime word. Taken together, these results identify the locus of lexical retrieval failures and offer the opportunity to investigate broader questions about semantic memory retrieval.

Pragmatic Reasoning in Structured Signaling Games

In this work we introduce a structured signaling game, an extension of the classical signaling game with a similarity structure between meanings in the context, along with a variant of the Rational Speech Act (RSA) framework which we call structured-RSA (sRSA) for pragmatic reasoning in structured domains. We explore the behavior of the sRSA in the domain of color and show that pragmatic agents using sRSA on top of semantic representations, derived from the World Color Survey, attain efficiency very close to the information theoretic limit after only 1 or 2 levels of recursion. We also explore the interaction between pragmatic reasoning and learning in multi-agent reinforcement learning framework. Our results illustrate that artificial agents using sRSA develop communication closer to the information theoretic frontier compared to agents using RSA and just reinforcement learning. We also find that the ambiguity of the semantic representation increases as the pragmatic agents are allowed to perform deeper reasoning about each other during learning.

Origins of Art: the Intersection of Cognitive and Cultural Evolution

In the field of cognitive archaeology, the origin of art has been recurrently explained as a result of the transition to a fully symbolic mind in our species, H. sapiens. Recent data is challenging that view as increasing evidence shows that the cognitive differences between ‘premodern’ and modern human populations are smaller than previously thought. Yet, possible cases of Neanderthal and other hominin art are few and far between, rendering artistic practices mainly a H. sapiens phenomenon. To explain this, it is necessary to redefine art and understand it not only as the product of cognitive operations, but as a behavior embedded in modern human social interactions.

No evidence for short-timescale temporal declines in expectations within a controlled cognitive task

People waiting to receive information about a personally relevant future event often become increasingly pessimistic as the event draws near. These temporal declines in expectations have been demonstrated robustly across both naturalistic and laboratory settings. However, the low-level cognitive processes that give rise to temporal declines in expectations remain unclear. Here, we investigated the temporal boundary conditions of this effect. In a controlled cognitive task involving repeated probabilistic gambles, we assessed the dynamics of participants' reward expectations over a 12-second waiting period prior to revelation of the gamble outcome. Across two experiments (total N = 120), we found no evidence for temporal declines in expectations over this short waiting period, no matter whether expectations were measured via direct probability report (Experiment 1) or via an incentive-compatible `cash-out' decision (Experiment 2). These results demonstrate that temporal declines in expectations are not an invariant characteristic of human expectations regarding personally relevant future events.

Representational Smoothing to Improve Medical Image Decision Making

We demonstrate how medical-image classification decisions can be denoised by aggregating decisions on similar images. In our algorithm, the final decision on a target image is cancerous if a percentage t of the k most similar images are cancerous, else it is not cancerous. Similarity between images is calculated as the distance between representations from an artificial neural network. We vary k and t for novice and expert participants using data from Trueblood et al. (2018) and Trueblood et al. (2021). We show that increasing k improves performance for novices, with their performance approaching that of experts. We also show that the algorithm is biased towards identifying cancerous cells, which is reflected in the representational space. The percentage t allows greater control over sensitivity and specificity and can be used to debias decisions. This algorithm is less effective for experts, partially explained by them giving similar responses on similar images.

Category Exceptions Change Category Boundaries

In order to successfully guide generalization of knowledge, category representation needs to be both: flexible enough to account for new evidence and stable enough to resist harmful change. Here we present a set of experiments designed to test how items that violate our expectations (i.e., category exceptions) affect category representation. Specifically, we wanted to know whether learning a category exception can change category boundaries. Does learning about penguins changes the way we think about birds? Do features of penguins contribute to making decisions as to whether a novel item is a bird? Across two experiments we found evidence that exceptions can change category boundaries and thus significantly affect future generalization. We discuss implications these findings have for the extent models of category learning and memory.

The logic of guesses: how people communicate probabilistic information

How do people respond to a question when they are not certain of the answer? Probabilistic theories of cognition assume that the mind represents probability distributions over possible answers, but in practice people rarely recite these probability distributions out loud: instead they make simple guesses. Consider how you would express your belief about how many people live in the European Union. You would probably not say ``a Gaussian with mean 300 million and standard deviation 50 million" -- you would make a simple guess, such as "between 200 and 400 million". Here we present a simple rational analysis of these guesses. We assume that communicating the full probability distribution in one's head would take too much time, so people offer simple guesses in order to communicate a compressed version of this distribution. Drawing on information theory, we show that it is possible to measure how well a guess encodes a given probability distribution, and suggest that people tend to make guesses that provide the best such encoding. Two experiments provide preliminary evidence for the model. Our theory explains from first principles why guesses seem to strike a balance between accuracy and informativeness.

Category learning across the menstrual cycle: Learning exceptions to the rule varies by hormonal milieu

Ways in which ovarian hormones affect cognition have been long overlooked in psychology and neuroscience research despite strong evidence of their effects on the brain. In order to address this gap, we study performance on a rule-plus-exception category learning task, a complex task that requires careful coordination of core cognitive mechanisms, across the menstrual cycle. Results show that the menstrual cycle distinctly affects learning of exceptions in a manner that matches the typical estradiol cycle. Furthermore, participants in their high estradiol phase outperform participants in their low estradiol phase, and show steeper learning slopes than men in exception-learning. These results provide novel evidence of the role of estradiol in category learning, underscore the importance of recruiting diverse samples in cognitive neuroscience research, and highlight the ways in which cognition varies throughout the fundamental biological cycles of the human experience.

Acceptability of technology involving artificial intelligence among future teachers

Technology has been used in the service of learning for a long time. Nowadays, the use of Artificial Intelligence (AI) is developing but its acceptability among future teachers still needs to be investigated. Moreover, differences between elementary and middle-school teachers could arise, due to the comparison between their role and those of technology involving AI. The current study aims at evaluating the acceptability of technology involving AI among future teachers, using a well-known model and more specifically regarding several tasks. Results show that elementary school teachers expect more performance from technology involving AI, but mainly for a use of content generation (e.g., course content, exercises). Middle-school teachers are more willing to accept technology involving AI for more high added value tasks such as help in writing learning or in diagnosing learning difficulties. Future studies should focus on identifying action levers to favor higher acceptability and actual use.

Analogy Use in Parental Explanation

How and why are analogies spontaneously generated? Despite the prominence of analogy in learning and reasoning, there is little research on whether and how analogy is spontaneously generated in everyday settings. Here we fill this gap by gathering parents' answers to children's real questions, and examining analogy use in parental explanations. Study 1 found that parents used analogy spontaneously in their explanations, despite no prompt nor mention of analogy in the instruction. Study 2 found that these analogical explanations were rated highly by parents, schoolteachers, and university students alike. In Study 3, six-year-olds also rated good analogical explanations highly, but unlike their parents, did not rate them higher than causal, non-analogical explanations. We discuss what makes an analogy a good explanation, and how theories from both explanation and analogy research explain one’s motivation for spontaneously generating analogies.

Loaded Language and Conspiracy Theorizing

Loaded language is an umbrella term for words, phrases, and overall rhetorical strategies that have strong emotional implications and intent to sway others. Belief in conspiracy theories is tied to a range of strong emotions (van Prooijen and Douglas, 2018). Accordingly, language with strong emotional and persuasive content may be expressed by people experiencing the strong emotions associated with conspiracy theorizing. In this research, we examine multiple types of loaded language in two online parenting forums: one historically against vaccination, and another historically accepting of vaccination. It is well-established that conspiracy theories are the most influential contributor to anti-vaccination views (Hornsey et al., 2018) and anti-vaccination beliefs are strongly correlated with belief in unrelated conspiracy theories (Goldberg & Richey, 2020). Results indicate that users of an anti-vaccination forum use a greater frequency of loaded language to express themselves than users of a vaccination-neutral forum.

The Source-Goal Asymmetry in Motion Events: Sources Are Robustly Encoded in Memory but Overlooked at Test

Previous research demonstrated an asymmetry between Sources and Goals in people’s linguistic and non-linguistic encoding of motion events: when describing events such as a fairy going from a tree to a flower, people mention the Goal (“to a flower”) more often than the Source (“from a tree”) and are better at detecting Goal changes in a Same-different memory test. Many take these findings as evidence for a homology between linguistic and conceptual representations: an unmentioned event component is also conceptually less robust. Here, we show that the nonlinguistic Source-Goal asymmetry disappears when memory is probed with a Forced-choice task instead of a Same-different task. We argue that, despite frequent absence from linguistic descriptions, Sources are robust in event memory, but not attended to during Same-different tests due to people’s task-relevance assumption. This result bears on the nature of the Source-Goal asymmetry and calls for a finer-grained account for language-cognition homology.

Expectations and Noisy-Channel Processing of Relative Clauses in Arabic

Some sentences are hard to read, and we don’t fully understand why. Memory-based and expectation-based constraints both attempt to explain sentence processing difficulties, and decades of sentence processing literature have found evidence in support of both theories. We further investigate theories of sentence processing by exploring subject- and object-extracted relative clause processing in Standard Arabic. We conducted a self-paced reading task and found that SRCs are easier to process than ORCs in Arabic, in line with expectation-based theories. A follow-up analysis of comprehension question answers revealed that when suggested with the possibility of a noisy interpretation, readers preferentially accept an SRC interpretation over an ORC interpretation. Our future research will explore these findings and test the threshold for acceptance of noisy interpretations.

Three systems interact in one-shot reinforcement learning

Human adaptive decision-making recruits multiple cognitive processes for learning stimulus-action (SA) associations. These proceses include reinforcement learning (RL), which represents gradual estimation of values of choices relevant for future reward-driven decisions, episodic memory (EM), which stores precise event information for long-term retrieval, and working memory (WM), which serves as flexible but temporary, capacity-limited storage. However, we have limited understanding of how these systems work together. Here, we introduce a new one-shot RL task to disentangle their respective roles. In 16 independent 8-trial blocks, 144 participants used one-shot rewards to learn 4 new SA associations per block. Each block provided one chance to obtain feedback for pressing one of two keys for each stimulus (trials 1--4), followed by a chance to use this feedback to make a choice in a short-term association task (trials 5--8; no feedback), primarily targeting WM. In a subsequent testing phase designed to assess long-term retention through RL or EM, all 64 stimuli were shown in randomized order and subjects were asked to press the correct key for each, without feedback. Trials 5--8 revealed WM-dependent strategy effects on choice accuracy, as well as a role for both RL and EM when WM is overwhelmed. Testing phase accuracy depended on feedback interacting with initial presentation order, revealing signatures of both RL and EM in learning from one-shot rewards. Computational modeling suggests that a mixture model combining RL and EM components best fits group-level testing phase behavior. Our results show that our new protocol can identify signatures of each of the three memory systems' contributions to reward-based learning. With this approach, we create new possibilities to better understand how each integrates a single bit of information, what their exact contributions to choice are, and how they interact.

Expectations of Causal Determinism in Causal Learning

Causal learning is shaped by people’s prior beliefs, including their expectations. In this paper, we specifically examine expectations of determinism: do they vary with perceptual features of physical causal events, and how do they influence subsequent causal learning from data? We show that perceptual features lead adults to different expectations of determinism for different causes of launching (Exps. 1A & 1B). Those expectations lead to significant differences in responses to causal “failures”; that is, we show a difference in violation-of-expectation effect after a failed launch (Exp. 2). Actual data can reduce or eliminate the impact of these expectations, but they do not override the effect of perceptual features (Exp. 3). Overall, spatiotemporal contiguity cues and expectation of determinism have similar effects on causal learning outcomes, but neither is fully reducible to the other.

How Virtual Work Environments Convey Perceptual Cues to Foster Shared Intentionality During Covid-19 for Blind and Partially Sighted Employees

The Covid-19 pandemic altered workplaces. For those with ‘office jobs,’ this meant working ‘virtually,’ or remotely, from home. This transition forced organizations and workplaces to exercise flexibility, adapt workflows and rely on Information and Communication Technologies (ICTs) to work remotely. However, Blind and Partially Sighted Individuals (BPSI) face challenges accessing work digitally and remote communications through ICTs. In response, we report on the results of our longitudinal participatory design study investigating the impact of working and training over a distance for BPSI. What emerged is a conceptual model to assist in understanding how ICT interfaces convey spatial-topological cues for the construction of shared intentionality in virtual work environments. The implications of our model could be significant, as it aids understanding of what is lost and gained when transitioning to virtual work environments. This could inform the development of ICTs with cross-sensory interaction and national accessibility policies for the workplace.

Handedness and Creativity: Facts and Fictions

Are left handers more creative than right handers? In both popular belief and scientific literature, left-handedness is linked with higher creativity. Here, we evaluate whether left handers are better at divergent thinking, and whether left handers are overrepresented in creative professions, in a qualitative review supported by meta-analysis. We argue that plausible mechanisms for a creativity-handedness link can be found within influential theories of the neural basis of creativity. However, our meta-analysis does not find evidence that left or mixed handers are better at divergent thinking; in fact, right handers score slightly higher on the Alternate Uses Test. Additionally, we conclude that while left and mixed handers may be overrepresented in Art and Music, they are underrepresented in creative professions in general. We find that although both right and left handers tend to believe that left handers are more creative, this belief is not supported by the available empirical evidence.

A Perceptually Grounded Neural Dynamic Architecture Establishes Analogy Between Visual Object Pairs

Detecting analogy is an important high-level cognitive skill that is involved in many aspects of human reasoning. While Structure Mapping Theory (Gentner, 1983) is a well-recognized high-level theory of analogy, it lacks a neural process implementation that links to perception and attention. Avoiding algorithmic computation on ungrounded symbols, we present a dynamic neural architecture built from interacting neural populations that establishes analogy between objects in two visually presented scenes. Consistent with SMT, it accounts for how humans find such analogies.

Who owns your information? Young children’s judgments of who owns the general and personal information users share with apps.

The present study investigates young children’s reasoning about who owns the information users share with apps. 87 children ages 5-years to 10-years were asked to judge who owned two types of information after it had been willingly shared by users: general and personal information. Based on an informational autonomy account, we predicted that young children would judge that the user owns their personal information but not their general information. We found that by 8-years-old children were indeed more likely to judge that users own the personal information they share with apps than they were to judge that users own the general information they share with them. However, younger children judged that the general information was owned by the user at similar rates to the personal information. Further exploration of our data suggests these changes are likely driven by beliefs about the ownership of general information.

Prosodic input and children’s word learning in infant- and adult-directed speech

This study examines (1) whether infant-directed speech (IDS) facilitates children’s word learning compared to adult-directed speech (ADS); and (2) the link between the prosody of IDS in word-learning contexts and children’s word learning from ADS and IDS. Twenty-four Dutch mother-child dyads participated when children were 18 and 24 months old. We collect mothers’ ADS and IDS at both ages and test children’s word learning from ADS and IDS at 24 months using an Intermodal Preferential Looking Paradigm (IPLP). We find that Dutch 24-month-old children could reliably learn novel words from both ADS and IDS, and IDS had a facilitative effect. Also, children’s word learning from IDS (but not ADS) is predicted by IDS pitch range when mothers introduce unfamiliar words to children at 18 months. Our findings contribute to an understanding of the role of IDS prosody in language development, highlighting both individual differences and contextual differences in IDS prosody.

Exaggeration of Stimulus Attributes in the Representation of Relational Categories

We investigated whether the representation of relational categories is different from that of featural categories. Earlier work has suggested an extreme-value hypothesis: when a category is defined in terms of a relation, exemplars with exaggerated values along this stimulus dimension are judged as better members of the category. Featural categories, on the other hand, are not exaggerated. To test this hypothesis, we trained participants to categorize two fictional diseases defined either by a deterministic relation or a deterministic feature. After the categorization task was mastered up to a predefined learning criterion, we provided a graphical user interface that enabled participants to construct good examples of the acquired categories by adjusting the stimulus attributes. We constructed a novel index of relational exaggeration based on residual deviations from a non-exaggerated response strategy. These results supported the extreme-value hypothesis. This replicates and extends an earlier quasi-experimental study (Du et al., 2021).

Coding Strategies in Memory for 3D Objects: The Influence of Task Uncertainty

Memory is limited in capacity, which means that we must choose what information to prioritize for storage. Part of knowing what to prioritize is predicting future needs. For example, if you view a 3D object, later on you may wish to recall exactly how it was oriented. Alternatively, you might need to remember its shape, independent of viewpoint. Given this kind of uncertainty, a good strategy would be to store multiple kinds of information about the objects we observe, and then decode in a task-dependent manner. We tested whether people apply these strategies in the specific domain of short-term memory for novel faces. To test whether people store various kinds of information about a face, and then decode in a task-dependent manner, we modeled their responses in a memory task using features (extracted from deep neural networks) that varied in how much 3D information they carried. We found strong evidence for a mixed-storage strategy, which did not vary in response to task demands. Our results suggest that in order to fully understand resource allocation and retrieval strategies in human memory, it may be critical to consider not just the distribution over tasks in people's natural environments, but also task uncertainty at the time of encoding.

The Impact of Prior Knowledge in Narrative-Based Learning on Understanding Biological Concepts in Higher Education

Fundamental concepts in biology are often challenging to understand. More strikingly, studies also report incorrect or incomplete understanding of such concepts for undergraduate natural science students even after instruction. Recent research suggests that embedding conceptual information in a narrative could support students’ learning process and facilitate conceptual change. Therefore, we designed learning materials covering complex concepts in biology either in the form of a narrative presenting the to-be-learned concepts in a historical context or as an expository text as control. We then assessed conceptual understanding and potential learning mechanisms. Results indicate that students learned from narrative texts and expository texts to a similar extent. However, if the prior knowledge was higher, the effect on learning was bigger in the narrative group than in the expository group. Moreover, the narrative led to better enjoyment and a higher germane cognitive load than the expository text material.

Grapho-Syllabic Systematicity in Chinese: Chinese Pictographs Have a Non-Arbitrary relation with their Pronunciations

It was recently found that letter-shapes have a non-arbitrary relation with their canonical pronunciations, in multiple orthographies and quantified across the whole of each orthography: letters that look similar tend to have similar pronunciations. Similarly, there is phonosemantic systematicity at the word level: words that sound similar tend to have similar meanings. We investigated for the first time whether a similar systematicity exists in Chinese characters. We measured all the pairwise phonological distances and all the pairwise orthographic distances of the 58 Chinese pictographic characters that are taught to year 1 and 2 in Chinese primary schools. The correlation was tested between the two lists of distances and verified by a Mantel test. We found a significant negative correlation between characters and their segmental pronunciations: characters that look similar tend to have dissimilar segmental pronunciations. This contrasts with the positive correlations found in previous similar research with alphabetic writing. We conclude, first, that questions of systematicity in the Chinese writing system are tractable in the same terms and by the same methodology as that applied to alphabetic writing systems. Second, segment-based processing requires to be augmented by tones for there to be systematicity that is comparable to that found in alphabetic writing systems. Any non-arbitrary relation between letter shapes and sounds may help bootstrap the acquisition of literacy.

Cognitive diversity promotes collective creativity: an agent-based simulation

In an agent-based simulation, we investigate the implications of social interaction and cognitive diversity on creative processes of divergent thinking. Agents performed a verbal association task individually and jointly in pairs. We created pairs of varying cognitive diversity by manipulating properties of the vector spaces defining their semantic memory. We find that cognitive diversity positively stimulates the flexibility of agents’ collective cognitive search, giving rise to higher fluency (more solutions) and originality (more ‘rare’ solutions). While cognitively similar agents tend to exploit local semantic neighborhoods, diversity promotes more explorative search, with longer distances traveled in semantic space. This helps diverse pairs reach more distant areas of semantic space and escape cognitive fixation. However, our model also suggests that too high levels of diversity can have detrimental effects, as overly exploratory behaviors make pairs leave solution saturated areas prematurely and increase the risk of reaching semantic “dead ends”.

A dynamic neural field model of phonetic trace effects in speech errors

Speech errors are often perceived as categorical substitutions of one sound for another, but phonetic analyses have consistently revealed that errorful productions retain a phonetic trace of the target category. These trace effects have been taken as evidence for the simultaneous activation of multiple categories, both exerting influence on speech production. We develop a dynamic neural field model of voice onset time (VOT) planning, showing how multiple activated categories can be resolved in the field to show trace effects. We evaluate model predictions against measurements of VOT for voiced and voiceless stops in speech error experiments and naturalistic corpora.

Bridging the prosody GAP: Genetic Algorithm with People to efficiently sample emotional prosody

The human voice effectively communicates a range of emotions with nuanced variations in acoustics. Existing emotional speech corpora are limited in that they are either (a) highly curated to induce specific emotions with predefined categories that may not capture the full extent of emotional experiences, or (b) entangled in their semantic and prosodic cues, limiting the ability to study these cues separately. To overcome this challenge, we propose a new approach called 'Genetic Algorithm with People' (GAP), which integrates human decision and production into a genetic algorithm. In our design, we allow creators and raters to jointly optimize the emotional prosody over generations. We demonstrate that GAP can efficiently sample from the emotional speech space and capture a broad range of emotions, and show comparable results to state-of-the-art emotional speech corpora. GAP is language-independent and supports large crowd-sourcing, thus can support future large-scale cross-cultural research.

Assessing the learnability of process interactions using grammatical spaces

A challenge in learning phonological grammars is learning how phonological processes interact. It has been argued that some process interactions are easier to learn than others. One basis for this argument is asymmetries observed in experimental settings: artificial languages generated from certain process interactions are more likely to be successfully reproduced by participants than others. In this paper, we argue that asymmetries in production do not necessarily provide direct support that some phonological interactions are easier to learn. Rather, we show that these asymmetries can instead emerge due to differences in the number of consistent or nearly-consistent grammars each pattern has. We present a noisy channel model of morpho-phonological learning and apply it to a recent behavioral study examining the learnability of phonological process interactions. We find that, due to the relative difference in the number of grammars that can exactly match or nearly match the observed data, the model achieves the same qualitative results as those observed in experimental settings.

Developmental changes in children's training strategies

Effective practice is key to learning. Yet, it is unclear whether young children have the ability to make effective and adaptive training choices. In this project, we investigated 4- to 7-year-old children’s (n=146) ability to tailor their training strategies to optimize performance outcomes. Children were presented with one easy and one difficult guessing game and were asked to choose which game they wanted to practice. Crucially, before they chose, they were told that they would eventually be tested either on the game of their choice (Choice condition) or on the game the computer would randomly pick (Random condition). Contrary to our hypotheses, we found that condition per se did not predict children’s training choices. However, we found that older children were more likely to make effective and adaptive training choices than younger children. Overall, our results indicate that children’s training choices improve from ages 4 to 7 and inform the development of interventions to support strategic learning.

Communicating understanding of physical dynamics in natural language

Our ability to share abstract knowledge with others is a defining feature of modern human intelligence. What information do people choose to include in such communication? Here we develop a novel physics-based video game to elicit natural language responses on how this game works to teach other people. We collected data from 238 participants and found that people explicitly described the latent physical properties of the game environment like mass and gravity in their responses. We also found that people who performed better in the game also produced responses that covered more latent physical properties. Taken together, our study provides novel insight into how people communicate their understanding of physical dynamics in natural language.

Radial Basis Leaky Competing Accumulator Model: A Biologically Plausible Framework for Decision-Making in a Continuous Option Space

In many real-life situations, we make decisions between a defined set of options, which can be either discrete (as when deciding between going on driving and stopping the car) or continuous (as when stirring the wheel, the possible range of angles goes from $-30$ to $30$ degrees). However, most computational models for decision-making focus on decisions between a discrete set of options. While there are a few sequential sampling models that can explain behavioral patterns (i.e., choices and response times) of decisions in a continuous option space (i.e., the CDM and the SCDM), these models have a few limitations. For example, these models assume no leakage in the evidence accumulation process and no spatial inhibition (i.e., inhibition among different areas of the option space depending on their distance to each other). In this paper, we propose a novel sequential sampling model based on an existing computational model (i.e., the leaky competing accumulator model) for decisions in a continuous option space. Our proposed model includes leakage and spatial inhibition and is thus more biologically plausible.

Locating past and future: The Influence of Spatial Ability on Time Representation

The representation of time depends heavily on spatial skills. Saj et al. (2014) demonstrated that left-hemispatial neglect patients, who lost the ability to detect objects in their left visual field, have a selective deficit in remembering items corresponding to the past, i.e., the left side of their mental timeline. The current study used the same memory task but tested neurotypical individuals (N = 76) to examine whether individual differences in spatial ability as well as learning order (chronological vs. random) predict how well participants remember items and associations between the item and time (past or future). Our results indicate that higher spatial ability and chronological learning both lead to better memory. This study is among the first to demonstrate how individual differences may impact time representation and memory that relies on a mental timeline.

“A fork is a food stabber”: Linguistic creativity in English L1 and L2 speakers

Knowing more than one language provides a speaker with an increased pool of linguistic experiences and concepts. This expanded language knowledge is thought to benefit bilingual speakers on standardized tests of creative ability. However, relatively little research has explored bilingual performance on tests of linguistic creativity. In this study we compare the production of creative, attenuated descriptions produced by English L1 and English L2 speakers. Using computational measures of text similarity, we find that English L2 answers were significantly less similar than L1 answers, suggesting a greater number of concepts and topics were used by the L2 participants. Additionally, unsupervised cluster analysis found no strong differences in the number of cluster topics between the L1 and L2 data. As such, the L2 answers contained more breadth, whereas the L1 answers contained more depth. The results may reflect fundamental differences in the storage and use of L1/L2 language knowledge.

Efficient learning through compositionality in a CNN-RNN model consisting of a bottom-up and a top-down pathway

Learning to write is characterized by bottom-up mimicking of characters and top-down writing from memory. We introduce a CNN-RNN model that implements both pathways: It can (i) directly write a letter by generating a motion trajectory given an image, (ii) first classify the character in the image and then determine its motion trajectory `from memory', or (iii) use a combination of both pathways. The results show that, in one-shot and few-shot learning, the model profits from different combinations of the pathways: The generation of different character variants works best when the top-down is supported by the bottom-up pathway. Refilling occluded images of efficiently learned characters works best when using the top-down pathway alone. Overall, the architecture implies that a weighted merge of bottom-up and top-down information into a latent, generative code fosters the development of compositional encodings, which can be reused in efficient learning tasks.

Measuring Quality of General Reasoning

Machine learning models that automatically assess reasoning quality are trained on human-annotated written products. These “gold-standard” corpora are typically created by prompting annotators to choose, using a forced choice design, which of two products presented side by side is the most convincing, contains the strongest evidence or would be adopted by more people. Despite the increase in popularity of using a forced choice design for assessing quality of reasoning (QoR), no study to date has established the validity and reliability of such a method. In two studies, we simultaneously presented two products of reasoning to participants and asked them to identify which product was ‘better justified’ through a forced choice design. We investigated the criterion validity and inter-rater reliability of the forced choice protocol by assessing the relationship between QoR, measured using the forced choice protocol, and accuracy in objectively answerable problems using naive raters sampled from MTurk (Study 1) and experts (Study 2), respectively. In both studies products that were closer to the correct answer and products generated by larger teams were consistently preferred. Experts were substantially better at picking the reasoning products that corresponded to accurate answers. Perhaps the most surprising finding was just how rapidly raters made judgements regarding reasoning: On average, both novices and experts made reliable decisions in under 15 seconds. We conclude that forced choice is a valid and reliable method of assessing QoR.

Source independence affects argument persuasiveness when the relevance is clear

Making inferences about claims we do not have direct experience with is a common feature of everyday life. In these situations, it makes sense to consult others: an apparent consensus may be a useful cue to the truth of a claim. This strategy is not without its challenges. The utility of a consensus should depend in part on the sources of evidence that underlie it. If each person based their conclusion on independent data then the fact that they agree is informative. If, instead, everyone relied on the same primary source, the consensus is less meaningful. However, the extent to which people are actually sensitive to this kind of source independence is still unclear. Here, we present the results of three experiments that examine this issue in a social media setting, by varying the sources of primary data cited via retweets. In each experiment, participants rated their agreement with 12 different claims before and after reading four tweets that were retweeted on the basis of either the same or different primary data. We found that people were sensitive to source independence only when it was clear that the tweeters had relied on the primary data to reach their conclusion. Implications for existing research are discussed.

Pre-boundary lengthening and pause signal boundaries in action sequences

In order to probe the production of kinematic cues to signal boundaries in action sequences, adults performed sequences of three actions on an object, with or without an action boundary following the second action. Movement of the hand was recorded via motion tracking, and it was found that the boundary was marked by a lengthening of the pre-boundary action and by a pause. These cues are also found in prosody to signal phrase boundaries in speech, suggesting a close coupling of the mechanisms underlying boundary production in both domains.

Learning from Word Books: Does the Type of Illustration Matter?

Picture books are a popular medium through which to promote language acquisition in young children. However, not much is known about how the pictorial context in which words are introduced in such books impacts word learning in toddlers, or how joint book reading further mediates this relationship. The present study introduced words to 19-23-month-old toddlers through books in either contextually rich, semantically relevant illustrations, or on a white background in isolation. Children and their parents participated in three lab visits during which a range of language and environmental measures were taken. Parents read our intervention materials at home between the first and second visits. We found that the pictorial context in which vocabulary words are presented was significantly related to language measures throughout our study. Further, this context also influences parents’ reading techniques, with longer interactions and more target words produced when reading contextually illustrated books. Our minimal book intervention shows promise in promoting vocabulary development in typically talking toddlers.

“He only changed his answer because they shouted at him”: children use affective cues to distinguish between genuine and forced consensus

Learning frequently forces us to rely on the good judgment and epistemic vigilance of sources with no more firsthand knowledge of a topic than ourselves, but who may have more second or third- hand knowledge. Yet, being forced to rely on their judgment doesn’t prevent us from evaluating their judgment: one might trust information because it was passed on to you by someone whose epistemic vigilance you trust, but reject it from someone whom you believe lacks good judgment. We present two experiments suggest that by integrating affective cues like anger and surprise along with perceptual access and consensus, children infer what others believe and what the correct answer to a question is. We discuss implications for consensus-based social learning strategies.

Causal versus Associative Relations: Do Humans Perceive and Represent Them Differently?

Research has shown that visual diagrams facilitate people’s understanding of and communication about abstract relations. In addition, the distinction between causal versus associative relations is important in human reasoning However, previous research has not directly compared how humans represent these two types of relations through visual diagrams. The current study examined whether causal and associative relations differ with respect to how people cognitively represent and interpret them in a spatial context using diagrams. We found that participants perceived relatedness of causal relationships to be stronger than that of associative relationships. This difference was reflected in their drawing of diagrams. Participants connected variables that shared a causal relationship with a shorter line than they did with variables that shared an associative relationship. The results shed light on the difference between causal and associative relations, and suggest new directions for future research to explore the spatial component of causal reasoning.

Towards Augmenting Humans in the Field: A Review of Cognitive Enhancement Methods and Applications

Efforts have always been deployed to surpass limitations in human cognitive abilities to enhance aspects such as task accuracy, work effectiveness and error management. Cognitive enhancement is a field aiming at improving human cognition in order to overcome those limitations. It bears important interest from the human factors community given its potential for reducing errors in complex operational environments. Yet, cognitive enhancement strategies are rarely used outside the lab and practical applications are scarce. The current paper presents a brief summary of the literature on human cognitive enhancement and discusses key operational applications of the main methods reported in this field. Using a human factors perspective, the paper also outlines how such techniques could be integrated into decision-support tools to support operators facing cognitive challenges in complex operational domains, including those experiencing functional limitations preventing them to contribute to the workforce.

Is the Language Familiarity Effect gradual ? A computational modelling approach

According to the Language Familiarity Effect (LFE), people are better at discriminating between speakers of their native language. Although this cognitive effect was largely studied in the literature, experiments have only been conducted on a limited number of language pairs and their results only show the presence of the effect without yielding a gradual measure that may vary across language pairs. In this work, we show that the computational model of LFE introduced by Thorburn, Feldman, and Schatz (2019) can address these two limitations. In a first experiment, we attest to this model's capacity to obtain a gradual measure of the LFE by replicating behavioural findings on native and accented speech. In a second experiment, we evaluate LFE on a large number of language pairs, including many which have never been tested on humans. We show that the effect is replicated across a wide array of languages, providing further evidence of its universality. Building on the gradual measure of LFE, we also show that languages belonging to the same family yield smaller scores, supporting the idea of an effect of language distance on LFE.

The contingency symmetry bias as a foundation of word learning: Evidence from 8-mont-olds in a matching-to-sample task

The contingency symmetry inference, the inference to generalize a learned contingency to a reverse direction, is known to be extremely difficult for non-human animal species (Lionello-DeNolf, 2009). In contrast, humans are known to have the “affirming the consequent fallacy”, which reverses the antecedent and the consequence (if P then Q: Q therefore P). The contingency symmetry bias has been long discussed in relation to the ontogenesis of language learning, as word learning requires understanding of bidirectional relationship between symbols and objects. But how this bias emerges has not been known. This research tested whether 8-month-old human infants have this bias on a matching-to-sample task. The results demonstrated the possession of this bias in human infants before they start active word learning. This bias is likely a uniquely human cognitive bias, which may explain why only humans have language.

Set Size Effects on the P3b in a BCI Speller

Data were collected from a brain-computer interface speller that utilized the P3b as a control signal. Stimuli consisted of letters and their "segments". Importantly, different letters were made up of different numbers of segments from a 10 segment library. Subjects were instructed to mentally note whenever segments from their letter (targets) were flashed. We found P3b amplitudes of target segments decreased as the number of segments in a letter (target letter complexity) increased. In contrast, the P3b attenuation was not affected by the total number of letters a segment belonged to (segment frequency). These results may reflect higher task difficulty caused by increased working memory load with increased target letter complexity. Alternatively, it's possible that despite the target rate being fixed at 30% within each block, subjects erroneously believed the target rate increased with target letter complexity. Further work to disentangle these possibilities may enrich our understanding of the P3b.

Making Predictions Without Data: How an Instance-Based Learning Model Predicts Sequential Decisions in the Balloon Analog Risk Task

Many models in Cognitive Science require data to calibrate parameters. Some modelers calibrate their models’ parameters for each individual in a data set, and others work at the aggregate level. Generally, the accuracy of a model is judged by the degree to which human data are replicated, and the model parameters are interpreted accordingly. It is not too surprising that models that are developed for a particular task and fit to each individual’s data in such a task replicate the human data well. The question is, however, whether those models can make predictions in the absence of human data. In this paper, we present a theory-driven model of a well-known sequential decision task (the Balloon Analog Risk Task, BART) which is able to make predictions in the absence of human data. The cognitive model is grounded on the processes and mechanisms of Instance-Based Learning (IBL) Theory of experiential choice. We demonstrate the simulation predictions from an IBL model and those of a well-known model of the BART, which depends on the fits to human data. We further show that when making predictions without data, the IBL model provides predictions that are both theoretically founded and accurate, while the Two-Parameter model performs much worse than when fit to data. We conclude with a discussion of the benefits of making theory-based predictions in the absence of human data for our community.

A Quantitative Symbolic Approach to Individual Human Reasoning

Cognitive theories for reasoning are about understanding how humans come to conclusions from a set of premises. Starting from hypothetical thoughts, we are interested which are the implications behind basic everyday language and how do we reason with them. A widely studied topic is whether cognitive theories can account for typical reasoning tasks and be confirmed by own empirical experiments. This paper takes a different view and we do not propose a theory, but instead take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning, which we call plausibility. For this purpose, we employ techniques from non-monotonic reasoning and computer science, namely, a solving paradigm called answer set programming (ASP). Finally, we can fruitfully use plausibility reasoning in ASP to test the effects of an existing experiment and explain different majority responses.

Rule-Based Categorization: Measuring the Cognitive Costs of Intentional Rule Updating

The ability to categorize visual information is essential for human cognition. Often, this categorization is achieved via internalized rules. In rule-based categorization tasks, participants categorize stimuli according to given decision rules. In this study, we created a framework aimed at measuring the respective impact of single memory operations on task performance. We present a study investigating two central mental operations - the addition of a new and the update of an existing rule - by confronting participants with Alien images they needed to assign to planets. Both conditions showed interference effects for task performance with previously learned ones. We found improved categorization task performance when old and new rules were in accordance, but no significant effect for conflicting situations. Our experimental setting promises to be well-suited to investigate the impact of memory operations on participants' behavior in a controlled environment.

Viewers Spontaneously Represent Event Temporal Structure

Events are considered as temporal segments with a beginning and an endpoint. Philosophical and linguistic literature on events distinguishes between bounded events that include distinct temporal stages leading to culmination (e.g., fix a car) and unbounded events that include largely undifferentiated stages and lack an inherent endpoint (e.g., drive a car). The present study shows that event viewers spontaneously compute this distinction through an interruption detection task. People watched videos of bounded or unbounded events with a visual interruption lasting .03s at the midpoint or close to the endpoint of the event stimulus. People indicated whether they saw an interruption after each video (Experiments 1) or responded as soon as possible during each video (Experiment 2). In both cases, the endpoint-midpoint difference depended on whether participants were watching a bounded or an unbounded event. As people perceive dynamic events, they spontaneously track boundedness, or the internal temporal structure of events.

Priming Counterintuitive Scientific Ideas

Intuitive explanations for natural phenomena are typically our default explanations, even after we have learned more accurate, scientific explanations (Shtulman & Valcarcel, 2012). The current study examined whether priming students with scientific images improves their ability to verify counterintuitive scientific statements, like “bacteria need nutrients” and “bubbles have weight.” Participants (100 college undergraduates) verified scientific statements interspersed with images relevant to the predicates of those statements; the images depicted either schematic diagrams (scientific primes) or everyday scenes (intuitive primes). Scientific primes increased the accuracy of participants’ responses, relative to intuitive primes, but not the speed of those responses, indicating that scientific primes facilitate a preference for scientific ideas over intuitive ones but do not eliminate the initial conflict between them.

Evidence for Dynamic Consideration Set Construction in Open-Ended Problems

Many decision problems can be divided into three parts: Generating a set of options to consider, evaluating them, and choosing the best. Prior models often assume that the ``consideration set'' is established in a single step prior to evaluation. Alternatively, people may dynamically and continually assess whether to expand the consideration set based on the quality of the actions considered so far. We use modeling to derive a signature property of dynamic consideration set construction and then demonstrate it in two experiments on human participants.

A Resource-Rational Process Model of Violation of Cumulative Independence

Human decision-making is filled with numerous paradoxes and violations of rationality principles. A particularly notable example is violation of cumulative independence (VoCI). Recently, there has been a surge of interest in theorizing and developing a resource-rational foundation for many such phenomena. Here we ask whether VoCI could be given a resource-rational basis too. To what extent could VoCI be explained in terms of the optimal use of limited cognitive resources? In this work, we look at VoCI through the lens of modern psychological theories of bounded rationality, presenting the first resource rational account of VoCI.We discuss the implications of our work for risky decision-making, and more broadly, human rationality.

Understanding and Modeling Coordination in the Minimum Effort Game

Groups of individuals need to coordinate in many real world domains. However, coordination failure is common and not well understood. There are few coordination measurements, analyses focus on averaged data, and models lack coordination strategies and clear correspondence to cognitive mechanisms. Here, we present a thorough analysis of human data from a difficult coordination scenario and a cognitive model implemented within the ACT-R cognitive architecture to fit and explain the data. Data were explored to better understand coordination strategies and group dynamics. The cognitive model included pre-game preferences, coordination strategies like signaling, and other player choice predictions. This work highlights the need for deeper data explorations and presents challenges for modeling related to coordination dynamics, strategies, and how players form beliefs about others.

Generalizing physical prediction by composing forces and objects

Our ability to make reliable physical predictions even in novel settings is a hallmark of human intelligence. Here we investigate how people infer multiple physical variables simultaneously and compose them to generalize to a novel scenario. Participants (N=203) observed a series of balls launched at different angles in a 2D virtual environment and generated predictions about their trajectories. We found that people could infer the masses of different balls based on these observations, as well as the existence of a latent "wind" force, and compose knowledge of these two variables to generalize to novel situations in a subsequent test phase. We modeled this generalization as the consequence of being able to simulate trajectories by independently combining force and mass information in accordance with Newtonian mechanics. To validate this approach, we also tested several alternative models and compared their generalization behavior to one another and to that of people. Together, our study points to the value of using generalization to probe the underlying representations supporting physical prediction.

A Property Induction Framework for Neural Language Models

To what extent can experience from language contribute to our conceptual knowledge? Computational explorations of this question have shed light on the ability of powerful neural language models (LMs)---informed solely through text input---to encode and elicit information about concepts and properties. To extend this line of research, we present a framework that uses neural-network language models (LMs) to perform property induction---a task in which humans generalize novel property knowledge (has sesamoid bones) from one or more concepts (robins) to others (sparrow, canary). Patterns of property induction observed in humans have shed considerable light on the nature and organization of human conceptual knowledge. Inspired by this insight, we use our framework to explore the property inductions of LMs, and find that they show an inductive preference to generalize novel properties on the basis of category membership, suggesting the presence of a taxonomic bias in their representations.

Leveraging Intentional Factors and Task Context to Predict Linguistic Norm Adherence

To enable natural and fluid human-robot interactions, robots need to not only be able to communicate with humans through natural language, but also do so in a way that complies with the norms of human interaction, such as politeness norms. Doing so is particularly challenging, however, in part due to the sensitivity of such norms to a host of different contextual and intentional factors. In this work, we explore computational models of context-sensitive human politeness norms, using explainable machine learning models to demonstrate the value of both speaker intention and task context in predicting adherence with indirect speech norms. We argue that this type of model, if integrated into a robot cognitive architecture, could be highly successful at enabling robots to predict when they themselves should similarly adhere to these norms.

Modeling Fixation Behavior in Reading with Character-level Neural Attention

Humans read text in a sequence of fixations connected by saccades spanning 7–9 characters. While most words are fixated, some are skipped, and sometimes there are reverse saccades. Previous work has explained this behavior in terms of a trade-off between the accuracy of text comprehension and the efficiency of reading, and modeled this using attention-based sequence-to-sequence neural networks. We extend this line of work by modeling the locations of individual fixations down to the character level. We evaluate our model on an eye-tracking corpus and demonstrate that it reproduces human reading patterns, both quantitatively and qualitatively. It achieves good performance in predicting fixation positions and also captures lexical effects on fixation rate and landing position effects.

How Do Children Combine Pointing and Language in the Earliest Stages of Development? A Case Study of Russian and Chintang

Learning to establish joint reference is an important milestone of communicative and linguistic development. Pointing is one of the first entry points into this process, since gestures often precede verbal communication. During early development, as well as later language use, pointing and linguistic utterances interact in many ways, complementing each other. However, little is known about the development of this relationship during development. In this paper, we focus on the development of the co-occurrence of finger pointing and accompanying utterances in two different cultures: Russia and Chintang (Sino-Tibetan, Eastern Nepal). We show that despite the differences in environment, the development of finger pointing and accompanying language use show substantial similarities. Early on, a larger proportion of points is not accompanied by language. As the children's linguistic abilities develop, children first use language to specify what is being pointed at, and later elaborate on some aspect of the referent.

Dynamics of Interaction with the Environment in Creativity: Embodied Imagination Framework

In creativity, the importance of interaction with the environment through bodily movement and perceptual information acquired therein has been discussed anecdotally. However, past creativity studies have mainly focused on the connection of creativity with memory and knowledge and the relationship between creativity and cognitive manipulations. The above process of bodily movement and environment was not sufficiently discussed. In this study, we developed a model of the above process and partially checked its validity through an experiment. Our model and the results of our experiment suggested the following processes. The interaction with the environment through the bodily movement changes the content and quality of the ideas generated. That interaction also changes the content of the cognitive manipulations in the idea generation. The above change in the cognitive manipulations partially described the change in the content and quality of the ideas. In these processes, the acquisition of perceptual information that differs greatly from the prediction has an important function. The dynamical relationship between the bodily movement, perception, and cognition in creative activities will require further investigation.

Hints and the Aha-Accuracy Effect in Insight Problem Solving

The Aha-Accuracy effect refers to the finding that experiencing an Aha! moment is associated with reaching correct solutions on insight problems. Because this effect has generally been demonstrated with verbal problems, this study tested for this effect on spatial problems (matchstick arithmetic). In addition, this study also explored the effect of hints on the Aha! experience and the Aha-Accuracy effect. Overall, there was no Aha-Accuracy effect in the no-hint control condition. There was an Aha-Accuracy effect in the hint condition, but it was limited to problems with solutions that were not directly cued by the hint. When the hint was directly relevant for solution, then many participants were able to reach a correct solution without an Aha! experience. These findings provide evidence that providing hints may not simply increase the likelihood of reaching a solution, but it may also alter the Aha! experience.

Order effects in choice are selectively modulated by cognitive load

The order in which options are presented influences choice in ways that parallel primacy and recency effects in memory, but the depth of this connection remains underexplored. I present sequences of art to experimental participants who select their favorite pieces, and find evidence that cognitive load can selectively weaken choice primacy or recency depending on its timing, analogous to established findings in memory research. The data suggests that primacy is reduced by an externally-imposed distractor task in between each option or by natural fatigue, while recency is reduced by an extra delay containing a distractor after the last option is presented. Thus, order effects in choice may be predictably modulated by the targeted disruption of processing.

Examining Prioritization in Working Memory for Verbal and Visual Stimuli

The effect of prioritization on information in working memory has primarily been examined in tasks containing a single type of stimulus and with one item that is prioritized. However, many theories of working memory posit different types of components for the maintenance of verbal or visuospatial information. This study examined differences between prioritized and nonprioritized items as well as word and image stimuli. Participants completed an association learning task in which working memory demands were varied along with the number of items to be prioritized. Following a short delay, retention was tested. Prioritization effects were identified during both the learning and testing phases of the experiment, and the impact of prioritization was moderated by working memory demands of the task. Significant differences in accuracy between word and image stimuli were only observed in the testing phase, with accuracy for verbal information being worse. While prioritization improved accuracy and response times during learning, it led to decreases in the testing phase.

Do people use social information to improve predictions about everyday events?

Following Griffiths and Tenenbaum (2006), we explore whether people use relevant social information to improve their already nearly optimal predictions about quantities in everyday events. We tested this question in two experiments involving quantities in three domains: cake baking times, movie runtimes, and podcast lengths. In Experiment 1, we found that participants were sensitive to the difference between relevant and irrelevant social information. In Experiment 2, we found that people consistently used relevant social information to adjust their predictions in the expected directions. We introduce an optimal social prediction model but find that it does not consistently perform better at accounting for our participants' social predictions than an optimal non-social prediction model. We conclude by discussing whether people use social information for prediction in an optimal way.

