Proceedings of the Annual Meeting of the Cognitive Science Society

Please, see the workshop's website here: https://sites.google.com/view/cogconcepts The goal of this workshop is to initiate an interdisciplinary conversation about reconceptualizing cognitive science disciplines. This workshop will bring together researchers proposing new conceptualizations in their disciplines, cognitive scientists investigating the mechanisms of concept learning and the role of concepts in human cognition, researchers building infrastructures to study and improve cognitive concepts, and philosophers analyzing scientific conceptualizations. The workshop will include activities which will prompt the audience to think about the conceptual foundations of their respective areas, and about ways to improve these foundations. These activities are designed to maximize audience participation and include panel discussions, as well as mind-matching sessions. One of the outcomes of the workshop is identifying the diversity of approaches for improving cognitive science concepts that could be relevant to both discipline-wide and more specific efforts.

Compositionality, or the ability to build complex representations from discrete elements, is an essential ingredient of human intelligence. Compositionality enables people to think productively, learn fast from limited experience, and generalize knowledge to new contexts without re-learning from scratch. It is also essential in information processing systems to efficiently represent structured data and has seen application in compression and symbolic Artificial Intelligence (AI). Historically, the notion of compositionality played a central role in linguistic theory and philosophy of mind. More recently, it is attracting a surge of interest throughout the domains of cognitive science. Compositional processes are leveraged for elucidating the nature of mental representations in cognition (Dehaene et al., 2022), understanding the functional organisation of the brain (Agrawal et al., 2019), or building Artificial Intelligence systems that are robust to changes in the environment (Hupkes et al., 2020).

In-context learning refers to the ability of a neural network to learn from information presented in its context. While traditional learning in neural networks requires adjusting network weights for every new task, in-context learning operates purely by updating internal activations without needing any updates to network weights. The emergence of this ability in large language models has led to a paradigm shift in machine learning and has forced researchers to reconceptualize how they think about learning in neural networks. Looking beyond language models, we can find in-context learning in many computational models relevant to cognitive science, including those that emerge from meta-learning.

The field of psychometrics has undergone substantial evolution over the past several decades, both in terms of advances in methodology and improved software and hardware for deploying new methods. Despite these strides, many of these developments have not been integrated into the broader field of psychology, as highlighted by Embretson (2005) and Borsboom (2006). Understanding and incorporating these psychometric advances is crucial to enable cognitive scientists to address growing concerns about validity and reliability, as well as to develop robust theoretical frameworks for understanding cognition.

In recent years, the field of computer graphics has achieved its longstanding dream of photorealism: modern graphics algorithms produce images that are indistinguishable from reality. Much like art at the advent of photography, then, computer graphics is now turning its gaze to the beholder: researchers are increasingly looking to cognitive science to engineer new modes of visual expression. Recent work has sought to apply insights from cognitive science to a variety of traditional graphics topics: from taking a perceptual approach to perspective, to studying the theory of mind behind animation, to applying theories of abstraction learning to build tools for geometry processing. At the same time, a wave of recent work in cognitive science has addressed fundamental questions about visual expression: for example, how humans understand and create sketches, shapes, and symbols. The field has also benefited greatly from tools and methods from computer graphics: differentiable rendering, physics simulation, and game engines have become indispensable in modeling human perception and intuitive physics. Recognizing this growing interdisciplinary exchange of ideas, we are proposing a workshop to begin building formal bridges between the cognitive science and computer graphics communities.

Cognitive science research has far-reaching implications, but many graduate students are trained solely for tenure-track faculty positions. Academic training develops a wide range of skills in behavioral research, literature reviewing, data analysis, scientific publishing, grant writing, teaching, and student mentorship. These skills have direct application in many other careers, but training within academia typically neglects to address how these skills translate to other work environments and career paths. As growth in the number of doctoral trainees continues to outpace permanent academic positions (Kolata, 2016; Larson et al., 2013; Lederman, 2016), more doctoral recipients have been seeking employment beyond faculty positions and academia (National Science Board, 2018). Those who are interested in exploring alternative career paths may not know where to turn for guidance. Our goal in this professional development workshop is to offer such guidance and an opportunity to network with scholars in similar situations.

We have a simple thesis: the relationship between academic and industry-based cognitive science is broken, but can be fixed. Over the last few decades, there has been a huge increase in the representation of cognitive science in industry. Beyond just machine learning, businesses are increasingly interested in human behavior and cognitive processes. Large proportions of our Ph.D. students, post-docs, and even faculty choose to go through a largely one-way door to corporate jobs in data science, behavioral experimentation, machine learning, user experience, and elsewhere. Currently, people who choose industry careers often lose their social and intellectual networks and their ability to return to tenure-track positions. Valuable insights from industry about memory, decision-making, learning, emotion, distributed cognition, and much more never return to the academic community. We believe that deep, theory driven, theory building work is being done in industry settings–and that the rift between communities makes all our work less effective

To communicate is to move. There is no way around that. If we pick up comprehensive handbooks or introductory texts in movement science (Hong and Bartlett (2008)) we see that there is very rich knowledge and tractable mathematical models about different aspects of movements. Yet, we find no chapter on communicative movements. While the field of speech motor control is a developed area on its own (Parrell and Lammert (2019)), there is no movement science of communication proper, which would include whole-body-, hand-gestural-, signed-, and inter-bodily actions.

In this Symposium we link different perspectives and traditions of research on language and concepts in the cognitive sciences to better understand the process of abstraction from the social-interactive standpoint. In contrast to the usual benefits of abstraction – such as the ability to categorize and generalize – we underscore the possibility of abstract concepts and words to remain underspecified, “open to the context, the suggestions of others” (Borghi, 2023) or to processes of real-time negotiations (Christiansen & Chater, 2022). This is enabled by their largely non-perceptual rela-tional character (Gentner, 2005) and by the fact that they arise in cognitive systems continually embedded and active in their environments (Cisek 1999; Mannella & Tummolini 2023).

Events are a fundamental part of human experience. Research on event cognition is rapidly developing and is revealing central aspects of how humans perceive, conceptualize, communicate about, and remember events. This symposium offers an interdisciplinary look at recent advances in the study of event cognition. The symposium brings together cognitive scientists from across continents, who are experts on the subject. The symposium contributors come from a variety of backgrounds and disciplines in developmental psychology, cognitive psychology, neuro-computational psychology, and linguistics. They combine a variety of innovative and integrative approaches and methodologies and study diverse populations across the lifespan and across languages. The overall goal of this symposium is to foster an interdisciplinary conversation on different aspects of event cognition.

Goals are a central pillar of everyday mental activity. From finding your way home to solving a puzzle and ordering food delivery, much of human action and cognition is goal-directed. Perhaps unsurprisingly, theories of goals are a central focus in the psychology of motivation (Elliott & Dweck, 1988), in social and personality psychology (Fishbach & Ferguson, 2007), as well as research aimed at understanding factors contributing to task achievement in educational and industrial settings (Ames & Ames, 1984; Locke & Latham, 2002). In this symposium, we highlight recent work emphasizing a goal-centric outlook on learning, exploration, and communication.

Reducing inequality, mitigating climate change, and responding to public health crises are large-scale goals that require the cooperation and coordination of many individuals. These goals cannot be achieved by one individual alone, and contributing is not always beneficial to each individual. And yet, individuals must contribute in order to make a difference. How do we hold individuals and groups responsible for collective action?

Predicting the actions and reactions of others is crucial to suc- cessful social interaction. When deciding whether to bluff in a game of poker, we consider the chances that the other players will fold or continue to play and unmask our bluff. When deciding whether to tell our boss that their plans are likely to have adverse effects, we consider a range of reac- tions, from being grateful for our honesty to being dismissed out of spite. Such predictions are highly uncertain and com- plex, not least because the other's (re)actions usually result from them making equally complex and uncertain inferences about us. Nevertheless, we are often remarkably successful – although sometimes utterly wrong – in our social inferences. How do we explain these successes and failures?

Deep Learning is a neural network approach where a network of multiple layers is trained to process complex data. Applications for deep learning include recognizing complex patterns in pictures, text, and sounds to, in some cases, produce insights into how human process information. Deep learning has garnered widescale interest, fostered by advances in, for instance, natural language processing and the release of innovative tools like Chat GPT. But does deep learning have implications for theory in the cognitive sciences; that is, is deep learning the answer for understanding human cognitive dynamics?

Tracing the evolutionary milestones of our species has been the focus of much exciting research. Yet, we are still unable to ‘locate' the divergence point of our cognitive evolution, which has made us so unique in the animal kingdom (Tomasello & Rakoczy, 2003). We have identified a gap in our understanding, and that is the absence of a systematic exploration into the symbiotic relationship between Homo sapiens and fungi. This highly interdisciplinary symposium aims to address this oversight, emphasizing the important – yet underappreciated – role of fungi in cognitive contexts and challenge traditional views of cognition.

Large language models (LLMs) like OpenAI's GPT-4 (OpenAI et al., 2023), or Google's PaLM (Chowdhery et al., 2022) generate text responses to user-generated text prompts. In contrast to work that evaluates the extent to which model-generated text coheres with linguistic rules (i.e., formal competence) (Chomsky et al., 2023; Piantadosi, 2023), the present symposium discusses the work of cognitive scientists aimed at assessing the extent and manner in which LLMs show effective understanding, reasoning and decision making, capacities associated with human higher cognition (i.e., functional competence) (Binz & Schulz, 2023; Mahowald et al., 2023; Webb et al., 2023). Given both their expertise and their interest in clarifying the nature of human thinking, cognitive scientists are in a unique position both to carefully evaluate LLMs' capacity for thought (Bhatia, 2023; Han et al., 2024; Mitchell, 2023) and to benefit from them as methodological and theoretical tools. This symposium will thus be of interest not only to cognitive scientists concerned with machine intelligence, but also to those looking to incorporate advances in artificial intelligence with their study of human intelligence.

Humans engage in a wide variety of search behaviors during their lifetime. These search behaviors may be external such as foraging for food in the wild, or searching for mates, or internal, such as searching for concepts in memory. Decades of work on search processes among humans has suggested that these two types of search behaviors share common characteristics and physiological mechanisms. Despite this progress, understanding the dynamics of internal search is an ongoing challenge in the field.

The brain evolved as a sensory-motor machine that drives behavior while being linked to the world through sensors. Human cognition abstracts from these sensory-motor roots but retains intimate ties. The brain's structure reflects this history. How do neural processes at different distances from the sensory and motor surfaces integrate to achieve meaningful and grounded cognition? This is a challenge given the time-continuous and graded nature of sensory-motor processing, which enables continuous online updating. It is also a major challenge to understanding development and autonomous learning, in which the coupling across functional boundaries evolves under the influence of online activation patterns.

In this session, representatives from IES, NSF, UNESCO, and ERC will discuss intellectual, sociological, and practical issues that arise in doing research in Cognitive Science, Education, and at their intersection. Some of these issues are due generally to the multidisciplinary nature of the work, but others are specific to education. The mobilization of research knowledge and human capital for translation to practice and policy remains a significant challenge that each of these agencies seeks to address. The speakers will highlight relevant initiatives and point to similarities and differences in the grant review processes between programs, providing tips for successful grant writing along the way. They will discuss where their programs are placed in relation to one another in the funding landscape and along the continuum from the most basic to the most applied research – and the extent that such a distinction is meaningful. They will also contrast their funding emphases and the implications those emphases have for the kinds of projects that can be engaged.

To facilitate language comprehension, the brain uses contextual information and prior knowledge to predict future content. Recent breakthroughs allow us to study pre-word onset prediction during naturalistic narrative listening by mapping contextual word embeddings from Large Language Models onto ECoG data. Long-range prediction encoding has been observed in fMRI data, where including multiple upcoming word embeddings enhances the model's fit to brain data. This study examines if similar predictive information is detectable in MEG data, which offers higher temporal resolution than fMRI but lower signal-to-noise ratio than ECoG. We found that pre-onset predictive signatures are present in MEG, even in data of limited length (1 hour) and in single participants. Unlike in fMRI, adding future embeddings does not improve encoding. These findings offer a novel avenue for studying predictive processing using MEG signals and call for further investigation to explain the differences observed between fMRI and MEG approaches.

Non-invasive vagus nerve stimulation has proven effective in modulating parasympathetic autonomic nervous system activity and various cognitive functions. This study investigated the effects of transauricular vagus nerve stimulation (taVNS) on body ownership and interoception in healthy subjects using a within-subjects experimental design (active taVNS/sham). The rubber hand illusion (RHI) and the Heartbeat Counting Task (HCT) were employed. Cardiac activity was recorded throughout the procedure to measure physiological indices of heart rate (HR) and heart rate variability (HRV). Ownership for the fake hand was observed in both active and sham stimulation, as indicated by drift and scores on illusion-relevant items (Q1-Q3). HR and HRV showed no variations between synchronous/asynchronous RHI or between stimulation conditions. Active taVNS resulted in decreased interoceptive meta-awareness. Individuals with lower interoceptive abilities exhibited heightened susceptibility to RHI during active taVNS, possibly due to perturbation of interoceptive signals and increased reliance on exteroceptive signals in constructing body representation.

This study investigated the effect of encoding context on the formation of false memories, using Category Associates Procedure with pictorial stimuli. In the literature, distinctiveness is suggested to decrease false memories; however, the mechanisms of this effect are still of debate. Participants studied objects from several categories, each category list either presented on congruent or incongruent backgrounds. Later, they performed a recognition test for three item types: studied, critical lure and unrelated items. We expected that incongruent condition should require more distinctive encoding, which may lead to decreased false recognition of critical lures compared to congruent condition. The results revealed a false memory effect consistent with existing literature; however, there was no difference between congruent and incongruent conditions in terms of false memory rates. The results were discussed in the light of encoding-based and retrieval-based theories. Additionally, visual imagery measures and reported strategies were also considered.

Which are the psychological mechanisms shaping people's risky choices? While the behavioral sciences have produced many theories to address this question, attempts to integrate their different assumptions are sparse: Verbal theories may explain real life decisions with high face validity but lack precisely testable predictions. Conversely, formal theories allow for clear mathematical predictions but often focus on artificial lab tasks. We aim to bridge this gap and harness each approach's respective advantages. To this end, we created a taxonomy of the most important psychological mechanisms involved in risk taking spanning different aspects of cognition, including attentional, affective, motivational, and psycho-social mechanisms proposed in the literature. As such, this taxonomy is the basis for an integrative process model quantifying the psychological mechanisms involved in decisions under risk and uncertainty, and may help researchers to identify similarities and discrepancies between different theories of choice.

Children learn nouns more readily than verbs in early development. Research on candidate explanations for this noun advantage has suggested that, while noun meanings can be easily gleaned from their observational contexts, verb meanings require access to their syntactic constructions, which remain inaccessible until later in development (Gentner, 2006; McDonough et al., 2011; Piccin & Waxman, 2007). This study asks whether previous demonstrations of tenuous verb learning from their observational contexts are partly due to the assessment method. In an adapted version of the Human Simulation Paradigm (HSP; Gillette et al., 1999), we assessed verb learning using multiple tasks. When verb learning was assessed via a free-response task, learning was minimal, replicating the challenge of learning precise verb meanings via observational contexts. The findings from both a categorization and semantic similarity task, however, suggest that learners do acquire partial knowledge of both action and mental verbs via their observational contexts.

Transcranial alternating current stimulation (tACS) is an efficient neuromodulation technique to enhance cognitive function in a non-invasive manner. Using electroencephalography and functional magnetic resonance imaging, it was investigated whether a tACS with different phase lags between central executive and default mode networks modulated cognitive performance in perception, working memory, and inhibitory control. It was found that phase-lag-dependent tACS mediated improvement in task performance, neurodynamically reflected in task-relevant cortical and subcortical activation as well as prefrontal-based top-down functional connectivity. Our observations provide neurophysiological correlates of network-wise tACS-phase-dependent neuromodulation and a feasible non-invasive approach to effectively modulate fundamental cognitive functions.

This study compares creativity test performance in Individual versus Team settings, addressing a gap in research that mostly focuses on individual outcomes. A total of 120 individuals participated in two sessions. The first session involved cognitive assessments, including the Advanced Progressive Matrices (APM), the Creative Reasoning Test (CRT), and the Test for Creative Thinking–Drawing Production (TCT-DP), as well as mood and personality questionnaires. In the second session, participants were assigned to either an Individual or a Team condition (N=3 each), based on and controlling for APM scores. The same assessments, except for the personality questionnaire, were conducted in this second session. In the Team condition, members were encouraged to collaborate in solving the tasks. We tested whether the conditions have a differential effect on the second session performances, particularly on divergent and convergent thinking scores in CRT, and/or on TCT-DP scores and/or on APM scores.

The theory of language prediction posits a competitive preactivation of semantic (meaning) and form (sound/grapheme) information, aiding in the selection of the most likely lexical candidate. Hypothetically, multiple semantic and form cohorts are preactivated before the actual lexical candidate is activated. This study explores this by examining young adults reading constrained sentences (discretely), with simultaneous electroencephalographic recording. Representational similarity analysis was conducted to assess word-specific, semantic-related, and form-related pair of sentences (focusing on the word preceding the expected word). To examine the temporality, cluster permutation and divergence point analyses were performed. The results indicated a semantic coactivation effect occurring before the phonological one and the recovery of the specific words. However, despite the phonological information being recovered before the word specific information, there were no significant differences in temporality. These findings indicate a semantic coactivation process for meaning selection during prediction, with form coactivation dependent on the expected word's selection.

This study introduces culture relatedness of words as a novel variable and explores its impact on emotional responses of English-Mandarin bilinguals living in the UK, where their second language (L2) is dominant. First, we conducted a norming study to identify emotive words related to participants' native (e.g., bamboo) and residential (e.g., scones) cultures. We then used event-related potentials (ERPs) to examine whether culture relatedness affects emotional responses to words presented in L1 and L2. We were particularly interested in investigating whether the well-established emotional distance from L2 may be due to cultural distance, and whether concepts related to one's native culture in L2 may enhance affective responses. Initial evidence from ongoing data analyses seems to suggest an interaction of culture relatedness and emotional valance on affective responses. This research offers new insights into the interplay of language, culture, and emotion in bilingual contexts, examining how cultural salience modulates emotional responses.

Cognitive development has been hypothesised to be stagelike between the ages of 5-8 years (e.g., Piaget). Yet, cognition varies from moment to moment, in every task, for every child. Studies have demonstrated that cognitive variability is non-trivial, non-random, and meaningful, but attempts for systematic and large-scale longitudinal measurements of cognitive variability have scarcely been undertaken. This project's goal is to create a more detailed empirical record and dynamical account of intra-individual variability in cognitive development of children. We aim to do this with a 3-year longitudinal and multimodal data collection starting at 5 years of age. Half-yearly measurements will be complemented with periods of daily measurements. Our ultimate aim is to build a variability corpus in which we can study variability patterns and developmental transitions, and to connect our findings to “Piaget's new theory”. Our poster will present our methodology and findings from a pilot study.

Attachment is crucial for infants' cognitive development and social relationships. Traditional attachment research has been qualitative, lacking a model to explain how infants' attachment styles develop from experience and how these are influenced by personal traits and environmental factors. We propose such a model, predicting how infants balance interaction with caregivers against exploring their surroundings. Our study is based in a grid-world environment containing an infant and caregiver agent. We vary the infant's temperamental factors (e.g., ability to regulate emotions and preferences for social vs. environmental reward), and caregiver behavior (whether positive or negative interactions are more likely). We find that different equilibria result that qualitatively correspond to different attachment styles. Our findings suggest that the characteristic exploratory behavior of each attachment style in real infants may arise from interactions of infant temperament and caregiver behaviors.

We identify theoretical challenges for developing a computational explanation of flexible linguistic inference. Specifically, the human ability to interpret a novel linguistic expression (like “mask-shaming”), where inferring plausible meanings requires integrating relevant background knowledge (e.g., COVID-19 pandemic). We lay out (i) the core properties of the phenomenon that together make up our construal of the explanandum, (ii) explanatory desiderata to help make sure a theory explains the explanandum, and (iii) cognitive constraints to ensure a theory can be plausibly realised by human cognition and the brain. By doing so, we lay bare the ‘force field' that theories of this explanandum have to navigate, and we give examples of tensions that arise between different components of this force field. This is an important step in theory-development because it allows researchers who aim to solve one part of the puzzle of flexible linguistic inference to keep in clear view the other parts.

We tailor the explanations we give depending on the person asking for them – you would explain why an event happened differently depending on which of the contributing causes the listener already knows. While significant prior work focuses on how causal structure in the world influences explanation, we focus on how explanation production is modulated by listener belief. We propose a computational model framing explanation as rational communication about causal events, using a recursive theory-of-mind and language production framework to choose amongst possible explanatory utterances that minimize the divergence between speaker and listener belief about a why an event happened. We evaluate our model using some partial observer stimuli, which manipulate the listener's stated prior knowledge about an event, and find that our model well-predicts human judgements about which of several contributing causes is the best explanation for a speaker to provide by modeling their communicative value to the listener.

This study investigated the impact of illusory contour clarity and surface representations on visual search for Kanizsa figures. Experiment 1 manipulated illusory contour clarity through inducer size, while Experiment 2 manipulated clarity by varying the number of arcs in the inducer pacman. Both experiments compared Kanizsa figures with non-illusory figures under the same manipulation conditions. The findings from both experiments suggested that illusory contour clarity did not significantly influence Kanizsa figure search performance, but rather suggested a Kanizsa advantage over non-illusory figures, underscoring the importance of surface representations. Experiment 3 explored the effects of surface alterations on Kanizsa figures and smoothed counterparts, and confirmed that surface alterations had discernible effects on visual search for Kanizsa illusory contours. The results indicated that visual search for Kanizsa illusory contours remained robust, unaffected by variations in illusory contour clarity, thereby emphasizing the role of surface representations in guiding visual search processes.

Adolescence is a critical period for developing higher-order processes, such as the ability to selectively switch attention in response to changes (cognitive flexibility) and employing strategies for regulating attention (metacognitive skill). We adapted a measure of cognitive flexibility, the cued task-switching paradigm, by allowing participants to control their preparation time. Adjusting preparation time according to the demands of the upcoming trial requires metacognitive awareness of task demands and cognitive processing limits. Therefore, we propose that this strategy of preparation adjustment captures metacognitive skill. In a large-scale study (N = 141) with adolescents aged 11-15 years, results indicate that participants spontaneously adopted a preparation adjustment strategy. Increased self-paced preparation time was associated with decreased cognitive flexibility costs and was positively related to questionnaire measures of metacognitive skill. Overall, these findings suggest that individual differences in metacognitive skill impact the extent to which adolescents spontaneously adopt a strategy to improve cognitive flexibility.

Agreement, a systematic formal mapping between linguistic elements, adds redundant complexity to languages (e.g., in ‘she writes' the -s adds no information), and yet is crosslinguistically prevalent. A prominent hypothesis argues that the ubiquity of agreement may be due to a functional advantage it confers for child learners. Here, we test this using an artificial language learning experiment with 56 English-speaking children (mean age 5;11). We investigate whether agreement can facilitate learning of noun classes (e.g., ‘masculine'/'feminine'). In one condition, agreement appeared as a redundant cue to noun classes, whereas in the other condition there was no agreement. Following exposure, we tested children on noun classification for both nouns they were trained on and novel nouns. Results reveal that children classified nouns equally well in both conditions. However, novel nouns were classified better in the agreement condition compared to the no-agreement condition, suggesting agreement can facilitate generalization for child learners.

The study aims to determine if the positive ERP effect associated with self-relevance extends from first to second person pronouns and whether it is independent of the pronoun's referent. Two EEG experiments were conducted with 72 participants listening to two distinct audiobooks, "Tschick" and "Auferstehung der Toten" (AdT). The chosen novels differ in narrative structure, allowing for a comparison of the ERP response of 2sg pronouns that potentially refer to the listener with personal pronouns that do not. The narrative design of "Tschick" directed all 2nd person pronouns to characters in the story, while in "AdT" the listener was the most likely referent. The results reveal a significant positive ERP effect for second person pronouns in "AdT" compared to "Tschick," supporting the hypothesis that the self-relevance effect generalizes to second person pronouns. The findings suggest that this positivity in ERP reflects attentional processes enhancing the cortex's sensitivity to self-other distinctions.

Navigating social situations is complex due to others' hidden intentions and evolving strategies, requiring learning from past experiences. Anxiety complicates adaptation to uncertainty, especially in non-social settings. However, research on the anxiety's impact on learning within social uncertainty remains scarce. In a preregistered study (N = 190), we investigated whether individuals with higher trait anxiety struggled to adjust learning rates in a social context with stable or volatile outcomes utilizing various learning models (e.g., additive, multiplicative, betrayal). Participants engaged in a modified trust game with stable and volatile players, alongside a non-social task with slot machines. Participants showed higher learning rates in social than non-social contexts, with notably elevated social learning rates in individuals with heightened fear of negative evaluation (FNE)—a crucial trait linked to anxiety, especially social anxiety. This suggests individuals with increased FNE might be more sensitive to learning under social uncertainty.

Researchers typically have a fair amount of freedom when it comes to data processing and analysis selection. In many instances, there isn't one correct way to, for example, deal with outliers, which gives rise to a multitude of reasonable analysis pathways, each with its own outcome. Computational advances provide researchers with a unique opportunity to view the impact of such researcher degrees of freedom on the results from a study. Multiverse analyses involve the computational analysis of all these potential pathways, which can demonstrate the robustness of a particular phenomenon, or the lack thereof. However, even though multiverse analyses are less susceptible to biases compared to the typical single-pathway approach, it is still possible to selectively add or omit pathways. To address this, we propose a more principled approach to conducting multiverse analyses, and illustrate how it can be applied using the Semantic Priming Across Many Languages project.

Undetected fake news can influence opinions and behaviors. Therefore, it is crucial to understand under which conditions people can detect fake news, and how aware they are of their detection performance. Building upon a study on phishing emails (Canfield et al., 2019), we investigated metacognition for both fake and legitimate news, along with related individual and task factors. In a single-factor within-subjects design, 175 participants read 19 sampled legitimate and 19 automatically generated fake news in random order. They were tasked with detecting fake news and providing metacognitive confidence judgments. Overall, participants displayed overconfidence with 68% correct detection and 73% confidence. However, they showed better calibration and resolution for fake news compared to legitimate news. Notably, there was a tendency for participants to misjudge legitimate news at high confidence levels. Prior knowledge positively impacted performance, whereas agreement with fake and disagreement with legitimate news resulted in performance falling below random.

Linguistic strategies humans use for communication are designed to meet the informational needs of their addressees. Speakers not only adapt their speech but also increase the rate of iconic gestures to enhance the clarity of a message for children. Although sign languages allow signers to take advantage of iconicity far more than what is possible in speech, little is known about whether signers use iconicity as a strategy in child-directed communication. In the present study, we used automatic video pose estimation to analyze descriptions aimed at a child (12yo) vs. an adult produced by 7 deaf adult signers of Italian Sign Language. Overall, signers used iconic constructions more than lexical signs and with comparable frequency in descriptions for both age groups. However, iconic constructions were longer in duration for children. Thus, the present study presents the first evidence that, unlike speakers, signers do not modulate quantitative but only qualitative aspects of iconicity for children.

In personalized-schedule learning, previous research has shown the benefit of initial attempted retrieval of study-items on short-term retention and later test performance. As a way of prior-knowledge identification, initial attempted retrieval may help to optimize learning and long-term performance further, through the removal (or ‘drop') of items from the learning set that are answered correctly on the first attempt. This study sought to support this hypothesis through a real-world, within-subjects experiment, comparing vocabulary test performance of Dutch middle school students after the use of a drop- and non-drop adaptive learning algorithm. The results show that short- and long-term item retention was higher for material studied using the drop-algorithm, while dropping items did not lead to worse retention compared to items that were kept upon initial correct responses. This suggests that initially-known items are correctly identified as ‘mastered', and that their removal from the learning material allows students to focus their efforts on unknown items, leading to increased learning gains.

We investigated whether inner language enhances memory for events in a naturalistic, non-verbal task where participants constructed simple models from memory after watching an instructional video. Across three experiments, we used linguistic suppression to manipulate access to language and tested its effect on overall memory performance. Results showed that access to inner language consistently affected event memory: when inner language was disrupted at encoding, participants were poorer at recalling the models and remembered fewer events. Critically, the effect of linguistic suppression on memory performance was greater than a control secondary task that did not affect access to language (i.e., poorer performance was not solely due to dual-task effects). These findings support the proposal that inner language enhances event memory via a mechanism of linguistic bootstrapping, which in turn extends theories of event memory and adds to a growing body of evidence that inner language is a highly valuable cognitive tool.

How can we know whether a child really understands a fraction and how they understand it? We argue that number-to-line tasks are a great probe, as children need to think about magnitude and have many opportunities for error. We tested 26,000 pupils from 6th to 10th grades and analyzed their errors. In 6th grade, 80% of the responses were wrong; 45% were still so in 10th grade. We observed seven error patterns. In particular, younger and lower-performing children mostly confused fractions with decimals; older and higher-performing children rather placed the inverse of the target fraction. All grades also confused the roles of the numerator and the denominator. We propose that children use two strategies: they either convert the target fraction into a decimal or partition the line into units to count. We discuss theoretical (strategy choice vs. strategy execution) and pedagogical (identify and remediate misunderstandings) implications.

Having better knowledge of fractions is causally related to the ability to learn algebra, so what happens when teaching and learning about fractions is disrupted, as was the case during the COVID-19 pandemic? In this study, we examine how educational disruption caused by a pandemic differentially impacted students' fraction learning relative to students who were learning other mathematics content during that time. This study provides results from a cross-sequential project examining various facets of mathematics knowledge for students in 4th-10th grades over three years (2021, 2022, 2023; N=903 students). We investigate differences in fractions and algebra knowledge based on students' grade levels across cohorts to determine if there are particular periods at which students' learning was differentially affected by the disruption. Individual differences in students' self-regulation, self-efficacy, and personality will also be explored as potential buffers.

Tuning into a novel language is a particularly difficult task for many adults. While the rhythmic and melodic patterns, i.e. prosody, bootstrap language acquisition in infancy, they are considerably challenging to learn in adulthood. Is it because of an age-related decline of the language-learning ability or because of unfavourable learning conditions? We investigated whether adults can auditorily sensitise to the prosody of a novel language, and whether such sensitisation is affected by concurrent presentation of alphabetic transcription. After 5 minutes of exposure to Māori, Czech listeners could reliably recognize this language in a post-test using low-pass filtered clips of Māori and Malay recorded by new speakers. Recognition accuracy was lower for participants exposed to the novel-language speech along with deep-orthography transcriptions or shallow orthography with unfamiliar characters. Adults can thus attune to novel-language prosody, but orthography hampers this ability. This has implications for language acquisition theories and learning practice.

Research on infants' observational behavior has predominantly focused on retrospective information gain, leaving the role of prospective evaluation of information gain unclear. We examined 12-month-olds' use of information sources in an eye-tracking study, where participants could use their gaze to 'shake' two out of three boxes to locate a hidden character through auditory cues. Across two pre-registered experiments, we manipulated the probability distributions for character locations to assess forward-looking exploratory strategies. Findings from Experiment 1 with a uniform distribution suggest that while infants learned task contingencies, their choices did not align with maximizing expected information gain, leaning instead towards confirmatory hypothesis testing. Experiment 2 employs a non-uniform probability distribution for character locations to rule out alternative explanations of Experiment 1. In this setup, one box pair provides more information gain, while the other provides confirmatory evidence. Data collection is in progress, results will be presented at the conference.

How are features and categories of objects represented in the brain? While numerous studies have identified category-specific regions for different categories of objects, the nature of the representation for individual objects remains elusive. We investigated this question by employing representational similarity analysis (Kriegskorte et al., 2006) to identify different types of object information reflected in fMRI activation patterns. Relying on Clarke et al's (2014) object naming data, we conducted a searchlight mapping analysis to assess whether the object dissimilarity predicted by various theoretical models of object categories and features corresponded to the dissimilarity defined by fMRI activity patterns. The object feature models we contrasted were based on three different sets of feature norms: (a) norming data we obtained from a dataset of 78,000 features produced by 100 participants for a set of 264 pictures (Antal et al., 2024), (b) the CLSB word feature norms (Devereux et al., 2014), and (c) McRae et al's (2005) word feature norms. Results will address the contribution of feature information to the representation of different object categories.

Recent decision making theories have explained behaviour using mental sampling mechanisms where people imagine possible outcomes to guide their choices. Simultaneously, work in other domains has found evidence of particular mental sampling patterns, such as autocorrelations between samples and moderation by prior assumptions, which current decision making theories do not generally consider. Here, we seek to unify this work, developing a new sampling model of preferential choice incorporating these findings in other domains. Our model, based on the Autocorrelated Bayesian Sampler, predicts choice, reaction time, confidence and valuation from a common underlying process. We find a strong correspondence between our model's predictions and empirical choice data, though performance remains below leading explanations for such tasks. Our model does however cover a broader set of response types than existing theories, suggesting the advantages of considering of a wider range of behaviours than are commonly examined in current decision making studies.

Imbalances between the brain's excitatory (E) and inhibitory (I) systems can lead to structural and functional cortical deviances which have been associated with various developmental conditions including autism. However, the developmental trajectory of such EI imbalances across childhood and adolescence as well as its relationship to autism traits is not well understood yet. In this study, we determined a functional measure of the EI balance from resting-state electroencephalogram recordings of 92 autistic and 100 allistic children (6-17 years of age) and related it to behavioral assessments of autism traits and language ability. Our results revealed differential EI trajectories for the autistic compared to the allistic children. Moreover, the EI trajectories related to individual language ability in which elevated excitability in late childhood and early adolescence was linked to decreased listening comprehension. Our findings therefore show that the developmental trajectory of EI balance shares variance with autism trait development.

The use of language within context is pragmatics. Since, there is no tool to assess Hindi pragmatics among Indian children, the present research aimed to develop a task to asses the same. In phase one, naturalistic observation, expert interviews, and text analysis of Hindi storybooks were conducted to understand the use of pragmatics. In phase two, Hindi Pragmatic language story narration (HPSN) and Hindi Pragmatic language video tasks (HPVT 1.0 and 2.0) were constructed to assess pragmatics. These were refined to develop Kids pragmatics Hindi videos (KPHV), used in phase three to investigate age and gender differences, further relationship with theory of mind was also examined. Children became significantly better in pragmatics with age. A significant relationship between pragmatics and theory of mind was also found. No significant effect of gender on pragmatics was observed. The findings of the study are useful for development of rehabilitation programs for children with Social Pragmatic Disorder (SPD). Keywords: Linguistics; Psychology; Development; Language development; Theory of Mind; Field studies; Statistics.

Virtual Reality (VR) can offer insights into realistic human defensive behavior. In the present work, we sought to elucidate the interplay between feelings and actions in VR-simulated threatening scenarios. Participants (n = 30) encountered various animal threats in VR during a fruit collection task. We retrospectively assessed participants' feelings after each episode on several dimensions, namely valence, arousal, potency, surprise, and anxiety. As predictor variables, we included scenario characteristics, behavioral responses, and personality traits. Our results indicate that the primary determinants for subjective feelings except potency were ultimate survival, the availability of self-defense weapons, and the animals' behavior (attack or not). No strong determinants for potency could be found. Notably, participants' behavioral responses did not independently influence feelings reported later. These findings highlight VR's potential in expanding our understanding of subjective feelings in threatening situations. Our research suggests that behavior and feelings in defensive situations might not be closely linked.

According to Bayesian accounts, perception is the consequence of integrating sensory input with prior expectations, resulting in biased percepts attracted towards our expectations. Contrary to this logic, Phan et al. show that downward motion is perceived as less accelerating than upward motion: a repulsion from the expectation that downward-moving objects should accelerate. This is one of a small number of reported effects where perception is repulsed from expectation. The question then arises, what conditions result in repulsive effects, and why? Here we manipulated the expected acceleration profiles for context and object identity along the horizontal axis, asking whether we see repulsion effects similar to those observed by Phan et al. We find repulsion when expectations are related to the context in which a ball moves, but attraction when an association is made between the ball's colour and the acceleration profile. We discuss possible reasons and implications for the contradictory results.

It takes children considerable learning and development to accurately predict whether an object is safely balanced or will fall -- something that happens if its center of mass is not supported from below. In the meantime, children go through a characteristic set of mistaken beliefs. Here we use an adapted version of the classical balance task to evaluate whether different models go through the same stages. Preliminary results show that convolutional neural networks (CNNs) do learn the task but do not necessarily go through the same stages. We are also testing several simulation-based accounts. We anticipate completing this work in time for the conference. The findings will help clarify the space of possible accounts of children's acquisition of intuitions about gravity and balance.

Speech disfluency refers to the errors, pauses, or repetitions in speech production. Co-speech gestures are known to help resolve disfluency, suggesting a metacognitive involvement. Here we ask whether (1) disfluencies and gestures act as metacognitive cues in speech, and (2) they have different functions in conversational vs. non-conversational settings. Fifty participants responded to trivia questions, and rated their confidence in their answers (i.e. metacognitive judgement), either with a visible or a non-visible listener. They audibly elaborated on their answers during which we measured the frequency and type of disfluencies and co-speech gestures. We predict confidence ratings to change as a function of the rate of disfluency and the gestures produced by the participants. We also expect the rate of disfluencies and gestures change depending on the conversational setting. Our findings will contribute to understanding the multimodal nature of language and the role of metacognition in speech and gesture production.

Trust is an important factor in interaction with automated agents. This study tracks users' trust calibration to automated agents in a vocabulary learning task. We hypothesize that trust declines as agent reliability declines and that anthropomorphism should buffer against this decline. Replicating de Visser et al. (2016), 60 participants guessed the meaning of 96 foreign words in a 4x4x2 mixed experiment. In each trial, they guessed alone, then got an agent's recommendation and gave trust judgments, and made a final decision. Four pedagogical agents varying in anthropomorphism (within-subject: human, robot, smart speaker, computer) recommended answers with decreasing reliability (within-subject: 100%, 67.5%, 50%, 0%). Furthermore, participants either did or did not watch an introductory video about the agents (between-subject). Behavioral and judgment data were analysed via mixed-effects models and ANOVAs. Two-way interaction shows that trust declined differently in various agents, but there is little evidence supporting trust resilience in any agent.

Extensive research from both sensorimotor and perceptual domains has shown that people make decisions by combining prior and current information according to their relative uncertainties, following Bayesian statistics predictions. However, less is known about visual search, a task that requires people to determine the presence/absence of a search target (T) amongst distractors (Ls). Here, we examined decision-making behavior in a visual search task which manipulated the target prevalence rate (prior: 25% or 50%) and the portion of the display that was visible (sensory information: 0%, 30%, or 60%). Participants' (N=56) decision-making behavior qualitatively reflected Bayesian predictions, relying more on the information that was less uncertain. When no items were visible, participants were highly accurate in making present/absent decisions based on the prevalence rate learned through feedback. But when provided sensory information, participants' decision-making was more strongly influenced by visibility. Thus, reliance on sensory information may dominate priors in visual search.

The ability to manipulate and recognize abstract representations seems to be a fundamental aspect of human nature, existing since the dawn of our species and transcending cultural barriers. In contrast, non-human primates exhibit very limited proficiency in recognizing abstract representations. This research delves into this human singularity for visual abstraction, through neuroimaging experiments conducted in both humans and non-human primates. Stimuli presenting the same concept (e.g. a house or a face) but varying in abstraction levels (photos, drawings, symbols, and words) were initially presented to a monkey, while intracranial recording of his brain were obtained (16 Utah arrays distributed in V1, V4 and IT). Preliminary results indicate that monkey display early signs of abstraction, particularly for evolutionarily ancient categories such as faces. MEG and fMRI recordings of human subjects are also currently underway, striving to unveil the neuronal mechanisms that set our species apart in the domain of visual abstraction.

Program induction is an appealing model for human concept learning, but faces scaling challenges in searching the massive space of programs. We propose a computational model capturing two key aspects of human concept learning – our ability to judge how promising a vague, partial hypothesis is, and our ability to gradually refine these vague explanations of observations to precise ones. We represent hypotheses as probabilistic programs with randomness in place of unresolved programmatic structure. To model the evaluation of partial hypotheses, we implement a novel algorithm for efficiently computing the likelihood that a probabilistic program produces the observations. With this, we guide a search process whereby high-entropy, coarse programs are iteratively refined to introduce deterministic structure. Preliminary synthesis results on list manipulation and formal grammar learning tasks show improvements in sample efficiency when leveraging likelihood guidance, and a preliminary human study explores how model intermediate hypotheses compare to those of participants.

This study investigates metacognitive awareness among students, focusing particularly on 'subjective confidence' as a predictor of potential conceptual change. In our study, 132 eighth graders completed a basic number knowledge test and evaluated their confidence level for each answer. Our analysis revealed that metacognitive accuracy—the alignment of confidence levels with actual performance—was significantly related to academic achievement scores in the 'Two-Numbers Comparison' task (e.g., choosing the correct inequality such as '1/2 > 1/3' or '1/2 < 1/3'), but not in the 'Number Approximations' task (e.g., choosing the closest result to '21/10 + 60/31' from options such as 2, 4, 41, or 81). Additionally, we observed distinct behavioral patterns in response times: the 'Two-Numbers Comparison' task elicited rapid responses, whereas the 'Number Approximations' task resulted in slower, more reflective responses. In conclusion, our results indicate that metacognitive processes are more actively engaged during intuitive thinking compared to reflective thinking.

In Visual working memory (VWM), the top-down goal selectively maintains and recalls items, while, bottom-up attention induced by perceptually similar items prioritizes recalling these VWM items. In this study, we focussed on whether repeated items have facilitated access in VWM and can also act as task-irrelevant interference hindering recalling task-relevant not-repeated items. In this VWM-based EEG study, human participants (n = 25) responded to a probe for an item's presence or absence in a memory array containing repeated and not-repeated items. Significantly slower response times and poor accuracy were observed for probe matching for not-repeated items. Also, Event-related spectral perturbation analysis showed an increase in mid-frontal theta (4-7Hz) and parietal alpha power (8-12 Hz) demonstrating that default prioritized repeated items interfere with recalling items corresponding to the not-repeated probe matching. This study shows how default prioritized repeated items; a relational property of stimuli can interfere with recalling task-relevant VWM items.

Math affect (i.e., attitudes/beliefs about math) and feedback are predictors of mathematical performance. How these factors jointly influence cognition during mathematical problem-solving is less understood. A computer mouse-tracking task was used to assess math affect and computation ability of 78 undergraduate volunteers, before and after feedback (none; positive; negative). Positive affect toward math significantly predicted better accuracy on mathematical computations, but performance improved noticeably after positive feedback. This led to the question of whether or not feedback and affective components of math impact decision-making. Post-baseline, participants' ability to calculate the mathematical problems sped up significantly — evidence of a practice effect. Individuals with more negative attitudes toward math exhibited more indecision in their responses when they received feedback, whereas participants with more positive attitudes toward computation reduced their indecision after feedback. This suggests that feedback interacts with math affect in important ways, impacting in-the-moment cognitive processing during mathematical calculation.

At birth, the human brain is tuned to spoken language in general and to some extent also to native language in particular. In behavioral studies, infants also prefer to listen to infant-directed speech (IDS) to adults-directed speech (ADS), apparently most robustly in their native language. Recent studies demonstrated that this preference has correlates at the neural level as well. We test whether newborns show differential neural tracking of native over foreign, rhythmically different, language. We assess neural tracking of native and non-native speech in Czech-exposed newborns. Newborns' were played a children's story in two rhythmically different languages, Czech (lacking acoustic cues to word-level stress) and Russian (acoustically salient word-level stress), in IDS or ADS, while their EEG was recorded. We predicted stronger neural tracking of the native Czech, evident in larger inter-trial phase coherence (ITC), and total power. Preliminary data (n = 27 out of planned 60) suggest this language-specific effect is most prominent in the theta band corresponding to the syllable rate. We will further test whether this native-language effect would be more prominent in ADS or IDS. Data collection is underway and the results will be presented & discussed at the conference.

Humans can learn from observing diverse others, even when they know little about their exact preferences, skills, or goals. Yet, while our remarkable social learning abilities have been a popular research topic, prior work has generally been limited to tasks in which observer and demonstrator share the same value function. To address this discrepancy, we use the socially correlated bandit task, where participants explore positively correlated, rather than identical, environments in groups. We extend existing work using this paradigm by comparing behaviour across individual and social rounds within participants. We replicate findings that humans are able to use correlated social information effectively, with behaviour being best described by a model noisily integrates social information. In comparing individual and social search behaviour, we find that social learning partially replaces directed exploration. In conclusion, we find that humans use social information flexibly, employing it as an exploration tool, despite our differences.

Linguists propose the existence of linguistic trees and define the merge operation to construct complex sentences from simpler elements. Previous neuroimaging studies, primarily utilizing 3T scanners, have identified an extensive fronto-temporal network involved in forming linguistic structures and executing merge operations. Intracranial recordings in these areas reveal a more distributed picture, with adjacent regions undertaking diverse linguistic tasks. We designed a 7T fMRI visual task to investigate the neural coding of syntactic operations. In healthy French-speaking participants, we initially identified the language network using a localizer. Subsequently, we employed short 3-word stimuli, presented briefly (200ms), to explore the response profiles within the language network. These stimuli included control conditions, affirmative statements, and interrogative sentences, all matched for letter and character count. Preliminary results indicate that 200ms is sufficient to differentiate between sentences and non-sentences, and suggest a finely-tuned specialization for syntactic operations within language network subregions.

Unveiling the Path to Phonological Anticipation: Insights from Infants' Eye Movements Phonological anticipation, predicting upcoming words based on phonological cues, is crucial in language processing (Brunellière et al., 2018; Ito et al., 2018). While infants show predictive abilities in language domains, mechanisms and developmental trajectories in native Spanish-speaking populations are less explored. This study investigates phonological anticipation in Mexican Spanish-speaking infants using eye-tracking. It examines if infants of different ages can anticipate phonologically related words in semantically restrictive sentences. Auditory sentences with restrictive contexts were presented, and visual stimuli included phonologically related and unrelated competitors. Participants were 18, 24, and 30-month-old infants. Results show 18- and 24-month-olds didn't anticipate based on semantics alone, requiring auditory presentation. However, 30-month-olds demonstrated phonological anticipation, signaling developmental changes. Understanding this trajectory is vital for comprehending language processing. This study contributes insights into the emergence and maturation of phonological anticipation, impacting language acquisition theories.

The impact of visual attention on choice processes has been established over the last decades. Several studies are consistent with the view that visual attention increases the subjective value of the attended option. However, a few computational models have been proposed to investigate how attention and subjective values interact in multi-attribute choices. Moreover, these models disagree in terms of whether value is modulated by attention additively or multiplicatively. The additive theory states that the boost up subjective value depends only on gaze duration, and gaze on an option magnifies the subjective value at a constant rate. On the other hand, the multiplicative theory assumes that the magnitude of the attention-driven boost is value-dependent, and gazing at a high-value option yields a more significant boost in subjective value. Although there is a long debate on these two theories, recent studies have shown that both additive and multiplicative interactions between subjective value and gaze time may be essential for explaining empirical data and have suggested hybrid theories. For multi-attribute decisions, however, extant attentional models only consider the multiplicative interaction. This work introduces a new computational framework to account for attention in multi-attribute decisions. Our model assumes a hybrid attentional mechanism for the interaction between subjective values and gaze duration. We have tested the model on four datasets from various domains (e.g., clothing/brand, food/nutrition, food bundle, and money risk tasks). The results from the nested model comparison show that the proposed hybrid model works better than the other computational models.

Code-switching, that is, the alternation between different languages in a single utterance, provides a unique window into language control mechanisms. Prior studies suggest that bilinguals upregulate their cognitive control when reading sentences that start in one language and end in another (e.g., Adler et al. 2020; Bosma & Pablos, 2020). The current project investigates whether more common types of code-switches and different modalities engage cognitive control differently. We had early Spanish-English bilinguals listen to (Experiments 1, 2, 4), or read (Experiment 3) sentences that were in Spanish only, or included dense or insertional switches to English. After each sentence participants responded to a Flanker trial. In contrast to prior findings, we either found no effect (Exp. 1), or a larger Flanker conflict effect after a switch vs. a unilingual sentence (Exp. 2 - 4). We therefore have no evidence that processing common types of code-switches upregulates cognitive control.

People's color-concept associations influence many processes underlying visual cognition from object recognition to information visualization interpretation. Thus, a key goal in cognitive science is developing efficient methods for estimating color-concept association distributions over color space to model these processes. Here, we investigated the ability of GPT-4, a multimodal large language model, to estimate human-like color-concept associations. We collected human association ratings between 70 concepts spanning abstractness and 71 colors spanning perceptual color space and compared these ratings to analogous ratings from GPT-4, when it was given concepts as words and colors as hexadecimal codes. GPT-4 ratings were correlated with human ratings, comparably to state-of-the-art image-based methods. Variation in human-GPT rating correlations across concepts was predicted by concept abstractness, but this effect was superseded by specificity (peakiness; inverse entropy) of color-concept association distributions. Our results highlight the viability of using model-generated color-concept association ratings to better understand human color semantics.

The ability to flexibly interpret signals in context is at the core of human communication, as even the most conventionalized linguistic signals are necessarily ambiguous and subject to inter-individual variability. We introduce a novel communication game (the Pizzini game) requiring pairs of participants to exchange linguistic signals that are successfully interpreted by using contextual information freshly generated by each pair. By allowing this common ground between once-strangers to be developed interactively in the lab, we are able to characterize the pair-specific contextual information available to participants when inferring intended meanings. We present preliminary data testing the predictions that (1) interactants align on an abstracted conceptual representation of a set of stimuli during the context-building portion of the task and (2) that the characteristics of this pair-specific conceptual representation predict the dynamics of how participants later resolve context-specific references to the same stimuli.

Understanding and modulating cognitive aspects of decision-making and reinforcement learning are crucial for addressing neuropsychiatric problems like substance use disorders (SUD). We developed a non-invasive stimulation method to modulate theta phase synchronization between the medial prefrontal cortex and right lateral prefrontal cortex. Our EEG-informed modulation led to bidirectional changes in learning-based decision-making, including error-related components and brain signatures. In fact, by combining HD-tACS with mathematical modeling, we revealed that in-phase/antiphase HD-tACS over the mPFC and rPFC significantly altered (synchronized/desynchronized) theta phase coupling between these regions, influencing decision accuracy (improved/impaired), and neurocomputational parameters of learning-based decision-making. Additionally, this modulation rescued/disrupted the causal link between brain error monitoring and cognitive control systems in healthy/SUD participants, and reshaped punishment-guided decision and learning components. We concluded theta rhythms in the mPFC and mPFC-rPFC coupling play a unifying causal role in regulating choice, learning, and behavioral adaptation in both healthy and patient populations.

Despite the widespread adoption of public-facing large language models (LLMs) over the past several months, we still know little about the complexities of machine-generated language in comparison to human-generated language. To better understand how lexical complexity differs between human- and LLM-produced texts, we elicited responses from four commercially-available LLMs (ChatGPT 3.5, ChatGPT 4.0, Claude, and Bard), and compared them to writing from humans from different backgrounds (i.e., L1 and L2 English users) and education levels. We also investigated whether the LLMs demonstrated consistent style across targeted prompts, as compared to the human participants. Through an analysis of six dimensions of lexical diversity (volume, abundance, variety-repetition, evenness, disparity, dispersion), preliminary results suggest that LLM-generated text differs from human-generated with regards to lexical diversity, and texts created by LLMs demonstrate less variation than human-written text. We will discuss the implications of these differences for future research and education in applied linguistics.

Emotional physiological responses are altered not only by external events, but also by the emotions of the people in front of us. While these interpersonal emotional linkages are considered an important aspect of empathy, it is unclear how they relate to cognitive empathy, that is, how we perceive others' emotions. We investigated the relationship between emotional linkage and emotional cognition in an experiment in which two participants estimate each other's emotions while their heart rates were measured during a thrilling joint task using a block game. We also collected data from the two observers because, in reality, in addition to understanding the emotions of the interacting partner, it is sometimes necessary to understand the emotions of a non-interacting person from a third-person perspective. The results suggest that, for game players, their own heart rate is related to perceived partner emotion, and for observers, the degree of heart rate synchrony between observers is related to perceived player emotion. Capturing emotional cognition in a joint task requires consideration of both individual emotion and interpersonal emotional linkages.

This study examined how vowel length in words affects the gustatory imageries (i.e., sweetness, saltiness, bitterness, sourness, and spiciness). We presented pseudowords with long and short vowels to native Japanese speakers using different modalities and instructions. The stimuli were presented visually (Studies 1, 3, and 4) or auditorily (Study 2). In addition, half of participants in Study 3 were instructed to subvocalize the stimuli and the other half were instructed not to subvocalize. Words with long vowels were associated with sweetness when presented in katakana characters (Studies 1 and 3). Words with short vowels were associated with saltiness and bitterness when presented in katakana characters (Study 3). Our findings revealed a role of vowel length in taste-sound correspondences in Japanese. It advances the understanding of how people obtain information about the taste expectations from word forms.

This study investigated the effects of consonant alliteration and meter on valence, arousal, and aesthetic evaluations. In Study 1, native Japanese speakers evaluated valence, arousal, beauty, and understandability of classical Japanese poems after listening to both alliterated and non-alliterated versions. The alliterated poems were rated as slightly calmer than the non-alliterated ones, although the difference was not statistically significant. In Study 2, native Japanese speakers listened to poems that consisted of pseudowords. The poems used as stimuli were systematically made in terms of alliteration and meter. The metered poems were perceived as more preferable, calmer, and more beautiful than the non-metered ones, regardless of the presence or absence of alliteration. Additionally, the alliterated and metered poems were perceived as more exciting than non-alliterated and metered poems. These results suggest that metered poems make people feel beautiful and comfortable. It might be applicable to clinical treatment.

The use of controlled processes to resolve cognitive conflict can have various effects on performance in memory tasks. There are two hypotheses in this regard. On one hand, the use of controlled processes required to resolve cognitive conflict may impair a deep stimulus encoding, and consequently its recall. Otherwise, it would favour the encoding and subsequent memory of the stimuli involved in it. The objective of the study is both to investigate conflict effects (i.e., stimulus and response level conflict) on memory performance and the role of encoding level in modulating that effect using different paradigms (e.g., the flanker, and task switching paradigm). The preliminary results show that conflict effects seem to be independent by the level of stimulus processing. Therefore, task-switching paradigm seems to nullify both stimulus and response-level conflict effects on memory performance. Otherwise, Flanker paradigm seems to be useful to highlight conflict effects on memory.

Studying physical rotation (i.e., rotation tasks during which figures can be physically rotated, such as through gestures) can offer insights also into problem solving processes at work during mental rotation. We present a novel method for behavioral pattern analysis which we applied to data from 2,999 physical rotation tasks gathered in-class from 50 secondary school students. The method uses normalized, resampled, time-dependent data on angular offsets between figures over time and agglomerative, correlation-based clustering. Each cluster represents a distinct behavioral pattern and its respective prototype a problem solving strategy. Results indicate that multiple strategies were employed: The dominant strategy matches the classical model of mental rotation, in which angular offsets between figures are decreased over time. For the secondary strategy, angular offsets were actually increased. A subsequent analysis shows that the secondary strategy was more frequently used for symmetric figures, possibly indicating problems with correctly matching segments across figures.

Results from both cross-language priming and code-switching studies suggest that syntax is shared between languages in a bilingual's language system. However, it is not clear how these bilingual language phenomena interact. We tested whether, under an implicit learning account, code-switching in the prime increases syntax sharing, leading to stronger cross-language priming. We conducted four simulated Spanish to English structural priming experiments using the Bilingual Dual-path model. The primes either had an English (code-switched) determiner and noun or noun only, at the beginning or end of the sentence, or were entirely in Spanish. Mixed effects analyses only revealed a significant positive interaction between code-switch condition and priming, indicating stronger priming, with a code-switched English noun phrase at the very beginning of the sentence, but non-significant interactions otherwise. These results provide further support for the idea that code-switching and cross-language structural priming can be interpreted as evidence for shared syntactic representations bilinguals.

How does experiencing a rare event, like a car accident or a lottery win, influence decision-makers consecutive decisions? Studying these so-called recency effects holds a long tradition in research on experience-based decision-making. Previous work suggests opposite behavioral patterns after experiencing a positive rare event: People have been found to be more likely to either repeat their choice (positive recency) or to avoid it (negative recency). The effect is thought to persevere for multiple choices and decrease over time. In this study, we provide new insights into recency effects by analyzing people's repeated choices from an extensive database—consisting of 3 million choices by 8,000 participants across 12 different decision-from-experience paradigms collected from 139 studies. We provide a conceptual framework clarifying patterns of positive and negative recency, including how the direction and magnitude of impact change over time.

Understanding and predicting the relevant risky choices of modern life is a key goal of the behavioral sciences and cognitive decision research specifically. However, do researchers study those choices that people actually face in their lives, or choices that at least capture the same cognitive processes? To address these open questions, we compare 214 risky choices from three perspectives (research, layperson, life outcomes) and use semantic embeddings extracted from a LLM to assess the similarity of choices between perspectives. Furthermore, by means of a Bayesian mixed effects model we examine the potential overlaps and gaps between the three perspectives regarding which cognitive mechanisms may be at play when people make the various choices. Our research informs theories of risk taking by revealing discrepancies of behavioral research with real-life choices, both regarding the choices that are considered timely as well as the cognitive underpinnings that influence these choices.

Cognitive search processes are generally affected by the number of available items. We investigated if this also applies to long-term memory retrieval. Specifically, we explored the effect of set size on retrieval times of cued memories from long-term memory. Participants learned lists of word pairs that varied in the number and the semantic similarity of the pairs. An increase in set size resulted in slower retrieval times, indicating the influence of set size on memory retrieval efficiency. However, participants were faster in retrieving more semantically similar word pairs. These findings are consistent with a search-based model of retrieval, illustrating its sensitivity to the number of memory candidates, while highlighting the role of the quality of the cue in optimizing search performance. Furthermore, we established the validity of using similarity values based on Word2Vec embeddings by showing a high correlation with human similarity ratings and similar model results.

This study assesses how partially sleep-deprived individuals learn regularities in a predictable yet uncertain environment and evaluates the impact of their expectations on inhibitory control performance. Participants were randomly assigned to undergo either an 8-hour (well-rested, WR, n=36) or a 4-hour (sleep-restriction, SR, n=32) sleep period before performing a Go/No-Go task in which we systematically varied the proportions of Go and No-Go trials (20%-80%, 80%-20%, 50%-50%). Preliminary results showed faster reaction times with increasing "Go" probability for both groups. The WR group showed a growing Go-Probability effect over time, unlike the SR group, suggesting potential differences in the underlying learning styles (e.g., meta- learning). As for accuracy, commission errors were more frequent as the probability of “Go” increased, irrespective of the group. To delve further into the effects of sleep deprivation on learning, a Bayesian model for individual learning under uncertainty will be implemented.

Languages differ in what meanings their lexical items encode: The meaning covered by English 'blue' is famously split into 'sinij' (darkblue) and 'goluboj' (lightblue) in Russian. Recent years have seen novel interest in functional explanations of such variation, pointing to a correlation between greater communicative need of a lexical field and a finer-grained lexical inventory. Here, I develop the position that rather than the mere difference in “need” to mention lexical field, it is the field's discourse-pragmatic diversity that predicts whether languages “lump” or “split” more. I will demonstrate this with computational techniques and a typologically diverse corpus of spontaneous spoken data from 51 languages (DoReCo), first for the field of verbs of visual perception ('see'-'look'), then on a lexicon-wide level. There are implications: our notions of what a comparable concept is in lexical semantics, what lexical knowledge entails, and the dimensions along which languages differ require re-examining.

The generation effect, demonstrating improved memory performance through self-generating information, was explored in this study. Participants engaged in semantic and perceptual generation tasks, where semantic tasks involved meaning-related associations, and perceptual tasks focused on surface characteristics. While previous studies separately examined these tasks, our project directly compared their impact. Experiment 1 revealed higher recognition performance for semantic generation over perceptual generation, with no significant difference in recognition across perceptual and semantic reading conditions. Experiment 2 incorporated judgments-of-learning (JOL) and no-JOL groups, demonstrating that participants accurately predicted and performed better on memory tasks involving generation and semantic manipulations. Additionally, JOL-group participants outperformed the no-JOL group, suggesting that predicting one's memory performance enhances actual memory performance. Experiment 3 aimed to see the effects of the match between encoding and retrieval. The results showed that the JOL group outperformed the no-JOL group, and this effect was observable through semantically meaningful testing.

Human ecological success stems from our ability to absorb and build upon cultural knowledge, a process we aim to model computationally by integrating individual and cultural learning from language — one of the main vehicles of cultural transmission (e.g. instructions, explanations, stories). In simple video games, our model infers game rules from both interaction data (individual learning) and partial causal models extracted from game descriptions (cultural learning). Given exhaustive descriptions (either hand-written or generated by a model given access to oracle data), models leveraging the two learning sources induce more accurate game rules from limited data than both the individual- and cultural-only controls. Interestingly, descriptions from human game players do not consistently yield better rule induction. We hypothesize that players may preferentially communicate information that will be essential to the others' future decision-making and we aim to investigate cultural transmission by integrating individual and cultural learning with both causal understanding and decision-making.

A suppression of the cortical beta rhythm is a ubiquitous neural correlate of action observation. However, it remains unclear to which extent low-level action detection and higher-order recognition of actions' kinematics and goals contribute to beta suppression. Here, 24 participants, equipped with EEG, watched videos of kinematically natural goal-intact (Normal), kinematically unnatural goal-intact (How), and kinematically natural goal-violating (What) actions. We investigated the beta suppression at the time of action onset and at the time of action recognition. Across conditions, the beta rhythm was suppressed at action onset above both hemispheres, and no further change in the already suppressed beta rhythm was observed at the time of action recognition. Furthermore, beta suppression did not differ between Normal, How, and What videos. In conclusion, beta suppression is an ubiquitous characteristic of action observation but does not seem to be sensitive to the higher-order characteristics of observed action.

Quotational constructions as in 'This phenomenon is called “moonbow”' involve predicates like call and are used to introduce a lexicalized word, i.e. “moonbow”, to the addressee. The name of a lexicalized concept is mentioned which is why we refer to this type of sentential construction as a name-mentioning construction (NMC). Although there is substantial philosophical research on the notion of quotation, empirical evidence is sparse. In our empirical investigation, we use eye-tracking data to look into the nature of the processing of quotation marks in NMCs. The results of Linear Mixed Models indicate that there is no statistically significant difference for early eye-tracking measures, but a significant effect for the expression in the target Interest Area Dwell Time. Words enclosed in quotation marks are processed longer than target words without quotes. We argue that our findings suggest the involvement of higher cognitive processes in the processing of quotes.

Throughout evolutionary history, and in everyday lives, it has been a crucial task to identify good and reliable cooperation partners. A good way of assessing potential partners' quality and willingness is to engage in conversation with them. We investigated if non-verbal behaviours during such conversations can be reliable indicators of interactants' cooperativeness – in contrast to the semantic content of utterances that can be easily faked. Specifically, we predicted that interactants who align in their use of non-verbal behaviours would also act more cooperatively in other tasks beyond the conversation. To test this, we analyzed gestures in the British TV game show Golden Balls, where contestants discussed and faced a game-theoretic decision to split or steal a monetary prize. Results suggest that individuals choosing to split indeed align their non-verbal behaviours more than those choosing to steal. This implies that subtle movements can serve as reliable indicators of trustworthy cooperation partners.

Background: Research on adults with ADHD has recently identified, in addition to cognitive-executive difficulties, significant impairments in emotion regulation. Objective: This study aimed to assess the efficiency of emotion regulation in adults with ADHD using three strategies: observe, reappraise, and suppress. Method: Adults with ADHD (n = 68) and without ADHD (n = 69) were exposed to neutral or negative IAPS image sets and reported their emotions while employing these strategies. Results: The ADHD group displayed significant emotion dysregulation, depressive symptoms, and anxiety compared to the non-ADHD group. Suppression of negative emotions was shown to be the mechanism by which the ADHD group achieved greater suppression efficiency, although both suppression and reappraisal were equally utilized as regulatory strategies. Conclusion: These results highlight the efficiency of suppression in controlling negative emotions in the ADHD population, while also suggesting potential for effective training in reappraisal.

The CONTROL IS UP metaphor is an embodied cognitive mechanism that helps western speakers reason about power relations in terms of vertical spatial organization, This paper explores its multimodal elaboration in visual manifestations of pyramidal structured arrangements and kinetic practices such as genuflexion in medieval miniatures in order to (i) demonstrate the role of multimodal representations of metaphors in reasoning, (ii) unveil power relationships between different societal strata. 34 miniatures (12th ct. Liber feudorum maior) were analised with the “multimodal genuflexion test” to describe and measure (CAD-software) distances between kinetic elements (facial/manual gestures, postures). Results indicate that (i) power relations are not just vertically represented (kneeling & bowing), physical distance between characters and position of hands are crucial, (ii) there is a significant positive correlation [U = 61,119; p = 0.003] between the power figure and the degree of “body bending” (bowing, kneeling) and hand distance.

Causality is a shared general experience, but languages differ in the way they encode it. This research explores the possible correlation between society type, language and causal attribution in the way Spanish speakers think and judge causality. 202 native speakers of European and American Spanish participated in three different studies: (i) an adaptation of Singelis' (1994) psychological questionnaire for social in(ter)dependency; (ii) a non-verbal categorisation task for the attribution of causal responsibility; and (iii) a multimodal description task for causal events. Data were elicited with a set of 58 causal videoclips from the CAL project (NSF,BCS-1535846). Results show that all Spanish speakers, regardless of their Western (Spain) or Eastern (Latin America) backgrounds, categorise and linguistically describe causality based on the degree of the action's intentionality. A strong correlation between language and causal categorisation was found, supporting the idea that language is a determining factor in the causal attribution.

This study explores the influence of social contexts on the efficiency of information search in children (6-14 years), adolescents (15-17 years), and adults. Participants are placed on a team for a competition. When the championship trophy goes missing, the participant's team has either won or lost. Participants are then tasked with playing a 20-Questions game to try to find the trophy. Beyond the developmental trajectory in their ability to select the most informative questions, we found, as hypothesized, that all participants actively biased their search strategies: the efficiency of their questions was contingent upon whether it was in their best interest to find the culprit. In particular, they were more likely to select the most efficient question when they were winning and were more motivated to identify the target. Overall, our findings suggest that social contexts play a strong role in modulating the efficiency of information search across age groups.

Outrage has often been interpreted as a shorthand for “moral outrage,” anger upon a moral standard being violated (Batson et al., 2007). We ask whether a violation of a moral standard is necessary for producing outrage or whether other variables can also produce it. By presenting participants with a series of potentially outrage-inducing scenarios and measuring their emotional responses, we seek to identify the predictors of outrage. We find that anger and disgust are the strongest predictors of level of outrage compared to sense of threat, level of surprise, level of uncomfortableness, severity of the moral violation, and how much one values the moral being violated. Mediation analyses suggest that moral violations do not mediate the effects of anger and disgust on outrage. However, anger and disgust do mediate the effect of moral violations on outrage. Our findings suggest that moral violations elicit anger and disgust, which in turn produce feelings of outrage.

While humans have the remarkable ability to learn concepts from few examples, machine learning algorithms oftentimes require complex architectures that struggle to learn from minimal data. We introduce a simple computational framework for one-shot learning to encode higher-level representations of visual concepts using Gaussian Mixture Models (GMMs). Distinct topological subparts of concepts are represented as inferred Gaussian components, which can generate abstracted subparts to build robust prototypes for each concept. Our framework addresses both one-shot classification tasks through a similarity metric inspired by Tverksy's (1977) contrast model, as well as one-shot generative tasks through a novel pipeline employing variational autoencoders (VAEs) to generate new class variants. Our approach yields impressive classification accuracy while also performing a breadth of conceptual tasks that most approaches do not even attempt. Results from human judges reveal that our generative pipeline produces novel classes of visual concepts broadly indistinguishable from those made by humans.

Creative thinking is associated with improved academic performance, social proficiency, problem-solving skills, and emotional wellbeing in children. Here, we explore the potential of ChatGPT, a language model developed by OpenAI, as an avenue for fostering creativity in children through prompting new ideas and ways of thinking. Six- to 11-year-old children's (N=140) performance on the Alternative Uses Test (AUT) was measured before and after completing one of three possible activities: (i) hearing single-word AI-generated uses for three objects, (ii) hearing sentence-long AI-generated uses for the three objects, or (iii) drawing a picture containing the three objects. Blind coding of children's own AUT responses (for different objects) before and after these activities suggested that children showed greater improvements in creativity in the two AI conditions (M=.55, SE=.14) than in the drawing condition (M=.04, SE=.15), F=4.17, p=.018. Our results provide initial support for ChatGPT as a useful tool for promoting children's creative thinking.

Utilizing a large-scale human mobility dataset, this study explores the influence of income inequality on status-seeking behaviour. Existing research suggests that income disparity, typically measured using the Gini coefficient, leads to increased status enhancement tendencies. Our study advocates the use of alternative multi-parameter metrics that capture inequality concentrated within specific income distribution segments. The findings of this analysis, based on foot traffic information from approximately 24,000 clothing stores, suggest that income inequality at both the lower and top ends of the income distribution promotes people's status-seeking behaviour, with lower-concentrated inequality exhibiting a larger effect. Furthermore, our data reveal a negative correlation between visits to “high-status” brands and an important element of social capital – civic engagement, indicating community participation could potentially counterbalance the need for status enhancement through consumption. Thus, this research provides a nuanced lens on the complex dynamics between income inequality, status-seeking behaviour, and social capital.

The study investigated the temporal dynamics of the sensorimotor integration between the noun and the adjective. Forty-two participants categorized an object noun as natural or artifact performing a precision or a power reach-to-grasp response. Responses were compatible or incompatible with the grip typically used to manipulate the object denoted by the noun presented on the screen for 250ms. After three different SOAs (0ms, 200ms, or 500ms) an adjective replaced the noun (250ms). The adjective could indicate a positive (e.g., round) or a negative (e.g., sharp) object property. Reaction times revealed that the SOAs modulated the grasp-compatibility effect (incompatible–compatible conditions). At 0ms of SOA, a standard compatibility effect emerged with positive adjectives, while negative adjectives reversed the effect. No modulatory effects were detected at 200 and 500ms. The present results provide first evidence about the temporal dynamics of sensorimotor integration process between these two classes of words.

Numbers are not only used for quantification (cardinals), but also for sequencing (ordinals), and identifying entities (nominals). For example, the sentence ‚ÄúPlayer number 23 took 2nd place by scoring 3 goals‚Äù features nominal, ordinal, and cardinal uses of numbers, in that order. Claims about the relative prevalence of these uses (Wiese, 2004, Niederer 2005) have never been tested. We present the first large-scale analysis of 3,600 numbers in context, showing that cardinal uses are dominant (83.4%), followed by ordinals (11.8%), and then nominals (4.8%). Round numbers, which are associated with approximation, dominate for cardinals (76.4%) but not ordinals (31.1%) or nominals (23.3%). The prevalence of round numbers increases with magnitude only for the cardinals. We discuss implications for the logarithmic scaling of the mental number line (Dehaene & Mehler, 1992), the approximate number system (e.g., Rinaldi & Marelli, 2020), and children's acquisition of number concepts (e.g., Colomé & Noël, 2012).

Artificial Neural Networks can be used to build a general theory of intelligent systems, connecting the computational, algorithmic and implementational levels. I analyze the generalization of learning in simple but challenging problems as a way to build the theory. I report simulations of learning and generalizing sameness, using Simple Recurrent Networks (SRN), Long-Short Term Memories (LSTM) and Transformers. We show that even when minimal requirements to implement sameness in SRNs are met, and a SRN network that can compute sameness theoretically exists, we failed to obtain it by training with backpropagation using all the possible input pairs. LSTMs come close to learn sameness, but the best networks require an inordinate amount of examples and the enrichment of the sample with positive examples. The same happens with Transformers. A similar task applied to ChatGPT revealed related problems. We discuss what this implies for Cognitive and Neural Sciences.

Studies have shown that readers who do not always experience an inner reading voice (less-IRV readers) move their eyes more freely and do more efficient silent reading than those who always experience IRV (full-IRV readers). This conclusion suggests that less-IRV readers may not be suited for studying with vocalization. In this study, forty students were assigned to full- and less-IRV reading groups. The main task in the experiment was to read short stories and answer comprehension tests. The reading materials comprised 12 stories, the same as those used by Morita and Takahashi (2019). Participants read them with audio assistance and answered three comprehension tests after reading each story. While reading the stories, the readers' eye movements were recorded. The results of the eye-movement index showed no difference in eye movement patterns (fixation, fixation time, saccade size, regression) and comprehension between the two kinds of readers. We found no relationship between inner reading voice styles and eye movements in audio-assisted reading.

Observational learning has been studied using the serial reaction time task (SRTT) reporting inconsistent findings on its nature. When present, observational learning appears to be due to explicit learning, even for complex second-order sequences (SOC). In contrast, statistical learning has been studied using the rapid serial visual presentation (RSVP) reporting implicit observational learning of simple sequences. We combined elements of the SRTT and RSVP to investigate whether observational learning of SOC can occur. Two groups were exposed to either a repeated or a random sequence in RSVP. A completion and a recognition tasks were performed as a measure of explicit learning, and an SRTT as a measure of implicit learning. Although results showed no difference between groups in the SRTT, the early learning index predicted the recovery from interference exclusively in the experimental group, which also showed a greater awareness of the repetitiveness of the sequence.

Mice are increasingly used to study the neural circuitlevel basis of behavior, often with the ultimate goal to extrapolate these insights to humans. To generalize insights about neural functioning between species, it is crucial to first ensure correspondence in behavioral and cognitive strategy. Here, we analyzed decision-making behavior in both humans and mice, and identified the same cognitive strategy of history-dependent evidence accumulation. Specifically, individual differences in choice repetition were explained by a history dependent bias in the rate of evidence accumulation – rather than its starting point. Evidence integration over multiple temporal scales thus reflects a fundamental aspect of decision-making, conserved across mammalian species. These findings set the stage for linking the computations of decision-making to neural dynamics at the single-cell and population levels.

A considerable number of non-central nervous system (non-CNS) cancer survivors face long-term cognitive impairments after successful treatment, which affects various domains of cognition. Two tests used to measure working memory and attention are the digit span forward and digit span backwards, which were computerized to assess cognitive deficits in cancer survivors. We aim to investigate which cognitive processes are impaired in cancer survivors, by separating the various processes measured in the digit span tests. To this end, we formulate a hierarchical Bayesian cognitive process model which uses raw input data from the digit span and identifies metrics of working memory capacity, attentional control, and executive control. We compare these outcomes between non-CNS cancer survivors and healthy controls, to better localize which processes are affected by cancer and its treatment. Formal modeling allows for the extraction of more precise information in describing the cognitive deficits faced by patients.

Sequential information is encoded through various systems, among which, chunking, rule recognition and nested tree structures. However, the computational and neural mechanisms connecting these systems remain largely unknown. Dehaene et al. (2022) propose that humans possess internal languages governed by symbolic rules, coined Language of Thought (LoT). Based on this assumption we developed the Language of Thought (LoT) algorithm, which processes sequences and produces descriptions as minimal programs. In an online experiment, participants reproduced spatial sequences. Structured sequences, defined by temporal regularities, were notably better reproduced than controls for temporal structure. Participants demonstrated the ability to compress structured sequences in working memory. Response times and performance suggested chunking around a repetition rule. Further analysis, suggested hierarchical organization of those chunks, following a syntactic rule - recursive repetition. LoT-complexity, equal to minimal description length (MDL) of the sequence in our LoT, outperformed other information theory models, aligning best with the data.

To behave efficiently, individuals must decide when to exert cognitive effort by weighing its benefits and costs. While adults often make such economical choices, less is known about how these decisions develop. Here, we tested whether children and adolescents (N=150, 10-20 years) also learn about the value of cognitive effort during a task-switching experiment manipulating the reward benefits (higher vs. lower incentives) and difficulty costs (easy vs. hard conditions) of engaging cognitive effort. Mixed-effects modeling analyses examining the influences of age, learning over time, and the reward and difficulty manipulations on task-switching performance revealed that accuracy improved significantly more rapidly for higher than lower incentives with increasing age, especially during the beginning and middle of learning. Meanwhile, accuracy improved marginally more rapidly for the easy than hard condition with increasing age. Together, these results suggest that reward and difficulty information distinctly guide cognitive effort across time and age.

Cognitive scientists attribute representations to complex systems in order to explain their behavior. The shocking facility with which Large Language Models (LLMs) perform difficult linguistic and non-linguistic tasks has generated an increasing amount of speculation concerning what sorts of internal representations might underlie this behavior (whether personal, sub-personal, and of which kinds) and what properties such representations might have (for instance, whether they are grounded). This paper aims to elaborate and defend a conservative explanatory methodology, based on analyses of particular LLM behaviors, according to which attribution of sub-personal representations is key to explaining model performance which is robust, systematic, and flexible, especially in zero-shot settings, and that behavioral benchmarking alone is insufficient to resolve questions about representation due to the mutual underdetermination of performance and competence. The resulting view should help frame future explanations of LLM behavior, and provide an empirically grounded alternative to mere a priori speculation.

Individuals make biased and variable probability judgements. Recent models such as the Bayesian Sampler (Zhu, et al., 2020), Probability Theory Plus Noise (Costello & Watts, 2014), and the Quantum Sequential Sampler (Huang et al., 2023) capture a wide range of effects by assuming people are biased towards indifference (i.e., 0.5). However, in some experiments participants instead showed asymmetric bias, defined as a pull toward non-0.5 values. We investigated asymmetric bias in 5 experiments, where participants judged the probabilities of dice rolls. While participants' judgements were independent of whether they were in a high or low probability environment or the number of alternative options displayed, participants showed a bias toward low (<0.5) estimates. Furthermore, participants showed the highest variability for judgements below 0.5. This latter effect can be captured by an asymmetric prior in the Bayesian Sampler, but not by the biasing mechanisms in the other models.

People's mental representations of complex stimuli, such as images of facial affect, are difficult to elicit. To address this challenge, methods such as Markov Chain Monte Carlo with People (MCMCP), integrate human agents into computer-based sampling algorithms. However, such methods suffer from slow convergence, making them impractical for recovering the representations of individuals. Here, we extended MCMCP by introducing an adapted Variational Auto-Encoder (VAE) with domain knowledge as an auxiliary agent, guiding the sampling process away from less useful experimental trials. To test this approach, we ran a new experiment comparing such a VAE-guided MCMCP against baseline MCMCP in terms of convergence speed and quality of recovering human representations of facial affect. Preliminary results demonstrated that most guided chains converged on an individual's facial affect representation within a single experimental session, faster than the baseline methods, and results showed the extent of individual differences in facial affect representations. Thus, VAE-guided MCMCP provides a promising framework for interfacing machine intelligence with psychological experiments to enhance our understanding of human cognition.

As children age, the ability to make decisions about perceptual information improves in terms of both speed and accuracy. However, understanding the delicate changes within both the decision-making process and the ability to optimize the trade-off between speed and accuracy with age remains a challenge. This study employed the diffusion decision model to investigate age-related developments in perceptual decision-making. Additionally, the impact of practice and end-of-block feedback on achieving optimal decision-making was investigated. We gathered behavioral data from 299 children aged 6 to 12 and 50 adults while they performed a motion discrimination task. Adults and older children had narrower decision criteria, higher drift rates, and shorter non-decision times compared to younger children. Furthermore, individuals tended to approach the optimal policy as they aged, and for both children and adults, practicing and receiving detailed feedback could speed up the attainment of the optimal policy.

Working memory (WM) is an essential component of cognition, believed to be involved in several cognitive processes. Persistent neural activity (PNA) during WM maintenance has been widely reported. In this study we tested whether stimulus-selectivity constited a predictor of increased PNA during WM maintenance. We performed single-cell recordings on medial temporal lobe (MTL) neurons and measured PNA during encoding and maintenance. We identified image-selective neurons, based on the observed firing rate (FR) elicited by exposure to different images. We compared the FR of such neurons during encoding and maintenance when the maintaining the prefered image in WM with the FR for maintenance of a non-preferred image. We observed PNA for both conditions, and measured a higher FR during maintenance of the preferred image. In alignment with the existing literature, the results of our analysis suggest that stimulus-selectivity is a potential predictor of PNA during WM maintenance.

Some humans, like other animals, may sense magnetic fields: Gurindji people from Australia can locate a hidden magnet solely based on magnetoreception, but an American control group cannot (Meakins, 2022). Why can only some humans use magnetoreception? One possibility is that human magnetoreception is experience-dependent: the fundamental capability may be universal, but the Gurindji learn to use it reliably because, unlike Americans, their language and culture promotes paying constant attention to cardinal directions and thinking about space using a geocentric cognitive map, which sensing the Earth's magnetic field would help with. If so, we might expect other cultures using geocentric thinking, such as the Hai//om people from Namibia, to have also learned to use magnetoreception. We tested this and found that, unlike Gurindji, Hai//om people could not locate a hidden magnet at above chance levels, suggesting that learning to think geocentrically may not be sufficient to acquire magnetoreception.

Understanding visual narratives requires readers to track dimensions of time, spatial location, and characters across a sequence. Previous work found cross-cultural differences for situational changes across adjacent panels, but few works have examined situational dimensions across extended sequences. We therefore investigated situational “runs” – uninterrupted sequences of the situational dimensions (time, space, characters) – in a corpus of 300+ annotated comics from the United States, Europe, and Asia. We compared runs' proportion and average lengths and found that across books, semantic information changed frequently and run length correlated with proportion. Yet, cross-cultural patterns arose, with American and European comics using more continuous runs than Asian comics. American and European comics used more and longer temporal and character continuity, while Asian comics used more spatial continuity. These findings raise questions about comprehenders' processing strategies for visual narratives across cultures and how general frameworks of visual narrative comprehension account for variations in situational (dis)continuity.

The semantic representation of abstract words is a topic of discussion within the embodied cognition framework. Existing theories propose the involvement of emotional, linguistic, and social experiences in abstract word representation. Focusing on ‘socialness' — a variable with limited empirical evidence — our study explores whether abstract words are associated with richer social experiences. Two semantic categorization tasks (explicit and implicit) with socialness priming and a lexical decision task with socialness priming were conducted to examine the effect of socialness on abstract words recognition. Additionally, we run similar experiments with affective priming to examine the effect of valence on abstract words recognition. Our results indicate that only valence facilitates the recognition of abstract words and only in the explicit task. Conclusively, we find no evidence supporting a non-strategic effect of socialness and valence on abstract words recognition, thus challenging existing theories on the grounding of abstract words in social information and emotion.

To investigate, in an ecological way, how people evaluate the truthfulness of universal and probabilistic statements we introduce a modified version of the Wason Selection Task. Participants see four decks of cards (instead of four cards), and are asked to turn as many cards as they deem necessary to judge if a given statement is true or false, both for the observed sample (deductive task) and for an imaginary reference population (inductive task). Participants encounter universal (“All P are Q”) or probabilistic statements (“more/less than x% of P are Q”; between-subjects) with abstract, realistic neutral, and realistic polarizing statements (within-subjects). Half of the participants receive an endowment for each turn, correct (incorrect) deductive judgments are rewarded (penalized), and turning a card incurs a cost (other half: fixed participation fee). We report results from two online experiments, thereby also contrasting prescriptive models of evidence search with actual behaviors.

Recent advances in generative language models, such as ChatGPT, have demonstrated an uncanny ability to produce texts that appear to be comparable to those produced by humans. Several key empirical results related to human processing of language, such as analogical reasoning, have been replicated using these models. Nevertheless, there are some important differences between the language generated by these models and language produced by humans. In this paper, I examine how LLMs performs on two pronoun disambiguation tasks reported on by Rhode, Levy, and Kehler (2011) and Sagi and Rips (2014). While LLMs performed reasonably in these tasks, their responses demonstrate stronger language-based biases while the influence of world knowledge, such as causal relationships, was lessened. Because LLMs replicate language produced by humans, these results can help shed light on which aspects of language use are directly encoded in language and which require additional reasoning faculties beyond language processing.

Success in competitive matches hinges on psychological and mental preparations, such as strategic decision and emotional control. Although relevant cognitive functions and corresponding neural activity have been reported in a simple short-term laboratory task, the contribution of neural activity to the outcome of a more complex and prolonged match-format task has not been examined. Therefore, we focused on esports players engaged in a fighting video game (FVG). We examined the association between electroencephalography results in the pre-round of FVGs and consequences of the rounds. The results showed that parietal beta and frontal alpha/gamma activities are associated with winning and losing, respectively, depending on the match's situation. Furthermore, parietal beta activity exhibited approximately 80% accuracy in win-loss predictions using machine learning. Our findings suggest that the performance of skilled video game players is influenced by psychological and mental preparations with fluctuations in neural oscillations.

Networks are a useful mathematical tool for capturing the complexity of the world. Using behavioral measures, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences (such as communities) even when presented with incomplete information. Their performance was best explained by a mathematical model following associative learning principles and based on the integration of the transition probabilities between adjacent and non-adjacent elements with memory decay. In a follow up MEG study, we explored the neural correlates of this hypothesis. First, the comparison of the brain responses to tone transitions adhering or not to the community structure revealed an early difference, suggesting an automatic encoding of sequence structure. Second, time-resolved decoding allowed determining the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones, enabling associative learning through Hebbian rule. 

Engaging in science lessons requires creative thinking skills. These skills are expressed in the verbalized ideas of students during these activities. The objective of this study was to analyze how momentary teacher behavior is associated with the level of novelty of scientific reasoning in teacher-student interactions. Participants were 14 teachers together with a small teaching group of 4-7 year old students (around 64 students in total). One lesson per teacher was recorded prior to a professional intervention. We categorized the verbalizations of both teachers and students in real-time, assessing level of novelty for all student statements and categorizing teacher utterances as divergent, convergent, or neutral. Preliminary analyses showed that interaction patterns are specific for each teacher and class. Generally, students tended to express lower levels of novelty following teachers' convergent statements. However, teachers' divergent statements did not lead to higher levels of student novelty.

How can we judge if conversation partners will be good cooperation partners in other tasks? A recent proposal is that low-level linguistic similarity such as subconsciously matching others' language use may be a signal of cooperativeness. To elucidate the mechanisms behind this relationship, we conducted two experiments, in which we found that conversation partners that matched the participants' syntax and pitch were perceived as more cooperative and were chosen more often as cooperation partners. Our findings further suggest that the sheer act of adapting to someone's linguistic production was not as crucial for choosing cooperation partners, even if it involved an initial cognitive investment. Rather, the decisive factor was sharing someone's linguistic preferences and thereby indicating group membership. This may have important implications for understanding cooperation partner choice and for understanding the (co-)evolution of language and cooperation, which both are traits that are particularly prominent in humans.

What do people think about climate change or artificial intelligence? How do people understand communication on risk and uncertainty? Answers to such questions are important for psychological research and policymakers. One powerful but under-explored way to answer such questions exists in using free associations. We present a guide on collecting, processing, mapping, and comparing people's free association responses using the 'associatoR' R package. We showcase this approach using a free association data set generated by GPT-4-Turbo that reveals its understanding of the concept of 'intelligence'. We discuss design choices and concrete analysis decisions, including steps to uncover the structure and topics of mental representations using different natural language processing approaches, such as the network analysis of the co-occurrences of responses or text embeddings from large language models. We believe that free associations present a powerful approach to revealing how people and artificial intelligence represent key social and technological issues.

Peripersonal space (PPS) is the multisensory representation of the near-body space. Several factors modulate PPS size and the sharpness of the boundary separating PPS and the far extrapersonal space, suggesting that PPS may be involved in the subjective experience and in the self-other representation. Such representations seem to be shaped by mindfulness meditation (MM); however, evidence on the effects of MM on PPS is limited. To test the hypothesis that MM modulates both PPS size and the sharpness of PPS boundary, we enrolled 26 non-meditators, who performed an audio-tactile task before and after a 15-minute guided focused attention meditation (FAM). Despite no changes of PPS size, after FAM we found a significantly reduced sharpness of PPS boundary, as if it dissolved. We suggest that the reduced separation between the self and the environment, reported by meditators in some phenomenological studies, may relate to the altered PPS sharpness.

Gesturing helps cognitive load (Kita et al., 2017) and access to details in autobiographical memories (Aydin et al., 2023). We examined gestures' roles in narrating emotional autobiographical events in first (L1) and second language (L2), expecting (1) gesture use to increase phenomenological ratings (reliving, visual imagery, auditory imagery, and importance) of the events and emotional intensity in both languages and (2) the effect to be more prominent in L2 than L1, where cognitive load increases. Twenty-nine participants (Mage=21.24) narrated positive and negative events in L1-Turkish and L2-English. No difference between L1 and L2 was found in phenomenological and emotional intensity ratings controlling for English proficiency and current mood. Representational gestures predicted imagery for negative events in L1, and non-representational gestures predicted emotional intensity and reliving for negative events in L2. Representational gestures' recollecting and non-representational gestures' fluency-resolving functions might have increased phenomenological ratings, particularly for negative events.