Adjusting the Use of Generalizations Based on Audience Expertise

Generalizations are a fundamental linguistic tool for efficiently passing along information. To interpret the intended strength of a generalization, listeners rely on prior knowledge. Experienced and inexperienced listeners may interpret the same generalization differently, potentially leading to miscommunication. Speakers could mitigate such miscommunication by avoiding generalizations that inexperienced listeners are likely to misinterpret. However, experienced speakers may struggle to understand the perspective of an inexperienced listener. The present study examined whether experienced speakers adjust their use of generalizations based on the expertise of their intended audience. Results showed that any such adjustments are minimal and insufficient to avoid miscommunication as operationally defined. Future research may clarify the practical impact of such miscommunication by examining how generalizations are used in relation to speakers’ and listeners’ goals.

User-Generated Star Ratings Are Not Inherently Comparable: How Star Ratings Structure Leads to Poor Choices

User-generated ratings — often elicited and presented as “star ratings” — have become a ubiquitous feature of the online consumer experience. While most research agrees that these user-generated ratings influence individual consumer decisions and overall consumer demand, there is less consensus as to whether user- generated ratings help consumers make better, welfare-enhancing decisions. In this manuscript, we expound on an intrinsic problem with the use of user-generated ratings in product choice decisions. Specifically, product ratings are typically given in an isolated (non-comparative) context, but are typically used in a comparative context, where relative differences in ratings may not reflect relative differences in quality. We provide a simple empirical demonstration of how this structural misalignment can lead consumers to choose suboptimal products and, ultimately, yield reduced consumer welfare.

Interaction in Acting Training and its Manifestations in Novices and Actors

To explain the importance of interaction for a truthful performance in acting, the present study captures the characteristics of interaction and attempts to probe the underlying intrapersonal changes through interaction during an acting course which emphasizes paying attention to a partner. Novice participants tend to change their way of communication as the course progresses, the pattern of which further differs from that of professional actors. While actors devote themselves more to the connection with their partner and demonstrate more balanced communication, novices rely on general inference to speculate about others’ affective states. This study offers a new perspective to elucidate the construction of interaction in acting, and emphasizes the significance of involvement in interaction when applying acting approaches to general training with the aim of improving social understanding.

Evolution of moral semantics through metaphorization

Although language is critical to supporting morality within society, it is not clear how moral language itself evolved. We investigate the evolution of moral semantics, hypothesizing that words evolved to take on moral meanings from concrete experiences through metaphorization. We test this hypothesis by analyzing moral semantic change in words from the Moral Foundations Dictionary and the Historical Thesaurus of English over the past hundreds of years. In contrast with the observation that words become concrete over time, we demonstrate that moral words in the English lexicon undergo concrete-to-abstract shifts, reflecting systematic metaphorical mappings to the moral domain. Our results provide large-scale evidence for the role of metaphor in the historical development of the English moral lexicon.

Using “Semantic Scent” to Predict Item-Specific Clustering and Switching Patterns in Memory Search

Elucidating the mechanisms that underlie clustering and switching behavior is essential to understanding semantic memory search and retrieval. Hills, Jones, and Todd (2012) proposed a model of semantic foraging based on the observation that statistical signatures in memory search resemble optimal foraging in animal behavior. However, the original model was postdictive in explaining when a switch would occur, as opposed to predictive, and was agnostic as to the cues used by humans to make a decision to switch from local to global information. In this paper, we proposed a switching mechanism, \textit{Semantic Scent}, as a predictive model underlying such behavior. Semantic Scent extends optimal foraging theory, reproducing the same switch behavior observed animal foraging behavior in memory search. We evaluated Semantic Scent against competing models including \textit{Random Walk} and \textit{Fixed Count} to determine its effectiveness in classifying switches made in fluency tasks. A quantitative model comparison between the switch models demonstrated Semantic Scent's superior performance in fitting human data. These results provide further evidence of the importance of optimal foraging theory to semantic memory search.

The Counterintuitive Interpretations Learned from Putatively Intuitive Simulations

Reasoning about sampling distributions is notably challenging for humans. It has been argued that the complexity involved in sampling processes can be facilitated by interactive computer simulations that allow learners to experiment with variables. In the current study, we compared the effects of learning sampling distributions through a simulation-based learning (SBL) versus direct instruction (DI) method. While both conditions resulted in similar improvement in rule learning and graph identification, neither condition improved more distant transfer of concepts. Furthermore, the simulation-based learning method resulted in unintuitive and surprising kinds of misconceptions about how sample size affects estimation of parameters while the direct instruction group used correct intuitive judgments more often. We argue that similar perceptual properties of different sampling processes in the SBL condition overrode learners’ intuitions and led them to make conceptual confusions that they would not typically make. We conclude that conceptually important differences should be grounded in easily interpretable and distinguishable perceptual representations in simulation-based learning methods. Keywords: education; statistics; learning with simulations; sampling distributions

Learning from Failure with Self vs Task Focused Feedback

Decades of feedback research have suggested that feedback is more effective in correcting errors than confirming the right responses. A study conducted by Eskreis-Winkler and Fishbach (2019) challenged this notion by showing that people learn less from feedback that indicates their answer is incorrect (failure feedback) than feedback that indicates their answer is correct (success feedback) even after incentivizing learning, manipulating response correctness, and controlling for background knowledge and mental inferences required for learning across conditions. Across two randomized experiments, we extended this work to investigate whether changing the focus of feedback from the self (“You answered this question correct/incorrect!”) to the task (“The answer was correct/incorrect!”) would reduce the difference between success and failure feedback. We replicated the previous study’s main finding that people learn less from failure feedback than success feedback. However, the focus of feedback message (task vs self) did not have the hypothesized effect. We suggest future research further investigate the impact of feedback focus using in-person experimental settings with more powerful designs and we recommend a set of motivational factors to investigate to determine how learning from failure feedback can be optimized. Keywords: learning; education; feedback; motivation; ego threat; replication

Improving the Perception of Fairness in Shapley-Based Allocations

The Shapley value is one of the most important normative division schemes in cooperative game theory, satisfying basic axioms. However, some allocation according to the Shapley value may seem unfair to humans. In this paper, we develop an automatic method that generates intuitive explanations for a Shapley-based payoff allocation, which utilizes the basic axioms. Given any coalitional game, our method decomposes it to sub-games, for which it is easy to generate verbal explanations, and shows that the given game is composed of the sub-games. Since the payoff allocation for each sub-game is perceived as fair, the Shapley-based payoff allocation for the given game should seem fair as well. We run an experiment with 630 human participants and show that when applying our method, humans perceive the Shapley-based payoff allocation as more fair than the Shapley-based payoff allocation without any explanation or with explanations generated by other methods.

Quantifying the Socio-semantic Representations of Words

Quantifying the meaning of a word is a complex challenge. Humans can encode semantic information along a large and diverse range of semantic dimensions for any given word. Whilst a number of studies have applied a range of techniques to quantify word meaning along specific dimensions, little work has focussed on the socio-semantic dimensions of meaning. Here, we present data that quantifies the socio-semantic representations of 2,700 Czech words along the dimensions of gender, location, political, valence and age. We also demonstrate the utility of the data set by calculating an estimate of socio-semantic similarity between all words, which can be used to identify words that are either proximally close or distant in socio-semantic space.

The Discrete, the Continuous, and the Approximate Number System

This paper explores the value of skepticism towards the Approximate Number System (ANS). I sketch some of the main arguments levied against ANS-based interpretations of numerical cognition data and argue that there are empirical and conceptual reasons to reject wholesale replacement of the ANS with an Analog Magnitude System (AMS). To simplify the discussion, I focus for the most part on a recent critical review representative of this new wave of revisionist skepticism (Leibovich, T., Katzin, N., Harel, M., & Henik, A., 2017). I start with a brief review of some of the reasons offered to deny that experiments studying our numerical abilities reveal the presence of a system dedicated to representing quantities of discrete objects, before turning briefly to empirical responses to these worries. I then offer a few reflections on why even if the empirical rebuttal were to fail, there are conceptual reasons to doubt that we are only equipped with an AMS. While some of these reasons involve methodological implications of AMS-based theories, other conceptual reasons to doubt AMS skepticism revolve around how ANS-skepticism seems to go against the history of the relation between the continuous and the discrete, and how one cannot be derived from the other. I then end with a potential reply to my worries involving an appeal to the Object-File System (OFS) as a source of discrete content in our numerical abilities and find it wanting.

How Stimuli Availability Effects Novel Noun Generalization in a Free-Choice Design.

A common result in novel word generalization is that comparison settings (i.e., several stimuli introduced simultaneously) favor conceptualization and generalization. We investigated which type of items four-, five- and six-year-old children would choose as referents in a free-choice novel noun generalization task. We manipulated the generalization items availability at test (i.e., generalization stimuli introduced sequentially or simultaneously). We also manipulated the semantic distance between items. In a signal detection theory framework, results showed that a simultaneous presentation of generalization items improves children’s sensitivity and helps them use a neutral strategy to generalize. Conceptual distance at learning also affects generalization performance. We discuss the cognitive constraints that both types of presentation bring into the task, and how distance might impede or favor conceptual alignments.

Modeling Reward Learning Under Placebo Expectancies: A Q-Learning Approach

Although expectancy effects induced by placebo treatment are reported to attenuate depressive symptoms in the long run, mechanisms underlying situational dynamics are not well understood. Improved reward learning has been discussed as a candidate mediator for effects of positive expectancies on more positive mood. Here, we fitted a series of Q-learning models to measure the effect of sham antidepressant treatment vs. open-label placebo in a probabilistic reinforcement learning task. Treatment effects were observed mainly in those Q-learning models justified by the task structure. Additionally, interindividual variability remained the largest origin of unexplained variance in predictive match across models. These findings provide further support for the role of expectancies in reward learning. They also highlight the need for unraveling individual differences in cognitive mechanisms that account for differences in reward learning, and obtaining reliable estimates for them.

Word-final orthographic priming as a function of word frequency, prime duration, and morphological status

One of the key issues in visual word recognition is the role of orthographic overlap in priming. However, most research investigating this topic has focused on priming with orthographic neighbors. In this study, we investigate priming effects of word-final letter overlap and their interaction with word frequency, prime duration, and morphology. In Experiment 1 with briefly presented primes (SOA=34 ms, N = 123), we obtained similar facilitation from non-morphological overlap (compel-TRAVEL) and inflectional suffix overlap (turned-CALLED), regardless of word frequency. In Experiment 2 when primes were fully recognizable (SOA=150 ms, N=123), only non-morphological overlap showed inhibition among lower frequency prime words. These results are inconsistent with predictions of the Interactive Activation model (McCelland and Rumelhart, 1981), and suggest (i) different weights of inhibition and facilitation depending on prime duration and morphological structure of words as well as (ii) the involvement of a reset mechanism in long SOA conditions.

How Do People Use Star Rating Distributions?

It may seem pointless to compare two products with the exact same average rating and total number of reviews without other review information. Now imagine a scenario in which the distribution of star ratings is also available to decision makers in addition to these two attributes. Will the decision still be uncertain as it is before or the distributions of stars will engender a preference towards one of the products? To answer this question, the current study used variability of star ratings as an approximation of a product’s distribution. The behavioral studies showed that participants exhibited distinctive choice patterns when the distribution of ratings was provided even when the average rating and total number of reviews were the same between two products involved in a comparison. A utility-based cognitive model was therefore developed to identify the underlying mechanism as to why people chose the way they did.

Where would you stand on the subway? A Bayesian framework for modeling commuter positioning choices in simulated subway coaches

Subway systems in large cities witness high volumes of commuter traffic, with crowded coaches and limited seats. In such scenarios, commuters often carefully position themselves in strategic locations with the aim of maximizing their chances of getting a place to sit. While user behavior in subways around the world have been the focus of multiple studies in the past, these everyday acts of ‘optimal decision making’ is of particular interest to the cognitive scientist. This paper inquires into commuter positioning choices in simulated subway coaches, within the framework of Bayesian probabilistic modelling. Data on preferred standing positions were collected across 20 subjects for 30 co-passenger configurations, through an interactive computer game. A generative model based on a Bayesian network involving three key spatial parameters was constructed, and used for inferring preferred positions conditioned on the specific configurations. The model was able to accurately simulate the quick and intuitive decisions made by the players under constraints of time, and also effectively capture noise in responses across subjects

Task-Based and Individual Differences Influence the Effect of Gesture Observation on Novel L2 Speech Sound Learning

This study sought to replicate the effect of observing pitch gesture and clarify the effect of observing representational gesture on L2 lexical tone learning and to explore the influences of individual differences in lexical and non-lexical tone perception on these effects. The results revealed that observing representational gestures facilitates lexical tone discrimination, albeit to a lesser extent than observing pitch gestures, suggesting that task difficulty may influence its effect. Moreover, they revealed that individual differences in non-speech tone perception predict discrimination of lexical tones learned by observing pitch gesture and no gesture, but not representational gesture. Together, these findings suggest that task difficulty as well as individual differences in sensitivity to non-speech sounds influence the effects of observing gesture on novel L2 speech sound learning.

Backchannel Behavior in Child-Caregiver Zoom-Mediated Conversations

An important step in children's socio-cognitive development is learning how to engage in coordinated conversations. This requires not only becoming competent speakers but also active listeners. This paper studies children's use of backchannel signaling (e.g., "yeah!" or a head nod) when in the listener's role during conversations with their caregivers via video call. While previous work had found backchannel to be still immature in middle childhood (i.e., 6 to 11 years of age), our use of both more natural/spontaneous conversational settings and more adequate controls allowed us to reveal that school-age children are strikingly close to adult-level mastery in many measures of backchanneling. The broader impact of this paper is to highlight the crucial role of social context in evaluating children's conversational abilities.

Individual-specific versus shared cognitive states differently support complex semantic and perceptual judgments

Cognitive processes that underpin performance on a given task may vary both within and across individuals. Yet, it is unclear how individual-specific versus shared cognitive processes each support behaviour. Here, we used a functional magnetic resonance imaging (fMRI) pattern classifier approach to ask how individual-specific and shared neural cognitive states differently relate to an individual’s ability to detect consecutive repeats in semantic (story) meaning versus perceptual (artist style) dimensions of illustrations that depicted well-known stories. Both states were related to participants’ task performance overall but differently for story versus artist style behaviours: individual-specific states were related to story performance, whereas shared states were related to artist style performance. These findings suggest that behaviours relying upon prior knowledge—likely varying across individuals—may be supported by idiosyncratic versus shared states. In contrast, unfamiliar judgments associated with a smaller number of eligible strategies may be supported by a state shared across individuals.

Language-induced categorical perception of faces?

Categorical perception (CP) facilitates the discrimination of stimuli belonging to different categories relative to those from the same category. Effects of CP on the discrimination of color and shape have been attributed to the top-down modulation of visual perception by the left-lateralized language processes. We used a divided visual field (DVF) search paradigm to investigate the prospective effects of CP for face identity and gender processing. Consistent with visual processing of face identity in the right hemisphere, we found CP facilitated perception only in the left visual field (LVF). In contrast, and consistent with language-induced CP, we observed a between-category advantage for processing face gender only in the right visual field (RVF). Taken together, our results suggest that language-induced CP plays a role in the category-based visual processing of faces by the left hemisphere, but face familiarity processing might be dependent on different, identity-specialized networks in the right hemisphere.

Transitive inference in non-humans? Not so fast!

A capacity for transitive inference (i.e. if aRb and bRc then aRc) was thought to be uniquely human. However, evidence of transitive inference in other species suggests that this capacity is ubiquitous throughout the animal kingdom. This apparent ubiquity raises two basic questions for cognitive science. (1) Why is transitive inference so prevalent? (2) What is special about transitive inference in (adult) humans? Formal (category theory) methods are used to address these questions. To the first question, different (implicit and explicit) forms of transitive inference follow from a common (universal) operation over the premises, aRb and bRc, i.e. a category theory version of transitive closure, hence the ubiquity of this capacity. To the second question, this construction involves rapid (one-shot) premise integration in older humans, but not other cohorts. This formal comparison points to rapid encoding and integration of relational data as underlying the evolution and development of higher cognitive capacities.

Dimension-Based Statistical Learning in Older Adults

The ability to perceptually “reweight” acoustic dimensions in response to changes in distributional statistics is known as dimension-based statistical learning (DBSL). However, it is currently unknown whether DBSL imposes a cognitive load. Older adults, who typically have age-related declines in cognitive ability, may be sensitive to this load. We examined young and older adults’ categorization of beer and pier sounds when the statistical relationship between VOT and F0 was consistent with that of American English, followed by a condition in which those statistics were reversed. Listeners made categorization decisions on each stimulus (Experiment 1), or after passive exposure to a string of stimuli (Experiment 2). In both experiments, younger and older participants demonstrated DBSL following exposure to the reversed statistics. Older adults tracked distributional statistics even when learning required accumulation of statistics over 8 sec, suggesting that rapid adaptation to regularities in speech input is robust across differing perceptual loads.

How people use the past as cues to the present

Humans must often make decisions in temporally autoregressive environments (e.g., weather, stock market). Here, current states of the environment regress on their previous states (either across consecutive timesteps or from several timesteps back in a patterned fashion). The current work investigates people’s abilities to utilize previous states of autoregressive sequences as cues to its current state. In Experiment 1 we determine whether utilization of autoregressions reduces as the temporal distance of the predictive timestep increases; and in Experiment 2 we explore whether participants’ utilization of previous timesteps in predictions compete such that they reduce utilization of one timestep when increasing utilization of another timestep. We also fit data from both experiments with a trial-by-trial decision model. Overall, we find that participants significantly reduced utilization of a cue with its increased temporal distance. However, we obtained less conclusive results on competition among timestep cues. These results can explain people’s predictions in sequential decision tasks (e.g., their tendencies to perceive clumpiness in random environments).

Les Liaisons Dangereuses: Quantifying French liaison-induced homophony

The French phonological rule of liaison, whereby certain underlying word-final consonants surface only when the following word starts with a vowel, sometimes creates homophony. For instance, un œuf ‘an egg’ and un neuf ‘a nine’ are both pronounced [ɛ̃.nœf]. While homophony is cross-linguistically frequent, there is evidence that it is constrained in various ways. Here, we quantify liaison-induced homophony by comparing its occurrence in real French to that in a benchmark consisting of versions of French with modified liaison consonants. We find that liaison induces more homophony in the benchmark than in real French. This is the first evidence that a phonological rule that applies across words is subject to an anti-homophony bias.

Predicting Human Judgments of Relational Similarity: A Comparison of Computational Models Based on Vector Representations of Meaning

Computational models of verbal analogy and relational similarity judgments can employ different types of vector representations of word meanings (embeddings) generated by machine-learning algorithms. An important question is whether human-like relational processing depends on explicit representations of relations (i.e., representations separable from those of the concepts being related), or whether implicit relation representations suffice. Earlier machine-learning models produced static embeddings for individual words, identical across all contexts. However, more recent Large Language Models (LLMs), which use transformer architectures applied to much larger training corpora, are able to produce contextualized embeddings that have the potential to capture implicit knowledge of semantic relations. Here we compare multiple models based on different types of embeddings to human data concerning judgments of relational similarity and solutions of verbal analogy problems. For two datasets, a model that learns explicit representations of relations, Bayesian Analogy with Relational Transformations (BART), captured human performance more successfully than either a model using static embeddings (Word2vec) or models using contextualized embeddings created by LLMs (BERT, RoBERTa, and GPT-2). These findings support the proposal that human thinking depends on representations that separate relations from the concepts they relate.

Perspective taking and reference frames for spatial and social cognition

When considering the location of objects and places, we often take perspectives in reference to ourselves or someone/something else. Using ourselves as a reference is considered using an egocentric reference frame, while using something external as a reference is considered using an allocentric reference frame. Of interest is the similarity of how these reference frames inform our understanding of both spatial and social cognitive processes. Similar to how we understand objects in relation to ourselves or an external reference, mentalizing and theory of mind processes have also been described using reference frames. Whether there is a common mechanism for using reference frames for processing both spatial and social information is unclear. The present study explored this idea with an online study where participants performed both a spatial and social (i.e., mentalizing) perspective taking task, along with questionnaire gauging personality, visualization ability, and anxiety. Participants who were better at taking someone else’s spatial perspective tended to be better at mentalizing. This relationship was not present when taking one’s own spatial perspective or when mentalizing was not necessary. We provide preliminary evidence that reference frames contribute to both spatial and social cognitive processes.

Gesture and Speech Disfluency in Narrative Context: Disfluency Rates in Spontaneous, Restricted, and Encouraged Gesture Conditions

Gestures facilitate speech production by helping speakers reduce cognitive load. Studies on gesture-speech interaction mostly examined the effect of representational gestures on spatial contexts. However, abstract deictics (e.g., pointing at objects that are not visually present) might also have a role in facilitating cognitive processes. The present study investigated the effect of gestures on disfluency rates by presenting a narrative task in three conditions: spontaneous, restricted, and encouraged gesture use. We found that disfluency rates across three conditions did not significantly differ. The use of abstract deictics in the spontaneous gesture use condition was a significant predictor of disfluency rates in the gesture restricted condition. Results indicate that gestures’ facilitative roles might be manifested differently depending on the context. Abstract deictics might also benefit speakers, especially in a narrative context. Studying abstract deictics can provide new insights on gesture and speech production interaction.

Connecting Exploration, Generalization, and Planning in Correlated Trees

Human reinforcement learning (RL) is characterized by different challenges. Exploration has been studied extensively in multi-armed bandits, while planning has been investigated in multi-step decision tasks. More recent work has added structured rewards to study generalization. However, past studies have often focused on a single one of these aspects, making it hard to compare results. We propose a generative model for constructing correlated trees to provide a unified and scalable method for studying exploration, planning, and generalization in a single task. In an online experiment, we find that people use structure (when provided) to generalize and perform uncertainty-directed exploration, with structure helping more in larger environments. In environments without structure, exploration becomes more random and more planning is needed. All behavioral effects are captured in a single model with recoverable parameters. In conclusion, our results connect past research on human RL in one framework using correlated trees.

Predicting Individual Discomfort in Autonomous Driving

Given considerable advancements in automated driving systems, the day when autonomous vehicles will be regularly present in our everyday life is impending. It is, therefore, very significant to put emphasis on the effect that giving up autonomy might have on an individual. We take into consideration an experimental data set regarding participants' reported discomfort levels to tackle the following questions: How can we represent a discomfort measurement in a meaningful way? Using this representation, can future discomfort reactions be predicted? We identify key features, identify baseline models, and develop a new approach based on the k-nearest neighbor model to considerably improve the prediction of individual user's discomfort measurements. A discussion of limits and potentials concludes the paper.

Does Expressive Writing Blunt the Effects of Math Anxiety on Math Performance? A Conceptual Replication and Extension of Park et al. (2014)

Math anxiety (MA) is negatively related to math performance. One proposed intervention with potential to disrupt the MA-math performance link is expressive writing. The current study aimed to conceptually replicate Park and colleagues (2014). In that study, the authors concluded that expressive writing effectively boosted math anxious students’ performance. In our current sample of 168 college students, participants randomly assigned to the expressive writing condition were no more accurate at posttest than were other participants assigned to a math self-concept intervention, active control, or passive control. Additionally, participants in the math self-concept and active control conditions reported lower state MA immediately following the intervention; participants in the expressive writing and passive control conditions reported no differences between pretest and posttest state MA. The current study provides boundary conditions for the effectiveness of expressive writing interventions in ameliorating MA during difficult math tasks and illuminates potential mechanisms underlying MA.

Modeling atypicality inferences in pragmatic reasoning

Empirical studies have demonstrated that when comprehenders are faced with informationally redundant utterances, they may make pragmatic inferences to accommodate the informationally redundant utterance (Kravtchenko & Demberg, 2015. Previous work has also shown that the strength of these inferences depends on prominence of the redundant utterance – if it is stressed prosodically, marked with an exclamation mark, or introduced with a discourse marker such as “Oh yeah”, atypicality inferences are stronger (Kravtchenko & Demberg, 2015; 2022; Ryzhova & Demberg, 2020). The goal of the present paper is to demonstrate how both the atypicality inference and the effect of prominence can be modelled using the rational speech act (RSA) framework. We show that atypicality inferences can be captured by introducing joint reasoning about the habituality of events, following Degen, Tessler, and Goodman (2015); Goodman and Frank (2016). However, we find that joint reasoning models principally cannot account for the effect of differences in utterance prominence. This is because prominence markers do not contribute to the truth-conditional meaning. We then proceed to demonstrate that leveraging a noisy channel model, which has previously been used to model low-level acoustic perception (Bergen & Goodman, 2015), can successfully account for the empirically observed patterns of utterance prominence.

Framing biodiversity Conceptions

Biodiversity is a complex concept entailing scientific and political aspects. The usage of analogies, especially metaphors, that have positive influences on the understanding of complex concepts, on attitudes and behaviors, seems an interesting strategy to achieve this goal. Based on biodiversity analogies elaborated by 259 participants, a first study aims to identify two important protective approaches: preservationism that encourages humankind to limit their intervention on nature and conservationism that allows humankind to exploit nature with parsimony. We analyzed their analogies and results highlight three major groups: a scientific, a conservationist and a preservationist dimension. A second study investigates the effects of metaphorical framing on environmental attitudes and behaviors. 277 University students read a short text framing biodiversity with a preservationist or conservationist metaphor or without metaphor framing. A decision-making task and an environmental concern scale were completed. Results showed an effect of the conservationist metaphor on the decision-making task.

Identifying a Phonetic Factors of Onomatopoeias Correlated to Sound Symbolic Commons between Japanese and Non-Japanese Speakers

Although the relationship between the sound of each word and its meaning is generally arbitrary, onomatopoeias are said to have the unarbitrary link, which called sound-symbolism, between them. In this study, we investigated whether sound symbolic words are widely common in a class of onomatopoeias in some natural language. We conducted an experiment, which asked Japanese and non-Japanese speakers to match each given Japanese-onomatopoeia-like sound with a shape to which the sound referred. The result of analysis showed a similar structure for both speaker groups, in which round shapes were associated to a particular set of sounds and pointed shapes associated to the other set of sounds. Moreover, the round/ pointed shapes are correlated to pseudo onomatopoeias with sonorants/ fricative phonetic features. This finding supports the sound symbolic hypothesis asserting that the major component of Japanese onomatopoeias forms a bouba-kiki like sound-shape correspondence even for non-Japanese speakers.

Modelling Dual-Processes in a Connectionist Network

This paper presents a connectionist network simulation of Livesey and McLaren’s (2009) results. In that paper they showed that participants with post-discrimination gradients that were initially peak shifted became monotonic as they were exposed to the full range of test stimuli. While the authors suggest that this is the result of rule-based processes ‘taking over’ responding, we show how a connectionist network with an attentional parameter and realistic activation functions for the input can simulate both the peak shifted and monotonic gradients. Although we do not infer that the monotonic gradient obtained in peak shift paradigms is entirely the result of associative, rather than propositional processes, we suggest that perhaps it is a change in the allocation of attention, in conjunction with the underlying representational structures used for the stimuli that facilitates rule induction in this case.

Infants infer motor competence from differences in agent-specific relative action costs

Determining others’ motor competence is critical for action prediction and social decision making. One aspect of competence judgements involves assessing how costly a given action is for a particular agent (e.g., whether climbing 4 floors of stairs is a piece of cake or a tough physical exercise). Such information is not given away by the agents’ physical appearance but can be inferred based on their behavior. Across two looking-time experiments, we show that 10-month-olds can infer and compare agent-specific costs of different actions. After being familiarized with agent A jumping over low obstacles and walking around high obstacles, and agent B jumping over both low and high obstacles, infants worked out that for B jumping bears little cost, while for A jumping high is more costly than detouring the obstacles by walking. Furthermore, they used this motor competence judgements to predict both agents’ actions in a new environment. These findings suggest that basic building blocks competence evaluations are available in infancy and may be rooted in infants’ action interpretation skills.

A Bayesian Drift-Diffusion Model of Schachter-Singer’s Two-Factor Theory of Emotion

Bayesian inference has been used in the past to model visual perception (Kerstenm 2004), accounting for the Helmholtz principle of perception as “unconscious inference” that is constrained by bottom-up sensory evidence (likelihood) while subject to top-down expectation, priming, or other contextual influences (prior bias); here "unconsciousness" merely relates to the "directness" of perception in the sense of Gibson. Here, we adopt the same Bayesian framework to model emotion process in accordance with Schachter-Singer’s Two-Factor theory, which argues that emotion is the outcome of cognitive labeling or attribution of a diffuse pattern of autonomic arousal (Schachter & Singer, 1962). In analogous to visual perception, we conceptualize the emotion process, in which emotional labels are constructed, as an instance of Bayesian inference, either consciously or unconsciously combining the contextual information with a person’s physiological arousal patterns. Drift-diffusion models were constructed to simulate emotional processes, where the decision boundaries correspond to the emotional state experienced by the participants, and boundary-crossing constitutes “labeling” in Schachter-Singer’s sense. Our model is tested against experimental data from the Schachter & Singer's study (1962) on context-modulated emotional state labeling and the Ross et al. study (1969) on fear reduction through mis-attribution. Two model scenarios are investigated, in which arousal pattern as one factor is pitted against contextual interaction with an confederate (in Schachter-Singer case) or explicitly instructed mis-attribution (in Ross et al. case) as another factor, mapping onto the Bayesian prior (initial position of the drift) and the likelihood function (evidence accumulation or drift rate). We find that the first scenario (arousal as the prior and context as the likelihood) has a better fit with Schachter & Singer (1962) whereas the second scenario (context as the prior and arousal as the likelihood) has a better fit with Ross et al. (1969).

Symmetry as a Representation for Intuitive Geometry?

Recognition of geometrical patterns seems to be an important aspect of human intelligence. Geometric pattern recognition is used in many intelligence tests, including Dehaene’s odd-one-out test of Core Geometry (CG)) based on intuitive geometrical concepts (Dehaene et al., 2006). Earlier work has developed a symmetry-based cognitive model of Dehaene’s test and demonstrated performance comparable to that of humans. In this work, we further investigate the role of symmetry in geometrical intuition and build a cognitive model for the 2-Alternative Forced Choice (2-AFC) variation of the CG test (Marupudi & Varma 2021). In contrast to Dehaene’s test, 2-AFC leaves almost no space for cognitive models based on generalization over multiple examples. Our symmetry-based model achieves an accuracy comparable to the human average on the 2-AFC test and appears to capture an essential part of intuitive geometry.

Effects of task and visual context on referring expressions using natural scenes

We explore contextual adaptation of referring expressions with respect to referential ambiguity and communicative intention. We focus not only on whether people adapt, but also on how by contrasting lexical specification (e.g., "batter") and syntactic modification (e.g., "man in white pants") when discriminating between objects in natural scenes (e.g., a batter wearing white pants and a referee). There are three main results. First, we replicate that speakers adapt their expressions to avoid ambiguity. Second, communicative intention has an effect: participants tended to use more specific names in a discrimination task than in a descriptive task, even without referential ambiguity in the context. Third, when given the choice, participants tended to prefer more specific words over adding modification - that is, using lexical rather than syntactic means to resolve ambiguity. This suggests that it may be less demanding to increase informativity of referring expressions with lexical specification than syntactic modification.

Landmark Modality in Wayfinding: Does it Make a Difference?

Navigation is a process that humans use to get from A to B. Landmarks used during navigation and wayfinding can address different sensory modalities. We examined landmark information in four different variants: as a written word, as a spoken word, as a picture, or as an odor. Our 51 participants were separated into four groups. Each group received one specific variant of landmark information integrated into a learning and wayfinding video of a virtual maze with 12 intersections. At each intersection, one landmark information was presented. To assess how well the relevant landmarks could be distinguished from unknown distractor items of the same condition, the experiment concluded with a recognition phase, where 24 stimuli were presented (12 landmarks + 12 distractors). Relative frequencies of correct responses and mean response times were measured for wayfinding and recognition. Odors lead to similar correctness in wayfinding compared to the more common landmarks (pictures, written and spoken words), even though requiring longer response times. We stepped away from the traditional but limited view on landmarks towards a more holistic (i.e. including all senses) view of human orientation. Implications for future scientific research are being discussed.

Intentional commitment through an internalized theory of mind: Acting in the eyes of an imagined observer

The ancient Greek hero Ulysses chose to bind himself to resist the temptation of Sirens, highlighting the fact that humans may voluntarily sacrifice their freedom of choice to achieve committed goals. In this work, we propose a computational model for such commitment under the framework of Bayesian Theory of Mind. The model is based on the idea that even when alone, humans act to better demonstrate their intentions to an imagined third-party observer (ITO) censoring their actions. Our model successfully captures the Ulysses-constraint of freedom, as the freedom confuses the ITO’s inference of their intention. We further show that, trajectories generated both by human actors and actors modeled with ITO censorship are easy to interpret both in the eyes of an actual human ob- server and an ITO. The results demonstrate that under conflict- ing desires, humans achieve commitment by spontaneously censoring their actions with an internalized theory of mind.

Cross-Cultural Sensitivity to Context when Reasoning about the Impossible

When judging the relative difficulty of impossible actions within the context of a magical world like that of Harry Potter, individuals honor real-world causal principles (e.g., assuming that heavier objects would be harder to levitate than lighter ones even though levitation itself is impossible; Shtulman & Morgan, 2017). We examined whether this effect persists when events are presented outside of this context. U.S. (Studies 1 and 2) and Chinese (Study 2) adults were asked to rate the relative difficulty of two impossible events that varied according to an irrelevant causal principle in one of three contexts: present science, future science, or magical. Though Chinese and U.S. adults honored irrelevant causal principles to a similar degree across the three contexts, Chinese adults’ confidence in their judgments varied by context. Additionally, individual differences in cognitive reflection (U.S.) and fantasy engagement (Chinese) related to judgments. Findings indicate that adults honor irrelevant causal constraints when reasoning about the impossible across multiple contexts, though subtle differences exist at both the cultural and individual level.

Looking into the past: Eye-tracking mental simulation in physical inference

Mental simulation is a powerful cognitive capacity that underlies people's ability to draw inferences about what happened in the past from the present. Recent work suggests that eye-tracking can be used as a window through which one can study the process of mental simulation in intuitive physics tasks. In our experiment, participants have to figure out in which of three holes a ball was dropped in a virtual Plinko box. We develop a computational model of human intuitive physical reasoning in Plinko that runs repeated simulations in a noisy physics simulator in order to infer in which hole the ball was dropped. We evaluate our model's behavior against multiple human data signals: trial judgments, response times, and eye-movement data. We find that a model that sequentially samples simulations while balancing uncertainty and reward best explains the patterns of participant behavior we observe in these three signals.

Identifying the distributional sources of children’s early vocabulary

Children’s early word learning is to a large extent driven by the prevalence of words in their language environment, with words that are spoken more often to children being learned earlier. However, children receive language from a variety of sources, including books, television, and movies meant for children, as well as speech and media that is meant for adults, but over- heard by children. Despite considerable similarity of word frequency distributions from these different input sources, there is also significant and predictable variability between them. For example, function words are far more frequent in books than in everyday speech, while early-learned nouns (e.g., ‘ball’ and ‘mommy’) are more frequent in child-directed speech than in other sources. Children receive a mixture of these different frequency distributions. The goal of this paper is to better understand the shared and unique variance in these input sources – in both English and French – and to evaluate how predictive these distributions are of children’s early word learning.

Time to get attention: The effect of temporal values on health, income and happiness

We study the effect of people’s temporal values (habits of attending to past or future events) on their health, labour market performance and happiness. Participants’(N=1177) data were initially collected in 2016 and followed in 2020-2021. We find that habitually more attending to the future negatively correlates to diseases (heart attack; high cholesterol; diabetes; high-blood pressure), but positively associates with health-related behaviour (eating vegetables and fruit; less smoking), health status (e.g., healthy weight; long life expectancy), income, hourly wages, financial satisfaction and happiness. Furthermore, such temporal values predict participants’ future situation of these aspects in 2020-2021, even after controlling for the 2016 baseline situation, IQ, self-control, patience, risk aversion and demographic information. We propose a temporal values and well-being hypothesis, suggesting that individuals’ temporal values can predict their concurrent and longitudinal all-around well-being. Our findings have strong implications for theories of time perception, and for a better understanding of factors that influence people’s health, income, and happiness.

What Is the point? a Theory of Mind Model of Relevance

Although pointing is sparse, overloaded, and indirect, it allows humans to effectively decode shared information, (ex)change their minds, and plan accordingly. Pointing is an invitation to jointly attend to an object, which triggers the mutual inference between agents of each other’s mind. Relevance is a fundamental assumption underlying all human communication, including pointing. We define relevance as how much a signaler’s belief can make a positive difference to its receiver’s well being. We build a Theory of Mind (ToM) model to test our definition of relevance and use pointing as a case study. In two experiments, we test our relevance model in a classic artificial intelligence (AI) task, the Wumpus world, with the key difference that there is a guide that points to help a hunter. Agents with our relevance model gain significantly higher rewards than agents who ignore signals from the guide. Agents with our model also achieve better performance than agents who receive an additional observation of the environment. The results show that the power of pointing comes from the ToM inference of relevance, rather than providing more precise individual perception.

Toward Automated Detection of Phase Changes in Team Collaboration

Team science research heavily relies on communication data—that is, data derived from audio, video, or text-chat communication streams between team members. Between transcription and content analysis, significant overhead is required to work with these data. Recent developments in natural language processing (NLP) may help ameliorate time constraints in this domain. Using transcript data, the present study, presented as a proof-of-concept, assesses how the BERT NLP model performs in a team communication categorization task, in comparison to ground truth measures. This work builds upon past work that relied on human-coded transcripts to identify phase transitions in team collaboration. Results suggest BERT’s capabilities at phase change detection are promising for experienced teams, though further iteration is needed on the methods in the current study. Applications of this work extend to real-time collaboration with an artificial agent, as this requires the real-time semantic processing of human communication data.

Does error-driven learning occur in the absence of cues? Examination of the effects of updating connection weights to absent cues

The Rescorla-Wagner model has seen widespread success in modelling not only its original target of animal learning, but also several areas of human learning. However, despite its success, a number of studies with humans have found effects that are not predicted by the model, thus inspiring proposals for modifications to the model. One such proposal, by Van Hamme and Wasserman (1994, VHW), is that humans not only learn from present cues to all (present and absent) outcomes, as in the original model, but also learn from the absence of cues. They set out to test this hypothesis with a causal rating experiment. However, behaviour in learning studies may depend on the task. We propose that error-driven learning should be considered to be a form of implicit learning and that the results of VHW’s contingency judgement task might stem from explicit strategies involving logic and reasoning. The present study investigates this question by a) running simulations with both the original and modified versions of the model; b) replicating the VHW experiment (Experiment 1); and c) extending the experiment with new stimuli and by including unseen stimuli following the learning phases (Experiment 2). Simulations show that the VHW modified model predicts that cues learnt at the beginning will be unlearnt when absent over the following blocks, so that they become negative predictors over time. In contrast, the original RW predicts that the absent cues remain steady (positive) predictors over the blocks. Results showed no significant difference in cue assignment between training and test, in line with the original RW model. Moreover, predictive cues in the training phase showed significantly higher ratings than a new cue introduced in the test phase, at least in some cases, also partially supporting the original RW. We propose that in the development of human learning theory, attention should be paid to whether the behaviour (or other learning data) to be modelled results from implicit learning or involves higher level cognitive processes. We suggest that the RW may best capture implicit error-driven learning.

Rhythmic Coordination Affects Children’s Perspective-Taking during Online Communication

We examined how rhythmic activities affect children’s perspective-taking in a referential communication task with 69 Chinese 5- to 6-year-old children. The child first played an instrument with a virtual partner in one of three coordination conditions: synchrony, asynchrony, and antiphase synchrony. Eye movements were then monitored with the partner giving instructions to identify a shape referent which included a pre-nominal scalar adjective (e.g., big cubic block). Participants with awareness of their partner’s perspective could, in principle, identify the intended referent before the shape was named when the target contrast (a small cubic block) was in shared ground whereas a competitor contrast was occluded for the partner. Children in the asynchrony and antiphase synchrony conditions, but not the synchrony condition, showed anticipatory looks to the target, suggesting that playing instruments asynchronously or in alternation facilitates perspective-taking, likely by training self-other discrimination and inhibitory control.

Predicting Human Similarity Judgments Using Large Language Models

Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting similarity judgments can be prohibitively expensive for naturalistic datasets as the number of comparisons grows quadratically in the number of stimuli. One way to tackle this problem is to construct approximation procedures that rely on more accessible proxies for predicting similarity. Here we leverage recent advances in language models and online recruitment, proposing an efficient domain-general procedure for predicting human similarity judgments based on text descriptions. Intuitively, similar stimuli are likely to evoke similar descriptions, allowing us to use description similarity to predict pairwise similarity judgments. Crucially, the number of descriptions required grows only linearly with the number of stimuli, drastically reducing the amount of data required. We test this procedure on six datasets of naturalistic images and show that our models outperform previous approaches based on visual information.

Low Spatial Proximity Between Text and Illustrations Improves Children’s Comprehension and Attention: An Eye Tracking Study

Learning to read is a critical skill; yet only a small portion of children in the United States are reading at or above grade level. Attention is one crucial process that affects the acquisition of reading skills. The process involves selectively choosing task relevant information and requires monitoring competing demands. Many books for beginning readers include illustrations, but this design choice may require learners to split their attention between multiple sources of information. This study employed eye tracking to examine whether embedding text within illustrations in children’s e-books inadvertently induces attentional competition. The results showed that spatially separating illustrations from the text in beginning reader books reduces attentional competition and improves children’s reading comprehension. This study shows that changes to the design of books for beginning readers can help promote literacy development in children.

Exploration is Higher in Social Contexts at the Cost of Rewards

In decision-making situations that arise repeatedly, there are tradeoffs between: (i) acquiring new information to facilitate future, related decisions (exploration) and (ii) using existing information to secure expected outcomes (exploitation). Exploration choices have been well characterized in nonsocial contexts, but choices to explore (or not) in social environments are less well understood. Social environments are of particular interest because a key factor that increases exploration in nonsocial contexts is environmental uncertainty, and the social world is appreciated to be highly uncertain. Here, participants searched for rewards in a series of grids that were either described as comprising real people distributing previously-earned points (social context) or as the result of a computer algorithm or natural phenomenon (nonsocial context). Participants explored more, and earned fewer rewards, in the social versus nonsocial context, suggesting that social uncertainty prompted exploration at the cost of task-relevant goals.

How Feedback in Interactive Activation Improves Perception

We follow up on recent work demonstrating clear advantages of lexical-to-sublexical feedback in the TRACE model of spoken word recognition. The prior work compared accuracy and recognition times in TRACE with feedback on or off as progressively more noise was added to inputs. Recognition times were faster with feedback at every level of noise, and there was an accuracy advantage for feedback with noise added to inputs. However, a recent article claims that those results must be an artifact of converting activations to response probabilities, because feedback could only reinforce the “status quo.” That is, the claim is that given noisy inputs, feedback must reinforce all inputs equally, whether driven by signal or noise. We demonstrate that the feedback advantage replicates with raw activations. We also demonstrate that lexical feedback selectively reinforces lexically-coherent input patterns – that is, signal over noise – and explain how that behavior emerges naturally in interactive activation.

Using efficiency to infer the quality of machines

When assessing the quality of a machine, people might consider the machine’s outputs—how well it serves its function. Alternatively, people might also consider the efficiency of the machine. We investigated this possibility in two experiments (N = 392). In each experiment, participants saw pairs of machines, one with simple inside parts and one with more complex inside parts. Machines either had the same output or unknown outputs, and people judged which of the two machines was better. When the machines had the same output, participants in both experiments judged that machines with simpler inside parts were better than ones with more complex insides. However, when machines’ functions were unknown, people predominantly judged that machines with complex insides were better. Together, our work shows that people consider both parts and functions of machines when inferring quality.

Modeling Social Influences on Indirectness in a Rational Speech Act Approach to Politeness

Politeness is a social linguistic phenomenon. Modeling polite language production and understanding is difficult, as it may contradict conversational maxims and is shaped by extralinguistic social influences, such as the speaker-hearer relationship. This paper extends Yoon et al.'s (2016) Rational Speech Act-based model of politeness by mapping speaker-hearer relationship influences to the utility weights of the model and instantiates it in German. Three online experiments, for empirical analysis and collection of behavioural data for model training and evaluation, are presented. These confirm the influence of the speaker-hearer relations on indirect politeness. Furthermore, two versions of the model are trained and evaluated to find out which part of the model is better suited for the integration of social influences. Overall, both model versions yielded similar results and were able to predict the meaning of polite speech acts.

Open System Model of Choice and Response Time

Sequential sampling models have provided accurate accounts of people’s choice, response time, and preference strength in value-based decision-making tasks. Conventionally, these models are developed as Markov-type processes (such as random walks or diffusion processes) following the Kolmogorov axioms. Quantum probability theory has been proposed as an alternative framework upon which to develop models of cognition, including quantum random walk models. When modeling people's behavior during decision-making tasks, previous work has demonstrated that both the Markov and quantum models have their respective strengths. Recently, the open system model, which is a hybrid version of the Markov and quantum models, has been shown to provide a more accurate account of preference strength compared to the Markov and quantum models in isolation. In this work, we extend the open system model to make predictions on pairwise choice and response time and compare it to the Markov and quantum random walk models.

Relative Numerical Context Affects Temporal Processing

Several studies have reported that numerical magnitudes biases temporal judgments, i.e., large numerical magnitude, were perceived to last longer than small numerical magnitude. However, these predictions have been predominantly verified only when the large and small numerical magnitudes were presented in an intermixed fashion where numerical magnitudes varied randomly from trial to trial. We conducted two experiments (Blocked-magnitude and Mixed-Magnitude) using a temporal bisection paradigm to investigate whether numerical context affects temporal processing in a sub-second timescale. The numbers were presented with varying durations. Participants were asked to judge whether the presented durations were shorter or longer. The results suggest that the temporal judgments were affected when small and large numbers were randomly presented in an intermixed manner. However, such effects disappeared when the number magnitudes were presented separately. These results indicate the modulation of attention in number-time interaction, and such crosstalk may not require a generalized magnitude system.