Causal inference plays a crucial role in humans' success in navigating the world. Fortunately then, numerous studies suggest that people are highly adept at making these inferences. Examining judgments of causal structure across numerous studies, Griffiths and Tenenbaum (2005) found that people's average judgments tracked the predictions of an optimal Bayesian model of the task. However, Tauber et al. (2017) show that aggregate behavior may appear optimal even when few individuals exhibit optimal patterns of responses. Here, I applied hierarchical Bayesian cognitive modeling approaches to a new study of causal structure judgments (N = 80) to examine the optimality of causal structure judgments at the individual level. Expanding the findings of Griffiths and Tenenbaum (2005), I found that the majority of participants' causal structure judgments were well-explained by an optimal Bayesian model (avg. r = .86). These findings suggest that human cognitive capacities are truly well-attuned to the causal inference task.

As adults, we readily work through alternative possibilities and their potential consequences in our minds before acting. This capacity for mental simulation enables us to internally explore alternatives without incurring costs of acting in reality. Young children are highly exploratory in the real world, but little is known about their ability to engage in internal exploration via mental simulation. This preregistered study (1) examines developmental changes in the use of mental simulation when solving problems with multiple options, and (2) investigates the influence of resource availability on the tendency to simulate. Adults (N=30) and 4-to 7-year-olds (target N=120; data collection ongoing) completed computer-based puzzles where they chose where to drop balls into a vertical maze to hit a goal. Accuracy and latency to act were measured as indices of mental simulation. Our findings will contribute to understanding children's problem-solving, and could lead to a new conceptualization of their exploratory behaviour.

Generalizations in thought and language are powerful tools to share information agents need to predict and control their environments. However, some generalizations are restricted to “sociocultural bubbles” (e.g. “women have trouble getting tenure in math”). How do we communicate such patterns? We examined how 4-7-year-olds (N=110) and adults (N=159) respond to context cues signaling that the speaker uses a generic generalization to convey a broad vs. contextually-restricted regularity. Adults endorsed generics flexibly, tracking context cues (p<.001), but younger children struggled, over-attributing socially contingent properties to the group beyond the “bubble”, on par with context-general regularities. This reveals a troubling discrepancy between children and adults' interpretations of generics, opening doors for miscommunication. When adults highlight problematic patterns with the hope of promoting social change, children may perceive their assertions as claims about group's broad, unalterable attributes. We discuss strategies to mitigate this in educational and family communication settings.

We investigated the impact of nameability, presentation configuration, and placement structure on object location memory. In Experiment 1, participants memorized photos of nameable and unnameable objects presented at one of four screen locations, either one or four objects at a time. After a filled retention interval, they were presented again centrally, and participants indicated the position where they were previously located. Results revealed a substantial memory advantage for nameable objects. Presentation configuration had no effect. In Experiment 2, we replicated these findings and additionally investigated the role of placement structure by presenting objects either in the corners or on the left/right/top/bottom position of the screen. Memory was better for horizontal/vertical placement, but only for nameable objects. Thus, nameable objects profit from semantic encoding, which can be further improved by simple orientation cues. Notably, with an average accuracy of 50%, location memory was not as "massive" as suggested in previous studies.

Curiosity has been suggested to reflect a drive for learning and to constitute a learning opportunity signal. If rational, curiosity should incorporate the reliability of the information source: more credible sources should instill more curiosity and learning. We tested these hypotheses in a lab experiment (n = 23) and online replication (n = 64), where we randomly assigned 100 zoology trivia questions and answers to one of three different sources claimed to be .99, .90 and .75 valid, respectively. Participants rated their curiosity for each source-indicated answer, read the answers, rated their credibility, and then took a retest on the questions. We found that indicated source credibility significantly affected curiosity ratings yielding an average of .56z,.22z and -.78z, respectively. Similarly, response update (learning) increased with 87% (exp 1) and 67% (exp 2) per z-score rated credibility (both p < 10^-18). Manipulated source credibility thus influenced both curiosity and learning.

In the context of online education, the impact of a speaker's gestures on children's spatial performance during learning still needs more exploration. Previous work found that spatial directions presented with gestures enhance children's performance on physical visuospatial arrays (Austin & Sweller, 2014). Here, we investigate whether spatial directions with or without gestures relate differently to 5-year-old monolingual Turkish children's spatial performance on a computerized map task. Children engaged in a task on a tablet that required them to recall the route directions presented in the videos either multimodally (speech-gestures combined) or in speech alone. Responses were coded for the target information in the route descriptions for actions (running), locations (school), and spatial directions (behind). Results only revealed better performance for encoding spatial directions presented multimodally (p=.013). Summarizing, the results emphasize the importance of multimodal input in enhancing children's spatial performance and highlight gestures' role in virtual visual-spatial learning environments.

To fully grasp the underlying behavioral and neural processes of social cognition, it has been argued that interactive experimental paradigms and multi-person neuroimaging are needed. However, few studies have examined group interactions, beyond the dyad, as well as how higher-level social properties map onto coordination dynamics. Here, we investigated the role of friendship in group social coordination, by mapping student social networks, and recruiting groups of participants by manipulating their friendship strength. Participants were tasked with pressing their own individual pressure measuring (force) device to reach a designated target force together in groups of three (two friends, one non-friend), with or without live visual feedback, whilst three-person EEG hyperscanning was employed. Preliminary behavioral results indicate that lack of friendship with the other two participants results in greater force production relative to the other participants. We plan to explore the relationship between the social, behavioral, and neural dynamics.

How children acquire the rules governing past tense production has been for many years a test bed case for nativist-constructivist debates about the nature of innate knowledge and its role in language acquisition. However, previous studies have tested the acquisition of past tense via corpus analysis, in which errors are rare, or elicitation tasks, in which tested items are few, resulting in limited between-item variability. To address these weaknesses, we analysed data from a uniquely large and longitudinal dataset containing 694 verbs, collected via an educational online platform. We examined whether form-frequency, phonological neighbourhood density (PND), and telicity predict the verb-level difficulty of past tense forms in Dutch. Our sample consists of Dutch-speaking children aged 8-12 years old, the age at which children are still making past tense over-regularisation errors. Analyses are ongoing but preliminary results suggest a role for all three factors and an interaction between frequency and PND.

Computerised technology is an increasingly popular tool for cognitive testing with non-human animals and has numerous benefits, such as tighter control over stimuli presentation and recording responses. Recently, virtual environment (VE) software has been successfully implemented in cognitive research with non-human primates. In VEs, novel stimuli can be presented in innovative ways allowing us to study phenomena in novel ways unrestricted by real-world space. We present evidence from capuchin monkeys (Sapajus apella) in a delayed-response task within a VE presented on a touchscreen. We compared capuchins' short-term memory performance between a VE task and an equivalent physical task. Preliminary data shows an effect of delay on accuracy in the VE, as in the physical task. We show that VE are a feasible method for studying cognition with capuchin monkeys, offering an engaging way to study primate cognition in without the physical constraints that are often present when designing apparatuses.

Learning a function from input-output pairs often follows exemplar or rule learning. Speed of exemplar learning and generality of rule learning were suggested to be promoted by visually aligning familiar and unfamiliar input-output exemplars. To test this, undergraduates (n=47) were randomly assigned to Full-, Partial- and No-Alignment groups. On each trial, students estimated fractions on number lines, and functions used to generate estimates were examined on 9 trial blocks. On pretest, estimates were (incorrectly) a linear function of denominators alone (Full 50%; Partial 50%; No 40%); on post-test, estimates were (correctly) a linear function of the whole fraction (88%; 44%; 47%). Virtually all change in the Full group occurred after training just two exemplars (75%). Also, regression to the denominator-only function differed across groups (0%; 38%; 33%). Finally, Full-Alignment group generalized to untrained problems more broadly than other groups. Findings demonstrated efficiency of visual alignment in function learning.

Identifying strongly with a salient future self increases future-oriented behaviour. Self-report measures can detect variations in future self-identification within and between individuals on the dimensions of vividness, similarity, and positivity. We adapted the Self Association Task (SAT) to detect preconscious perceptual processing biases towards future self-related information. Participants were instructed to imagine different versions of their future selves, constructed using one of the three dimensions mentioned above. These imaginations were followed by the implicit SAT and explicit self-report measures of future self-identification. The similar and positive future self-imaginations led to increased subjective future self-identification. While a classic self-prioritisation effect was prominent throughout, similarity constructs of the future self also elicited processing biases on accuracy but not response time. As suggested by philosophical theories on self-continuity, the construct of a future self can influence future self-identification and direct future-oriented cognitions and behaviours.

Metaphor comprehension studies have investigated how we process metaphors by pitting conventional metaphors against novel ones. Although these studies have yielded much data on how we comprehend metaphors, no research has examined how we process novel spatial metaphors of time. We constructed a stimuli pool of 80 spatial metaphors of time, 40 were conventional time metaphors, whereas the other 40 were novel spatial metaphors of time evenly distributed among Moving-Ego or Moving-Time perspectives and Path or Manner metaphorical motion in the main verb. Ratings by 40 participants showed that the novel metaphors had more possible interpretations, were more ambiguous, difficult to interpret, less apt, and less conventional. A pilot study with 10 participants showed that these properties of metaphors were linked to metaphor interpretation and temporal gesture production in various ways. This study will continue to investigate how we process and represent novel spatial metaphors of time with more participants.

Hand gestures have been shown to enhance overall verbal divergent and convergent creative thinking, especially for people with high imagery. In the present study, we tested whether gestures can also boost creative imagery, or creative visual imagination, as both creativity and gestures might rely on visuospatial skills. Participants first generated ideas regarding a simple unfinished figure and then completed the figure with their favorite idea. Spontaneous and encouraged gesture frequencies during idea generation and verbal descriptions of the idea before drawing it were calculated. We found that iconic gestures produced when generating ideas could lead to more vivid and original creative imagery. However, gesturing during idea description could result in reduced transformativeness (i.e., reduced modification and flexibility when drawing). These findings suggest that iconic gestures can be beneficial for visual creative imagery when generating ideas. However, once we settle on a particular idea, gesturing about it might hinder creative flexibility.

People systematically deviate from the rational Bayesian updating of beliefs, as notably evidenced by conservatism and base-rate neglect. The primary cognitive models that explain these biases include simple heuristics (Woike et al., 2023, https://doi.org/10.1016/j.cogpsych.2023.101564) and stochastic sampling approximations of the Bayesian solution, like the Bayesian Sampler (Zhu et al., 2020, https://doi.org/10.1037/rev0000190). However, neither type of explanation appears entirely complete, as the data fall between the two; only about half of participants' responses align with heuristics. Could these results be explained by a new class of models that blend heuristics with Bayesian models? We test both simple mixtures of heuristics and the Bayesian Sampler, as well as a hybrid model in which heuristics are used to set a prior that improves estimates based on stochastic samples. Our analysis indicates that neither heuristics nor the Bayesian Sampler alone are sufficient to explain the data.

Self-other integration plays a vital role in efficient synchronization with other humans. Previous research has shown that in simple rhythmic joint action tasks (e.g., tapping), self-other integration can be described using mathematical models of coupled oscillators, representing within- and between-person action-perception links. The present study focuses on investigating self-other behavioral and inter-brain dynamics (dual-EEG) when synchronization is either the goal of the task itself or rather an emergent phenomenon in complex continuous interactions. More specifically, participants produce improvised movements in a ‘mirror-game' paradigm while being explicitly asked to synchronize with the partner (synchronized condition) or produce independent movements with visual feedback of each other (spontaneous condition). Mathematical models of coupled oscillators will be used to reveal emergent dynamics of self-other integration on behavioral and neural level. Moreover, we hypothesize that stronger interpersonal synchronization in the spontaneous condition will lead to stronger sensorimotor alpha and beta desynchronization and higher inter-brain synchronization.

Humans are born with an intuitive representation of the physics world. How accurate is intuitive physics? Researchers from education focus on the failures, students' errors and misconceptions while cognitive psychologists argue humans anticipate and manipulate physical environments in ways betraying veridical knowledge of classical mechanics. One solution is to hypothesize there are distinct systems of “cognitive physics” with different limitations and deployment in the tasks favored by the two literatures. The goal of current study is to gather evidence from psychometric studies by estimating how many distinct factors explain performance on intuitive physics assessments. We build an augmented concept inventory including several previously-validated concept inventories, around 120 items. The pilot study indicated that participants recruited online from Prolific displayed expected accuracy on the tasks. We are now collecting around 1,000 participants and applying multidimensional item response theory (MIRT) analyzes to identify the latent structure.

Face-to-face communication is highly complex, with information being transmitted via multiple channels simultaneously. Social gaze can regulate conversation, express emotions, and signal interest or disinterest, and eye contact, or a lack thereof, is a powerful visual cue that influences the dynamics of communication. While previous research has shed light on gaze in autism in general, there remains a lack of 1) evidence on interactions in dialogue between autistic adults (rather than mixed dialogues) and 2) investigations on the influence of gaze on conversational dynamics and interpersonal rapport. We have developed a novel setup with mobile dual eye-tracking glasses that allows for the automatic detection of mutual eye contact. Our exploratory analyses of conversations in homogeneous autistic dyads provide new insights into autistic gaze dynamics and their interrelation with rapport, ultimately helping to advance the current understanding of cognitive diversity and of the fundamental elements of social interaction.

The pretesting effect suggests that attempting and failing to guess unknown information can improve memory compared to errorless study. A key question is when it is the best moment to give feedback after testing. In this study, we explored two factors: (1) the timing of feedback after unsuccessful pretest, provided immediately or after 24 (Experiment 1) and 48 hours (Experiment 2); and (2) the timing of the final test after feedback, immediately or after 24 hours (Experiment 1). We assessed their impact on recall accuracy, comparing with an errorless (read-only) learning condition. Results showed superior accuracy for pretesting than read-only condition; for immediate feedback than delayed; and for immediate test than delayed. Furthermore, although smaller, there was still pretesting effect after 24 and 48 hours of feedback delay. This flexibility in timing could be particularly useful in educational settings where logistical constraints may force a delay in feedback or test.

Language models have traditionally been trained on massive digitized text corpora. However, alternative data sources exist that may increase the representational alignment between language models and human knowledge. We contribute to the assessment of the role of data sources on language model representations. Specifically, we present work aimed at understanding differences in the content of language representations ('embeddings') trained from text, behavioral data (e.g., free associations), and brain data (e.g., fMRI). Using a method from neuroscience known as 'representational similarity analysis', we show that embeddings derived from behavioral and brain data encode different information than their text-derived cousins. Furthermore, using an interpretability method that we term, 'representational content analysis,' we find that, in particular, behavioral embeddings better encode dimensions relating to dominance, valence, and arousal, which are likely critical for the representational alignment of language models.

Texts about fictional characters are often written in a third person singular (3SG) perspective. In a self-paced reading study, Child et al. (2018) found that emotional information is processed more easily when the narrative uses second person singular (2SG) rather than 3SG. In the current study, we explore how 2SG and 3SG are processed in Dutch. Because Dutch is a language with a formal-informal distinction in 2SG, we also contrast formal 2SG-V (e.g., u, 'you') to informal 2SG-T (e.g., jij, 'you') forms. We find a main effect of perspective on target processing, with 2SG read faster than 3SG. In contrast, spillover regions (three words following the target) are read slower following 2SG than 3SG, and spillover regions were read slower following 2SG-V than following 2SG-T. This means that processing emotional narratives through a 2SG perspectives induces a processing cost compared to 3SG perspectives, and this increases with formal 2SG.

Children frequently use under-informative expressions (e.g., Side) while describing Left-Right relations between objects but use gestures to disambiguate the relative locations of objects (Karadöller et al., 2022). Here we ask how children collect visual information about the spatial relations they express when planning such descriptions. Twenty Turkish-speaking 8-year-olds saw displays with four pictures of the same two objects in various spatial configurations. Target pictures described to a confederate depicted left-right relations (e.g., lemon left to box). Descriptions were coded whether they were informative in speech, informative with gesture, or under-informative. Children had more target fixations when planning (1) informative than under-informative descriptions (β=0.515, SE=0.131, p<0.001); (2) descriptions that are informative with gesture than informative in speech (β=-0.827, SE=0.171, p<0.001). Results extend previous literature showing that visual attention changes as a function of informativeness and the modality (Ünal et al., 2022) of the description for 8-year-old children.

The role of gestures in cognition extends beyond communication as people gesture not only when they speak but also think. This also holds for individuals who are blind from birth. However, studies showed that blind speakers produce fewer spontaneous gestures than sighted speakers when describing events. The present study aims to go beyond quantitative measures and gain insight into gesture kinematics. We compared the duration, size, and speed of path gestures (showing the trajectory of a movement) used by 20 blind, 21 blindfolded, and 21 sighted Turkish speakers when describing spatial events. Blind speakers took more time to produce larger gestures than sighted speakers, but the speed of gestures did not differ. The gestures of blindfolded speakers did not differ from those of blind and sighted speakers in any of the measures. These suggest a lifetime of blindness influences the kinematics of gesture production beyond a temporary lack of vision.

Work in psycholinguistics continues to demonstrate new ways in which perspective-taking guides language processing. E.g., recent work shows that, in sentences like “Sophie [told Amanda that]/[asked Amanda if] she likes learning new languages”, readers use perspective reasoning to judge the ambiguous pronoun as near-categorically referring to the subject antecedent in TELL (because Sophie possesses the relevant knowledge) and the object antecedent in ASK (Amanda’s knowledge). Although these patterns demonstrate a robust perspective effect, could they instead arise from shallow lexical cues provided by TELL/ASK? Experiment 1 rules out lexical-cue explanations by showing that preceding context sentences can compel readers to actually reverse the “default” antecedent judgements otherwise found for TELL/ASK sentences. Experiment 2 further explores the pragmatic basis of perspective-taking in stand-alone sentences by simply varying character properties, e.g., “Max asked his [son/tutor] Gerald if he understood the assignment correctly”, where Gerald’s role shifts who likely holds the relevant knowledge.

This paper explores the cognitive processes underlying how and why trust in informational sources fluctuates. If information from experts and mainstream media is broadly more accurate than peer networks', why do we sometimes lose trust in experts? Counterintuitively, we often prefer information from authoritative sources, even if they become distrusted. We built a computational model of these dynamics. It includes a decision process sensitive to information processing costs and a learning process driven by prediction error minimization. We hypothesized that human information-processing biases could explain why experts are preferred as default sources of information and why their legitimacy is less resilient than peer networks' when both provide inaccurate information. We ran simulations over a wide range of parameters and found that the processing advantages of following experts can be outweighed by overreacting to their mistakes. This effect is higher when the environment is unstable and the epistemic authorities are biased.

The sense of agency (SoA) refers to the experience of controlling one's actions and their effects, while representational momentum (RM) denotes a bias in the perceived trajectory of a moving object induced by one's anticipation of movement. Research in cognitive science suggests that control over action modulates anticipative mechanisms. In the present study, we question the influence of SoA on RM. Participants viewed two dots, one of which moved horizontally on the screen. Its movement was either triggered by the computer or by participants. In the former case, participants either could freely choose or were commanded on which dot to trigger. Additionally, given the role of affordances in motor control and movement perception, we tested the effect of adding a tunnel through which the dot could pass. The results showed that agency and affordances influenced movement anticipation with no interaction between the two. Freedom of choice yielded no difference.

How do humans infer the structural interpretations of a piece of music from its basic elements? Since recursive elaboration is an important structural principle in several musical traditions, generative probabilistic models are a useful tool for characterizing musical interpretation as a probabilistic inference problem. However, due to the high degree of ambiguity and combinatorial complexity of even short excerpts of music, exact inference (e.g. finding the "best" structural interpretation of a piece) is usually not feasible. The present work proposes a hybrid approach to this problem. An explicit and interpretable probabilistic top-down model is complemented with a heuristic parser that reverses the generative process in a greedy fashion and adapts to feedback from the top-down model via deep reinforcement learning. The combination of these two models bridges the gap between explicit but slow top-down knowledge and immediate musical intuitions on various levels of musicianship.

Humans are often involved in complex social relationships, where they exhibit biased behavior when they process information from neighbors (e.g., irrational DeGroot learning) and cognitive biases on perceiving social network structures (e.g., egocentric biases, network centrality, etc.). But little is known about the cognitive reason behind. Here we purpose a unified computational framework (reduced representation model, RRM) to deal with the problems, which assumes people represent an optimal reduced network based on the trade-off of utility and cognitive cost for the representation, and make rational inference on it, where DeGroot-like behavior emerges. We did simulations to show RRM can provide an underlying explanation for DeGroot model and human perceptual biases, and tested model predictions in previous dataset (n=209), lab experiment (n=248) and field data. Our work provides an optimal way to depict social network representation when considering human cognitive limitations, and may help understand widespread human biases in social environments.

Some have speculated that higher-order cognitive functions repurpose mechanisms that evolved for perception and action. Expanding on these ideas, we explored whether creativity builds on our ability to strategically navigate through space ('Creativity as Strategic Foraging'). We establish a connection between different types of creative thinking—divergent and convergent—and corresponding spatial search strategies. Participants completed tests of both divergent and convergent creativity. Before each creativity trial, they searched a city map for which we manipulated the search pattern: half the participants searched for multiple dispersed locations, the rest converged repeatedly on a single location. Participants who engaged in divergent spatial search exhibited superior divergent thinking but poorer convergent thinking, while the opposite held true for participants who repeatedly converged on a single location. These findings highlight a targeted association between spatial foraging and creativity, contributing to a deeper understanding of the underpinnings and mechanisms of high-level cognitive processes.

Self-regulated learning may be crucial for goal setting, progress monitoring, and adaptive problem-solving. The ability to find and recognize relevant and reliable information has become increasingly valuable. Therefore, to understand self-regulated learning processes, we interviewed 138 9-11-year-olds to analyze their information-seeking behaviors when learning either novel or partially known concepts by themselves. Children's responses were categorized into two groups: Human-Sources Learners and Platform Learners. Results revealed an overall preference for Platforms (73.23%). Interestingly, when learning novel concepts, the proportion favoring Human-Sources increased significantly (34.56% versus 18.80%). Most of the children mentioned changing their strategy when stuck during the learning process (79.93%), with Platform Learners showing higher adaptability (89.34%) than Human-Sources learners (54.17%). These findings deepen our understanding of children's decision-making regarding learning, aiding teachers in guiding their learning processes more efficiently, valuable not only in educational settings, but also in their personal and professional lives.

Numbers and space are associated in the mind, and in language. We investigate 6,400 instances of verbs indicating vertical movement (e.g., rise, fall, decline) or size-based changes (e.g., contract, grow, extend) in four corpora, showing that 60% of all uses occur in quantitative contexts (e.g., ‘prices rose'). For concrete spatial language, it has been found that movement goals are more likely encoded than sources (e.g., Lakusta & Landau 2005, Stefanowitsch, 2018). We demonstrate that this asymmetry carries over to spatial-numerical language, which more often encodes goals (e.g., ‘revenue went up to 48 million') than sources (e.g., ‘share prices rose from $7.13'). In line with their path-related meaning, vertical verbs showed a much higher propensity to encode endpoints (20%) than size-based verbs (10%), a large effect (Cohen's d = 2.0). These results show that the goal bias attested for spatial language carries over to abstract conceptual domains.

When making financial forecasts, individuals often overreact to recent information, as consistently observed in both laboratory studies with naïve participants and professional consensus real-world forecasting. Current models attribute this overreaction to either an overestimation of recent information or memory constraints favoring more accessible information. An alternative explanation suggests individuals accurately integrate all available information into their mental posterior probability distribution for forecasting, but are unable to directly access this distribution, leading to dependence on approximation methods such as sampling. Local sampling algorithms have received recent support in other forecasting contexts and may introduce overreaction as a consequence of a starting point bias. By reanalyzing existing data from a price prediction task with a random walk price series, we observe increasing variability in predicted values and forecast errors as the horizon expands. Employing this heightened variability and overreaction, we differentiate between competing explanations for the observed forecasting behavior.

Paired-associate learning is a classic paradigm addressing a most fundamental memory task: recalling examples of arbitrary associations between elements. One lesson is that semantics matters to this otherwise episodic task in that elaborative rehearsal connecting the words facilitates cued-recall. We ask whether two forms of semantic elaboration, inspired by higher-order cognition, can produce even better performance. Control groups performed ordinary elaborative study tasks (integrated imagery and compare/contrast) within a traditional paired-associate learning task. Experimental groups either: (1) completed a conceptual combination task requiring them to invent a novel concept aptly captured by the noun-noun pair; or (2) invoked relational cognition skills by predicating a propositional statement wherein the two concepts each fulfilled roles in a semantic relationship. Across three experiments relational predication showed a sizable advantage in cued-recall relative to controls; and additional evidence revealed a less robust benefit of conceptual combination. Implications for theory and application are discussed

Dancers vividly express joy, grief, and other emotions through their body movements, which reflect the deliberate expression of certain emotions and also the unintentional emotions. Research has shown that the speed of performers' movements varies according to the emotions deliberately expressed. However, no study has examined the non-deliberate emotions. Therefore, this study examined dancers' unintentionally exposed emotions through their movements. Seventeen semi-professional dancers performed a neutral choreography to three music types—joy-inducing music, sadness-inducing music, and a metronome—and their performances were compared. Changes in the dancers' body movements were measured using a motion-capture system. Results showed that body movements were generally faster and more dynamic with emotion-inducing music compared to the metronome. While the speed of pelvic movements was more when they danced to joy-inducing music, arm movement was more apparent for sadness-inducing music. These findings help understand the unintentional emotion-expression dynamics in dance.

Mandarin Resultative Verb Compounds (RVCs, e.g., bo-kai “peel-open”) consist of two verbal components. The second component (V2) denotes a resultant state associated with the action denoted by the first component (V1) (Tham, 2015). RVCs emerge in child speech by age 2 and become productive at age 3 (Deng, 2010). However, comprehension difficulties persist until age 6 (e.g., Chen, 2016). Given the puzzling gap between early production and delayed comprehension, we conducted an event description and a sentence comprehension experiment to investigate children's knowledge of the compositional nature and resultative meaning of RVCs. In both experiments, we highlighted the contrast between realized and unrealized resultant state in the visual stimuli. 4- and 5-year-olds were sensitive to the result component of RVCs and differentiated RVCs from mono-morphemic V1s. Our findings demonstrate Mandarin-speaking children's ability to map appropriate verb forms onto unfolding events and provide evidence in favor of continuity in language development.

When children solve cognitive problems together, they coordinate their speech, hand movements and head movements. Previous studies with adults have shown that such multimodal coordination is related to better collaboration. We do not know whether this is true for children, however. In this study, dyads of children (6-10 years) discussed and solved balance scale problems together. To investigate children's multimodal coordination, we measured their speech, hand movements and head movements throughout their bouts of discussion, and applied multidimensional Recurrence Quantification Analysis (MdRQA) on these timeseries. We coded the type of collaboration the children engaged in during these bouts of discussion. We measured performance regarding predicting to which side the balance scale would tilt. We will analyse how children's multimodal coordination is related to the type of collaboration and to their performance on the balance scale problems. Our results will show how successful collaboration between children emerges from their multimodal coordination.

For the claim that child-directed speech (CDS) aids language development to be generalisable, superior learning from CDS compared to adult-directed speech (ADS) must be demonstrated across multiple input domains and learning outcomes. To determine availability of relevant evidence we performed a study space analysis of the research literature on CDS: 942 peer-reviewed studies were coded with respect to CDS features, learning outcomes and whether they included a comparison between CDS and ADS. The results showed that only 290 (16.2%) studies compared outcomes between CDS and ADS, almost half of which focussed on the ability to discriminate between the two registers. Only 20 studies showed learning benefits from CDS for some morphosyntactic and lexico-semantic features and none for pragmatic and extra-linguistic features. Thus, CDS-ADS comparison studies are very unevenly distributed across input features and outcome measures. Until these research gaps are filled claims that CDS facilitates language development should be moderated.

Serial dependence is a phenomenon in which perception of the current stimulus is influenced by that of past stimulus. Previous studies have shown that serial dependence does not occur between modalities, however, it has only been validated with limited types of tasks. We examined the cross-modal serial dependence in numerical estimation task. Participants were asked to estimate the number of flashes presented sequentially for visual stimuli and the number of white noises presented sequentially for auditory stimuli. We observed significant serial dependence from visual to auditory, but not in the reverse direction. The reason we observed serial dependence between modalities may be due to the high-order processing required to perform the numerical estimation task. We need to further investigate the nature of the visual stimuli (sequential or simultaneous) as well as their temporal properties to determine why only serial dependence from visual to auditory was observed in this experiment.

This paper discusses a case study of a 10-year-old Chinese-English bilingual boy, who has developmental dyslexia. The boy exhibits a discrepancy in his reading and writing abilities in both languages, which is believed to be due to the distinct orthographic characteristics and cognitive requirements of the two languages. The study investigated the reasons for his literacy skills profile from both orthographic and cognitive perspectives by evaluating the boy's working memory, literacy skills, receptive vocabulary, and cognitive abilities in both languages. Preliminary findings revealed that while the child's cognitive profile was consistent across both languages, his reading and writing accuracy in Chinese was lower compared to TD Chinese-English bilinguals, with greater difficulties in Chinese writing. This case study reinforces the cognitive account theory, suggesting that the varying cognitive demands needed for literacy skill development can result in differences in these skills, particularly regarding accuracy, in bilingual children (Sambai et al., 2022).

Age-related differences in autobiographical memory recall studies focused on the differences between young and elderly adults. Episodic details and phenomenological experiences in young and middle-aged adults were less studied. To obtain a trajectory, it is important to depict the changes in episodic and phenomenological details in middle-aged adults. The present study aimed to fill this gap by comparing young (ages 18 - 30 in Study 1, 20 - 30 in Study 2) and middle-aged (ages 30 - 60 in Study 1, 40 - 50 in Study 2) adults on early and recent memories. We collected data from 303 participants and asked questions about their phenomenological experiences. We coded episodic details based on the episodic richness scheme (Levine et al., 2002). We found that younger adults recollected more detailed memories than middle-aged adults. Also, young adults recollected events that were more important to their identity. Findings are discussed regarding retrieval/encoding-related advantages and their change across the lifespan.

The role of working memory (WM) in maintaining, monitoring, and executing intended actions in prospective memory (PM) is debated in recent neuropsychological literature. In this study, WM load is manipulated twofold: in an ongoing n-back task (2-back vs. 3-back) and by the stimulus complexity of the cues (high vs. low). Event-related brain potentials (ERPs) in 57 young adults were used to examine the neural correlates of strategic monitoring, maintaining intentions, and detecting PM cues. We observed faster and more accurate responses when the ongoing task is a 2-back and the complexity of the cues is low. The ERP results showed that increased activation during strategic monitoring and maintenance of the intention as the n in the n-back load was increased. In contrast, manipulation of stimulus complexity affected ERPs related to cue detection. In sum, these findings demonstrate, that different types of WM load manipulations affect distinct stages of PM.

Drift-Diffusion Models (DDMs) are a widely-used class of models that assume an accumulation of evidence during a quick decision. These models are often used as measurement models to assess individual differences in cognitive processes, such as an individual's evidence accumulation rate and response caution. An additional underlying assumption of these models is that there is internal noise in the evidence accumulation process. However, fitting DDMs to experimental choice-response time data alone cannot yield estimates of an individual's evidence accumulation rate, caution, and internal noise at the same time. This is due to an intrinsic joint-unidentifiability of these parameters when fitting DDMs to behavioral data. We introduce methods of estimating these parameters at the same time with additional data types. The methods to estimate model parameters rely on Bayesian inference and simulation-based Bayesian inference. We show why these methods are useful without making strong assumptions.

Large Language Models (LLMs) have captured the world's attention for their surprisingly sophisticated linguistic abilities, but what they might reveal about human cognition remains unclear. Meanwhile, members of the public routinely share “prompt engineering” tips for eliciting “better” responses from LLMs such as OpenAI's ChatGPT. These efforts parallel research on linguistic framing, which shows that subtle linguistic cues shape people's attitudes and decision-making in a variety of contexts. In this study, we tested whether state-of-the-art LLMs would exhibit similar framing effects as human participants. We adapted a range of linguistic framing stimuli for use with LLMs based on a recently developed taxonomy of framing effects (e.g., lexical, figurative, and grammatical framing). Results revealed that some but not all framing effects replicated with LLMs. These findings have practical applications for interacting with AI systems and inform our understanding of the cognitive mechanisms that underlie the effects of framing.

Expectations play a critical role in children's learning. Prior studies suggest that children selectively focus on and better remember details of expectation-violating events (Stahl & Feigenson, 2017; 2019). Yet, it remains unclear whether this enhanced memory persists across varying degrees (e.g., somewhat vs. very surprising) and types of expectation violations (core-knowledge vs. schema-based violations). Adapting a surprise storybook paradigm from Foster and Keane (2019), we measure children's (5-8 years; N=20) surprise and recognition memory for six stories that span different expectation-related domains and contain outcomes that are expectation-congruent, somewhat expectation-violating, or completely violating. While preliminary data revealed no significant difference in recognition accuracy by level of surprise, a trend towards better memory for violations of well-entrenched versus schema-based expectations was observed. This preliminary work points to potential differences in how varying types of expectations influence memory in young children and has important implications for learning.

The McGurk effect is a demonstration of the multimodal nature of speech perception; listening to /b/ while watching visual mouth movements for /g/ is expected to result in a “fusion” perception of /d/. A majority of studies on the effect use isolated syllables, whereas our goal was to enhance ecological validity by examining word stimuli. We varied task (forced-choice vs. open-ended) and stimuli (words vs. non-words) between participants. In the word condition, all three stimuli formed words (e.g., beer/deer/gear), and in the non-word condition, the B, D, or G stimulus was a word while the other two were nonwords (e.g., besk/desk/gesk). Fusion responses were much lower than in previous studies, but importantly, participants showed the most fusion responses when the D stimulus was a word and B and G were non-words. These results challenge assumptions about the underlying mechanisms of the McGurk effect, arguing against a purely perceptual illusion.

Cognitively reappraising a stressful situation—reinterpreting it to lessen its emotional impact—is effective for regulating negative emotions. When reappraising, English speakers engage in linguistic distancing, spontaneously using words that are more abstract or impersonal. Previous work showed that this pattern generalizes to Spanish but was equivocal as to whether Spanish-specific markers of psychological distance (e.g., “estar”—“to be” for temporary states) are signatures of successful emotion regulation for Spanish speakers. Here we revisited this possibility. Spanish-English bilinguals in majority Spanish-speaking countries (N = 138) transcribed their thoughts in each of their languages while responding naturally to negative images or reappraising them. Reappraisal increased the use of distance markers common to both languages as well as the use of “estar,” which was associated with reduced negative affect when reappraising. Our findings suggest that people distance their language in both cross-linguistically shared and language-specific ways when regulating their emotions.

Children learn words that name objects (“ball”) and those that name abstract concepts (“story”). One view of learning is that different inputs matter for different words (Snedeker & Gleitman, 2004). That is, many argue that although the observational contexts in which words occur are sufficient for learning object names, they are not for learning abstract “hard words” (Gleitman et al., 2005). This study revisited the contributions of observational contexts to learning one type of hard word: nouns denoting non-basic level object categories (“hard nouns” like “friend”; Kako, 2005). In a new artificial learning paradigm, we reveal that although observational contexts were insufficient for full hard noun learning, they afforded learners partial knowledge that allowed them to succeed in some learning tasks. These data highlight how observational contexts may lay the foundation for learning hard nouns, and underscore how definitions of learning impact our understanding of how the input shapes it.

Filled pauses (e.g., um) help speakers to maintain a conversational floor with their listener(s). Considered an advanced form of disfluency; they emerge when children obtain some language competency. Bilinguals might frequently produce filled pauses as they are sensitive to communication. This longitudinal study examined Turkish-English bilingual (N=50) and Turkish monolingual (N=48) children's production of filled pauses in L1-Turkish and L2-English narratives. Children in three age groups were recruited in Time1 (5-,7- and 9-year-olds) and Time2 (6-,8- and 10-year-olds) and were asked to narrate a story from a picture book. Results showed that controlling for L1-Turkish proficiency scores, the filled pause frequency in L1-Turkish narratives increased from Time1 to Time2, both for bilinguals and monolinguals for all age groups. We obtained the same findings for bilingual children's English narratives, controlling for English proficiency. We suggest that filled pauses might stem from metacognitive processes, which become more prominent with age.

Pupil dilation is assumed to be a slow and indirect reflection of latent cognitive events. Deconvolution approaches promise a more precise study of these events, assuming that they all trigger a delayed pupil response. However, conventional deconvolution approaches neglect the possibility that between-event timings and the shape of the pupil responses differ between subjects, trials, and cognitive events. Accounting for this variability however is crucial to 1) achieve precise recovery of latent events and 2) to investigate how trial-level predictors influence cognitive processes. We present a new method that performs trial-level deconvolution by combining generalized additive mixed models with Hidden semi-Markov models. We tested this method on synthetic data and subsequently applied it to data from a lexical decision experiment (N=24) and recovered six processing events. Investigating the trial-level durations of the recovered events revealed that early visual and late decision-related processing were influenced differently by frequency and word-type.

Problem-solving and sequential decision-making research have a long-standing tradition of utilizing various tasks in experiments to gain insights into different aspects of human behavior. Choosing the right task for investigating these aspects is crucial since human solution approaches depend on features and dynamics of tasks. For a complete theory of sequential decision-making, we must consider this relationship between behavior and task features. We developed a taxonomy and identified nine structural task features that allow us to describe the relationship between tasks and the behavior in the tasks. We categorize sequential decision-making tasks and show how their features link to the demands on solution approaches that leverage their structure. We argue that this taxonomic view on tasks can guide research processes as it can help select the right task for a research question at hand and can be used to relate the results of behavioral studies to each other.

Cross-linguistically, lexicons tend to be more phonetically clustered than required by their phonotactics; that is, words are less distinct than they could be. We use an agent-based exemplar model to investigate how this property arises over generations of language transmission under different functional pressures from learning and communication. We find that, in isolation, learnability pressures rapidly give rise to maximally clustered lexicons. When communicative pressures are also at play, clustering increases in line with a producer-side pressure to maximise similarity between words, but the rate of change is modulated by a listener-side preference for dispersion of word forms: a speaker who is trying to be understood considers what the listener is likely to understand before choosing a word to send. Overall, this work sheds light on how organisational properties of the lexicon may arise as a result of an ongoing trade-off between pressures from language learning, production and comprehension.

Cognitive graphs represent relationships of learned associations between items or concepts, such as social relationships within a friend group or a network of streets. It is unknown what properties of graphs affect the ability of individuals to mentally represent and navigate these structures. Primary candidates are 1. the number of states (nodes) within a graph, 2. the number of connections among states (edges), and 3. community structure. We independently manipulate these factors to examine how they affect both the ability to identify paths between nodes and the efficiency of paths chosen in abstract graphs (associative networks) of object pictures with no overt spatial properties. Consistent with our hypotheses that changes in graph size, edge number, and community structure impact learning, we observed that these factors affected accuracy and efficiency in reaching targets. The findings demonstrate the influence of graph structure on learning, with implications for both spatial and non-spatial graphs.

Electric vehicles (EVs) are quickly replacing internal combustion cars, which will soon become obsolete. Nonetheless, how drivers' perception and cognition deal with certain features of EVs remains largely unknown. In this study we focus on the role of in-car sound, specifically the artificial engine sounds, on drivers' speed perception and control. Previous studies indicate that removing or reducing engine sound leads drivers to underestimate speed and, consequently, to drive faster. Furthermore, evidence suggests that specific sound frequencies could play a role in this process, highlighting the importance of in-car sound features. We consider benefits and limitations of different research paradigms used in the field (mostly video based technique and driving simulation) and we propose an experimental protocol to systematically investigate the phenomenon. Finally, we suggest that the wider use of psychophysical methods on video recordings would benefit the research in the field and overcome some limitations of simulation studies.

Second language learners must often learn categories which may not map well with those of their first language. Prepositions often differ between languages, for example, German uses different words for vertically “on” and horizontally “on” which would be novel to an English-speaker. Additionally, learners must contend with varying degrees of noise in the learning environment. A spatial continuum of images was created depicting prepositions such as “above” and “below” (familiar) or horizontal “on” and vertical “on” (novel). We used an artificial preposition learning task in adult English-speakers to explore both the influence of familiarity (familiar or novel) and the degree of statistical regularity in the learning material (noisy or consistent labeling of continuum steps) on learning outcomes. Our results suggest that learners are sensitive to statistics and familiarity and revealed individual differences in the sensitivity to these statistics, suggesting differences in efficiency of learning novel prepositions.

Cultural evolution is changing humanity much faster than genetic evolution, but at present we lack a way to empirically ground models of cultural evolution in a quantitative, content-agnostic way analogous to counting alleles in models of genetic evolution. A way to measure what information ends up in which minds would permit quantitative models of the many different processes that govern the flow of memes through minds. We offer a method for estimating the amount of information retained based on previous exposure to a cultural artifact. Entropy estimates that are generated based on a test set from e.g. Harry Potter will differ between a treatment group (Readers, people who have read Harry Potter), and a control group (Non-Readers). This difference is an expression, in bits, of how much information from the book stored in Readers' minds and therefore capable of influencing behavior.

In general, it is still unclear, to what extent, that autistic children would develop the ability to recognize facial expression by age and which basic emotion expressions are consistently difficult to learn. Moreover, what crucial processing and mechanisms would play a key role for the autistic behavior patterns in early social interaction. To answer these questions, a deep learning model is constructed to simulate the eye movement records during judging emotion expression of typical developed and autistic children. The simulation results are: 1. for older autistic models, if the gaze fixations for eyes and mouth of positive emotion is longer, it would lead to greater recognition performance; 2. in contrast, for younger autistic models, it takes longer training sessions to correctly recognize most of negative emotions as too much inferences of internal information occurred while establishing reliable prototypes of facial figures in differentiating the angry, sad, and disgusting expressions.

The digital environment has a significant impact on our everyday lives, but there is a lack of studies on how it affects cognitive processes like attention and working memory (WM). This study aims to compare attention and WM in digital and non-digital environments. In Experiment 1, we compared attention and working memory under paper and computer-based environment tasks. The findings showed that under non-digital condition attentional sustainability and visual working memory were better. In Experiment 2, we examined attentional shifting and sustainability at different levels of digital saturation (the presence of perceptual feedback on a website). Attentional sustainability was better in a saturated condition, but attentional shifting was not affected. Thus, the real environment is suggested to be superior due to lower saturation and higher motor-visual coherence. Digital saturation, along with the ACD idea, can guide attention. These results have applications for enhancing the user experience with interfaces.

There is debate regarding the role of number words in numerical cognition, especially for understanding exact large numbers. Studies of languages with number words for only small numbers suggest those languages do not provide symbolic scaffolding for exact large numerical cognition. This study investigates numerical cognition in speakers of the Amazonian language Awet√Ω which has twenty number words. In experimental tasks with numbers/objects up to 20, Awet√Ω participants demonstrated high accuracy in counting, verbal number comprehension, verbal and non-verbal one-to-one matching, and exact subtraction. Awet√Ω speakers also performed with high accuracy on approximate non-symbolic number comparison with more than 20 items, i.e. beyond their number word range. Awet√Ω participants performed as well as Portuguese speaking control participants across tasks. These findings demonstrate that knowledge and use of a system of twenty numeral words is sufficient for understanding exact numerical equivalence, at least up to 20, and basic arithmetic proficiency.

Cultural evolutionary theory has shown that social learning is adaptive across a broad range of conditions. However, humans frequently under-utilise beneficial social information in experimental settings – a phenomenon termed egocentric discounting. We tested the hypothesis that influence is affected by expected reliability using a two-player online task in which both participants answered the same questions in series. After a first attempt, player 2 saw either advice from player 1 or their actual answer (spying). In addition, we manipulated the payoff structure of the task such that it had either a cooperative, competitive, or neutral incentive. As predicted, advice was least honest and social information overall had the least influence in the competitive condition. Player 2 also chose to spy rather than receive advice when offered the choice. Unexpectedly and regardless of the payoff structure, advice was more influential when player 2 could choose information but spying was more influential otherwise.

Attention-Deficit / Hyperactivity Disorder (ADHD) is known to be associated with racing thoughts. Christoff et al. (2016) posit that the main determinant of the dynamics of spontaneous thoughts is the presence of constraints on cognition, be it automatic or deliberate. In the present project, we operationalized the unfolding of spontaneous thoughts with a word generation paradigm (Andrews-Hanna et al., 2021; Benedek et al., 2012; Jung, 1910): participants had to generate series of 10-30 words aloud, following a metronome. We set out to contrast two levels of constraint on associations (strong and weak) to test their impact on the dynamics of thoughts, and to relate it to sub-clinical ADHD-like symptomatology. Using reaction times and semantic metrics, we show that the participants who scored higher on an ADHD diagnostic questionnaire produced words that were less related, but only in the "weak constraint" condition - akin to free thoughts.

Teaching plays a crucial role in human learning, from formal educational environments to mentorship scenarios, yet its cognitive underpinnings remain underexplored. We focus on the distinction between teaching by reasoning using Theory of Mind (i.e., explicitly inferring what a learner knows) and teaching using heuristics (i.e., relying on a simple rule). We use a graph-navigation task where a learner agent with limited knowledge attempts to navigate through the most rewarding trajectory, with guidance from a human teacher. Our findings reveal that teachers utilize a blend of learner-specific strategies and general heuristics. We model learner-specific strategies using Bayesian Theory of Mind (Baker, Saxe, & Tenenbaum, 2009) and demonstrate that the most effective teachers incorporate this strategy. Intriguingly, we show that teaching strategies can be altered without explicit feedback. This suggests that subtle changes in the environment may significantly alter teaching approaches, highlighting the importance of understanding the cognitive processes behind teaching.

Probabilistic categorization (PC) has been used mostly to distinguish between memory systems (declarative vs. procedural). Most literature on PC has relied on the Weather Prediction Task. However, this task doesn't provide the flexibility in assigning probabilities to exemplar features that is often required in more ecological settings. Recently, Marchant and Chaigneau (2021) developed a PC task that allows flexible classification probabilities by computing p(category|feature). In this study, we implemented Marchant and Chaigneau's PC task under two feedback conditions (i.e., 70% and 90%) counterbalanced by three features' relevance conditions. In the transfer phase, subjects rated exemplars' category membership. During learning, we implemented eye-tracking to capture the number of fixations to each exemplar's features. Our work in progress shows that fixations on relevant features predict transfer responses, suggesting that people show declarative knowledge of critical features according to their relevance. Interestingly, declarative retrieval varies with the reliability of feedback.

Space helps us understand abstract math concepts (Winter et al., 2015). Mental rotation is often studied for its predictive role in math development (Casey et al., 2015; Geer et al., 2019). The association between spatial location knowledge and math development remains overlooked despite the significance of left-right body space encoding in numbers (SNARC effect, Dehaene et al., 1993). This ongoing study investigates the link between preschoolers' mental rotation skills, spatial location knowledge, and various mathematical abilities (symbolic, non-symbolic, counting). Preliminary analyses (N= 20; Mage= 4;6) using R showed a significant relationship between spatial location knowledge and symbolic math (r= .43; p= .05) and counting skills (r= .51; p= .02), while no such association is found between mental rotation skills and mathematical abilities (all ps> .05). These findings demonstrate a strong link between spatial location knowledge, but not mental rotation, and development of preschoolers' mathematical skills. Keywords: space; number; preschoolers

Metacognitive monitoring of uncertainty is critical for the development of self-regulated learning because recognition of uncertainty triggers information-seeking or a strategy change. Uncertainty monitoring is assessed with the calibration of explicit self-reports of certainty with objective levels of certainty. Typically, this is done with cognitive tasks where each trial is independent from the last, such as with perceptual judgments of noisy images. However, uncertainty monitoring is perhaps most important when there are multiple consecutive decisions to be made that are contingent on each other, such as problems requiring multiple steps to solve. Reasoners have to reflect on each step and consider if they are getting closer or further from a solution. In the current experiment, both children aged 5-10 and adults calibrated their initial certainty judgments similarly, showing sensitivity to differing initial levels of certainty. However, only adults updated their judgments as they progressed through consecutive decision steps.

What are we saying when we say a body is cognitive? In various turns, we might be saying (or taken to be saying) that it is conscious, that it has mind, or that it is intelligent. But consciousness does not imply mind, and cognition may not imply consciousness. Still, this ambiguity is an unnecessary confusion that pervades scientific, philosophical, and everyday language. This paper proposes that we clarify this as follows: An embodied act can be assessed as cognitive if its activity can be modelled as a trajectory towards a goal, if this trajectory takes place in some state space (i.e., geographical, linguistic) that can also be modelled, and if, within this modelling, an affordance vector can be established from the agent to goal that does not depend upon another body for its relevance (i.e. a hammer would not have this vector because it acquires its directedness from another body).

In an era in which people are bombarded by claims, often from unreliable sources (e.g., generative AI, click-bait headlines), understanding what leads people to believe such claims is imperative. Building on work demonstrating the role of explanation in learning, we test how the process of explaining a claim affects people's beliefs in it (3 studies; N=476, 17,580 observations). In Study 1, participants read 30 scientific news headlines. For each, participants either: generated an explanation for the reported phenomenon, wrote down any thoughts they had about it, or retyped it word-for-word. Participants rated the likelihood that the headline was true. In Study 2, participants also provided baseline ratings one week before the manipulation. Study 3 added a control condition where participants simply read the headline. Across studies, participants believed claims of fact were more likely to be true after trying to explain them compared to any control condition.

Previous research has effectively employed the fast-paced action puzzle video-game Tetris for understanding the acquisition of extreme expertise in complex, dynamic environments. A common approach when contrasting expert to novice performance has been the dissection of their interactions with the environment into disjoint sub-tasks – such as Reaction Time (RT), measured by the input latency to new events on screen. The crucial, underlying assumption to this paradigm is task consistency at all levels of expertise. Using data collected from participants of the Tetris World Championship 2019 and from novices in our lab, we show that this assumption does not hold. While for novices the RT task type remains the same across all conditions, for experts - depending on environmental parameters - the nature of the RT task undergoes a shift and under specific conditions does not represent a RT task anymore. Thus, expert vs. novice sub-task comparison may not be a valid paradigm.

Understanding and quantifying listening effort (LE) is important to a better understanding of speech perception in acoustically challenging environments. EEG alpha power has shown promise as a measure of LE, but relationships between acoustic challenge and alpha have been inconsistent in prior work. We test whether these mixed findings are attributed to differences in alpha power measurement across studies. We compared traditional bandwidth measurement of alpha power to an algorithmic spectral parameterization (SP) approach which separates alpha from background changes in broadband aperiodic activity. Whereas the traditional approach yielded no significant difference in alpha between speech in quiet versus in background noise, the SP approach, which accounts for flattening of the broadband slope in noise, yielded a significant increase in alpha power to speech in noise. These results highlight the importance of accounting for aperiodic brain activity when considering oscillatory EEG markers of cognitive demand in speech perception.

In decision-making, the Levy flights model (LFM), an extension of the diffusion decision model, adopts a heavy-tailed distribution with the pivotal 'alpha' parameter controlling the shape of the tail. This study critically examines the theoretical foundations of alpha, emphasizing that its test-retest reliability is essential to classify it as a cognitive style measure. Our analysis confirms the alpha parameter's test-retest reliability across various occasions and tasks, supporting its role as a trait-like characteristic. The study also explores LFM parameter interrelations, despite low correlation among the other parameters (so representing distinct aspects of data), there is a pattern of moderate correlation between alpha and non-decision time. Investigating the practice effect, our analyses indicate a consistent decrease in non-decision time, threshold, and often alpha across sessions, alongside the drift-rate increase. We also employ Bayesflow for parameter estimation, evaluating its precision with different trial counts. These findings provide valuable guidelines for future LFM research.

Learners face metacognitive challenges in planning efficient allocation of limited study time and cognitive resources. Our work draws cognitive science research showing how humans use reinforcement learning to adaptively develop metareasoning heuristics that balance deliberation and exploitation in learning sequence planning. We model this framework computationally by formulating adaptive content sequencing as a Markov Decision Process with meta-level states, actions, and rewards. A neural meta-policy module governs deliberation on building new personalized learning plans versus the reuse of prior recommendations through simulated user interactions. Testing using 100 simulated agents exhibiting the evolution of knowledge, interests, and consumption patterns provided longitudinal data on meta-policy responsiveness to dynamic learning requirements. Analyzing trends over time and trigger-reaction lags quantified opportunities for improving deliberation latency and relevance. The simulated experiments demonstrate promising progress in computationally modeling the metacognitive capacity for resource-rational planning by strategically balancing plan quality and computational effort in education content recommendation.

Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the agent-based models (ABMs) commonly used for such simulations often over-simplify human behavior. We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs). Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. This bias limits their utility for understanding resistance to consensus views on issues like climate change. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research. These insights highlight the promise and limitations of LLM agents in this domain and suggest a path forward: refining LLMs with real-world discourse to better simulate the evolution of human beliefs.

Autobiographical memory arises from an integration of memories with the self-model, which means that the recall of one's past needs to be congruent with one's self-model. Memories invoking self-incongruent emotions pose a particular challenge for maintaining a stable and positive self-image, which makes them a good target for research into processes of self-memory integration. Expanding on our previous research, we developed an fMRI paradigm that uses subject-specific recalls of shameful episodes from the past, compared with neural and fearful episodes as control conditions, in order to identify the neural correlates of self-incongruent episodes. To this end, we employ multivariate methods (representational similarity analysis) to compare neural activation patterns of natural images associated with the autobiographical episodes. We expect higher similarity for items associated with the incongruent (shameful) episode in areas previously connected with self-related processes.

Mandarin Chinese has a logographic writing system consisting of characters (e.g., 朋 and 友) that are monosyllabic morphemes often combined to form words (i.e., 朋友, “friend”). The vast majority of Chinese words consists of two monosyllabic characters. This research describes the construction and properties of the Chinese character network and demonstrates how its network structure has implications for the lexical processing of Chinese characters through an analysis of Chinese megastudy data. Capitalizing on a database of over 25,000 double-character Chinese words, a network representation was created to represent how single characters are combined to form double-character Chinese words. Network measures such as degree and closeness centrality were retrieved from the network representation and included as predictors in a regression model to predict visual lexical decision performance of single Chinese characters. Network measures contributed additional variance beyond traditional variables such as number of strokes and character frequency.

Previous work has found that when adults are asked for "the first thing that comes to mind", they will provide something that falls between the descriptive average and the prescriptive ideal. In two experiments, we tested whether children would also be influenced by prescriptive information in their first-to-mind judgments, but also when they were asked to predict a randomly sampled item. In Experiment 1, providing information about whether being longer or shorter made a fictional tool better or worse led children to provide judgments that were biased toward the prescriptively 'best' tool, regardless of what they were asked for, while adults ignored prescriptive information when asked for a random sample. Experiment 2 replicated this result but further showed that the effect was specifically driven by information about which objects were prescriptively good, and did not also arise when the only salient information was about which objects were prescriptively bad.

A hallmark of human vision is the ability to rapidly recognize objects in a complex naturalistic scene. However, the exact mechanisms behind the computational invariance of object recognition remain unknown. In this study, we investigate object constancy by estimating how the ventral visual stream processes shading, shadows, textures, and specularities. To accomplish this, we use object meshes from the Objaverse dataset to create distinct multiclass classification tasks. For every task, we render a dataset by excluding exactly one of the previously stated features at a time. Subsequently, we train a ResNet50 model on each dataset. The trained model is evaluated on Brain-Score; deviations in these metrics indicate the importance of a brain region in achieving invariance to a specific feature. A reduced score for a removed feature in a particular region implies its crucial role in processing that feature since the model classifies objects based on remaining scene intrinsics.

Sentence completion studies have shown that L1 English readers use verbal aspect (VA) as a cue to disambiguate pronouns in the context of sentences with transfer of possession verbs. Specifically, in the context of a sentence like “Mary gave/was giving a book to Bill”, a subsequent pronoun is more likely to refer to the source referent (“Mary”) when the aspect is imperfective (“was giving”) than when it is perfective (“gave”). L2 studies have shown mixed results on whether L2 speakers, living within an L2 country or outside, can utilise VA as a discourse cue. The current study tested L2 English speakers using an online Maze task, where a pronoun (“He” or “She”) referring to either the source or goal referent had to be chosen at the critical point in the sentence. The results showed that both L1 speakers and L2 speakers, regardless of location, used VA as a disambiguation cue.

Even when early implanted, children with cochlear implants show heterogeneous language skills and often struggle with pragmatic communication aspects. Research aimed at elucidating specific weaknesses at the interactional level has yielded inconsistent findings. We analyse dyadic interactions involving nine hearing-impaired children and fourteen normal-hearing children engaging with an adult during a referential (treasure-hunting) task, periodically alternated with role-reversal sub-tasks (e.g., child-led referential-tasks, child-storytelling). Our investigation employs a multi-level analysis approach, encompassing acoustic features (F0, intensity, speech duration, speech rate), turn-taking dynamics (duration, gaps, overlaps), laughter responsiveness and pragmatic functions, convergence of these features, dialogue acts, contingency, and task success. We compare interactional patterns across groups and conditions. The adoption of a multi-level characterization is grounded in the hypothesis that alignment at "lower levels" serves a functional role and concurrently offers insights into alignment at a conceptual level, thereby facilitating mutual understanding and conversational success, giving insights on underpinning neuro-psychological processes.

Kinship terminology varies cross-linguistically, but there are constraints on which kin may be categorised together. One proposed constraint on kinship diversity is internal co-selection: an evolutionary process where terminological changes in one generation of the kinship paradigm co-occur with parallel changes in other generations, increasing system-wide predictive structure. We compared kinship systems from 544 natural languages to simulated baselines and found higher-than-chance mutual information (MI) between generations of kin, suggesting a selective pressure for internal co-selection. We then tested experimentally whether this systematicity increases learnability. Participants were taught artificial kinship systems with either maximum or minimum MI between generations. We predicted the high-MI system would be easier to learn, but participants showed little evidence of learning in either condition. A follow-up experiment tested whether predictive structure facilitates generalisation rather than learning. Although other strategies are common, we found that participants often maximise predictive structure when generalising terms to new kin.

Spatial demonstratives exist in all languages, but currently there is much debate regarding the parameters that affect their use both between and within languages. In this work, we explore ‘perspective taking' as a means of accounting for variation in demonstrative use both between and within languages. Analysing primary and secondary data, we test the effects of egocentric distance and addressee position on demonstrative production in speakers of two languages with two purportedly different demonstrative systems: English and Japanese. Based on individual differences between speakers, we propose a framework unifying different theoretical accounts of demonstrative systems in which demonstratives require a spatial reference frame to be chosen prior to the application of a range of routines to select the appropriate term in a given context.

This study delves into the dynamics of dyadic communication within a predefined acting scenario by analyzing how the utterance and behavior of paired participants change over time and influence each other. Assigned specific roles and objectives within this preset context, participants focus on and verbalize each other's actions. Prior research, which compared verbal characteristics between professional actors and novices, underlines the importance of shifting focus from self to partner in attaining naturalistic performances, referring to authentic communication in an acting setting. The present study incorporates pose estimation into the video analysis of acting training, assessing behavioral dynamics in a natural state. By extracting the correlations in movement changes of the paired participants during role-playing, the dynamic process of interaction in a specific context is traced, elucidating how natural performances develop through intensive mutual attention and interaction. Additionally, examining concurrent changes in utterance provides insights into the reasons behind behavioral changes. Overall, this research not only sheds light on the nuances of performing arts training but also makes a contribution to the broader understanding of action patterns and communication dynamics within specific social roles and interactions.

Words that are learned early were shown to be semantically more stable, and vice versa (Cassani et al. 2021, Cognitive Science). Semantic change, however, has multiple aspects. In this diachronic corpus study, we examine the relationship between the age of acquisition (AoA) of words and a set of different measures of semantic change: change in a word's polysemy; overall semantic displacement; and average extent of semantic fluctuation. All measures are based on diachronically layered sense distributions (Hu et al. 2019, ACL) derived from the Corpus of Historical American English. AoA is taken from Kuperman et al. (2012, Behav. Res. Meth.). Taking interactions with frequency into account, we show that semantic displacement and fluctuation are positively associated with AoA as expected. Early acquisition is associated with an increase in polysemy. This hints at the relevance of semantic (metaphorical) extension in the early acquisition of the lexicon.

Notifications convey important information, but they can also act as distractions, leading to resumption errors. Previous research has primarily focused on two types of notifications: pop-up notifications that appear quickly (1 second) and transparency reduction notifications that appear slowly (2 seconds). Pop-up notifications in an environment with perceptual feedback tend to result in the highest number of errors, while transparency reduction notifications may go unnoticed in an environment without feedback. To bridge this gap, the third variant of notification speed (1.5 seconds) was introduced in this study. The aim was to strike a balance between the noticeability of notification and minimizing the negative impact of attention redirection. Participants were instructed to perform the Modified Bourdon Test and close notifications. The findings revealed that the third variant, combining the features of pop-up and transparency reduction notifications, led to a decrease in resumption errors while still effectively capturing users' attention.

Studies have reported that prestimulus brain oscillations guide perceptual experiences during AV speech perception. However, 'what' features in such oscillations drive perception remains unknown. In this EEG study (n=18), we investigated how prestimulus periodic oscillations and aperiodic components influence the perception of the McGurk illusion on a trial-by-trial basis. Using logistic mixed-effect models, we determined the topology of spectral markers that predict the brain response to illusory perception. We found lower levels of alpha (8-12 Hz) and beta (15-30 Hz) band oscillations over parieto-occipital sensors, lower aperiodic offset values over parietal-temporal sensors, and a lower global effect of exponent over the scalp that predicted the response to McGurk illusion. We conclude that the predominant source of variations in the prestimulus oscillatory state is manifested by aperiodic background activity and that variations in these oscillations and aperiodic activity, account for inter-trial and inter-individual variability in perception of the McGurk illusion.

While students typically prefer multiple-choice learning, open-answer questions have frequently been found to be more effective, attributed to their role in promoting recall as opposed to recognition. Here, we examine increasing the effectiveness of multiple-choice testing as a learning tool, by using foils (incorrect answer options) that are similar in meaning and word form to the correct answer. Participants studied French-Dutch vocabulary in three learning conditions: one with unrelated foils, another with open questions, and a third using multiple choice questions with related foils. The related foils were either semantically or orthographically similar to the correct answer. The results showed no significant difference between the open questions and the related foils condition, indicating comparable effectiveness. Replicating earlier studies, the unrelated foils condition yielded significantly poorer learning outcomes. Overall, these results suggest that multiple-choice testing can be a viable alternative to open answer testing when utilizing related foils.

This study investigates social context's effect on human perceptual decision-making in animacy recognition, a crucial skill for identifying potential social interaction partners. Visual cues, particularly goal-directed and synchronized motion, are essential in animacy inference. We hypothesize a bias (evidenced by response frequency, response time, and confidence levels) toward perceiving motion as animate when in the presence of others. Participants assess animations featuring two moving disks engaging in interactions characterized by varying degrees of synchronized and goal-directed motion. These assessments are conducted individually and alongside another participant performing the same task. During each animation, participants indicate via button press whether they perceive the disks as being alive. Subsequently, they rate their confidence in their response using a 1-5 Likert Scale. By employing Bayesian and Drift Diffusion Models, we aim to uncover how the presence of others impacts animacy perception, thereby shedding light on the role of social factors in perceptual decision-making.

Humans are remarkably adaptive instructors who can adjust advice on complex tasks based on a learner's current knowledge and goals. However, paradigms in cognitive psychology generally examine pedagogy in constrained and discrete tasks, like categorization or feature learning. We examine teaching in continuous domains, where there are theoretically infinite hypotheses, and model how teachers can formulate a computationally tractable Bayesian inference using Gaussian process regression. Taking inspiration from function learning tasks, we investigated how one teaches visual underlying functions by giving pedagogically-informed point examples. Preliminary evidence suggests teachers are sensitive to learners' priors about continuous functions. For instance, when learners expect a diverse range of function types (linear, quadratic, periodic, etc.) then teachers tend to select examples that help distinguish between those types. Conversely, teachers relaxed this constraint if learners had not seen multiple function types. Our results provide insight into mechanisms of pedagogical guidance in complex, continuous task domains.

Human bodies follow similar designs. Yet, languages differ in how they divide the body into parts to name them (Brown 1976; Enfield et al. 2006; Majid et al. 2015; Huisman et al. 2021). In this study, we investigate the similarities and differences in naming two separate body parts with the same word, i.e., colexifications. Using a computational approach, we analyze networks of body part vocabularies across 1,028 languages. The analyses focus on the influence of perceptual features that lead to variations in body part colexification networks and on a comparison of network structures in different semantic domains. Results reveal that adjacent body parts are frequently colexified, while variations in vocabularies are influenced by perceptual features like shape and function. Compared to semantic domains like emotion and color, body part colexification networks show less variation across language families. This research presents the first large-scale comparison of body part vocabularies and provides important insights into the variability of a universal human domain.

Social identity and situational information guide how people morally judge others. A journalist is judged differently than a doctor if they expose private information, which may also depend on whether the reason was to prevent a public health crisis vs. for monetary gain. What is less known, is how people decide how much and what type of information (identity vs. situation) is more relevant for them to make a moral judgment. To investigate this, participants received limited information about a case with a potential moral violation. Then, they could get new pieces of information about the case (varying in importance as normed in our pre-study) incrementally, or stop collecting information and instead judge the violation. This study elucidates how people accumulate and use evidence to judge others. Our findings can reveal underlying biases in decision-making and be used to inform legal and criminal proceedings, news coverage strategies, and others.

I formalize the problem of care in the mathematical language of sequential decision-making. Drawing upon insights from developmental psychology, robotics, and computational cognitive modeling, I conceptualize care as a dynamic interplay between the caregiver ('one-caring') and the care recipient ('cared-for'). Caring actions maximize the utility of the cared-for at a future point when they are required to act autonomously. Since this quantity cannot be directly optimized, the focus is on enabling increasing levels of autonomy through environmental shaping, risk reduction, and safe exploration. I distinguish caregiving from helping and teaching by care's focus on exploration and autonomy that increase capacity over time. In the context of elderly care, the emphasis shifts towards preserving rather than enhancing capacity. Finally, I consider the role of caregiving in the development of moral values and the possibility of artificially intelligent agents that might someday care for us.

Polysemy is pervasive in modern language and represents one key factor that allows for the unlimited expressive potential of human language. One important process of historical meaning extension is that of metaphor (Anderson 2017). Recently, the paradigm of Experimental Semiotics has been proposed as a novel methodology to investigate semantic change (Bowerman & Smith 2022). In experimental semiotics, participants have to converge on a novel signalling system in the absence of a shared language. Here we adopt this approach to experimentally investigate the role of metaphor in meaning extensions. Specifically, we will present results of a study in which participants have to communicate about a novel meaning space in a referential communication game. Importantly, participants will only be able to use symbols for which they have previously established symbol-meaning mappings in a prior game. The results show to which degree participants make use of metaphor in in this task.

People have different intentions when sharing information online. However, are others able to interpret these motives and form accurate impressions of the poster? To investigate this, we put participants (N = 307) in imaginary opinion-based ingroup and outgroup online forums. In each, people were presented with different types of statements and asked for their impressions of the poster as well as of their own ingroup and outgroup. Negative impressions and intentions were more commonly linked to posters thought to be outgroup members, even when they exhibited similar behaviours to posters thought to be members of the ingroup. Notably, most types of contact increased people's liking of the ingroup and disliking of the outgroup. That said, a perceived effort to engage in genuine discussion over group matters by perceived outgroup posters appeared to shift outgroup impressions to be more positive. This highlights the potential benefit of deliberation in mitigating intergroup animosity.

People rely on communicative cues when assessing others' knowledge levels about a topic. Speech fluency has been shown to inform these assessments (Brennan & Williams, 1995), however little is known whether co-speech gestures also impact how we judge others' level of expertise. To address this, we showed 42 participants (Mage=21.05) short videos of speakers in four conditions (fluent or disfluent speech with gestures, fluent or disfluent speech without gestures). Participants then provided FOAK (feelings of another's knowing) judgements of the speaker. A mixed effects regression analysis, with conditions as fixed and trial and subjects as random effects, revealed that fluent speech elicited higher FOAK ratings than disfluent speech, p<.01. Surprisingly gestures did not affect FOAK ratings. This is a first study to suggest, fluency can be a more prominent cue than gestures when assessing others' knowledge levels.

Human communication, known to occur between two individuals using various modalities, has attracted significant interest, particularly in the context of neural dynamics' studies. Embodied communication, especially in cooperative or competitive situations, has been a focal point of these studies. However, the neural activity during this process is not well understood from the viewpoint of motor intention in communication. It is crucial to note that intentional motor switching occurs following motor coordination within a pair. In this study, we conducted a simultaneous recording of EEG, motion, and gaze of two players engaged in our newly devised coordination game involving bodily motions. We observed significant differences in time-frequency power during cooperative and competitive situations in intentional motor switching. This finding suggests that the EEG power differences in local brain regions and in the alpha and high-frequency bands are effectively related to the process of intentional motor switching.

It has been highlighted that non-invasive stimulation of the auricular branch of the vagus nerve (taVNS) have a neuromodulatory effects on several cognitive functions. In-fact, tha vagal system is central for the organism's homeostatic regulation and it has widespread connections with various cortical and subcortical areas. Hence, our focus on studying the impact of this technique on a multifaceted cognitive process essential in human experience, time perception. Healthy subjects underwent explicit (duration discrimination) and implicit (prediction) temporal tasks during two distinct experimental sessions of stimulation with taVNS: a sham condition (offline stimulator) and an active stimulation condition. Participants' cardiac activity (Heart rate variability) was monitored throughout the experiment. Preliminary results show improved performance during the active stimulation condition, particularly for predictive temporal tasks. TaVNS may enhance brain activity in areas crucial for implicit timing (e.g. upper temporal cortex, lower parietal cortex) and supporting the adjustment process of temporal prediction errors.

Transauricular vagus nerve stimulation (taVNS) is increasingly spreading both in research and clinical practice; however, literature often presents non-uniform results in various cognitive domains. We propose a procedure based on the use of taVNS to investigate its effect on executive functions, also considering the modulation of the homeostatic balance between the sympathetic and parasympathetic systems. 40 (22F) volunteers participated in two separate sessions (stimulation/sham). After baseline measurements (heart rate variability) and a preliminary stimulation phase, they performed Stroop and go/no go tasks. Throughout the procedure, cardiac activity was recorded to obtain HRV parameters in different experimental conditions. Although performance differences were not identified in the tasks, the modulation of HRV parameters during the tasks indicates how taVNS can influence the balance between the sympathetic and parasympathetic systems during the execution of cognitive tasks. This effect is likely attributed to the taVNS acting on vagal tone, supporting the parasympathetic component.

Long COVID is defined as the persistence of COVID-19 symptoms for more than 12 weeks following infection (NICE, 2022). This condition is estimated to affect nearly 2 million people in the UK (ONS, 2023). Long COVID patients experience symptoms affecting multiple organ systems (Davis et al., 2023; Raveendran et al., 2021) including the CNS, and Cognitive symptoms (Davis et al., 2021; Guo et al., 2022a) and deficits (Guo et al., 2022b; Hampshire et al., 2021) have been demonstrated in adult sufferers. Despite the condition occurring in 13% of children who contract COVID-19 (NICE, 2022) there is little research on the cognitive impacts of Long COVID in pediatric samples. This study explores memory (item- and associative) and language (semantic and syntactic) across 80 6-12 year olds with and without history of covid infection, relating these to parent-reported cognitive symptoms including brain fog and short-term memory problems.

It has been claimed that language learners are better at acquiring phonetically motivated phonological patterns compared to unmotivated patterns; this hypothesis is known as substantively biased phonological learning. We test this hypothesis by exposing French-speaking participants (n=120) to either a vowel harmony pattern (phonetically motivated) or a vowel disharmony pattern (comparable formal complexity but phonetically unmotivated) in an artificial language. Participants were trained with noun roots and a single suffix, but at test were required to add multiple suffixes to roots, including a novel suffix with a vowel unobserved during training. Although participants performed equally well when adding a single suffix, only those in the harmony condition generalized when adding two suffixes (including the held-out suffix). This work expands on previous research by showing feature-based generalization of harmony, but not disharmony, to novel affixes held out from training. It provides strong evidence for the substantively biased phonological learning account.

Implicit biases are a common source of prejudicial decision-making in society. While the use of language models might be intended to eliminate human bias and prevent harmful prejudice, they are trained on human-generated linguistic data and thus inherit human-like biased attitudes. We conducted a narrative review of implicit attitudes in linguistic models, drawing on literature from artificial intelligence, social psychology, and cognitive science. Drawn from experimental data, our findings suggest an important link between statistical patterns in language and the implicit biases displayed by people. While several efforts have been made to capture the levels of bias in language models, there is no contribution yet that focuses on the causal nature of the relationship between language and implicit bias in language models. This literature review highlights the state of the art in this growing field, identifies gaps in the literature, and showcases challenges for further research in the future.

Proficient speakers of a second language (L2) show similar processing of affective content as native speakers, but reduced magnitude, later latency, and a less differentiated emotional neural response (e.g., Hsu et al., 2015). Because language and culture are intertwined, this study examined whether cultural relevance of short stories affects immersion during reading, independent of language proficiency. Hong Kong (HK) and Mainland Chinese (MLC) readers were exposed to identical short stories featuring events and traditions related to either culture. Their level of attention, transportation and emotional engagement after each story was measured using the Story World Absorption Scale (Kuijpers et al., 2014). Preliminary results show that HK participants were significantly more immersed in HK cultural stories than in MLC stories, especially when they described modern events. Instead, MLC participants showed no difference in immersion. The results will be discussed considering historical common origins and modern stark distinction between the two cultures.

Stochastic events in our daily environments, such as a missed bus or forgotten keys, require adaptive understanding for efficient exploitation of the environment. In this study we tested how humans acquire and use such understanding if the either the type or probability of events change. We asked 281 participants to predict the location of an animated fly, which hid from the observers. Over the first 10 trials we induced a strong prior model of the task environment and subsequently introduced stochastic changes and new content to manipulate the rate of model violations. Prediction errors derived from a specific world model drove information seeking actions, leading to new explanations and associated probability estimates. Current world model constrained possible updates, often only leading to partial investigation and suboptimal strategies, especially when behavior had positive utility. Additionally, evidence for accurate understanding but failure to identify and exploit ideal behavior was a characteristic result.

Our senses receive a continuous stream of complex information. According to Event Segmentation Theory (EST), we parse this information into meaningful events, allowing us to extract relevant information, remember it, and act upon it. Previous research has related these events to so-called ‘neural states': temporally and regionally specific stable patterns of brain activity, which tend to coincide with events in the stimulus. Here we show that these neural states additionally align with stable features in a movie stimulus that are relevant to a specific brain region. This supports the idea that many brain areas across the cortex apply event segmentation in a hierarchical manner. Using intracranial measurements, we further investigate whether neural states are present at a much smaller timescale and how their characteristics correspond to EST. Our findings provide support for the idea that neural states could underlie the cognitive skill of event segmentation.

Random generation studies have shown that people struggle to be unpredictable – they slowly and effortfully produce autocorrelated sequences instead. However, true random processes (such as radioactive decay) are also not instantaneous. In this project we explore how long it takes people in a random generation task to be random. We do so in two experiments asking people to draw samples from naturalistic domains (lifespans and heights), manipulating either the rate of generation or the requirement to be random (within participants). Irrespective of pace or instructions, we find that people can produce a random sample every four to five seconds. Additionally, the time a person needs to produce random samples is consistent across conditions, but varies widely between people. Following previous literature, we model random generation performance as an autocorrelated sampling algorithm, giving a process level account of how people do these tasks and why they need time to be random.

Others not only influence our behavior, but also our metacognitive evaluations of those behavior (i.e. decision confidence), even when feedback is random and uninformative. Here we ask if metacognitive ability to monitor reliability of one's decisions predicts social susceptibility. We also ask if mood (anxiety and depression) further modulates this effect. We gave 46 healthy participants a perceptual task and presented them with random social feedback (positive, negative, neutral). Participants rated their confidence in their decisions before and after feedback, and lastly had an opportunity to change their initial decisions. In a separate task we also measured their metacognitive abilities, as well as their anxiety and depression scores. Results showed metacognitive ability to increase susceptibility to random social feedback. Surprisingly for those with high levels of metacognitive ability anxiety exacerbates this effect, whereas depression suppresses it.

In social network research, free recall name generators are central tools for measuring individuals' perceptions of their social relationships. This study addresses the patterns that individuals exhibit when recalling their social relationships. Specifically, it examines the influence of social contexts, groups, and demographic factors on the order and relative sequences in which individuals are named. By analyzing responses of a friendship name generator in a longitudinal dataset of over 1000 students from the Swiss StudentLife study, we aim to shed light on the cognitive patterns that govern the recall of social bonds. The results shed light on how cognitive mechanisms shape perceived social networks and highlighted the importance of strong relations, similarity in characteristics, and group structures for their recall. The results show that memory is strongly influenced not only by the relationship between the nominator and the nominated person but also by the relationship between the nominated persons.

Natural languages often exhibit agreement, where two words must be matched for certain features. It's well known that people use knowledge about agreement to drive expectations during online processing. What is less well known is how the type of dependency mediates this expectation and thus the processing difficulty of a gender-mismatched word. To test this, we collect incremental processing data on three types of gender agreement mismatches in Russian: (i) past-tense verbs and subjects, (ii) attributive adjectives and nouns, (iii) predicate adjectives and nouns. We collect two types of incremental processing data: eye-tracking and Mouse-Tracking-for-Reading (MoTR), in which a participant reveals and reads text by moving their mouse, whose position is recorded. We find that while participants are surprised by ungrammatical conditions, this is mediated both by the type of agreement as well as the gender of the agreeing noun.

This study extends the application of Diffusion Drift Modeling (DDM) to examine the lexical access of monosyllabic Chinese real words and pseudo-words in an auditory lexical decision task. Here, the pseudo-words were constructed from phonological segments based on real words, allowing us to assess lexicality derived from suprasegmental information—specifically, tones—and to match their frequency to that of corresponding base forms. Following Ratcliff (2004), we manipulated the drift rate to vary with log-transformed frequency and lexicality while maintaining other DDM parameters as constant. Our results revealed that pseudo-words generally led to slower drift rate compared to real words. Additionally, for real words, an increase in log-frequency was associated with higher drift rate, whereas for pseudo-words, an increase in frequency unexpectedly corresponded to lower drift rate. This differential impact of frequency on drift rate may suggest the distinct cognitive pathways activated in the processing of suprasegmental information in lexical access.

How do children bootstrap language through noisy supervision? Most prior works focused on tracking co-occurrences between individual words and referents. We model cross-situational learning (CSL) at sentence level with few (1000) training examples. We compare reservoir computing (RC) and LSTMs on three datasets including complex robotic commands. For most experiments, reservoirs yield superior performance over LSTMs. Surprisingly, reservoirs demonstrate robust generalization when increasing vocabulary size: the error grows slowly. On the contrary, LSTMs are not robust: the number of hidden units needs to be dramatically increased to follow up vocabulary size increase, which is questionable from a biological or cognitive perspective. This suggests that that random projections used in RC helps to bootstrap generalization quickly. To our knowledge, this is a new result in developmental learning modelling. We analyse the evolution of internal representations during training of both recurrent networks and suggest why reservoir generalization seems more efficient.

The alignment between large language models and humans' knowledge and preferences is central to such tools' safe and fair deployment. A number of approaches to quantifying alignment exist, but current work is fragmented, preventing an overview across categories of stimuli and demographic groups. We propose that free associations from massive citizen-science projects can advance representational alignment by helping evaluate both content and demographic inclusivity. We assess the representational alignment of GPT-4 Turbo and data from the English Small World of Words Study (ca. 80.000 respondents, 3.7 million responses). Our results indicate that while the language model can capture some procedural signatures of human responses, it shows heterogeneous alignment across stimuli categories, poor representational alignment for controversial topics (e.g., religion, nationality), and differential representation of demographic groups (e.g., males, females). All in all, our work suggests that free association can be used to evaluate the representational alignment of large language models.

Imagine that a river becomes polluted if two plants generate too much waste. One might be more inclined to say that a plant caused the river to become polluted when it produced more waste than expected. While similar normality effects on causal judgments have been observed in cases with binary variables, little work has focused on cases with continuous variables. To test whether the statistical normality of continuous variables influences causal judgments, we had participants learn statistical norms over repeated iterations of a vignette and make a causal judgment about an instance of that vignette. Following Icard et al. (2017), we manipulated the causal structure and the normality of each cause. By testing whether normality effects on causal judgment generalize to cases with continuous variables, our results help determine whether these effects are central to human cognition, or simply apply to a subset of cases studied thus far.

Human cognition is highly flexible– even when posed with novel questions and situations, we are able to manipulate our existing knowledge to draw reasonable conclusions. All human cognition requires flexibility, yet we lack a well-justified computational explanation for how humans might learn and manipulate conceptual knowledge in a way that allows for cognitive flexibility. Here, we develop and test a neural network model of how humans learn and use concept representations. The core of this model frames concepts as latent vector representations that are learned through observations across multiple context domains. The architecture we propose gives rise to a natural mechanism for generalization of conceptual knowledge between familiar domains. This work integrates findings and methods across cognitive science, neuroscience, and machine learning, and holds promise to advance the understanding of conceptual representations within each of these fields.

Learners are often unaware of effective learning strategies, hindering their actual utilization. We investigated intervention methods to increase the utilization of effective learning strategies in self-regulated learning settings, specifically focusing on the interleaving strategy in category learning. Undergraduate students were either informed or not about effective learning methods and studied painting styles of various artists in a self-paced order. In Experiment 1, participants who received instructions about specific goals and critical skills required for category learning at the beginning were more likely to recognize the importance of identifying between-artists differences, but did not necessarily increase interleaving during their study. In Experiment 2, however, participants provided with procedural and conditional metacognitive knowledge (i.e., why interleaving is effective and how to interleave exemplars) in the middle of their study significantly increased interleaved practices. Our findings suggest that enhancing metacognitive knowledge can help encourage the use of effective learning strategies in self-regulated learning.

In social contexts, learners need to infer the knowledge and intentions of the information provider and vice-versa. In this study, we tested how well participants could infer the intentions of different information providers in the rectangle game, where a fictional information provider revealed clues about the structure of a rectangle that the learner (a participant) needed to guess. Participants received clues from either a helpful information provider, a provider who was randomly sampling clues, or one of two kinds of unhelpful providers (who could mislead but could not lie). We found that people learned efficiently and in line with the predictions of a Bayesian pedagogical model when the provider was helpful. However, although participants could identify that unhelpful providers were not being helpful, they struggled to learn the strategy those providers were using, even when they had the opportunity to practise being a deceptive information provider.

The counterfactual emotions of regret and relief arise from considering how the present would look had one taken an alternative past action. We investigated if 4- to 9-year-old children (N = 192) could identify others' regret and relief by watching videos of actors choosing between two boxes that concealed a better or worse prize. Each actor first looked inside the chosen box and made a happy or sad facial expression, and children were then shown the contents of that box. Critically, the actor then looked inside the non-chosen box and made either a happy or sad facial expression, and children were asked what they thought was inside. Children aged 6 years and older were able to identify that the non-chosen box concealed a better prize when the actor was sad, and a worse prize when the actor was happy. The abilities to experience and recognise counterfactual emotions may develop concurrently.

Video game competition, called esports, is an intriguing subject for the investigation of psychological skill differences between players; such skills are more weighted toward achieving optimal performance than physical skills are. We looked for differences in psychological skills between advanced and intermediate players and the kind of psychological skill that is critical for defining a player's skill level. We measured the physiological states of players in esports matches and found that the temporal heart rate pattern during competitive matches was highly correlated among advanced players, rather than among intermediate players or players of different levels. Additionally, physiological synchrony among advanced players decreased under sparring situations in which no winner or loser was determined. These results suggest that the unique superior psychological skills of advanced players are motivation control, which is characterized by the ability to maintain and demonstrate a high motivation to win.

People readily identify interactions based on resource transfer, such as giving. In the present study, we examine whether adults bind two agents in an interactive unit even if one caused the other to gain a resource indirectly — i.e., as a side effect of pursuing another outcome. Across five behavioral and EEG experiments, we found convergent signatures of social binding (change sensitivity and alpha-band suppression) when adults were presented with an action resulting in the collateral gain of a resource for a passive agent. No binding was observed when the action caused the collateral loss of the agent's pre-existing possession, revealing an asymmetry in how gains and losses are perceived to affect agents. Together, these findings suggest that adults interpret actions resulting in the provision of material gains as interactive, even when these are indirectly brought about.

Recent evidence suggests that infants interpret giving as indicative of a relationship based on reciprocal exchange. Monitoring such a relationship requires tracking its participants irrespective of the role they occupy in a given interaction, as these are assumed to alternate over time. We explored this hypothesis in a label-mapping paradigm by testing whether 14-month-olds interpreted a trained label as referring to the features of an agent pointed at (a stable identity-tagging information) or to the action role it carried out (a temporary information). Across four eye-tracking experiments, infants consistently mapped the trained label onto the agent's features, when the agent gave a resource to someone. Superficial similar actions not resulting in social transfer (i.e., disposing of an object) did not induce such mapping. These findings suggest that the observation of giving highlighted identity-preserving information over transitory action roles, possibly due to the relational assumptions this action engendered.

Social interaction plays a fundamental role in language acquisition. Although adult learners can acquire language through passive instruction, they also benefit from interaction. We asked whether these benefits are due to interaction providing information about communicative context. We designed an online interactive game where participants communicated in an artificial language with a computer partner. We contrasted 3 conditions: a fully interactive condition, a passive condition in which participants learned through passive exposure, and a third condition, in which participants were exposed to the language in context but without involvement in interaction. We found that interaction produced the best results, and that mere exposure to context did not help: even when tested interactively, passive learners did better than participants who had been exposed to, but not involved in, interaction. The main benefit of interaction therefore, at least in an online learning environment, is not merely to provide context for language use.

The automaticity of syntax has been a long-debated topic in psycholinguistics. One strategy to establish it involves finding significant evidence of syntactic priming in experimental tasks that restrict conscious awareness. Two common criteria to assess the unconscious nature of priming are that visibility (d') of masked words is not significantly different from zero, and that visibility is not positively correlated with the size of the priming effect. Unfortunately, these outcomes may also arise from low statistical power in visibility data and low reliability of dependent measures. We report results of a meta-analysis and a Bayesian re-analysis, which revealed low statistical power and evidence that "subliminal" words were actually visible for participants. Additionally, reliability analyses on Berkovitch and Dehaene's (2019) dataset showed that noisy measures may account for the lack of correlation between visibility and priming. These findings question the validity of previous results supporting the automatic nature of syntactic processing.

Inferring mental states from faces during social perception is sensitive to context and higher-level semantic information. This is vividly observed in captioned humor, like memes, where a single line can dramatically reshape scene perception and understanding. This EEG-study explores the real-time neural dynamics of these sudden insights in social perception. Across three experimental phases (pre-insight, insight, post-insight), 40 participants viewed images of 120 scenes showing public figures. During the insight phase, humorous captions (e.g., "trying to set somebody on fire with his mind") either matched or mismatched the following image (e.g., a politician, mid-speech, rubbing his temples). Comparing event-related potentials between trials with vs. without sudden insight revealed distinct changes in the N170, early posterior negativity (EPN), and N400 components from pre-insight to insight phase. These results link sudden, humorous insight in social perception to instant alterations in visual processing, fast affective responses, and higher-level semantic processing.

Childhood adversity (e.g., poverty, violence exposure) has been associated with broad cognitive deficits as well as with cognitive adaptations in specific abilities. Integrating these perspectives requires a process-level understanding of how deficit and adaptation processes operate. We investigated how adversity was associated with inhibition, attention shifting, and mental rotation in the Adolescent Brain Cognitive Development (ABCD) study (N ≈ 10,500). Using Hierarchical Bayesian Drift Diffusion Modeling, we distinguished between speed of information uptake, response caution, and stimulus encoding/response execution. We further used structural equation modeling to isolate task-general and task-specific variances in each of these processing stages. Youth with more exposure to household threat showed slower task-general processing speed, but showed intact task-specific abilities. In addition, youth with more exposure to household threat and material deprivation tended to respond more cautiously in general. These findings suggests that traditional assessments might overestimate the extent to which childhood adversity reduces specific abilities.