Transfer of Learning-Guided Cognitive Control through Congruency Cues: a study involving two variants of Flanker task

Transfer of cognitive abilities has often been described in regard to Working Memory, while little has been said about Cognitive Control. Recent studies have proposed that congruency cues can be used to investigate learning-guided cognitive control adaptations in a trial-by-trial fashion during conflict tasks. In the present study, we employed congruency cues within an inducer/diagnostic paradigm to (1) induce a control learning between cue and string congruency in a Flanker task variant and (2) test whether this learning could transfer to a different Flanker variant. Results provided evidence that participants can learn to strategically employ congruency cues to adapt their cognitive control and that these learned control strategies/routines can be transferred to a very similar task variant (near transfer). Further experiments will be performed to explore the extent of this transfer.

Long-Term Plausibility of Language Models and Neural Dynamics during Narrative Listening

Several popular sequence-based and pretrained language models have been found to be successful for text-driven prediction of brain activations. However, these models still lack long-term cognitive plausibility as well as insights on the underlying neural substrate mechanisms. This paper studies the influence of context representations of different language models such as sequence-based models: Long short-term memory networks (LSTMs) and ELMo, and popular pretrained Transformer language model (Longformer). In particular, we study how the internal hidden representations align with the brain activity observed via fMRI when the subjects listen to several narrative stories. We use brain imaging recordings of subjects listening to narrative stories to interpret word and sequence embeddings. We further investigate how the representations of language models layers reveal better semantic context during listening. Experiments across all language model representations provide the following cognitive insights: (i) the representations of LSTM cell states are better aligned with brain recordings than LSTM (hidden state), the cell state activity can represent more long-term information, (ii) the representations of ELMo and Longformer display a good predictive performance across brain regions for listening stimuli; (iii) Posterior Medial Cortex (PMC), Temporo-Parieto-Occipital junction (TPOJ), and Dorsal Frontal Lobe (DFL) have higher correlation versus Early Auditory (EAC) and Auditory Association Cortex (AAC).

Semantic priming across speakers and listeners of Latino varieties of English

We examine how the variation present in a Latino variety of English spoken by Miami-based Cuban Americans, which is not a foreign accent, affects processing for two distinct listener populations, General American English listeners and LA-based Mexican American English listeners. Past research has appealed to notions of standardness and familiarity when explaining processing costs associated with foreign and regional accents. Studying two listener populations that have different relationships with standard and Latino varieties of English has the potential to disentangle these factors (i.e. familiarity, standardness). Through three semantic priming experiments, which measure online processing, it’s shown that the variation present in Cuban American speech does not affect priming facilitation for General American English listeners or LA-based Mexican American listeners, suggesting that our human processing system is generally flexible at accommodating variation and that it’s worth studying the effects of variation at levels beyond the extremes.

Leveraging psychometrics of rational inattention to estimate individual differences in the capacity for cognitive control

Recent years have witnessed significant advances in our understanding of bounds on rationality in both cognitive psychology and economics. These two fields have been making separate progress, but time is ripe for unifying these efforts. In this article, we introduce recently developed economic tools, themselves rooted in the psychometric tradition, to quantify individual differences in the capacity for cognitive control. These tools suggest that a reliable assessment of the capacity for cognitive control may be accomplished by examining task performance as a function of reward. We demonstrate through simulation studies that an incentive-informed measure of task performance does a better job of recovering individual differences in one’s capacity for cognitive control, compared to the commonly used congruency effect. Furthermore, we show that the economic approach can be used to predict control-dependent behavior across different task settings. We conclude by discussing future directions for the fruitful integration of behavioral economics and cognitive psychology with the aim of improved measurement of individual differences in the capacity for cognitive control.

Interaction dynamics affect the emergence of compositional structure in cultural transmission of space-time mappings

People talk about time using the language of space. The future is "ahead." Endless events are "long." Cross-linguistically, these conventions exhibit both universality and striking diversity. These mappings in language, therefore, might originate from a combination of shared cognitive biases and sociocultural processes. To investigate the mechanisms involved in the emergence of space-time mappings - and linguistic metaphor more broadly - we conducted an experiment in which participants had to communicate about abstract temporal concepts using entirely spatial signals. The spatial signals developed by one pair of participants were then transmitted to the next pair, creating chains of multiple generations. Together, these processes of interaction and transmission sometimes generated fully systematic, compositional systems - although sometimes also generated systems that lacked structure entirely. The deciding factor may have been how people responded to errors - with incremental adjustments or radical reconfiguration. Systematic metaphors, therefore, may emerge from a heterogeneous mix of mechanisms.

Uninvited and unwanted: False memories for words predicted but not seen

Previous demonstrations of false memories for predicted but not presented words used slow encoding and immediate retrieval conditions, potentially exacerbating false memory effects. We present two experiments that investigated whether false memories also occur under self-paced encoding and delayed retrieval conditions, and whether false memories are reduced when the initial prediction was disconfirmed by an implausible word, thought to elicit false memory suppression. Results showed that previous demonstrations of false memories were not contingent on the task conditions: False memories also occur when language processing is self-paced, and they affect longer-term memory structures. Crucially, false memories emerged regardless of whether the prediction-disconfirming word was plausible or not. Results are evaluated against a recent psycho-linguistic account that makes diverging predictions regarding the processing consequences of mild and severe violations of plausibility.

Timing relationships between representational gestures and speech: A corpus based investigation

Theories suggest that representational gestures depicting properties of referents in accompanying speech could facilitate language production and comprehension. In order to shed light on how gesture and speech are coordinated during production, we investigate whether representational gestures are time-locked to the onset of utterances (hence planned when full events are encoded) or Lexical Affiliates (LAs; words most closely aligned with the gesture meaning; hence planned when individual concepts are encoded) in a large corpus of naturalistic conversation (n = 1803 gestures from n = 24 speakers). Our data shows that representational gestures are more tightly tied to LA onsets than utterance onsets, which is consistent with theories of multimodal communication in which gestures aid conceptual packaging or retrieval of individual concepts rather than events. We also demonstrate that in naturalistic speech, representational gestures tend to precede their LAs by around 370ms, which means that they could plausibly allow for an addressee to predict upcoming words (ter Bekke, Drijvers & Holler, 2021; Ferré, 2010; Habets et al., 2011).

Communicative need modulates lexical precision across semantic domains: A domain-level account of efficient communication

Different domains exhibit different degrees of lexical precision. Existing work has suggested that communicative need may modulate the precision of word meaning in individual domains. We extend this proposal across domains by asking why languages have more precise vocabulary in some domains than others. We hypothesize that lexical precision for a domain reflects how frequently speakers need to refer to it. We test this proposal using a cross-linguistic dataset of word-concept mappings for nine diverse domains from seven languages, and word frequencies from independent corpora. We find that the more frequent domains (except for kinship) tend to be more precise in every language, supporting a domain-level account of efficient communication on the precision of the lexicon.

Children, but not adults, prioritize relational over dispositional interpretations of dominance interactions

Humans routinely monitor social interactions to learn about the relational make-up of their groups and select social partners. It is unclear however whether social interactions primarily invite inferences about the dispositions of the participants involved or about underlying social relations. In the present study we tested which of these two inferences children and adults draw when observing interactions based on dominance. Children expected dominants to prevail over previous subordinates but did not generalize this expectation to interactions with novel agents, whereas adults did. These results suggest that children interpreted dominance as specific to a particular social relation, whereas adults interpreted it as a stable, target-invariant trait. This asymmetry supports the proposal that children may first interpret social interactions through a relational stance, and only later in development apprehend them through the lenses of trait attribution.

Effects of negation and knowledgeability on pragmatic inferences

Language use can be characterised as transparent, stating facts about the world, or non-transparent, requiring additional meaning to be inferred. The challenge faced by addressees is recognizing when language use is transparent or not. The current study investigates two factors that may influence how readily participants interpret utterances as instances of transparent or non-transparent language use; speaker knowledgeability and utterance form. When utterances involved negation participants were more likely to recognize this as non-transparent language use and infer that the situation is usually different. Whereas speaker knowledge did not influence how utterances were understood.

Accessibility factors that lead to good-enough language production

Accessibility plays a major role in speech production. Here we investigate and measure four factors that influence speakers to produce one word over another more optimal word form. Three experiments asked participants to label images of insects and instruments. Participants were incentivized to produce an accurate specific label (e.g., bee), over a more general label (e.g., insect), so that specific labels were more optimal. Each of three experiments manipulated a different factor that could influence accessibility – word frequency, priming, and interference – and all experiments additionally varied whether labels had to be produced under time pressure or not. Results showed that each variable significantly influenced the accessibility of labels: participants produced more specific labels when those labels were higher frequency, when they were primed, when a visually-similar label had not been primed, and when participants were unconstrained by time pressure. These findings demonstrate that multiple factors influence the accessibility of familiar words during production, regularly leading participants to rely on “good-enough” rather than optimal options to convey their message.

Why do People fit to Benford’s Law? – A Test of the Recognition Hypothesis

Burns & Krygier (2015) demonstrated that people could exhibit a strong bias towards the smaller first digits, in a way similar to that described by Benford’s law. This paper sought to expand the scope of this phenomenon and to test a possible explanation, the Recognition Hypothesis that a Benford bias is due to life-long environmental exposure to this statistical relationship. Participants completed three numerical tasks: A Generation Task requiring answering trivia questions; a Selection Task requiring selecting between two numerical responses; and an Estimation task requiring estimating the number of jelly beans in a jar. The results found no evidence of any first digit effect in the Recognition Task, some evidence of Benford bias in the Generation Task and strong evidence in the Estimation Task. Future research should focus on alternatives to the Recognition Hypothesis and investigate the parameters of Benford bias in generation tasks

How children talk about their desires: A corpus study of ‘want’

Children’s production of mental state verbs can reveal evidence of their theory of mind and general cognitive development. Children produce a certain class of mental state verbs, namely desire verbs such as want, wish, and hope, early in development. Among these desire verbs, they produce want the most frequently. We report on a corpus study of 450+ instances of want as gathered from children’s dialogues with caretakers in the CHILDES database. We developed a novel coding scheme to measure children’s use and understanding of want utterances: i.e., we sought to track the contents of their desires and the agents children predicated desires about. We report on the frequencies of these features across the ages of 2- 4, and highlight noteworthy trends in the way children learn to use want. Children appear to talk about their own desires most often; they primarily use questions to talk about second person desires; and they desire more complex objects as they mature. We describe how these patterns of linguistic competency may serve as an index of a developing theory of mind.

Integrating Experience into Bayesian Theory of Mind

Other people's mental states---what they want, what they know, and how they combine the two to act---are structured by the experiences that they've had. In line with this, we propose that inferences about other people's experiences are a central, but often neglected, aspect of human Theory of Mind. We explore this idea by presenting and testing a computational model that jointly infers others' desires, knowledge, and experience. We find that, by focusing inferences on others' experience, our model can make richer inferences about other's knowledge than would be otherwise possible. Our model quantitatively fits participant judgments on two experiments above an and beyond an alternative model. Overall, our work extends the richness of human Theory of Mind judgements that can be formalized as Bayesian inference over a generative model.

What’s in a Feature that an Object Concept May Have it?

We investigated how the properties of lexical items, which label object features, affect concept tokening. We addressed this issue by modeling data from three sources: (1) norms obtained from a dataset of 78,000 features to a set of pictures representing living and nonliving objects; (2) accuracy data from a picture-word priming congruency task with stimuli presented for 50-60 milliseconds; and (3) corpus data on the lexical properties of four different social usage count measures. We conducted two sets of analyses: one relying on sample count-based measures (i.e., measures based on the norming study: sample frequency, cue validity, feature distinctiveness), and a second relying on the social usage count-based measures (i.e., word frequency (WF), contextual diversity (CD), discourse contextual diversity (DCD), and user contextual diversity (UCD). Contrasting count and social usage-based measures allowed us to gain insight into the contribution of diverse semantic and socially oriented contextual measures of lexical items, and how they may affect concept tokening. Our results show that cue validity and feature distinctiveness were negative predictors of participants’ accuracy to congruency decisions—an effect which was more pronounced for distinctive features of living things. There was also a noticeable advantage for the UCD and DCD variables, over CD and WF. Overall, our results suggest that the conceptual system may be organized as a function of both, intrinsic properties of object features and usage based contextual measures of lexical items that label these features.

Metonymy as a Universal Cognitive Phenomenon: Evidence from Multilingual Lexicons

Metonymy is regarded as a universally shared cognitive phenomenon; as such, humans are taken to effortlessly produce and comprehend metonymic senses. However, experimental studies on metonymy have been focused on Western societies, and the linguistic data backing up claims of universality has not been large enough to provide conclusive evidence. We introduce a large-scale analysis of metonymy based on a lexical corpus of 20~thousand metonymy instances from 189 languages and 69 genera. No prior study, to our knowledge, is based on linguistic coverage as broad as ours. Drawing on corpus and statistical analysis, evidence of universality is found at three levels: systematic metonymy in general, particular metonymy patterns, and specific metonymy concepts. These findings imply that a shared conceptual structure for these patterns and concepts holds across societies.

Modeling cognitive diversity in group problem solving

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers (Hong and Page 2004). This striking claim about the power of cognitive diversity is highly influential within and outside academia, from democratic theory to management of teams in professional organizations. Our replication and analysis of the models used by Hong and Page suggests, however, that both the binary string model and its one-dimensional variant are inadequate for exploring the trade-off between cognitive diversity and ability. Diversity may sometimes beat ability, but the models fail to provide reliable evidence of if and when it does so. We suggest ways in which these important model templates can be improved.

Know your network: people infer cultural drift from network structure, and expect collaborating with more distant experts to improve innovation, but collaborating with network-neighbors to improve memory

We suggest that some of the mechanisms underlying network effects on cultural evolution are intuitively accessible to laypeople, and may be part of the suite of social learning strategies underlying the human capacity for cumulative culture. Interest in the psychological mechanisms underlying this capacity typically focuses on learners’ ability to identify reliable sources and capacity for high-fidelity imitation. Yet, at the population level, research suggests that network structures themselves may influence cumulative learning by changing individuals’ explore-exploit patterns. In our experiments, adults infer that more proximal or distal clusters in a fragmented network will have more similar or dissimilar technological “styles”, and prefer to seek advice from more distant experts when asked to innovate, but more proximate experts when asked to remember. Commonsense intuitions about how social networks shape our access to information and diversity- fidelity tradeoffs for memory and innovation may make us more effective social learners.

Data-driven Crosslinguistic Syntactic Transfer in Second Language Learning

Second-language (L2) learning is characterized by both positive and negative transfer from the first language (L1). However, psycholinguistic studies focus on a few syntactic phenomena and L1-L2 pairs at a time, resulting in an incomplete picture. We apply machine learning to seven learner corpora in English and Spanish with 39 language pairs, showing that statistical models combined with simple $n$-grams of part-of-speech tags and syntactic dependency relations achieve good performance in recovering the L1, indicating structural transfer from L1 to L2. Further machine learning using a rich hand-curated linguistic feature set allowed us to identify aspects of L2 linguistic structure particularly influenced by L1 (verbal morphology, average dependency tree parse depth, and headedness of clausal structures) as well as those with minimal influence (distributions of dependency relations, basic word orders, or non-projective dependencies).

Selection of goal-consistent acoustic environments by adults and preschool-aged children

Children are navigating a world with massive amounts of auditory input, sometimes relevant while other times purely noise, and must somehow make sense of it all. The early auditory environment is critical for speech perception and recognition, auditory discrimination, and word learning, all of which support language outcomes. What strategies do children use to learn in noisy environments? One potential strategy is environmental selection, which allows children to seek environments that align with particular goals. In the current paper, we examined whether children and adults make decisions about their environments by integrating auditory information and goal-states. While 3- and 4-year olds struggle with discriminating the level of noise in noisy speech streams (and likely do not use this information for environmental selection), 5-year-old children and adults can. Further, we show initial evidence that they can use this information to reason about acoustic environments that are consistent with specific goals.

Eliciting Human Beliefs using Random Generation

Elicitation methods, such as asking people to produce the deciles of a distribution, are standard practices in policy or applied statistics. However, these approaches often only capture a rough outline of what people know. We investigated whether tasks in which participants generate random sequences of items can be used to elicit people’s implicit beliefs about the distribution of these items. Because it remains unclear if, and at what level of detail, people represent distributions, we applied both decile elicitation and random generation tasks to uncover the kinds of environmental statistics investigated by Griffiths and Tenenbaum (2006). We found that random generation is competitive with decile elicitation in predicting participants’ expectations. Both random generation and decile elicitation revealed that people know the rough shapes of environmental distributions. Random generation, however, goes beyond decile elicitation in establishing the novel finding that people are aware of fine details of environmental distributions

What is moral ambiguity and when does it trigger curiosity?

Morality is a critical aspect of life––it influences how we think, design systems, and even the stories we tell. Looking to the popularity of true crime stories and characters like Dexter Morgan, it seems that our preferences are toward exploring moral ambiguity and moral badness. Across two experiments, we examine what moral ambiguity is and what kinds of moral information spark curiosity and explanation-seeking. In Experiment 1, we manipulate moral ambiguity to mean someone with conflicting moral character, and we predict those individuals will trigger curiosity more than morally consistent people. Results suggest that both morally ambiguous and immoral minds pique curiosity for explanations. In Experiment 2, we find that when ambiguity is instead operationalized as what is typical or average, we are curious about morally deviant things. This research points to critical differences in the kinds of moral minds we are curious to learn more about.

Manipulating the face contour affects face recognition performance leaving the Face Inversion Effect unaltered

The following studies investigated the perceptual processes that are the basis of the face inversion effect (FIE). We evaluated the effects of disrupting holistic information conveyed by the face contour/outline. In Experiment 1 (n=144) we blurred the contour of the faces and using an old/new recognition task we found that a robust inversion effect similar to that for normal faces remains for these new no-contour faces. However, a significant reduction in overall performance was found for no-contour vs normal faces. In Experiment 2 (n=74) instead of blurring we inserted a novel face contour to replace the normal one and found the same pattern of results as in Experiment 1. Our results suggest that the holistic information provided by the face contour does not on its own influence the FIE, however it plays a role in face recognition more generally.

Linguistic Encoding of Inferential Evidence for Events

How people learn about events often varies with some events perceived in their entirety and others are inferred based on the available evidence. Here, we investigate how children and adults linguistically encode the sources of their event knowledge. We focus on Turkish – a language that obligatorily encodes source of information for past events using two evidentiality markers. Children (4- to 5-year-olds and 6- to 7- year-olds) and adults watched and described events that they directly saw or inferred based on visual cues with manipulated degrees of indirectness. Overall, participants modified the evidential marking in their descriptions depending on (a) whether they saw or inferred the event and (b) the indirectness of the visual cues giving rise to an inference. There were no differences across age groups. These findings suggest that Turkish-speaking adults’ and children’s use of evidential markers are sensitive to the indirectness of the inferential evidence for events.

Multilingual and Bi-dialectal Irony Processing

We examined the effects of multilingualism and bi-dialectalism on irony interpretation by comparing multilingual, bi-dialectal, and monolingual young adults. We used an act-out task with three Meaning (literal positive, literal negative, ironic) and four Cue conditions (context-only, intonation-only, intonation + face, context + intonation + face). Results revealed that irony interpretation was (1) difficult, as shown by slower and less accurate responses to ironic compared to literal items; (2) facilitated by the presence of more ironic cues. Moreover, evidence suggested that linguistic context had a greater and facilitative effect on the speed of irony processing compared to literal meanings. Finally, we found no evidence for group differences in accuracy or speed of irony processing or in the way that different (combinations of) cues affected irony. Overall, our findings support a view of multilingual pragmatics according to which pragmatic interpretation is no different in multilinguals compared to monolinguals.

Reward Prediction Error Neurons Implement an Efficient Code for Reward

Dopaminergic reward prediction error neurons in the midbrain are the most prominent type of neurons encoding rewards. To explain the coding properties of these neurons, we apply the efficient coding framework to derive how neurons should encode rewards to maximize efficiency. The optimal populations qualitatively explain two recently made observations about real reward prediction error neurons: First, reward prediction error neurons represent rewards relative to a range of quantiles of the expected reward distribution, not relative to a single value. Second, the tuning of these neurons is asymmetric around their base firing rate and the asymmetry of each neuron is related to its threshold quantile. Furthermore, we achieve a good quantitative agreement with the neuronal recordings that were recently used to establish distributional reinforcement learning as a mechanistic explanation for these observations. Our analyses suggest the new interpretation that reward prediction error neurons might efficiently encode reward. Furthermore, it establishes an interesting theoretical link to the sensory processing literature, where efficient coding principles were developed.

Retrieval Practice Promotes Learning of Turkish as a Foreign Language: A Computer-Assisted Language Learning Study

Variation in second language acquisition is evident from earliest stages. This study examined effects of learning tasks (retrieval practice, comprehension, verbal repetition) on comprehension of Turkish as a new language. Undergraduates (N = 156) engaged with Turkish spoken dialogues in a computer-assisted language learning session via Zoom, with learning tasks manipulated between-subjects. Participants completed pre/posttests assessing comprehension of Turkish number and case marking, a vocabulary test, and open-response questions gauging explicit awareness. The retrieval-practice group showed highest performance overall, after controlling for significant effects of nonverbal ability and pretest. For comprehension of number/case marking, the comprehension group performed comparably to the retrieval-practice group. For vocabulary comprehension, the verbal-repetition group performed comparably to the retrieval-practice group. Differential performance associated with learning tasks indicates benefits of testing and production and aligns with transfer-appropriate processing. As predicted by the noticing hypothesis, explicit awareness of number and case marking correlated with comprehension accuracy.

Syntactic harmony arises from a domain-general learning bias

Syntactic harmony occurs when heads and dependents align within and across different types of phrases in a language. Harmony is a well-known (statistical) typological universal: in most languages, many if not all heads and dependents are consistently ordered (i.e., either head-dependent, or dependent-head). Despite decades of work, from every conceivable theoretical perspective, the origins of syntactic harmony remain opaque. However, recent work using artificial language learning has suggested that harmonic patterns are easier to learn than their non-harmonic counter-parts. Thus at least part of the explanation for this tendency may be linked to learning. Here, we explore whether the mechanism behind the learning bias for syntactic harmony is fundamentally domain-general by instantiating harmony in non-linguistic stimuli. Our findings support the claim that the origins of syntactic harmony lie in a domain-general bias for simplicity acting on linearized, language-specific categories.

Investigating Adults’ Strategy Use During Proportional Comparison

Adults show numerical interference during discrete proportional reasoning. Although children’s similar errors are attributed to incorrect counting strategies, it is unlikely that adults use a counting strategy. We investigate two behavioral phenomena of proportional reasoning, numerical interference errors and holistic ratio-dependent responding, and use a Bayesian model-based approach to test whether these behavioral patterns can be explained by adults’ differential use of numerator comparison versus proportion comparison strategies. We find evidence of numerator interference and holistic ratio dependent responding for both discrete (i.e., individual dots) and continuous (i.e., undivided pie charts) proportions, but numerical interference is stronger for discrete stimuli. Importantly, adults’ continuous proportion comparisons were best captured by a proportion strategy, whereas discrete proportion comparisons showed a mixed pattern, with a slight preference for a numerator strategy. These findings provide insight into the mechanisms underlying proportional reasoning and provide a novel model-based approach for investigating strategy use.

The speed of statistical perception

In virtually every activity we engage in — from analyzing economic trends, to predicting which of two football teams is more likely to win a game — our minds are tasked with separating signal from noise. Such computations benefit from the fact that our minds are highly attuned to the statistical structure of the world. But how quickly do we detect statistical structure — and to what extent is our sensitivity to structure rooted in perceptual processes? To address this, we asked observers to judge whether briefly presented visual stimuli were generated randomly or non-randomly. In as little as a tenth of a second, people exhibited the same stable biases of statistical perception that they exhibit in classic cognitive tasks (i.e., without time constraints). These results suggest that certain biases of subjective probability may arise not from how we think about randomness, but from how we perceive statistical information in the first place.

The Myside Bias in Argument Evaluation: A Bayesian Model

The "myside bias'' in evaluating arguments is an empirically well-confirmed phenomenon that consists of overweighting arguments that endorse one's beliefs or attack alternative beliefs while underweighting arguments that attack one's beliefs or defend alternative beliefs. This paper makes two contributions: First, it proposes a probabilistic model that adequately captures three salient features of myside bias in argument evaluation. Second, it provides a Bayesian justification of this model, thus showing that myside bias has a rational Bayesian explanation under certain conditions.

Limits on Neural Networks: Agent-First Strategy in Child Comprehension

This study investigates how neural networks reveal developmental trajectories of child language, focusing on the Agent-First strategy in comprehension of an active transitive construction in Korean. We develop three models (LSTM; BERT; GPT-2) and measure their classification performance on the test stimuli used in Shin (2021) involving scrambling and omission of constructional components at varying degrees. Results show that, despite some compatibility of these models’ performance with the children’s response patterns, their performance does not fully approximate the children’s utilisation of this strategy, demonstrating by-model and by-condition asymmetries. This study’s findings suggest that neural networks can utilise information about formal co-occurrences to access the intended message to a certain degree, but the outcome of this process may be substantially different from how a child (as a developing processor) engages in comprehension. This implies some limits of neural networks on revealing the developmental trajectories of child language.

Is the learning of artificial phonotactic rules interfered with by the concurrent experience of English?

Adults can rapidly learn new first-order phonotactic constraints like /f/ only occurs at the beginning of syllables, by producing strings of nonsense syllables such as "hes feng neg kem". The learning is measured by observing their speech errors, e.g., whether /f/’s then always slip to syllable onset position. Context-dependent (second-order) constraints such as /f/ occurs at the beginning of syllables if the vowel is /æ/, but occurs at the end of syllables if the vowel is /ı/ can be learned as well, but errors only follow these constraints after a period of sleep. It has been suggested that the knowledge of newly-learned second-order constraints is isolated from English knowledge in a separate "mini-grammar" and that the creation of the mini�grammar requires a period of sleep. The present study investigates the mini-grammar notion in the learning of first-order constraints, which are learned quickly in a single session. We interleaved trials in which participants produced strings of nonsense syllables with trials in which they repeated English sentences. The English sentences and nonsense sequences either showed the same consonant-position constraints or the opposite constraints. Speech error data showed that the English sentences interfered with the learning of the first-order constraints within the nonsense sequences, suggesting that the constraints in the nonsense context were not separated from ordinary English in a mini-grammar. We hypothesize that the formation of mini-grammars may require consolidation and that no mini�grammar is created for first-order constraint learning.

Effortful Control of Attention and Executive Function in Preschool Children

Attention is widely considered a core process of Executive Function (EF), but it is not clear if it is a separable or integral component of EF in preschool children. Preschool children (n=137) completed a battery of tasks which included EF (i.e., response inhibition, working memory) and attentional control (AC) processes (i.e., sustained attention, selective attention). Confirmatory Factor Analyses (CFA) indicated that a two-factor model with EF and AC as separate factors fit the data better than a unitary one-factor model. These findings are consistent with the view that EF and AC are developing at different rates during the preschool years, and thus are not yet fully integrated in the processing of information. The implications of how EF and AC should be conceptualized in early childhood are discussed.

Learning Biases for Syncretic Morphological Systems

Morphological syncretism occurs in languages when one morphological category ‘merges’ with another. Cross-linguistic research on the prevalence and types of syncretic patterns has revealed that some types of syncretism are more common than others. For example, syncretism in nominal morphology is more likely to occur in non-singular categories (Baerman et al., 2005). In two artificial language learning experiments, participants were exposed to words from a miniature language with suffix markings for gender (feminine and masculine) and number (singular, dual, and plural). Participants in Experiment 1 showed no evidence of a bias for syncretism in non-singular forms. However, participants in Experiment 2 showed a general bias to infer that a suffix that marked a novel category should be identical to a known form. This bias was strongest for non-singular items, in line with the cross-linguistic typology of syncretism. Implications for learnability and typology are discussed.

The paradox of learning categories from rare examples: a case study of NFTs & The Bored Ape Yacht Club

Collectible items, such as stamps, coins, paintings, and trading cards, are often valued for their rarity. A side effect of rarer items being more highly valued is that they are also more often traded, discussed, and displayed. A new collector's experience of the category defined by a set collectible items is thus heavily biased towards the rare items. Theories of category learning predict that these conditions make for a uniquely challenging environment in which to learn a category because rarity-based sampling can invert the distribution of associated attribute frequencies. Here, we show that under these conditions, the demand for rarity is self-defeating: when newcomers do not correct for the sampling bias present in their experience, they will have a distorted sense of the category and misunderstand which items are in fact rare, causing rarity to become devalued over time. We find evidence for this dynamic in the context of The Bored Ape Yacht Club (BAYC), a collection of 10,000 non-fungible tokens (NFTs), each with a set of attributes that vary in rarity. We demonstrate that, in line with our theory, over time the influx of newcomers learning about BAYC has been associated with a decrease in the demand for tokens with rare attributes.

Pupil Diameter as Implicit Measure to Estimate Sense of Embodiment

We explore pupil diameter (PD) as estimator of Sense of Embodiment (SoE) using data of three user studies. We hypothesize that pupil diameter reflects SoE in a direct and indirect way. If individuals feel strongly embodied, presenting an emotional stimulus like a threat to the surrogate will produce a strong response, as if the stimulus would be presented to their own body. This would lead to a positive correlation between SoE and pupil dilation during the presentation of emotional stimuli. Besides this direct effect, there may also be an indirect effect. It is postulated that higher degrees of embodiment reduce workload when controlling a surrogate. This indirect effect of embodiment through lower workload on the PD would result in a negative correlation between SoE and PD since lower workload results in smaller PD. These direct and indirect effects were partially confirmed by the results of three experiments. We observed that PD and SoE are positive and direct correlated in case of emotional stimuli subjected to the surrogate (e.g. a threat), and that PD tended to be smaller for participants who experienced a condition designed to provide high SoE compared to one designed to provide low SoE.

Powering up causal generalization: A model of human conceptual bootstrapping with adaptor grammars

Human learning and generalization benefit from bootstrapping: we arrive at complex concepts by starting small and building upon past successes. In this paper, we examine a computational account of causal conceptual bootstrapping, and describe a novel experiment in which the sequence of training data results in a dramatic order effect: participants succeed in identifying a compound concept only after experiencing training data in a “helpful” order. Our computational model represents causal relations as reusable, modular programs, which can themselves be “chunked” and flexibly reused to tackle more complex tasks. Our specific approach is based in combinatory logic and adaptor grammars, building on previous theories that posit a “language of thought” for concept representation, but making the learning process more sensitive to a learner’s experiences than any particular choice of conceptual primitives. Crucially, we demonstrate that a caching mechanism like that used in adaptor grammars is key to explain human-like bootstrapping patterns in causal generalization.

Aligning Language and Memory Accounts of Semantic Interference

Parallel accounts of interference resulting from the generation of related words can be found in the retrieval-induced forgetting (RIF) and the cumulative semantic interference literatures. Recent work on the language production side suggests that the same adaptive learning process may underlie both. However, the literatures remain separate. They use different procedures and dependent measures, and theoretical accounts focus on underlying conceptual representations (memory research) vs. conceptual-lexical links (language research). We propose that the accounts should be reconciled. As an initial step toward this goal we combined a retrieval/generation procedure with a continuous picture-naming test phase to assess their combined effects on interference. We observed both costs and benefits in error data. There were more naming errors (including many time-outs) for non-generated items from activated categories and fewer for previously generated items. Perhaps due to a too-severe cutoff, naming times did not show a RIF influence, only a marginal facilitation effect for generated items. However, naming time showed typical cumulative interference within the picture-naming phase independent of previous retrieval experience. Future work will investigate the locus of interference in conceptual memory representations versus in links to word representations with the goal of producing a unified account of semantic interference.

Bayesian rational memory model simulates temporal binding effect

Temporal Binding (TB) is standardly regarded as an implicit measure of the sense of agency (Haggard, 2017). Though the TB effect is robust, an underlying mechanism has not been agreed upon (Hoerl et al., 2020). Here we propose a memory process as an explanation for the observed error in two publicly available datasets. We first replotted the data and found that on average, across both experiments, participants overestimate the length of the shortest timing interval and underestimate the longest interval, a classic regression to the mean pattern. Summary statistics extracted from the data from each experiment were then used as parameters in a simple Bayesian model of memory. Model simulations reproduced the behavioral data for almost all timing intervals and experimental trial-types across two experiments. Adjusting one of the parameters in the model (prior mean for actions) resulted in an improved qualitative fit. We suggest that other more likely sources of error, apart from experienced agency, may account for this result.

Does Mental Effort Avoidance Depend on the ‘Type of Effort’?

The propensity for people to avoid mentally demanding tasks in the absence of reward is well documented. As a result, humans are often described as cognitive misers. This characterisation, while consistent with the psychological literature, contradicts everyday instances of effort being sought: reading, board games, and brain-teasing puzzles. Such examples however are markedly different from the types of tasks typically used in the mental effort literature (e.g., working memory tasks, demand selection tasks). The current set of experiments assessed whether the type of task (i.e., N-Back, Number Sequence Problems [NSP], or Anagrams) affects people’s aversion to, or desire for, increased effort. On average, across 3 experiments, participants showed an aversion to effort regardless of whether the effort required was more attentional (N-Back) or cognitive (NSP and anagrams) in nature, and were willing to forgo financial reward in order to avoid more difficult tasks. A minority of participants, however, sought more effortful tasks for equal or lesser reward.

Efficient and Effortful Theory of Mind Reasoning in the AToM Cognitive Model

Apperly and Butterfill (2009) argue that adult theory of mind (ToM) requires two parallel systems. One system, efficient but inflexible, enables rapid judgements by operating without explicit modeling of beliefs, while a separate, effortful system, enables richer predictions over more complex belief encodings. Here, we agree with their qualitative distinction but propose a different model: a single process, but with effortful re-representation leading to two phases of ToM reasoning. Efficient reasoning, in our view, occurs over representations that include actions, but not necessarily explicit belief states. Effortful reasoning, then, involves re-representation of these initial encodings in order to handle errors, resolve real-world conflicts, and fully account for others’ belief states. We present an implemented computational model, based in memory retrieval and structural alignment, that illustrates our approach.

Familial Guilt: A Cross-Society Comparison of Judgments of Collective Family Responsibility

When a group member commits wrongdoing, people sometimes assign responsibility and blame not only to the wrongdoer but also to other members of the same group. We examined such assignment of collective responsibility in the context of exploitation of one family by another. Participants were recruited from the United States and South Korea, which are known to vary in cultural norms and endorsement of collectivistic values. Participants in both countries rated the degree to which an agent (grandson) should be held responsible for his grandfather’s exploitation of a victimized family, while varying the closeness of familial connection. Participants’ responsibility judgments showed sensitivity to whether the grandson received financial benefit from the wrongdoer and to the perceived closeness between the grandson and the wrongdoer. Korean participants imposed greater responsibility on the agent than did American participants. Implications for understanding the influence of social norms on moral judgments are discussed.

Representations of emotion concepts: Comparison across pairwise, appraisal feature-based, and word embedding-based similarity spaces

A question that has long interested cognitive scientists is how to best represent the different emotions we experience and attribute to others. For example, constructionist and appraisal theories propose that differences between emotions can be captured in part by their variation along a set of appraisal dimensions. More recently, researchers have used language models to capture the differences across different emotion terms. Both approaches allow us to represent emotions as occupying different locations in high-dimensional representational spaces. To ask how well these different approaches capture the similarity between emotion concepts, we collected pairwise similarity and appraisal feature ratings for 58 different emotion concepts and then employed representational similarity analysis to investigate the overlap between people’s pairwise similarity judgments and emotion similarity in a 14-dimensional appraisal space and three word embedding spaces from two word2vec models (300 dimensions) and the newer GPT-3 model (12288 dimensions). The results indicate that while there is a high correlation between appraisal feature-based similarity and pairwise similarity judgments, word embedding-based similarity exhibits lower correlations, though GPT-3 showed much better performance than the word2vec models. Finally, characterizing the errors made by word embedding models showed that they can be largely attributed to an over-reliance on the valence of emotion concepts.

IPOWER: Incremental, Probabilistic, Open-World Reference Resolution

Referring expression understanding and generation are critical for robots to communicate about the world around them. Recently there have been significant advances on the problem of referring expression understanding, also known as reference resolution, with researchers presenting approaches to both incremental reference resolution (i.e., processing referring expressions word by word in real-time as they are spoken) and open-world reference resolution (i.e., resolving references both to known and previously unknown entities). In this work, we combine insights from these approaches to present IPOWER: the first algorithm for performing reference resolution incrementally in open-world environments.

Clickbait’s Impact on Visual Attention – An Eye Tracker Study

In this paper, we have studied the impact of clickbait headlines on the distribution of visual attention on hyperlinked news articles. Visual attention is a driving factor in ad-based revenue models that support online journalism. Importantly, it is also an indicator of cognitive processes involved in reading and comprehension. We hypothesize that articles with clickbait headlines receive lesser visual attention when controlled for articles’ content. This is based on the premise that a significant proportion of clicks on clickbait headlines are driven by readers’ specific epistemic curiosity rather than knowledge acquisition. An eye-tracker setup was used to infer visual attention from the gaze-fixation analysis conducted on data from 60 participants. Our results suggest that clickbait headlines significantly reduce the visual attention on news articles. Though, article content comprehension measured by a recall test was comparable for clickbait and non-clickbait headlines. Our findings add to the discussions on the cognitive attention and the implications of using clickbait headlines for news publishers, newsreaders, and advertising agencies alike.

Comparing Machine and Human Learning in a Planning Task of Intermediate Complexity

Deep reinforcement learning agents such as AlphaZero have achieved superhuman strength in complex combinatorial games. By contrast, the cognitive science of planning has mostly focused on simple tasks for experimental and computational tractability. Using a board game that strikes a balance between complexity and tractability, we find that AlphaZero agents improve in value function quality and planning depth through learning, similar to human in previous modeling work. In addition, these metrics reflect causal contributions to AlphaZero's playing strength. Yet the strongest contributor is the policy quality. The decrease in policy entropy also drives the increase in planning depth. The contribution of planning depth to performance is lessened in late training. These results contribute to a joint understanding of machine and human planning, providing an interpretable way of understanding the learning and strength of AlphaZero, while generating novel hypothesis on human planning.

From social identity to meaning interpretation: when looser speakers are treated more strictly

We explore the impact of speaker identity on the interpretation of number words in a T(ruth)-V(alue) J(udgment) task – a paradigm in which respondents assess whether a given description appropriately represents a given body of facts. We find that imprecise statements from speakers socially expected to be less precise – i.e. “Chill” ones – are rejected at a higher rate, and thus held to more stringent evaluation standards, than those from speakers socially expected to speak more precisely – i.e. “Nerdy” ones, and especially so when participants do not identify with the speaker’s properties. This shows that TVJ assessments are impacted by respondents’ social perception of the speaker; but that they are affected by social considerations in a different way from other experimental tasks similarly tapping into meaning interpretation, suggesting a nuanced interplay between social information and pragmatic reasoning

Modeling aberrant volatility estimates in Autism Spectrum Disorder

Computational cognitive theories of Autism Spectrum Disorder have received renewed attention in recent years. Consistent with the predictive processing framework, ASD has been re-conceptualized as a disorder of aberrant prediction and learning-rate estimation involving multiple levels of a putative cognitive computational hierarchy. Specifically, behavioral symptoms of individuals with ASD might manifest due to an aberrant overestimation of the volatility of environmental contingencies (i.e. tendency of change in cue-outcome probabilities) which in turn might induce a dysfunctional setting of learning rates. In this work, we attempted to conceptually replicate computational modeling analyses of an impactful study of the recent ASD modeling literature in an independent sample of subjects. We were not able to replicate some prior reported effects likely due to differences in model architecture and cognitive task setup. We found statistical trends in similar directions.

Modeling Sentence Processing Effects in Bilingual Speakers: A Comparison of Neural Architectures

Neural language models are commonly used to study language processing in human speakers, and several studies trained such models on two languages to simulate bilingual speakers. Surprisingly, no work systematically evaluates different neural architectures on bilingual speakers’ data, despite the abundance of such studies in the monolingual domain. In this work, we take the first step in this direction. We train three neural architectures (SRN, LSTM, and Transformer) on Dutch and English data and evaluate them on two data sets from experimental studies. Our goal is to investigate which architectures can reproduce the cognate facilitation effect and grammaticality illusion observed in bilingual speakers. While all three architectures can correctly predict the cognate effect, only the SRN succeeds at the grammaticality illusion. We additionally show how the observed patterns change as a function of the models’ hidden layer size, a hyperparameter that we argue may be more important in bilingual models.

Satiation effects generalize across island types

A recent proposal of syntactic satiation claims that it is driven by adaptation: comprehenders track and update their beliefs about the probability of observing certain sentences, leading to subsequent increases in the acceptability of those sentences. This leaves open what the representational targets of satiation are, that is: what is the tracked information that belief update is based on? In two acceptability judgment experiments, we show that exposure to one type of island violation can lead to the satiation of another island type, suggesting that island type-general representations are tracked by comprehenders in addition to island type-specific representations. The same experimental paradigm can be used for further exploration of the representational targets of satiation.

Extending the Predictive Performance Equation to Account for Multivariate Performance

Adaptive scheduling systems aim to estimate the ability of an individual in order to prescribe a personalized training schedule. These adaptive systems are often founded on regularities of human memory such as a learning, forgetting, and the spacing effect. One such model which has been developed to both account for regularities of memory and be used in applied contexts is the Predictive Performance Equation (PPE). One limitation of the PPE is that it is only able to account for and incorporate information about a participant’s accuracy on a task and cannot take into account additional performance measures such as reaction time. To expand the PPE, we propose a simple extension to the model, allowing it to account for both accuracy and reaction time measures. Our paper reports the extension to the PPE as well as a formal model comparison to another model of learning and retention (Pavlik and Anderson, 2005). The results of our model comparison reveal that the extended PPE can both better account and predict an individual’s performance than Pavlik and Anderson (2005) model.

Do humans recalibrate the confidence of advisers or take their confidence at face value?

Who we choose to learn from is influenced by the relative confidence of potential informants. More confident advisers are preferred based on an assumption that confidence is a good indicator of accuracy. However, oftentimes, accuracy and confidence are not calibrated, either due to strategic manipulations of confidence or unintentional failures of metacognition. When accuracy information is readily available, people are additionally vigilant to the calibration of informants, penalizing incorrect, yet confident advisers (Tenney et al., 2007). The current experiment tested whether participants can leverage inferences about two advisers' calibration profiles to make optimal trial-by-trial decisions. We predicted that choice of advisers reflects relative differences in the advisers' probability of being correct given their stated confidence (recalibrated confidence), as opposed to stated confidence differences. The prediction was not supported by data, but calibration had a modulating effect on choices, as more confident advisers were more influential only when they were also calibrated.

Conceptual Prerequisites for Proportional Analogy

Analogy plays an important role in cognitive development, but children often need cognitive supports to draw correct ones. Here, we examined the role of conceptual knowledge in proportional analogies, which are often depicted as a simple exercise in pattern completion. In Study 1, adults and children (N = 321) completed 4-term analogy tasks featuring letters, lines, integers, or fractions. Performance was lowest for fractions, and strongly impacted by educational background. In Study 2, we conducted an educational intervention focusing on either conceptual knowledge, procedural knowledge, or both for 3rd-to-5th graders (N = 343) using a pretest-training-posttest design. Children with poor pretest magnitude knowledge were more likely to fail analogical reasoning, and training on conceptual knowledge that fractions denote magnitudes improved children’s analogies. Together, these studies indicate that knowledge of fractional magnitudes is important to proportional analogy.

Query-Based Memory Approximates Rational Induction: Applications to Infant Statistical Learning

Query-Based Memory (QBM) models are heavily used in machine learning, though their relevance to human cognition is unclear. In this paper, we explore QBM models through both formal exploration and a simulation study to address this question. We found that QBM models are theoretically motivated, as they approximate rational induction with neurally-plausible mechanisms. Additionally, a simple implementation of the model could readily reproduce four benchmark findings in infant statistical learning. These results provide an encouraging starting point for further research using these formal tools to understand cognition across development.

The Neural Correlates of the Effect of Belief in Free Will on Third-Party Punishment: An Optical Brain Imaging (fNIRS) Study

Third party punishment (TPP), or altruistic punishment, is specifically human prosocial behavior. TPP denotes the administration of a sanction to a transgressor by an individual that is not affected by the transgression. In some evolutionary accounts, TPP is considered crucial for the stability of cooperation and solidarity in larger groups formed by genetically unrelated individuals. Belief in free will (BFW), on the other hand, is the idea that humans have control over their behavior. BFW is a human universal notion that, in some studies, has been found to be supportive of prosocial behavior. In our study, we examined the effect of BFW on TPP under high and low affect scenarios through optical brain imaging (fNIRS). We hypothesized that in low affect cases, there would be a positive correlation between the strength of the BFW and the severity of the punishment inflicted. Obtained results and related statistical analyses indicate that participants with higher degree of BFW have more neural activation in their right dorsolateral prefrontal cortex (DLPFC) (hbo and hbt measures) in high affect scenarios, whereas the participants with lower degree of BFW have higher levels of neural activation in the medial PFC (hbo and hbt measures) in low affect scenarios. These empirical findings are in line with the research findings in the relevant academic literature and support the hypothesis that the degree of BFW influences punishment decisions.

How ‘Good-Enough’ is L2 Sentence Comprehension? Evidence from Suffixal Passive Construction in Korean

This study investigates how L2 learners achieve the ‘good-enough’ comprehension in Korean. We focus on a suffixal passive construction, given the scarcity of this construction in the L2 textbook input. Results from acceptability judgement and self-paced reading tasks suggest two aspects of L2 comprehension. First, L1 and L2 comprehension do not qualitatively differ regarding ‘good-enough’ processing: the L2 processor utilises both heuristic and algorithmic parsing to reduce the burden of work at hand. Second, the divergence of L1 and L2 processing behaviours during comprehension may originate from various factors around L2 learners (e.g., L2 input, L1–L2 interface, task types), which are assumed to anchor the noisier representations of L2 knowledge.

Children’s Acquisition of the Concept of Antonym Across Different Lexical Classes

Understanding abstract relations, and reasoning about various instantiations of the same relation, is an important marker in human cognition. Here we focus on development of understanding for the concept of antonymy. We examined whether four- and five-year-olds (N= 67) are able to complete an analogy task involving antonyms, whether language cues facilitate children’s ability to reason about the antonym relation, and how their performance compares with that of two vector-based computational models. We found that explicit relation labels in the form of a relation phrase (“opposites”) improved performance on the task for five-year-olds but not four-year-olds. Five-year-old (but not four-year-old) children were more accurate for adjective and verb antonyms than for noun antonyms. Two computational models showed substantial variability in performance across different lexical classes, and in some cases fell short of children’s accuracy levels. These results suggest that young children acquire a solid understanding of the abstract relation of opposites, and can generalize it to various instantiations across different lexical classes. These developmental results challenge relation models based on vector semantics, and highlight the importance of examining performance across different parts of speech.