Scatterplots are commonly used data visualizations to depict relationships between variables. There are inconsistent findings in the literature regarding how outliers in scatterplots affect trendline estimates. Correll & Heer (2017) found no difference for trendline estimations between the no-outlier and the outlier conditions consisting of a separate group of items creating an outlier cluster. However, Ciccione et al. (2022) showed that single outlier points might be included in trendline estimations. To investigate whether an outlier cluster was perceived as a salient and separate unit and thus excluded from the remaining data points, we directly compared the effects of single and multiple outliers on trendline estimations, controlling for correlation strength, outlier position and trend direction. Participants drew trendlines. We found that participants included single outliers more than they've included outlier clusters into the trendlines; this pattern was similar across all other control variables; suggesting grouping might play a role in this process.

Societal expectations have been found to determine which social roles (e.g., jobs) people should occupy. Previously, however, these beliefs have been mainly explored using implicit measures such as sequential priming tasks where responding to expected (vs. conflicting) information is facilitated. We applied a random generation paradigm where participants said aloud the first names of hypothetical people working in various professions. This revealed that more female (male) first names were uttered for the female-typical (male-typical) occupations reflecting the societal gender stereotypes and the environmental statistics. Furthermore, the proportion of female and male names generated for each profession predicted participants' performance in a sequential priming task (prime = professions from random generation task, target = female vs. male face) better than the environmental statistics or participants' explicit gender ratio estimates of these jobs. Collectively, these findings offer a new method for exploring the internal representations elicited by cultural expectations.

Young children, like adults, conform to group consensus. However, it is unclear when children develop more sophisticated intuitions about how the composition of groups, the way in which they acquire and aggregate information, impacts advice quality. This experiment assessed children's developing sensitivity to source independence - that is, do they understand that statistically independent sources of information are more valuable than correlated ones? Children (5-to-11-year-olds, N=106) and teenagers and adults (N=99) played a space exploration game in which they made multiple 2AFC decisions based on the advice of 8 friendly aliens. Across trials and participants, we manipulated consensus (the relative number of advisers endorsing each option) and source diversity (the relative number of independent advisers endorsing each option). Results indicated that children were able to detect the correlations between sources, but their ability to exploit this knowledge was late emerging, likely in the early adolescence years.

Past research examining developmental differences in exploration behavior has shown that children are more likely than adults to seek out uncertainty. However, children's exploration behavior may be shaped by their distinct prior experiences and assumptions, differing from those of adults. We investigate these differences and their potential impact on exploration, using the game “Little Alchemy”, in which players can create new elements (e.g. clay) by combining previously discovered elements (e.g. stone and mud). Previous work found that adults use an empowerment strategy: They combine elements with the goal of creating new elements with the potential for many successful combinations. We observed that children were less likely to use an empowerment strategy, but relied more on their uncertainty compared to adults. This discrepancy decreased over age. In a follow-up experiment, we showed that this difference was indeed due to children using different strategies rather than the influence of different semantic priors.

Children are keen explorers of the world: They systematically explore surprising findings and test hypotheses during play. However, less is known about whether toddlers are similarly driven to learn about the self. Here, we ask whether toddlers are intrinsically motivated to explore their own competence. In ongoing work, 2-year-olds (N = 12) play Montessori practical life games along with their parents; toys were verified to be equally appealing and challenging to toddlers in an independent norming experiment (N = 14). Within each pair, parents guide the toddler's hands while playing with one toy, which provides ambiguous information about the toddler's competence, and take turns playing with the other toy independently, which provides unambiguous information. Toddlers are then offered both toys to freely explore independently. Preliminary results show that toddlers explored the ambiguous toy first in 71% of the 31 trials, suggesting that toddlers seek out opportunities to learn about their competence.

Humans systematically make poor financial judgments, a problem that can be mitigated with advice, whether it be from humans or, increasingly, artificial intelligence (AI). Yet, one potential obstacle in human-AI interaction is algorithm aversion, where people prefer humans over AI advisors. However, whether this preference affects the integration of advice remains unclear. We investigate AI advice integration in financial judgements and its underlying psychological drivers. In two studies, participants (N=716) engaged in incentivised investments, receiving AI or human advice. Results showed that participants integrated AI and human advice similarly, even if they preferred human advice. However, those with strong preferences integrated information better from their preferred source. We further find that different psychological factors impact preferences and advice integration, suggesting that advice preference and advice integration are independent of each other. These findings highlight the potential for AI to enhance financial judgements, even among individuals averse to its use.

Decision-making often involves a trade-off between risks and rewards, for which humans are susceptible to biases. Cognitive training could facilitate these processes, but, for applicability outside the lab, it needs to persist over time. In this double-blind study, participants were split into treatment and control groups to complete a decision-making task involving monetary gambles. All participants completed pre-training (Day 1), training (Day 2-7), and several post-training sessions (up to 6 months). During training, one group was given feedback to promote risk-neutral choice (treatment), whereas the other merely practiced the task (control). Following training, choices in the treatment group were significantly more risk-neutral than in the control group (with no improvements), and this pattern was replicated up to 6 months without any top-up training. Computational modeling revealed a complex pattern of change in the feedback group – whereby participants' initial risk preferences partially determined the effect of training on their post-training preferences.

Computational reinforcement learning models suggest that learning involves both model-free (MF) reward prediction errors and model-based (MB) state prediction errors, observed in instrumental and Pavlovian learning (Daw et al., 2011; Schad et al., 2020). Pavlovian-instrumental transfer (PIT) demonstrates Pavlovian values impacting instrumental responses. Single-lever PIT paradigms, often considered as MF, show correlations with reduced MB instrumental control (Garbusow et al., 2014; Review Cartoni et al., 2016; Sebold et al., 2016). To explore whether single-lever PIT effects are exclusively MF or also MB, we created a novel two-stage paradigm assessing MF and MB control trial by trial. Computational dual-control model simulations revealed a two-way interaction for MF and a three-way interaction for MB PIT. Thus far, Bayesian sequential analysis using Savage-Dickey density ratios (N=10) suggests the existence of MF (BF=3.93) and MB (BF=1.26) influences on PIT, aligning with Pavlovian learning and emphasizing the role of MB computations in single-lever PIT tasks.

Planning in complex environments is crucial in everyday life, yet the underlying cognitive abilities remain unclear. We investigated this through an online experiment (n=476) where participants completed nine cognitive tasks: Raven's Matrices, Mental Rotation, Corsi Block Task, Change-Detection Task, Pattern Recognition Task, Wisconsin Card Sorting Task, a complex two-player game called Four-in-a-Row, and two simpler planning tasks. We found moderate correlations across most metrics, aligning with existing literature on cognitive interconnectivity. Notably, performance in the Four-in-a-Row game significantly correlated with all other tasks, implying a shared cognitive basis for planning, regardless of task complexity. Additionally, latent variable analysis revealed distinct factors underlying planning in different state spaces, with working memory capacity playing a crucial role in navigating larger spaces. These findings shed light on the cognitive architecture of complex planning.

The relational matching-to-sample task (RMTS) is a gold standard in measuring abstract concepts. Most preschoolers and non-human animals do not spontaneously succeed in the classic, two-item version of the task. It is debated whether this failure indicates a lack of abstract reasoning ability, perhaps linked to limited language capabilities, or rather stems from learned biases for other bases of matching. We developed a physical, causal RMTS task for 4- to 5-year-old children based on matching the weight relations within object pairs by asking them to align two balance scale apparatuses. We presented conflicting object matches in half of the trials and a transfer phase with a new set of stimuli. By age five, children benefitted from the causal context of the task, suggesting that not solely abstract reasoning abilities but other factors, like biases to match individual object features, influence their performance in classic arbitrary RMTS tasks.

Recognising the future utility of a solution is fundamental to our capacity for innovation. However, developmental research has thus far focused on children's capacity to create solutions, rather than recognise existing solutions with ongoing utility. We examined children's capacity to retain and share a solution that would be useful again in the future. Across two rooms, 4- to 9-year-olds (N=83) were given a series of time-based tasks which could be solved by building and using a tool. When given the opportunity to transport a tool between the first and second rooms, children from age 6 onwards retained the tool that would be useful again above chance levels. When subsequently asked to secure a solution for another child, only 8- to 9-year-olds chose this tool above chance. Positive age-partialled correlations between children's retaining and sharing behaviours suggest that these behaviours may reflect a common underlying capacity for recognising future utility.

Generalisation of a linguistic rule can be facilitated by certain distributional characteristics. Previous work has shown that a rule is better generalised if it applies to items that (i) follow a skewed frequency distribution, or (ii) follow a uniform frequency distribution over many distinct item types. These two observations cannot be unified under explanations of rule generalisation that are based on entropy of the frequency distributions (since skewed distributions have low entropy, while a greater type count increases the entropy), nor explanations that focus on one highly-frequent type providing a basis for analogical extension (since all types in uniform distributions are equally frequent). Using an artificial language learning experiment and an agent-based model, we show that participants' generalisation behaviour is best matched by a model encoding preferential generalisation of rules containing long-tailed distributions—that is, containing a greater number of low-frequency types.

Round numbers are often interpreted approximately (Krifka, 2002), with "pragmatic halos" (Lasersohn, 1999) that encompass multiple permissible values. For example, stating "there were 200 people at the meeting" would be acceptable even if the exact count were 197 or 204. In line with the idea that larger numbers have more approximate representations (e.g., Cheyette & Piantadosi, 2020), we demonstrate that rounding and pragmatic halos are magnitude-dependent. First, an analysis of every single number in two large corpora (COCA, BNC) shows that indicators of rounding predict frequency (cf. Woodin et al., 2023), but crucially in interaction with magnitude, with round numbers over-represented for larger magnitudes. Second, we show that jigsaw puzzles often systematically deviate from what is advertised on the box in a way that depends on magnitude, e.g., a 1,000-piece puzzle may contain 1,024 pieces, whereas a 50-piece puzzle is more likely to contain the stated value exactly.

Combining words in new ways is a hallmark of linguistic generativity. Previous work has shown that people's understanding of novel noun-noun combinations is influenced by the linguistic distributional information associated with a compound's constituents - words closer in semantic space are more likely to be judged as sensible/interpretable, and processed more quickly, than constituents that are further apart in semantic space. We extend this work by investigating whether two levels of linguistic distributional knowledge (first-order local co-occurrences, second-order contextual similarity), and the sensorimotor similarity of the constituents, impact people's processing effort. In two experimental studies, we found that linguistic distributional information facilitated processing of novel combinations for both shallow sensibility judgements, and deeper interpretation generation. Effects were stronger for interpretation generation, and for distributional measures, but these effects were mediated by the concrete/abstract nature of a compound's head noun. The findings support embodied theories that propose a strong role for linguistic distributional information.

Involuntary mental time travel (MTT) is spontaneously reliving past events or envisioning future scenarios without conscious effort. We explored the phenomenological characteristics and contents of self-caught and probe-caught spontaneous thoughts, focusing on involuntary MTTs. These paradigms differ in the meta-awareness they demand, which may affect the nature of the captured thoughts, especially under attentional load. During a vigilance task with different attentional loads, participants reported their thoughts as they realized them (self-caught) or when the task prompted them (probe-caught). They then completed questionnaires regarding their thoughts' phenomenological characteristics. We predict that self-caught thoughts will have a higher proportion of involuntary MTTs, marked by episodic and self-related content. Under high attentional load, involuntary MTTs are expected to comprise a larger proportion of reported thoughts in both paradigms. Investigating the characteristics of spontaneous thoughts and their modulation by attentional load contributes to a deeper understanding of the metacognitive processes underlying involuntary MTTs.

Implicit mentalising involves the automatic awareness others' perspectives. The Joint Simon task demonstrates this as a Joint Simon Effect (JSE): A spatial compatibility effect is elicited more strongly in a Joint Simon versus an Individual go/no-go task. The JSE may stem from spontaneous action co-representation of a social partner's frame-of-reference, which creates a spatial overlap between stimulus-response location in the Joint (but not Individual) task. However, JSE's domain-specificity is debated. We investigated the potential content of co-representation during task-sharing—typical geometric stimuli were replaced with two coloured sets of animal silhouettes. Each set was assigned to either the participant themselves or their partner. Critically, a surprise image recognition task followed, aiming to identify any partner-driven effects in incidental memory exclusive to the Joint task-sharing condition, versus the Individual condition. Bayesian statistics indicated a robust absence of the key JSE, limiting interpretations of incidental memory findings, with implications regarding JSE's replicability.

Psychologists have noted that analogical reasoning is pervasive in argumentation (Kuhn, 1992; Holyoak, 1997), but the forms these arguments can take varies substantially. Memes are one common format or argument-by-analogy. Memes are widely recognized images or templates that compares two situations to each other for the purpose of making some (often questionable) point. Even though memes-as-arguments are readily visible on social media, the persuasiveness of this category of argument-by-analogy---and specifically the features that predict their persuasiveness---have not been established. This study investigates whether and in what ways arguments by analogy, delivered in the form of a meme, are persuasive. We develop a large set of memes representing common meme structures, political leaning, and familiarity and examined how these factors predict a meme's perceived clarity, persuasiveness, and memorability, along with these memes effects on beliefs about issues such as climate change, immigration, and racism.

A plethora of research has identified that undergraduate students experience higher levels of general anxiety disorder (GAD) with questionnaires primarily being used diagnose students. However, the potential for linguistic analysis and the emotional Stroop test as measurements of GAD remains unexplored, especially if both were to be used together. The present study aimed to produce a novel measure of GAD using both linguistic measures and the emotional Stroop task. This research study employed a quantitative approach and an experimental research design. A volunteer sample of 17 undergraduate students completed an online questionnaire, a written task, and an emotional Stroop task distributed via social media and the University of Birmingham Research Participation Scheme. This study produced a novel questionnaire for GAD and was utilised as a baseline measure. This study used two independent T-tests to measure the overall sentiment of written responses of participants and the frequency of first-person singular pronouns in written response. Additionally, two independent T-tests were used to measure reaction times and accuracy on the emotional Stroop Task. The findings highlighted no statistical differences between higher and lower levels of GAD and linguistic responses and reaction times and accuracy on the emotional Stroop test, suggesting that the measures utilised in the present study may not be able to predict GAD. As such, the findings of this study underscore the complexity of GAD and extend our understanding of GAD measurements. By illuminating the emotional, behavioural and cognitive factors of GAD, this study advocates for more awareness in university settings and proactive support for undergraduate students.

This study introduces an innovative approach to readability assessment, integrating cognitive science principles with artificial intelligence to evaluate text comprehensibility. Traditional methods of determining text readability have largely focused on surface-level features, neglecting educational complexity and curriculum alignment of the content. This study proposes a novel method that employs large language models (LLMs) to assess text difficulty by considering content depth and its alignment with educational standards. By leveraging the extensive knowledge encapsulated in LLMs, the method evaluates whether the content of the text corresponds to a specific educational level, ranging from elementary to university. Our readability assessment method provides a more nuanced understanding of text accessibility. The difficulty of the text content is assessed using a combination of language resources to measure the amount of scientific knowledge contained in the text. It promises to enhance educational resources' alignment with learners' capabilities, facilitating more effective learning experiences.

Many infant experiences are both visual and auditory in nature, but what is the role of auditory cues in visual attention? Using a Dynamic Field model of infant visual attention, we generated simulations of infant looking behaviour in both a tone and no tone version of the Infant Orienting With Attention (IOWA) task. The DF model predicted a significant difference in reaction times and accuracy between the tone and no tone groups with the tone group faster and less accurate. To test this, we ran the IOWA task with 70 infants between 4 and 10 months of age randomly assigned to either a tone or no tone condition. There were no significant between-group differences. We explore these empirical findings using the dynamic field model, extending the model in two directions. First, we utilise Tensorflow tools to optimise the model parameters, and second, we fit the model parameters to individuals.

Several studies show that lexical frequency influences linguistic processing and, when uncontrolled, can confound the results of psycholinguistic experiments. Given the scarcity of solid frequency data for sign languages, this study aims to know the subjective frequency of 277 signs of the Uruguayan Sign Language (LSU). The study is available online (its source code is publicly available) and allows the collection of frequency estimates. This tool was validated by running the experiment with Rioplatense Spanish words and comparing the estimates with measures of objective frequency based on corpora and reaction times observed in lexical decision tasks. The results will allow us to know the variation of frequency according to typical variables in psycholinguistic studies, such as region, age, and ethnicity, and according to variables more typical of sign language studies, such as the age of language acquisition, use of the language at home, and the educational background of the participants.

We often rely on others' testimony when learning about new topics, such as health benefits of a novel food. However, the sources are not always knowledgeable, helpful, or unbiased, necessitating an assessment of their credibility. Here, we present a Bayesian model of source credibility, where a listener simultaneously infers the expertise and intention of the source while trying to discern the truth. A key prediction is that rational inference of credibility requires anchoring it on some kernel of shared knowledge. We consider a scenario where both parties have noisy access to the ground truth of familiar topics (e.g., is broccoli healthy?), which serves as a basis for reasoning about a source's credibility on novel topics (e.g., is avocado healthy?). This approach provides a computational framework for understanding how people respond to information in domains like science communication and media consumption.

Voice identity recognition in auditory-only conditions is facilitated by knowing the face of the speaker. This effect is called the ‘face-benefit'. Based on neuroscience findings, we hypothesized that this benefit emerges from two factors: First, a generative world model integrates information from multiple senses to better predict the sensory dynamics. Second, the model substitutes absent sensory information, e.g., facial dynamics, with internal simulations. We have developed a deep generative model that learns to simulate such multisensory dynamics, developing latent speaker characteristic contexts. We trained our model on synthetic audio-visual data of talking faces and tested its ability to recognize speakers from their voice only. We found that the model recognizes previously seen speakers better than previously unseen speakers when given their voice only. The modeling results confirm that multisensory simulations and predictive substitutions of missing visual inputs result in the face-benefit

Rhyme is regarded as a powerful mnemonic device that facilitates cognitive processing. Previous studies mainly examined rhyme-perception development in the case of children (e.g., Kiràly et al., 2017); thus, instead, the present research focuses on information-recall processes in the adult population. In cultural transmission processes, rhyme and memory are closely connected (cf. Kirby et al., 2008); therefore, there is a need for research investigating whether and how the adult population's recall ability is enhanced by rhymes to gain a better understanding of the rhyme‚Äìmemory relationship. The present study examines whether rhyming words are more likely to be recalled than their non-rhyming counterparts. Results suggest that rhymes affect short- and long-term consolidation of real words and pseudowords in the case of adult participants (N = 38). By gaining insight into recall processes related to rhyming, it may be possible to understand information retrieval procedures in the context of cognitive poetics.

People tend to judge repeated information as more veridical, referred to as the Illusory Truth Effect (ITE). While recent findings show that the effect is still observed when we “know better”, how episodic experiences influence ITE and how metacognitive judgment (i.e. subjective confidence) of one's response changes with repetition remains unclear. To address this question participants watched a video and then judged the truth value of statements about the video, presented in varied repetitions (0,1,4). We compared truth and confidence judgments of repeated items that were false, true, or unknowable. We found that for true statements repetition increased confidence and truth judgments. For false items, it increased only confidence leaving truth judgments unaffected. Conversely for unknowable items, repetition increased truth judgments but not confidence. These results suggest that based on information's congruence with memory references, its repetition impacts truth and confidence judgments differentially.

Generalizations that hold across all languages (linguistic universals) provide important insights into cognition, language, and learning. In semantics, the best-known universal is determiner conservativity: the truth of sentences like “every/most/some/no fish swim(s)” depends only on the determiner's first argument (“fish”). This rules out cross-linguistically unattested determiners (e.g., “equi fish swims” meaning ‘the fish and the swimmers are numerically equivalent' isn't conservative because both fish and swimmers matter). Zuber & Keenan (2019) propose a weakening of conservativity: determiners depend on their first OR second argument, but not both. Which constraint do learners obey? We test whether adults are able to learn novel determiners that are classically non-conservative but are conservative on the weakened view. We compare these ‘weakly conservative' cases against novel determiners that are conservative on both views and non-conservative on both views. We find that adults can learn conservative meanings, but not weakly conservative meanings, supporting the classical understanding.

People judge an object's criterion value by relying on its similarity to previously experienced objects, the so-called exemplars. This work investigates exemplar-based processes in quantitative judgments by applying cognitive modeling to data from an eye-tracking experiment. Participants (N = 49) first learned the criterion value and location on the screen of each of four exemplars. Then, they assessed the criterion value of briefly presented test stimuli, and eye-tracking measured the gaze proportion to the now blank exemplar locations (looking-at-nothing). Participants who showed more looking-at-nothing also relied more on exemplars according to cognitive modeling of the test phase responses in the RulEx-J framework. Furthermore, looking-at-nothing was directed in particular at locations of exemplars similar to the test stimulus. Our multi-method approach thus suggests tight links between eye-tracking and cognitive modeling. The insights from process-tracing methods might be particularly valuable, when cognitive modeling cannot distinguish between alternative processes to perform quantitative judgments.

What sorts of graphical formats best convey effect size and degree of certainty of a finding? Confidence intervals are commonly used to show uncertainty, yet lay people and experts fail to correctly interpret their meaning. There has been a recent push to present individual data points rather than only presenting aggregated summary statistics (e.g., means, confidence intervals, lines of best fit). But it is unclear how well people can aggregate raw data presented in a graphical format. Across two studies, we presented participants with hypothetical study outcomes of two independent groups in three graph styles: dot plots, mean with 95% confidence interval (CI) plots, combined plots, and bee plots. We asked participants to make judgments about the effect size using the Common Language Effect Size or Bayes Factors. Participants were more likely to underestimate effect sizes and Bayes Factors for dot plots and bee plots compared to mean + 95% CI plots and combined plots. These findings suggest that people have trouble making statistical inferences when presented with raw data points in graphs.

To what degree does semantic foraging probe semantic network structure? We use a combination of foraging experiments (animals, concrete nouns) and simulations on networks based on nine approaches to semantic similarity to address this question. In data and simulations, we find a significant bias towards naming semantically similar items, and significant correlations between inter-naming time and semantic distance. In previous foraging experiments, a roughly power law distribution with a Lévy range exponent was found in the distribution of inter-naming intervals. We find the value of this exponent is not universal but is sensitive to the search space size in that the exponent decreases (moving further into the Lévy range) as the number of nameable items is exhausted. Moreover, these exponents are not unique to semantic networks but appear in censored random walks on other graphs. Our combined experimental results and simulations provide insights into the topology of semantic memory.

Procrastination is often characterized as minimal progress initially, with a significant increase in progress shortly before the deadlines. Yet, the cognitive mechanisms underlying this intriguing dynamic feature of procrastination—the time course of progress—remain poorly understood. We investigated this through an experiment where participants worked on a self-paced, week-long online reading task consisting of numerous work units (N = 611). We proposed two models that fit each individual's time course of progress. Both models consider the time course of progress as the output of sequential decision-making: whether to work now (and, if so, how much) or later. The first, a normative model, calculates the value of making progress using the Bellman equation; the second, a roll-out model, estimates this value by simulating future work progress. We found that the rollout model fit the data much better, suggesting some evidence against people behaving rationally and some evidence for people simulating future work progress.

Modern intelligent tutoring systems, exploiting technological advances in augmented and virtual reality and large language models, offer fluent natural language interaction between a virtual character and a student complemented with a multimodal interface, including recognition and synthesis of affects and intentions expressed in speech tonality, facial expression, gaze, and body language. Being concerned with consumer satisfaction, developers of such systems often miss the educational needs. Here we present a Virtual Tutor that, using the above technologies, helps students to self-regulate during learning. This is made possible based on the self-regulated learning theory integrated into an emotional cognitive architecture. Virtual Tutor uses its emotional intelligence to model, guide, and motivate students to engage in self-regulation. It does it in parallel with performing the basic tutoring functions. Results of our preliminary study provide some evidence of support for Virtual Tutor. This work was supported by the Russian Science Foundation Grant #22-11-00213, https://rscf.ru/en/project/22-11-00213/.

Dendrophilia — a widespread proclivity toward hierarchical behavior — has long been argued to be central to human cognitive uniqueness. Alternative views emphasize the developmental and evolutionary continuity of complex hierarchical psychological processes with simpler sequencing mechanisms. We investigated the predispositions of human adults and 3-to-6-year-old children to spontaneously generate hierarchical patterns in an open-ended sequence generation task. We also compared the human ability to learn hierarchical patterns with that of rhesus macaques and carrion crows. Our Bayesian mixture model quantified the extent to which distinct mechanisms — associative chaining, linear iteration, queues, and stacks — were implicated in hierarchical behavior. Our results suggest that hierarchical behavior is possible across species. It emerges early in cognitive development and may be scaffolded by simpler cognitive processes that eventually increase in representational and computational complexity. Thus, our findings contradict the dendrophilia hypothesis and point to shared psychological processes underpinning hierarchical behavior.

While it is difficult to find and persuade research participants to invest the famous 10,000 hours of practice necessary to develop expertise in any given task, one can more easily commit oneself to such a devoted undertaking. Through autoethnographic observation, the author, a retired semi-professional eSports competitor with no experience or knowledge of the new competitive eSport game Street Fighter 6, documented and livestreamed months of gameplay sessions as he acquired expertise and rose through the ranks of the game's competitive online mode, striving to reach the game's highest ranking of “Master.” The author critically examines the strategies and practices most useful for optimizing learning and performance – illustrating the contributions of reflexivity and reflection that are often overlooked in laboratory experimentation. Overall, this work demonstrates how autoethnographic insights developed “in the streets,” when combined with empirical research in the lab, contribute to a fuller picture of learning and expertise.

Traditional conceptions of inference emphasize explicit following of logical rules, often tied to the possession of natural language, thereby implying that non-human animals cannot make inferences. However, comparative research shows extensive evidence of the success of several species of non-human animals in nonverbal reasoning tasks, putting pressure on the traditional view. We deny two traditional assumptions about inference: the lingualism of thought, and the requirement of explicit rule following. We suggest instead a multidimensional model of inference illustrated through several case studies. Thereby, we categorize informational transfers across three dimensions by marking the degree of context-independence, the format of representation, and the type of perspectivity involved. By allowing for a more nuanced interpretation of empirical data than the traditional view, our framework is able to accommodate inferential behaviors of both linguistic and non-linguistic agents, and shed light on varied manifestations of inference across species and developmental stages.

We study repair in child-directed, child-surrounding and child speech in longitudinal corpora of 4 languages: English, Russian, Chintang and Indonesian (age range: 2;01-3;04). We distinguish open requests (e.g. 'huh?'), restricted requests (e.g. 'you saw what?'), and restricted offers (reformulation or recast, e.g. 'you saw a bird?'). Our results indicate that in the aggregated model, clarification requests develop in children independently from adult speech, pointing to early universal emergence. When we analyse repair types separately, only restricted offers in both CDS and CSS are significantly predictive factors for the number of reformulations in child speech. Since this repair type is used by caregivers to provide both positive and negative feedback to children, they follow a special path of acquisition dependent on input distributions. Therefore, we propose that early repair acquisition relies on individual cognitive development of children as well as language exposure to the recast frequency in the caregiver speech.

The most recent generation of Large Language Models owes its success not only to scale, but also a novel step in their training: reinforcement learning from human feedback (RLHF). In this study, we assessed the impact that this training regime has on the fit between model and human behavior in regards to linguistic behavior. We evaluated three versions of OpenAI's GPT-3 davinci – original, instruction-tuned, and RLHF-trained – using psycholinguistic tasks: subject-verb agreement, sentence acceptability, and event knowledge. We then compared their performance to human participants. We found that the RLHF model is significantly more human-like in its answers, including in the errors it commits. Moreover, the uncertainty of the distribution of its output is closely tied with between-subject variation in humans. This suggests that human feedback improves not only the overall quality of LLMs, but also the alignment between their behavior and the linguistic, metalinguistic, and discursive intuitions of humans.

Measurement of braille reading with high spatial and temporal accuracy could provide a unique window into incremental processing, complementary to eye tracking and speech perception measures. In braille reading, the fingers move continuously (not in discrete saccades) and perceptual processing is focal (unaffected by parafoveal preview or anticipatory coarticulation). We video-recorded (~60fps) nine congenitally blind adults reading linguistically rich passages from the Natural Stories Corpus presented in UEB English braille. Finger locations were tracked with computer vision software, mapped to page coordinates, and converted to word reading times (RTs). In the resulting dense data set, containing >3 million tracked locations and >50,000 word tokens, RTs increased with word length in cells (r=0.77), decreased with log word frequency (r=–0.62), and increased with context-based surprisal (r=0.40, all ps<0.001). These results establish lexical and contextual effects with a low-cost, automatic braille tracking method.

Learned fears of stimuli from phylogenetically fear-relevant categories (such as snakes and spiders) tend to be significantly more resistant to extinction than those from fear-irrelevant categories (such as birds and butterflies.) Olsson et al. (2005) demonstrated that representations of outgroup members, as defined by race, can act as fear-relevant stimuli in a classical conditioning paradigm. It is not as clear, however, whether (and how) persistent fear of outgroup members can be acquired vicariously. We investigate whether observers of interactions with negative outcomes associated with representations of outgroup members develop extinction-resistant fears. Our results indicate that outgroup members can act as fear-relevant stimuli in an observational scenario. The effect is not sensitive to self-relevance manipulations; importantly, however, other-oriented empathy may reduce the tendency toward forming extinction-resistant conditioned responses to outgroup members. Implications of these preliminary results, including limitations and suggestions for future research, are discussed.

When completing a task for a prolonged period, our ability to sustain attention fluctuates over time. Accordingly, in mice, disengaged behaviour has temporal autocorrelation (i.e., ‘disengagement states'), with lapses clustering in time, rather than occurring randomly. In this disengaged state, mice make more errors and provide responses biased towards one side. What neural and physiological processes trigger the transition into, and out of, disengagement states? Here, we investigated the role of pupil-linked arousal. We used a public dataset of 140 mice performing a perceptual decision-making task, including extracellular recordings alongside behavioural and pupil responses. We applied hidden Markov models to identify engagement states based on response times. Preliminary results show that disengaged trials are associated with larger and more variable baseline pupil, and suggest that pupil size changes precede state transitions. These findings will provide a starting point for exploring the cortical, subcortical and neuromodulatory processes preceding task (dis)engagement.

Adults have rich beliefs about children's development timelines, and they interpret and react to children's behaviors across ages, holding children responsible to some degree. While children's mental capacity and potential could motivate moral agency attribution, a question remains whether a consistent relation exists between the empirical beliefs about children's various capacities and the responsibility attribution to their behaviors that manifest the corresponding capacities. Here, we tested 361 adults (UK, US) on their folk psychology and moral beliefs about different ages with vignettes that reflect agential control in various domains (motor control, inhibitory control, theory of mind, planning, moral evaluation) combined with several variants of scenarios. We characterized the relation between adults' expectations and responsibility attribution with mixed models. We found that this moral reasoning varies for targets of different ages and the amount of responsibility is mostly determined by age. We suggest an alternative mechanism between capacity- and moral beliefs.

We explore how viewpoint phenomena interact with metacognition during dynamic, intertwined processes of thinking, speaking, gesturing, and improvising music. Taking a perspective on experienced or solely imagined situations involves physical and/or conceptual positioning within or outside a spatial, narrative or mental context, whereby speakers typically employ various bodily articulators to signal simultaneous or shifting viewpoints. Tapping into how viewpoint frames thought processes, we propose that shifting viewpoints are a metacognitive strategy to explore contextual possibilities through semiosis, embodied in gestures and other body movements. Changing viewpoints on an unfolding situation, including one's own mental activities, allows for both re-experiencing scenarios and exploring new ideas and perspectives. This theoretical groundwork prepares our empirical research into how metacognition and viewpoint jointly drive musical improvisation. Applying cognitive semantics and Peirce's semiotics, we present preliminary analyses of musicians' improvisation and their multimodal meta-discourses (including motion-capture data), thus exploring cognitive-semiotic processes in musical creativity.

Several studies showed that humans are incredibly accurate at extracting simple statistical information from noisy datasets, such as judging the linear trends of scatterplots. Crucially, these intuitions might serve as one of the building blocks of both graphical and mathematical skills. However, we do not know if such abilities are specific to our species or if they can be found in other animals as well. We tested several guinea baboons on a trend judgment task in which they had to judge whether linear trends (both noisy and noiseless) were increasing or decreasing. We show that they can and that they behave strikingly similarly to humans: they seem to base their judgment on the t-value of the graph, which is the index that a statistician would calculate to measure the significance of the linear relationship in the dataset. These findings suggest that the ability to extract statistical information from visual noise is not available only to humans.

This research studies how 24 experts in sport, music, and business were able to make successful and repeated career transitions to senior levels in their field. It examined – among other aspects – the roles of cognitive flexibility, personal intelligence, generative thinking, motivation, and contextual intelligence in career transitions. It also examined how identity changes and adapts during a career transition and how self concept evolves over the course of a career. In-depth interviews were analysed both qualitatively and quantitatively and served as the basis for evaluating several theories of expertise, cognition, motivation, and intelligence. Key findings include: deliberate practice was rarely mentioned as a contributor to transitions; the early development of expertise in multiple domains contributed to its generalizability; transition expertise evolved over the course of a career; and self concept did not unfold in a linear progression of sequential stages as predicted by many theories in the field.

This study looks at the benefits and functions of bilingualism in Indian schoolchildren with low vision impairment. Bilingualism, particularly in a multilingual country like India, can have considerable cognitive, social, and educational benefits. The study focuses on a sample group of N=60 (monolingual and bilingual) school-aged children with varying degrees of low vision impairment and analyses how bilingual (L1-Telugu and L2-English and L1-Hindi and L2-English) education effects their learning and social integration. Using the Language Experience and Proficiency Questionnaire (LEAP-Q), the study employs both qualitative and quantitative methods to assess cognitive development, language competency, and social interaction abilities in a bilingual situation. The findings indicate that bilingualism improves not only verbal abilities, but also cognitive flexibility, problem-solving ability, and social empathy in early children. This study suggests that bilingual education should be an integral part of the curriculum for visually impaired pupils in India, encouraging their overall development and integration into society. The findings have significant implications for educational policies and practices affecting special-needs children in diverse environments.

Children know a lot about counting, even before they can count; for instance, even toddlers know that the counting routine involves establishing one-to-one correspondence between number words and items counted. Here we varied the size, numerosity, density, and layout of elements of sets, and asked children which set was easier to count in pair-wise comparisons across twelve trials. We also asked children themselves to count 5 to 15 items arranged in straight lines. Even children who could not count to 15 recognized that it was easier to count fewer than more dots and recognized that structured sets were easier than random arrays; however, they failed to recognize that some layouts made tracking easier than others. This suggests that children's meta-knowledge about counting precedes their ability to count for some but not all properties of sets.

Many human-invented compositional systems (e.g., language, mathematics) embody hierarchical relational structures. How exactly these structures are acquired during learning remains an open question. Here, we examine how the structure of a system engages learners' attention and learning. Participants (N=88) learned an artificial language that describes novel combinations of unknown visual symbols while their eye movements were recorded. Participants were randomly assigned to one of two conditions. The ‘More' condition had three latent rules that connected components in verbal input to visual input. In contrast, the ‘Less' condition had only one latent rule. Despite having more regularities to learn, the ‘More' condition performed as well as the ‘Less' condition. Eye movement data further revealed that participants in the ‘More' condition selectively attended to target symbols more than those in the ‘Less' condition. These results suggest a counterintuitive ‘More is More' principle: the presence of multiple regularities organizes attention and potentiates learning.

Trained on text only, Large Language Models (LLMs) provide a unique way to approach the age-old question of how language captures sensory experiences. Such models have showcased human-level performance in several domains. However, what they capture about the sensory world remains uncertain. We prompted state-of-the-art LLMs (GPT-3.5 and GPT-4) as well as sighted and congenitally blind adults to judge the likelihood of successful visual and auditory perception using verbal scenarios. Scenarios varied in distance of the observer from the object (next to, across the street, a block away), duration of perception (glance vs. stare) and properties of perceived object (e.g., size for vision). Sighted and blind humans produced highly consistent perceptibility judgments, and these correlated highly with GPT-3.5 and GPT-4. GPT-4 showed human-like effects of size, distance, and duration, though both LLMs underestimated humans' ability to perceive. Language captures detailed quantitative information about perceptibility.

Across two experiments, we presented children (N = 168; 3 to 6 years) with a memory task in which three targets were hidden sequentially before a search period. In both experiments, younger children were significantly more likely to first search for the last target hidden (in line with the recency effect), whereas older children were significantly more likely to first search for the first target hidden (in line with the primacy effect). In a separate test phase where some but not all targets were were externally marked, younger children were biased towards selecting the marked target first, whereas older children were significantly more likely to search for unmarked targets before marked targets (thus reducing the time spent maintaining the location of the unmarked targets in memory). These results indicate marked shifts in young children's ordered memory recall tendencies, much earlier in development than suggested by previous research.

Just as we intuitively know that "chair" and "boy" denote referents in different categories, we know that "standing" falls into a different category from "walking": One of the events is static, the other dynamic. In three self-paced reading experiments, we show that such differences in event dynamicity leads to expectations about the temporal structure of complex events. We replicate and extend Gennari (2004): Participants read complement (Exp.1) and relative clause constructions (Exp.2,3) in which the event type in the subordinate clause (i.e., event/state) and temporal proximity between main and subordinate clause situations (i.e., close/overlap vs. distant/non-overlap) were manipulated. Consistent with Gennari (2004), we find evidence that people expect states to overlap (Exp.1,2), but only when in line with their expectation that states should happen first in time (Exp.3). Our results support a multifactorial model of language comprehension in which event structure is central to the formation of temporal expectations.

Understanding abstraction is a stepping stone towards understanding intelligence. We ask the question: How do abstract representations arise when learning sequences? From a normative perspective, we show that abstraction is necessary for an intelligent agent when the perceptual sequence contains objects of similar interaction properties appearing in identical contexts. A rational agent should identify categories of objects of similar properties as an abstract concept, enabling the discovery of higher-order sequential relations that span a longer part of the sequence. We propose a hierarchical variable learning model (HVM) that learns chunks and abstract concepts from sequential data in a cognitively plausible manner. HVM gradually discovers abstraction via a conjunction of variable discovery and chunking, resembling the process of concept discovery during development. In a sequence recall experiment that demands learning and transferring variables, we observe that the model's sequence complexity can explain human behavior in a sequence memorization experiment.

Perception Verbs (PVs) can have, besides their denotational interpretation that 'X perceives Y', other interpretations depending on context. For example, in narratives we often find contexts where seeing something introduces a new referent, heralds a pivotal event, or compresses redundant information about characters' inner states. We computationally model the emergence of such pragmatic use in children (4-12y) with recent Language Models (LMs). Since LMs are partly trained on narrative corpora and can model coherence in narratives, we assume that a LM can be used to identify PV contexts that humans recognise as having a pragmatic function. We sample PV contexts from ChiSCor, a corpus of Dutch children's freely told narratives, and use the confidence of LM predictions to identify developmental patterns in pragmatic use of PVs for children of different ages. Simultaneously, our setup allows us to identify types of pragmatic meaning that LMs still struggle with.

The idea of curriculum learning, whereby a model is first exposed to simpler examples before an increase in complexity, has long fascinated the AI community. Unfortunately, the experimental successes of curriculum learning have been mixed, particularly applied to natural language, where a vast body of literature appears to evidence its failures. However, recent work has shown that language models trained on transcribed-child-directed-speech (CDS) learn more grammar compared to those trained on Wikipedia. To a lesser extent, the same trend has been observed through training on transcribed speech and simple text data. Motivated by these findings, we revisit the idea of curriculum learning starting from CDS, before moving to simple data, and finally finishing with complex long form text. Unfortunately, through experimentation with an array of models and training step sizes, only in the smallest models trained for the least steps does curriculum learning show any advantage over random sampling.

Previous research about the intensity of emotional facial expressions has relied on stimulus sets of morphed facial expressions that have been generated artificially. Ecologically valid open-access facial stimulus sets with varying intensities of multiple different expressions are rare. However, there is a growing need for a validated facial stimulus set that would include multiple levels of intensities. This study aimed to develop and test the psychometric properties of a stimulus set with real facial expressions (8 men and 8 women) with 11 intensity levels for five facial expression categories: angry, happy, neutral, surprised and sad. 52 individuals rated the valence, arousal and intensity of the 656 stimuli. Descriptive statistics, internal consistency of the rating for each stimulus, emotion category, and intensity level were described. The stimuli and summary data are available upon request (https://osf.io/f8ews/).

Decision frames influence how people act. These frames and the resulting decisions can be changed by manipulating how a problem is described. Here, we ask if people themselves can induce frame changes when thinking about a problem and how these frame changes affect decision-making and choice satisfaction. In our experiment, participants (N > 700) generated as many options as they would like for day to day scenarios as choosing a costume for a party or finding a gift for a friend. Then, participants selected one of the options they generated and reported their choice satisfaction. We found that choice satisfaction was higher when the option selected was more semantically dissimilar to the rest of the option set. We argue that this suggests that participants use a novel strategy to facilitate decision-making: Participants aimed to construct decision frames by generating options sets with a uniquely dissimilar option, which facilitated choice and increased satisfaction.

Recipient design, tailoring one's message to an interlocutor's relevant requirements, is a core pragmatic process in human communication. The knowledge shared among interlocutors influences the form and content of the speaker's utterances addressed to a recipient. In a computerized experiment, we investigated whether recipient design is different for robot- and human-recipients and whether it is sensitive to dynamic changes in attributed competence of the addressee. In a word-guessing game. participants described objects and abstract concepts to a robot- and human-recipient, who later guessed the word. The recipient gave incorrect answers in half of the trials. We coded participants' descriptions for linguistic complexity in robot- and human-recipient conditions as well as in trials immediately following correct and incorrect trials. We predicted linguistic complexity of the descriptions to differ by recipient and trial type. Our findings will be discussed in relation to cognitive attributions' influence on recipient design in HRI.

The influence exerted by no longer relevant intentions that have been successfully executed or cancelled is called aftereffects. The current study investigated the effect of encoding a secondary intention on the aftereffects of non-relevant prospective intentions. The study used an active phase-finished phase paradigm with participants randomly assigned to either experimental or control conditions. In the experimental condition, participants encoded a secondary intention in the finished phase of the task. In the control condition, participants did not encode any additional instructions. Commission errors and response latencies were analysed in the finished phase for fulfilled intentions or encoded but unfulfilled intentions. Independent sample t-tests found significant (p<0.05) differences between experimental and control groups. Suspended cues displayed a higher accessibility due to anticipatory monitoring and pending response action, and also resulted in more commission errors in comparison to repeat cues.

Though working memory deals with different types of contents, the vast majority of studies on working memory updating have been conducted on non-sense syllables and numbers. The present study aims to understand the updating process of words, particularly, whether word length increases the response latencies for updating. The study hypothesized updating of longer words to be more time consuming than shorter words. A modified version of the working memory updating paradigm proposed by Artuso & Palladino (2011) is used for the study. A within-subject experimental design was employed. Repeated measures ANOVA of response latencies across conditions of 3,4 and 5 letter word updating, found no significant differences in reaction times on the basis of word length. The involvement of chunking and other long term memory processes can be cited as the reason for this.

Interlingual Respeaking (IR) is a new technique that enables real-time subtitling in a different language. This cognitively demanding technique involves collaboration between a language professional and automatic speech recognition software (ASR), creating a human-AI interaction (HAII) environment. Integrating technological tools with an individual's internal cognitive resources establishes an extended cognitive system. However, different types of errors are observed in terms of output accuracy. Our ESRC-funded research found that working memory (WM) (backward span) has a negative relationship with omissions, where content is dropped out (e.g., to save time). Nevertheless, additions, where the human adds content (e.g., to clarify meaning) and correctness, where form-related issues arise (such as grammar mistakes), had an inverse relationship with the N-back Task (the simultaneous maintenance, updating, and processing of WM). These findings suggest that the IR errors involve diverse types of WM resources.

Although most words have more than one meaning, the mechanisms underlying new-meaning learning have been understudied. This one-to-many mapping poses even greater challenges for second language learners. The present study examined the behavioral mechanisms underlying new-meaning learning among non-native speakers by focusing on the effects of word familiarity, an approximate measure of lexical quality. We found that learning new meanings for more familiar L2 words was easier, as indicated by better recognition and cued-recall performance throughout the learning phase and in delayed tests. Furthermore, new-meaning learning facilitated, rather than impeded, the processing of original meanings, especially after a delay. Comparing these findings with those from previous studies involving native speakers, it appears that lexical quality influences how new and prior knowledge interact during new-meaning learning.