A task-general model of human randomization

Does the human mind contain a task-general ‘randomization machine’? Stable biases of randomization have been identified that span multiple domains and modalities, in both lower-level perceptual tasks and in higher-level cognitive tasks. The stability of such biases indicates that the mind may rely on a stable set of properties to create and perceive randomness. But what computational principles support randomization? Here, we approach this question by building a computational model of human randomization that generalizes across spatial and numerical tasks. We show that simple computational heuristics capture higher-order properties of human-generated random sequences, in both numerical and spatial randomization tasks each with many possible options. Furthermore, we show that human behavior in both types of tasks can be approximated by the same low-dimensional model, implying that a domain-general set of computational principles may underlie randomization behavior in general.

Some forms of uncertainty may suppress the evolution of social learning

Social learning is essential to survival. It is likely to evolve when it is more efficient than asocial, trial-and-error learning. The consensus in cultural evolutionary theory holds that some amount of environmental variability and uncertainty about the best decisions are necessary for social learning to evolve. However, current models for the evolution of social learning tend to conflate forms of uncertainty, and rarely consider different ones in tandem. Moreover, many models are limited by considering only two possible behaviors and environmental states. Here we use evolutionary agent-based modeling to identify the complex ways in which different forms of uncertainty affect social learning. We model a time-varying environment with dozens of possible behaviors performed by agents engaging in individual and social learning. We show that ambiguous payoffs, larger possible decision sets, and shorter agent lifespans sometimes increase social learning prevalence, as expected. However we also find which concrete uncertainty conditions cause evolution to select against social learning.

UK bilingual toddlers show a lag in vocabulary size relative to monolinguals in both comprehension and production

A widely researched question in bilingualism asks whether bilinguals’ vocabulary growth is equal to or lower than that of monolinguals. Some studies have found smaller vocabularies in bilingual toddlers than monolingual toddlers when comparing in one language, but others have found no significant group differences. We compared 12 to 32-month-old bilingual toddlers growing up in the UK with English and one additional language (AL) to age-matched UK English monolinguals. We evaluated both vocabulary size in English and conceptual vocabulary. Bilinguals’ English vocabulary sizes in both comprehension and production were significantly smaller than monolinguals’ after controlling for age and socioeconomic status. This was seen across bilinguals of different levels of language dominance. The bilingual lag in vocabulary size was smaller when calculated using conceptual vocabulary but still significant for both comprehension and production. We discuss the implications for measurements of bilingual toddlers’ vocabulary size.

Identifying concept libraries from language about object structure

Our understanding of the visual world goes beyond naming objects, encompassing our ability to parse objects into meaningful parts, attributes, and relations. In this work, we leverage natural language descriptions for a diverse set of 2K procedurally generated objects to identify the parts people use and the principles leading these parts to be favored over others.We formalize our problem as search over a space of program libraries that contain different part concepts, using tools from machine translation to evaluate how well programs expressed in each library align to human language. By combining naturalistic language at scale with structured program representations, we discover a fundamental information-theoretic tradeoff governing the part concepts people name: people favor a lexicon that allows concise descriptions of each object, while also minimizing the size of the lexicon itself.

Can Children Detect Fake News?

Fake news has permeated online media, presenting consumers with the challenge of detecting it. At what age are we capable of undertaking this challenge? And what factors predict success? We explored these questions with elementary-school-aged children (n = 86), who were asked to judge the veracity of ten news stories, five fake and five real. Children also completed a developmental version of the cognitive reflection test (CRT-D; Young & Shtulman, 2020a). As a group, children were at chance at differentiating fake news from real news, and their individual performance did not vary by age or cognitive reflection. Adults (n = 271) given the same materials succeeded at detecting fake news, especially those high in cognitive reflection. These results suggest that children lack the knowledge or skill needed to evaluate news credibility and that cognitive reflection predicts fake news detection only after we have attained some baseline level of information literacy.

The relationship between individual differences in mental imagery vividness and emotional distress

Mental imagery is theorized to play a key role in mood and mood disorders due to the emotional impact of visualizations and biases in the processing of negative versus positive imagery. Although differences in emotional imagery have been linked to mental health outcomes, it is unclear if individuals experiencing emotional distress differ in their baseline ability to generate mental images (i.e., ‘imagery ability’). Recent research has highlighted linkages between imagery ability and facets of trait mindfulness, such as the tendency to observe and describe inner thoughts. Thus, we suspected that individual differences in trait mindfulness may help explain inconsistent findings regarding the relationship between imagery ability and emotional distress. A path analysis revealed that trait mindfulness significantly and fully mediated the relationship between imagery vividness and depression, indicating that mindfulness is a critical aspect of imagery phenomenology, as well as emphasizing the importance of mindfulness to mental health.

Intolerant Data: Testing The Tolerance Principle

Rule-based learning is an important aspect of language acquisition. Yang (2005,2016) proposed the Tolerance Principle (TP) to predict when a rule will be formed by the language learner. We present the derivation of the TP as originally proposed and test it on both hypothetical data and corpus data from 8 children. Results for the hypothetical data contradict the TP’s predictions, as do the data from 7 of the 8 children. We conclude that the original form of the TP does not explain rule-learning.

An End-to-End Imagery-Based Modeling of Solving Geometric Analogy Problems

Geometric analogy problems remain an intriguing part of intelligence scales, which is closely correlated to many cognitive studies, such as perception, conception, memory, abstract and inductive reasoning. The problems not only target the most fundamental element --- analogy-making --- in human cognition, but also require integration of multiple components and stages: looking at the test booklet, thinking for a minute or two, and deciding the answer. Great efforts and achievements have been made to explain different individual aspects of this process. In this paper, we take a more holistic approach from the perspective of problem-solving, by modeling the entire process, from the moment the visual stimuli are received to the moment an answer is decided. Therefore, we explore how the final solution can be built upon visual inputs and necessary components that lie between the perceptual input and conceptual output. Particularly, we designed a novel similarity metric and a correspondence-finding method based on mapping and optimization. With these two basic blocks, we implemented a computational model, and report our initial results on a classical problem set.

Cognitive and Emotional Impact of Politically-polarized Internet Memes About Climate Change

Public opinion polls have shown that beliefs about climate change have become increasingly polarized in the United States. A popular contemporary form of communication relevant to beliefs about climate change involves digital artifacts known as memes. The present study investigated whether memes can influence the assessment of scientific data about climate change, and whether their impact differs between political liberals and conservatives in the United States. In Study 1, we considered three hypotheses about the potential impact of memes on strongly-held politicized beliefs: 1) memes fundamentally serve social functions, and do not actually impact cognitive assessments of objective information; 2) politically incongruent memes will have a “backfire” effect; and 3) memes can indeed change assessments of scientific data about climate change, even for people with strong entering beliefs. We found evidence in support of the hypothesis that memes have the potential to change assessments of scientific information about climate change. Study 2 explored whether different partisan pages that post climate change memes elicit different emotions from their audiences, as well as how climate change is discussed in different ways by those at opposite ends of the political spectrum. Keywords: climate change, memes, metaphor, politics, beliefs, topic models

The role of reading test strategy in reading comprehension: An eye-movement study

The study examined the role of reading test strategies in reading comprehension performance of children by analyzing their eye-movements during a reading comprehension test. The Eye Movement analysis with Hidden Markov Model (EMHMM) with co-clustering discovered two representative eye movement pattern groups, with one more flexibly attending to either the passage beginning or the question in the beginning of the test displayed and attending to more contextual information in answering inferential questions. Participants adopted the more strategic pattern outperformed the other group in cognitive-linguistic skills and reading comprehension. Also, by quantifying participants’ eye movement behaviors along the contrast between the two pattern groups, their eye movement behavior explained unique variance on reading comprehension performance beyond general cognitive abilities and reading-related cognitive-linguistic skills. Thus, reading test strategy plays an important role in accounting for reading comprehension performance. These findings have important educational implications on teaching reading test strategies to help children improve comprehension performance.

The Role of Verb-Event Structure in Children’s Lexical Ambiguity Resolution

Recent evidence indicates that children represent and learn multiple meanings of ambiguous words from early in development (e.g., mail letter, alphabetic letter). This raises the question of which naturalistic factors might allow young children to resolve lexical ambiguities. Previous research has shown that children’s processing of ambiguous words is facilitated by verb-related information. However, it is still unclear whether such facilitation comes from bottom-up (lexical associations) or top-down information sources (verb-event structures). In this study, we leveraged a large sense-annotated child-directed speech corpus to disentangle the effect of bottom-up lexical and top-down event structure cues. Preliminary results show that 4-year-olds might rely on verb-event structures when these are put in competition with lexical association. We discuss implications for theories of sentence parsing and word learning.

Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning

Speakers' referential expressions often depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. Patterns of overmodification, in which a speaker uses a modifier that is redundant given their communicative goal, have proven especially informative in this regard. It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways. Unfortunately, systematically manipulating these factors during human language acquisition is impossible. In this paper, we propose to address this limitation by adopting neural networks (NN) as learning agents. By systematically varying the environments in which these agents are trained, while keeping the NN architecture constant, we show that overmodification is more likely with environmental features that are infrequent or salient. We show that these findings emerge naturally in the context of a probabilistic model of pragmatic communication.

Bayesian gates: a probabilistic modeling tool for temporal segmentation of sensory streams into sequences of perceptual accumulators

To explain how perception processes are performed, understanding how continuous sensory streams are temporally segmented into discrete units is central. This is particularly the case in speech perception where temporal segmentation is key for identifying linguistic units contained between consecutive events in time. We propose an original probabilistic construct, that we call "Bayesian gates", to segment temporally continuous streams of sensory stimuli into sequences of decoders. We first define Bayesian gates mathematically and describe their properties. We then illustrate their behavior in the context of a model of word recognition in speech perception. We show that, based on an event detection module, they sequentially parse the acoustic stimulus, so that each syllable decoder only processes a segment of the sensory signal.

Modeling the regular/irregular dissociation in non-fluent aphasia in a recurrent neural network

In the debate between single-route and dual-route models of verb inflection, the dissociation between regular and irregular verbs in the non-fluent variety of aphasia has been a key sticking point for the proponents of the single-route model. This paper adopts a state-of-the-art neural model which has previously been used to learn inflectional morphology, and shows that it can also be used to model data from non-fluent aphasia. This challenges the assumption that a dual-route model is necessary to capture apparent dissociations in aphasia data and encourages a reanalysis of the deficits involved in non-fluent aphasia.

Cognitive Differences in Human and AI Explanation

How do humans explain and cognize visual information? Why do AI explanations in radiology, despite their remarkable accuracy, fail to gain human trust? In a study of 13 radiology practitioners, we found that AI explanations of x-rays differ from human explanations in 3 ways. The first concerns visual reasoning and evidence: how humans get other humans to see an interpretation’s validity. Machine learned classifications lack this evidentiary grounding, and consequently XAI explanations like heat maps fail to meet many users' needs. The second concerns the varying needs of interlocutors. Predictably, explanations suitable for experts and novices differ; presuppositions on explainee knowledge and goals inform explanation content. Pragmatics matter. The third difference concerns how linguistic terms and phrases are used to hedge uncertainty. There is no reason XAI might not satisfy these human requirements. To do so, however, will require deeper theories of human explanation.

Bridging cultural and cognitive perspectives on similarity reasoning

Is a cow more closely related to grass or to a chicken? Responses vary by culture and age, among other factors. Those from western societies (or independent-leaning regions within interdependent non-western societies) are more likely to endorse the taxonomic match, the chicken, over the thematic match, grass (Chiu, 1972; Talhelm et al., 2014). This preference has been documented -- largely in western cultures -- to increase over development (e.g., Smiley & Brown, 1979). While neither development nor culture occur independently of the other, comparisons across these areas are problematic. We address one potential barrier to comparing cultural and developmental research using this classic paradigm -- stimulus format -- and show that the use of text (versus image) stimuli can bias participants toward taxonomic responding in some contexts. We present stimuli designed for cross-cultural use with children and adults and document country, regional, and demographic variation across the US and Italy.

Sampling-based probability construction explains individual differences in risk preference

Contemporary models of subjective probability distortions assume that distortions arise during probability encoding. However, such assumptions are inconsistent with the ability of humans to retrieve probabilities veridically in some elicitation formats. We present a sampling-based model of probability judgment for risky prospects that assumes that probability distortions occur because people read out probability judgments as biased averages from working memory contents. Simulations demonstrate that this model shows the classic inverse-S shaped distortion of probability judgments using only retrieval-stage assumptions. The model further predicts that observers with greater working memory capacity would show larger probability distortions on average, which should lead to a particular fourfold pattern of risk preference as a function of working memory capacity. Using cognitive ability measurements as a proxy for working memory capacity, we conducted an experiment with human participants and found results consistent with the model's predictions as well as previous empirical studies. Our results support a role for sampling during assessment of risky prospects, which in turn explains differences in probability distortions seen across different elicitation methods.

Pragmatics of Metaphor Revisited: Modeling the Role of Degree and Salience in Metaphor Understanding

One of the advantages of using metaphorical expressions over literal ones might be that speakers can convey not only the intended property, but also its degree. For example, when hearing “John is a shark”, the listener might infer that the speaker aims to communicate that John is as mean as a typical shark. We present experimental findings supporting this hypothesis, along with a novel metaphor interpretation model, which is implemented within the Rational Speech Act framework. We compare our model's predictions to those of an existing RSA model of metaphor understanding, within which the listener infers just the presence or absence of a feature as opposed to its degree, and find that our model produces a significantly better fit.

Comparisons in Adaptive Perceptual Category Learning

Recent work suggests that learning perceptual classifications can be enhanced by combining single item classifications with adaptive comparisons triggered by each learner’s confusions. Here, we asked whether learning might work equally well using all comparison trials. In a face identification paradigm, we tested single item classifications, paired comparisons, and dual instance classifications that resembled comparisons but required two identification responses. In initial results, the comparisons condition showed evidence of greater efficiency (learning gain divided by trials or time invested). We suspected that this effect may have been driven by easier attainment of mastery criteria in the comparisons condition, and a negatively accelerated learning curve. To test this idea, we fit learning curves and found data consistent with the same underlying learning rate in all conditions. These results suggest that paired comparison trials may be as effective in driving learning of multiple perceptual classifications as more demanding single item classifications.

The Treachery of Images: Objects, Pictures, Words and the Role of Affordances in Similarity Judgements

Categorization is a fundamental cognitive strategy employed to ease information processing and to aid memory formation. Past research on how humans categorize objects has used images of objects as experimental stimuli. Concurrently, studies in the past 10 years have found differences in the processing of images as compared to real-world objects. One proposed explanation is that these results are due to differences in the affordances of images versus objects. Using a similarity judgement paradigm, we explored the effect of affordances in a categorization task including words (object names), images, and objects. Consistent with previous research, we found significant differences in how participants made similarity judgements of images and objects. Moreover, we found that similarity judgments using object names were much more similar to the judgments of pictures than of objects. An exploratory cluster analysis opens the possibility of framing such differences as affordance driven. These results suggest a need for more ecologically valid categorization tasks, more conservative inferences when using images as stimuli in these tasks, and the need for further exploring the role of affordances in categorization.

Willingness to Interact Increases When Opponents Offer Specific Evidence

In polarized political climates, debate is ubiquitous but minds rarely change. This raises a question: what causes people to update their views? Recent work has shown that people are persuaded more by experienced-based explanations rather than factual ones. Yet, facts surely play (or ought to play) an important role in political discourse. Is it possible to leverage the persuasive power of personal experiences without sacrificing factual information? In Experiments 1 and 2, we replicate and build on previous findings showing that people who offer experienced-based (vs. fact-based) explanations are perceived as more rational and worthy of respect. In Experiment 3, we show that more complex explanations combining factual information with personal examples reveal more nuanced results. Collectively, this work sheds new light on how experienced-based and fact-based evidence can be used to persuade.

A neural implementation of MINERVA 2

The MINERVA 2 (Hintzman, 1984) model of human memory has been used to simulate a variety of cognitive phenomena. These simulations, however, describe cogni-tive phenomena at Marr’s (1982) representation/algorithm level, with little effort to link the core assumptions of the model to an underlying neural implementation (however, see Kelly et al., 2017). This article describes a possible neural implementation of MINERVA 2—one that is sim-ple and arguably biologically plausible. This implementa-tion suggests a novel method for generating response la-tencies and provides a concrete example to support Marr’s claim that the representations and algorithms that mediate human performance in a variety of different cognitive tasks (e.g., decision making; Dougherty, Gettys, & Ogden, 1999) can be investigated and simulated without reference to their underlying neural implementation.

The driving forces of polarity-sensitivity: Experiments with multilingual pre-trained neural language models

Polarity-sensitivity is a typologically general linguistic phenomenon. We focus on negative polarity items (NPIs, e.g. English 'any') -- expressions that are licensed only in negative contexts. The relevant notion of 'negative context' could be defined lexically, syntactically or semantically. There is psycholinguistic evidence in favour of semantics as a driving factor for some NPIs in a couple of languages (Chemla, Homer, & Rothschild, 2011; Denić, Homer, Rothschild, & Chemla, 2021). Testing the scale of this analysis as a potential cross-linguistic universal experimentally is extremely hard. We turn to recent multilingual pre-trained language models -- multilingual BERT (Devlin, Chang, Lee, & Toutanova, 2018) and XLM-RoBERTa (Conneau et al., 2019) -- and evaluate the models' recognition of polarity-sensitivity and its cross-lingual generality. Further, using the artificial language learning paradigm, we look for the connection in neural language models between semantic profiles of expressions and their ability to license NPIs. We find evidence for such connection for negation but not for other items we study.

Reinforcement Learning Agents for Interacting with Humans

We tackle the problem of an agent interacting with humans in a general-sum environment, i.e., a non-zero sum, non-fully cooperative setting, where the agent's goal is to increase its own utility. We show that when data is limited, building an accurate human model is very challenging, and that a reinforcement learning agent, which is based on this data, does not perform well in practice. Therefore, we propose that the agent should try maximizing a linear combination of the human's utility and its own utility rather than simply trying to maximize only its own utility.

That was close! A counterfactual simulation model of causal judgments about decisions

How do people make causal judgments about other's decisions? Prior work has argued that judging causation requires going beyond what actually happened and simulating what would have happened in a relevant counterfactual situation. Here, we extend the counterfactual simulation model of causal judgments for physical events, to explain judgments about other agents' decisions. In our experiments, an agent chooses what path to take to reach a goal. In Experiment 1, participants either made hypothetical judgments about whether the agent would succeed were it to take a certain path, or counterfactual judgments about whether the agent would have succeeded had it taken a different path. In Experiment 2, participants made causal judgments about whether the agent succeeded or failed because of the path that it took. Our computational model accurately captured participants' judgments in both experiments and we find that causal judgments are better explained by counterfactuals rather than hypotheticals.

5. Abstracts

How to Explain the Automaticity of Object Recognition

Capacities for object recognition are capacities to either categorize a perceived object as belonging to a conceptual category (e.g., being a cat) or identify it as being a specific individual (e.g., being Mr. Whiskers). There is wide agreement that at least some of these capacities operate automatically. Empirical studies have determined that recognition can occur as quickly as within 200ms after stimulus detection. This short time frame excludes any possibility for subjects to exercise conscious control over the development of the process. Though several detailed models have been advanced to explain how recognition processes work (especially in cases of face and object recognition), no adequate explanation of their automaticity has been offered. Here, we advance a proposal according to which recognition is automatic because it is the result of an associative processing mechanism. In particular, we propose that recognition implements a competitive learning mechanism to solve the match-selection problem.

Why do people gesture more during disfluent speech? A pragmatic account.

People are more likely to gesture when their speech is disfluent. Why? According to an influential answer to this question, people gesture when they are disfluent because gestures facilitate speech production (Krauss & Hadar, 1999). Here, we propose an alternative explanation: People may gesture when their speech is less fluent because gestures serve as a pragmatic signal by commenting on problems with speaking. If so, when the listener cannot see the speaker, the speaker’s pragmatic motivation to gesture more during disfluencies should disappear. As predicted, we showed that people were more likely to gesture when their speech is disfluent only when the listener can see their gestures, but not when the listener cannot see their gestures. These results suggest that people gesture more when speaking is difficult, not because gestures facilitate speech, but rather because gestures comment on speakers’ difficulty with presenting an utterance for the listener.

Predictors of Incidental and Intentional Learning: Curiosity, Construal Level, and Musical Arousal

Having an intention (or not) forms a categorisation system for learning. Correspondingly, incidental learning stands for learning without intention to learn. The current paper examines the influences of incidental and intentional learning. 170 adults participated in three experimental studies. In each experiment, participants examined some animal illustrations and read paragraphs about them. In the first study, animal illustrations were arranged as being common (i.e., low curiosity) or novel to examine the effect of curiosity. Low curiosity level was more effective on incidental learning and vice versa for intentional learning. In the second study, mental time travel manipulation showed focusing on current time increased intentional learning. In the third study, the effect of arousal was examined via the tempo of the music (i.e., faster tempo for high arousal level). Low arousal level increased incidental learning. Ultimately, curiosity, mental time travel, and arousal could be essential in intentional and incidental learning.

(How) Do Register and Morphosyntactic Congruence Effects Interact during Sentence Reading? Two Eye-Tracking Pilot Studies

In two eye-tracking pilots during reading, we investigated real-time processing of formality-register congruence and subject-verb morphosyntactic congruence, and their relation. Participants read, in German, two context sentences conveying a formal or informal situation, followed by a target sentence containing a high- or low-register verb (e.g., Engl. transl. "The policeman detained the activist" vs. "The policeman grabbed the activist") which matched or mismatched context formality. The second pilot additionally manipulated subject-verb morphosyntactic congruence (e.g., Engl. transl. "*The policeman detain the activist"; "*The policeman grabbed the activist"). We observed main effects of formality-register and morphosyntactic congruence on verb reading times, as well as an interaction effect at the post-verbal object noun. Higher degrees of context and target sentence formality resulted in longer reading times. Ongoing investigation will further clarify our pilot findings, which suggest some interference between formality-register and morphosyntactic congruence processing.

The link between language and cognition in infancy

The link between language and thought lies at the core of what it means to be human. This link is evident in the first months of life. As early as three months of age, a parent labeling an object that the infant is looking at influences how that infant thinks about those objects (Ferry et al., 2010). Prior research used face-to-face methodologies. Considering the constraints posed by the COVID-19 pandemic, we sought to validate an online categorization task. The results show that language labels facilitated category formation for 9- to 12-month-old infants. In contrast, a control condition that presented the same labeling phrases in reverse did not facilitate categorization. These findings suggest that infants can perform categorization using an online protocol such as Zoom and this paradigm provides a promising new avenue for studying the intersection between language and cognition.

Non-native English Speakers Are Not Disadvantageous in Humor Appreciation, but Detection, Compared with Native Speakers

Humor comprehension is a great challenge for foreign/second language (L2) learners. Previous studies on humor comprehension in L2 speakers have relied only on descriptive approaches or subjective ratings on humor materials. However, no study has quantitatively investigated the behavior of L2 speakers in different stages of humor comprehension including humor detection and appreciation. This study first developed/validated a novel method to separately assess joke detection and appreciation, and conducted a series of experiments to explore the difference between native and non-native English speakers. The results showed that the non-native speakers achieved significantly lower accuracy in the joke detection than the native speakers, whereas the appreciation ratings were comparable between them. The results suggested that the non-native speakers are not necessarily disadvantageous in the joke appreciation, but detection, compared with native speakers

Quantifying the Emergence of Symbolic Communication

We quantitatively study the emergence of symbolic communication in humans with a communication game that attempts to recapitulate an essential step in the development of human language: the emergence of shared signs. In our experiment, a teacher must communicate a first order logic formula to a student through a narrow channel deprived of common shared signs: subjects cannot communicate with each other with the sole exception of car motions in a computer game. Subjects spontaneously develop a shared vocabulary of car motions including indices, icons, and symbols, spanning both task-specific and task-agnostic concepts such as "square'' and "understand''. We characterize the conditions under which indices, icons, and symbols arise, finding that symbols are harder to establish than icons and indices. We observe the dominant sign category being developed transitions from indices to icons to symbols, and identify communicating in ambiguous game environments as a pressure for icon and symbol development.

Trait anxiety modulates negative affect-cued distribution of visuo-spatial attention.

Spatial deployment of visual attention in humans is crucial for selecting and prioritizing task-relevant visual information for efficiently navigating natural visual environments in daily life. As prominent landmarks of social environments, human faces conveying salient emotion information, have been found to influence attention. We investigated if facial emotions also modulate the spatial distribution of visual attention and whether any such effect associates with individual differences in internal affective states, e.g. anxiety. Participants (n = 28) discriminated the orientation of target Gabor patches co-presented with distractors, speedily and accurately. The key manipulation was randomly presenting a task-irrelevant, face emotion prime briefly (50 ms) at unexpected time points, conveying either Neutral/Disgust/Scrambled (null) emotion signal 150 ms before the target patches. Disgust signal modulated the gradient of attention (change in negative inverse attentional efficiency with unit change in distance from the source of emotion signal) in significant association with trait-anxiety scores, such that the direction of attention gradient flipped (spatial attentional shift) with increasing severity of trait anxiety. Neutral signal yielded attention gradients comparable to Scrambled with no clear association with anxiety, implying the presence of no anticipated effect. Altogether, the results suggest that individual trait-anxiety levels condition the effect of negative and physiologically arousing emotion signal (e.g., Disgust) on spatial distribution of visual attention. The findings may help furthering the understanding of visual distortions underlying affective states and disorders.

Math Self-Concept, Stereotypes, Achievement and Anxiety – A Cross-Sectional Study

The present study assessed both implicit and explicit math self-concept, math-gender stereotype, math achievement, and math anxiety in 260 Swiss children from 1st, 3rd, and 5th grade. In line with prior research, explicit math self-concepts were found to decrease as a function of participant age cohort. Furthermore, already children in the first grade were found to implicitly endorse the math-gender stereotype. In line with these findings, math anxiety was found to be higher in older cohorts than in younger, and it was non-trivial in all age groups. Lastly, as expected, math achievement was higher for older children. Taken together, the present study found patterns of stereotypes increasing as a function of age, with simultaneous increases in math anxiety, and simultaneous decreases in math self-concept. These findings underline the importance of studying math-related constructs in children. Understanding these patterns will contribute to sustaining STEM interests in young children and adolescents.

What’s Different About Improvised Rap?

Rap lyrics are a popular and understudied domain of human culture. In particular, improvised rap lyrics provide a unique window into the cognitive and linguistic constraints of creative language production. Although the vast majority of rap lyrics are written (premeditated), improvising lyrics has long been a core element of hip-hop culture. Very few efforts have investigated the neural underpinnings of improvised rap, and none so far have focused on the language output itself. This project compares phonemic, rhyme, and semantic features of written and improvised rap lyrics from 7 expert rappers in order to uncover related phonological structures. Here, I demonstrate that the phonemes of these two modes of production seem to be drawn from different distributions. In addition, across various metrics, improvised lyrics from these experts display smaller phonological structures and less variation than written lyrics from those same artists, all while consistently exhibiting large rhyming patterns (3+ syllable).

“Feels Like I’ve Known You Forever”: Empathy and Self-Awareness in Human Open-Domain Dialogs

As conversational agents become more human-like, people expect them to be engaging as well. However, developing agents that comprehend human desires and generate appropriate responses, continues to be a challenge. We, therefore, collected 2,300 human open-domain dialogs with self-labeled psychological variables such as empathy, connectedness, respect, and friendliness. We found that participants who talk coherently and disclose self-relevant information were engaging partners. Also, we found that various empathetic responses were critical for sincere interaction: agreement, perspective-taking, referring to someone as adorable, and asking questions. When comparing the most and least engaging dialogs, linguistic cues and length of sentences denoted different extents of perceived empathy and sincerity by the partner. Also, we found that a large language model, GPT-3, makes small talks in one shot, but it cannot generate many empathic expressions or sustain a lengthy conversation. We propose a new approach for enhancing conversational agents' social and engaging characteristics.

Conceptual Gender vs. Grammatical Gender: Exploring Implicit and Explicit Object Categorization in Turkish and French Speakers

Grammatical gender (GG) affects object categorization (e.g., Samuel et al., 2019). This study is among the first to examine the extent of this effect using both implicit and explicit tasks. Speakers of French (GG language) and Turkish (genderless) completed an Implicit Association Test (IAT) about classifying faces based on their gender (female/male) and objects conceptually associated with a specific gender (e.g., necktie) based on a criterion unrelated to gender (tool/clothing item). Participants also completed an explicit task of attributing gender to those objects and a survey assessing sexist attitudes. Turkish speakers were only affected by conceptual gender (CG) whereas French speakers showed the effect of CG in their explicit gender attributions and of both GG and CG in their IAT responses. These effects were stronger for participants who were high in their sexist attitudes. Thus, GG implicitly interferes with object categorization, and individual differences in attitudes may modulate this effect.

Predicting Domain Knowledge Using Natural Language Processing Tools

Individuals who possess extensive domain knowledge use their knowledge when understanding, discussing, and remembering events. The purpose of this study was to assess the extent to which natural language processing (NLP) tools could be used to predict domain knowledge from typed descriptions of events. Participants watched videos of basketball and recalled them after viewing. Knowledge of basketball was assessed. NLP tools were utilized to assess whether linguistic features of participants’ event descriptions could be used to predict domain knowledge. Moreover, the extent to which linguistic indices could be classified as relating to linguistic complexity or features of mental model construction was explored. Results from machine learning models suggest that domain knowledge (high, low) could be predicted with up to 90% accuracy. Additionally, 90% of predictors could be categorized. Higher knowledge individuals tended to describe events with more linguistic complexity and produced more words related to spatial, temporal, and social relations.

Does Sans Forgetica font facilitate word memory?

The new Sans Forgetica (SF) typeface was designed to promote desirable difficulty. Here, we investigate whether SF improves memory for words with within-subject designs. Participants studied words in Arial and SF (Exp 1 and 2) and completed old-new recognition tests where words retained their study fonts (Exp 1) or were in either Arial or SF (Exp 2). They had significantly better recognition (hit rate) in SF than in Arial (Exp 1) and significantly higher sensitivity indexes (d’) when words were tested in SF than in Arial (Exp 2). While encouraging, further examination of these results (e.g., response bias) suggest a less straightforward interpretation. Thus, we have reservations for the effectiveness and use of SF for improving word memory.

Improving Improv: Effects of Interpersonal Coordination on Music Improvisation

We examined the signatures of music improvisation following a targeted manipulation that involves a mirroring task. In 18 pairs participants completed a solo music improvisation performance using a percussion instrument (cajon), a mirroring task with a partner, and joint music improvisation. Across pairs, we manipulated three different types of mirroring to examine its effects on coordination during joint music improvisation (Hierarchical mirroring, partner A leading and partner B following; Turn-Taking mirroring, partner A leading the first half and partner B leading the latter half; and Egalitarian mirroring, partners co-creating spontaneous movement together). From audio recordings, we extracted signatures of interpersonal coordination from the acoustic performances using cross-correlation. There were only marginally significant differences in interpersonal coordination during improvisation, with pairs in the Turn-Taking condition exhibiting lower levels of cross-correlation than in the Egalitarian condition. This work provides some initial theoretical insights about downstream effects of interpersonal dynamics on music improvisation.

How robust and persistent are intuitive conceptions? Insights from production tasks

Intuitive conceptions are prevalent among young learners and can impose constraints to knowledge acquisition. Even though the data suggests that instruction does not eradicate them, this phenomenon has rarely been quantified. In this study we raise the question of how robust intuitive conceptions are. Moreover, we look at their persistence long after instruction of the studied notions. Production tasks concerning the four elementary arithmetic operations were used for measuring the degree to which they prevail and impose constraints among adults, 131 bachelor students as well as 168 high-school teachers and 57 mathematics teachers. The findings revealed that in most cases (88.93%) participants evoked examples that are congruent with an intuitive conception. This was observed for all the arithmetic operations and populations involved in the study. Even when explicitly prompted to find incongruent cases, they failed on two thirds of the cases. The educational entailments of these findings are discussed.

Dyslexia and Motor Skills: A Meta-Analysis

Research suggests individual differences in motor skills may be associated with reading ability in school-age children. This meta-analysis explored whether deficits in motor skills are evident in developmental dyslexia, using estimates from 33 studies (k) with 122 effects (m) from dyslexic and age-matched neurotypical samples (N dyslexia = 1248, M age = 11.6 years, range 7.0 to 25.3 years). An overall effect of moderate magnitude, g = –.52 [95% CI –.74; –.29], confirmed that motor skills are impaired in dyslexia. Meta-regression analyses indicated no significant effects of participant age or language. Subgroup analyses revealed significant group differences for tasks with nonlinguistic stimuli (k = 28, m = 83), e.g., pegboard task, g = –.46 [–.71; –.22], or linguistic stimuli (k = 10, m = 39), e.g., word copying task, g = –.66 [–1.09; –.24]. Effects were significant for groups with confirmed dyslexia diagnoses (k = 27, m = 103), g = –.56 [–.81; –.30], but not for groups identified as poor readers, (k = 6, m = 19), g = –.32 [–.93; .28]. Effects were significant for fine motor skills (k = 29, m = 106), g = –.61 [95% CI –.83; –.39], but not for gross motor or composite measures (k = 10, m = 16), g = –.26 [95% CI –.76; .24]. The results suggest that fine motor tasks might help to identify children at risk of dyslexia. Longitudinal research may further elucidate relations between motor and reading skills.

The Quantified Moral Self

Artificial Intelligence (AI) can be harnessed to create sophisticated social and moral scoring systems – enabling people and organisations to form judgements of others at scale. While this capability has many useful applications – e.g., matching romantic partners who are aligned in their moral principles, it also raises many ethical questions. For example, there is widespread concern about the use of social credit systems in the political domain. In this project, we approach this topic from a psychological perspective. With experimental evidence, we show that the acceptability of moral scoring by AI depends on its perceived accuracy, and that perceived accuracy is compromised by people's tendency to see themselves as morally peculiar, and thus less characterizable by AI. That is, we suggest that people overestimate the peculiarity of their moral profile, believe that AI will neglect this peculiarity, and resist for this reason the introduction of moral scoring by AI.

Forgetting in delayed recognition as generative compression with decreasing capacity

Recent research has proposed that systematic biases in human memory -- while seemingly highlighting a proclivity for failure -- can be understood as hallmarks of optimised lossy compression. Specifically, a form of compression termed semantic compression whereby an internal model of the environment is recruited to encode memories. Semantic compression casts memory errors in the normative framework of information theory, describing how limited memory resources should be distributed to optimise recall performance. Notably, the theory does not define a single best compression, rather a continuum of trade-offs between utilised capacity and expected distortion is possible. However, possible consequences of this characteristic feature have not been tested explicitly. Here we test the idea that gradual degradation of memories with time corresponds to a decrease in the amount of resources allocated to store memories. We apply the general framework to remembering synthetic words in a delayed recognition experiment and find that subjects are indeed less sensitive to intrusions generated by our model than generic distortions, and that delay length modulates recall rates in line with the predictions of the theory.

Including Errors and Errors Correction in a Cognitive User Model

We present a user error model that simulates a user's errors using an eyes and hands extension to cognitive architectures. We developed a complete interactive cognitive model that performs a spreadsheet task. This model is compared with an existing cognitive model that performs the same task in a different spreadsheet tool. Also, the predictions are compared to human data (N=23) on the same uninstrumented interfaces. The comparison suggests that the interactive cognitive model moves us closer to having a user model that can directly test interfaces by predicting human behavior and performing the task on the same interface that users interact. The error model also allows exploration of error detection, error correction, and different knowledge types.

Explicit and Implicit Coordination of Joint Action

People collaborate daily to achieve joint goals, negotiating sidewalks, preparing meals, constructing furniture. Here we analyzed how strangers coordinate a complex novel task, assembling a TV cart. Before beginning and before major actions, partners used a photo of the completed cart, speech, and gesture to establish a joint model of the structure of the completed cart. Most coordination was implicit, a conversation of actions that could be understood in the context of the current state of the object and the shared mental representation. Actions served a dual purpose, both to communicate and to advance the task. The basic unit of coordination was the dyad, not the individual.

The role of body representations in higher order cognition

Previous research using motor dual tasks and TMS has suggested that interference with cortical motor information impacts word reading. This research offers support for theories of grounded cognition by suggesting a functional role of sensorimotor information in conceptual representation. However, motor dual tasks and TMS are limited to partial interference of body representations. To address this, the current electroencephalography (EEG) study induces body illusions to interfere with the broader representation of body information during verb reading. During this task, participants categorized words related to hands or feet while we measured EEG. With spatiotemporal representational similarity analysis (RSA), we demonstrate that sensorimotor information is decodable in normal conditions and is delayed during illusion conditions. Our results suggest that during illusion conditions, sensorimotor information is disrupted by body illusions. This supports theories of grounded cognition for word reading.

Achieving Consensus to Learn an Efficient and Robust Communication via Reinforcement Learning

Human communication usually exhibits two fundamental and essential characteristics under environmental pressure, efficiency, i.e., using less communication frequency to achieve comparable performances in cooperation; and robustness, i.e., maintaining a relative performance when communicating in a complicated environment. Since a critical goal of designing artificial agents is making them human-like in many scenarios. How artificial agents could learn a human-like communication mechanisms in terms of efficiency and robustness is a long-existing problem which has not yet been solved. Reinforcement Learning, due to its trail-and-error paradigm, provides a promising framework in solving the above research problem. With reinforcement learning, this paper develops architectures that help agents learn an efficient and robust communication, and carries out extensive experiments which uncover that artificial agents are cognitively capable to learn such human-like communication protocol in various environments (tasks).

Cognitive Models as Simulators: The Case of Moral Decision-Making

To achieve desirable performance, current AI systems often require huge amounts of training data. This is especially problematic in domains where collecting data is both expensive and time-consuming, e.g., where AI systems require having numerous interactions with humans, collecting feedback from them. In this work, we substantiate the idea of cognitive models as simulators, which is to have AI systems interact with, and collect feedback from, cognitive models instead of humans, thereby making their training process both less costly and faster. Here, we leverage this idea in the context of moral decision-making, by having reinforcement learning (RL) agents learn about fairness through interacting with a cognitive model of the Ultimatum Game (UG; Nobandegani, Destais, & Shultz, 2020), a canonical task in behavioral and brain sciences for studying fairness. Interestingly, these RL agents learn to rationally adapt their behavior depending on the emotional state of their simulated UG responder.

Linguistic Anticipation in Children’s Correction Sentences

Adults anticipate semantically related information when a disfluency is presented using the syntactic and semantic information of the sentence context (Lowder & Ferreira, 2016). Anticipation skills depend on experience and language development, whether children present similar anticipation skills is unknown. This research aimed to explore the anticipation skills based on disfluencies in school children (8-9 years old) and adults. Participants heard disfluency (In the yard, I saw a dog, no, a rabbit) and coordination (In the yard, I saw a dog and a rabbit) sentences and observed four pictures: the first noun (dog), the second noun (rabbit), a critical distractor (cat), and an unrelated distractor (tiger). Results demonstrated that children and adults looked more at the critical distractor than at the unrelated image only in disfluency condition; however, children were slower than adults in predicting the next noun. Therefore, our results revealed that language prediction becomes more efficient with development.

Inter-Team Cognitive Diversity – Using Distributed Cognition for Analyzing Team Cognitive Diversity

Teamwork in contexts of remote and hybrid working has become increasingly prevalent. Thus, the importance of developing and enhancing this mode of working is important. Previous literature has focused on the study of within-team diversity, which is why this research takes the perspective of inter-team diversity. This study aims to bring insights into teamwork from a cognitive perspective, using ethnography to compare how teams in different domains diversely organize and function to achieve their goals as distributed cognitive systems. Distributed Cognition is selected as a theoretical approach, especially the Distributed Cognition for Teamwork – CL (DiCoT-CL), which has been used previously in numerous studies relating to teamwork contexts.

High-arousal positive emotion evoking is more effective in VR than on a 2D monitor based on computational affection

Virtual Reality (VR) technology has been widely used in researching situations that require high ecological validity but are difficult to copy. In this paper, we compare the effect of 360-degree videos in VR head-mounted display and 2D computer screen on evoking four types of emotional states, and innovatively assess the effect of emotion evoking using the Go/No-go attention paradigm. Based on the eye movement data collected from a considerable number of participants (N=48), the result reaches marginally significance in the effect of immersion level of evoking (p=0.075), emotion’s level of arousal (p=0.019), and the interaction between them (p=0.037) under the condition of positive evoking. We find that immersive device can better evoke positive emotions with high arousal, while non-immersive device cannot manipulate the intensity of evoked emotions. Our study empirically demonstrate that high-arousal positive emotion evoking is more effective in VR than on a 2D monitor based on computational affection.

How Optimal is Too Optimal? Expectations About Performance in the Traveling Salesman Problem

How effective do observers expect other problem solvers to be? What makes a decision seem "human"?" We addressed this question in the context of the Traveling Salesman Problem (TSP), a decision problem in which a perfectly optimal solution is intractable, but for which various kinds of approximate solutions are available. We conducted a series of experiments involving both "production tasks" in which we asked subjects to solve the TSP, and "perception tasks", in which we asked subjects to judge others' solutions to the TSP, rating them for intelligence or humanness. Results suggest that observers expect human solutions to be less optimal than algorithmic solutions: observers expect human problem solvers to exhibit a combination of local and global solution criteria, and to use a short look-ahead window when choosing a solution. These results shed light on human models of other humans' minds, a fundamental problem in social interaction and robotics.

Mouse-tracking meta-cognitive ratings of comprehension during garden-path sentences.

Research on movement in linguistic production and the perceptions of such movements are well established, with direct applications in co-speech gesture and signed languages. Underexplored, however, is leveraging movement itself to investigate language comprehension and perception. In the following experiment, we introduce a novel use of mouse-tracking as a tool for continuous self-reporting of comprehension felicity. This method yields a direct and informationally dense datastream whose properties may shed insight into real-time meta-cognitive sentence processing. The dense-sampling measure affords use of nonlinear time series analysis methods not yet applied to sentence comprehension. Participants (N=46) gave continuous ratings of ease- of-comprehension while listening to reduced relative clause garden-path and control sentences. We compare these ratings and examine our results in the context of competing garden-path processing theories: an all-or-nothing account and a competitive account.

Paths to Learning in Traditional Artificial Classification Tasks

There’s increasing evidence from studies of human performance in artificial classification learning tasks that a plurality of mechanisms or strategies are available to learners. A recent investigation from our lab group found that a linearly-separable category structure was harder to learn than a comparable non-linearly separable structure; and furthermore, that there were qualitative individual differences reflecting different paths to learning. In the present experiment, we take a deeper dive into performance on the LS category structure using a bank of test phase measures to more fully reveal what each individual has learned and represented. We identify a systematic set of profiles to characterize individual learners and demonstrate novel evidence on the nature and role of traditional psychological constructs: exemplars, prototypes, and rules. In particular we show classic prototype effects arising not as a broad-based phenomenon but only from a specific path to learning. Keywords: categorization; tacl: prototype effect; individual differences

Can Social Relations Influence Cooperation in Prisoner’s Dilemma?

The paper explores the impact of social role assignment and the corresponding payoff distribution on cooperation in the Prisoner’s dilemma following the types of relations according to Fiske’s relational models’ theory: communal sharing, authority ranking, equality matching, and market pricing. The corresponding roles and payoff distribution are teammates (each player receives the sum of the payoffs), partners (each player receives half of the sum of the payoffs), boss and subordinate (the boss receives 2/3 and the subordinate 1/3 of the sum of the payoffs), and opponents (each player receives the standard payoff). The results show that in the teammates’ and partners’ conditions cooperation was significantly higher than in the other conditions. Surprisingly, the results for the boss and subordinate condition although sharing a similar payoff distribution rule to the teammates’ and partners’ conditions were more similar to the opponents’ condition with significantly lower cooperation rate.

Linguistic aspect constrains event apprehension

This study addresses the interaction between language and event cognition. Previous research has shown that viewers follow perceptual and conceptual features of event structure, and these features influence how they process events. Here, we examine whether events are processed in different ways when preceded by descriptions that contrast in lexical aspect. In our experiment, viewers watched fifteen short movie clips and were asked to indicate whether they saw an interruption. Interruptions occurred in either the midpoint or the late point of the events. We found that telic vs. atelic sentences shown prior to the movie clips influenced event construal and hence the detection of interruptions at midpoints vs. endpoints. This novel finding supports a mapping between lexical aspect in language and temporal structure-building in event apprehension. Our study provides direct experimental evidence for the role of language as a top-down conceptual cue in event processing.

Effect of self-experience on empathic helping to virtual agents

What factors induce empathy for virtual agents? To explore this question, we examine the relationship between humans and agents in the inverted cyberball task where a participant chose to help an ostracised agent. This study especially controls experience in the preparation task from self-involvement as the base of empathy. In the one condition, participants have induced the same experience as the ostracised agent, while in the other condition, participants observed ostracising relations between two agents. As a result, self-involvement in the preparation task does not influence the helping behavior of the ostracised agent. The factor relating to the helping behavior is whether the participants noticed the relation. This notice was also associated with empathy trait. From these results, we can conclude that empathy can acquire others' perspectives even when they have no experience with the pain of ostracism.

Exploiting Embodied Simulation to Detect Novel Object Classes Through Interaction

In this paper we present a novel method for a naive agent to detect novel objects it encounters in an interaction. We train a reinforcement learning policy on a stacking task given a known object type, and then observe the results of the agent attempting to stack various other objects based on the same trained policy. By extracting embedding vectors from a convolutional neural net trained over the results of the aforementioned stacking “play,” we can determine the similarity of a given object to known object types, and determine if the given object is likely dissimilar enough to the known types to be considered a novel class of object. We present the results of this method on two datasets gathered using two different policies and demonstrate what information the agent needs to extract from its environ- ment to make these novelty judgments.