Distinguishing relevant features from noise is a central challenge for efficient behaviour. We asked whether humans address this challenge by leveraging the insight that behaviourally relevant processes change on a slower timescale than noise. To test this idea, participants were asked to learn the rewards of two-dimensional bandits when either a slowly or quickly changing feature of the bandit predicted reward. Participants accrued more reward and achieved better generalisation to unseen bandits when the reward-predictive feature changed slowly, rather than quickly. These effects were stronger when participants experienced the feature speed before learning about rewards. Computational modelling revealed that participants adjusted their learning rates based on feature speed. Those who learned better from slow features also had higher learning rates for it from the onset. These results provide evidence that human reinforcement learning favours slower features, suggesting a bias in how humans approach reward learning.

Infant word learning is a crucial process that is of great importance to early development. Indeed, delays in early word learning are linked to poor language and educational outcomes, including Developmental Language Disorder (DLD). However infant word learning is highly variable, and the correlates of successful and delayed early word learning are not well understood. Here, we examine the role of individual temperament in word learning, examining the dynamic interplay between category interest, general curiosity, willingness to engage, and motivated word learning in a novel word learning task, using changes in infant pupil diameter as the measurement. Preliminary data suggests category interest to be of key import to early word learning, supporting previous findings from Ackermann et al (2020). We also find differences in personality contribute to word learning, suggesting that the variability in infant word learning might be related to individual differences.

Political polarisation is growing along with its negative consequences – degradation of functional government and increases in stochastic violence. Polarisation can result from both cognitive factors affecting information processing and biased information ecosystems, but their interactions are poorly understood. We present an agent-based model combining a varyingly polarised media landscape with agents driven by homophily and uncertain (political) identity inference processes. Agents were motivated to find similar others to form an ingroup by comparing moral values expressed in response to environmentally imposed moral dilemmas. Media pushed moral values in line with either liberal or conservative values, varying in agreement and influence. Liberal agents were more satisfied (according to homophily motivations), formed larger, more stable clusters, and morally disengaged less than conservatives. Identity aligned media exposure increased liberal agents' satisfaction, but had no, or the opposite effect, on conservative agents. We conclude that media exposure asymmetrically affects political polarisation across political identities.

Emotions can be insensitive to certain attributes of a situation. A large body of evidence shows that information on probabilities, large numerical counts, or intentions is frequently disregarded in the elicitation and regulation of emotions. To date, no existing theory comprehensively accounts for the features that tend to be overlooked by emotion. In this paper I call attention to the common denominator of such features: they cannot be perceived nor contribute to the iconic representation of events. For instance, the exceedingly low probability of a plane crash does not affect its imagistic representation (i.e., the iconic representation of the event is silent about the event's probability). I introduce the Iconic Prioritization Hypothesis, positing that the prioritization of the iconic format in emotion can explain the neglect of information that is representationally silent in this format. Emotion may favour iconicity as it is the format of immediate, first-hand evidence about our surroundings.

Spatial orientation and spatial navigation are important abilities. However, large individual differences are common in these spatial abilities, yet satisfying explanations about the origin of such differences are lacking. In this work, we measured the eye-tracking data of 26 participants who freely explored a large city (244 buildings) in an immersive virtual reality for 150 min. After the exploration, participants performed a pointing-to-building task in the same city. For the analysis, we transform the eye-tracking data into gaze-graphs and calculate graph-theoretical measures. We then model participants' mean task performance with a linear model using global gaze-graph measures (R²=0.41). Moreover, a linear model with graph diameter only results in an R² of 0.4; thus, graph diameter can explain 40% of the variance in the mean task performance of participants. Overall, our results show visual behavior, specifically gaze-graph diameter, to be a strong predictor of individual differences in spatial navigation performance.

We propose a generative modeling framework of impaired language production and an inference framework that models rational comprehension of impaired language. Given a task (e.g. picture-description), we approximate the prior distribution over intended sentences using a language model trained on unimpaired speakers' utterances. We define a generative model of operations (e.g., semantic and phonological errors, retracing, filled pauses) that intervene on the intended sentence to yield an utterance. The model is implemented in the Gen probabilistic programming language, with data from AphasiaBank's ‘Window' picture-description task. Given observed utterances, a particle filter estimates posterior probabilities for latent variables (e.g. the speaker's intended sentence or sequence of errors). Our framework models comprehension as inference on a generative model of production, and provides a way to quantify incremental processing difficulty for impaired language in a way that combines a language model prior with explicit reasoning about errors.

Children's spatial and numerical skills are highly related, and predictive of concurrent and future mathematical ability (Lourenco et al., 2018). The number line estimation (NLE) task, in which children indicate the spatial positions of numbers on a line, is a commonly used index of spatial-numerical ability. Critically, training studies have demonstrated a causal link between NLE and math ability (Ramani & Siegler, 2008). Nonetheless, there is extensive debate about the role of numerical and domain-general abilities in the NLE task. Here, we used fMRI with young children to assess the neural mechanisms supporting NLE performance. Whole-brain and ROI analyses yielded significant activation in bilateral intraparietal sulcus (IPS) during the NLE task, relative to matched control conditions. Moreover, we found a positive association between neural maturity in bilateral IPS during the NLE task and behavioral measures of math ability (Cantlon & Li, 2013), controlling for analogical reasoning and spatial working memory.

Social isolation (SI) is a modifiable factor, thought to impact cognitive resilience, with the potential to impact cognition up to ADRD diagnosis and throughout disease duration. MMSE and/or MoCA cognitive function measurements, demographic (including marital and accommodation status SI proxies) and diagnosis data were extracted, using natural language processing, from electronic health records from Oxford NHS patients aged 50+ years. Longitudinal multilevel models were used to predict cognition as a function of the interaction between diagnosis duration, SI proxies and Covid-19, controlling for age, sex and diagnosis. Using MoCA, ‘lifelong single' marital status (

The mechanisms underpinning probability matching remain a disputed topic. Among common explanations of the effect is that people employ a win-stay, lose-shift (WSLS) strategy. We suggest an alternative framing of probability matching as the result of a Bayesian sampling process involving simulating a mental sequence of possible outcomes. In three within-subject tasks, we presented people with information about a six-sided die with four sides of one colour and two of another. Two of them involved predicting the next outcome in a series of die rolls, with and without feedback. The third explicitly asked participants to mentally generate sequences of rolls from the die. The patterns of autocorrelations in responses, the absence of an effect of feedback on the next response, and the elevated proportion of maximising responses on the first trial in all conditions are all consistent with a Bayesian sampling model but contradict the WSLS account of probability matching.

The emergence of deliberate ignorance, i.e. what influences children's deliberate decisions not to seek information, is a phenomenon so far notably overlooked. This project addresses this gap by investigating various factors that systematically modulate such decisions in children and adults. Across five studies, we presented participants with short stories illustrating social situations where a misdeed occurs, and measured participants' preference for knowing the identity of the wrongdoer. Studies 1-3 (N = 550) shows that both children and adults systematically manifest a preference for ignorance when the information value is low, and when the potential wrongdoer is suspected to be a friend. Studies 4-5 (N = 333) further investigate the role of information probability in this phenomenon. This first contribution shows that children's preference for deliberate ignorance is modulated by information value and the relationship frame proposed, suggesting a rational approach to ignorance.

Category-based induction in the food domain is of key importance to generalize food knowledge to new instances of food and therefore to enlarge children's dietary repertoire. Generics are well known linguistic cues for boosting induction in young children because they facilitate the access to pieces of conceptual knowledge. However, we hypothesized that some children could not benefit from this facilitating effect of generics because they are equipped with a poor system of conceptual knowledge about food. These children are those exhibiting intense food neophobia disposition (i.e. the fear of novel food). In experiment 1, 4-6 years old children (n=137) were asked to complete an induction task adapted from Gelman, 2002 depicting properties in two conditions (i.e., generics vs specific quantifiers). In experiment 2 (ongoing) we followed a similar procedure, except that we used conflicting triads paradigm. Our preliminary results confirmed that food neophobia hindered the facilitating effect of generics.

Decades of work exploring the development of children's analogical reasoning illustrates that 3- and 4-year-old children struggle with reasoning by analogy (i.e. glove:hand::sock:___), almost always preferring superficially related “object matches” (:shoe) over “relational matches” (:foot). However, one recent study demonstrated preschoolers' ability to choose relational matches when a traditional relational-match-to-sample task is embedded in causal scaffolding, framing the target abstract relation as one between beginning and ending states of a causal transformation. Current work aims to discover which factors of causal framing facilitate this boost in early abstract reasoning. In Study 1, we replicate this effect while adapting the transformation to involve two objects, showing that preservation of identity is not necessary for analogical reasoning in a causal context. In Study 2, we explore the replicated effect in a case of non-agentive causation, finding that the causal boost, while still present, is significantly weaker when scaffolding involves a machine vs. an agent. These findings demonstrate that causal framing can be a powerful tool in bolstering children's early abstract reasoning capabilities and show that this enhancing effect is even stronger when an agent holds causal power.

The research focus on adaptivity in complex systems has propelled an exploration of diverse interactions characterized by state transition processes. However, the investigation of functional variances among processes, rooted in fundamental operands, remains insufficient. Recognizing this gap is crucial for unveiling the constituents of state transitions and their functional boundaries during ongoing adaptivity. To address this, our central focus is on quantifying the functional variance in the interactions of fundamental operands. This approach enables a systematic study of complex adaptive networks grounded in the dynamics of cognitive abilities, where elements adapt and evolve based on cognitive processes. To underscore this point, we emphasize translating ontologically irreducible networks into functionally representable ones at the meso-level, which is essential for assessing their effectiveness. Our active investigation during state transitions explores external interventions, aiming to shed light on mutual influences.

Eye tracking is a promising and non-invasive method for assessing cognitive processes in neurodegeneration. Our study focuses on the use of stimulus-driven eye tracking as a tool for discovering neurodegenerative conditions. In this study, we examine perceptual organisation (grouping, segmentation), and accentuation (Pinna & Sirigu, 2011) in neurologically impaired and healthy individuals. Based on a preliminary analysis, there are differences in the average number of fixations between clinical and control groups. Additionally, there are variations in the scanned area within specific sets of stimuli between the control and clinical groups. By identifying these differences, our study contributes to a deeper understanding of the mid-level perceptual processes in neurodegeneration.

In decision making scenarios, reasoning can be viewed as an agent executing an algorithm p ‚àà P that selects an action a ‚àà A, aiming to optimize some outcome. Metareasoning extends this by selecting p itself through a meta-algorithm p^{meta}. Previous approaches to study metareasoning in humans have required that the transition/reward distributions are known by the agent, but the value function isn't. We extend these efforts to study metareasoning for agents acting in unknown environments by formalizing the meta problem as a meta Bayes adaptive Markov decision problem (meta-BAMDP). We formally investigate the theoretical consequences of this framework within the context of two-armed Bernoulli bandit (TABB) tasks. Not only do we make theoretical progress in making the (usually intractable) metareasoning problem tractable, but we also generate predictions for a resource rational account of human exploration in TABB tasks.

Whether logical inference is available without language is highly debated. One such inference is the disjunctive syllogism (A Or B, Not A, Therefore B). Evidence from non-linguistic search tasks suggests that that the syllogism may be unavailable before age 3 (Mody & Carey, 2016). However, in a replication of the same task using language (i.e., verbal negation), even 2.5-year-olds succeeded (Grigoroglou, et al., 2019). Here we explore the role of language in children's logical reasoning. 2.5-, 3- and 4-year-olds performed a non-linguistic search task, after a short training in reasoning by exclusion. Half of the children received linguistic training (e.g., heard “there is no coin in X cup”); half received non-linguistic training (i.e., saw that one location was empty). Results show that 2.5-year-olds who received linguistic training succeeded in disjunctive syllogism but those who received non-linguistic training failed. We conclude that the presence of verbal negation facilitated logical reasoning.

Do metaphors for learning influence how well we remember new information? We tested whether reading a learning-oriented metaphor (i.e., emphasizing learning processes and outcomes) versus a self-referential metaphor (i.e., emphasizing motivational or emotional aspects of learning) can affect how well new information is learned. Participants were randomly assigned to read either a paragraph likening learning to a long hiking tour (self-referential condition), a paragraph likening learning to expanding a library in one's mind (learning-oriented condition), or no paragraph (no metaphor condition). Then participants learned a new mnemonic technique, the Method of Loci, and had to apply it to a sentence-learning task. The effect of metaphor on sentence memory depended on participants' education level. People with college degrees learned better in the self-referential condition than the learning-oriented condition, whereas people without college degrees showed the opposite pattern. These findings identify novel individual differences in how metaphors for learning influence learning outcomes.

Languages across the world organize semantic categories in many ways. Research in semantic typology and efficient communication has shown that languages tend to be shaped by pressures for communicative efficiency. It was recently proposed, in addition to this principle of efficiency, that the cross-linguistic prevalence of a system is explained by considering and formalizing the Typological Prevalence Hypothesis. This recent research found that the interaction between communicative and developmental pressures infers the prevalence of color-naming systems across the world better than phylogenetic relatedness alone. However, it is not yet clear whether the information-theoretic framework developed by the authors can explain the typological prevalence of non-perceptual categories. Therefore, we extend this model to kinship systems to test if this formalization of the Typological Prevalence Hypothesis can generalize to other semantic domains.

Perceptual organization contains two interrelated sets of phenomena: visual grouping and figure-ground segmentation. Different types of grouping and segmenting (and interaction and competition between them) have been described. Less clear is what happens once an accent is added in grouping or segmenting. According to several eye tracking experiments (n=35), apart from pop-out effect of particular elements, the overall structure of the visual field is changed. If compared to the non-accentuated stimuli, adding single accent to both grouping and segmentation stimuli induces not only local changes in saccadic processes but also a more global difference in gaze alignment. Most importantly, accent assigns a directional effect to the visual structure, typically decreases the average fixation time (by 11-28% depending on stimuli) and changes location of fixations and decreases their variation (if compared to non-accentuated stimuli). However, no significant differences between the number of fixations in non-accentuated and accentuated stimuli can be observed.

Recent work using neural networks and crowd-sourced perceptual judgements has shown that human figure drawings contain latent structure that can predict many characteristics of the artist including parent-reported motor function and perceived gender. We extend these approaches to two-dimensional renderings of three-dimensional cubes, assessing whether latent structure in these cube drawings likewise predicts demographic characteristics and motor function measured via a paper-folding task. Drawings produced with marker and paper showed a large predictive relationship with paper-folding (accounting for 59% of the offset variance, 62% of the angle variance, ps < .01). We also observed a complex interaction with gender: better cube-drawings predicted better paper-folding for male-identifying participants, but this relationship was reversed for female-identifying participants, who demonstrated better paper folding abilities overall. The results suggest that cube drawings contain richer structure than previously recognized and can provide a useful nonverbal metric for characterizing aspects of cognitive and motor abilities.

Numerical and spatial skills are highly interrelated, and both contribute to mathematical cognition. Spatial-numerical associations are frequently examined using number line estimation (NLE); however, there is considerable debate about the relative contributions of number-specific and domain-general (i.e., working memory) processing involved in this task. Here, we used functional neuroimaging to examine the processes supporting NLE in adults (n = 47). Participants completed an in-scanner NLE task and number localizer. We found that within left and right parietal number regions, neural activity during the in-scanner NLE task differentially predicted out-of-scanner behavioral measures. Specifically, activity in the left (but not right) posterior intraparietal sulcus (IPS) predicted visuo-spatial working memory, and activity in the left (but not right) anterior IPS predicted performance on an out-of-scanner NLE task. These findings suggest that NLE relies on both spatial-numerical and domain-general capacities supported by left-hemisphere parietal regions, challenging hypotheses about right-lateralized visuo-spatial contributions to number processing.

The productivity of the human mind is rooted in the ability to flexibly combine concepts and computations. Developmental origins of this ability remain poorly understood. In two looking-time experiments, we investigated whether 15-month-olds (N = 48) can learn two distinct functions and compose them. We used a tool that transformed objects: it had two functions (i.e., it changed the kind of the object that went inside, or duplicated it), each triggered by a different handle. Experiment 1 showed that infants could learn both functions: at test, they looked longer when the outcome of the handle manipulation did not match the performed action than when it did. In Experiment 2, following a familiarization with individual manipulations and their outcomes, both manipulations were performed simultaneously at test. Infants displayed surprise when the outcome was inconsistent with a function composition. Infants readily learn two novel functions and spontaneously combine their outcomes.

There is a long line of research that has investigated how different kinds of visual information (e.g. optic flow) guide high-speed steering. Additionally, researchers have developed visual control models that capture the relationship between information and steering. Although models have been designed for a variety of steering maneuvers, they all assume that steering behavior remains consistent whether a driver has driven down a road once or numerous times. Thus, models do not address how behavior changes as drivers become familiar with the layout of the road. Our work investigates how drivers incorporate visual information and spatial knowledge to guide steering . We present a virtual driving experiment that examines how steering changes as humans become more familiar with a track, measuring metrics including speed, steering angle, and lane deviation. Results inform the development of a cognitive model that captures the relationship between visual information and spatial knowledge to guide steering behavior.

Cognitive scientists continue to debate gender/sex differences and similarities in basic problem-solving, including numerical and spatial cognition. While gender group differences may exist in these cognitive skills adulthood, it is unclear whether differences are fixed (early-developing, permanent) or plastic (late-developing, malleable). If fixed, they would relate more to gender categories; if plastic, they would relate more to gender socialization and spatial learning environment. To disentangle these hypotheses, we measured brain activity with fMRI in 51 children (4-8y; 20 boys / 31 girls) during numerical (vs. face) and geometric (vs. word) processing tasks. Activity occurred in bilateral superior and inferior parietal cortex during numerical and geometric processing, but activity within these regions was unrelated to gender category, gender socialization, or spatial learning environment. Bayesian analyses also revealed widespread gender similarities in numerical and geometric processing. These findings challenge the hypothesis of early, fixed gender differences in numerical and spatial development.

When speech is repeated, we sometimes perceive a musical quality in it, a phenomenon known as the speech-to-song transformation. Pitch information is shown to play a significant role in this process. However, this effect is less pronounced in tonal language speakers for ununderstood reasons. To explore this further, the current study recruited 140 participants, both tonal and non-tonal language speakers, and tested them using various languages and non-speech fragments. Results indicated that the reduced transformation effect in tonal language speakers was specific to speech materials and did not extend to non-speech materials. This suggests that while repetition invites listeners to perceive musical qualities in sound, the mechanisms underlying speech-to-song transformation seem to operate with an additional layer of linguistic processes. The findings provide a basis for further investigations into the dynamic information processing link between language and music.

Exemplar models (e.g., Nosofsky 1986) provide a highly influential account of the psychology of human category learning. However, the explanatory power of exemplar models may falter when applied to behavior outside of standard laboratory paradigms (Murphy, 2016) or even within the realm of traditional category learning experiments (Conaway & Kurtz, 2016; Kurtz & Wetzel, 2021). The present research poses new challenges that test the exemplar view within its wheelhouse of artificial classification learning tasks. Learners acquired categories based on two concentric circles (inner and outer) in feature space. Similarity-matched generalization tests reveal underlying global versus item-based category representation. Implications for exemplar and abstractive formal models of category learning are discussed.

American adults exhibit cognitive biases that favor processing relational information (e.g., comparative heights) over concrete metrics (e.g., surface area), but the bias's origin—cultural, developmental, or evolutionary—is debated. We explored this question by comparing American adults and children, Tsimane adults (with and without formal-education), and rhesus macaques. Findings indicate that relational biases emerge with increased exposure to formal-education. That is, educated Tsimane and Americans show a relational bias, unlike the concrete bias seen in uneducated Tsimane and macaques. Furthermore, young American children show less relational bias than older children and adults, indicating a progressive increase in relational bias. These findings suggest that while common ancestors of humans and macaques may have evolved to favor simpler concrete processing, this bias can be overridden by environmental influences (e.g., abstract language and symbols) that promote relational processing. Further investigations on early-life biases could help educators tailor teaching methods to cognitive predispositions.

This research explores computational models of decision-making under risk. Our models replace the conventional utility function with an episodic memory retrieval process, dynamically estimating utility by recalling past events. Rather than beginning deliberation with explicit knowledge of choice outcome utilities, the value of an outcome emerges from the stochastic recall of related past experiences. In order to reflect both the cognitive and neural dynamics of episodic recall during decision making, our approach incorporates a computational neuroscience model of the hippocampus. Comparisons between this account and previously published decision-making models demonstrate consistency with patterns of behavior captured by those models, while also making predictions concerning the specific effects of contextually cued memory retrieval. We also propose explorations involving the modeling of interactions between the hippocampus and the prefrontal cortex with the goal of shedding light on the neural basis of deliberation.

Humans can flexibly pursue goal-oriented behavior in the face of changes in the environment. Cognitive control refers to this set of processes allowing such adjustments, and is thought to rely on neural oscillations in the theta band (4-8Hz). First, theta amplitude increases when control is needed, and second, shifts of peak frequency in the theta band have been suggested to reliably balance task representation and gating of task-relevant sensory and action information. However, it remains unknown how these two characteristics of the control signal interact and how optimal configuration for task performance is achieved. To tackle this question, we developed a computational model that relies on reinforcement learning principles to find optimal control settings for task performance. Our simulations show that these different oscillatory characteristics play distinct roles in the flexible adjustment to task demands. This work opens new avenues for research on the mechanisms allowing cognitive flexibility.

Symmetry, a fundamental concept in perception and language, poses an interpretative challenge due to the disparity between its formal definition and linguistic expression. Formal symmetry is often distorted when expressed linguistically, such that e.g., 'North Korea is similar to Red China' is interpreted differently from 'Red China is similar to North Korea' despite their logical equivalence (Tversky, 1977). Gleitman et al. (1996) found this interpretive asymmetry stems from the syntactic positions of arguments, such that symmetry is restored when both arguments are on equal syntactic footing (e.g., a Conjoined NP Intransitive, “North Korea and China are similar”). Here a novel eye-tracking method tested how syntax and lexical semantics contribute to symmetrical interpretations. Participants were asked to rapidly sort spoken utterances by clicking on visible folders marked with a symmetrical or asymmetrical icon. Commitments to symmetry based on syntactic evidence emerged rapidly as the sentence unfolded over time.

This study examines how mental models of the Human-Nature Relationship (HNR) predict pro-environmental behavioral intentions directly and mediated through anthropocentric and biocentric environmental attitudes. We found that behavioral intentions relevant to environmental protection were directly predicted by two aspects of HNR: human superiority beliefs (negatively) and perceived human impact on nature (positively). Protection intentions were also indirectly predicted by these variables, as well as perceived impact of nature on humans (positively) via their association with biocentric attitudes (SRMR= 0.040). In contrast, no component of HNR directly predicted behavioral intentions relevant to environmental investment, although all three showed the same pattern of indirect association via biocentric attitudes (SRMR= 0.036). Results suggest that mental models of the human-nature relationship provide a cognitive foundation for environmental behavioral intentions both directly and through their association with environmental attitudes. These findings have implications for pro-environmental interventions that deal with conceptual and attitudinal change.

Adjusting expectations in response to errors is a cornerstone of several learning theories (Rescorla & Wagner, 1972; Rumelhart et al., 1986). Grimmick (2019) shows that individuals deploy attention during cross-situational word learning based on the strength of the error signal. The current study introduced an equal number of accurate and inaccurate expectations about word-referent pairs. This study manipulated the difficulty of cross-situational word learning trials to examine whether the impact of errors differs depending on task demands. Individuals learned the initially accurate items better than the initially inaccurate ones. Manipulating the demands during word learning did not significantly impact the tendency to benefit from accuracy. This research is part of an ongoing project. This ongoing research explores how individual differences in vocabulary, inhibition, and working memory abilities interact with contextual factors, such as task difficulty, as individuals learn word-referent pairs that violate their expectations.

Sometimes people ask us to do things we do not want to do. We may try to avoid the aversive consequences of non-compliance by finding a loophole: an interpretation of the request that is consistent with its literal but not intended meaning. Exploiting loopholes requires an integrated understanding of pragmatics, goal alignment, and rational planning. This kind of complex social reasoning may be challenging for people with autism. Here we surveyed parents to study the prevalence and development of loophole behavior in childhood among autistic and non-autistic children. Neither the tendency to produce loopholes nor their developmental trajectory differed between autistic (N = 200) and non-autistic children (N = 200). These results are consistent with previous work suggesting the heterogeneous nature of autism and the difficulty of finding single tasks that distinguish high-functioning children with and without autism; the results also demonstrate that autistic children are capable of this kind of complex social reasoning.

How are morphemes recognized and interpreted during incremental sentence comprehension? We investigated this question in a crossmodal primed lexical decision task employing words that contain semantically ambiguous roots (e.g., ‘bark'; with meanings related to both “dog” and “tree”) but which are disambiguated when affixed by “-ing” (e.g., ‘barking'; related to “dog” only). We aimed to understand whether access to the meaning of the root ‘bark' would be constrained by lower-level morphological affixation. In our experiment, participants listened to sentences containing an affixed ambiguous root while concurrently performing lexical decisions to a visual target related to the root-only meaning, the affixed meaning, or matched controls. Targets were presented for 80 ms at the recognition point of bark or 500 ms post-recognition. We found that both meanings of the root were activated, despite affixation. Results suggest that a parsing system blind to semantics decomposes morphologically complex words into morphemes before recognition.

Gonering & Corina (2023) argued that abstractions over visual scenes (i.e. schemas or situation models) provide a semantic scaffold for acquiring verb argument structures. We provide a systematic meta-analysis of 158 fMRI studies of verb processing (from NeuroSynth) and 208 fMRI studies of visual event processing (from NeuroQuery) suggestive of their hypothesis. Functional maps produced using Activation Likelihood Estimation via the Neuroimaging Meta-Analysis Research Environment package (Salo et al., 2022) (cluster-level family-wise error corrected using Monte Carlo method) showed overlapping regions of activation in the left inferior parietal lobule and Brodmann's area 47 bilaterally, suggesting shared neural resources for processing verbs and visual scenes. Meta-analyses on additional visual scene and verb processing studies from NeuroSynth and NeuroQuery, respectively, are also underway. We further intend to show that a hierarchical Bayesian model can learn verb argument structures from input statistics, even when they deviate from strong prior event semantic knowledge.

We know that context influences the facilitation of language comprehension. Previous research has shown that discourse coherence influences this contextual facilitation, with comprehenders making stronger predictions about upcoming words when reading highly coherent narratives. However, it is unclear whether the predictions made by Large Language Models (LLMs) exhibit similar discourse-level influences. As such, we investigate whether surprisal values from LLMs reflect longer context effects. We calculated word-level surprisal values (as a measure of prediction strength) for passages that vary in coherence. We used these to predict human reading times for the same passages collected from 289 participants. We found that surprisal only predicted reading times early in the target sentence, and that GPT-2's surprisal values were not influenced by discourse coherence, in contrast to human reading data. This has implications on the use of Transformer-based LLMs in modelling human cognition.

People use incomplete social network information to infer relationships. For example, if two individuals have many mutual friends, people infer they are friends with each other. We examined whether these inferences depend on domain-specific knowledge about social relationships, or instead depend on domain general-reasoning about statistics and proportions. In two experiments, participants (N=526) either saw partial information about social networks, like friendships between people, or about non-social networks, like wired connections between electrical parts. They then judged if two entities in each network were directly connected to each other. The entities varied in the number of connections and the proportion of mutual connections. People made similar judgments across social and non-social networks: with greater proportion of mutual connections, the two entities were judged as more likely to be connected to each other. In sum, inferences about networks might primarily depend on reasoning about statistics and proportions.

What is the developmental trajectory of probability learning in early childhood, and how do changes in choice behavior relate to changes in executive functions? We conducted a two-year longitudinal study with children between the ages of 3.5 and 6.5 years and complemented behavioral analyses with computational modeling to illuminate underlying cognitive processes. On average, children became more likely to choose the high-probability option as they grew older and increasingly diversified choices in line with probability matching by T3. Moreover, younger children in the cohort were more likely to maximize probability than older children. Our analyses suggest that increasing choice diversification across childhood may relate to improving executive functions and value-based learning, whereas probability maximizing may serve as an easily implementable satisficing strategy. Finally, our findings emphasize how children's variability in choice behavior may affect the estimated direction of change and highlight the need for longitudinal research.

A behavioural and two EEG hyper-scanning experiments are presented which investigate how predictive processing modulates the way interlocutors align behaviourally and at the level of the brain (Hasson, 2012; Pickering & Garrod, 2007). In the experiments interlocutors engaged in dyadic interactions performing a semi-controlled semantic association game and where the p predictability of the semantic associations was manipulated. The behavioural results showed that both interlocutors were around 400 ms faster in the predictable versus non-predictable conditions The results of the two EEG studies aim at demonstrating (1) whether we observe brain-to-brain synchronisation between the interlocutors at the level of word semantics, and (2) whether prediction enhances this synchronisation. To our knowledge, this is the first study to directly demonstrate prediction effects in an interaction.

Do verbally reported feelings follow reinforcement learning principles? Prediction errors—differences between expectations and outcomes—are key in models of learning across humans, animals, and machines. Historically, the emphasis has been on outcomes in the environment (e.g., money or food), focusing relatively less on the fact that humans can also report correspondingly expected and experienced affect (i.e., feelings). Recent research suggests that expected and experienced affect, including prediction errors, can explain behavior beyond outcomes in the environment alone. However, the properties of affective prediction errors underlying this explanatory power are unknown. We address this gap across two studies. We show that affective prediction errors can decrease over time, but that the decrease depends on introspection (Study 1). We then replicate this finding while additionally documenting transfer effects across tasks (Study 2). Crucially, decreases in affective prediction errors generally occurred independent of changes in behavior.

Various methods for social learning have been proposed within the reinforcement learning framework. These methods involve the social transmission of information within specific representational formats like policies, value, or world models. However, transmission of higher-level, model-based representations typically require costly inference (i.e., mentalizing) to ``unpack'' observable actions into putative mental states (e.g., with inverse reinforcement learning). Here, we investigate cheaper, non-mentalizing alternatives to social transmission of model-based representations that bias the statistics of experience to ``hijack'' asocial mechanisms for learning of environments. We simulate a spatial foraging task where a naïve learner learns alone or through observing a pre-trained expert. We test model-free vs. model-based learning together with simple non-mentalizing social learning strategies. Through analysis of generalization when the expert can no longer be observed and through correspondence between expert and learner representations, we show how simple social learning mechanisms can give rise to complex forms of cultural transmission.

Experimentally manipulating alcohol value reliably influences alcohol choice and consumption; however, the cognitive mechanisms that underpin these relationships are not well-understood. Here, we explore whether computational parameters of value-based decision-making (VBDM) change when people experience heightened craving to consume alcohol, and whether parameters of VBDM are predictive of actual drinking behaviour. Prior to completing a novel VBDM task, participants recalled either a positive drinking memory while being exposed to an alcoholic cue (alcohol craving), or a positive alcohol-unrelated memory while being exposed to a soft-drink cue (control). A drift-diffusion model (DDM) was fitted to reaction time and choice data to estimate evidence accumulation (EA) processes and response thresholds during the different blocks in each experimental condition. Subsequently, ad libitum alcohol consumption (disguised as a taste test) was measured. Using computational modelling techniques to quantify the internal processes of decision-making could potentially contribute to identifying innovative targets for treatment interventions.

In visual cognitive neuroscience, there are two main theories about the function of the ventral visual system. One suggests that it serves to classify objects (H1); the other suggests that it generates intermediate representations from which people can generate verbal descriptions, actions, and other kinds of information (H2). To adjudicate these, we trained two deep convolutional AlexNet models on 330,000 images belonging to 86 classes, representing the intersection of Ecoset images and the semantic norms collected by the Leuven group. One model was trained to produce category labels (H1) , the other to generate all of an item's semantic features (H2). The two models learned very different representational geometries throughout the network. The representations acquired by the feature-generating model aligned better with human-perceived similarities amongst images, and better predicted human judgments in a triadic comparison task. The results thus support H2.

A growing body of research indicates that moral decision-making is influenced by language status. Across studies and language combinations, participants make more utilitarian judgements when responding to moral dilemmas in a foreign (L2), compared to a native (L1) language. One explanation for the Foreign Language Effect is a reduced access to social norms in L2, since normative knowledge is acquired early in life in the native language. To test this account, we provided Chinese-English late bilinguals with “temporary social norms”: Upon dilemma presentation, response percentages of alleged previous participants were shown, representing either a deontological or utilitarian majority. We found that in English, participants conformed to utilitarian and deontological majority information, highlighting the malleability of moral decisions in an L2 context. In Chinese, participants only conformed to the utilitarian majority, potentially reflecting the influence of collectivist values. Our findings highlight the complex interplay between language, culture, and social norms in moral cognition.

Psycholinguistic dictionaries or lexicons have been used for text analysis in a variety of domains, from analyzing terrorist manifestos to congressional speeches. Methods for developing these dictionaries generally focus on identifying lexemes – single semantic units – that map to psychological categories such as health (containing words like yoga, disease, neurosis), positive sentiment (happy, joy), or interpersonal conflict (fight, kill). The focus on single lexemes neglects multiword expressions (such as kick the bucket, by and large, birds of a feather), which constitute a significant portion of any language and offer similar insight into human psychology and cognition. This paper proposes a methodology for developing lexicons of multiword expressions of psychological significance, and addresses the considerations specific to identifying and validating multiword expressions. Using this methodology, I developed two lexicons of multiword expressions that correspond to two cognitive processes and used them to analyze qualitative text data discussing belief in conspiracy theories.

Collaboration takes place everywhere in everyday life, and exposure to the ideas of others is a core process in collaboration. In this study, we investigated whether exposure to other people's ideas facilitates idea generation: 123 participants were asked to list as many ideas as possible to increase turnout in one of three conditions: constant exposure, intermittent exposure, or no exposure. Participants in the no exposure condition generated ideas without exposure to other's ideas. In the constant exposure condition, one of the sets of ideas generated by participants in the no exposure condition was presented on every trial. In the intermittent exposure condition, ideas were only presented in trials 1, 4, and 7. As a result, there was no significant difference in the number of ideas generated between conditions. The conditions under which exposure to the ideas of others facilitates idea generation were discussed.

Most alphabets are based on visual coding of phonemes and syllables, and similar visual codes were developed for visually conveying the sounds of speech to deaf people. Notably, Cued Speech (CS) allows for syllables to be specified by a combination of lip configuration, hand location and hand shape. The use of this communication system has been proven to improve general language skills in a deaf community characterized by low literacy. Meanwhile, the mechanisms of CS perception remain largely unknown. In an fMRI study involving 3 groups of participants (deaf and hearing people proficient in CS and a group of hearing people naïve to CS), we identify the brain areas that process and, more specifically, encode the various components of CS. Particular attention is given to the role of expertise, and to the links between CS and reading, two coexisting visual codes for language that both compete and support each other.

Universal constraints on word meaning apply to both lexical and logical words. Across languages, a well-known gap in the logical vocabulary is that 'not all' is never lexicalized. This gap extends beyond determiners to the modal and temporal domains; e.g. 'not must' and 'not always' are typically not lexicalized (Horn 1973). The challenge is to explain this gap. The non-lexicalization of 'not all' has been explained as resulting from a cognitive bias against intrinsically marked meanings (e.g., Katzir and Singh 2013). Recent alternative accounts, however, have explained this same gap relying on considerations of communicative efficiency rather than cognitive markedness (e.g., Enguehard and Spector 2021). In a series of word learning experiments, we disentangle these views by testing whether learners are more likely to infer that a novel word means 'some' rather than 'not all' and whether this varies depending on the communicative needs in the context.

Several studies found that word duration is predicted by its informativity (average past predictability) above and beyond predictability in the current context, suggesting retrieval of phonetically-specific tokens from memory. We show that a significant effect of informativity can emerge from noise in predictability estimates. We generate durations from a model in which 38% of log duration is predicted by log probability, as in our actual data, but the rest is normally-distributed noise. Estimated probability for each word in each context is then generated from a binomial distribution with success probability from the real sample and size matching context frequency. We compute informativity and fit the regression model we fit to the real data. Informativity is significant in 100% of simulations, even though probability is the only true predictor, although the effect of informativity is smaller in simulation (0.7 < b < 0.10) than in the actual corpus (b = 0.12).

Word embeddings trained on large scale text corpora are central to modern natural language processing and are also important as cognitive models and tools in psycholinguistic research (Pennington et al., 2014). An important alternative to these text-based models are embeddings derived from word association norms (De Deyne et al., 2019). Recently, these association-based embeddings have been shown to outperform text-based word embeddings of comparable complexity (such as GloVE, word2Vec & fastText) in semantic similarity rating tasks (Cabana et al., 2023; Richie & Bhatia, 2021). Here we evaluate English and Rioplatense Spanish association-based embeddings derived from the Small World of Words (SWOW) project on the Google Analogy set and the Bigger Analogy Test Set (Gladkova et al., 2016). We also developed a small analogy set that focuses on semantic relationships, such as event knowledge and category-exemplar relationships such as prototypicality. SWOW-derived word embeddings perform similarly as traditional text-based word embeddings in semantic analogies, and outform them in some categories. These results illustrate relevant similarities and differences between text-based and word association-derived embeddings. References Cabana, Á., Zugarramurdi, C., Valle-Lisboa, J. C., & De Deyne, S. (2023). The “Small World of Words” free association norms for Rioplatense Spanish. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02070-z De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M., & Storms, G. (2019). The “Small World of Words” English word association norms for over 12,000 cue words. Behavior Research Methods, 51(3), 987–1006. https://doi.org/10.3758/s13428-018-1115-7 Gladkova, A., Drozd, A., & Matsuoka, S. (2016). Analogy-based detection of morphological and semantic relations with word embeddings: What works and what doesn't. Proceedings of the NAACL Student Research Workshop, 8–15. https://doi.org/10.18653/v1/N16-2002 Richie, R., & Bhatia, S. (2021). Similarity Judgment Within and Across Categories: A Comprehensive Model Comparison—Richie—2021—Cognitive Science—Wiley Online Library. Cognitive Science, e13030. https://doi.org/10.1111/cogs.13030

Previous research with suggests that individuals show prioritized processing for self-referenced stimuli, from self-faces, self-names to momentarily associated arbitrary geometrical shapes. We asked our participants to perform an attentional blink task with self-associated arbitrary geometrical shapes and self-names where these stimuli were either presented as T1(Exp 1A & 2A) or T2 (Exp 1B & 2B). Given that the self-referential shapes would engage more resources a larger attentional blink was expected in Exp1A & 2A, and was found for self-names(2A) as compared to self shapes (1A); however no difference between shapes & names was found when these were presented as the T2 ( Exp 1B & 2B. We conclude that the higher familiarity of self-names drove the larger attentional blink observed with these stimuli and manifested in a bias relative to the control stimuli which were friend and stranger referenced stimuli.

Representational overlap is debated as the neural basis of multitasking costs. Cognitive theories propose that overlapping task representations lead to an unintended exchange of information between tasks (e.g., crosstalk). Recently, modality-based crosstalk was suggested as a source for multitasking costs in multisensory settings. Robust findings of increased costs for certain modality mappings, even when both tasks use non-overlapping stimulus and response modalities, may be explained by crosstalk between the stimulus modality in one task and sensory action consequences in the concurrently performed task. This study (N = 54) employs functional neuroimaging, multivariate pattern analysis, and modality-specific interventions to investigate neural overlap in multitasking, emphasizing modality compatibility. Noteworthy, differences in single-task representations were found in the auditory cortex but not in fronto-parietal regions. Improved auditory decoding accuracy in modality-incompatible tasks predicted dual-task performance gains, eliminating modality-specific costs, exclusively for the modality-incompatible intervention group. This study provides neural evidence for modality-based crosstalk in sensory regions.

Socially anxious individuals often engage in subtle avoidance behaviors (SABs) to mitigate their distress during feared social situations, such as avoiding eye-contact during a public speech. However, by preventing direct confrontation with their fears, SABs greatly hinder the efficacy of exposure therapy, the first-line treatment for social anxiety. Here, we test whether neural stimulation of the brain circuits controlling avoidance behavior can augment the efficacy of exposure therapy. This intervention relies on evidence that dual-site transcranial alternating-current simulation (tACS) of theta-gamma phase-amplitude couplings between frontal regions can improve control over social avoidance tendencies. Here, we use the same tACS protocol (active, or sham) on socially anxious individuals undergoing a standardized exposure to public speaking. Additionally, we implement quantitative, multimodal estimates of SABs using motion-tracking, eye-tracking, and prosodic analyses of participants' public speeches. We expect quantifiable reductions in multimodal measures of SABs during active-vs-sham tACS, ultimately enhancing exposure therapy's efficacy.

This research explores the potential of machine learning (ML) in identifying vocal markers for schizophrenia. While previous research showed that voice-based ML models can accurately predict schizophrenia diagnosis and symptoms, it is unclear to what extent such ML markers generalize to different clinical subpopulations and languages: the assessment of generalization performance is however crucial for testing their clinical applicability. We systematically examined voice-based ML model performance on a large cross-linguistic dataset (3 languages: Danish, German, Chinese). Employing a rigorous pipeline to minimize overfitting, including cross-validated training sets and multilingual models, we assessed generalization on participants with schizophrenia and controls speaking the same or different languages. Model performance was comparable to state-of-the art findings (F1-score ~ 0.75) within the same language; however, models did not generalize well - showing a substantial decrease - when tested on new languages, and the performance of multilingual models was also generally low (F1-score ~ 0.50).

We study hybrid confirmation trees, a simple heuristic for producing hybrid intelligence in high-stakes classification tasks. Hybrid confirmation trees first elicit the decision of one human expert and one algorithm. Whenever the two agree a decision is immediately made. In case of disagreement, a second human expert is called in to break the tie. We apply this approach to data on deepfake detection, recidivism prediction and skin tumor diagnosis and investigate how it performs for experts of varying levels of skill. Our approach proves to be a powerful alternative to human-only confirmation trees in all data sets we test and for all skill levels as it performs similar, if not better, at reduced cost. In addition, for high-performing individuals it can outperform both human confirmation trees and algorithms, producing complementary human-algorithm performance. We show that this effect exists because skilled experts disagree with the algorithm on the right instances.

Language models have great potential as cognitive models for studying human language acquisition, but current models are far less data-efficient than human learners. Children acquire language from 100 million words or less, but large language models are trained on trillions of words. We discuss the prospects for improving language models' developmental plausibility through a meta-analysis of results from the 2023 BabyLM Challenge. BabyLM was a competition that invited participants to train a language model on a 100 million-word corpus including transcribed speech and child-appropriate texts. Results from over 30 submissions showed that new machine learning techniques and increased training iterations yielded models that outperformed leading large language models in grammar, language understanding, and linguistic generalization, while cognitively plausible approaches such as curriculum learning were less effective. We discuss the implications of these and other findings for computational cognitive modeling and explore ideas to ensure future competitions' contributions to cognitive science.

Environmental stressors present negative consequences for development. However, their impact on relevant neurocognitive processes, particularly in underrepresented samples, is less clear. The current project aims to examine how adverse experiences influence efficiency of evidence accumulation and neural connectivity in adolescents. The study included 199 adolescents from the Future of Families and Child Wellbeing Study, a population-based longitudinal cohort study with substantial representation of youths from disadvantaged backgrounds. Participants completed an emotional-faces, gender-identification task while undergoing functional MRI. Reaction times (RT) and responses were recorded and fitted with a drift diffusion model. Parameters were estimated using the Dynamic Model of Choice software, which provides a better characterization of the underlying cognitive mechanisms compared with using RTs or cognitive batteries. Analyses investigating the impact of adverse experiences to drift rate and functional connectivity are underway. Results from this study will provide a better understanding of adversity on neurocognitive mechanisms in adolescents.

Time words like ‘yesterday' and ‘tomorrow' are abstract, and are interpreted relative to the context in which they are produced: the word ‘tomorrow' refers to a different point in time now than in 24 hours. We tested 112 3- to 5-year-old Hindi-speaking children on their knowledge of ‘yesterday' and ‘tomorrow', which are represented by the same word in Hindi-Urdu: ‘kal'. We found that Hindi learners performed better than English learners when tested on actual past and future events, but that performance for hypothetical events was poor for both groups. Compatible with a “syntactic bootstrapping” account, we conclude that syntactic tense information – which is necessary for differentiating ‘yesterday' from ‘tomorrow' in Hindi – may play a stronger role in learning these words than mapping of specific words to particular past and future events (“event mapping”).

We investigated the impact of teachers' multimodal cues on L2 word learning in naturalistic teaching. 169 university students randomly watched 12 of 54 clips of English vocabulary instructions and took subsequent word recognition and learning tests. The learning outcomes were analysed as a function of teachers' prosodic, linguistic and gestural input during the instruction of each vocabulary while controlling for students' characteristics and varying teachers' influences. Results showed that a shorter mean length of utterances, fewer L2 English words, and more questions for students and “phrase” teaching predicted better learning outcomes. Furthermore, students learning improved with teachers' slower speaking rate but fewer pauses and more iconic gestures. These results were robust even after controlling for other significant factors such as students' English proficiency, working memory, degree of liking of teachers and different teachers. Overall, multimodal cues enhance L2 vocabulary learning, with implications for educators, linguists, and cognitive scientists.