Study on Heterogeneous Roles in Coordinated Behavior of a Triad Using Force-based Models

Humans interact based on others' roles to achieve a group goal. A previous study indicated that the adjusting role is related to high task performance in the coordinated behavior of a triad. The action may handle others' or its misses resiliently and maintain an overall balance; however, the previous results alone can not explain the adjustment process in the crucial role. This study formulated the three heterogeneous roles in the coordinated drawing task using equations of motion, where a triad operate reels to change thread tensions and move a pen connected to the three threads to draw an equilateral triangle. The simulation results showed that, for drawing at least three sides, the adjusting role may use the degree of pen deviation on each side that is influenced by other operators to change the tension. Our findings contribute to understanding of complex and dynamically planned coordination through supplementing the experimental results.

What will they choose? Adults’ and children’s intuitive predictions of others’ numerical decisions

We constantly interpret and predict others’ decisions and behaviors when interacting with the world. Do our predictions of others’ decisions differ depending on whose behavior we are trying to predict? In four experiments, we probed how adults and children (N = 144, 6- to 8-years old) predict different agents’ intuitive numerical decisions. We varied the phylogenetic and ontogenetic history of the agents: an adult, a child, an infant, a chimpanzee, and an ant. We found that both adult and child participants predicted the adult and child agents to reliably choose the larger amount. Both adult and child participants predicted the infant and ant to choose at random. However, while adult participants attribute numerical decisions similar to adults and children to chimpanzees, children seem to think chimpanzees behave similarly to infants and ants. Overall, these results suggest that adults and children share similar intuitions about others’ numerical decisions based on agent identity.

Intercultural enactive ethics: an approach from science and technology understood as enactive practices

This work analyzes how we can understand science and technology as enactive practices, and how that characterization helps promoting an epistemology that does not rely only over the epistemological processes of science and technology, but rather brings into play other categories that help other types of reflections, such as science and technology in the face of cultural diversity. The idea that cognitive technologies can be understood as scaffoldings for developments and innovations within enactive practices is used and developed, to lead to the understanding that cultural variety plays an essential role in understanding the diversity of practices based on differentiation of the specialization of skills in relation to the media through affordances. This allows proposing a proposal for critical intercultural ethics based and understood from enactive practices.

Do humans have intuitive theories of time?

Children have intuitive theories of several conceptual domains, but it is unknown if adults’ common sense beliefs about time reflect an intuitive theory. Here, in an online survey, 165 3- to 6-year-old children judged whether 13 time-related phenomena (e.g., the future, going back in time) were real or not real and provided confidence ratings for their judgments. Beforehand, parents provided their own responses to the same items and predicted their children’s responses. As early as age 3, children’s responses to most items resembled those of adults. Children’s responses to past-related items (e.g. changing the past) were more similar to adults’ than were their responses to future-related items (e.g., changing the future). Parents predicted their children’s responses with high accuracy. These results suggest that many, but not all, adult beliefs about time emerge early in development, and may be part of an intuitive theory.

Active learning, feedback and hypercorrection effect in word learning

How do individuals select which of multiple sources of information to attend to, and which events and entities in their environment to solicit more information about? This study aims at understanding whether adults actively solicit information that they are missing to fill gaps in their knowledge of recently learned novel word-object associations. In other words, we ask whether adults actively solicit the labels of objects they are not confident about. Furthermore, given the role of confidence on the influence of feedback on word learning, we ask whether the beneficial effects of feedback on errors vary as a result of the confidence learners have in their knowledge of newly learned novel word-object associations. We will also compare the findings of this study to the results of a study with the same design that was conducted with preschoolers.

Adults’ Processing of Overextensions Reflects Category Violation. An ERP study

A previous research by Raynal et al. (2021) has demonstrated that unconventional verb extensions based on abstract similarities (i.e. semantic approximations) elicited a reduced N400 compared to pseudoverbs in preschoolers. Whereas this result reveals young children’s analogical abilities, the current study aims to shed light on the specificity of mature metaphorical processing. To do so, the design of the previous research was replicated with adults. Participants saw images of actions (e.g., peeling an orange) followed by an auditory presentation of sentences containing either a conventional verb (e.g., peeling), an approximate verb (e.g., undressing), a superficially related verb (e.g., pressing) or a pseudoverb (e.g., rauging). We found that approximate verbs elicited a strong N400 response, followed by a reduced Late Negative Component (LNC) compared to pseudoverbs. These findings suggest that mature metaphorical reasoning involves a category violation followed by a reinterpretation process allowing one to recategorize the source and the target concepts through a common abstract concept.

Frequencies of Metaphorical Expressions in Asperger Syndrome and Typical Development

Language studies in Asperger Syndrome (AS) report problems in intention interpretation, figurative language and pragmatic abilities. Those abilities require to differentiate constructional and contextual meaning. Previous research use a functional framework to look for literal and figurative language processes. Method. We use Conceptual Metaphor Theory (CMT) and Steen's five steps analysis to compare metaphorical expression frequencies in AS and Typical Development (TD) from an experiential-based perspective. We documented the conceptual metaphors (A is B form), and the metaphor's types: Structural, Orientational, and Ontological. We applied three tasks to elicit speech: (a) conversation task, (b) narration task, and (c) description task. Results. Our Mexico City’s data indicates that AS children are able to produce common metaphorical expressions at the same levels as TD children, at equal ages in Spanish. We found both populations use intentional and contextualized metaphor expressions, and the metaphors are mostly conventionalized expressions previously not considered.

Automated generation of sentence reading fluency test items

Psychometric testing is a valuable educational tool for the assessment and monitoring of students’ abilities in core subjects. However, the manual development of these tests is a tedious process requiring test specialists to produce and curate large volumes of high-quality items. In this paper, we consider whether automating test item generation with modern machine learning methods is a feasible solution for obtaining strong psychometric test items at low cost in the domain of sentence reading fluency. We assess the ability of the large neural language model GPT-3 to produce items ``few-shot’’--- from a short prompt with only a handful of examples. Our results show that generated items closely resemble standardized test items in terms of their factual ambiguity, content appropriateness, and complexity. Furthermore, after filtering for correct answer-labeling these generated items possess similar latent psychometric properties to standardized test items, even capturing subtle grade-level variation.

Evaluating locality in NMT models

With a series of theoretically-informed tests, Dankers, Bruni, and Hupkes (2021) investigated how compositional the behavior of neural networks that are trained on fully natural data is. Focusing on neural machine translation (NMT), one of their key findings is that models appear to be modulating poorly between local and global behavior, where local changes in the input often affect the output in an unwanted manner. While their study is based exclusively on the behavior of the models, we take one step further and investigate how this non-locality manifests itself within the model. We develop metrics to quantify internal locality on the encoder side of the model, focusing on the attention mechanism. We find strikingly different patterns in models trained on different amounts of data that go beyond what could be observed behaviourally and present a range of experiments showing how local and global behavior is modulated within different setups.

Familiarity plays a unique role in increasing preferences for battery electric vehicle adoption

Battery electric vehicles (BEVs) play an important role in efforts to reduce carbon emissions but widespread adoption is hindered by people's perceptions of BEVs. Here we examine the role of familiarity in influencing preferences for BEVs. Using a US-based survey, we measured people's familiarity with BEVs, BEV beliefs, belief uncertainty, and perceived barriers and measured how these cognitive factors influence preferences. We first find that familiarity increases BEV preferences independent of its effect through other factors. Second, exploratory mediation analyses find that familiarity also indirectly increases BEV preferences by increasing positive BEV beliefs. Third, although familiarity reduces belief uncertainty, the influence of uncertainty on preferences depends on belief valence. Taken together, these results propose that familiarity plays a unique role in improving people's perceptions and attitudes towards BEVs. We situate our findings within the broader cognitive science literature and highlight a familiarity-targeted intervention aimed at improving more widespread BEV adoption.

Typicality gradients in the category fluency task

In the category fluency task, participants are given a superordinate category (e.g., ANIMALS) and generate exemplars (e.g., dog, cat, lion, tiger, monkey, …). Cognitive neuropsychologists have shown that people with frontal lobe lesions are less flexible in switching between categories (e.g., Pets, Felines, Jungle Animals). Recently, cognitive scientists have developed formal models of the switching process. Our research builds a second bridge between the cognitive neuropsychology and cognitive science literatures. We utilize machine learning models of word meaning (GloVe, word2vec) to investigate typicality gradients of category exemplars over time. Within a category run, people produce more typical exemplars earlier, and across runs, they sample more frequent categories before less frequent categories. We also propose a novel ANIMALS category scheme that improves the classic one (Troyer et al., 1997). These findings expose the temporal structure of the category fluency task, and are relevant for theories of cognitive flexibility and cognitive control.

Explanations that backfire: Explainable artificial intelligence can cause information overload

Explainable Artificial Intelligence (XAI) provides human understandable explanations into how AI systems make decisions in order to increase transparency. We explore how transparency levels in XAI influence perceptions of fairness, trust and understanding, as well as attitudes towards AI use. The transparency levels – no explanation, opaque, simple and detailed - were varied in two contexts - treatment prioritization and recidivism forecasting. In eight experimental groups, 573 participants judged these explanations. As predicted opaque explanations decreased trust and understanding, but surprisingly simple explanations that were more limited in the information they provided had stronger effects on trust and understanding than detailed explanations. Transparency levels did not have an impact on perceptions of fairness and attitudes towards AI, but context did, with the recidivism AI being perceived as less fair. The findings are discussed in relation to information overload and task subjectivity vs objectivity.

Investigating the Effect of Synthetic Voice Naturalness on Gist Memory

As voice-AI technology becomes commonplace in today’s world, speech synthesis technology is rapidly becoming more naturalistic. While previous studies investigated the intelligibility of synthetic speech, it is not clear how the naturalness of a synthetic voice affects listeners’ memory of the meaning content of a spoken message. The present study investigates how listeners’ memory of semantic gist is affected when participants are exposed to a naturalistic synthetic or a roboticized synthetic voice. Participants completed a Deese-Roediger-McDermott (DRM) task to assess recognition accuracy for semantically related word lists. The naturalistic and robotic synthetic exposure voices showed similar levels of recognition accuracy across conditions. However, both synthetic voices resulted in worse recognition accuracy compared to previous research on DRM tasks when the lists were read by human talkers. These findings inform the development of synthetic voices used in information delivery contexts and point to future directions for memory research with synthetic voices.

The interplay of situation-formality register congruence and verb-argument relations

The semantic relation between a verb and its argument rapidly impacts language comprehension much like world knowledge and the linguistic context (Altmann & Steedman, 1988; Kutas & Hillyard, 1984; McRae et al., 1998). As part of the socially situated context, register could incrementally modulate comprehension and interact with standard language knowledge processing. Two self-paced reading experiments with an additional picture selection task examined how social-formality contexts and their (mis)matches with register use are comprehended in the presence of verb-argument semantic relation (mis)matches. We assessed whether comprehenders can rapidly adapt to shifting situation formality (Exp 2, N=64), or whether they benefit from habituation enabled by blocked presentation of formality (Exp 1, N=64). We successfully replicated incremental verb-argument (mis)match effects. No significant register effect was found, but the observed picture selection accuracy patterns could be taken to suggest that the processing of social contextual information might impact late sentence processing.

Enhanced Visual-Linguistic Interaction in Children within the Autism Spectrum: Evidence from the Visual-World Paradigm with Dynamic Scenes

How do children within the autism spectrum (ASD) integrate linguistic and visual information during real-time processing of sentences and dynamic scenes? We employed a visual word paradigm with sentences containing different verb types (causatives such as 'crack'; perception/psychological verbs such as 'examine') and with different scene contexts (agent in the scene moving towards a target object or remaining neutral). Participants were a group of 10-year old ASD and typically developing children (TD). We reasoned that the two dominant theories about ASD cognitive processing style—the weak central coherence theory (WCC) and the enhanced perceptual function (EPF)—make different predictions on how and where information about linguistic and visual information may be integrated. WCC predicts that ASD children would have greater difficulty with sentence and scene semantics while EPF predicts an early, post-perceptual integration via verb-specific and object (but not full scene) representations. The results suggest that ASD children differ from TD children by computing the link between verb and object faster, suggesting an enhanced post-perceptual integration.

Bizarreness Effect and Its Relation to Memory and Metamemory

Research shows that participants predict their memory performance to be lower when they experience disfluencies during encoding, even though encoding disfluency does not always affect actual memory performance. Bizarre statements are typically encoded slower than common statements, which constitutes an example of an encoding disfluency. The current study investigated how disfluencies during encoding for bizarre and common statements affect actual and predicted memory performance from a metacognitive perspective. Across two experiments under intentional learning instructions, participants made either memory predictions or vividness ratings for bizarre and common statements, followed by a free recall task. Participants predicted to remember common statements more than bizarre statements for both Experiment 1 (self-paced encoding) and Experiment 2 (experimenter-paced encoding), even though the actual memory performance was higher for bizarre than common statements. This demonstrates a metacognitive illusion for the bizarreness effect, similar to other manipulations of encoding disfluency.

Immediate Feedback Decreases False Recognition in the DRM Paradigm

Studying false recognition is highly important not only in furthering our general understanding of memory but also in applied situations like eyewitness testimony. The Deese Roediger McDermott (DRM) paradigm, utilizing study lists of words all semantically related to a theme word that is not actually presented, has been shown to be highly likely to produce false recognition of the theme word (critical lure) at test (Roediger & McDermott, 1995). Prior studies have aimed to combat this error by providing feedback, however the feedback provided was either delayed or only given for errors to the critical lure. Our study provides immediate feedback for every memory judgment given in a DRM recognition task, allowing subjects to evaluate their active memory traces. The results suggest that immediate feedback (compared to delayed) is more effective at reducing false recognitions, possibly due to a learned reliance on memory features that are diagnostic of true memories.

Not just if, but how much: Children and adults use cost and need to make evaluations about generosity across contexts

Evaluations of others’ generosity are critical for identifying quality social partners, yet the factors which systematically affect these evaluations and how they vary across development are still relatively unclear. In this work, we propose that two key evaluative dimensions are the cost associated with a giving action and the need of the recipient. In this way, we suggest that information about both the giver and the recipient influences generosity evaluations. Across two sets of studies, we establish that adults’ and children’s third-party evaluations of generosity indeed are sensitive both to the cost and need associated with the action. Variations in cost and need predicted responses across several, distinct manipulations. Further, these effects were observed both in comparative and standalone contexts, suggesting both dimensions are spontaneously invoked in third-party evaluations. Interestingly, children’s responses to cost manipulations were less consistent than to need manipulations, implying cognitive development could contribute to cost understanding.

Perceptual Grouping for War and Peace

In a ground war, achieving peace is challenging even when both sides suffer. Here we demonstrate that when humans jointly viewed a battlefield as perceptually grouped by colors, a factor irrelevant to the rules of land-war games, they waged fewer wars and accumulated more wealth due to the perceptual border’s constraint on their aggression. This facilitation of peace arose from perceptual grouping that serves as a visual common sense shared by humans, as a mismatched perceptual grouping between players failed to limit war. Moreover, it relies on the geopolitical principles that make ground wars destructive and disappeared when these principles were violated. Together, we show that perceptual grouping limits human ground war as it is a mutually acknowledged “rallying point” where expectations of peace converge.

Monster Math Race: Chasing Integrated Number Sense

Recent work has suggested that cross-notation understanding (e.g., 2/5 vs. 0.25) is important for math outcomes. In this study, equivalent fraction, decimal, and percent stimuli were used to examine individual differences in cross-notation and within-notation comparison in undergraduate students (N=183). Hierarchical linear regression analyses suggested that cross-notation magnitude comparison accuracy added explanatory power beyond that of within-notation magnitude comparison accuracy in predicting fraction arithmetic calculation and estimation skills, as well as ACT scores. Additionally, participants did not perceive equivalent rational numbers as equivalent when expressed in different notations (e.g., percentages were perceived as larger than equivalent fractions or decimals). Undergraduate students were also randomly assigned to one of two number line interventions: one focused on emphasizing connections among fractions, decimals, and percentages and another focused on developing fraction magnitude representations. Both interventions yielded improvements in rational number understanding, but there were some greater benefits of the cross-notation intervention.

Epistemic Cultural Constraints on the Uses of Psychology

This paper describes some epistemic cultural considerations which shape the uses of psychology. I argue the study of mind is bound by the metaphysical background of the given locale and era in which it is practiced. The epistemic setting in which psychology takes place will shape what is worth observing, how it is to be studied, how the data is to be interpreted, and the nature of the ultimate explanatory units. I argue epistemic constraints shape the praxes that arise from structural study of the mind. In order to illustrate this notion of epistemic cultural constraint, I discuss Soviet Psychology and provide a contrast between practical uses of psychoanalysis in India, Egypt, and rural Ghana. In response to these conceptual and practical epistemic limitations, psychology could adapt methods drawn from history and anthropology towards an interdisciplinary psychology.

Does Positional Level Structural Priming Depend on the Verb?

Structural priming is a heavily studied and multi-faceted phenomenon. Essentially, when other factors are equal, syntax shows a tendency to repeat across utterances with potential gains in fluency (Pickering & Ferreira, 2008). Lexical repetition, particularly repetition of the verb between utterances enhances structural priming significantly (Mahowald et al., 2016), but this lexical-boost is not as long-lasting (Hartsuiker et al., 2008). Previous research on structural priming has heavily leaned on measures of syntactic choice, but a few studies have also measured initiation time (Smith & Wheeldon, 2001). The current study looks to differentiate effects of lexical and structural repetition benefits in initiation latency as a measure of potential fluency increases in sentence production. Results suggest that benefits in fluency are dependent on lexical and structural repetition.

Learning Unnatural Language Quantifiers

The fact that all natural language quantifiers are conservative raises the question of whether people could hypothetically learn the conservative quantifiers more easily than the non-conservative ones. Some developmental studies attempted to answer this question, yet they did not reach any consistent results. This study offers an insight into this debate by investigating the learning of four unnatural language quantifiers with an eye-tracking experiment. This experiment employs the occluded referent paradigm, allowing us to identify which referents, hypothetical parts of the sets that the quantifiers relate to, the participants address when discovering the meanings of the quantifiers. The results show that when figuring out the quantifiers’ meanings, people refer to the lexically-related referents instead of limiting their hypothesis space based on conservativity. This implies that difficulty of learning is associated with the number and lexical-relatedness of referents that the quantifier is related to, rather than conservativity.

Minimizing Expected Uncertainty in Visual Word Recognition: Are Readers Sensitive to the Distribution of Information across Word Forms?

Skilled readers are typically most accurate at identifying words when fixating them slightly left of the central character, the so-called optimal viewing position. There are two main explanations for this effect, which are not mutually exclusive. The first claims that the optimal viewing position lies left-of-center due to the particular constraints of the human perceptual system. The second explains the effect in terms of the beginnings of words generally being more informative about word identity. The complexities of natural languages make it difficult to tease apart the relative contribution of each explanation. We explore this issue through the lens of a Bayesian cognitive model and two experiments using artificial lexicons in which we can carefully control how information is distributed across wordforms. Our results replicate previous findings and further suggest that readers may use implicit knowledge about information distribution to minimize uncertainty when targeting words.

What to Do When Someone Expresses a Misconception? A Cross-Cultural Examination of Children’s White Lie-Telling Behaviour

This study explored white lie-telling behaviour in 3- to 6-year-old children from three cultural groups: Anglo Canadian (n = 49), Chinese Canadian (n = 45), and Eastern-European Canadian (n = 11). In a video-conferencing setup, a female researcher expressed a misconception about her artwork and asked participants for their opinion, in the presence versus absence of a stated social consequence (i.e., two conditions). Parental measures of collectivism and parenting styles were also collected. The results indicated that the likelihood of children telling a white lie (versus challenging the researcher’s misconception) did not differ significantly across cultural groups or conditions and was not predicted by parental collectivism, authoritativeness, or authoritarianism. However, the effect of authoritativeness on white lie telling did approach significance. These findings are discussed in relation to possible factors that might have accounted for the lack of cultural differences.

Understanding of Linguistic Scales in Speakers with Williams Syndrome

Individuals with Williams Syndrome (WS) display an unusual cognitive profile with severe deficits in spatial skills along with fluent and arguably complex language. Our experiment focused on the comprehension of scalar expressions, such as `some', `two', and `or' as a window to study their semantic and pragmatic competence. We compared performance of individuals with WS (mean age = 16,4 (year, month), age range = 11,10-21,11) to children matched by Mental Age (MA, (mean age = 6,1, age range = 5,2-7,8) and typical adults. No differences between the WS and MA groups were found in their knowledge of truth conditions of scalar terms. We further tested whether participants accept the statements with scalar terms in contexts featuring their logical (semantic) readings. Individuals with WS accepted logical readings more often than children matched by MA, suggesting that individuals with WS have access to the abstract meaning of scalar expressions.

Culture and Category Learning: The Relationship between Analytic and Holistic Thinking Styles

Basic category learning mechanisms are largely thought to be universal (Shepard, 1987). However, research has shown differences in processing styles between Eastern and Western cultures (Nisbett et al., 2001; Norenzayan et al., 2002), though this has not always been replicated (Murphy et al., 2017). To explore category learning styles and effects of culture, this online study paired the Shepard, Hovland, and Jenkins tasks (Shepard et al., 1961) with the Analysis-Holism scale (Choi et al., 2007). These tasks vary in complexity and optimal strategy to test the reliance on single feature rules, disjunctive rules, and family resemblance. Cultural differences for holistic approaches in non-Western samples were expected when categories could be acquired in more ways than one. The results show expected learning trends for the SHJ tasks, signifying reliable data collection. Additionally, of the six tasks, higher holistic thinking was significantly correlated with the family resemblance task (Type IV).

Multiple representational theories explain non-human primate perspective-taking: Evidence from computational modeling

Humans’ ability to attribute mental states to agents has been hypothesized to underpin our unique social behaviors. However, questions remain about the extent to which our representational Theory of Mind (ToM) is shared with non-human primates (NHPs). Here, we present a set of computational models each built to formalize a different representational theory of a foundational ToM component—understanding what others can see—and compare each model’s performance to that of NHPs across a range of previously published perspective-taking experiments. Our results show that multiple competing theories can account for NHPs’ perspective-taking abilities, including both human-like ToM and less complex mentalistic theories, but not simpler, non-mentalistic theories. This work supports the idea that NHPs may reason about others’ mental states when assessing their visual perspectives, and provides promising avenues for future work using computational modeling to determine if and how NHPs represent more complex mental states (e.g., ignorance, belief).

Explain with, rather than explain to: How explainees shape their learning

Research about explanation processes is gaining relevance because of the increased popularity of artificial systems required to explain their function or outcome. Following an interactive approach, not only explainers but also explainees contribute to successful interactions. However, little is known about how explainees actively guide explanation processes and how their involvement relates to learning. We explored the occurrence and type of explainees’ questions in 20 adult–adult explanation dialogues about unknown present and absent objects. Crucially, we related the question types to the explainees’ subsequent recall of the unknown object labels. We found that explainees asked different types of questions, especially about the object’s label and facts. Questions about the object’s function were asked more when objects were present. In addition, requests for labeling were linked to better recall. The results contribute to designing explainable AI that aim to provide relevant explanations and to further experimental approaches to study explanations.

​​Learners Integrate Syntactic Frames and Semantic Hypotheses in Cross-situational Verb Learning

Previous research suggests that a verb’s meaning is learned partly through the aggregated profile of syntactic frames associated with it. For example, “turn” occurs with transitive and intransitive frames in causative alternation (“He turned the car”/“The car turned”), indicating it is a causal verb. Some evidence demonstrates that young children combine multiple frames to map verbs to appropriate events. However, previous work always presented these frames together, in a single dialogue. What remains unknown is how verb learning occurs when the frames are separated, uttered in different referential contexts, as is likely in children’s everyday life. In a series of cross-situational word-learning experiments, we show that adult learners update their hypothesis about a novel verb's meaning when they encounter the verb again in a new frame, integrating their previous hypothesis about the verb’s meaning with the new frame. These results shed light on the cross-situational mechanisms of syntactic bootstrapping.

Real-time processing of COVID-19 health messages: Talking about you, us and people

We used COVID-related health messages to investigate the real-time processing of indexical and generic expressions ('you,' 'we,' 'people'), to further our understanding of how these expressions are processed and to explore whether the ease of comprehending public health messages related to the COVID pandemic (as measured by reading time) is influenced by type of referring expression. Results from a self-paced reading study point to an increased processing load in messages with the non-indexical form 'people' (relative to 'we' and 'you'), which we suggest is separable from effects of word length and frequency. We interpret this as initial support for the Indexicality Hypothesis, which posits that expressions which can be indexical are easier to process than non-indexicals. To interpret the expression 'people,' an additional representation needs to be evoked, which does not 'come for free' as part of the speech situation, unlike the speaker and addressee referents of indexical pronouns.

Emotions, age, and subjective probability in children

Many of our decisions are based on probabilistic information. While probability theory is a useful tool for quantifying probabilities mathematically, subjective probability is a complex psychological phenomenon. We investigated developmental changes in subjective probability and the modulating role of emotions in probabilistic cognition. For this, we asked N = 45 children (M = 10.59, SD = 2.28, range 7-15) and N = 160 adults (M = 25.20, SD = 14.35, range 18-88) to estimate the probability of a series of three-item compound events generated from a known probability distribution. While children’s estimates largely resembled those of adults, conservatism (avoidance of the extremes) and representativeness judgments (basing estimates on similarity) were modulated by age and emotions. Our findings suggest that the way in which people use the representativeness heuristic develops with age and that emotions modulate subjective probability in children and adults.

Changing Perspectives: Examining Factors Related To Counterfactual Thinking In Ambiguous Social Judgments

There is much diversity in how people form theories and perspectives about situations. There are many instances today in which people may want others to question their perspectives as a first step to changing them. Aspects of counterfactual thinking such as mutability and availability may be involved in questioning a perspective. We explore this in an exploratory study (n = 80) using ambiguous social judgment scenarios. We performed path analysis on the data to compare four models, three of which are based on counterfactual generation. The best fitting model showed that availability was a mediator for mutability to predict our measure of questioning an initial perspective.

Modelling the Emergence of Linguistic Conventions for Word Order: The Roles of Semantics, Structural Priming, and Population Structure

We used agent-based modelling to study the emergence of linguistic conventions for basic word order (the order of subject, object and verb) in different populations. As a starting point, we take word order variation based on semantic properties, as observed in improvised gesture experiments. In our first simulation we explore the relative contributions of two pressures, one for semantically conditioned variation, and the other structural priming (which takes place when two individuals engage in communication), and show that a relatively increasing influence of structural priming best explains an increase in word order regularity. Next we implement a larger simulation, investigating how properties of the population affect regularization of word order. Our models compare population sizes with different population densities, and show that the speed of regularization in languages is heavily influenced by population density, and population size has little effect.

Neural mechanisms of Event Visibility in sign languages

Event structure in sign languages is reflected in the manual dynamics of verb production. As signed event structure is visible (iconic), non-signers are able to recognize it, despite having no sign lexicon. In this EEG study, hearing non-signers were presented with telic and atelic verb signs, followed by a lexical classification task in their native language. Behavioral data confirmed that non-signers classified both telic and atelic signs with above-chance accuracy. ERP waveforms indicated that non-signers identified the perceptual differences in motion features when viewing telic/atelic signs, and used different processing mechanisms when integrating the perceptual information with linguistic concepts in their native language. Non-signers appeared to segment visual sign language input into discrete events, as they attempted to map the observed visual forms to concepts, and label them linguistically. This mechanism suggests a potential evolutionary pathway for co-optation of perceptual features into the linguistic structure of sign languages.

Perception of a phoneme contrast in Singaporean English-Mandarin bilingual adults: A preregistered study of individual differences

Chinese phonology features a contrast between alveolar and retroflex places of articulation, particularly in the standard Beijing variety of Mandarin. However, studies have shown that ‘outer-circle’ varieties (such as in Taiwan and Singapore) have a less clear contrast, termed “deretroflexion”, which results in poor contrastive perception for Taiwan Mandarin speakers. However, our previous study did not find this deficit in Singapore Mandarin speakers. In this preregistered follow-up study, we investigate how Singapore Mandarin speakers perceive the alveolar-retroflex contrast and examine if differences in perception are linked to Mandarin understanding proficiency. Our results (N = 62) reveal that while Singapore Mandarin speakers perceive an alveolar-retroflex phoneme contrast, there is a wide range of differences in ambiguity resolution across the alveolar-retroflex acoustic spectrum. We did not find a link between perceptual differences and Mandarin understanding proficiency, indicating that highly ‘tuned’ perceptual sensitivity is not needed for high Mandarin understanding proficiency.

How to Build a Toddler Lexical Network

Understanding child language development requires accurately representing children’s lexicons. However, past work modeling children’s lexical-semantic structure typically utilized adult norms and corpora. The present work uses Word2Vec embeddings trained on a newly-created toddler-directed language corpus. Distributional approaches like Word2Vec calculate similarities taking into account not just when words occur together, but also when words occur in similar contexts. A network created from Word2Vec embeddings showed higher accuracy in predicting normed word acquisition from 16 to 30 months using network centrality measures, when compared to a network created using sliding window co-occurrences. We also compared predictions from the Word2Vec toddler network, a network created by training Word2Vec on typical adult input, and a model trained using both corpora. The toddler-only network outperformed the other two, indicating the importance of selecting language sources that reflect the population of interest. The present results reveal a promising new direction in understanding toddler word learning.

Cue integration in speech and music

Listeners attend to multidimensional cues in pitch processing, including the spectral shape. While work has shown that listeners normalize voice quality cues in linguistic pitch processing, listeners did not show normalization in non-speech (sawtooth waveform) sounds. It remains unclear whether speaker normalization is unique to speech, or common across all natural sounds, including musical sounds. This study uses manipulations to the spectral slope to compare listener's cue integration in pitch perception in speech vs. music. A forced-choice pitch classification task was conducted. Listeners were given either speech or violin stimuli pitch contour pairs that varied in combinations of F0 and spectral slope cues. They judged whether the second contour was higher or lower in pitch than the first. Results show that listeners integrated spectral cues in speech and violin conditions similarly, and listeners with higher musicality had more categorical responses. Overall results imply overlapping speech and music pitch processing domains.

Patterns of Causal Judgements Diverge from Patterns of Recall: a Test of the Outcome Density Effect

An outstanding issue in cognitive science is whether the computational principles that apply to causal reasoning also guide the way that participants encode the relations among events in memory. The outcome density effect is a behavioral pattern in causal reasoning in which participants’ causal ratings linearly decrease as the base rate of the effect also decreases, even while contingency remains 0. It is key evidence for Bayesian models of causal reasoning as it reflects decreasing uncertainty. We queried whether it may also, separately, affect memory for events. We measured both recall and causal ratings in a causal learning task to test whether the outcome density manipulation affects causal judgment, recall, or both. We replicated the outcome density effect on causal judgment, lending support to Bayesian models, but found that memory instead exhibited a U-shaped relationship with base rate, and therefore, causal judgment and memory had divergent signatures.

Lexical Categorization Variables for English and Mandarin Quantify Ambiguity in Object Naming

This study focuses on naming and rating ambiguously categorized objects to more accurately represent the real-life naming scenarios that language-users encounter. We examined 407 images of common object categories from a previous study collected in 2013 with monolingual undergraduate students in the United States and China. Of those images, we selected 150 that both (1) balanced the mean and range of naming variables between languages and (2) had low between-language correlations on the same variables. We elicited object names and typicality ratings for these 150 images from a more age-, education-, and geographically-diverse sample of English monolinguals across the US. Naming measures were significantly correlated between current and previous English samples, suggesting high consistency of these object categories within language, especially typicality ratings when conditioned on the object name typicality is not a strictly conceptual measure. Cross-language correlations were low, illustrating the unique categorization patterns between languages.

The role of predictability and sense of agency in time estimation

Our perception of how time passes changes dynamically, and is influenced by psychological factors such as sense of agency (SoA), predictability, and attention. Using a reproducing time estimation task, this study investigates how predictability cues can influence time estimation by individuals with stable and unstable SoA. The results show that participants with stable SoA tended to estimate longer durations than participants with unstable SoA. Further, a cue to predictability led to even longer estimations by participants with stable SoA but made no difference in the time estimations of participants with unstable SoA. The study discusses the relationship between individual differences in SoA, predictability, and time estimation.

A Study on Relative Performance of an Reinforcement Learning Agent and Human in a Psychometric Assessment Game

A previous study used the Antarjami gaming framework to determine the OCEAN personality traits. In this study, a reinforcement learning agent is being compared to its human counterpart through various levels in the game. A Deep-Q Network(DQN) is proposed here for playing the Antarjami game autonomously. The DQN takes as input the image of the game screen and decides the moves/actions it wants to play in the game. A collection of DQNs having uniform architectures is used, where each DQN is trained on a particular level of the game. The starting position of each player within a game level is random in the Antarjami framework, therefore the training of the reinforcement learning agent is agnostic to any bias about the initial positions of the players. The work shows how an RL-Agent gathers scores in a greedy fashion irrespective of any psychological inclinations.

The emergence of discrete and systematic communication in a continuous signal–meaning space

Language is simultaneously discrete (symbolic) and continuous (e.g., speech), and meaning-form associations are largely arbitrary. How and why did these properties emerge? To address this question, we study how people develop novel communication systems to refer to a continuous domain (color) using a continuous signal space (whistles). We conducted an experiment in which participants need to generalize from five learned signal-color pairings to a larger range of colors during an online communication game with another participant. We find that: (i) both discreteness and systematicity tend to emerge, such that signaling systems that reflect an underlying symbolic structure as well as systematic association with colors emerge more frequently; and (ii) these emergent systems achieve better communicative performance compared to emergent systems that exhibit only discreteness or only systematicity. These findings suggest a human cognitive bias not only toward symbolic communication, but also toward non-arbitrary meaning-form associations.

A computationally rational analysis of response strategy in a probability learning task

Intelligent behavior requires the ability to adapt to an ever-changing environment. But are humans rational or normative in this ability? We apply a resource-rational analysis to the data from a probability learning task (Gagne et al., 2020). Our analysis hypothesizes that people seek to maximize the expected utility of behavior, while simultaneously minimizing the complexity of their behavioral policies. We report evidence consistent with this hypothesis. We also show that people adopt simpler policies in situations of greater environmental stability, and interpret this as a consequence of reward maximization.

Interference and Case Marker Effects in Dependency Locality: Insights from Hindi

Decades of sentence comprehension research have focused on the cognitive factors that determine the processing difficulty. In this work, we investigate how locality and interference effects interact with each other in Hindi, a head-final language with flexible word order and a rich case-marking system. Using linear regression, we examine the extent to which the dependency distance of a sentence, which is based on retrieving previously-stored elements in the working memory can be predicted by various other backward-looking measures of processing complexity. We evaluate how dependency distance is influenced by similarity-based interference, case density, information structure, and finally, a forward surprisal measure proposed recently to model planning processes in comprehension and production systems. Overall, our results indicate that similarity-based interference and case density are significant positive predictors of dependency length, lending credence to the view in the literature that the mechanisms underlying locality might be driven by interference.

The Costs and Composition of Discontinuity in Visual Narratives

In discourse, entities that are discontinuous with the current storyline are seen as cues for an event boundary, as they are too irregular to be mapped to the existing scene. However, some instances of discontinuity can maintain coherency, as exemplified in Calvin & Hobbes comics, where visual discontinuities can be resolved by the understanding that they depict Calvin’s imagination rather than actual events. This requires the reconciliation of the original storyworld domain with a private, mental domain (the alternative). In our first experiment, we examined whether switching between domains and/or the nature of the presented domain(s) incurred processing costs. Our second experiment examined whether physical cues such as the contours between the discontinuous entities facilitated processing. The results indicate that switching domains is indeed more costly, despite still being understood as congruous sequences. Moreover, strong similarity in contours aided readers with greater comics proficiency. Overall, our results show that the processing of visual narratives extends beyond mere event understanding and is not universally transparent. Keywords: blending; discontinuity; rhyme; visual narratives

Syntactic adaptation to short-term cue-based distributional regularities

Syntactic adaptation to short-term exposure has been documented with both single-trial priming and cumulative priming paradigms. These studies usually involve repeated exposure to the same structure (e.g. reduced relative clauses), and therefore it remains open whether people can track context-dependent regularities through short-term exposure. In the current study, we present a self-paced-reading experiment that investigates context-dependent syntactic adaptation by manipulating the relationship between the animacy feature of the subject NP (animate vs. inanimate) and the corresponding parse of a verb following a subject NP. We analyzed the results in terms of a log-linear model for context-dependent syntactic adaptation. The results suggest that comprehenders can track and adapt to cue-based distributional regularities, but only when the short-term regularities are consistent with the long-term ones existent in their native language.

Schematic Diagrams and Semantic Processing of English Prepositional Polysemy

The paper investigates whether English native speakers (NSs)’ and non-native speakers (NNSs)’ online processing of prepositional phrases were primed by schematic diagrams and whether the priming effects were modulated by different prepositions and senses. 125 adult NS and NNS participants judged on over and in phrases that encode spatial and extended senses in a semantic priming task. Results showed an inhibitory effect of diagrams on NNSs’ processing but a marginal facilitatory effect on NSs’ processing. In addition, the inhibitory effect of diagrams only applied to the processing of extended senses, but not spatial senses. The current findings indicate that NNSs’ processing of English PPs could be negatively influenced by schematic diagrams particularly when the target phrases are more complex (Boers, 2011), presumably due to NNSs’ more limited cognitive capacity of processing complex second language expressions under time pressure than NSs.

The Comprehension of Broad Focus: Probing Roothian Alternatives

Research has shown that comprehenders represent alternative meanings to single focused words online, consistent with Rooth’s (1992) formal semantic account. However, focus can also take a broader scope over whole phrases ('read the manuscript'). We examined whether in these cases, alternatives are represented by testing for an interference effect of the particle only. Using probe recognition, we first tested unmentioned alternatives to the constituent parts of VPs, to object nouns (Exp 1, 'letter' for 'manuscript') and verbs (Exp 2, 'wrote' for 'read'). In Exp 3, we tested alternatives to whole phrases ('wrote the letter'). In all experiments, alternative probes were processed slower than unrelated ones. We found varying evidence of the interference effects of only with noun, verb and whole-phrase alternatives. Overall, this study does not provide support for the generalisation of the effects of only to broad focus. Additionally, we discuss the methodological implications of probing whole phrases.

Are There Bad Dual Character Concepts?

Dual character concepts have recently received significant attention from experimental philosophers. They are usually defined as possessing two independent criteria for categorization, each associated with its own sense. One of these criteria is descriptive, while the other is normative. Almost all examples discussed in the literature involve a positively evaluated normative element (e.g., the concept of scientist is associated with the pursuit of empirical truth, something that we value). But can dual character concepts also involve morally bad elements? We report the first evidence suggesting that they can. Our study contrasted pairs of positive and negative concepts (e.g., friend/enemy, joy/sadness) and neutral concepts (e.g., baker). We found that negatively valenced concepts such as traitor, enemy, superficial, or sadness have a dual character. We also observed that positive concepts were significantly more likely to have a dual character than negative ones, but the effect size of this difference was small.

Thinking into a machine's mind - Anticipation of an agent's behavior in a cooperative game

An accurate mental model of the partner's behavior is fundamental for efficient cooperation. The theory of mind demonstrates that humans are able to create such a model from repeated interactions with their human partners. However, it is an open question whether humans are also willing and capable of taking the perspective of artificial agents and creating similar mental models of agent behavior. We developed a repeated cooperative task that allows us to investigate the process that guides the formation of a specific partner model by repeatedly asking for a prediction of an artificial agent's actions. We found that humans learn to anticipate the artificial partners' behavior if it is goal-directed. An inability to explicitly explain the partner's behavior suggests that this is an implicit learning process. The role of the acquisition of task knowledge in the model of the other agent's behavior is discussed.

Hysteresis in training task of Approximate Number System: transfer effect to symbolic math abilities

From an early age, humans have access to the Approximate Number System (ANS), which allows an approximate sense of quantities. Several pieces of evidence show the emergence of a functional relationship between individual differences in ANS accuracy and mathematical performance, but the correlational nature of the studies do not allow us to clarify the nature of this relationship. In this study, we conducted a randomized controlled trial with a pre and post-test design, which aims to evaluate the hysteresis effect in modulating performance in an approximate quantity comparison task and the subsequent transfer effect on symbolic mathematical performance. One hundred and twenty-eight students from senior kindergarten and first grade of elementary school participated in this study. The results show a hysteresis effect in Reaction Time and efficiency index for First Grade, but no transfer effect to symbolic mathematical abilities was found.

Angry, sad, or scared? Within-valence mapping of emotion words to facial and body cues in 2- to 4-year old children

Previous studies report that children acquire emotion words gradually during ages 3--5 and beyond (e.g., Widen, 2013). Most of this work, however, has used tasks that are demanding for young children (e.g., asking children to produce emotion labels in a free-labeling task), and has asked children to map emotion labels to facial configurations alone. In our study, we tested children's ability to comprehend, rather than produce, emotion words, and used not only facial configurations but also body language. In two pre-registered online experiments, two to four-year-old children (N = 96) were asked to connect emotion words, including happy, sad, angry, and scared, to either facial configurations (Experiment 1) or to combined facial configurations and body postures (Experiment 2). While we found an overall pattern consistent with prior work (i.e., better performance when distractors were from opposite valences than when they were of the same valence), we also found much earlier competence in understanding same-valence emotions. Even 2-year-olds succeeded in differentiating the three negative emotion words and connecting them to facial cues (Experiment 1). Experiment 2 replicated this pattern and further showed that children performed equally well (but not substantially better) given additional body cues. These results suggest that before children can produce emotion words in an adult-like manner, they have some systematic understanding of those words and can map them to emotion cues within valence domains. (Preprint: psyarxiv.com/ka3ed)

Formalization and Implementation of ViolEx: An Active Inference perspective

Expectations play a critical role in human perception, cognition, and decision-making. There has been a recent surge in modelling such expectations and the resulting behaviour when they are violated. One prominent recent proposal is the ViolEx model. To move the model & the literature forward, we identified three areas of concern and addressed two in this study - Lack of formalization and implementation. Specifically, we aim to provide the first implementation of ViolEx using the Active Inference formalism by leveraging the rich overlap between them. As a result, we successfully simulated all expectation violation coping strategies of ViolEx and analyzed their potential mediators. Through this analysis, we identified novel predictors of immunization & assimilation and documented the computational mechanism of their mediation. We see this as the first step in developing a formal research framework to study expectation violations and hope to serve as a base for future ViolEx studies.

People learn other's preferences on a latent feature space using emotion expressions as labels

Inferring others' preferences is one of the central cognitive tasks of social life. The inferred preferences are used to predict the behavior of others in novel situations; therefore, a certain level of abstraction is required. We considered the inference of preferences as a classification problem on an abstract latent feature space. Participants (n=96) were asked to show 89 images of automobiles to either an agent that had been programmed with a specific person's preference and which showed facial expressions according to that preference or an agent that did not show facial expressions. Next, participants were asked to predict the agent's likes and dislikes for novel 23 automobiles. The results showed that participants could predict the agent's likes and dislikes for the unseen automobiles with 74% accuracy, indicating that they learned the decision boundary of agents' preferences on the latent feature space using expressions of emotion as labels.

Eliciting Proactive and Reactive Control during Use of an Interactive Learning Environment

The dual mechanisms of control framework describes two modes of goal-directed behavior: proactive control (goal maintenance) and reactive control (goal activation on task demands). Shifts between these modes may explain variations in user performance in computing tasks. Although these mechanisms are relevant to the design of interactive systems, and particularly educational systems, their relation to human-computer interaction (HCI) is under-researched. We propose a manipulation to induce proactive or reactive control in the context of mathematical problem solving on an online tutoring system. We present two experiments where students solved problems using either proactive or reactive control. Study 1 validates the manipulation by investigating behavioral measures that reflect usage of the intended strategy and assesses whether either cognitive control mode impacted learning. Study 2 investigates whether alternating between control modes during problem solving affects student performance.

Time Alignment between Gaze and Speech in Image Descriptions: Exploring Theories of Linearization

In describing images, visual and linguistic processes coordinate with each other and they both proceed in a linear fashion. Three main theories about the nature of the relation between these processes and how they unfold over time have been proposed in the literature of 'linearization'. In this work, we investigate the hypotheses put forward by these theories utilizing a corpus of spoken image descriptions with speakers' eye-movement data. We explore the time alignment between the fixations on objects and the utterance of the corresponding nouns in the data. In contrast to previous studies, this dataset allows us to inspect unrestricted language production in the context of real scenes on a larger scale. We find both confirming and conflicting evidence for each of the theories in question, suggesting that the intricate relation between eye movements and language production may involve mechanisms proposed by all three theories.

Preschoolers’ sensitivity to abstract correlations in the properties of sets and functions

Causal relationships can generate many different kinds of correlations among variables. However, research on children’s causal reasoning has focused almost exclusively on just one kind of regularity: the temporal covariation between candidate causes and effects, and in particular, the covariation between interventions and outcomes. Here we show that young children recognize more abstract correlations – in the ways that object properties are distributed over sets, or change over time – and constrain their causal hypotheses accordingly. Specifically, we show that children (range: 48-84 months) distinguish candidate causes based on correlations in the distribution of discrete (set size, arity, and proportion) and continuous (mean and mode) properties of sets (Experiment 1), and also within monotonic, quadratic, and periodic functions (Experiment 2). Keywords: children; causal reasoning; abstract concepts; sets; functions

Towards Capturing Scientific Reasoning to Automate Data Analysis

This paper describes an initial cognitive framework that captures the reasoning involved in scientific data analyses, drawing from close collaborations with scientists in different domains over many years. The framework aims to automate data analysis for science. In doing so, existing large repositories of data could be continuously and systematically analyzed by machines, updating findings and potentially making new discoveries as new data becomes available. The framework consists of a cycle with six phases: formulating an investigation, initiating the investigation, getting data, analyzing data, aggregating results, and integrating findings. The paper also describes our implementation of this framework and illustrates it with examples from different science domains.

Self-report vs. objective data. What impact of monitoring on smartphone use regulation?

Smartphones are now the most widely used devices in the world, and their usage monitoring applications have become a general interest topic. However, few experimental studies investigate the reflexive effects of this monitoring on users. To address this point, this paper presents a longitudinal experiment on the effects of monitoring on various variables (e.g. screen time, types of uses). Objective and subjective data from 60 participants, divided into treatment and control groups, were collected over a 3 weeks period. Both groups had to estimate their daily usages, but the treatment group subsequently had access to their real data. Results have shown a normalizing influence of monitoring on smartphone usage, by improving estimation of screen time, reducing time spent on some underestimated applications and increasing use of others overestimated applications. This research paves the way for public policies promoting mastery of its own technological uses and responsible digital usage.

People Have Systematically Different Intuitions about Ownership even in Seemingly Simple Cases

Ownership is pervasive across human societies but invisible to the naked eye, and so people need to infer who owns what. Given its ubiquity, one might expect that people's intuitive theories of ownership would converge. Yet, ownership disputes are common, raising the question of whether these intuitions might actually vary across people. We explored this question using a set of simple, parametrically varied object-transfer events where participants determined how much two agents owned an object. Participants showed strong consensus about ownership in some events, but opposing intuitions in others. Subject-level analyses suggest that these disagreements reflect two overarching intuitive theories, one where intentions are central to ownership, and another where physical possession is prioritized. Our results suggest that people have stable but different intuitive theories of ownership, which stem from their beliefs about the relationship between mental states and possession.

Contrasting the semantic typology biases of Deaf and hearing people in their conceptualization of time and space

How might differences in linguistic modality influence the mental lexicon? The mental lexicon offers a window into conceptual domains and the shape of memory. On the other hand, the distinction between concrete-abstract offers a suitable approach for a comparative study. So far, studies comparing the organization of the mental lexicon between Deaf and hearers indicate similarity in lexical knowledge. This research asked if Deaf and hearing people have similar conceptual organizations of the domains of time and space. In their respective language, sixty-two participants made a repeated free association task with dual-class pieces in a concurrent domain clue format. The results showed less clustering for the deaf than the hearing group and opposed tendencies to establish semantic relations. The results matched those of previous studies done on different aged groups. The results suggest that factors associated with linguistic modality modulate the abstractness or concreteness of concepts.

An explanation of representativeness: contrastive confirmation-theoretical reasoning motivated by question-answering dynamics

Although there are several representativeness-based models of the Lawyer-Engineer task, it remains unclear just why people rely on representativeness-based heuristics rather than on posterior probabilities. This is especially striking because subjects have access to the rational answer: irrational answers decrease dramatically in frequency formats (Gigerenzer&Hoffrage,1995). We argue that the availability of representativeness is explained by the fact that subjects (1) engage in question-answering behavior, as predicted by theories of linguistic semantics, and (2) recursively reason on each other’s mental states, as predicted by the Rational Speech Act Theory (Frank&Goodman,2012). To test this, in a norming study, we asked participants for frequency judgments on the components of Bayes' law, using pairs of real-world professions and related descriptions. In the main experiment, an independent group gave probability judgments on lawyers-engineers problems. We compared different models built from the normed values, and found that those incorporating (1) and (2) best predicted main-experiment responses.

Multiple-Object Search in Cluttered Scenes

Visual clutter impairs performance for single target searches in scenes. Our study investigated the effect of clutter on visual search performance in scenes where multiple targets were present. Observers completed a search task which required detecting multiple targets when given a word representing the target. The number of targets in scenes was varied and the visual clutter was measured using a clutter algorithm. Results showed that search performance declined with increasing levels of clutter. In particular, participants searched longer and made more errors in highly cluttered scenes. Further analyses suggested that participants elicited a tendency to overestimate the number of targets in scenes where clutter was high. Overall, these findings suggest that visual clutter impairs performance for multiple target searches.

Context-based Prediction Error Updating of Memory Representations is Modulated by Event Boundaries

Event boundary advantage (EBA) refers to greater memorability of information at boundaries than for any other part of an event. Recent studies have identified post-encoding processes as a likely source of EBA. The current study investigated whether boundaries are distinctly remembered because they act as gateways for retrieval of associated event-elements by using a trace modification paradigm where memory for the last item of an encoded triplet (A-B-C) is suppressed by replacing it with a novel item on re-exposure (A-B-D). Two hierarchical Bayesian models tested whether the immediate associate, boundary item or only the category difference between old and new item of the triplet predicts suppression. Results indicate suppression is predicted by an interaction between memory of A and category, but successful updating is predicted by an interaction between memory of B and category. We discuss the implications of the result for understanding role of event boundaries in trace updating.

Multimodal Communication in Virtual and Face-to-Face Settings: Gesture Production and Speech Disfluency

Online data collection has become a prominent option due to the COVID-19 pandemic. It is crucial to understand to what extent online studies can be compared with face-to-face studies, particularly in multimodal language research on which the modes of communication have a crucial effect. This study investigated multimodal communication across face-to-face and videoconferencing settings, focusing on gesture production and speech disfluency in a daily routine description task (N=64). Results suggested that overall disfluency rate was higher for those who communicated via videoconferencing than those who communicated face-to-face. The use of specific disfluency types also differed across the two settings, signaling an interplay between cognitive and communicative strategies. Overall gesture frequency and iconic gesture use were comparable across the two settings. Iconic gesture use negatively predicted the overall disfluency rate, regardless of the setting. Using different contexts is required to understand whether multimodal language differs between face-to-face and online communication.

Toward Digital Transformation of Personality Questionnaire: Development of Digitalized Questionnaires and Correlation Analysis between Personality Traits and Reactions Obtained during answering the Questionnaires

Personality traits evaluation is useful for effective supports of work and mental care. However, answering personality questionnaire demands much time and mental load for subjects (target persons). To reduce such time and load, one of the solutions is a digital transformation of personality questionnaire. Toward digital transformation of personality questionnaire, we developed digital questionnaires, which enable us to obtain not only answers but also answering reaction to questions. By using correlation analysis, we found significant correlations between 12 types personality traits and six reaction indicators obtained by the questionnaires. To develop a simpler and accurate digital questionnaires, it would be effective to develop a personality estimation method by using a combination of answer and reaction obtained by the questionnaires

Exploring the Richness of Human Causal Reasoning with Think Aloud Data

The paper aims to examine participants’ open-text ‘think aloud’ explanations of their reasoning while making a judgement about an ambiguous scenario. It aims to consider this data in light of frameworks such as causal modelling, intuitive theories, coherence and the story model. Consistent with these frameworks, we find that participants bring in a large amount of world knowledge to connect ambiguous evidence to unobserved, inferred variables and, via these, to the target judgement. We attempt to represent these chains of inferences using causal diagrams and find that participants interpretations of the scenario can be lumped into one of two distinct causal models, each presenting an internally coherent ‘image’ of the ambiguous scenario. Furthermore, participants’ judgement predicts which of those two models they adhere to. We discuss the limitations and merits of this methodological approach for investigating these types of frameworks.

Psychological Flexibility Determines COVID-19 Peritraumatic Distress and Severity of Depression

COVID-19 implied social distancing, forced behavioural changes, and the economic downturn has been associated with poor mental health and wellbeing. Depression and suicide are the highly predicted psychosocial risks caused by the pandemic crises. Early studies evaluating the effect of COVID-19 on psychiatric health have succeeded in developing screening measures. However, they have been limited in understanding its relations with individual psychological flexibility. An individual's psychological flexibility determines the ability to fight against such adversities on an immediate time scale and the future psychotherapeutic treatment. We conducted an online study to examine the relationship between psychological flexibility and risks to depression and their relationship with COVID-19 peritraumatic distress. We used Multidimensional Psychological Flexibility Inventory (MPFI), Beck's Depression Inventory-II (BDI-II) and COVID-19 Peritraumatic Distress Index (CPDI) to measure the above psychological factors. The results are discussed in light of individual psychological flexibility and its association with BDI-II and CPDI outcomes.

Expanding Understandings of Embodied Mathematical Cognition in Students' Fraction Knowledge

This exploratory study provides evidence that middle-grade students' symbolic fraction knowledge relates to grounded and embodied cognitive learning processes such as spatial ability and anxiety. These findings (N = 89) are consistent with several previous findings on fraction knowledge and mathematics learning more generally while highlighting several novel associations. Three key findings include: 1) mental rotation and spatial visualization are specifically predictive of fraction knowledge scores; 2) spatial anxiety may moderate the relationship between spatial ability and fraction knowledge scores; and 3) fraction knowledge is not only grounded in processes operating at biological and cognitive timescales individually, but components of these processes are interconnected. Though exploratory, these findings may provide the foundations for future work exploring the mechanisms behind these associations.

Curvature Effect on Aesthetic perception

Aesthetic perception is an inseparable part of the decision-making process in daily life. It also is an important part of the beauty and therefore tastes. The determination of preferences is directly related to the subregions of the PFC. The contour is the essential visual attribute for accurately perceiving the form of an object. It has been known that sharp angles cause an implicit perception of threat, and perceived security is related to aesthetic pleasure. The aim of the study is to investigate the effect of contour type on decision making and aesthetic perception in PFC. The study using the fNIRS method has shown that there is a marginal significant relation between liking, contour type, and PFC areas (F(3.81)=2.225, p>.092, η2=.076). Current findings suggest that left mPFC, FPC, and right dlPFC have a significant contribution to the liking of curved objects.

Attentional Momentum Effects on Addition Verification

The direction of our attention can influence our performance on a variety of tasks. For example, reading from left to right relates to people associating small numbers on the left and large numbers on the right. In contrast, reading from right to left relates to people associating small numbers on the right and large numbers on the left. The current study tests if this type of “attentional momentum” can be induced by storytelling based on pictures and whether it affects college students’ reaction time on an arithmetic verification task with equations in a traditional (e.g., 2+2=4) or non-traditional (e.g., 4=2+2) direction. Our results show that students were faster at verifying simple traditional math problems after telling stories based on pictures arranged from left to right, but faster at verifying simple non-traditional math problems after telling stories based on pictures arranged from right to left.

A computer mouse-based throwing task to study perceptual-motor skill learning in humans and machines

Perceptual-motor tasks offer redundant solutions to achieve a goal. However, not all solutions are equally robust to error-producing noise or variability and thus, skill learning can be viewed as a search process to identify behaviors that are error-tolerant. Throwing a ball to hit a target is one such example of a complex perceptual-motor skill that has been studied in the laboratory via the virtual “skittles” task, a simplified 2D task involving throwing a tetherball around a pole to hit a target. We implemented the task as a Unity3D environment (code here: https://github.com/ShortFox/SkittlesTaskEnvironment/) which enables participants to complete the task with a computer mouse and replicated key findings from previous research. Our implementation allows for remote data collection and can serve as a pedagogical tool to teach concepts in skill acquisition. Future work will use this task to explore human versus machine skill acquisition by leveraging Unity’s MLAgents reinforcement learning package.

Typicality Gradients in Computer Vision Models

Rosch (1975) proposed that some exemplars are more typical of a category than others. Typicality gradients have historically been estimated from data collected from human participants. Here, we investigated whether they can be estimated from a computer vision model, VGG19, guided by cognitive science models of concepts and categorization. Following prototype models, we estimated the similarity of each exemplar to the concept prototype computed in two ways (average across all exemplars, most typical exemplar). Following exemplar models, we computed the pairwise similarities between pairs of exemplars and used MDS to order them on a continuum. The prototype-average model achieved the highest rank-order correlation to human typicality ratings of exemplars of the bird category. Thus, computer vision models may have some promise for generating human-like typicality gradients. We are extending this work to utilize newer computer vision models such as ResNet, and to encompass a broader range of categories.

Grounding Action Verbs in Egocentric Visual Perception

It has been conjectured that verb learning is hard because verb meanings are not readily "packaged'' from the physical world. To provide new empirical evidence on this account, we analyzed egocentric video collected from natural toy-play interaction and focused on the naming events when action verbs were uttered in parent speech. Using the Human Simulation Paradigm, we showed egocentric videos of those naming events to adult observers and asked them to guess the target verb in parent speech. We found that adult observers used many different verbs to describe the same visual event, and only one of them matched with the verb in parent speech. We analyzed mismatched verbs and identified several sources of mismatch, and found that all of the mismatched verbs are relevant to the target verb, but they capture different properties (temporal, semantic, etc) of the visual event. We also found that different naming events for the same verb also differ in terms of the degree of ambiguity. Taken together, the results in the present paper provided new evidence from the child’s view, showing that verb learning is hard not only because multiple possible meanings are embedded in each learning situation, but also because these candidate meanings expand across multiple dimensions of the physical world, overlap with each other, and relate to the target meaning in many different ways.

Infant Action Prediction of Everyday Food Preparation

Infants’ ability to anticipate the actions they observe is thought to be a critical skill in early social-cognitive development. A considerable body of research has demonstrated that infants can anticipate observed actions from a variety of cues in different kinds of visual contexts. However, most of this work has relied on screen-based eye-tracking paradigm using highly controlled computer stimuli. Less is known is known about whether and how infants anticipate live actions in natural settings. One prior study demonstrated that infants did anticipate their parents’ actions during live, free-flowing parent-infant play, though they did so infrequently. Using head-mounted eye-tracking, the current study aimed to build upon this work by exploring whether infants make gaze predictions while observing their parents perform an everyday activity in a home-like environment—assembling several peanut-butter and jelly sandwiches. Preliminary findings reveal that infants anticipate their parents’ actions at relatively high rates, indicating a close coupling between parent actions and infant gaze. Rates of prediction did not change over the course of the observation, suggesting that infants already possess prior knowledge of the action sequence structure that they utilize to successfully predict their parents’ ongoing actions.

Investigating Expert and Novice Programming Problem Solving

Programming is a complex problem-solving domain, requiring the coordination of different types of knowledge and skills. The present study investigates expert and novice programming problem solving by analyzing talk-aloud transcripts and the code generated. Based on this analysis a set of basic goal and step components used by novice and expert programmers are identified, which will inform on the generation of cognitive models in the next phase of this research.

Assessment of Mathematical Competence by the Transcriptions of Formulas: An Exploration of Spatial and Temporal Metrics

Previous studies have shown that temporal metrics of writing behavior in simple transcription tasks have some potential for use in the assessment of student learning. This study explores whether spatial metrics, specifically the distance between written strokes, may also have potential for the assessment of competence. Students, N=219, copied sets of equations with different spatial layouts and equation complexity. Although students’ level of competence is manifest to an extent in distributions of distance metrics, the effects of spacing are weaker than with temporal metrics. Stimuli format contrary to the standard mathematical spacing formats may differentiate high and low competent students.

Key Open Door: Simulating Children’s Early Three-word Utterances Reveals Simple Statistical Regularities Underlying Telegraphic Speech

A central question for language development is whether very early telegraphic speech, ungrammatical but intelligible, reflects adult-like abstract linguistic competence, or instead is the result of statistical learning without the reliance on the abstract knowledge. In this paper, we develop a simulation paradigm to evaluate the predictive fit of different accounts of three-word utterances children (<20 months) produced. The simulation subjects are a) human adults with full linguistic competence, b) a statistical model that captures local statistical regularities of language input, and c) a deep neural model that learns and processes language input with global context-aware learning mechanisms. The statistical language model predicted child three-word utterances, both grammatical and ungrammatical ones, better than the neural models and even better (but not statistically) than human adults. The findings suggest simple local statistical regularities underlying child early telegraphic speech.

Pragmatic comprehension of implicatures — consistency within individuals across types and time

While some first results in the literature indicate a relationship between pragmatic processing and specific personality traits or cognitive properties, no results to date show whether an individual makes consistent inferences across different pragmatic tasks or throughout time. In the present longitudinal study, we address these questions by collecting the data on seven types of implicature tasks (including classic scalar implicatures, embedded scalars, and implicatures based on informational redundancy), for the same set of participants. Additionally, we relate the propensity of drawing pragmatic inferences to participants' cognitive and personality profiles. Results show a strong consistency in pragmatic inferences within individuals across time, and between highly similar implicature tasks, but no correlation between different classes of implicatures such as those based on quantifiers vs. not, suggesting that these are subject to different processes. Furthermore, of the individual differences examined, only memory updating was associated with pragmatic competence in bare numerals.

6. Member Abstracts

Nature in the Balance: Symmetry in perceived human-nature relations predicts pro-environmental attitudes

In the face of global climate change, investigating pro-environmental behaviors and perceptions of the human-nature relationship is increasingly important. We investigated perceived asymmetries in how humans and nature impact each other across two studies. Participants (N=331) rated the degree to which they believe humans and nature impact each other (Study 1) and were asked to list and assess instances of human-nature and nature-human impact (Study 2). Study 1 results suggest that participants generally perceive humans to have a stronger impact on nature than vice versa, whereas Study 2 results suggest that participants perceive humans to have a strong-negative and a weak-positive impact on nature and nature to have a similarly strong-positive and weak-negative impact on humans. These findings suggest that while humans’ relationship with nature is broadly perceived anthropocentrically and unidirectionally, when probed about specific human-nature impacts, the human-nature relationship is perceived as bidirectional, but still unbalanced.

Validating child-friendly neuroimaging language localizer in adults

Toddlers undergo massive changes in their language abilities, but are almost never studied with awake functional magnetic resonance imaging. For future use in toddlers, we developed two child-friendly, engaging, well-controlled tasks that robustly activate the language network. The first task presents 20-second edited audiovisual clips from Sesame Street: a single puppet addressing the viewer or two puppets speaking to each other, while the auditory speech is played forwards or backwards. The second task presents 1-3 minutes of continuous dialogue, in which the speech of only one character is played in reverse. Twenty adults heard our two novel tasks, along with a validated auditory language localizer (Scott et al, Cognitive Neuroscience, 2017). The same cortical regions were active in our tasks (Forward>Backward speech) as in the localizer (Intact>Degraded). These results validate our new tasks, which we hope will enable cognitive neuroscience studies of language in challenging but important populations, like toddlers.

Exploration as a Learning Strategy to Support Children's Pattern Learning

Knowledge of repeating patterns is foundational for early mathematical thinking. While interventions that rely on direct instruction help children to master specific patterns, they often struggle to transfer this knowledge to new patterns. This paper investigates exploration as a learning strategy for abstract patterning and improving knowledge transfer. In a yoked, between-subjects design, 5- and 6-year-old children (n = 90) were tasked with finding up to three hidden stars in a repeating shape pattern (ABB). Exploration participants (n=45) explored pattern materials themselves, while demonstration participants (n=45) observed the experimenter revealing the contents of each shape in the same order as the exploration counterpart. In a transfer task, children saw the same repeating pattern with different stimuli. We found that 6-year-olds significantly outperformed 5-year-olds in the exploration condition and used more sophisticated patterning strategies. These findings suggest that exploration may support and extend older children’s emerging understanding of underlying repeating pattern units.

Human Information Seeking in Architectural Spaces Simulated in Virtual Reality

Previous research has shown that proportions, ornateness, and lighting of indoor architectural spaces affect observers' mental states (Negami & Ellard 2021). However, most studies are limited to verbal self-reports, and focus either on photographs, or CAD-modeled rooms in which certain elements, (e.g. ceiling height), are experimentally manipulated. We study human psychological and physiological responses to historical indoor sites reconstructed in virtual reality (VR). Such spaces are often designed to evoke affective responses -- for example, sacred architecture is meant to evoke feelings of calm. Using drone footage, we record 3D geometry, visual and auditory sensory data of an indoor space. We recreate the space in VR, and record humans’ eye-movements, heart-rate, galvanic skin response, and reports of affective states, during free exploration. We propose a cognitive model that interprets physiological responses as information-foraging, and identify the correlates of reported changes in affective states with specific properties of architectural space.

How do young children interpret conditionals?

Conditionals have multiple interpretations: the statement "If it rains, Abbie will buy an umbrella" is true when it rains and Abbie buys an umbrella (conjunction), when it doesn’t rain and Abbie doesn’t buy an umbrella (biconditional), and when it doesn’t rain and Abbie buys an umbrella (conditional). Prior research shows that children understand all three interpretations in adolescence, but the reasons for this delay remain unclear. For some researchers, children struggle to represent multiple possibilities due to limited resources. For others, task-related factors underlie children’s difficulties. In a truth-value judgement task, 3-10-year-olds matched a conditional statement with one of two pictures: 1 depicting a false interpretation of the conditional vs. one of the 3 true interpretations. Results showed that children understood conjunction since age 3 but comprehended all three conditional interpretations at age 9, earlier than previously thought. Findings point to a task-related explanation of children’s difficulties.

Conditional probability of novel word sequences predicts immediate serial recall performance

Many theories of verbal working memory have claimed that novel word lists are unlike natural language. As a result, recall of the novel word lists is not affected by sequential statistics in natural language and must be governed by short-term memory. We analyzed memory for novel, dative-like word lists (e.g., ADJ-NOUN-GAVE-ADJ-NOUN-NOUN) across three experiments. We used a powerful neural language model (Ng et al., 2019) to compute the surprisal associated with the critical words in each list, because surprisal indexes psycholinguistic processing difficulty (Levy, 2008). Surprisal at critical words strongly predicted the number of words recalled in each list. These results demonstrate lexico-syntactic factors can support syntactic structure even in novel word lists and that continuous measures of processing difficulty may be used to characterize similarity of novel lists to natural language. We discuss implications for theories of verbal working memory.

When Newton beats Euclid: intuitive physics underlies sensitivity to geometry

Reasoners consider some differences in geometric features as more similar than other differences in geometric features. For example, humans and other animals consider horizontal mirror images more similar to each other than vertical mirror images or objects that have different orientations around a point in the vertical plane. Previous theories that explain sensitivity to geometry in terms of Euclidean principles cannot account for these categorization tendencies because in Euclidean geometry, rotation is an invariant transformation. There is thus no difference between the vertical and horizontal axes in Euclidean geometry. We propose a new theory that can account for these tendencies because it considers the role that gravity plays in how minds categorize geometric objects. Our theory makes new predictions about how reasoners categorize geometric objects in different contexts: 3D contexts with gravity, 3D contexts with gravity where objects were manipulated, and 2D contexts. Five experiments with adults support our theory.

Using fMRI to study the neural basis of violation-of-expectation

In studies of infant cognition, why do babies look longer when objects pass through each other, or someone behaves inefficiently? We test 3 candidate explanations (domain-specific prediction error, domain-general endogenous curiosity, and perceptual novelty), each with a distinct, non-mutually exclusive, predicted pattern of neural activity. We scanned 17 adults using fMRI while they watched videos of agents and objects, adapted from infant behavioral research [1–4]. Cortical regions preferring social vs physical information [5,6] showed similar preferences for these stimuli. These regions, in the first run of the experiment, responded to physical and social violations (unexpected > expected outcomes), with a greater response to violations from the corresponding domain. Regions that respond to general perceptual novelty also responded to social and physical violations, regardless of domain. Thus both domain-specific, and general perceptual, cortical regions encode violations of expectation involving agents and objects. References 1. Baillargeon R, Spelke ES, Wasserman S. Object permanence in five-month-old infants. Cognition. 1985;20: 191–208. 2. Needham A, Baillargeon R. Intuitions about support in 4.5-month-old infants. Cognition. 1993;47: 121–148. 3. Gergely G, Nádasdy Z, Csibra G, Bíró S. Taking the intentional stance at 12 months of age. Cognition. 1995;56: 165–193. 4. Woodward AL. Infants selectively encode the goal object of an actor’s reach. Cognition. 1998;69: 1–34. 5. Koster-Hale J, Saxe R. Functional neuroimaging of theory of mind. Understanding Other Minds. 2013. pp. 132–163. doi:10.1093/acprof:oso/9780199692972.003.0009 6. Fischer J, Mikhael JG, Tenenbaum JB, Kanwisher N. Functional neuroanatomy of intuitive physical inference. Proc Natl Acad Sci U S A. 2016;113: E5072–81.

Investigating the impacts of an immersive learning mode and graded feedback on category learning

Categorization research often employs traditional artificial classification learning (TACL) and a foundational category structure based on family resemblance (FR). In TACL trials, each training item is presented for classification with arbitrary labels followed by corrective feedback. In the FR structure, categories are organized around two opposite configurations (i.e., prototypes) of a handful of binary features such that each category consists of its prototype plus the off-by-one variations. Despite convention, there is reason to question whether these choices align with natural category processes and structures. We employ a richer instantiation of FR using prototypes in opposite corners of a 2D space with four levels of variation. In addition, we investigate variations on the TACL paradigm: 1) situating classification learning within an immersive, dynamic, goal-driven setting; and 2) altering the core task to predicting a graded level of category membership (akin to typicality). Results provide implications for theoretical accounts of categorization.

The cognitive neuroscience of social learning in human evolution

Cumulative culture is believed to be a uniquely human form of social learning, and is therefore believed to be key in understanding how humans evolved such complex social organization and technologies. It is reasonable to believe that our capacity for cumulative culture is the result of a series of interconnected, complex evolutionary processes, but humans abilities for teaching, imitation and communication are believed to play a central role. We plan on combining brain imaging techniques and a transmissions chain design with transmission of evolutionary relevant tasks (knot tying and symbol-production) in order to look at the mechanisms of cumulative culture in a new way. By looking at the brain activation involved in acquiring and transferring these skills we aim to offer new insight on the cognitive and behavioral demands of these technologies and their effect on cultural evolution.

Look For Adjectives In the Face: How Facial Expressions Contribute To Meaning In Signed Languages

Sign languages are visual-spatial languages, articulated not only by hands but also with facial expressions and other body parts. Facial expressions, in particular, have important grammatical roles, such as defining whether a sentence is a question or not and differentiating the meaning of a sign when assuming the role of a quantifier. Despite the importance of facial expressions, technologies to recognize and generate sign language mainly focus on manual signs, ignoring the information from facial expressions. In this work, we focus on collecting and annotating data to evaluate the role of facial expressions in communicating adjectives. We study the role of facial muscle activity to express the intensities of manual signs and show how modeling facial action units change the overall meaning of a sentence. We also explore how facial expressions allow a deeper understanding of sign language when included in the design of machine learning models.

Measuring social curiosity-driven attentional differences in children with autism using an augmented reality-based phone app

In learning about and building models of their social and physical worlds, children exhibit a wide range of complex, spontaneous, intrinsically motivated, curiosity-driven behaviors. To understand how curiosity-driven behavior in social environments differs in diverse learners, we experimentally measure attention allocation in children on the autism spectrum and their typically-developing peers. We designed an augmented reality smartphone activity where children freely explore novel stimuli. Some agents behave “socially” (i.e., animate), while others have more regular (i.e., periodic, static) or irregular (i.e., random) inanimate behavioral patterns. This project is part of a broader program in which we attempt to model curiosity with artificial intelligence. Our augmented reality systems will lead to large and diverse data acquisition, allowing us to model a comprehensive range of learning behaviors and enable more inclusive, personalized pedagogical and diagnostic tools.

Mapping the Structure of Metaphorical Concepts of Undergraduate Research Mentoring

Many scientists describe themselves metaphorically as a “gardener” or “sculptor” of the undergraduate students in their lab and the students’ own research ideas. Across two preregistered studies (N = 1200), we applied existing methods for mapping the entailments of metaphorical concepts to provide insight into lay conceptions of undergraduate research mentoring. Participants were presented with one of several mentoring metaphors and rated how closely a series of mentor attributes fit the metaphor. Iterated exploratory factor analysis revealed a small set of dimensions along which the metaphors differed systematically, reflecting the mentor’s role as a nurturer, authority figure, communicator, and collaborator. The metaphors were also associated with different intuitions about mentors’ responsibility for and power to influence student learning in the lab. Our findings show that mentoring metaphors capture distinct mentor attribute profiles, with implications for how scientists understand and communicate the value of their mentoring approach to their students.

Causal imprinting in the pandemic: the persistence of the surface-model of Sars CoV2 transmission

Laboratory experiments have shown that the first causal explanation people hear “imprints” on the mind. New causal explanations become combined with the first, rather than replace it. The COVID-19 pandemic presented a real-world version of these experiments. The initial causal model communicated by scientific publications focused on how the virus spread via contact with surfaces. However, by July 2020 the science was clearly showing that aerosol transmission was more important, yet there has still been a greater focus on cleaning surfaces than ventilation since then. The current studies show how the surface model and aerosol model coexist in people’s minds, are associated with risk perceptions, and drives preferences for pandemic control strategies. In addition, the studies shows that a tutorial based on a deeper explanation of aerosol transmission focused on the density of virus particles in the air helps causal model updating.

Graphicacy skills across ages and cultures: a new assessment tool of intuitive statistics' abilities

Graphical representations such as scatterplots have become the elective tool to transmit quantitative information in an efficient manner. However, little is known about the way humans extract statistical information from graphs, and if these abilities correlate with higher level cognition, such as numerical skills. We proposed a new assessment tool of intuitive statistics: the judgment of trends in noisy scatterplots with varying slope, noise and number of points. We found that human performance is beautifully predicted by the t-value associated with the regression of the scatterplot; our findings reveal a remarkable human ability at performing visual statistics tasks fast and accurately, close to an optimal observer model. Crucially, we found that these "graphicacy" skills are present in both educated (n=4000) and uneducated adults living in non-industrialized societies (n=50), and even in preschoolers (n=23); furthermore, they correlate with numerical skills.

The Impact of Dyslexia Awareness Month on Search Volume Using GoogleTrends

Dyslexia is one of the most common learning disabilities, but is also severely underdiagnosed. Early intervention is demonstrated to produce a better prognosis, but lack of awareness can impede pursuing and accessing treatment. Dyslexia Awareness Month has been implemented in both Canada and the United States during the month of October as a way of increasing the public’s knowledge of this learning disability. The objective of this study was to ascertain the effectiveness of these campaigns between the years of 2016 – 2019 in both countries. We employed ‘Google Trends’, which is a statistical tool that can be used to compare volumes of specific Google searches, to evaluate the relative number of searches for the term ‘dyslexia’ before, during, and after the awareness month campaigns each year. Our results show a significant increase in dyslexia-related search inquiries during October in both countries, but the timeline of these differences varies.

The emotional value in objects: Insights from coping with the COVID-19 pandemic

How possessions gain personal meaning has been a topic of interest to diverse fields studying the mind. Traditionally, the personal value of possessions is hypothesized to be driven by their special histories (Belk, 1988). We provide evidence that people value possessions for their ability to regulate distress. During the pandemic, people (N=333) reported having coping-possessions that helped them to regulate emotions (49%), solve problems (26%), or both (25%). Coping-possessions had high personal and monetary values (M=4.82 and 4.45, respectively; one-sample t-tests against midpoint, ps < .001) with problem-focused possessions having higher monetary values than emotion-focused ones (p < .001). The majority of coping-possessions, however, had mundane histories (e.g., bought at a store) rather than noteworthy ones (p < .001), suggesting that the emotional functions of objects contribute to their perceived personal value. This work also has implications for how adults use objects to regulate their emotions (Timpano & Port, 2021).

Adults' Evaluations of Rote and Reflective Teachers

Decision-making can be automatic (“rote”) or on-the-fly (“reflective”). Are people sensitive to whether others are behaving rotely or reflectively? The rote-versus-reflective inference may be particularly relevant when learning from others: good teachers should be actively considering the learner’s needs. When teachers rely on rote systems, this may “break” the mental state recursion that facilitates learning from pedagogy. This study takes a first step in investigating learning and inference when teachers use rote-versus-reflective reasoning, using uniqueness of feedback as a cue to reflectivity. Adult participants viewed videos of teachers providing either identical, similar, or unique feedback to three different groups of students; participants evaluated that teacher along several metrics. Consistent with our predictions, rote teachers were evaluated as poorer informants than reflective teachers, and students paired with rote teachers were expected to learn less. These results are the first to demonstrate sensitivity to, and impact from, inferences about others’ rote-versus-reflective behaviors.

Procrastination and the Intention-Behavior Gap

Procrastination is universal but not monolithic; a second dimension, the Intention–Behavior Gap (IBG), has been suggested to be relevant. The IBG is the difference between one’s intentions and one’s behavior. So far, the IBG has not been directly measured within a behavioral experiment. We assigned subjects a week to work on a lengthy online reading task. Before the task started, subjects reported their plan by the number of reading units for each day. We found great variability in their plans, actual time course and the difference between the two (IBG). The time course of work ranged from completing earlier than planned to not finishing. We calculated correlations between the mean unit completion day, the IBG, the Causal Dimension Scale, indices of regret and satisfaction, and scales for procrastination (General, Active and Irrational). Besides the correlations expected based on definitions, we did not find any significant correlations.

Walking in or Talking with Others’ Shoes: Studying the Role of Perspective Getting and Mimicry on Interlocutors’ Interpersonal Accuracy and Feelings of Empathy

We investigated the extent to which two interventions (perspective-getting versus mimicry) affect individuals’ cognitive empathy (interpersonal accuracy) and affective empathy differently. Participants were invited to a Zoom-session where they met another participant (the target) who they had never met before. Participants were instructed to assess the stranger’s attitude on several topics (interpersonal accuracy) and to report their empathic feelings towards the target (affective empathy). Beforehand, participants were explicitly instructed to either (1) get the target’s perspective on these topics in a five-minute discussion (perspective-getting), to (2) mimic the target’s facial expressions (mimicry) while watching the target explain her general experiences with the topics, or (3) to watch the same monologue without the mimicry instructions (control). Results showed that perspective-getting increased predictors’ interpersonal accuracy versus mimicry and the control. However, the mimicry instructions did not result in higher feelings of affective empathy compared to the other two conditions.

What creates violations of linguistic universals? Correlates of linguistic rarities and their importance in studying linguodiversity

Absolute linguistic universals (ALUs) are theoretically useful. However, do non-trivial ALUs exist? Two reasons for doubt can be identified. First, many proposed ALUs transpired to be only strong statistical tendencies. Second, computational modeling suggests that rarely can positive typological evidence alone indicate an ALU. Thus, the debate over ALU existence persists. We advance this discussion by analyzing what languages tend to violate previously proposed ALUs and whether they are distributed randomly in terms of sociogeography and phylogeny. Non-randomness would suggest bias in ALU estimation using existing data. We find non-random distribution of ALU violations: languages with more speakers, certain language families, including Indo-European, and language isolates tend to violate ALUs more often than expected. The results suggest that linguistic diversity is underdescribed, and that many ALUs are put forward due to an underestimation of the space of possible linguistic structures due to sampling biases of researchers.

Multimodal integration as predictions. An explanation of the McGurk effect

During speech perception, humans constantly sense multiple sources of information. As so, the McGurk effect has been typically conceived as a prototypical example of language multi-modal integration, specifically, it has been used to study audio-visual integration during speech. The McGurk effect arises when in-congruent audio-visual stimuli} are paired and perceived as a different syllable ( auditory /ba/ + visual /ga/ = percept “da” ). We developed a hierarchical computer model, based on self-organizing maps and Hebbian learning to study speech multi-modal integration. Our architecture allows studying the McGurk effect purely on bottom-up processing of audio-visual information. We trained several versions of the model and measured the activation similarity between McGurk alike stimuli and congruent ones by means of mutual information. Our results suggest that the illusory percept arises from the best congruent representation that reduces uncertainty. Furthermore, the reliability of each sensory modality determines the best congruent representation.

Joint Content-Context Analysis of Scientific Publications: Identifying Opportunities for Collaboration in Cognitive Science

This work studies publications in cognitive science and utilizes mathematical techniques to connect the analysis of the papers' content (abstracts) to the context (citation, journals). We apply topic modeling on the abstracts and community detection algorithms on the citation network, and measure content-context discrepancy to find academic communities that study similar topics but do not cite each other or publish in the same venues. These results show a promising, systematic framework to identify opportunities for scientific collaboration in a highly interdisciplinary and diverse field such as cognitive science.

The Perceived Dilution of Causal Strength

Dependency theories of causal cognition, like causal Bayes net theory, postulate that the strength of causal links is independent of the causal structure in which they are embedded. We propose a new theory that postulates that people's concept of causality is richer, and that predicts an influence of causal structure on strength intuitions. According to the theory, people's concept of causality involves the idea that causal powers behave like phenomena studied in fluid dynamics: Continuous causes are assumed to spread their capacity across causal pathways, akin to fluids running through pipe systems. The theory predicts a perceived dilution of causal strength. A series of experiments (N = 3,733) and a meta-analysis corroborate the theory. For common causes, people perceive the strength of individual links to decrease with the number of links. In causal chains, people perceive a link strength gradient. This dilution effect disappears for genuinely binary causal variables.

Play as intuitive modelling for fostering creativity in interacion

Pursuing the mechanisms and methods to improve the creative potential in team activities becomes significant in modern society. One candidate of the creative method is LEGO Serious Play (LSP), where intuitive modelling with LEGO blocks, corresponding to Play in LSP, is considered effective in teams’ creative problem-solving. To understand the mechanism for Play to influence creativity in interpersonal interactions, we compared Play (intuitive modelling) and Non-Play modelling with LEGO blocks. Our experiments consisted of two phases, individual and collaborative modelling, and two groups conducted the former phase, intuitive modelling before thinking (Play) and thinking before modelling (Non-Play). We found that Play significantly promoted utterances about abstract concepts during the collaborative phase, while utterances about concrete models showed no difference between the two groups. This finding suggests that the interplay between mentioning abstract concepts and handling concrete objects may be a mechanism of Play as intuitive modelling to improve team creativity.

Exploring the Functional Role of Post-Error Adjustments during a Flanker Task

After committing an error, people slow down to avoid subsequent errors. This post-error slowing (PES) is sometimes considered a functional adaption that leads to improved post-error accuracy (PIA). However, the co-occurrence of these two phenomena is inconsistent, bringing into question the functional role of PES. We first reanalyzed previous flanker data to investigate the functionality of these adjustments by comparing RT and accuracy between post-correct and post-error trials. Using a flanker task with absent, congruent, or incongruent distractors, we can explore whether cognitive demand differences yield different effects on PES and PIA. Instead, a significant PIA effect was uniformly observed across conditions, while a not significant post-error speeding effect suggests they are distinct phenomena. A second experiment with a shortened cue period and intertrial interval produced only a marginally significant post-error speeding effect. In sum, these data suggest these phenomena are distinct and their occurrence is reliant on task demands.

‘Sheep’ and ‘Ship’: An investigation into English vowel merger in multilingual Singapore

English is often discussed with models that have a ‘Centre/Standard’ variety e.g., in US, and UK where English is spoken as a first language, and varieties of spoken elsewhere are regarded as ‘Outer’ or ‘Non-standard’. ‘Standard’ varieties are often regarded as prestigious and worthy of documentation, and to be taught in schools. This is problematic when the speech sounds in the local varieties of English differ from ‘Standard’ varieties. In Singapore, English is the working language among the multilingual population. Despite its importance, speech sounds in Singapore English are not well documented and not used in teaching. 60 Singaporean speakers of English completed a same/different judgement task of 206 words to investigate possible vowel mergers e.g., are ‘sheep/ship’ pronounced the ‘same’? They also read aloud the same words. We analyzed the acoustics (F1, F2) of their articulations and analyzed whether participants’ same/different judgements predicted the way they pronounced the words.

People think of others as more prosocial when they are motivated by aesthetic goals vs. instrumental goals

People expect others to take efficient paths toward goals. Inefficiency changes how we categorize actions, leading us to see actions as play (Chu & Schulz, 2020), or as movements performed for their own intrinsic value (Schachner & Carey, 2013). Here we find that performing actions for their own value (e.g., aesthetic value), versus for instrumental purposes, provides social information about others. In a pre-registered experiment (N=360), participants judged which character in a pair was more compassionate, or more selfish/manipulative. For one key pair (among distractors), both characters performed the same activity (music, painting, eating, exercising, math, being in nature), and we manipulated why: Either for its own aesthetic value, or as a means-to-an-end (instrumental value). Across all activities, aesthetically-motivated characters were judged as more compassionate and less selfish/manipulative than instrumentally-motivated characters (p’s<0.01). Aesthetically-motivated behavior may signal others’ emotionality moreso than instrumentally-motivated activities, driving inferences about prosociality.

The role of primary motor cortex in inhibition of return: An investigation using transcranial magnetic stimulation

Inhibition-of-return (IOR) is demonstrated by longer response times (RTs) for stimuli presented at previously attended locations than unattended locations. While facilitatory cueing effects occur at short stimulus onset asynchronies (SOAs) between a cue and target, IOR emerges at later SOAs. Frontoparietal areas including posterior parietal cortex have been implicated in producing IOR, but the contribution of primary motor cortex (M1) is unclear. In the present study, participants executed key-press responses to peripheral targets following a cue presented at the same or opposite location at different SOAs (100/300/600/1000ms). Single-pulse transcranial magnetic stimulation (TMS) was applied over right M1 on 50% of trials in a randomized (Experiment 1) or blocked (Experiment 2) order. IOR appeared to be attenuated by TMS, although RT profiles differed between experiments. Motor-evoked potentials were not altered by the cue-target relationship in either experiment. These findings do not support a key role of M1 in mechanisms of IOR.

Event Segmentation In Story Listening Using Deep Language Models

Event segmentation theory posits that we segment continuous experience into discrete events and event boundaries occur at large transient increases in prediction error, often related to context changes. Identifying event boundaries a priori has been difficult in naturalistic settings. To overcome this challenge for story listening, we used a deep learning language model (GPT2) to compute the predicted probability distributions of the next word, at each point in the story. For 3 stories, we computed the surprise, the entropy, and the Kullback-Leibler divergence (KLD). We then asked participants to listen to these stories while marking event boundaries. We used regression models to compare the GPT2 measures and the human segmentation data. Preliminary results indicate that event boundaries are associated with transient increases in KLD. This supports the hypothesis that prediction error serves as a control mechanism governing event segmentation, and points to important differences between operational definitions of prediction error.

Representation matters: A PDP model of impression formation and person knowledge

When we encounter a person we rapidly form an impression. This impression is shaped by factors inherent to the person and event, including gender cues, skin colour, clothing, and context, along with pre-existing knowledge about the world. We present a PDP model of impression formation and person knowledge that has roots in the study of semantic cognition. We trained our model to learn about people of different races, genders, and occupations (e.g., black female doctor), along with context related to those occupations (e.g., hospital bed), and semantic attributes related to those occupations (e.g., intelligent). We demonstrate and explain the interesting finding that the model encodes knowledge about gender and race in early ‘visual’ hidden layers, despite no visual similarity being built-in, and no feedback connections from ‘semantic’ regions. This suggests a third mechanism through which the knowledge we hold about people may quite literally shape the way we see them.

Motor-related word association differences in Parkinson Disease for verb processing

Sensory-related word association differences in Parkinson's Disease for verb processing Free word association tasks are tools to study spontaneous lexical organization in children and adults. Lexical organization resembles a network of words grounded on relevant sensory information; for example, verbs engage the most with motor information. Studying lexical networks in populations with motor disorders, like Parkinson's Disease (PD), will insight into the effect of relevant information deprivation in lexical organization. We applied a free word association task with early-acquisition Spanish nouns as stimuli. Participants consisted of 20 with PD -divided into two groups depending on their cognitive state- and ten healthy older adults for control. Then, we used an artificial neural network to determine the semantic distance between stimuli-response dyads. We found differentiated semantic distances for verb responses in all groups in an inverse u-shaped curve depending on their cognitive and PD state. Our findings suggest that lexical organization and meaning are not crystallized, but adapt from sensory information.

Neighborhood effects for composite (i.e., non-prime) numbers

We investigated individuals’ representation of composite numbers (i.e., those with factors other than 1 and themselves). Some numbers are “more composite” (have more factors) than others (e.g., 72 vs. 74). We hypothesized that people mentally represent numbers in factor neighborhoods. This should result in a compositeness penalty: performance on factoring problems (e.g., x * 9 = 72) should be slower for more composite products, because retrieving the corresponding factor requires inhibiting neighboring factor pairs. We measured 50 undergraduates’ time to correctly solve factoring problems for composite products (6 to 100). We found that for well-practiced facts (i.e., the 12x12 multiplication table), performance slowed as compositeness increased, even after controlling for problem size and factor size. However, there was no compositeness penalty for “non-table” facts. These findings support our factor neighborhood hypothesis for well-practiced facts and suggest that people's representations of composite numbers are influenced by mathematics instruction (i.e., “multiplication tables”).

Limits and risks of GPT-3 applications

Limits and risks of GPT-3 applications Recent GPT-3 applications have fed new optimism in AI research. To explore limits and risks, we investigated how far we can get in developing a so-called digital replica of Daniel Dennett, generating text outputs from a large language model trained on Dennett’s philosophical writing. In consultation with Dennett himself, we compare several fine-tuning strategies and evaluate outputs. Analyzing the failures and the successes will allow us to address technical and ethical issues such as: 1. How accurate can such models be with current technology? 2. To what extent (if at all) might it be acceptable to present a model’s outputs as representing an author’s view? 3. Should copyright holders have control over such models? 4. How one can address risks of making such models public (including possible over-reliance on their accuracy)? 5. How good are experts and non-experts in distinguishing humans from replicas?

Children as Design Partners: Digital Media, Learning Analytics, and Metacognition

Children may participate in the design of digital technologies as users, testers, informants, and design partners (Druin, 2002). Yet, more often than not, teachers, researchers, and engineers are design partners, while children remain as mere users and testers (Krumm, Means, & Bienkowski, 2018). This paper explores how children are designers of digital artifacts and learning analytics on an online platform called Knowledge Forum (Scardamalia & Bereiter, 2021). Eight children participated in a group interview to reflect on their online notes and drawings from grade 1 to grade 6, using the analytic tools to mine developmental patterns in their online discourse (e.g., vocabulary growth, diversity and complexity of ideas). Implications of this work are discussed by extending Druin’s framework to consider children’s roles in simultaneously improving usability, augmenting the impact of technology on their learning, and the advancement of learning theories within the context of hybrid learning in a post-pandemic world.

An investigation of the cognitive and neural correlates of semantic memory search related to creative ability

How creative ideas emerge remains unclear. We designed an associative fluency task based on polysemous words to explore the neurocognitive correlates of semantic search related to creativity. We distinguished two search components by assessing clustering and switching performance from participant’s responses. We related these components to creativity, executive abilities, and semantic memory structure explored via semantic networks, and identified their predictive functional brain connectivity patterns. Clustering correlated with divergent thinking, and relied on interactions between control, salience, and attentional networks. Switching correlated with the ability to combine remote associates, memory structure, and executive abilities, and was predicted by connectivity between and within the default, control, and salience networks. These results suggest that switching captures interactions between memory structure and control processes guiding the search, while clustering may capture controlled processes related to persistent search. These findings shed new light on the neurocognitive mechanisms of semantic search supporting creativity.

Controlling the narrative: Euphemistic language affects judgments of actions while avoiding perceptions of dishonesty

The present work examined the extent to which evaluations of actions could be biased by the strategic use of euphemistic (agreeable) and dysphemistic (disagreeable) terms. Across multiple studies, we demonstrate that evaluations of actions are made more favorable by replacing a disagreeable term (e.g., torture) with a semantically related agreeable term (e.g., enhanced interrogation) in an act’s description. Notably, the influence of these terms was reduced (but not eliminated) when actions were described in a less ambiguous (i.e., more detailed) manner. Despite their influence, participants judged both agreeable and disagreeable action descriptions as largely truthful and distinct from lies, and judged agents using such descriptions as more trustworthy and moral than liars. Overall, the present findings suggest that a strategic speaker can, through the careful use of language, sway the opinions of others while avoiding many of the reputational costs associated with less subtle forms of linguistic manipulation (e.g., lying).