Reduced cognitive function in major depression (MDD) is often interpreted as a reduced ability to exert cognitive control. Here we used the Effort Foraging Task to test the hypothesis that reduced cognitive function may be due, in part, to decreased willingness to exert control in MDD because of increased cognitive effort "costs". Contrary to our predictions, neither cognitive nor physical effort costs differed with MDD diagnosis (N MDD=52, N Comparisons=27). However, we found distinct patterns of symptom relationships for cognitive and physical effort costs. In MDD, greater anxiety symptoms were selectively associated with lower cognitive, but not physical effort cost (i.e. greater willingness to exert cognitive effort), whereas greater anhedonia and behavioral apathy symptoms were selectively associated with increased physical (but not cognitive) effort costs. These findings support the measurement of both cognitive and physical effort as decision-making function markers that may inform heterogeneity of MDD.

Analogical studies demonstrate that participants often fail to retrieve a well-learned base analog during the subsequent processing of a semantically-distant target analog. We evaluated whether presenting the target analog before the base analog increases analogical retrieval during hypothesis-generation. Experiment 1 revealed a higher rate of analogical retrieval when the target analog preceded the base analog, as compared to the traditional “base-target” sequence. Using a factorial design, Experiment 2 assessed whether spontaneously acknowledging the relevance of a subsequently encountered explanation for resuming a failed explanatory attempt requires the presence of structural similarities between the base and target situations. Results demonstrated that the primary contributor to spontaneous reactivation of a failed explanatory attempt is the presentation of an analogous phenomenon, while the presence of a useful explanation alone did not yield a significant impact. These findings contribute valuable insights to the dynamics of analogical retrieval and offer relevant implications for educational strategies.

Emotions, including stress and anxiety, strongly influence cognition and learning experiences. This study investigates the impacts of stress on cognitive load during learning, considering baseline anxiety levels and fluctuating stress. With a focus on technology-based learning, a web-based HTML introduction module was used. Using a social stress test, 15 participants underwent a stressful situation during learning, while the control group of 15 were in a neutral condition. Results indicate significantly elevated stress levels in the experimental group throughout the experiment, with a corresponding decrease in learning performance. For high perceived difficulty, the stressed condition demonstrated a significant increase in response time compared to the control condition. In contrast, when experiencing low perceived difficulty, a significant difference in response time across conditions was not found. Findings emphasise the importance of managing stress in educational contexts to optimise learning outcomes in the evolving landscape of technology-based learning.

Here we used deep neural network + hidden Markov model (DNN+HMM) to provide a computational account for the relationship among holistic processing (HP), face scanning pattern and face recognition performance. The model accounted for the positive associations between HP and eyes-focused face scanning pattern/face recognition performance observed in the literature regardless of the version of the composite task used to measure HP. Interestingly, we observed a quadratic relationship between HP and face scanning pattern, where models being highly eyes-focused or highly nose-focused had lower HP. By inspecting fixation locations and associated attention window size in the model and XAI methods, we found that the eyes- and nose-focused models both developed local and holistic internal representations during training, and their difference was in the temporal dynamics of how these representations were used. Our findings demonstrated how computational modeling could unravel the mechanisms underlying cognition not readily observable in human data.

Electroencephalogram (EEG) signals are physiological indicators of brain activity, offering the advantage of high temporal resolution for capturing subtle emotional changes and providing rich information for emotion recognition. However, extracting effective features from EEG data with a low signal-to-noise ratio poses a significant challenge that hinders progress in this research field. To address this issue, we propose a multi-view time-frequency contrastive learning framework called MV-TFCL to enhance the information representation capability of EEG signals from multiple perspectives. Firstly, we introduce a recursive neural network based on multi-scale time-frequency consistency, which integrates global semantic information across different scales through gated units. To our knowledge, this is the first proposal of the theory of multi-scale time-frequency consistency applied in emotion recognition research. Subsequently, we design a tree-structured time-frequency encoder to capture local semantic information within the time-frequency domain. Finally, we incorporate semantic consistency constraints from both global and local perspectives to learn more generalizable and robust features. Extensive experimental results on two publicly available datasets demonstrate the effectiveness and superiority of our proposed method.

The framework of event perception suggests that people segment continuous perceptual input into discrete events by forming mental representations of ongoing activity. Prior work extending the segmentation framework to emotion perception shows that a richer emotion vocabulary is associated with segmentation of emotion events in greater agreement with the cultural ingroup. However, little is known about how labeling behaviors themselves shape the segmentation of emotion events. Here, we look at the effect of labeling on emotion segmentation. Participants were randomly assigned to simply segment videos into discrete emotion events or to segment only when an emotion label is available and to label the segmented event. We found that compared to the group that segmented without providing labels, the group that segmented with explicit labeling behaviors were less sensitive at discriminating emotion events from non-emotion events and more conservative to identify an emotion event. The results are discussed with respect to competing theoretical accounts of the impact of labeling on emotion perception and suggest that the conceptual broadening account (where labels invoke idiographic emotion representations) may best account for the findings.

Inspired by inductive reasoning models, we test whether generalized shared reality (i.e., the sense of being on the same page) arises through probabilistic inference about latent commonalities. Using a naturalistic text-based chat paradigm, we manipulated whether conversation partners discussed a belief they shared, a belief on which their opinions differed, or a random prompt. Participants discussing shared opinions reported experiencing greater shared reality compared to those discussing differences or random topics. Moreover, participants who made broader inferences about additional beliefs they might share with their partners also reported greater shared reality. While discussing shared opinions can induce an overall greater sense of shared reality, participants discussing differences leveraged their conversation to establish shared realities about other topics. We demonstrate that shared reality can emerge in multiple ways during initial interactions, establishing a foundation for future mechanistic investigations within an inductive inference framework.

Since their inception, drift diffusion models have been applied across a wide range of disciplines within psychology to uncover the mental processes that underlie perception, attention, and cognitive control. Our studies contribute to ongoing efforts to extend these models to abstract, social reasoning processes like moral or legal judgment. We presented participants with a set of social rules, while manipulating whether various behaviors violated the rule's letter and/or its purpose–––two independent standards by which to decide what constitutes a transgression. In this framework, cases that violate or comply with both a rule's text and its purpose can be seen as congruent or ‘easy' cases, and cases that elicit opposing verdicts as incongruent or ‘hard' cases–––in a manner analogous to widely-studied conflict tasks in cognitive psychology. We recorded 34,573 decisions made by 364 participants under soft time pressure, and investigated whether hierarchical drift diffusion modeling could explain various behavioral patterns in our data. This approach yielded three key insights: (1) judgments of conviction were faster than judgments of acquittal owing to an overall bias (z parameter) toward conviction; (2) incongruent cases produced longer reaction times than congruent cases (an interference effect), due to differences in the rate of evidence accumulation (v parameter) across case-types; and (3) increases in the ratio of congruent-to-incongruent cases amplified the interference effect on reaction times, by fostering greater response caution—revealed by a larger threshold (or a parameter). Thus, our studies document dissociable effects of the drift diffusion components on rule-based decision-making, and illustrate how the cognitive processes that subserve abstract and social decision-making tasks, such as the enforcement of communal and legal rules, may be illuminated through the drift diffusion framework.

Neuroimaging, especially through Functional Magnetic Resonance Imaging (fMRI), plays a pivotal role in understanding brain activity by leveraging blood-oxygen level dependent (BOLD) signals to estimate neural activities across the brain. The interpretation of these signals through functional connectivity (FC) matrices facilitates the application of Graph Neural Networks (GNN) for analyzing brain network structures, offering insights into both normal and abnormal brain functions. Despite the potential of centralized learning methods in this domain, challenges related to data privacy and the feasibility of sharing sensitive medical datasets across institutions limit their application. This study introduces the Federated Graph Learning Framework for Brain Connectome (FGLBC), addressing these concerns. This novel approach enables the collaborative training of GNN models across multiple entities, such as hospitals, without compromising data privacy. The FGLBC framework implements a privacy-preserving local GNN training (PPGT) algorithm that incorporates Differential Privacy (DP) to safeguard sensitive information during model training. Furthermore, we introduce a unique similarity-weighted aggregation (SWA) algorithm that enhances the aggregation process, thereby boosting the global model's utility and performance. Our comprehensive evaluation across benchmark datasets demonstrates that the FGLBC not only preserves user privacy but also achieves or surpasses the performance of existing methods.

Outward differences between cultures are very salient, with Western and East Asian cultures as a prominent comparison pair. A large literature describes cross-cultural variation in cognition, but relatively less research has explored the developmental origins of this variation. This study helps to fill the empirical gap by replicating four prominent findings documenting cross-cultural differences in children's reasoning, visual attention, and social cognition in a cross-sectional sample of 240 3-12-year-olds from the US and China. We observe cross-cultural differences in three of the four tasks and describe the distinct developmental trajectory that each task follows throughout early and middle childhood.

Cognitive processes underlie economic relations. In this paper, we develop a conceptual, mathematical, and computational framework for modeling market exchange as a series of dynamically interacting cognitive processes. Specifically, we show how advertisers can build trust and gain confidence in their pricing power to the point that they erode trust and undermine the efficacy of their advertising. Customers conversely orient towards advertisers seeking information or turn away from them as unreliable communicators. These behaviors and the patterns they generate occur inside a state space of unallocated perceived value. They constitute a small subset of the full range of possible strategic and adaptive responses that define cognitive microeconomics.

Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a paradigm similar to the game of telephone, where one person observes a stimulus and reproduces it for the next to form a chain of reproductions. Past serial reproduction experiments typically employ a single sensory modality, but humans often communicate abstractions of the world to each other through language. To investigate the effect language on the formation of abstractions, we implement a novel multimodal serial reproduction framework by asking people who receive a visual stimulus to reproduce it in a linguistic format, and vice versa. We ran unimodal and multimodal chains with both humans and GPT-4 and find that adding language as a modality has a larger effect on human reproductions than GPT-4's. This suggests human visual and linguistic representations are more dissociable than those of GPT-4.

Aphantasia, namely the inability to voluntarily form visual mental imagery, does not, counterintuitively, impair the affected from successfully performing mental imagery tasks. One way of explaining this finding is to posit that aphantasics, despite their claim to the contrary, can form visual imagery, a position here referred to as aphantasia skepticism. This article outlines and rejects two types of aphantasia skepticism and argues that the position results from what is coined the visualizer's fallacy, namely the false belief that visual mental imagery is necessary to carry out mental imagery tasks. Furthermore, it is argued that the visualizer's fallacy and the resulting aphantasia skepticism are not only potentially harmful to aphantasics but may also lead to an impoverished view of the dynamics of cognition in general.

Spontaneous recovery of fear after extinction is a well-established behavioral phenomenon. Different theories in psychology account for spontaneous recovery by proposing that it may result from temporal weighting, reduced processing of stimuli over time, enhanced salience of adverse events or return of the acquisition context. We propose a novel mechanism of spontaneous recovery: selective maintenance of adverse events, and ground this mechanism in a computational model of latent cause inference. To investigate the proposed mechanism, we collected behavioral data with an aversive conditioning and extinction task (N=280) and fit the data with computational models formalizing our and others' theories. Quantitative and qualitative model comparisons indicated that selective maintenance of adverse events accounts for spontaneous recovery better than alternative theories. As spontaneous recovery of fear after extinction can serve as a model of relapse after exposure therapy, we use this mechanistic understanding of spontaneous recovery to propose and simulate the effect of add-on interventions to prevent relapse after exposure therapy.

Pointing is an intuitive and commonplace communication modality. In human-robot collaborative tasks, human pointing has been modeled using a variety of approaches, such as the forearm vector or the vector from eye to hand. However, models of the human pointing vector have not been uniformly or comprehensively evaluated. We performed a user study to compare five different representations of the pointing vector and their accuracies in identifying the human's intended target in an object selection task. We also compare the vectors' performances to that of domestic dogs to assess a non-human baseline known to be successful at following human points. Additionally, we developed an observation model to transform the vector into a probability map for object search. We implemented our system on our robot, enabling it to locate and fetch the user's desired objects efficiently and accurately.

Counterfactual reasoning is at the centre of human daily life and plays a key role in shaping our moral and social judgments. Its effect on moral judgment in adulthood, such as justifying immoral behavior (e.g., “If you had not left your phone on the table, it would not have been stolen.”), has been studied for years. However, we still know very little about when counterfactual reasoning starts to affect humans' moral judgments. To test this, we examined the effect of better and worse counterfactual alternatives on 5-6-year-olds' (N = 91) moral and happiness judgments. We found that children judged social exclusion (e.g., a new kid has to play alone while other children play together) as less morally acceptable after imagining how it could have been better (e.g., the new kid and other children at the school could have played all together), but, contrary to past work with adults, they did not justify it after imagining how it could have been even worse (e.g., the other children could have broken the new kid's toy). However, children's happiness judgments showed the opposite effect: they reported feeling happier about reality after imagining a worse counterfactual alternative compared to children who only thought about what actually happened. Keywords: counterfactuals; moral judgment; children; happiness judgment

We examine the relation between event segmentation in language and cognition in the domain of motion events, focusing on Turkish, a verb-framed language that segments motion paths in separate linguistic units (verb clauses). We compare motion events that have a path change to those that did not have a path change. In the linguistic task, participants were more likely to use multiple verb phrases when describing events that had a path change compared to those that did not have a path change. In the non-linguistic Dwell Time task, participants viewed self-paced slideshows of still images sampled from the motion event videos in the linguistic task. Dwell times for slides corresponding to path changes were not significantly longer than those for temporally similar slides in the events without a path change. These findings suggest that event units in language may not have strong and stable influences on event segmentation in cognition.

Pragmatic theories assume that during communicative exchanges humans strive to be optimally informative and spontaneously adjust their communicative signals to satisfy their addressee's epistemic needs. To investigate this ability in infants, we designed a task in which 18-month-olds had to point at the target object they wanted to receive. In Experiment 1, we found that when the target was placed behind a distractor object, infants appropriately modified their pointing to avoid mistakenly indicating the distractor to their partner. When the objects were covered, and their communicative partner had no information (Experiment 2) or incorrect information (Experiment 3) about the target's location – as opposed to being knowledgeable about it – infants pointed at the target more often and employed modified pointing more frequently when it was necessary. This demonstrates that 18-month-olds can take into account their communicative partner's epistemic states and provide her with relevant information through optimally informative deictic gestures.

Comparison of visibility inferences across congenitally blind and sighted people provides insight into the contribution of first-person sensory experience to intuitive theories. We hypothesized that both groups understand others' visual experiences via an intuitive theory incorporating variables known to influence visual psychophysics (distance, looking duration, and feature size). Adults born blind (n=20) and sighted (n=40) listened to short scenarios that described an observer looking at another person from different distances and for varying durations. Participants rated how likely the observer would perceive appearance features of the person that varied in size (e.g., eye color vs. hat). A probabilistic formalization of intuitive visibility fit the ratings with high accuracy across scenarios and features. Model parameters were qualitatively identical across groups but blind adults weighted distance and size less. A quantitative and generative intuitive theory of vision develops without first-person sensory access, possibly through linguistic communication, and is fine-tuned by visual experience.

This study delves into how various musical factors influence the experience of auditory illusions, building on Diana Deutsch's scale illusion experiments and subsequent studies. Exploring the interaction between scale mode and timbre, this study assesses their influence on auditory misperceptions, while also considering the impact of an individual's musical training and ability to discern absolute pitch. Participants were divided into non-musicians, musicians with absolute pitch, and musicians with relative pitch, and were exposed to stimuli modified across three scale modes (tonal, dissonant, atonal) and two timbres (same, different). The findings suggest that scale illusions occur less frequently with different timbres and vary with scale mode. Crucially, the absolute pitch ability appears to have a more significant impact on the perception of illusions than the duration of musical training. This research contributes to understanding the complex interplay between various factors in auditory perception and the mechanisms behind the experience of auditory illusions.

Previous research has shown that our subjective sense of certainty doesn't always accurately reflect the strength of the evidence that has been presented to us. We investigate several key factors that drive children's certainty using a Boolean concept learning task. We created an idealized learning model to predict children's accuracy and certainty during the experiment, given past evidence that they have seen in the task, and we compared its predictions with our behavioral results. Our results suggest that while predictors from the idealized learning model capture children's accuracy, behavioral predictors generated by the behavioral data can better predict children's certainty. We also show that younger children's certainty can be explained by the idealized learning model, while older children's certainty is primarily predicted by how well they observed themselves doing in the experiment.

This paper introduces a novel perspective to significantly mitigate catastrophic forgetting in continuous learning (CL), which emphasizes models' capacity to preserve existing knowledge and assimilate new information. Current replay-based methods treat every task and data sample equally and thus can not fully exploit the potential of the replay buffer. In response, we propose COgnitive REplay (CORE), which draws inspiration from human cognitive review processes. CORE includes two key strategies: Adaptive Quantity Allocation and Quality-Focused Data Selection. The former adaptively modulates the replay buffer allocation for each task based on its forgetting rate, while the latter guarantees the inclusion of representative data that best encapsulates the characteristics of each task within the buffer. Our approach achieves an average accuracy of 37.95\% on split-CIFAR10, surpassing the best baseline method by 6.52\%. Additionally, it significantly enhances the accuracy of the poorest-performing task by 6.30\% compared to the top baseline. Code is available at https://github.com/sterzhang/CORE.

Functional relations are prevalent in everyday life and science. Do children have intuitive knowledge of functional relations, and can they learn these relations by active information gathering (i.e., choosing a few input values and observing the corresponding outputs)? We found that 6- to 9-year-olds can learn different families of functions (linear, Gaussian, and exponential) through both informative data provided by an experimenter and data they gather from the environment for themselves. Overall, children learn linear functions more accurately than non-linear functions. When choosing data points to learn about, some children select highly similar points that only shed light on a narrow region of a function, while others choose more variable inputs and gain a more holistic view of a function. Children who use this latter, globally informative strategy have higher learning accuracy, particularly for non-linear functions. Results suggest that children are in the process of developing effective strategies for active function learning.

As a foundation for social interaction, interpersonal coordination is facilitated by positive social qualities (e.g., cooperation), but undermined in negative contexts (e.g., conflict). Exactly how social factors shape coordination is less clear. Previous literature notes that the way people attend to others impacts how interactions unfold. It is possible therefore, that patterns of social attention also govern coordination. We examined this proposition by using virtual reality to investigate how attentional focus (self vs. other) and partner gaze (direct vs. averted) influence the spontaneous emergence of coordination. The results indicated that: (i) coordination was enhanced in the other (cf. self) focus condition; (ii) coordination was diminished in the averted (cf. direct) gaze condition. These findings suggest that changes in social attention impact interpersonal coordination. More broadly, this work provides further evidence that the emergence of interpersonal coordination fluctuates as a function of social context.

Adults are known to have superior working memory to children, but whether this improvement is driven primarily by differences in storage capacity or attentional control is debated. In particular, the understanding of how capacity and control influence the development of working memory is hampered by the fact that most theorizing about the effect of variation in either on behavior has been verbal. To address this, we extended a computational model of working memory to clearly separate the contributions of capacity and control, fitting the model to a recent developmental study. We find that the combined influence of capacity and control on working memory may be more complicated than previously appreciated. In particular, the general pattern of qualitative differences between children and adults could be produced by increasing either capacity or control alone. These results point to a need for additional experimental paradigms to clearly parse the differential impact of working memory components.

This review explores the complex relationship between Fear of Falling (FoF) and Fear of Heights (FoH), and their impact on human postural control. FoF encompasses a spectrum of psychological and physiological responses that dynamically influence postural control, while FoH involves perceptual distortions and heightened physiological arousal in response to elevated environments. Through a comprehensive literature review, we examine the research methods and findings of studies on FoF and FoH. We further propose that Optimal Feedback Control (OFC) theory is a suitable framework to understand the computational aspects of how these fears modify postural control. We aim to provide a nuanced understanding of FoF and FoH, not only as psychological phenomena but as complex, dynamic interactions of cognitive, physiological, and motor processes influencing an individual's interaction with their environment.

As AI systems like language models are increasingly integrated into decision-making processes affecting people's lives, it's critical to ensure that these systems have sound moral reasoning. To test whether they do, we need to develop systematic evaluations. We provide a framework that uses a language model to translate causal graphs that capture key aspects of moral dilemmas into prompt templates. With this framework, we procedurally generated a large and diverse set of moral dilemmas---the OffTheRails benchmark---consisting of 50 scenarios and 400 unique test items. We collected moral permissibility and intention judgments from human participants for a subset of our items and compared these judgments to those from two language models (GPT-4 and Claude-2) across eight conditions. We find that moral dilemmas in which the harm is a necessary means (as compared to a side effect) resulted in lower permissibility and higher intention ratings for both participants and language models. The same pattern was observed for evitable versus inevitable harmful outcomes. However, there was no clear effect of whether the harm resulted from an agent's action versus from having omitted to act. We discuss limitations of our prompt generation pipeline and opportunities for improving scenarios to increase the strength of experimental effects.

Children rapidly learn to use language to effect a variety of communicative acts, such as proposing actions, asking questions, and making promises. While prior work has characterized this development in cross-sectional corpora, these analyses have been unable to comprehensively track individual differences in children's acquisition of communicative acts. We analyzed a longitudinal corpus of parent-child interactions from ages 14 to 58 months. We find that children's repertoires of communicative acts diversify over this period, with stable individual differences in the diversity of children's communicative act repertoires. Further, the diversities of parents' and children's communicative act repertoires are correlated. Children with more diverse communicative act repertoires also have larger vocabularies and use more diverse syntactic frames, suggesting links between discourse development and lexical and syntactic knowledge. Taken together, this work provides new insight into individual trajectories of communicative development and connections between communicative act use and other levels of language structure.

Spatial cognition is fundamental to human behavior, but people differ in how they remember spatial relations, variably using body-based (egocentric) and environment-based (allocentric) spatial reference frames. Despite decades of study, the causes of this variation and flexibility in spatial memory remain unclear. Here we show that people spontaneously use different reference frames on different spatial axes at the same time. When remembering the placement of a target object in a 2-dimensional array, Indigenous Tsimane' adults preferentially used allocentric space to determine lateral placement and egocentric space to determine sagittal placement in the same action. This effect of axis was also significant among US university students, whose overall preference for egocentric space was stronger on the sagittal than lateral axis. These findings support a novel account of spatial cognitive diversity and suggest that people across cultures habitually integrate egocentric and allocentric spatial reference frames into the same action.

Temporal language grounding (TLG) aims to localize query-related events in videos, which explores how to cognize relationships of video content with language descriptions. According to selective visual attention mechanism in cognitive science, people's cognition and understanding of what happens often rely on dynamic foreground information in the video. Nonetheless, background usually predominates the scenes so that query-related visual features and irrelevant ones are confused. Thus, we propose a Foreground Enhanced Network (FEN) to diminish the background effect from two aspects. FEN at first in spatial dimension explicitly models the evolving foreground in video features by removing relatively unchanged background content. Besides, we propose a progressive contrastive sample generation module to gradually learn the differences between the predicted proposal and its elongated proposals that include the former as a portion, thereby distinguishing similar neighborhood frames. Experiments on two common-used datasets show the efficacy of our model.

Human learning does not stop at solving a single problem. Instead, we seek new challenges, define new goals, and come up with new ideas. What drives people to disrupt the existing conceptual landscape and create new things? Here, we examine the decision to create new things under different levels of expected returns. We formalize innovation as stochastically recombining existing ideas, where successful and more complex combinations generate higher returns. This formalization allows us to cast innovation-seeking as a Markov decision process, and derive optimal policies under different settings. Data collected through an online behavioral experiment confirm our prediction that people should invest more time and effort in seeking innovations when they know the chances of success are high and the potential new ideas would be rewarding. However, people also deviate from being optimal, both innovating more and less than they should in different settings.

Computational psycholinguistics has traditionally employed a complexity metric called Node Count, which counts the number of syntactic nodes representing syntactic structures and predicts processing costs in human sentence processing. However, Node Count does not dissociate distinct syntactic operations deriving those syntactic structures, so that how much processing cost each syntactic operation induces remains to be investigated. In this paper, we introduce a novel complexity metric dubbed Composition Count, which counts the number of syntactic operations deriving syntactic structures, allowing us to understand the computational system of human sentence processing from the derivational, not representational, perspective. Specifically, employing Combinatory Categorial Grammar (CCG) which is equipped with multiple syntactic operations and thus suitable for the purpose here, we investigate (i) how much distinct syntactic operations of CCG contribute to predicting human reading times, and (ii) whether the same holds across languages. The results demonstrate that distinct syntactic operations of CCG have independent and cross-linguistic contributions to predicting human reading times, while Node Count turns out not to be robust cross-linguistically. In conclusion, these results strongly suggest the importance of Composition Count to dissociate distinct syntactic operations, not whole syntactic representations, and understand the computational system of human sentence processing.

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learning from diverse information sources. We first introduce a semi-supervised variant we call the delusional hedge capable of learning from both supervised and unsupervised experiences. In two experiments, we examine the alignment between human judgments and predictions from the standard hedge, the delusional hedge, and a heuristic baseline model. Results indicate that humans effectively incorporate both labeled and unlabeled information in a manner consistent with the delusional hedge algorithm---suggesting that human learners not only gauge the accuracy of information sources but also their consistency with other reliable sources. The findings advance our understanding of human learning from diverse opinions, with implications for the development of algorithms that better capture how people learn to weigh conflicting information sources.

Understanding spoken words requires listeners to integrate large amounts of linguistic information over time. There has been considerable debate about how semantic context preceding or following a target word affects its recognition, with preceding semantic context often viewed as a constraint on possible future words, and following semantic context as a mechanism for disambiguating previous ambiguous input. Surprisingly, no studies have directly compared whether the timing of semantic context influences spoken word recognition. The current study manipulates the acoustic-perceptual features of a target word, a semantic cue elsewhere in the sentence biasing toward one interpretation, and the location of the semantic context. We find that the two cues are additively integrated in participants' word identification responses, and that semantic context affects categorization the same regardless of where it appears relative to the target word. This suggests that listeners can optimally integrate acoustic-perceptual and semantic information across time.

A wealth of evidence indicates that children use their developing linguistic knowledge to incrementally interpret speech and predict upcoming reference to objects. For verbs, determiners, case-markers, and adjectives, hearing linguistic information that sufficiently constrains referent choice leads to anticipatory eye-movements. There is, however, limited evidence about whether children also use spatial prepositions predictively. This is surprising and theoretically important: spatial prepositions provide abstract semantic information that must interface with spatial properties of, and relations between, objects in the world. Making this connection may develop late because of the complex mapping required. In a visual-world eye-tracking task, we find that adults and 4-year-olds hearing 'inside' (but not 'near') look predictively to objects that afford the property of containment. We conclude that children make predictions about the geometric properties of objects from spatial terms that specify these properties, suggesting real-time use of language to guide analysis of objects in the visual world.

Prior studies of Theory of Mind have primarily asked observers to predict others' actions given their beliefs and desires, or to infer agents' beliefs and desires given observed actions. However, if Theory of Mind is genuinely a causal theory, people should also be able to plan interventions on others' mental states to change their behavior. The intuitive causal model of Theory of Mind predicts an asymmetry: one has to instill both the relevant belief and desire to cause an agent to act; however, to prevent a likely action, it suffices to remove either the relevant belief or desire. Here, we use these asymmetric causal interventions to probe the structure of Theory of Mind. In Experiments 1 and 2, both adults (N=80) and older children (N=42, 8-10 years) distinguished generative and preventative cases: selecting interventions on both mental states (both belief and desire) to induce an agent to act and just one of the mental states (either belief or desire) to prevent an action. However, younger children (N =42, 5-7 years) did not. To probe this age difference, in Experiment 3, we asked younger children(N=42, 5-7 years) just to predict the outcome of others' mental state interventions. Children predicted that interventions were more likely to prevent actions than to cause them, but failed to predict that intervening on both the relevant beliefs and desires is more likely to generate a novel action than intervening on either alone. These findings suggest that by eight to ten years old, people represent the causal structure of Theory of Mind and can selectively intervene on beliefs and desires to induce and prevent others' actions.

To fully understand human memory, it is necessary to understand its lifespan development. However, memory assessments often rely on significantly different methodologies for different age groups, and their results are typically not directly comparable. In this paper, we present a quantitative assessment of memory function spanning an age range of five to 85 years that is based on a model-based memory assessment. This approach yields a uniform metric that is directly interpretable and can be compared across different tasks and materials that are appropriate for different age groups. The results show a robust U-shape function, with long-term memory function at age 5 being comparable to that of cognitively impaired elderly individuals. These results and the method utilized could provide a new foundation for future studies on memory development across life stages.

Daily experiences are conceptualized as events involving multiple participants and their relations (i.e., thematic roles). When describing events, speakers often do not include all event participants involved. Here, we explore how underlying conceptual requirements and language-specific encoding options influence the content of event descriptions in speech and gesture in two typologically different languages (English, Turkish). Focusing on conceptually peripheral instruments whose status is highly debated, we manipulated the conceptual status of event participants by including events that ‘require' or ‘allow' otherwise syntactically optional instruments. Results showed that the require-allow distinction did not manifest uniformly in Turkish and English in speech, gesture, or when both modalities were considered. However, mention of highly optional event participants (e.g., allowed instruments) was affected by language-specific syntactic encoding options. We conclude that, under more naturalistic elicitation conditions, planning descriptions of instrument events is more heavily affected by language-specific encoding than conceptual prominence of the roles.

Exploration in sequential decision problems is a computationally challenging problem. Yet, animals exhibit effective exploration strategies, discovering shortcuts and efficient routes toward rewarding sites. Characterizing this efficiency in animal exploration is an important goal in many areas of research, from ecology to psychology and neuroscience to machine learning. In this study, we aim to understand the exploration behavior of animals freely navigating a complex maze with many decision points. We propose an algorithm based on a few simple principles of animal movement from foraging studies in ecology and formalized using reinforcement learning. Our approach not only captures the search efficiency and turning biases of real animals but also uncovers longer spatial and temporal dependencies in the decisions of animals during their exploration of the maze. Through this work, we aspire to unveil a novel approach in cognitive science of drawing interdisciplinary inspiration to advancing the field's understanding of complex decision-making.

Whereas principles of communicative efficiency and legal doctrine dictate that laws be comprehensible to the common world, empirical evidence suggests legal documents are largely incomprehensible to lawyers and laypeople alike. Here, a corpus analysis (n=59 million words) first replicated and extended prior work revealing laws to contain strikingly higher rates of complex syntactic structures relative to six baseline genres of English. Next, two pre-registered text generation experiments (n=280) tested two leading hypotheses regarding how these complex structures enter into legal documents in the first place. In line with the \textit{magic spell hypothesis}, we found people tasked with writing official laws wrote in a more convoluted manner than when tasked with writing unofficial legal texts of equivalent conceptual complexity. Contrary to the \textit{copy-and-edit hypothesis}, we did not find evidence that people editing a legal document wrote in a more convoluted manner than when writing the same document from scratch. From a cognitive perspective, these results suggest law to be a rare exception to the general tendency in human language towards communicative efficiency. In particular, these findings indicate law's complexity to be derived from its performativity, whereby low-frequency structures may be inserted to signal law's authoritative, world-state-altering nature, at the cost of increased processing demands on readers. From a law and policy perspective, these results suggest that the tension between the ubiquity and impenetrability of the law is not an inherent one, and that laws can be simplified without a loss or distortion of communicative content.

Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experience with highly generic learning algorithms or do they require strong inductive biases? Recent advances in collecting large-scale, longitudinal, developmentally realistic video datasets and generic self-supervised learning (SSL) algorithms are allowing us to begin to tackle this nature vs. nurture question. However, existing work typically focuses on image-based SSL algorithms and visual capabilities that can be learned from static images (e.g. object recognition), thus ignoring temporal aspects of the world. To close this gap, here we train self-supervised video models on longitudinal, egocentric headcam recordings collected from a child over a two year period in their early development (6-31 months). The resulting models are highly effective at facilitating the learning of action concepts from a small number of labeled examples; they have favorable data size scaling properties; and they display emergent video interpolation capabilities. Video models also learn more robust object representations than image-based models trained with the exact same data. These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases.

Philosophers have long been arguing the precise semantics of different deontic terms within normative statements. However, little research has been done on the human reasoning side of understanding such terms. In this paper, we propose a normative scheme with bitstring semantics that is expressive enough to cover the basic normative concepts in most mainstream schemes proposed in deontic logic research. Even though further confirmation is needed, our explorative experiments on human deontic reasoning have shown results that are consistent with our proposed scheme.

In this paper, we explored the attribution of causal responsibility in a causal chain of events, where an agent A instructs an intermediate agent B to execute some harmful action which leads to a bad outcome. In Study 1, participants judged B to be more causally responsible, more blameworthy, and more deserving of punishment than A. In Study 2, we explored the effect of proximity on judgments of the two agents by adding a third, subsequent contributing cause, such that B's action no longer directly caused the final outcome. Participants judged both agents A and B to be less causally responsible and deserving of punishment (but not less blameworthy) when they were less proximal to the outcome, and there were no differences in judgments between the two agents. In Study 3, we varied whether each of the two agents (A and B) intended for the final outcome to occur. We find an interaction between role and intent, where participants only mitigated judgments for A when A did not intend for the outcome to occur – regardless of B's intent. We discuss possible explanations for our findings and its implications for moral and legal decision-making.

Spatial demonstratives are deictic expressions used to point to a referent with language. In the standard view, they encode a spatial proximal\distal contrast between “near” (this) and “far” (that) from the speaker. Several studies have shown that such contrast maps on a perceptual contrast between peripersonal and extrapersonal space. Still, other factors beyond spatial distance influence demonstrative choice. Here we investigate whether the proximal/distal contrast maps also onto a more general contrast between being in physical control/not in control of a target referent. Participants were presented with two circles (red and blue) on a screen. They had to move them with the mouse to find the target circle (the one with two gaps). One circle followed the mouse trajectory (controllable), while the other moved randomly in the center of the screen (not controllable). Unknown to the participants, the gaps only appeared if the stimuli crossed a distance threshold. Importantly, participants had to use stimulus controllability to solve the task. They were instructed to answer by indicating the target to the experimenter using this/that and red/blue (in Italian questo/quello and rosso/blu). Results show that participants used the proximal demonstrative more frequently to refer to the target stimulus when in control. These findings suggest that, similarly to spatial distance, physical control influences demonstrative choice.

Speakers are often over-informative, referring to the color and shape of a referent even when all objects in a scene are unique. Interestingly, this helps listeners locate the target. If speakers are indeed sensitive to listeners' online processing demands, they should be more over-informative when addressing someone whose processing is especially slow. Here we show that English-speaking adults produce more redundant color adjectives when speaking to children than adults (Exp 1); that although Spanish-speakers produce fewer redundant color adjectives than English-speakers overall, they too do so more often for children (Exp 2); that these results are independent of experience with young children (Exp 3), and that children themselves (ages 4-10) are more over-informative when speaking to younger children than adults (Exps 4 and 5). Collectively, these results suggest that sensitivity to listeners' online processing demands is robust, emerges early in development, and may be especially tailored to young learners.

An important issue in the semantic memory literature concerns the relative importance of experience-based sensorimotor versus language corpus-based distributional information in conceptual representations. Here we examine how each sort of information is associated with the EEG response to words in a property verification task in which participants indicated whether or not a property term (such as ”red”) is typically obtained for a concept term (such as ”APPLE”). To define and measure each type of information, we operationalized distributional and sensorimotor information using cosine distance measurements derived from GloVe Embeddings and Lancaster Sensorimotor Norms respectively. We then modeled single-trial EEG responses to property words in a property verification task using regression models. Our findings indicate that semantic processing in this task simultaneously incorporates distributional and sensorimotor information, and their contribution is shaped by task-relevant linguistic context. We aim for our study to contribute to a critical examination of such information operationalizations and also encourage a systematic evaluation of their performance across tasks, particularly for EEG measurements.

Adults routinely tailor their communication to others' auditory access, such as substituting gestures for speech in noisy environments. Yet, assessing the effectiveness of different communicative acts given others' perceptual access—especially when it differs from one's own—requires mental-state reasoning, which undergoes significant developmental change. Can young children tailor their communication to others' auditory access? In Study 1, parental report (n=98) indicated that most children, by age 4, adjust their communicative behaviors in noisy settings. Study 2 elicited these behaviors experimentally with 4- to 5-year-olds (n=68). Children taught how a novel toy works to a learner who wore headphones playing either loud music or nothing. Children were more likely to use physical demonstrations, and less likely to use verbal explanations, when the learner's auditory access was obstructed. These findings illustrate how mental-state reasoning might support children's ability to communicate successfully across perceptually-compromised contexts and individuals.

In spoken languages, new information is often expressed with a longer duration than given information. We investigated whether signers use duration to mark information status. Fifty deaf Chinese Sign Language (CSL) signers retold a cartoon clip, and we examined how they tracked references. The results showed that CSL signers mostly used nominals, classifiers and constructed actions, but rarely used any pointing or zero anaphora. When focusing on nominals, newly introduced references had a longer duration than the maintained and re-introduced ones, while the durations of maintained and re-introduced nominals did not differ. Additionally, there was a gradient decrease in sign duration over the first three mentions followed by an increase for the fourth and fifth mentions. Furthermore, between two nominal mentions, the more non-nominal referring there were, the shorter the duration of the current nominal mention. Thus, CSL signers vary the duration of nominals to indicate the degree of accessibility.

An important aspect of language comprehension is learning and generalizing complex lexical relations. For instance, having learned that the phrase preserve cucumbers predicts vinegar and that preserve berries predicts dehydrator, one should be able to infer that the novel phrase preserve peppers is more compatible with vinegar, because pepper is more similar to cucumber. We studied the ability to perform such (compositional) generalization in distributional models trained on an artificial corpus with strict semantic regularities. We found that word-encoding models failed to learn the multi-way lexical dependencies. Recurrent neural networks learned those dependencies but struggled to generalize to novel combinations. Only mini GPT-2, a minified version of the Transformer GPT-2, succeeded in both learning and generalization. Because successful generalization in our tasks requires capturing the relationship between a phrase and a word, we argue that mini GPT-2 acquired hierarchical representations that approximate phrase structure. Our results show that, compared to older models, Transformers are architecturally advantaged to perform compositional generalization.

Humans have increasing opportunities to offload internal cognitive demand, such as by setting reminders to aid future memory performance. Here, we examine how children begin to balance mind and world: weighing up when to offload cognition and when to rely on their unaided capacities. Australian children aged 6 to 9 years (N = 120) were tasked with remembering the locations of 1, 3, 5, and 7 targets hidden under 25 cups. In the critical test phase, children were provided with a limited number of ‘tokens' to distribute across trials, which they could use to mark target locations and assist future performance. Following the final search period, children were invited to evaluate and adjust their initial allocation. Results showed that 8- to 9-year-olds prospectively allocated proportionately more tokens to difficult trials, whereas 6- to 7-year-olds did so only in retrospect. Throughout childhood, humans become increasingly adept at balancing internal and external cognition.

Events of social exchange, such as givings and tradings, are uniquely prevalent in human societies and cognitively privileged even at early stages of development. Such events may be represented as having 3 or even 4 participants. To do so in visual working memory would be at the limit of the system, which throughout development can track only 3 to 4 items. Using a case study of trading, we ask (i) whether adults can track all four participants in a trading scene, and (ii) whether they do so by chunking the scene into two giving events, each with 3 participants, to avoid placing the visual working memory system at its limit. We find that adults represent this scene under a 4-participant concept, and do not view the trade as two sequential giving events. We discuss further implications for event perception and verb learning in development.

When reasoning about a claim, it makes sense to be more persuaded if lots of other people agree. But, there are many factors that make weighing the evidence behind a consensus complicated. For example, a consensus might be more or less informative depending on the type of claim, or whether each consensus member formed their opinions independently. These factors might also influence people differently depending on their own assumptions or preferences. In this study we used a mock social media paradigm to assess how persuaded people were by two factors: the presence of consensus (no consensus vs. consensus), and source independence (a consensus based on independent information sources vs. a consensus formed off shared, dependent sources). We varied these factors at both the group and individual level. At the group level, we assessed a third factor: whether people were influenced by the type of claim being reasoned about (we assessed 60 different claims divided into 4 categories). Almost everyone was more persuaded by consensus trials compared to no consensus trials. However, the strength of this effect was credibly stronger if the claim was likely to have a ground truth. We found that around one third of participants were sensitive to source independence. Of these, three quarters were more persuaded by a consensus based on independent sources, but the quarter who were more persuaded by dependent sources were persuaded just as strongly.

During sentence processing, comprehenders have to maintain a mapping between lexical items and their position in the sentence (syntactic position). We propose a model of morpheme-position binding in working memory, based on models such as 'serial-order-in-a-box' and its SOB-complex-span version. Like those working memory models, our sentence processing version derives a range of attested memory interference effects from the process of item-position binding. We present simulation results capturing similarity-based interference and item-distortion. These two major classes of interference effects have not received a unified account before, and are not fully captured by cue-based retrieval models.

Dual language learners (DLLs) constitute a large portion of the population, but relatively little is known about the best ways in which to assess their vocabulary knowledge. Past research has used both conceptual vocabulary knowledge, assessing whether a child knows a word in either language, as well as total vocabulary knowledge, assessing what words a child knows in each language separately. The present work uses neural networks to predict specific word learning for individual Cantonese-English DLLs. As its input, The model utilizes word2vec embeddings that either represent children's' conceptual word knowledge or total word knowledge. We find that using total word knowledge results in higher predictive accuracy, suggesting that knowing what specific words DLLs know in each of their languages provides the most accurate picture of DLLs' vocabulary knowledge. The present work has many implications for both identification of at-risk individuals and the creation of learning materials for DLL populations.

This study investigates how people assign blame to autonomous vehicles (AVs) when involved in an accident. Our experiment (N = 2647) revealed that people placed more blame on AVs than on human drivers when accident details were unspecified. To examine whether people assess major classes of blame-relevant information differently for AVs and humans, we developed a causal model and introduced a novel concept of prevention effort, which emerged as a crucial factor for blame judgement alongside intentionality. Finally, we addressed the “many hands” problem by exploring how people assign blame to entities associated with AVs and human drivers, such as the car company or an accident victim. Our findings showed that people assigned high blame to these entities in scenarios involving AVs, but not with human drivers. This necessitates adapting a model of blame for AVs to include other agents and thus allow for blame allocation “outside” of autonomous vehicles.

Humans seem to arbitrate between automatic and controlled processing by optimizing a trade-off between cognitive effort and performance. Previous research has described ways of how these costs and benefits can be quantified and how the trade-off between them can be performed. However, it remains unclear how the costs should be weighed relative to the benefits and how the cost of the arbitration mechanism itself factors in. Here, we derive measures for these separate factors from a single objective: the variational free energy. We demonstrate that by minimizing this objective, the trade-off between automatic and controlled processing as well as meta-control is optimized implicitly. As a proof of concept, we show that the congruency and proportion congruency effects in the Stroop task directly result from this optimization, given an environment with specific statistical regularities.

Adults often hold different goals for children's achievement: Sometimes adults want children to learn as much as possible, while at other times adults discount children's learning in favor of high performance. How do children reason about the achievement goals adults have for them? Across 3 preregistered studies (n = 120), we asked whether 5- and 6-year-old children understand the causal relationship between adults' achievement goals, their task choices, and children's competence. In Experiment 1, we found adults are more likely to give harder tasks to children when they hold learning versus performance goals and when the child is more competent. In Experiment 2, we found that children make similar inferences about adults' task selections given the adult's achievement goal and the receiving child's competence. Finally, in Experiment 3, children inferred that adults would pick harder tasks for them when they possessed a learning goal versus a performance goal, which matched their own task choice given the same achievement goals. Thus, young children can infer the relationship between adults' child-directed achievement goals and actions and may use this information to learn about what adults prioritize for children across contexts.