Simplicity beyond probability: Simplicity’s role in evaluating explanations goes beyond providing cues to priors and likelihoods

People often evaluate explanations by considering various ‘explanatory virtues’, such as an explanation’s simplicity (i.e. the number of unexplained causes referred to). Simplicity has been thought to guide these evaluations by providing a cue to the inputs of Bayesian inference (priors and likelihoods), thus indirectly helping compute the outputs: the posterior probability of an explanation being true. Yet simplicity may also play other, more direct, roles in explanation evaluations. While study 1 supported the mediating role of priors and likelihoods in people’s simplicity preferences, study 1 and 2 found that participants still preferred simpler explanations after statistically controlling for priors and likelihoods (either elicited or provided). These results suggest that simplicity guides explanation evaluations not just by providing cues to the inputs of Bayesian inference, but also by serving as a direct cue to the outputs of these inferences – perhaps providing a simplifying heuristic for these evaluations.

Analyzing the Effect of External Environments on Mind Wandering during a Perceptual-Motor Task

To maintain performance in perceptual-motor tasks involving interactions with the external environment, appropriate regulation of the arousal level is essential. Both internal (learning and saturation) and external (stimuli) factors can be assumed to affect such arousal levels. We investigated these factors by using a line-following task, in which participants had to follow a scrolling line with a circle. Participants recruited through crowdsourcing engaged in this task for 30 minutes. They responded to periodical probes during the task to indicate their level of concentration thereon. Experiment 1 tested the effect of external stimuli designed to decrease/increase arousal levels in this task. Based on the results of this basic experiment, Experiment 2 switched the stimulus pattern from high to low arousal / low to high arousal during the task. As a result, we found differences between task conditions, suggesting the effect of adaptive stimulus presentation to maintain the arousal level.

The role of vocabulary and executive functions in the comparison process for novel word generalization

Comparing several examples of a target category leads to better generalization than single presentations. It has been argued that the more children know about the world the more likely they will discover new conceptually relevant dimensions (Gentner & Hoyos, 2017). From another point of view, the comparison process is underpinned by control processes such as executive functions (Augier & Thibaut, 2013). This study contributes to this debate with a correlational approach in which we assessed, word generalization in a comparison design, inhibition, cognitive flexibility, working memory, and vocabulary. 120 five-years-old children performed the generalization task with unfamiliar objects and 120 other five-years-old children performed the generalization task with familiar objects. Whatever the familiarity of the objects in the generalization task, we found positive and significant correlations between categorization and flexibility but not with vocabulary. It suggests that executive functions are more at play in the comparison process than word knowledge.

Decision Variables in the Case of Police Lineup Rejections

Though high-confidence identifications from a police lineup can provide strong evidence of guilt, confidence in a lineup rejection often provides little additional information beyond the rejection itself. Here, we investigate whether participants use different decision rules for positive identifications vs. lineup rejections. In this experiment, we compared confidence ratings from a standard simultaneous lineup (where confidence in the lineup rejection is not applied to any particular face) to the confidence ratings from a rate-them-all simultaneous lineup (where confidence that a person is or is not the perpetrator is applied to every face in the lineup). We hypothesize that, in the case of a lineup rejection, confidence in the standard condition will be best predicted by the average memory signal in the rate-them-all condition. The possible reliance on an average memory signal could help to explain why previous research has not found a strong confidence-accuracy relationship for lineup rejections.

Applying the pragmatics-first approach to agent-based models of emergent communication – insights from a study in experimental semiotics

Recent theoretical shift in the discussion on the evolutionary origins of human communication points towards cognitive pragmatics as a foundation of open-endedness of our expression (Heintz & Scott-Phillips 2022). This perspective, however, has not been acknowledged yet within computational modeling of language emergence, which suffers from a lack of flexibility and generalization. We present the results from our experimental semiotics study inspired by an agent-based model (Mordatch & Abbeel 2018). Replicating conditions from a multi-agent simulation in the real world with human participants allowed us to: a) disclose commonly shared model assumptions that are potentially harmful and neglect the importance of social cognition and context; b) identify communication strategies that emerged among “human agents” and pragmatic behaviors that helped to establish them. Building on these results, we argue that the models of emergent communication could benefit from applying the pragmatics-first approach, sketching a direction towards more flexible and human-like communication.

Sensitivity to time discrepancy: A computational model of human response to expectancy disconfirmation

Traditional expectancy disconfirmation theories have focused on the discrepancy between expectations and perceptions. Here, we propose a new Bayesian framework model that takes into account human responses triggered by disconfirmation. It aims to explain, for example, the differences of responses to waiting time at a hospital reception, where the wait time discrepancy is ideally expected to be zero, by introducing the sensitivity of individuals which corresponds to the decision threshold for responses when coping with disconfirmation. The model was applied to a real-world clinical appointment dataset. The dataset contains multiple appointments with expected and actual times and individual response patterns depending on the disconfirmation. Individual sensitivity was estimated from the dataset. The sensitivity was correlated with the response patterns such as sticking to expectation. In addition, with agent-based simulations, results suggest that the introduction of sensitivity contributes to identifying different groups of responses and predicting negative responses such as "No-Show".

The Neurobehavioral Basis of Parallel Individuation and Numerical Approximation

Research shows the existence of a small-number system (~1-3) invoking parallel individuation, and a large-number system (~4+) based on Weberian magnitude estimation. A 128-channel EEG system was used to investigate ERPs while participants detected numerical changes in dot arrays during an oddball task. During “No Change” trials, the N170 over the parietal-occipital-temporal areas showed distinct waveforms to the habituation of small (but not large) numbers, indicating that more items encoded in working memory leads to stronger N170 amplitudes. We also observed decreased accuracy for increasing vs. decreasing changes in magnitude. Converging over the mid-parietal area (Pz), lower P3b amplitudes were observed for harder, numerically-larger conditions, while Increasing-Large changes showed longer reaction times compared to Decreasing-Large. Our findings suggest a neurobehavioral differentiation in perceiving small vs. large numbers and increasing vs. decreasing change at early stages of processing, and a later stage that involves higher-order numerical processing linked to context-updating.

Affect and grammatical anomaly in discourse

We investigated the relationship between dispositional affect and the interpretation of sentences containing modal auxiliaries (must, should, might, would), that were embedded in factual vs. non-factual contexts. 49 participants read 2-sentence discourses followed by superficial questions, e.g., The art collector is admiring the statue. It would cost thousands of dollars. Question: The art collector is appreciating the statue. 1) True 2) False A negative correlation (r= -.355) was found for question-response accuracy scores and positive affect for must, should sentences vs. might, would. Thus, low positive affect individuals were more accurate after reading sentences with must, should vs. might, would. Questions were always about the context sentence; less attention at the second sentence would allow for higher accuracy rates. Given that must, should do not require restrictors for interpretation (Stump, 1985) whereas might, would do, low positive affect individuals paid less attention to these sentence types allowing for higher accuracy.

Second language vocabulary learning: When do pictures speak louder than words?

Second language vocabulary learning research has so far been predominantly focused on concrete nouns. However, recent work suggests that different word classes lead to differences in grounding, processing and learning of words (Markostamou, 2017; Martin & Tokowicz, 2019). To contribute to the basic understanding of the learning mechanisms, this study investigates how different learning methods influence learning of different types of words (nouns, verbs and prepositions) through either text-based or picture-based learning methods in adult population (N=160). Selected words vary in their translational ambiguity to additionally explore the modality effects in words with different levels of cross-linguistic (dis)similarity (Jarvis & Pavlenko, 2010). Production and recognition tasks are introduced after both learning sessions and again after a one-week delay to obtain comprehensive data about the learning curve across conditions. The results will contribute to understanding how words are represented and conceptualised and how they interact with different modalities during the initial learning phases.

Rates of gender representation in children’s literature across cultures: A comparison of US vs. Chinese children’s books

Male-protagonist overrepresentation exists in US children’s books and varies as a function of author-gender and target audience age (Casey et al., 2021). We investigate whether these patterns appear across cultures by coding 437 bestselling children’s books from China and comparing them to the US dataset. Mixed-effects and chi-square models revealed cross-cultural male-overrepresentation. Effects of author-gender showed that books written by American male authors and Chinese female authors were associated with greater male-overrepresentation. Effects of target audience age showed that books written for 3-8-year-olds in the US and 3-5-year-olds in China exhibited the most male-overrepresentation, suggesting that children in these age groups may be particularly vulnerable to inequitable gender representation in print media. This is the first study to examine gender representation in Chinese children’s books on a large scale and to directly compare rates of gender representation across cultures. Implications for educational practices and literacy development will be discussed.

Metacognition and marking-for-self in theatrical dance as a multimodal pattern of semiotic activity

Metacognition ("thinking about thinking") depends on representations and metarepresentations, which are usually treated as internal knowledge structures, rather than as external-oriented processes. We explore how multimodal patterns of external semiotic activity (not monomodal and internal symbolic-based processes) can provide a more accurate description of metacognition. To develop our ideas, we examine the act of marking-for-self in theatrical dance. To mark is to perform a dance phrase in a simplified, schematic, or abstract way. When marking, dancers use their bodies in motion to represent some aspects (properties, dynamics, or structures) of the complete dance-phrase they are thinking on. Marking-for-self occurs when dancers mark in their own idiosyncratic manner, a process that potentializes real-time reflection on its own dancing sequences and structures. According to our view, these representations are metacognitive embodied and externalized diagrammatic structures. We detail this phenomenon in light of recent developments in Cognitive Semiotics and diagrammatic reasoning approach.

Relationship of time perception with working memory and number sense using gaussian process regresion

Time is one of the most fundamental phenomena in our universe. From the cognitive perspective, it is interesting how we perceive time. To verify the most common cognitive model of time perception (the Scalar Expectancy Theory) time reproduction experiment was conducted. Participants underwent a standard number sense procedure (comparing the number of dots). Next, they were asked to memorise some random letters. Afterwards, a face picture (neutral, happy, angry, fearful) was displayed for 400-1600 ms. After that, participants were asked to reproduce the string of letters and the picture's duration by pressing the space key. The final stage of the study was data modelling using Gaussian Process Regression. The results showed inconsistency with the previous results on the relationship between emotion and time perception. The relationship of memory overload with time perception may have been found.

Playing the Lottery of a Lifetime: The Effect of Socially Induced Aspiration on Q-Learning Agents

Our aspirations are influenced by the rewards obtained by people around us. How adaptive are these inherited aspirations in stochastic, lottery-like environments? We study the behavior of social Q-learning agents in two multi-armed bandit (MAB) settings: 1) a standard task where one arm gives a higher reward than others and, 2) a lottery task where all arms give a high reward with some small probability. We define aspiration as a function of rewards attained by a previous generation, and happiness as a linear combination of rewards and aspiration. We find that in the standard MAB task, higher aspiration encourages exploration, and agents who learn from the ‘top’ agents accumulate more rewards and happiness. However, in the lottery task, higher aspiration doesn’t improve performance; instead, agents who learn from the ‘top’ agents are more unhappy. Together, this research highlights the context-dependent nature of aspirations and their implications to modern society.

Exploring perceptual decoupling in the context of smooth pursuit eye movement

Recent work suggests that perceptual decoupling (i.e., eye behavior becoming less determined by the sensory environment) is responsible for eye behaviour changes between externally and internally directed cognition. In the current study we investigated perceptual decoupling effects on smooth pursuit eye movements elicited by simultaneous engagement in internal visual and arithmetic task under two workload conditions. The results of multilevel modelling showed that effects of perceptual decoupling were moderated by task type (higher for visual internal activity), workload (higher for high internal demands) and follow a characteristic time course relative to internal operations. The findings indicate that perceptual decoupling is a central mechanism underlying differences in eye behaviour between internally and externally directed cognition and shed light on relevant conditions of this effect.

A meta-inference model of confidence

The conventional theory of decision confidence suggests that confidence reflects the probability that a decision is correct—the posterior probability of the chosen option. However, due to factors including inference noise and incorrect generative models, the brain is incapable of computing exact posteriors. We propose a new model of decision confidence by assuming that the brain possesses an internal generative model of these imperfections, which we describe as “true posteriors plus noise”. Using this generative model, the brain performs “meta-inference”, by computing confidence as the probability that the chosen option (whose noisy posterior is the highest) also has the highest true posterior. In other words, we propose that confidence reflects the probability that the observer made the best possible decision under the noisy posteriors. We found that this model outperformed the conventional model in explaining confidence in multiple-alternative perceptual decisions, and it quantitatively accounts for empirical confidence ratings in value-based decisions.

Joint online inference of material properties and object shape

Humans remarkably can perceive 3D surfaces that are literally invisible – when their shapes are implied through dynamic physical interaction with various materials (liquid, granular, elastic). To study this ability and its interaction with perception of material properties, we produced videos simulating unfamiliar non-rigid or non-solid materials interacting with differently shaped rigid objects and containers. Crucially, videos rendered only the materials, while the rigids remained invisible. Observers correctly identified the underlying surface shape from two alternatives in most, but not all cases; observers also simultaneously inferred the internal properties of the material and identified a matching sample from how it interacted with novel surfaces. In fact, judgments about materials were comparable to when observers saw fully visible renderings of all objects in the scenes. Our results highlight the role of an internal physics model in the joint perception of shape and material properties, and in the loop of perception more generally.

Restructuring problem-related semantic associations promotes solving success

While problem-solving is central in our daily life, its underlying mechanisms remain elusive. The dominant theory states that one must restructure a problem (i.e., reorganize problem-related representations) to solve it. As empirical evidence supporting this mechanism is scarce, we used network science methodology to demonstrate the key role of restructuring in problem-solving. Individual semantic memory networks were estimated before and after participants attempted to solve a riddle. These networks represent the organization of solution-relevant and irrelevant words as nodes, with edges representing the strength of the relationship between them based on the participants’ relatedness judgments. Restructuring was quantified as the difference in semantic network metrics between pre- and post-solving phases. Successful problem-solving was predicted by local semantic network restructuring, only in edges and nodes assessed as helpful to solve the riddle. These results shed new light on the mental restructuring in problem-solving and provide a new method to quantify it.

Neurodiversity and anthropomorphism in social insect research

When researching social insects, interpretations drawing parallels between behaviours and social systems of humans and animals are particularly prevalent. While these comparisons have value for illustrative purposes, failing to recognize them as metaphors can result in erroneous interpretations of data. Previous studies have found this anthropomorphic bias to be connected to extraversion and sociability. Autistic individuals are overrepresented in the life sciences, may be less prone to cognitive bias, yet still face significant stigma in the workplace. In this interdisciplinary study, we therefore surveyed an international sample of social insect scientists to explore the impact of autistic traits on their research. We herein present the first results of this study, and discuss them from the standpoints of cognitive science, philosophy of psychiatry, and entomology. We thereby hope to elucidate the role of neurodiversity and atypical modes of perception in science, and contribute to creating a more inclusive academic landscape.

Deep networks as cognitive models: the case of reading in different orthographies

Although Artificial Neural Networks were born as Neurocognitive models, the architectures used nowadays in AI are not conceived as models of the brain. In the last few years, Deep networks have been fitted to brain activity to use them as neural models, but their use as cognitive models is less prevalent. Here we use a transformer model complemented with a simplified visual input to model reading acquisition. First, we trained the network to recognize the speech input. After that, we use letter sounds and letter visual representations to train the network to output the correct letters. We apply this model to our empirical previous results, comparing learning in a transparent (Spanish) and an opaque (French) orthography as in transparent orthographies, phonological awareness is much less important than in opaque orthographies as a predictor of reading. We show that the difficulty of training correlates with opaqueness, and interpret the results.

The relations between parent–educator communication, the home environment, and children’s outcomes in preschool

This study examines the relations between parent–educator communication and preschoolers’ numeracy, literacy, and executive function (EF), and explores if these relations work through the home numeracy environment (HNE) and the home literacy environment (HLE). Children (51.33% female, M = 57.52 months, SD = 3.62) were enrolled in a state-funded prekindergarten program (n = 211) or in another community-based preschool program (n = 52). Analyses revealed a significant relation only between parent–educator communication and numeracy skills in the spring of preschool (β = -.14, p = .002). Unexpectedly, higher parent–educator communication scores in the spring of preschool were related to lower numeracy skills at that same time. Furthermore, the indirect effects for the HNE and HLE were not significant for all outcomes. The findings suggest that it may be beneficial for educators to engage with families early in the academic year to support children’s numeracy skill development.

Differential patterns of object-location binding in children and adults: Pruning the special role of location

Object location and identity interact with each other asymmetrically in adults: Participants tend to judge two sequentially presented objects as more similar when they appear in the same location compared to in different locations (a phenomenon referred to as Spatial Congruency Bias). Yet no comparable identity congruency bias is found, suggesting that location is special in object-binding. To investigate whether such a special role of location is inherent in our visual system or acquired during development, we conducted experiments in both 5-year-old children and adults performing location-judgment and identity-judgment tasks. The study replicated asymmetric bias in adults by finding that location biased their identity judgments, but not vice versa. However, symmetric bias was found in children, suggesting fused processing and reciprocal influence between location and identity in early childhood. The results indicate that location maintains its influence across development, whereas object identity’s influence on location gets pruned away during development.

Studying the long-term dynamics of reciprocity based on welfare tradeoff ratios

People reciprocate another person’s altruistic or spiteful intentions toward themselves over repeated interactions, but it is unclear how such other-regarding intentions are represented in people’s minds and how the dynamics of reciprocity unfold. Recent work formalizes other-regarding intentions as welfare tradeoff ratios (WTR) and demonstrates that people reciprocate by adjusting their own WTR in response to the perceived WTR of another person. However, due to the complexity and inaccuracy of existing WTR measurements, it is still infeasible to study the long-term dynamics of people’s WTR adjustment beyond a few trials. Here we develop an experimental paradigm based on nonlinear continuous decomposed games that precisely reveals to the participant the opponent's WTR and measures the participant’s WTR in a single trial. We find that participants are sensitive to the opponent’s WTR and adjust their WTR accordingly. This experimental framework enables the fine-grained investigation of the long-term dynamics of people’s intention-based reciprocity.

People’s evaluation of programs that drive agents' behavior

We examine whether people evaluate the performance of other agents using only behavioral metrics (intuitive behaviorism) or by also taking into account the program that is driving an agent’s performance (intuitive cognitivism). In an online study, 200 participants, most without programming experience, learned to use a simple block programming language that controls a maze-solving robot. Participants then evaluated which of two programs was “better”, for 18 pairs of robots and programs. The programs varied in 3 metrics. Two of these, action efficiency and representation efficiency, were motivated by work in Reinforcement Learning and philosophy of mind, and the third, semantic generalization, is novel. We found that people’s judgements are in line with intuitive cognitivism, that people are sensitive to the program features, and that people intuitively evaluate and care about which other problems a program solves beyond the given task.

Predicting Fixation Locations in 43 Languages based on Perceptual Constraints and Information Theory

Why do readers typically fixate near the center of a word, with a bias towards word onset? Alhama, Siegelman, Frost, & Armstrong (2019) proposed an account based on (1) perceptual constraints that reduce the likelihood of perceiving a letter the further it is from the fixated location, and (2) the information available from the perceived letters for identifying the word. We expand this work to predict the fixation location distributions of 7-letter words for 43 languages from 9 language families. We found that in the majority of the languages, words were most likely to be correctly recognized when fixating near the center, slightly toward onset. However, there were deviations from this trend, such as predicting more accurate recognition when fixating slightly towards offset in Slavic languages. Our results provide novel predictions for experimental work that considers a flexible language system that optimizes initial fixations based on information theory constraints.

Analyzing Reliability of Interpretable Parameters in Deep Learning Language Models

Deep-learning has made remarkable progress in recent years. However, its parameter estimation methods are very different from those of traditional statistical models. They are typically iteration-update formula, such as Adam, designed to avoid falling into a local solution even when there are a large number of parameters. While deep-learning-based models still have interpretable parameters such as difficulty or ability, how reliable are these parameters estimated by such estimation methods? In this study, we compared the estimation methods used in conventional statistical models, such as Marginalized Maximum Likelihood Estimation and MCMC, with those used in deep learning, such as Adam, for simple item response models. In experiments, the parameter values estimated by the methods using Adam and others and the parameter values estimated by the methods used in conventional statistical packages showed statistically significant correlation. This result supports that the parameter estimates by deep learning can be trusted to some extent.

Individuals with Mild Cognitive Impairment are impaired in a gambling task

Decision-making is commonly impaired in those with cognitive decline. I compared individuals with Mild Cognitive Impairment (MCI; n = 45) and cognitively healthy older adults (n = 45) in their ability to improve their decision-making in the Iowa Gambling Task over time. On each trial, participants chose from one of four decks of cards and received a net gain or loss. The four decks differed in their expected value (EV). They continued choosing among the decks for one hundred trials, trying to maximize their winnings. Results indicate that both groups learned to choose from the higher EV decks over time, but those with MCI learned more slowly. The data were fit to a reinforcement learning model that indicated those with MCI performed worse in part because of task strategy: those with MCI were less likely to perseverate on wins and updated their beliefs more slowly to losses.

Agreement can facilitate learning of noun class systems

Agreement, a systematic formal mapping between linguistic elements, adds redundancy to languages (e.g., in ‘she writes’ the -s adds no information), and yet is crosslinguistically prevalent. Here, we suggest that agreement may be functionally advantageous by providing additional cues for language learning. We conducted an artificial language learning experiment to test whether agreement, and especially, alliterative agreement – where the agreement is expressed by repetition of the same form, can facilitate learning of noun classes (e.g., ‘masculine’/’feminine’). To this end, we compared the learnability of noun class systems in three input conditions: no agreement, alliterative agreement, and non-alliterative agreement. We found that participants who learned the non-alliterative agreement were better at generalizing the noun class system to novel nouns with the relevant semantic features. There was no difference between the alliterative and the no-agreement conditions, suggesting that the possible learnability advantage of agreement marking lies in having distinct forms as cues.

Comparative Evaluation of Multivariate Coordination Methods to Assess Team Cognition in Multi-Modal Team Interactions.

Team cognition is an essential component of team functioning. Although traditionally assessed through (retrospective) questionnaires or ratings, there is increasing interest in using real-time data (e.g., skin conductance, movement) to assess team cognition through team coordination dynamics (TCD). TCD involve two or more processes or elements of the team that covary across time and conditions (e.g. synchrony or alignment). Various methods and modalities have been used to calculate TCD that show connections with team outcomes and cognition. Yet, it is unclear which ones are the most functional. In our research, we use data from four-persons teams engaging in a collaborative game to calculate various TCD indices (entropy, MdRQA, coherence, synchrony coefficient) for several modalities (skin conductance, heart rate, movement) and compare them to metrices of team cognition obtained through questionnaires. We aim for our research to facilitate the use of (multimodal) TCD for monitoring and managing team functioning.

Studying the Effect of Moderator Biases on the Diversity of Online Discussions: A Computational Cross-linguistic Study

The methods by which people harm others evolve with changes in, and in access to, technology. Several cognitive, linguistic, and behavioral theories have suggested that biased language use is correlated with dominance and can reduce the diversity and inclusivity of a community (e.g. Poteat et al, 2010). We present a cross-cultural and cross-linguistic study of moderators on Reddit in English, Arabic, and French. We collect and analyze a large Reddit moderation dataset and use machine learning models to study cognitive and behavioral differences of moderation across cultures. We then work with expert linguists who analyze and evaluate our results. Finally, we explore the implications of our models for studying how we might shut down voices from different communities by not moderating online content properly. Our preliminary results reveal biases towards women and minority groups, and more broadly affirm our hypothesis that culture and topic of discussions bias moderation decisions.

Testing the testing effect with featural and relational categories

Testing the testing effect with featural and relational categories Theoreticians have recently proposed that the benefits of testing depend on the extent to which the to-be-learned concepts are interconnected, with testing effects emerging from low element interconnectivity and becoming weaker with greater element interconnectivity. We tested this idea by using a classification task and manipulating the amount of element interconnectivity in the categories that subjects learned. Some subjects learned featural categories, which lack element interconnectivity, whereas others learned relational categories, which are defined by how its elements are interconnected. This factor was crossed with type of training, wherein some subjects learned through classification (i.e., testing) and others through observation (i.e., studying). For featural categories, it was predicted that classification training would produce better learning than observational training, whereas this benefit should become weaker or disappear for relational categories. However, preliminary results show that both types of training lead to similar learning for both category types.

Learning Causal Overhypotheses through Exploration in Children and Computational Models

Human children are proficient explorers, using causal information to great benefit. In contrast, typical AI agents do not consider underlying causal structures during exploration. To improve our understanding of the differences between children and agents—and ultimately to improve AI agents’ performance—we designed a virtual Blicket experiment to test childrens’ ability to leverage causal information while exploring a novel environment. This experiment doubles as an RL environment with a controllable causal structure, allowing us to evaluate exploration strategies used by both agents and children. Our results demonstrate that there are significant differences between information-gain optimal RL exploration and the exploration of children: in particular, children appear to consider a wide range of creative overhypotheses, including stochasticity, total weight, object ordering, and more. We leverage this new insight to lay the groundwork for future research into efficient exploration and disambiguation of causal structures for RL algorithms.

The effects of prediction in conversational alignment.

In this experiment we set out to investigate how predictive processing may modulate alignment between interlocutors in a dyadic setting (Pickering & Garrod, 2013). The experiment presents a novel interactional task where participants are involved in a partially controlled association game where speaker A names a picture and speaker B responds with a semantically related word. Importantly, the predictability for the upcoming object is manipulated. Data has been collected from 20 dyads, and the results show a prediction effect with a mean difference of 400 ms between predictable and non-predictable conditions. Crucially, this prediction effect was not only present in speaker A who had to name the predictable or unpredictable object, but also for the interlocutor. To our knowledge, this is the first study to demonstrate speaker-listener prediction effects in a dyadic interaction. This will be further tested in a dual-EEG setting to explore this question at the neural level.

Categorising images by generating natural language rules

The ability to generate rules and hypotheses plays a key role in multiple aspects of human cognition including concept learning and explanation. Previous research has framed this ability as a form of inference via probabilistic program induction. However, this modeling approach often requires careful construction of the right grammar and hypothesis space for a particular task, and cannot easily be transferred to other domains. In this work, we present an alternative computational account of rule generation, leveraging advances in multimodal learning and large language models. Taking naturalistic images as input, our computational model is capable of generating candidate rules that are specified in natural language, and verifying them to determine their fit to the data. We show that our model can generate, in a zero-shot manner, plausible rules for visual concepts across multiple domains.

Visual perception of vertical movements in word learning

People understand abstract ideas (e.g., positive/negative valence) through concrete concepts (e.g., up/down; Lakoff & Johnson, 2013). Empirical research has shown that upward/downward motor actions stimulate positive/negative feelings and memories (Casasanto & Dijkstra, 2010), and congruent motor actions facilitate word learning (Casasanto & de Bruin, 2019). Although prior studies reveal a close link between language and perceptual experiences, no study has tested whether the visual perception of upward/downward movements enhances the learning of words whose meaning involves either higher/lower spatial position (e.g., cloud, road), positive/negative emotional valence (e.g., joy, grief), or higher/lower social status (e.g., doctor, unemployed). The effects of directional congruency in word learning are discussed based on the results of an experiment in which Japanese speakers learned 54 English-based pseudowords presented with automatic visual movements that were congruent (e.g., upward-positive) or incongruent (e.g., downward-positive) with the pseudowords’ assigned meaning, or controls (e.g., rightward/leftward-positive).

Priority-Adjusted Replay for Successor Representations

Intelligent agents are capable of transfer and generalization. This flexibility in adapting to new tasks and environments often relies on representation learning and replay. Among these algorithms, successor representation learning and memory replay offer biologically plausible solutions. However, replay prioritization algorithms remain largely limited to value prediction errors. Here we propose PARSR, Priority-Adjusted Replay for Successor Representations, to address this caveat. Decoupling learning of the environment dynamics and rewards, PARSR can use prediction errors from either representation learning or values to prioritize memory replay. We compare PARSR to SR-Dyna, Dyna-Q, and a number of state of the art algorithms using replay and successor representations in cognitive neuroscience. We find that PARSR is able to reproduce human behavior in a number of revaluation tasks while also representing a performance improvement over SR-Dyna, its closest counterpart.

Race moderates the effect of tactility on children's learning from counting books

Based on prior work, we predicted that traditional, 2-D counting books would be better than tactile counting books at promoting young children's numeracy. However, the first author suspected that the effect of tactility on learning would differ for Black versus non-Black children. To test this, we examined data from an existing project on preschoolers' learning from shared counting book reading. Participants included 325 preschoolers, ages 2 to 6, 41% of whom were Black. Findings suggest that race moderates the effect of tactility on numeracy. Non-Black children conformed to the original hypothesis that non-tactile counting books would be best for promoting children's numeracy, but Black children did not. This finding is important because much of the research on children's early numeracy is conducted with homogeneous, convenience samples, so theories and predictions are being built on incomplete data. Without studying diverse samples, the field risks making inaccurate conclusions about how children learn.

Combining mental simulation and abstract reasoning explains people’s reaction time in an intuitive physics task

How do people reason intuitively about everyday physical events? Two broad camps provide very different answers to this question. The first camp emphasizes mental simulations: when people predict where a bouncing ball will end up, they run a mental process that roughly approximates the motion of the ball. The second camp emphasizes rules and abstractions: when reasoning about the bouncing ball, people may reason ‘the ball and table are solid, the ball can't go through the table’. Here we bring the two views together, with a model of physical reasoning that combines mental simulation and abstraction. We show behavioral signatures of both rule-usage and mental simulation in a response-time study in which participants reason about the trajectory of a ball. We present a novel model that can account for these signatures, and compare it to current models of physical reasoning that cannot.

Lightness constancy in reality, in virtual reality, and on flat-panel displays

Virtual reality (VR) technology is being used in an increasing number of applications, but we sometimes perceive surface properties differently in real and virtual environments. To evaluate how well virtual platforms support realistic lightness perception, we measured lightness constancy for 12 observers in three conditions - in a physical scene, in VR, and on a 2D flat-panel display. Observers performed a lightness matching task where they adjusted the match patch until it appeared to be the same shade of gray color as a reference patch. We found significantly greater Thouless ratios in the physical condition (mean±95% confidence interval: 0.87±0.04) than in the flat-panel condition (0.79±0.08). However, lightness constancy levels were not significantly different in the VR condition (0.83±0.08) than in the physical condition or the flat-panel condition. Our results suggest that VR can be a flexible alternative to flat panel displays and a reasonable proxy for real environments.

Critical transitions in belief and identity

Sudden personal ruptures figure prominently in literature, religious scripture, and “great man” historical narratives. Scientists experience sudden insights. Religious devotees abandon their faiths—or find new ones. Political organizers switch parties. Are these restricted to the lives of a few exceptional individuals—or are they widespread, commonplace, a recurring feature of human experience? To address these questions, we conducted a survey of people’s experiences of sudden transformation across a range of life domains: morality, aesthetics, mathematical or scientific belief, religion, politics, social life, and mental health. Sudden personal transformations were surprisingly widespread. The magnitude of a rupture was associated with changes in social networks, and with changes in belief dissonance. In other words, new friends or challenging beliefs could precipitate large personal transformations. Critical transitions in the human experience may reflect universal dynamical mechanisms.

Exploring cognitive pathways to sustainability – development and validation of personas for sustainable behavior

Over the last years, we could observe increasing awareness of sustainability and climate change in society. Individual sustainable behavior emerges by various influencing factors, resulting in different degrees of sustainable behavior. An important factor is the intention behind pro-environmental behavior, which can be goal-directed, motivated by other goals, or habitual. At the same time, good intentions do not always translate into sustainable actions. To develop interventions that promote pro-environmental behavior, we need to shed light on cognitive mechanisms underneath sustainable thoughts and how they stimulate actions. We conducted ten semi-structured interviews with representative individuals asking about their intentions, influencing factors of sustainability and examples from everyday life. Based on their scope of reflection, knowledge, and predominant intention, we could identify five different sustainability personas: sustainability-oriented, open-minded, opportunistic, careless, and dismissive. We present characteristics of these personas, discuss the validation process, and investigate cognitive mechanisms of reflection in the context of sustainable behavior.

Supporting Information Processing and One Instant Teaming of Humans and Cyber-Technical Systems by Conceptual Chunking

Human skills and expert knowledge are valuable in increasingly complex human-machine systems. Efficient one instant teaming between operators and cyber-technical systems requires deeper understanding of human cognition. Conceptual chunking is one strategy to optimize memory performance by integrating small information units and their interactions to a larger one. Graphical visualizations (e.g., in industrial control panels) can support teaming and understanding of complex interactions by highlighting these relationships. The present study investigates whether graphical design elements are able to enhance conceptual chunking. In an experiment (N = 40), graphical design elements were used to induce or inhibit conceptual chunking. Response accuracies, response times, gaze data, and strategies were assessed. Results reveal that participants rely more often on graphical design elements presenting relations between variables. Hence, they show a deeper understanding, faster and more accurate responses when using the presentations for the first time. This indicates high potential for one instant teaming.

Spatial organization of tactile localization in adults

Reaching to targets on the body is an important adaptive behavior, but little is known about how such reaching is spatially organized. Here, we tested right-handed adults (n = 25) in a tactile localization task. A vibrotactile target was placed, one at a time, at 15 different sites on the face: six pairs of corresponding sites on the left/right sides of the face (forehead to mouth region) and three midline sites (chin, mouth, forehead). Participants reached more with the right hand to right-side face targets (134/148 right hand reaches to right-side trials) than to left-side targets (70/152 right hand reaches to left-side trials, x2(1) = 39.56, p < .001). For midline target locations, right-hand reaching dominated (65/75 trials, p < .001). Results are discussed in relation to how tactile localization is jointly influenced by the body’s spatial structure and hand dominance.

Gender bias in grammatical gender systems across languages

A non-trivial number of the world’s languages have grammatical gender linked to biological sex. It is known that grammatical gender classification is not arbitrary, often following a semantic “core”. We investigate whether grammatical gender systems exhibit gender bias in different semantic domains, particularly terminologies of technology (e.g., microcomputer), a domain that has been considered male-dominant, and disease terminologies (e.g., hepatitis), a domain susceptible to negative connotations. In an initial analysis with five languages, we found a baseline bias towards masculine classification of nouns compared to feminine. The technology domain showed no significant bias in grammatical gender with respect to the baseline, but two of the studied languages, French and German, showed statistically significant biases towards feminine classification in disease terminologies, while Arabic showed a significant bias towards masculine classification. These initial findings suggest that languages may be subject to domain general and specific gender biases through overt grammatical features.

A neurophysiological investigation of noisy channel sentences

We used event-related brain potentials (ERPs) to investigate noisy channel models of sentences containing errors of deletion vs. errors of insertion. Ditransitives e.g., (i) The aunt mailed the letter to her niece by post were compared to deletion conditions (ii) #The aunt mailed the letter_ her niece by post. Furthermore, double object constructions (iii) The aunt mailed her niece the letter by post were compared to insertion conditions (iv) The aunt mailed her niece #to the letter by post. All sentences were followed by yes/no comprehension questions. The Bayesian size principle proposes that deletion errors are more likely to occur than insertion; accordingly, perceivers should be more likely to revise sentences with deletion, resulting in P600 effects. Instead, results (N=41) revealed long-lasting negative-going waveforms for deletion errors at niece and positivity at by for insertion errors. Results are interpreted in terms of perceivers' decisions to revise via search space considerations.

Chinese Dialect Proficiency and Executive Function: Evidence from the Simon Task

A considerable number of studies have investigated whether speaking two languages improves executive function. Results, while controversial, have highlighted the influence of second language proficiency on the emergence of these cognitive advantages. To date, few studies have investigated whether other related linguistic experiences, such as dialect use, confer similar advantages. The diverse range of dialects found across the People’s Republic of China provide ideal conditions under which to explore this question. Using a heterogeneous sample of Mandarin-English bilingual, bidialectal young adults (n = 73), the present study investigated whether differences in self-reported dialect proficiency impacted on Simon Task performance. While results showed reduced Simon effects associated with higher reported dialect proficiency, it was a consequence of slower performance on congruent trials, not improved performance on incongruent trials. The observed reduction in facilitation from congruent trials associated with higher reported dialect proficiency suggests a cognitive disadvantage, but merits further investigation.

Contingency learning decreases when associations are shared

In the color-word contingency learning paradigm, each word appears more often in one color (HI contingency) than in other colors (LO contingency). Despite the words being irrelevant, responses to the relevant colors quickly become faster to HI than to LOs—the contingency learning effect. Across four experiments (N = 1,490), the number of response-irrelevant word stimuli linked to each of the three response-relevant colors varied from 1 to 2 to 4. Our prediction, derived from the Parallel Episodic Processing (PEP) 2.0 model, was borne out: The magnitude of the contingency learning effect declined monotonically as more words were linked as HIs to each color. Inconsistent with the PEP model, however, we observed changes in response times not only in HI but also in LO trials, indicating a need to amend the model. Associative learning may therefore be a function of prioritizing high probability items at the expense of low probability items.

How words can be learned by observation depends on what is meant by “learned”

Word learners experience naming events differing widely in their referential quality. Whereas referents of some naming events are transparent from their extralinguistic contexts, referents of many naming events are ambiguous. Word learning theories are divided in whether learners mainly learn from a few transparent events or whether learners also aggregate across ambiguous ones. Data consistent with the former view are evident in the Human Simulation Paradigm (HSP) in which naïve observers must identify parents’ words from muted vignettes of parent-toddler interactions. The HSP reveals that even adults struggle to identify the identity of parents’ words across ambiguous vignettes. Our work revisits the HSP by examining how alterations to its dependent variable affects the conclusions about the naming events that shape learning. This work underscores how one’s definition of learning has implications for both accounts of the mechanisms of learning as well as accounts of the relevant input into those mechanisms.

Altercententric interference vs. bias in 7.5 month-old infants: a pupillometry study

In a preregistered pupillometry study we tested whether a perspective cue can both increase and decrease 7.5-month-olds' surprise. In the congruent condition infants saw an agent watching a ball roll behind an occluder. After the agent left, infants saw the ball rolling outside the stage (an informational asymmetry). In the baseline the agent watched the final event as well. At outcome, the occluder was lowered to reveal empty space: congruent with reality, as the ball is out. In the incongruent condition the outcome is identical, but the ball should have been there. Informational asymmetry was manipulated again. We found overall larger pupil dilation in the incongruent condition, indicating infants’ remembering the ball’s existence. As hypothesised, we could increase their surprise in the congruent condition by having the agent last see the ball inside, but found mixed results when trying to use the perspective cue to also decrease infants’ surprise.

Efficient exploration of spatial environments through Map Induction using adaptable compositional map representations

How do humans find their way in new environments, so quickly and efficiently? Humans reuse old knowledge to build new concepts in many non-spatial domains, such as language and drawing. Could people learn maps by a similar process, that extracts common structure to speed up learning, and generalize across maps? Understanding the computational cognitive mechanisms that support this efficiency can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that human map learning relies on inferences over the structure of unobserved spaces, based on spatial priors informed by previous experience. We model this by combining Program Induction with a Hierarchical Bayesian framework that explicitly reasons about uncertainty through strong spatial priors. Using a new behavioral Map Induction Task, we demonstrate that this computational framework explains human exploration behavior better than non-inductive models and outperforms state-of-the-art planning algorithms in a realistic spatial navigation domain.

Alternative Outcomes, Surprise, and Predicting Replication of Psychological Research

When we read research findings, what facilitates consideration of different possible outcomes? 112 Amazon Mechanical Turk Workers read about four psychological studies, and either predicted the results, received the results without explanation, or received the results with a plausible explanation. We hypothesized that 1. receiving results would increase the difficulty of explaining alternative outcomes, and that this would be amplified by receiving explanations, 2. As difficulty explaining alternatives increases, participants would be less surprised by actual results and predict higher likelihood of replication. We did not find the expected differences in difficulty across conditions. However, across all four research studies, surprise was negatively correlated with likelihood of replication, indicating that more surprising results are considered less likely to replicate. We consider reasons why our manipulation did not affect difficulty of explaining alternative outcomes, and discuss implications for how research can best be presented to promote consideration of alternative outcomes.

What is Noise Music? A Psychometric Approach

Can noise music fans tell us about benignly masochistic art and how sound becomes music? Noise music often includes “non-musical” sounds (electronic static/ feedback/screaming), while both exaggerating features of musicality (excessive monotony/surprise) and/or stripping them away (melody/harmony). Noise enthusiasts’ (N=395) preferences and attitudes about sound and music were recorded. We tested a five factor model using diagonally weighted least squares. Results suggest a good fit of data to the hypothesized model (CFI=0.967; RMSEA=0.043). Noise/music descriptions varied along five dimensions. Noise as: 1-Intentional (“Noise music is different from environmental noise because it requires deliberate compositional and stylistic choices”); 2-Medium (“Environmental noise can be music if used in a certain way”); 3-Art (“Environmental noise is art”); 4-Musical (“Environmental noise can have interesting timbres”); and 5-Pleasurable (“I often find environmental noise beautiful”). Experiencing noise-as-music involves a range of perceptual styles and beliefs about artistic intention.

Processing singular "they" is harder than plural "they", but does not cause referential failure

The English use of "they" to refer to a singular referent that is non-specific or of unknown gender dates back to the 1300s. Recently, "they" has emerged as the dominant pronoun for individuals who identify as gender nonbinary, and a coherent subset of English speakers (innovators) explicitly accept "they" when referring to a specific individual of known gender ((i) “Sarah slept because they were tired”) in off-line judgments. The present work examines whether on-line comprehension difficulty patterns with off-line judgments. Using a maze-task, participants' read sentences like (i) containing “they” with plural or singular subjects, or “he/she” with named subjects that matched or mismatched in their typical gender. Gender mismatched evoked reading difficulty. Plural “they” also caused difficulty, but significantly less. Innovators experienced less difficulty with singular “they” than non-innovators, but still processed plural “they” faster than singular “they”. This work suggests that the grammar of “they” may have yet to stabilize even for innovative speakers.

Towards metacognitive learning in depression

Recent studies have linked depression to aberrations in metacognition, particularly metacognitive bias (overall confidence) and sensitivity (ability to differentiate correct/incorrect trials). However, whether these alterations stem from shifts in metacognitive learning is unclear because previous paradigms have tested either (retrospective) performance-beliefs isolated from performance-feedback or (prospective) performance-expectations on a few trials only. Here, we examine perceptual performance, self-performance-beliefs and how performance-feedback alters self-evaluation; we develop a novel paradigm that enables evaluating performance continuously and derive metacognitive measures using linear-mixed-modelling of performance and performance-beliefs. Our results suggest that depressive traits are associated with negative shifts in metacognitive bias but not sensitivity. Furthermore, feedback incorporation was independent of depressive traits and generally more pronounced after desirable feedback. Our study contributes towards a better understanding of how disadvantageous self-beliefs are formed and maintained in depression and offers promise for a computational cognitive science of metacognitive learning beyond the study of depression.

The relation between gaze and turn-taking in dyadic avatar-mediated conversations

Human communication comprises a complex and dynamic interplay of verbal and nonverbal communication channels. The investigation of these channels therefore represents a major methodological challenge. Technical developments in interaction platforms using virtual characters provide tools for these investigations. Paradigms in which participants interact with algorithmically controlled agents have already enabled the investigation of individual nonverbal communication channels with the necessary experimental control. However, it is unclear how these results relate to human-human communication. Here, we present a study with a new system for human-human interactions mediated by avatars. As a proof-of-concept, we tested the generalisability of gaze patterns during turn-taking in avatar-mediated conversations. Results show that given our Bayesian mixed effects model, priors and data, there is compelling evidence that gaze patterns are comparable to natural interactions. Exploratory analyses show that our system is suitable to shed light on variability in individual-specific gaze behaviour, which we plan to investigate further.

Learning cognitive and linguistic prosodic categories for automatic cross-lingual sign language understanding

End-to-end sign language understanding and generation models do not accurately represent the prosody of the languages. This lack of temporal and spatial variation in generated signs leads to poor quality and lower human perception. We seek to improve prosody in automatic models that understand sign language by modeling intensification in a data-driven manner with strategies grounded in the linguistics of sign language by enhancing the representation of intensity modifiers in gloss annotations. In this work, we identify four major categories for sign intensification across two different sign languages. Through an extensive data exploration, we compare annotated datasets for German Sign Language and British Sign Language for intensification. We analyze the effects of the domain of the dataset on the intensifier abundance and the changes of intensifier use across different sign languages.

Conflict between self and other in the development of perspective tracking

We examine whether infants and young children experience “conflict” between their own perspective and that of another in a false belief scenario. Based on the altercentric hypothesis, we propose that young infants can track the perspective of others because they lack a competing self-perspective. With the emergence of self-awareness, children may then be able to generate a representation of their own perspective and only then does this become a competitor to the perspective cued by others. To test this, we presented 18- and 42-month-olds with a perspective-conflict scenario and used pupil diameter as an index of conflicting processing. Half of the 18-month-olds passed the mirror self-recognition (MSR) task. Functional t-tests showed that MSR recognizers had greater dilation during the anticipatory phase compared to non-recognizers. Data collection with 42-month-olds is ongoing; preliminary results from pilot data suggests that the pupil trace of the 42-month-olds is similar to the MSR recognizers.

All Animals are Conscious: A New Premise in the Cognitive Science of Consciousness

The Distribution Question (DQ) asks: Can we know which animals beside humans are conscious? The current best practices for answering DQ rely on a version of a marker approach, which seeks to find some observable features that indicate consciousness. I argue that the marker approach all but guarantees a positive answer regarding any animal selected for investigation-including sponges. This makes answering the DQ currently unachievable. What will turn it into an achievable goal is to have a robust theory of consciousness, which, I argue, requires we adopt the premise that all animals are conscious.

Simulating progressive neurodegeneration in silico with deep artificial neural networks

We recently proposed a novel paradigm of using convolutional neural networks (CNNs) to model information processing in the diseased brain. Previously, we simulated posterior cortical atrophy (PCA), a form of Alzheimer’s disease primarily impacting the visual cortex and manifesting as visual cognition deficits, by ablating CNN weights. However, this approach modelled a synaptic ablation injury, which resulted in the rapid onset of functional impairments. Here, we investigate using a weight decay function to simulate a gradual synaptic injury. In contrast to ablation injury, the onset of functional deficits was slower with the proposed weight decay injury. If only a subset of the network weights were subject to a decay injury, the delayed onset of functional deficits was even more pronounced. This approach may better reflect the subtle atrophy that precedes symptoms and the gradual onset of functional impairments seen in patients with neurodegenerative diseases such as PCA and Alzheimer’s disease.

Bilingualism protects social cognition in aging: Effects of early bilingualism on older adult theory of mind

The ability to understand and speak more than one language (i.e., bilingualism) may offer some protection against age-related cognitive deterioration. Although research on children and young adults has reported that bilinguals outperform monolinguals in some theory-of-mind (ToM) tasks, a similar positive effect of bilingualism on older adults’ ToM has yet to be established. Here, we examined the effects of bilingual experience (i.e., onset age of bilingualism) on ToM in normal aging using the ToM Task Battery with 60 young adults (aged 18-30) and 72 older adults (aged 56-79). Results revealed that older adults showed deficits in ToM and their performance gradually declined with age compared to young adults. Importantly, early bilingualism mediated the age differences in ToM; earlier onset age of bilingualism predicted better ToM performance in older adulthood. Findings suggest a possible protective effect of early bilingualism against age-related declines in socio-cognitive functions.

Local versus global coherence in the generalization of category training

In recent evidence, classification training can elicit two qualitative patterns of generalization: one is exemplar-based such that close proximity to known members of a category best predicts membership in that category; the other involves inducing a global form of coherence in the mapping between input space and category membership. Such global coherence is an abstraction about category membership – not in the form of clusters or prototypes, but grounded in regularities like categories alternating in input space (Kurtz & Wetzel, 2021) or one category having correlated feature values while the other is anti-correlated (Conaway & Kurtz, 2017). We investigate the extent to which categorization is driven by local match to exemplars versus conforming to global structural regularities using generalization items as critical tests: proximal to members of one category but conforming to the global regularity underlying the other. Results are discussed in terms of implications for theoretical accounts of category learning.

Does delayed interim testing enhance subsequent learning?

Testing on previously learned materials can enhance the learning of new materials studied after the test (the forward effect of testing). We examined how the timing of interim testing influenced subsequent learning. Participants studied trivia facts in two learning sections (A & B) and provided a mental effort rating for each section. Between the two sections, participants either restudied, took an immediate test, or a 15-min delayed test on Section A. They then proceeded to Section B and took a final test on both sections. The results demonstrated that both the immediate and delayed testing groups outperformed the restudy group on Section B, indicating that the forward effect of testing was independent of the test timing. Furthermore, the testing groups showed a bigger increase in their mental effort from Section A to B than the restudy group, suggesting that the forward testing effect occurred probably due to more mental effort.

Perceptual Similarity Affects Relational Judgements

In STEM instruction, the effectiveness of teaching by analogy is often limited by students’ focus on superficial features of the source and target exemplars; the strategy of progressive alignment (moving from perceptually similar to different targets) has been suggested to address this issue (Gentner & Hoyos, 2017). In contrast, computational models suggest maximizing surface feature variation to improve the relation learning, and human behavioral studies find a relational bias in learning (Austerweil & Ehrens, 2018). Here, participants were explicitly instructed to match stimuli based on relations while perceptual similarity of stimuli varied parametrically. We found that lower perceptual similarity reduced accurate relational matching (F = 9.53, p<.001), and observed a similar trend for reaction times. This finding demonstrates that perceptual similarity may interfere with relational judgements, but also hints at why progressive alignment may be effective. Implications for instructional sequence design will be discussed.

Sharing of mental effort in a joint task

Previous research has shown that when people engage in a joint task, they tend to minimize the total required effort. This is the case even when minimizing total effort increases the amount of effort individuals would need to expend. This tendency towards co-efficiency has previously been investigated in the motor domain. The aim of the current experiments was to investigate whether these findings extend to the sharing of mental effort. Five experiments were conducted using a multiple-object tracking task and two used a memory-based task. The first two experiments confirmed that, individually, participants prefer easier tasks. The following three experiments manipulated task difficulty for a participant and their ostensible partner and measured preferences for different difficulty combinations. Our findings provide support for egoistic effort distribution strategies, with participants mostly minimizing their own effort.

Expectations bias moral judgments

People’s expectations play an important role in their reactions to events. There is often disappointment when events fail to meet expectations and a thrill to having one’s expectations exceeded (e.g. Mellers et al., 1997). Here, we examined how expectations influenced people's reactions to events with serious moral consequences, and their moral decisions concerning such situations. Participants judged pairs of events where a victim experienced a similar harm, but where victims were at different prior risk of being harmed (e.g. someone killed by a drunk driver on a Friday night versus a Saturday morning). People found unexpected harms to be more upsetting and they were more likely to choose to prevent an unexpected harm than an expected one. Further studies explored other factors that might have influenced judgments beyond expectations. This bias may have pernicious moral consequences, potentially leading to reduced concern for victims in most need of help.

Performance incentives enhance alerting, orienting, and executive attentional processes

A growing body of research has documented that people enhance cognitive control when they are motivated, likely by proactively upregulating goal-relevant stimulus processing. However, there are multiple attentional mechanisms that can facilitate stimulus processing (such as better alerting and orienting), and little is known about how incentives reconfigure these component processes. To address this question, we developed an incentivized version of the well-validated Attentional Networks Task. This task combines Posner cueing with flanker inhibition, allowing us to isolate the effect of reward across a range of attentional domains. Consistent with previous research, we found that in a sample of online participants (N=120), overall accuracy was enhanced under incentives, without sacrificing reaction time. We further found that incentives increased participants' sensitivity to alerting cues and altered how orienting cues interacted with flanker facilitation. These results provide new insight into how people control multiple attentional processes to earn rewards.

A First Look at Zipf's Law and the Speech of Children with Autism Spectrum Disorder

Zipf’s law asserts that words form a power law distribution: word frequency is inversely proportional to rank. Relatively recent cognitive and usage-based linguistics argue that speech differs structurally from writing. Except for a few older analyses performed on tiny corpora, studies of Zipf’s law prior to 2021 have been done on written corpora and use informal methods to determine Zipfianness. We argue that recent work indicating that transcribed speech forms a Zipfian distribution can be extended to the speech of traditionally developing children. Further, we show that the transcribed speech of children with a clinical diagnosis of autism spectrum disorder is non-Zipfian. These judgements are made using formal statistical techniques developed in Clauset (2009). They include the Kolmogorov-Smirnov statistic for goodness-of-fit and likelihood ratio to rule out other distributions. Clauset, A., Shalizi, C. R., and Newman, M. E. J. (2009). Power-Law Distributions in Empirical Data. SIAM Review, 51(4), 661–703.

Inferences from Disagreement

To figure out what happened in the past, we often rely on others’ testimony. One challenge is that people can disagree in their interpretations of what happened. We will investigate children’s use of disagreement as evidence for what happened, specifically, inferring that the event itself was ambiguous and could generate multiple interpretations. Children (N = 50; 7 to 11 years) will hear two observers’ testimony, in which the observers either agreed about another speaker’s desires (e.g., both observers agreed that the speaker wanted an intervention) or disagreed (one observer was sure the speaker wanted the intervention, while the other observer was sure the speaker did not want it). Children will then be asked to infer which of three events happened: the speaker uttered an unambiguous request (should be inferred more in agreement trials), an ambiguous request (should be inferred more in disagreement trials), or a random statement (neither trial type).

What's the relationship between mechanical reasoning and different types of spatial skills?

From previous work we know that mechanical reasoning is associated with spatial skills, and that it could even be possible for mechanical reasoning training to lead to meaningful gains in spatial abilities. However, it is unknown whether the connection with mechanical reasoning applies to different types of spatial skills or is specific to only one or a subset of them. To answer this question, we presented participants with six different tasks, four of which corresponded to spatial assessments that were organized along a 2 (intrinsic/extrinsic) x 2 (static/dynamic) matrix of factors, and two evaluations that measured mechanical reasoning (gears and pulleys task and DAT-5 mechanical reasoning test). Three hundred adult participants from Chile responded from their personal computers to the stimuli presented on Open Lab, an online platform. The results shed light on the link between mechanical reasoning and the different subtypes of spatial skills.

Intractability of Bayesian belief-updating during communication

Imagine a friend asks: "could you pass me the dragonfruit''. You have no idea what they mean and inquire, "do you mean the orange fruit in the bowl?'', but they respond: "no, the pink one''. Now you know which fruit they want and in future contexts you will likely also understand their request. People seamlessly update beliefs about speakers' (word) meanings. Through interaction they somehow infer what "dragonfruit'' means: are all dragonfruits "pink'', or just this one? Is its shape more diagnostic, or some combination of the two? The number of possible belief updates is vast, growing exponentially with the number of features. Using formal complexity analysis, we prove that state-of-the-art models of communication are computationally intractable. Hence, they cannot yet explain how people can navigate this search space efficiently and communicate seamlessly. The intractability result holds for different model variants, suggesting a fundamental computational challenge for explaining communicative interaction.

Undoing in human planning

From writing to hiking, people’s real-world sequential decision-making often benefits from “undoing” (e.g. deleting sentences or backtracking). Surprisingly, undoing has not been studied in experiments on human planning. To investigate how much, when, and why people undo, we introduce a task that is a cross between the “Traveling Salesperson” and the “Knapsack” problems with an undo option. Within a length budget, subjects sequentially connect as many dots as possible on a map. On “undo” trials, they are allowed to take back actions without constraints. We find that undoing is beneficial, that subjects exhibit great individual variability in the number of undos, that undos are more frequent after errors than after correct actions, and that long response times tend to precede sequences of undos. Together, these results suggest that undo actions serve a dual role of correcting errors and of exploring alternative paths, where path evaluation benefits from full play-outs.

Intertwining Generalization and Memorization

Over-paramaterized neural models have become dominant in Natural Language Processing. Increasing the size of a neural network seems to result in improved performance across a a broad range of tasks. Despite their size these models have been shown to generalize poorly outside their training data. Seemingly failing to extract the systematic generalizations that humans use to generate and interpret language. Increasingly work has questioned whether these models are learning to generalize or memorize, with larger capacity models potentially just memorizing their data more and more effectively. We suggest the tradeoff between memorization and generalization may be more nuanced; with the capacity of a model shaping the kinds of generalizations they are likely to acquire. Our results on a linguistic task suggest that while all models develop generalization strategies, smaller models may arrive at a smaller distribution of strategies that generalize more robustly to novel data.

Decomposing objects into parts from vision and language

How people decompose objects into parts provides insight into how object concepts are organized (Tversky & Hemenway, 1984). How strongly is such knowledge grounded in how parts appear in visual images? Some parts may loom larger in the mind than they do in the world, while others may be more perceptually salient than they are accessible from memory. In our study, participants were either cued with a photograph (N=50) or a category label (N=96) before decomposing each of 32 objects into parts. We found that parts were generally listed at similar frequencies in both conditions, but identified several parts that diverged from this pattern (Jensen-Shannon divergence=0.157, p<0.001), suggesting that the salience of certain parts can depend on task context. In ongoing work, we are exploring how these part decompositions relate to the parts that people include in drawings of those objects (Yang & Fan, 2021).

Using Dual Systems Models of cognitive development to understand risky sexual behaviors in adolescents from Montevideo (Uruguay)

Adolescence is characterized by an increase in risky behaviors. The Dual Systems Models of cognitive development suggest that this could be explained by an imbalance between the development of the Socioemotional Reward System and the Cognitive Control System. Also, there is a normative increase in sexual exploration. However, some sexual behaviors are considered health risks. With a prospective longitudinal panel design, we aimed to analyze the effect of change in risk propensity, self-regulation, and consideration of future consequences (CFC) on sexual risk behaviors in adolescents aged 15-20 years from Montevideo (Uruguay). In a sample of 49 adolescents, we measure cognitive variables at T1 and T2 using behavioral tasks and scales. We surveyed sexual behaviors at T2. Congruent with Dual Systems Models, we observed a decrease in risk propensity and CFC-Immediate, and the decrease in risk propensity impact the use of contraceptive methods. No significant results found for self-regulation and CFC-Future.

Face-preferring regions in FFA, STS, and MPFC have different functions.

Faces are an important source of perceptual and social information. Multiple cortical regions including the fusiform faces area (FFA), the superior temporal sulcus (STS), and medial prefrontal cortex (MPFC) respond more to dynamic faces than videos of toy objects, human bodies, and pastoral scenes. Do face-preferring regions in FFA, STS, and MPFC have different functions? To address this question, we re-analyzed functional magnetic resonance imaging (fMRI) data from seven different experiments that included a dynamic faces versus objects localizer. Each of the seven experiments tested different perceptual and social features using dynamic videos, point light displays, narratives, and animated cartoons. Using a functional region of interest approach, we observed a significant condition by region interaction in four of the seven experiments. Thus, although FFA, STS, and MPFC respond more to dynamic faces than objects, bodies, and scenes, these three regions differ from each other functionally.

Walking in her shoes: Pretending to be a woman role model increases young girls’ persistence in science

Pretend play is a ubiquitous learning tool in early childhood, enabling children to explore possibilities outside of their current reality. Here we demonstrate how pretend play can be leveraged to empower girls in scientific domains. Four- to seven-year-old children (N = 240) played a challenging science activity in one of three conditions. Children in the Exposure condition heard about a successful gender-matched scientist; children in the Roleplay condition pretended to be that scientist; children in the Baseline condition did not receive information about the scientist. Girls in the Roleplay condition, but not in the Exposure condition, persisted longer in the science activity than girls in the Baseline condition. Furthermore, pretending to be the scientist equated girls’ persistence to that of boys. These findings suggest that pretend play of role models is an effective strategy to motivate young girls in science and may help reduce gender gaps from their roots.

Reduced phonological processing during speech-planning

In conversation, interlocutors rapidly exchange well-timed turns-at-talk, generally starting to plan their contributions already during the incoming turns. Previous research showed that while this early-planning-strategy supports fast turn-taking, it comes with increased planning difficulty compared to planning after the incoming turn. The present study investigates whether concurrent planning is also detrimental to speech input comprehension, targeting the level of phonological processing. 60 subjects conducted a dual-task experiment, verbally responding to auditorily presented quiz-questions while simultaneously monitoring them for pre-specified target phonemes (60% present), pressing the space-bar upon encounter. Subjects’ monitoring performance for phonemes encountered during response planning was significantly worse than for phonemes encountered when response planning was not yet possible. Additionally, phoneme detection latencies were significantly longer when concurrently planning a verbal response than in a single-task phoneme-detection control experiment. Thus, results indicate that parallel speech planning, while common, leads to decreased input processing, possibly due to interference effects.

The impact of categorization rule on categorical visual search

The results of research by Hélie, Turner, and Cousineau (Hélie et al., 2018) show that category representation (rule-based and information integration categories) may influence performance in the visual search categorization task. In the present experiment, we replicated their results using other types of categories that contained discrete features: categories based on verbal rules and prototypes. We found that after learning the verbal rule as well as after learning rule-based categories target-present and target-absent trials are not much affected by display size. The effect of display size is bigger for prototypes, but unlike the results of Hélie et al, it does not collapse for target absent trials. We explain these results by the fact that the categories with discrete features have a stronger and more conscious representation, which is easier to transfer to the visual search task. The reported study was funded by RFBR, project number 20-013-00698.

Role of Implicit Emotion in Response Inhibition and Response Adjustment

Inhibitory control is a crucial executive control function that enables us to adapt to a changing environment. Previous studies have tried to investigate the role of emotion in response inhibition; however, few studies have investigated role of emotion in response adjustment. Here, we examined how emotion influences our ability to inhibit a pre-planned response and then program another appropriate response with the help of the double-step saccade task. Each trial had either a single target or two targets. Upon a single target onset, subjects were required to make a quick saccade, but upon two target onset, subjects were required to inhibit their initial saccades and redirect their gaze to the second target. In Experiment 1, the first target was a geometrical square box, and the second target was a facial stimulus. In experiment 2, this order was reversed; the first target was facial stimulus, the second target was a square box. Finally, in the control experiment, both targets were geometric shapes. This manipulation allowed us to study the effect of emotional stimuli on response inhibition in varying contexts (task relevance). We found that subjects were less successful at inhibiting their initial saccades as the inter-target delay increased. Further results showed that facial stimuli as first target impaired response inhibition compared to geometric shape as first target. While inhibiting saccade to geometric shape first target, angry faces as the second target interfered with both response inhibition and response adjustment compared to happy and neutral faces. Angry faces take more attentional resources to be processed leaving fewer resources available for ongoing activities and hence interfere with inhibitory control.

Semantic Congruence Across Sensory Modalities

Multisensory processing often has facilitation and/or interference effects (Chen & Spence, 2010; Thomas et al., 2017) yet the mechanisms remain unclear. This experiment used a Stroop-like task (Stroop, 1935) to examine how congruent, incongruent, and irrelevant information presented in one sensory modality affects processing and responding in a different modality (see Figure 1 for trial types). Participants were simultaneously presented with pictures and sounds of animals or vehicles. In separate blocks they had to respond to either the visual or auditory stimulus, while ignoring the other modality. Results suggest that visual stimuli have a larger effect on auditory responding than vice versa. Incongruent visual stimuli had a larger effect on accuracy while congruent visual stimuli had a larger impact on response time. We will present additional data at the poster focusing on factors that may affect congruency effects including individual differences and types of auditory stimuli.

Visual attention to threat in the Himba, a remote people of Namibia

Threatening stimuli capture visual attention more rapidly than benign stimuli. The canonical interpretation of this robust finding is that the brain evolved a “fear module” enabling rapid detection of threats common at the time of mammalian evolution, such as snakes and spiders. This rapid attentional capture is thought to enable prioritized processing of threatening stimuli, providing a survival advantage, and is assumed to be universal. However, these findings have been documented almost entirely in WEIRD (white, educated, industrialized, rich, and democratic) populations. Here, we address this gap by examining threat detection in a remote African culture, the Himba. Using a touch screen visual search task, we found that both evolutionary-relevant (snakes and spiders) and modern threats (knives and syringes) captured attention more rapidly than benign stimuli. To our knowledge, this is the first study showing that the same kind of threats that rapidly capture visual attention in the West also rapidly capture visual attention in the Himba.

Understanding of differences in verbal and figurative representations by children 4-6 years old.

In our study we examined the ability to distinguish between verbal and visual representation in preschool children. We developed tasks in which children had to learn new signs denoting different types of representation and then use the new symbols to compare different formats of imagination (inner speech and visual images) in story characters, and then apply these symbols to describe their own representation in solving different tasks. We will present the results of testing children between the ages of 4 and 9, which show that the majority of preschool and school-age children successfully learn the symbols and use them to distinguish verbal representation from visual representation in characters. We will discuss the further development and application of this technique to the study of preschool children’s meta-cognitive abilities. The reported study was funded by RFBR, project number 20-013-00698.

Supporting Word Learning from Shared Book Reading Sessions with Preschoolers

Shared book reading with preschoolers has been linked to lasting, positive effects on children’s vocabulary development. However, some evidence shows children quickly forget target words learned from books, which suggests these words may need extra memory supports. The current studies examined two memory supports that could help word learning: variability and presentation timing. Study 1 examined the effect of question variability by showing 4-year-olds novel words accompanied by irrelevant questions (control), the same questions (no variability), or different questions (variability) each time the target word was presented. Study 2 examined the effect of presentation timing by presenting 4-year-olds with novel words either massed or spaced in time. Children’s memory of the novel target words was tested in one of three delays: 0 minutes, 5 minutes, or 24 hours. Ultimately, these studies inform word learning theories and help improve the quality of shared book reading sessions between caregivers and children.

Are children mindful decision makers? Investigating underlying mechanisms of Turkish-learning children’s recency bias

We investigated recency bias (i.e., choosing the second option among two) of 3- to 5-year-old preschoolers (n=59) in a free order language, Turkish, regarding individual differences in working memory and verbal skills. Children were presented with a decision-making task in which they responded to questions about an imaginary character’s decisions with familiar and unfamiliar objects in 3 word orders (both options at the beginning, both at the end, or first option at the beginning while the second at the end). Children exhibited bias in all word orders. WM skills, not language skills, predicted bias, even after controlling for age. As children aged, their bias decreased for unfamiliar options when both were at the beginning. Recency bias also decreased as children’s WM skills improved when both options were at the beginning. Children are not mindful decision-makers and their WM skills interact with the way questions are asked.

The moderation effect of Illusion of Explanatory Depth of Knowledge towards National and International Issues

According to Rozenblit & Keil (2002), attempting a mechanistic explanation of a concrete phenomenon results in reduced self-reported knowledge, suggesting that the latter is normally overestimated (Illusion of Explanatory Depth or IoED). Fernbach and Sloman (2013) demonstrated the effect in sociopolitical issues, finding that causal explanation results in short-term moderation of belief extremity. While the effect of explanation on knowledge self-estimation has been robustly replicated, debates about the impact on extremity continue. Here, we replicate both effects in an online sample of Iranians (N=100 and p<.05). Given IoED’s reliance on mechanistic framing, we further hypothesized that the more concrete construal afforded national hot-button issues would produce a more robust effect compared to international ones. Our experiment indeed found stronger IoED effects on both knowledge estimation (p<.005) and belief extremity (p<.003) across national issues. We discuss how this moderation intervention can be helpful in extremism context.

A process-oriented approach to creative metacognition

The role of metacognition in creative thinking is beyond doubt, but, so far, most research has focused on just one metacognitive subcomponent – monitoring, especially idea evaluation. Less is known about the role of metacognitive knowledge and control. Here, we present an extended theoretical conceptualization of creative metacognition that distinguishes metacognitive processes associated with pre-task, during-task, and post-task phases, followed by an empirical examination of this process-oriented approach based on an online study (N = 425). We examine effects of creative knowledge, monitoring (metacognitive feelings and discernment of others’ ideas), and control on creative performance and explore relationships with surface (creative self-concept) and core personality characteristics (openness, narcissism). Our findings indicate the involvement of various predicted metacognitive processes in creative ideation and thereby provide initial empirical support for the proposed model of creative metacognition.

Understanding Lightbulb Moments: Meaning-making in visual morphology from comics and emoji

How do we interpret a lightbulb above a head to mean inspiration? We investigated the semantic processing of these “upfixes” like lightbulbs or gears that float above characters’ heads. We examined the congruity of face-upfix dyads presented sequentially with words describing their literal (“lightbulb”) or non-literal meanings (“inspiration”). To examine if upfixes alone sponsor meanings, upfixes either matched or mismatched the facial expression (ex. lightbulb over an excited vs. sad face). Literal words always evoked faster response times when presented before images. When images appeared before words, literal words were responded to slower than non-literal words for matching dyads, but faster times for mismatching dyads. Non-literal words were rated as more congruous with matching dyads, while literal words were more congruous with mismatching dyads. Thus, non-literal upfix meanings (e.g., inspiration/lightbulb) are ingrained in memory only when matching their faces, supporting that they belong to a constrained visual lexicon.

Extending the Bayesian Causal Inference of Body Ownership Modell Across Time

Bayesian Causal Inference (BCI) models multisensory perception as inference about two causal structures: either the sensory data stems from several separate causes or from a common cause. If the sensory evidence supports a common cause, the multimodal sensory input is integrated into a single percept. BCI has been applied to the rubber hand illusion, in which the subject integrates tactile stimulation with seen brush strokes on a rubber hand and experiences said hand as their own. A model of body ownership is relevant for virtual reality design, especially for strengthening avatar embodiment. In previous work, we have criticized the applied BCI model for its extraordinarily wide priors. Here we investigate whether the priors can be narrowed by increasing the sensory evidence. We try to accomplish this by extending the model across time. Preliminary results look encouraging, but further research, especially psychophysical experiments to inform the parameter choices, is needed.

Modeling grammatical breakdown under processing load

We present proof-of-concept modeling results which demonstrate a novel way to evaluate theories of the structure of the phonological grammar: We generate predictions of how the behavior of constraint-based grammars and analogical models is likely to change under processing load. Previous results (Moore-Cantwell and Kush, 2019) show systematic differences in participant behavior when generating novel forms under working memory load, vs not. Under load, participants observed fewer generalizations of the language, produced less intra-speaker variation, and differed more participant-to-participant. We modeled memory load in constraint-based grammar (MaxEnt, Goldwater and Johnson, 2003) via probabilistic ignoring of constraints. Memory load in analogy (TiMBL, Daelemans et al., 2018) was modeled as a restricting of the search space for an analogical set, based on word-frequency. Our constraint-based model predicted a more systematic breakdown, more in line with previous experimental findings, while our analogical model predicted more word-to-word variation.

The Generation of Original and Known Explanations: How the presence or absence of relevant information influences sense-making and information foraging

Explanation generation is ubiquitous, and is shaped by our individual and collective knowledge about a phenomenon. Some phenomena are not easily explained by known explanations and require the creation of original or unknown explanations (How does scientific knowledge grow?), while others are strongly associated with known explanations (What caused the Battle of Dunkirk?). The processes underlying explanation generation when known/unknown have different characterizations, and broad implications for learning and innovation. However, this distinction is not studied in prior work. We hypothesize that unknown (compared to known) explanations will: require relatively more sense-making––manipulation of acquired information; exhibit self-directed information search not captured by simple information foraging models; and result in looser knowledge networks containing semantically dissimilar foraged-for information. We embed these hypotheses into a model framework, and show that it captures the behavior of participants asked to generate explanations in unknown/known conditions using information they forage for on Wikipedia.

Self-Explaining the Notional Machine to Improve Novice Programmers’ Learning and Mental Models, Supported by a Computer Tutor System

A notional machine is an abstract representation of how a computer works. Prior research shows that students often form incomplete or inaccurate mental models of the notional machine, reflecting misunderstandings and causing programming errors. To date, however, there is very little work on how to help students acquire accurate models of the notional machine. To fill this gap, this research focuses on novice programmers and experimentally investigates the impact of direct instruction of the notional machine compared to standard instruction that does not include the notional machine. The instruction is embedded in an online computer tutor that we implemented and compared to a version of the tutor that does not refer to the notional machine but otherwise includes the same materials. In particular, both tutor versions include instructional videos followed by self-explanation prompts. We will present data from an evaluation of both tutor versions.

Is there an Own-Age Advantage in Talker Recognition?

Adults are far better at identifying adult talkers than child talkers (e.g., Cooper et al., 2020). Why is this the case? Are child talkers acoustically less distinguishable (e.g., Lee et al., 1999)? Or perhaps adults better identify adult talkers, while children better identify child talkers (e.g., see Anastasi & Rhodes, 2005, for evidence that an own-age advantage exists in face recognition). Here, we test adults (N=72) and 6.5-year-olds (N=71) on a voice identification task featuring single word recordings by 8 children and 8 adults. While all listeners successfully identified all talkers above chance (ps<.05), adults significantly outperformed children with both adult (Madult=0.66 vs. Mchild=0.54; p<.001) and child talkers (Madult=0.64 vs. Mchild=0.57; p<.05). Thus, we find no evidence of an own-age advantage in talker recognition. Rather, we find additional evidence that adults are more skilled than children in voice identification tasks, suggesting that talker recognitions skills take time to fully mature.

Test Before Study: Maximizing Adaptive Learning Gains using Prior Knowledge Assessment

Digital adaptive fact learning systems tailor learning sessions to the individual learner. Most adaptive learning systems assume that the learner has no prior knowledge of the material and therefore use an initial ‘passive rehearsal’ trial for all facts. Here, we test a system that uses active retrieval trials instead of passive study trials when introducing items. This minimizes time loss associated with studying familiar materials and maximizes potential benefits of attempted retrieval before study. We test the system by having participants learn the association between the outlines and names of countries, a domain in which they are likely to have varying degrees of prior knowledge. We show that using attempted retrieval to identify known items is valuable in real-world applications, where partial knowledge of study materials before the start of a learning session is very common.

Looking for a second opinion: Epistemic emotions and the exploration of information sources

Epistemic emotions affect learning and exploration. Specifically, beliefs held with high confidence elicit high levels of surprise and curiosity when proven wrong. In turn, these emotions lead to searching for more elaborative details about the belief topic. Do epistemic emotions also motivate exploration of how widely a belief is held? After answering a trivia question and indicating how confident they were, participants were shown an answer submitted by another participant, reported their surprise and curiosity, and then given the option of seeing up to three responses from different participants. The results supported serial mediation, with certainty predicting surprise, surprise predicting curiosity, and curiosity predicting the number of additional sources explored. However, unlike prior findings, high-certainty errors did not result in stronger emotions or more exploration than low-certainty errors. Thus, epistemic emotions motivate not just elaborative exploration but also exploration of opinion convergence - two complementary ways to justify beliefs.

Perception of Risk and Intention for Vaccination in times of Covid-19 emergency in an Indian sample

Many countries including India are struggling with the Covid-19 emergency. India started an official nation-wide immunisation campaign before the outbreak of the second wave in March 2021. However, the acceptance of vaccines was shaped by several factors including risk perception. The current study aimed at investigating the perception and susceptibility to the risk of ongoing Covid-19 emergency, and intention to get vaccinated among the Indian population. The online study involved 308 participants from different regions across India. Results showed that intention to get vaccinated was positively correlated with perception of risk, pro-sociality, use of preventive behaviours, and trust in science and medical professionals. The study highlighted the psychological and demographic variables which can be useful to tailor public health outreach programs in future pandemics.

An Account of Interoceptive Affordances

Lately there has been an increase in work on the role that interoception can play in motivating action. Given that research programs such as the ecological approach have an action-oriented account of perceptual content that has proved successful for explain a host of psychological phenomena, one would expect there to be work caching out interoceptive content in ecological terms. But little work has been done exploring interoception from the perspective of the ecological approach. Here I will attempt to remedy that by showing just how we can conceive of interoception as perception of action possibilities. More precisely, I will argue that in interoception what we perceive are affordances. And then I will attempt to develop an account of interceptive affordances which conceives of them as action possibilities an agent can take to regulate internal bodily processes.

Machines and Molds: An Exploration of Brainless Cognition

Cognition as a grouping of mental processes has always been fundamentally linked to that of the brain. Yet more recently, the acellular slime mold known as P. polycephalum has been observed to complete various tasks associated with cognition without the need for a brain. The most notable feat of P. polycephalum has been repeatedly solving mazes with greater efficiency on each subsequent run. This has led to the speculation that P. polycephalum may possess the ability to learn. Comparatively, various developments in computation have led to algorithms and robotic systems that also appear to have the capacity to “learn.” Utilizing the same maze structure to test both P. polycephalum and various computational methods, we quantitatively map and compare the effectiveness and means by which each solves the problem. With this, we hope to provide greater insight into brainless problem-solving in both biological organisms and machines.

Statistical Learning Guides Auditory Attention

The statistics of the auditory environment can be computed globally (the probability of single ‘sound events’), and locally (how often does one sound precede another). We examine how the global probability and temporal predictiveness of sounds of different acoustic frequencies affect decisions on an orthogonal sound dimension, duration. An ideal observer could perform this task perfectly by selectively attending to duration. Yet listeners judge duration faster for tones whose frequency has a high global probability compared to those with a frequency of low probability. Moreover, when a preceding 'cue' tone’s frequency predicts that of a subsequent 'target' tone, listeners are faster at judging the duration of the target tone. This latter effect is not solely a result of temporal cueing in that cue-target relationships that are not predictive do not speed response. Statistical regularities may drive attention to perceptual dimensions, even when the dimensions are irrelevant to optimal task performance.

Adaptive Learning Technology based on Working Memory Capacity

Learning processes are impacted by individuals’ capabilities (e.g., working memory capacity). Nowadays, the technological improvements like those promised by deep learning methods open new opportunities to adapt learning environments. However, studies using the abilities of these models to individualize learning remain currently rare. The current research project aims to tackle one of the major constraints imposed by video learning related to the transitory aspect of delivered information. The purpose is to individualize the learning, based on learners working memory capacity (WMC). To this end, several elements need to be taken into account: (1) How to automatically and efficiently evaluate WMC without impacting learning; (2) Analyze in details the actual effect of WMC on video learning; (3) Get the most efficient and acceptable solutions to reduce the effects of WMC.

A Closer Look at Bridging Inferences: Onlookers and Framing in Visual Narratives

Like verbal discourse, visual narratives use various techniques to motivate readers to infer unstated events. One technique of sponsoring bridging inferences in visual sequences is to replace climactic events with an “onlooker”, a bystander viewing the off-panel climax reacting with textual and/or bodily cues to varying degrees. In two self-paced reading experiments, we explored how onlookers sponsored inferencing. Experiment 1 compared five types of onlookers ranging from a passive face with no cues to explicit facial or textual cues, and Experiment 2 investigated the effect of zooming in on onlookers’ bodily cues. Less explicit onlookers are processed faster, but at the next panel all onlookers required equal updating costs and zooms elicited further costs. Moreover, textual cues facilitated understanding. Overall, these findings show that complexity and explicitness of information in visual narratives affect subsequent event inferencing. Keywords: backward inferences, explicitness, event cognition

N400 amplitudes as Bayesian surprise at the level of meaning

In the study of human language comprehension, the N400 brain potential is often used as an index of on-line processing of meaning. However, a more precise account of its underlying computational process is still debated. Previous neural network modelling work simulated N400 amplitudes as change in a probabilistic representation corresponding to an internal implicit prediction error or Bayesian surprise at the level of meaning (Rabovsky, Hansen, & McClelland, 2018). Here we further test this account using a Bayesian learner model to simulate semantic processing of nouns from different semantic categories presented in an oddball like roving paradigm. We show that Bayesian surprise at the level of meaning significantly predicts N400 amplitudes and does so to a significantly higher degree than a non-Bayesian baseline. This sets the N400 in relation to ERP mismatch negativities in perceptual oddball paradigms, which have featured prominently in Bayesian accounts of brain function.

Robustness of graph theoretic representations of semantic networks

Recent network-based approaches leverage graph theoretic analyses to study individual differences in semantic networks and how they relate to other cognitive processes. However, it remains ambiguous whether individual differences captured via semantic network analyses reflect true differences in latent knowledge representations, or strategic differences in how people approach semantic relatedness tasks. To determine the robustness of content- and structure- based metrics of individual semantic networks we test their reliability across different tasks. We find both weighted and unweighted graph theoretic representations can predict individual differences in connections between semantic units across tasks. Furthermore, node centrality, a content-based metric which captures relative ‘importance’ of units within a network, is reliable across tasks, but metrics of structural properties of semantic networks, i.e. average clustering coefficient and shortest path length, are less robust. These results highlight the importance of validating graph-theoretic measures in the study of individual differences in semantic memory.

Forward and Backward Serial Recall

Although recall proceeds most naturally in the order of encoding, people can also recall items in reverse order. Prior studies raise the question of whether recall performance differs as a function of direction. To shed new light on this question, we examined the temporal dynamics of forward and backward serial recall. Consistent with prior work, we find similar levels of recall accuracy in forward and backward recall. However, detailed analyses of recall dynamics demonstrated higher accuracy for recall initiation and lower accuracy for recall transitions in backward compared to forward recall. Pre-cuing subjects to the direction of recall eliminated the difference in recall initiation and reduced, but did not erase, effects seen in recall transitions. We show that backward recall benefits and suffers from the recency effect: By promoting access to end of list items, recency facilitates initiation, but it hinders correct transitions by promoting fill-in-errors following omissions.

Do Judgments of Learning Improve Inductive Learning?

The present study investigated whether making judgments of learning (JOLs) can enhance the learning of the studied materials (backward effect) and newly studied materials (forward effect) in inductive learning. In two experiments, participants studied various butterfly species through Sections A and B. After learning Section A, participants made either target-present JOLs, target-absent JOLs, or merely restudied Section A (Experiment 1). We also manipulated whether participants make an explicit retrieval attempt before making JOLs (Experiment 2). Then all participants learned Section B and took a final test for both sections. The results revealed that simply making target-present or target-absent JOLs did not produce learning benefits. However, when people made the target-absent JOLs combined with explicit retrieval attempts they outperformed the other groups in both sections, showing backward and forward effects of JOLs. In conclusion, JOLs seem to be effective only when learners actively retrieve the learned information while making metacognitive judgments.

Choice blindness persists despite explicit instructions to detect false feedbacks: preliminary evidence for a Bayesian model of choice blindness

Choice blindness is a puzzling phenomenon, showing how people often fail to detect a mismatch between their intentions and outcomes and then confabulate reasons for choices they never made. Despite extensive replications, little is known about the cognitive mechanisms underpinning it. To tackle this question, we report an online experiment showing for the first time that people still illusorily self-attribute unchosen outcomes despite being explicitly instructed to detect false feedbacks. Comparing standard implicit choice blindness with our new explicit task, we show how prior beliefs about manipulations and stimuli discriminability influence detection. Building on these results, we sketch a preliminary Bayesian model of choice blindness suggesting that choice blindness occurs when beliefs in the reliability of feedback override monitoring related cues indicative of a mismatched outcome. Finally, we discuss practical advantages of explicit task to study choice blindness and implications for the sense of agency and self-knowledge.

Morphological Strengthening of Phonological Performance in Child Speech

First language learners use various linguistic cues to construct their language(s)’ grammar. I study how phonological and morphological structures interact in the minds of two young learners of Russian (1;6 - 2;11 years of age) and their caretakers. While phonology is traditionally recognized to develop ahead of morphology, my data show a ‘backward’ directionality for the younger child only: the grasp of consonant cluster inventory depends on the rising morphological complexity, measured as a number of morphemes per word. This interaction is first backed by consonant clustering, being a natural result of phonological alternations at the morpheme boundary; and second, by language pathology data, an aphasic patient attempting to improve the coda sonority in inflected words but not monomorphemic ones during speech production. If this result is validated on a larger sample, the finding is meaningful both theoretically and practically for the speech therapy procedures.

The P600 and P3 ERP components are linked to pupil dilation as correlate of norepinephrine activity

The P600 event-related potential component is elicited by linguistic violations and continues to inform neurocognitive models of language comprehension (e.g., Kuperberg, 2021). It has also been suggested to be a variant of the oddball-sensitive, domain-general P3 component (e.g., Coulson et al., 1996). In a sentence comprehension and an oddball paradigm we find that the task-elicited pupil dilation, a putative correlate of norepinephrine release from the locus coeruleus, is similarly affected by both manipulations. Crucially, the size of the pupil dilation predicts the amplitude of both components on a trial-by-trial basis. This suggests that both components rely on a shared neural generator and, more specifically, that both may be linked to norepinephrine release in response to rare and motivationally significant stimuli (Nieuwenhuis et al., 2005; Sassenhagen et al., 2014; 2015). Our findings thus question the domain-specificity of the P600 and give further insights into the role of neuromodulators in cognition.

Role of Prior Knowledge in Feedback Timing

Several individual differences have been suggested to explain neutral or negative effects of feedback on learning (Hattie & Timperley, 2007; Kluger & DeNisi, 1996). Specifically, Fyfe and colleagues (2016) have found that feedback has been useful for students with low prior knowledge, but has mixed effects on students with high prior knowledge. In contrast to prior studies that measure prior knowledge, here we manipulate students' propensity to transfer from knowledge of a more familiar concept to a more difficult one using knowledge activation (Sidney, 2020). Undergraduates (N = 138) were randomly assigned in a 3 (delayed, immediate, or no feedback) x 2 (activate prior knowledge or not) between-subjects design. Both activating prior knowledge and providing immediate feedback enhanced performance during learning trials. On a no-feedback posttest, feedback effects were moderated by knowledge activation. Importantly, combining knowledge activation with immediate feedback had a large positive impact on performance.

The effect of language on the approximate number system

The approximate number system (ANS) underlies our rapid and intuitive sense for quantities (Feigenson et al., 2004). The ANS, tapped into when performing rapid number judgement (DeWind et al., 2015), is affected by the properties of visual stimuli (e.g., object density and grouping). In these studies, we test whether the ANS is also affected by semantic information, an effect previously found in the processing of other types of spatial information (e.g., language presented at encoding affects spatial memory, see Gudde et al., 2016; Loewenstein & Gentner, 2005). We present the results of four experiments manipulating vague quantifiers (few, several, many, lots, 'no quantifier') prior to flashing a visual scene containing a number of objects. If higher-level cognition can penetrate the ANS, we expected that quantifiers presented at encoding would bias approximate number judgments towards the previously presented quantifier. Such results would provide compelling evidence that expectation mediates low-level visual processes.

Semantically homogenous item displays improve memory for item recall but not item location

We often organize items in space according to their semantic and/or functional properties (e.g., cookware in the kitchen, clothing in the closet), yet little research has been conducted on the memorial effects of these types of spatial configurations. Across three experiments, we examined how a semantically homogenous display influenced memory for item locations compared to a heterogenous/scrambled display. Participants learned the locations of words in a semantically homogenous display (where all words belonged to a single category), as well as a scrambled display (where the words belonged to an assortment of different categories). While the semantic display improved recall memory for the words themselves (i.e., item memory), it resulted in a cost to item location memory. We hypothesize that semantic structuring can impair memory for item locations by increasing similarity and hence confusability between items from the same category.

Trait mindfulness is associated with dynamic transitions in a combination of affective qualities

The study aimed to examine the relationship between mindfulness and affective thought dynamics. We hypothesized two principle components that are associated with trait mindfulness. Twenty-three participants wrote ten successive words from six seed words, rated affective dimensions on these words, and completed the five-facet mindfulness questionnaire (FFMQ; act-aware, describing, non-judgment, non-reactivity, and observing). Principle Components Analysis (PCA) was used reduce the affective dimensions and Markov-Chain to analyze state changes. Linear regressions were then used to examine the relationships between the PCs and FFMQ. Our results revealed one PC (valence, arousal, and importance). Linear regressions revealed that observing, act-aware, and describing predicted the probability of transitioning from negative to positive [P(N->P)] (B = 1.77, p = 0.040), P(PP) (B = 1.77, p = 0.040), and P(PP) (B = 1.77, p = 0.040), respectively. These state shifts may explain improvements in emotion regulation through mindfulness, which directs these affective shifts.

Spatial alignment facilitates visual comparison in diagonal structures

To grasp visual relationships, such as those presented in figures or diagrams, viewers must often compare the spatial relationships between visual structures (e.g., the slopes of two lines, sequences of DNA strands). Past work has found that this visual comparison process is optimized when visuals are placed directly, such that the relational correspondences are obvious (e.g., horizontal spatial structures placed vertically), and weakened when relational correspondences are impeded (e.g., horizontal spatial structures placed horizontally) (Matlen et al. 2020). However, this principle of spatial alignment has yet to be tested with other spatial structures beyond horizontal and vertical structures. Here, we tested this principle using diagonal spatial structures. The results bore out the spatial alignment principle – participants were faster and more accurate when diagonal structures were in direct relative to impeded placements. These findings have important implications for theories of comparison as well as for design and instruction.

Language Experience Modulates L2-Related Representational Change when Learning Novel Categories

When learners are exposed to multiple languages, semantic categories have been shown to undergo a process of convergence wherein concepts that overlap across natural languages come to be represented more similarly. Recently, we replicated this convergence effect using a simulated bilingual language learning paradigm in which participants learn one language (i.e., category boundary and associated labels) before then learning a second language with a shifted category boundary. This work, however, only assessed English-speaking monolinguals. In the present study, we extend this paradigm to bilinguals—asking whether extensive experience maintaining different label mapping systems modulates degree of semantic convergence when learners face two novel (artificial) languages. We first assessed the language experience of Polish-English bilinguals then measured representational change via the simulated bilingual language learning paradigm. We report on evidence that language history moderates the extent, and direction, of representational change, and we conclude with implications for theories of bilingual representation.

Investigating social norms in nonhuman animals

Social norms—rules governing which behaviors are deemed appropriate or inappropriate within a given community—are typically taken to be uniquely human. Recently, this position has been challenged (Andrews 2020; Danón 2019; Fitzpatrick 2020; Kappeler et al. 2019; von Rohr et al. 2011). The view that norms are human unique stems from commitments regarding the psychological capacities required for having them, and skepticism that animals possess these prerequisites (Birch 2020; Rakoczy and Schmidt 2019; Schlingoff and Moore 2017; Tomasello 2016). However, among norm cognition researchers there is little agreement about the cognitive architecture that underpins social norms in humans. To move forward, we draw inspiration from the progress made in the animal culture research, develop an operationalized account of social norms as *a socially maintained pattern of behavioral conformity within a community* (Westra and Andrews, in preparation), and offer methods for studying social norms in wild and captive primate populations.

Exploring the Role of Pragmatic Reasoning in Linguistic Framing Effects

Myriad linguistic cues, subtle and otherwise, have been shown to shape thinking and reasoning. Such linguistic framing effects are attributed variously to irrational biases, affective processes, and analogy. Across two studies, we explored pragmatic reasoning (PR) as a potential unifying mechanism underlying disparate framing effects. In Study 1, we adapted a measure of PR used in attribute framing research, assessing the ability to infer information subtly communicated by a speaker’s choice of frame. Our measure captured individual differences in PR distinct from cognitive reflection and mentalizing ability. In Study 2, PR predicted the effect of identity labels on attitudes towards illegal immigration, but not other framing effects. Two such effects were driven, however, by participants’ explicit recognition of the influence of the framing language. These results suggest that framing effects may indeed be mediated by PR, but that our measure of PR does not fully capture its mediating role.

What sort of explanation should we expect from the algorithmic decision-making system?

The requirements of transparency or explainability draw considerable attention in AI ethics. Still, it is not clear what it is for, whom AI is explainable to, what kind of explanation is demanded. First, I take the principle of explainability to state that there is a prima facie duty to make AI explainable when used in morally significant situations. Second, I show that explainability has a dual nature. Most of the existing literature is based on the unjustified assumption that there is only one purpose for explainability and one kind of explanation should be given to end-users. I argue, however, it is directed toward both decision-makers for their control to decide and decision-recipients for their trust in the algorithmic decisions. Consequently, different explications need to be given to different stakeholders for different purposes.

Fun as maximizing learnability: Balancing difficulty and prior knowledge

Theories of intrinsic motivation describe how behavior is driven by inherent satisfaction beyond only rewarding outcomes. One computational theory quantifies “fun” as the pleasure derived from improving one’s model of the environment. Here, we refine and test this theory by predicting maximal fun occurs when learning progress is also maximal, corresponding to a balance between ability (or knowledge) and task difficulty. Across multiple natural data sets (e.g., “Super Mario Maker”, “Trackmania”, or “Robozzle”), we confirm our prediction that human judgments of fun are highest at intermediate levels of difficulty. We provide further evidence through a number guessing experiment, where we manipulated the lea