Surprisal values from large language models (LLMs) have been used to model the amplitude of the N400. This ERP component is sensitive not only to contextual word expectancy but also to semantic association, such that unexpected but associated words do not always induce an N400 increase. While LLMs are also sensitive to association, it remains unclear how they behave in these cases. Moreover, another ERP component, the P600, has shown graded sensitivity to plausibility-driven expectancy, while remaining insensitive to association; however, its relationship to LLM surprisal is not well researched yet. In an rERP analysis, we evaluate surprisal values of two unidirectional transformers on their ability to model N400 and P600 effects observed in three German ERP studies isolating the effects of association, plausibility, and expectancy. We find that surprisal predicts an N400 increase for associated but implausible words, even when no such increase was observed in humans. Furthermore, LLM surprisal accounts for P600 effects elicited by violations of selectional restrictions, but captures neither P600 effects from more subtle script knowledge violations nor graded P600 modulations. The results of our investigation call into question the extent to which LLM surprisal offers an accurate characterisation of the functional generators of either the N400 or P600.

Aspectual coercion occurs when there is a semantic mismatch between constituents in terms of their lexical aspect. Despite the long psycholinguistic history of this phenomenon, we currently lack direct measures of how people interpret coerced sentences. We introduce a novel method combining aspectual comprehension with event cognition, allowing us to detect changes in how individuals construe events after reading sentences with varying aspectual information. This study involved two experiments where participants read sentences—either telic or atelic, with or without coercion—followed by a video clip related to the sentence. They assessed if the actor completed the task and identified any brief interruptions during the event, located at the midpoint or late points. The focus was on whether coerced sentences altered participants' event construals, impacting their responses. Results uncovered distinct cognitive responses to aspectual coercion and highlighted differences between coercion types. This method advances our understanding of how lexical aspect influences event representation, offering insights into the nuanced effects of aspectual coercion on cognitive processing and event perception.

Adults often learn new semantic information in face-to-face communication with other adults (e.g., teachers, colleagues). More knowledgeable individuals provide an ensemble of multimodal behaviours that can shape the information that their interlocutors learn. Using the naturalistic ECOLANG corpus of dyadic conversations, we ask whether multimodal behaviours (pitch, speaking rate, representational gestures, points, object manipulations, and gaze) support adults' semantic learning of unknown objects above and beyond verbal properties of utterances (number of utterances, lexical diversity, mean length of utterances, concreteness) and learners' individual differences (vocabulary, working memory). We found that individual differences, pointing and object manipulations affected learning, with verbal and multimodal factors also interacting to predict adult semantic learning. Our results highlight the relevance of accounts of multimodal learning in adulthood and the importance of considering naturalistic interaction in its complexity to understand the factors that influence adult learning.

Reservoir computing (RC) offers distinct advantages in extracting spatiotemporal information with low training costs by separating recurrent neural networks into a fixed network with recurrent connections. The quality of the fixed network, known as the reservoir, plays a pivotal role in the performance of the RC system. Our work aims to provide a unified synaptic development framework for RC, constructing a more biologically plausible reservoir to model and understand the neural networks development within the human brain. In this paper, we propose an Autonomous Synaptic Development Reservoir Computing model (ASD-RC) based on an adaptive network of phase oscillators. The reservoir autonomously adjusts the distribution of connection weights in response to external stimuli, forming a task-specific structure. Through experiments and theoretical analyses, we demonstrate that ASD-RC can emulate various synaptic development rules of biological neural networks in \textit{vivo}, including the Hebbian rule and STDP. Furthermore, experiments reveal that combining different development rules can enhance performance on prediction tasks compared to using a single development rule, showcasing the emergence and effects of synergistic development that improve information processing capacity.

We study referential communication about concepts at different levels of abstraction in an interactive concept-level reference game. To better understand processes of abstraction, we investigate superordinate referring expressions (animal). Previous work identified two main factors that influence speakers' choice of referring expressions for concepts: the immediate context and the basic-level effect, i.e. a preference for basic-level terms such as dog. Here we introduce a new concept-level reference game that allows us to study differences in the basic-level effect between comprehension and production and to elicit superordinate referring expressions experimentally. We find that superordinate referring expressions become relevant for groups of objects. Further, we reproduce the basic-level effect in production but not in comprehension. In conclusion, even though basic-level terms are most readily accessible, speakers tailor their expressions to the context, allowing the listener to identify the target concept.

Computational neuromodeling methods for evaluating representational dynamics involve intricate analysis choices at every stage of the analysis pipeline. Analysis choices for data processing pipelines are generally chosen based upon end to end accuracy metrics and corresponding performance metrics. Psychology research has recently begun to acknowledge the importance of controlling for potential bias introduced by degrees of freedom in data analysis, with specification curve analysis introduced as a principled method for correcting for such biases. In this paper, we conduct a specification curve analysis (SCA) for representational similarity analysis pipelines reported in the literature for fMRI and EEG datasets, respectively. We show that EEG-based RSA analyses are relatively robust to alternative specifications but that fMRI based analyses are not. Using a novel decision-tree analysis to supplement SCA, we present a potentially more robust pipeline for such analyses.

Telicity is an important semantic feature pointing to event construal: telic verb phrases denote bounded events with an inherent endpoint while atelic verb phrases denote unbounded events without such an endpoint. Languages encode telicity in different ways. Unlike English, Mandarin lacks an overt count-mass distinction and allows bare noun objects to form verb phrases. Would this cross-linguistic difference influence event perception? Experiment 1 elicited descriptions of bounded vs. unbounded events from English and Mandarin native speakers. A clear cross-linguistic difference was found: English speakers mostly used telic predicates for bounded events and atelic predicates for unbounded events while Mandarin speakers gave atelic predicates with bare noun objects for both event types. Experiment 2 explored how English and Mandarin speakers tracked the temporal structure of bounded vs. unbounded events. The two language groups performed similarly. The way people describe events may not affect the way they track event temporal profiles.

In this paper, we test the effect of manipulating discrimination difficulty on subsequent generalization of learning and in particular, on the peak shift effect. Participants learned a discrimination where one stimulus led to an outcome (S+) and another stimulus led to no outcome (S-). Difficulty was manipulated by varying the degree of similarity between the S+ and S- across groups (easy/medium/hard). In contrast to similar studies in animals, we found that increasing the difficulty of the discrimination resulted in less peak shift. Using a hierarchical mixture model, we characterize the effects of discrimination difficulty on relational- and similarity based responding, and show for the first time, a similar mixture of responding on stimulus identification gradients. We conclude that peak shift on generalization and identification measures can be explained by mixtures of participants responding in different ways.

Decoding naturalistic stimuli from neural recordings is a significant challenge in systems neuroscience, primarily due to the high-dimensional and nonlinear nature of stimulus-response interactions, and is further exacerbated by the limited availability and noisiness of neural data. While contemporary approaches that incorporate generative models, such as Generative Adversarial Networks (GANs), attempt to address these issues by mapping neural responses to latent representations, they do not fully overcome these obstacles. In this work, we present a novel paradigm of differential neural decoding (dicoding) that focuses on the relative changes in response patterns, which not only expands the neural training data quadratically but also inherently denoises it. To determine the corresponding stimulus changes, this method leverages the Euclidean and feature-disentangled properties of the underlying latents through vector arithmetic. As such, we not only effectively exploit the latent space but also achieve semantically meaningful latent offsets in the context of the stimuli, resulting in improved sample efficiency. We trained a decoder to predict changes in latent vectors based on the corresponding changes in neural responses. The absolute latent vector itself was derived by vector addition of the predicted latent change (indicative of stimulus shift) to a reference latent, which was fed to the generator for the reconstruction of the perceived stimulus. Our results show that this geometrically principled approach facilitates more effective reconstruction of naturalistic stimuli from noisy and limited neural data.

Food neophobia – the reluctance to eat novel food – has been associated with poorer performance in category-based tasks within the food domain among preschoolers. This research aims to unravel this negative relationship and determine if this association is specific to food items or reflects general cognitive rigidity in considering alternative ways to represent entities. In study 1, 123 children between 3 and 6 years were tested on an inductive reasoning task, comparing food and animals. In study 2, 112 children aged 4 to 6 engaged in a cross-categorization task comparing food, animals and artifacts. Results indicated that neophobic children exhibited poorer induction and cross-categorization performance in all domains compared to their neophilic counterparts. These findings highlight the importance of child characteristics in shaping the general development of category-based abilities and suggest that food neophobia, rather than a fear of novelty, reflects instead difficulties in changing perspectives once items have been classified.

What factors contribute to the growth of children's early vocabulary? One method for exploring this question is investigating predictors (e.g., frequency) that differentiate words learnt earlier from those learnt later. A more comprehensive account requires the examination of multiple language families and multiple levels of linguistic representation (e.g., phonological, morphosyntactic, semantic). Here, we studied 10 predictors of word ages of acquisition across 27 languages and dialects. We found that words that were more frequent, concrete, and associated with babies were learnt earlier, whereas words that had greater length in phonemes and mean length of utterance were learnt later. There was no reliable effect of other morphosyntactic predictors, or of phonological neighbourhood. We found evidence of a tradeoff between a language's morphological complexity and the effect of syntactic complexity for predicates, supporting the competition model. Predictor coefficients revealed broad consistency across all languages, along with variability that reflected language family classifications.

Retrieval practice—the process of actively calling information to mind rather than passively studying materials—has been proven to be a highly effective learning strategy. However, only recently, researchers have started to examine differences between learners in terms of the optimal conditions of retrieval practice in applied educational settings. In this study (N = 118), we focus on learners with dyslexia: a group that is usually not included in the retrieval practice literature. We compare their performance to the performance of typical learners in an adaptive, retrieval practice-based word learning task using both typing-based and speech-based response conditions. We find that typical learners outperform learners with dyslexia when they are asked to respond by typing, but that this difference is much smaller when learners can respond by speech. These results can contribute to the development of educational technology that allows for effective and inclusive learning in neurodivergent individuals.

The Abstraction Reasoning Corpus (ARC) is a visual analogical reasoning test designed for humans and machines (Chollet, 2019). We compared human and large language model (LLM) performance on a new child-friendly set of ARC items. Results show that both children and adults outperform most LLMs on these tasks. Error analysis revealed a similar "fallback" solution strategy in LLMs and young children, where part of the analogy is simply copied. In addition, we found two other error types, one based on seemingly grasping key concepts (e.g., Inside-Outside) and the other based on simple combinations of analogy input matrices. On the whole, "concept" errors were more common in humans, and "matrix" errors were more common in LLMs. This study sheds new light on LLM reasoning ability and the extent to which we can use error analyses and comparisons with human development to understand how LLMs solve visual analogies.

What impacts what we remember about objects we have just encountered? Influential theories of learning suggest that more active engagement leads to stronger memories than passive observation. However, it is not clear which aspects of interaction lead to stronger memories, nor what kinds of memories are supported by active engagement. Here we conduct several experiments to investigate the impact of assembling an object on subsequent recognition and recall performance. We found that reconstructing a block tower by copying it part-by-part could impair subsequent memory for that tower, compared to passively viewing that tower. By contrast, when participants initially encoded each tower by building it from working memory, their subsequent recall was enhanced relative to when they held the tower in working memory without building it. Together our results suggest a complex relationship between the nature of our interactions with objects and our subsequent memories of them.

English allows for both compounds (e.g., London-made) and phrasal paraphrases (e.g., made in London). While these constructions have roughly the same truth-conditional meaning, we hypothesize that the compound allows less freedom to express the nature of the semantic relationship between the participle and the pre-participle nominal. We thus predict that the pre-participle slot is more constrained than the equivalent position in the phrasal construction. We test this prediction in a large corpus by measuring the entropy of corresponding nominal slots, conditional on the participle used. That is, we compare the entropy of α in compound construction slots like “α-[V]ed” to the entropy of α in phrasal constructions like “[V]ed by α” for a given verb V. As predicted, there is significantly lower entropy in the compound construction than in the phrasal construction. We consider how these predictions follow from more general grammatical properties and processing factors.

We offer philosophical motivations for a method we call Virtual World Cognitive Science (VW CogSci), in which re- searchers use virtual embodied agents that are embedded in virtual worlds to explore questions in the field of Cognitive Science. We focus on questions about mental and linguistic representation and the ways that such computational modeling can add rigor to philosophical thought experiments, as well as the terminology used in the scientific study of such representations. We find that this method forces researchers to take a god's-eye view when describing dynamical relationships be- tween entities in minds and entities in an environment in a way that eliminates the need for problematic talk of belief and concept *types*, such as *the belief that cats are silly*, and *the concept CAT*, while preserving belief and concept *tokens* in individual cognizers' minds. We conclude with some further key advantages of VW CogSci for the scientific study of mental and linguistic representation and for Cognitive Science more broadly.

We offer a computational framework for modeling explanation as cooperative rational communication. Under our framework, when an explainer is faced with a ``why?'' question, they reason about the question-asker's current mental model, and intervene on that mental model in order to maximize the listener's future utility. We instantiate our framework in a planning domain, and show that our framework can model human explanations about plans across a wide variety of scenarios.

Disparate impact rules are formally neutral but indirectly discriminate against protected groups (i.e., by targeting a characteristic that is more prevalent in a given group). Because these rules are not obviously malicious, they have been widely enacted to circumvent policies against explicit discrimination. In a series of four experiments, we show that adults and children are sensitive to the moral implications of disparate impact rules. However, we also find that they are more accepting of these rules when strong justification is provided, compared to rules with no justification. Crucially, demographic differences also impact people's judgments of disparate impact rules and their creators. We find that conservatives and those from groups not directly affected by the rule tend to be more accepting of it. By studying people's reasoning about disparate impact rules, this work aims to identify the mechanisms by which these rules may evade detection. Finally, we discuss how these insights may inform the development of interventions that highlight the problematic effects of indirectly discriminatory policies.

Advancements in deep neural networks have led to significant progress in computer vision and natural language processing. These networks, trained on real-world stimuli, develop high-level feature representations of stimuli. It is hypothesized that these representations, stemming from different inputs, should converge into similar conceptual systems, as they reflect various perspectives of the same underlying reality. This paper examines the degree to which different conceptual systems can be aligned in an unsupervised manner, using feature-based representations from deep neural networks. Our investigation centers on the alignment between the image and word representations produced by diverse neural networks, emphasizing those trained via self-supervised learning methods. Subsequently, to probe comparable alignment patterns in human learning, we extend this examination to models trained on developmental headcam data from children. Our findings reveal a more pronounced alignment in models trained through self-supervised learning compared to supervised learning, effectively uncovering higher-level structural connections among categories. However, this alignment was notably absent in models trained with limited developmental headcam data, suggesting more data, more inductive biases, or more supervision are needed to establish alignment from realistic input.

Studies of meaning in human and primate communication face, in principle, similar methodological problems. In both cases, meaning is not observable directly, but must be inferred from more indirect sources, such as directly observable behavior. Recent work in probabilistic cognitive modeling of language use has therefore developed methods of inferring latent se- mantic meaning through the lens of a probabilistic model of language use. In this paper, we explore how to adapt such an approach for insightful investigations of primate communication. Towards this end, we develop a suitable probabilistic model of processes that generate communicative behavior by making use of functionally specified latent meaning representations. As a proof of concept, we apply this model to a rich, annotated data set of orangutan communicative dyadic interaction and conclude that explicit probabilistic modeling can provide additional insights for the study of animal communication pertaining to the context-dependent nature of signals and the gradual evolution of human communication systems.

This study investigates the phenomenon of verbal overshadowing in olfaction. It focuses on how odor recognition is impacted after individuals sniffed and then described odors. Three key findings emerged. First, participants who refrained from describing a previously encountered target odor (control group) showed significantly superior performance in recognizing the target odor compared to those who had described it (verbal group). Second, the verbal overshadowing effect tended to diminish or completely disappear when participants were required to respond rapidly. Third, providing participants with instructions highlighting potential conflicts between olfactory and verbal representations did not alleviate the influence of the verbal overshadowing effect. To conclude, describing an odor elaborately can adversely affect odor memory, even when one is aware of this, but this is mitigated under speeded conditions.

Music-making relies on precise temporal control and mutual coordination among performers, particularly to maintain tempo. We evaluate the impact of human-machine interaction and rhythmic subdivisions on tempo change in musical trios. A synchronization-continuation task was performed by trios of human participants interacting with confederates or with algorithmic (i.e. machine) models. Sounded tone onsets were produced by a linear error-correction model, delay-coupled model, and Kuramoto model that replaced a human participant. Inter-onset intervals were examined from participants who performed rhythms in both in-phase and anti-phase conditions while a third group member was either a human or algorithmic model. Trios drifted toward faster tempi more when they contained a human than an algorithmic model. Tempo drift also increased for the aligned rhythms (in-phase) compared to rhythms with rhythmic subdivisions (anti-phase). Finally, the tested algorithmic models replicated the confederate's tempo drift without the use of any period correction mechanisms. This research advances our understanding of unintentional tempo drift, offering insights into ensemble dynamics and models of temporal coordination in groups. Implications for musical coordination and avenues for future research are discussed.

Moral inference is an emerging topic of critical importance in artificial intelligence. The contemporary approach often relies on language modelling to infer moral relevance or moral properties of a concept such as "smoking". This approach demands complex parameterisation and costly computation, and it tends to disconnect with psychological accounts of moralization. We present a simple cognitive model for moral inference grounded in theories of moralization. Our model builds on word association network known to capture human semantics and draws on rich psychological data. We demonstrate that our moral association graph model performs competitively to state-of-the-art language models, where we evaluate them against a comprehensive set of data for automated inference of moral norms and moral judgment of concepts, and in-context moral inference. Moreover, we show that our model discovers intuitive concepts underlying moral judgment and is applicable to informing short-term temporal changes in moral perception.

This pupillometric study investigates the relevance of domain-final intonation for attention-orienting in German, employing a changing-state oddball paradigm with rising, falling and neutral intonation on deviant stimuli. Pupil dilation responses (PDR) to deviants were shown to be affected by their intonation contours, strengthening the case for the role of intonational edge tones in attention-orienting. Moreover, the magnitude and duration of the PDR response was higher for rises than falls, indicating the fundamental role of intonational rises for the activation of the attention-orienting mechanism in speech perception.

We investigate whether more accurate representation of syntactic information in Transformer-based language models is associated with better alignment to brain activity. We use fMRI recordings from a large dataset (MOUS) of a Dutch sentence reading task, and perform Representational Similarity Analysis to measure alignment with 2 mono- and 3 multilingual language models. We focus on activity in a region known for syntactic processing (the Left posterior Medial Temporal Gyrus). We correlate model-brain similarity scores with the accuracy of dependency structures extracted from model internal states using a labelled structural probe. We report three key findings: 1) Accuracy of syntactic dependency representations correlates with brain similarity, 2) The link between brain similarity and dependency accuracy persists regardless of sentence complexity, although 3) Sentence complexity decreases dependency accuracy while increasing brain similarity. These results highlight how interpretable, linguistic features such as syntactic dependencies can mediate the similarity between language models and brains

While Vector Symbolic Architectures (VSAs) are promising for modelling spatial cognition, their application is currently limited to artificially generated images and simple spatial queries. We propose VSA4VQA – a novel 4D implementation of VSAs that implements a mental representation of natural images for the challenging task of Visual Question Answering (VQA). VSA4VQA is the first model to scale a VSA to complex spatial queries. Our method is based on the Semantic Pointer Architecture (SPA) to encode objects in a hyperdimensional vector space. To encode natural images, we extend the SPA to include dimensions for object's width and height in addition to their spatial location. To perform spatial queries we further introduce learned spatial query masks and integrate a pre-trained vision-language model for answering attribute-related questions. We evaluate our method on the GQA benchmark dataset and show that it can effectively encode natural images, achieving competitive performance to state-of-the-art deep learning methods for zero-shot VQA.

Video recording is a widely used tool for studying animal behavior, especially in fields such as primatology. Primatologists rely on video data to analyze and research topics such as social grooming to uncover subtle mechanisms behind complex social behavior and structures. Insights into these social behaviors may provide us with a better understanding of our closest living relatives, but also new theories and insights into our own behavior. However, analyzing this type of data using manual annotation is currently a time-consuming task. Here we present an end-to-end pipeline to track chimpanzee (Pan troglodytes) poses using DeepLabCut (DLC) which then serves as input to a support vector machine. This classifier was trained to detect role transitions within grooming interactions. We replicate a recent study showing that DLC has usability value for chimpanzee data collected in natural environments. Our combined method of tracking and classification is remarkably successful in detecting the presence of grooming, indicating the directionality and a change in turn with an accuracy above 86% on unseen videos. We can identify particular pose features used in the classification of grooming, which will contribute to the exploration of turn-taking dynamics on a scale that would otherwise be difficult to attain with traditional methods. Finally, our pipeline can in principle be applied to recognize a variety of other socially interactive behaviors that are largely recognizable by (joint) postural states.

People thinking creatively will shift their bodies, wander around, move. Why? Here we investigate one explanation: Movement is a canny strategy for changing the information that is available visually, in ways that facilitate insight. We first analyzed video footage of mathematicians engrossed in creative thought. We found that sudden "aha" insights were reliably preceded by movements away far from the blackboard, as if mathematicians were stepping back to "see the big picture." To confirm the causal impact of changing proximity on creativity, we conducted an experiment that manipulated proximity to a whiteboard while participants worked on insight puzzles represented by diagrams. Participants had greater creative success when they could survey the entire whiteboard from a distance. Whether in real-world expert reasoning or a controlled experiment, movements away and toward visual representations facilitated insight. Wandering is sometimes a kind of epistemic action, facilitating the discovery of novel connections.

It has long been assumed that infants' ability to discriminate between languages stems from their sensitivity to speech rhythm, i.e., organized temporal structure of vowels and consonants in a language. However, the relationship between speech rhythm and language discrimination has not been directly demonstrated. Here, we use computational modeling and train models of speech perception with and without access to information about rhythm. We test these models on language discrimination, and find that access to rhythm does not affect the success of the model in replicating infant language discrimination results. Our findings challenge the relationship between rhythm and language discrimination, and have implications for theories of language acquisition.

There has been exciting recent progress in computational modeling of moral cognition. Work in this area tends to describe the cognitive mechanisms of human moral judgment using symbolic models, which are interpretable and written in terms of representations that carry meaning. However, these models fall short of capturing the full human capacity to make moral judgments in that they fail to process natural language inputs but instead rely on formal problem specifications. The inability to interface with natural language also limits the usefulness of symbolic models. Meanwhile, there have been steady advances in conversational AI systems built using large language models (LLMs) that interface with natural language. However, these systems fall short as models of human reasoning, particularly in the domain of morality. In this paper we explore the possibility of building neuro-symbolic models of human moral cognition that use the strengths of LLMs to interface with natural language (specifically, to extract morally relevant features from it) and the strengths of symbolic approaches to reason over representations. Our goal is to construct a model of human moral cognition that interfaces with natural language, predicts human moral judgment with high accuracy, and does so in a way that is transparent and interpretable.

Social media is often used as a platform where individuals engage in debate regarding topics that are important to them. Not all arguments are equally convincing, and whilst a given argument may be persuasive to some people, it is often seen as inadequate by others. We are interested in both the individual and argument level differences that make ‘everyday' arguments such as those on social media persuasive. In a replication of our Everyday Belief Bias Task (Deans-Browne & Singmann, 2024), we investigate this question using a paradigm that consists of two parts. In the first part, we measure participant's individual beliefs about eight claims each referring to a political topic (e.g., Abortion should be legal). In the second part, participants rated an argument for each of these claims that was deemed as either good, inconsistent (containing internal inconsistencies), or authority-based (being centered around appeals to authority). We replicated the belief consistency effect – participants preferred arguments that were also in line with their beliefs. We also found that authority-based arguments were rated as worse than inconsistent arguments, and that both types of arguments were rated as worse than good arguments. The implications are first that people do not evaluate arguments independently of the background beliefs held about them. Secondly, people are willing to ignore inconsistencies in arguments more than they are willing to accept the endorsement of authority figures as adequate evidence for arguments.

Our visual system tends to prioritise novel information, and this allocation of attention, as examined with the Visual Paired Comparison Task (VPC), is taken as an indirect index of memory processes. At present, research on the emergence of a novelty preference (NP) remains unclear about its temporal dynamics and agnostic about the role that the organisation of conceptual knowledge may play in it. These two gaps are addressed in this eye-tracking study, which adapts the VPC task to enable a finer temporal tracking of the NP while manipulating categorical and functional relationships between pairs of real-world visual objects to examine the impact conceptual associations bear on it. We found that NP significantly increases with increasing delay between the familiarisation and the test phase, especially for pairs of objects that were both categorically and functionally related (e.g., dart/dartboard). Our findings provide fresh evidence about the interplay between overt attention, conceptual knowledge and memory processes on novelty preference while offering valuable insights into the temporal dynamics of NP and its conceptual implications for mechanisms governing visual short-term memory.

Toddlers are sensitive to the reliability of speakers in their environment \cite{koenig2010}. While previous work suggests that children prefer labels from reliable speakers, it remains unclear how these social representations guide lower-level information-seeking processes that affect speaker preferences. The current study introduces a gaze-contingent eye-tracking paradigm to investigate how children engage in sampling during word learning. Toddlers (22-24m) view videos of two speakers labeling familiar objects; one speaker provides reliable labels and the other speaker provides unreliable labels. Toddlers then sample novel labels from the speakers using a gaze-contingent interface: only the speaker they are fixating on provides a novel label. Preliminary data (N = 18) suggests that participants prefer to sample first from the reliable speaker over the unreliable speaker. However, there is little difference in overall sampling preferences. Our findings suggest that toddlers can assess speaker reliability, but remain open to exploring information from unreliable speakers.

Early word learning involves mapping individual words to their meanings and building organized semantic representations among words. Previous corpus-based studies (e.g., using text from websites, newspapers, child-directed speech corpora) demonstrated that linguistic information such as word co-occurrence alone is sufficient to build semantically organized word knowledge. The present study explored two new research directions to advance understanding of how infants acquire semantically organized word knowledge. First, infants in the real world hear words surrounded by contextual information. Going beyond inferring semantic knowledge merely from language input, we examined the role of extra-linguistic contextual information in learning semantic knowledge. Second, previous research relies on large amounts of linguistic data to demonstrate in-principle learning, which is unrealistic compared with the input children receive. Here, we showed that incorporating extra-linguistic information provides an efficient mechanism through which semantic knowledge can be acquired with a small amount of data infants perceive in everyday learning contexts, such as toy play.

Toddlers can only say one or two words at a time. What do they choose to talk about? We report preliminary results (N=167; mean; 19.5 months) from a pre-registered online experiment on productive language. Toddlers saw six movies. A curtain opened on an introductory scene, the parent closed their eyes, and a new event happened. The curtain closed and the child was asked what happened. On two trials the unseen event was new to the parent (Novel event); on two trials, one of two animals ate the only food in the scene (Agent ambiguous); on two trials, the only animal ate one of the two foods (Patient ambiguous). We predicted that toddlers would selectively generate informative utterances (i.e., referring to the novel event, the agent, and the patient, respectively). Toddlers' productive language was indeed sensitive to what listeners' know; however, unlike adults, they selectively referred to information in common ground.

People who have no experience with programming can create informal programs to rearrange the order of cars in trains. To find out whether they rely on kinematic mental simulations, the current studies examined participants' eye movements in two experiments in which participants performed various moves and rearrangements on a railway consisting of a main track running from left to right and a siding entered from and exited to the left track. In Experiment 1, they had to imagine different sorts of single moves of cars on the railway. The sequences of their fixations resembled iconic gestures: they tended to look at the starting location of the imagined move, and then at its final location. In Experiment 2, the task was to create descriptions of how to solve four sorts of rearrangements that differ in their Kolmogorov complexity. It predicted the time to find the correct solution and the relative number and duration of fixations recorded during the description of each move for rearrangements of different complexity. Participants were more likely to fixate on the symbols on the cars than anything else, and they fixated longer when the rearrangement was more difficult. They also tended to fixate regions of the tracks where a car's movement began or ended, as if they were imagining a car moving along the tracks. The results suggest that humans rely on a kinematic mental simulation when creating informal algorithms.

Learning names for novel objects has been shown to be impacted by the context in which they appear. Manipulations of context, therefore, provide a key pathway to explore these learning dynamics. Here we use a neural process model that instantiates the details of ‘context' to generate novel, counterintuitive predictions about how similarity in object properties influence learning. Specifically, we use a dynamic field model, WOLVES, to simulate and predict learning in a cross-situational word learning task in two conditions: one where the two objects presented on each learning trial are metrically similar in a property (‘NEAR') and another condition where the two objects are always dissimilar (‘FAR'). WOLVES predicts—counterintuitively—that participants should learn better in the ‘NEAR' condition (where objects are potentially confusable) than in ‘FAR' condition (where objects are distinctive). We then tested this prediction empirically, finding support for the novel prediction. This study shows the utility of process models which instantiate the details of ‘context' during learning and provides support for WOLVES. We know of no other theory of cross-situational word learning that captures these novel findings.

Infants' bodies, brains, and environments are ever-changing. Although this continuous transformation is a fundamental feature of development, how infants actively adapt and learn amidst such volatility is still unknown. To address this, we devised a novel learning task in which the location of a reward was systematically altered, transitioning from stable to volatile periods. Through computational modelling, we inferred from the infants' gaze and pupil data the learning processes that enabled them to navigate these changing environments. We found that infants' tonic pupil size reflected trial-by-trial changes in the level of environmental volatility. Moreover, phasic changes in pupil size when observing the reward indicated that infants relied on the information about volatility to optimize their learning. This resulted in the successful performance of the task, as indicated by the pattern of anticipatory looks to the correct reward locations. Together, these results identify the active role that infants play in adapting to change.

Seeing a person preparing to perform an action and later remembering having seen subsequent phases of the action, but not previous phases. This is what a theory on the role of the motor system in the creation and recovery of memories predicts can happen. We investigate memory for action after viewing an image representing an actor acting on a series of everyday objects. The participants in one experiment viewed a series of still photos of unfolding actions on objects (e.g., blowing the nose), and 15 minutes later they were asked to complete a recognition task. At recognition, participants viewed photos representing temporally distant moments, backward or forward in time compared to the original, along with the same photos seen at encoding. Results showed that participants tended to accept forward photos more than backward photos. In a pilot study, we explored the role of the temporal distance between encoding and recognition. Results showed that when 3 days elapsed between the encoding and recognition phases, participants did not tend to accept forward photos more than backward photos.

In this paper we investigate whether subject doubling in French is affected by the Uniform Information Density (UID) principle, which states that speakers prefer language encoding that minimizes fluctuations in information density. We show that, other factors being controlled, speakers are more likely to double the NP subject when it has a high surprisal, thus providing further empirical evidence to the UID principle which predicts a surprisal-redundancy trade-off as a property of natural languages. We argue for the importance of employing GPT-2 to investigate complex linguistic phenomena such as subject doubling, as it enables the estimation of subject surprisal by considering a rather large conversational context, a task made possible by powerful language models that incorporate linguistic knowledge through pre-training on extensive datasets.

Choice deadlines are commonly imposed in decision-making research to incentivize speedy responses and sustained attention to the task settings. However, computational models of choice and response times routinely overlook this deadline, instead simply omitting trials past the deadline from further analysis. This choice is made under the implicit assumption that parameter estimation is not significantly affected by ignoring these omissions. Using new tools from likelihood-free inference, here we elucidate the degree to which omitting omissions, even in seemingly benign settings, can lead researchers astray. We explore the phenomenon using a Sequential Sampling Model (SSM) with collapsing boundaries as a test-bed.

Generic statements (e.g., “girls wear makeup”) tie properties to groups and are a common way of transmitting stereotypes. One natural but untested way that people might try to undermine these statements is by making a similar statement about salient but not mentioned contrastive groups (e.g., “boys can wear makeup too”). Do can generics license the same judgments as do generics? Four studies examined how adults judge two novel groups when one group does a property (e.g., Zarpies make pizzas) while the other group can do the property (e.g., Gorps can make pizzas too). Compared to do generics, adults consistently judged groups described with can generics to be less likely to have, less interested in, less competent at, and for it to be less permissible for them to do the property. Overall, these results suggest that can generics are unlikely to be an effective means at equating beliefs about two groups.

This study explores human capabilities in distinguishing and recognizing entities that change over time from those that do not. We specifically investigate the linguistic distinction between "individual-level predicates" (ILPs) and "stage-level predicates" (SLPs). Our empirical approach focuses on how humans visually distinguish these two types. We performed a corpus analysis, in which a set of image captions were randomly extracted and annotated by experts with either SLP or ILP labels. The findings indicated a predominance of SLPs over ILPs in the image captions. We then performed automatic annotation on a large dataset of image captions and conducted a machine-learning experiment on image classification based on ILSs and SLPs. Our results demonstrated that SLPs were identified with high accuracy, while ILPs were identified with about chance level, substantially lower than human capabilities. Given the analyses, we discuss what features of the image contribute to distinguishing between ILPs and SLPs.

Content-curating algorithms provide a crucial service for social media users by surfacing relevant content, but they can also bring about harms when their objectives are misaligned with user values and welfare. Yet, potential behavioral consequences of this alignment problem remain understudied in controlled experiments. In a preregistered, two-wave, collaborative filtering experiment, we demonstrate that small changes to the metrics used for sampling and ranking posts affect the beliefs people form. Our results show observable differences in two types of outcomes within statisticized groups: belief accuracy and consensus. We find partial support for hypotheses that the recently proposed approaches of "bridging-based ranking" and "intelligence-based ranking" promote consensus and belief accuracy, respectively. We also find that while personalized, engagement-based ranking promotes posts that participants perceive favorably, it simultaneously leads those participants to form more polarized and less accurate beliefs than any of the other algorithms considered.

The cognitive processes underlying mental folding have been investigated for decades, while the neural correlates associated with this spatial transformation are barely understood. This study combines cognitive modeling with EEG recordings from 41 subjects to investigate the general mechanisms of mental spatial transformation. By linking model-based simulation and electrocortical activity, we identified brain areas involved during mental folding. Our novel approach showed active central parietal and left parietal, as well as occipital areas during spatial storage, while the right parietal cortex was associated with spatial transformation. The left occipital and parietal regions were active especially during visual baseline trials, while the right parietal region exhibited stronger activity for more difficult folding trials, replicating previous results. The varying activation patterns imply different cognitive loads for storage and for transformation depending on task difficulty.

Typical models of learning assume incremental estimation of continuously-varying decision variables like expected rewards. However, this class of models fails to capture more idiosyncratic, discrete heuristics and strategies that people and animals appear to exhibit. Despite recent advances in strategy discovery using tools like recurrent networks that generalize the classic models, the resulting strategies are often onerous to interpret, making connections to cognition difficult to establish. We use Bayesian program induction to discover strategies implemented by programs, letting the simplicity of strategies trade off against their effectiveness. Focusing on bandit tasks, we find strategies that are difficult or unexpected with classical incremental learning, like asymmetric learning from rewarded and unrewarded trials, adaptive horizon-dependent random exploration, and discrete state switching.

Current models of cognitive control frame its allocation as a process of expected utility maximization. The benefits of a candidate action are weighed against the costs of that control allocation (e.g. opportunity costs). Recent theorizing has found that it is normative to account for the value of learning when determining control allocation. Here, we sought to test whether learning expectations could explain people's initial control allocation in a standard dot-motion perceptual task. We found that subjects' initial skill level and learning rate in a first block were able to predict their initial willingness to accumulate evidence in a second block, interpreted as a greater control allocation for the task. Our findings support the hypothesis that agents consider the learnability of a task when deciding how much cognitive control to allocate to that task.

Innovation in children is typically studied by examining their capacity to create novel tools. However, innovation also involves recognising the future utility of a solution. Across two experiments, we examined children's capacity to recognise and construct a tool for future uses. Experiment One presented 3- to 5-year-olds (N=55) with a future-directed problem-solving task. When given a tool construction opportunity in anticipation of returning to the task, only 5-year-olds made the correctly shaped tool above chance levels. Experiment Two assessed 3- to 7-year-olds' (N=92) capacity to build a tool with future, as well as present, utility in mind. Age was positively associated with constructing a tool of greater utility than necessary to solve the present task. Children's propensity to construct longer tools was associated with their capacity to prepare for two alternative possibilities on a secondary task, suggesting performance on our innovation task reflects emerging future-oriented cognition.

Decisions about extinction risks are ubiquitous in everyday life and for our continued existence as a species. We introduce a new risky-choice task that can be used to study this topic: The Extinction Gambling Task. Here, we investigate two versions of this task: a Keep variant, where participants cannot accumulate any more earnings after the extinction event, and a Lose variant, where extinction also wipes out all previous earnings. We derive optimal solutions for both variants and compare them to behavioural data. Our findings suggest that people understand the difference between the two variants and their behaviour is qualitatively in line with the optimal solution. Further, we find evidence for risk-aversion in the Keep condition but not in the Lose condition. We hope that this task can facilitate further research on this vital topic.

Compositional generalization that requires production and comprehension of novel _structures_ through observed constituent parts has been shown to be challenging for even very powerful neural network models of language. However, one of the test cases that poses the greatest difficulty---generalization of modifiers to unobserved syntactic positions---has not been empirically attested in human learners under the same exposure conditions assumed by these tests. In this work, we test adult human learners on whether they generalize or withhold the production of modification in novel syntactic positions using artificial language learning. We find that adult native speakers of English are biased towards producing modifiers in unobserved positions (therefore producing novel structures), even when they only observe modification in a single syntactic position, and even when the knowledge of their native language actively biases them against the plausibility of the target structures.

Humans preferentially recall items that are presented in close temporal proximity together -- a phenomenon known as the "temporal contiguity effect". In this study, we investigate whether this phenomenon emerges naturally when training a recurrent neural network with episodic memory on free recall tasks. The model learns to recall items in the order they were presented, consistent with the human contiguity effect. The strength of this effect predicts the performance of individual networks, mirroring experimental findings in humans where stronger contiguity effects predict higher recall performance. The contiguity effect in the model is supported by a neural representation of item index, resembling the `method of loci'. This differs from prominent computational models of human memory, which use a slow decay of past information to guide sequential retrieval. Our findings provide insights into the mechanisms underlying episodic memory and pave the way for future studies of its interactions with other cognitive processes.

In term series problems where multiple mental models can be constructed, partial-order models can be created as mental representations, which make it easier to perceive the symmetry of the terms. To test these hypotheses, we categorized multi-model (indeterminate) term series problems according to the patterns of partial-order models that could be constructed, and analyzed the reasoning performance for each pattern. These results suggest that reasoners tend to use the symmetry of terms to reduce the cognitive load of reasoning. Analysis of the patterns of incorrect answers also suggests that attempts to exploit the symmetry of the term may be biased, leading to errors in reasoning.

Cognitive control is thought to regulate the conflict between stability---maintaining the current task in the face of distraction---and flexibility---switching to a new task of greater priority. However, evidence conflicts regarding when and to what extent stability and flexibility trade-off. A normative theory of flexibility and stability may help clarify when and why we should expect such trade-offs to occur. Towards such a theory, we model task-switching as a problem of decision-making under uncertainty, in which the decision-maker must simultaneously infer both the identity of a stimulus and the task governing the correct response to that stimulus. We find that optimal behavior is either extremely stable or extremely flexible, but not both, indicating a normative basis for a trade-off between the two. However, we also show that a sub-optimal but more realistic decision-maker exhibits behavior between these two extremes, and more closely resembles experimental data.

Prospect Theory has been highly influential; however its experimental paradigm lacks higher orders of uncertainty. To introduce this, participants are asked to imagine themselves facing a choice between two bags containing 100,000 blue or red balls in unknown proportions. A red ball wins £500. Participants are shown samples from each bag; e.g., 5 balls from Bag 1 (3 red) and 100 balls from Bag 2 (55 red). The bags can be represented by distributions with Bag 1 having a higher mean probability estimate (60% vs 55%), but more variance (second order uncertainty) in that estimate. By varying observed frequencies and gain vs loss formats, we seek to determine if classic findings remain when higher order uncertainties are present. Results consistent with the four-fold pattern are seen for gains (uncertainty seeking at low probability values, uncertainty aversion at high probability values) but for losses, uncertainty aversion is seen at all values.

We test whether the support one holds about an event is influenced by other hypotheses. We addressed this by examining context effects in subjective probabilities (SPs) when forecasting NCAA men's basketball team rankings. A challenge in investigating context effects with naturalistic stimuli is the need to model the different representations of the options. To do so, we adapted the Spatial Arrangement method to capture individual representations and developed an algorithm to select stimuli. We asked participants steeped in basketball knowledge to create spatial maps for 50 teams. They were then presented with customized triplets of teams and asked to estimate their SP that one team would outrank the others. The study uncovered context effects in SPs, and moderators of the effects. Our findings suggest that similar cognitive processes may govern the construction of belief and preference and highlight the importance of modeling mental representations to understand forecasting scenarios.

The recent upswing in both use and acceptance of they/them as a singular pronoun has led to it becoming potentially ambiguous between singular and plural interpretations in cases like “Alex went running with Liz. They fell down” in which Alex is known to use they/them pronouns. The current work uniquely investigates how children interpret they in these ambiguous cases. Specifically, 5-year-olds, 8-year-olds, and an adult control group underwent a partial replication of Arnold et al. (2021), wherein they answered comprehension questions regarding a series of two-sentence stories. Results show that children can successfully map the pronoun they onto a singular individual when there are no plural competitors and that they interpret ambiguous they similarly to adults, although 5-year-olds interpret this pronoun as singular more often than 8-year-olds. These findings indicate that older children potentially undergo a form of overregularization of they due to grammatical rules enforced at school.

Autoregressive Large Language Models (LLMs) trained for next-word prediction have demonstrated remarkable proficiency at producing coherent text. But are they equally adept at forming coherent probability judgments? We use probabilistic identities and repeated judgments to assess the coherence of probability judgments made by LLMs. Our results show that the judgments produced by these models are often incoherent, displaying human-like systematic deviations from the rules of probability theory. Moreover, when prompted to judge the same event, the mean-variance relationship of probability judgments produced by LLMs shows an inverted-U-shaped like that seen in humans. We propose that these deviations from rationality can be explained by linking autoregressive LLMs to implicit Bayesian inference and drawing parallels with the Bayesian Sampler model of human probability judgments.

We conduct a large-scale study of online community variation in language. We show that factors of efficient communication, which have been shown to drive crosslinguistic variation in lexical semantic systems, also play a role in within-language variation across 1926 English-language Reddit communities. We study variation in stancetaking behaviour, a domain where efficient communication may be influenced by social motivations for language use. We find that communities indeed have efficient stancetaking systems, particularly with respect to their own communicative needs. However, contrasting with crosslinguistic work, we find that communities are often not optimized for their needs. Moreover, we find that community-level social factors correlate with how optimized they are. These results highlight the importance of accounting for social pressures for language use when studying how efficient communication drives variation.

Helping is a universal human behavior, and is a core aspect of a functioning society. However, the decision to provide help, and what type of help to provide, is a complex cognitive calculation that weights many costs and benefits simultaneously. In this paper, we explore how various costs influence the moment-to-moment decision to help in a simple video game. Participants were paired with another human participant and were asked to make repeated decisions that could benefit either themselves or their partner. Several preregistered manipulations altered the cost each person paid for actions in the environment, the intrinsic resource capacity of individuals to perform the task, the visibility of the other player's score, and the affordances within the environment for helping. The results give novel insight into the cost-benefit analyses that people apply when providing help, and highlight the role of reciprocity in influencing helping decisions.

Math anxiety is a pervasive issue in higher education that is often associated with poor performance outcomes. A hypothesized reason for this association is that individuals with math anxiety experience negative and intrusive thoughts related to the situation, their performance, and its consequences. These distractions are thought to be specific to math-related contexts. However, recent empirical evidence from the test anxiety literature calls the anxiety-distraction association into question. Here, we demonstrate that (a) math anxiety is associated with higher average reports of negative distraction, (b) that math anxiety-induced distraction is specific to the math problem-solving domain, and (c) that test anxiety also accounts for higher ratings of math-specific negative distraction. Investigating potential mechanisms underlying the math anxiety–poor math performance relationship is necessary for implementing effective interventions that foster math success, both in educational settings and in everyday life.

Entities in the spatial domain (objects) and the temporal domain (events) are characterized by parallel distinctions that are supported by a shared notion of individuation that runs across domains. This work investigates whether conceptual considerations of individuation are language-independent. We test speakers of English, which uses count-mass syntax and telicity to mark linguistic individuals in the nominal and verbal domain respectively, and Mandarin, which lacks these linguistic features. Our results throw light onto the nature of entity categories in the human mind: both English-speaking and Mandarin-speaking viewers process individuated and non-individuated entities differently, with only the former having a well-defined (temporal/spatial) structure with integrally-ordered, distinct parts. Crucially, these features of non-linguistic individuation are conceptualized in similar ways cross-linguistically and are potentially universal.

Our choices often require us to prioritize some features of our rich sensory experience over others. Past work suggests that humans solve this problem by focusing on relevant information while discarding that which is irrelevant. Yet learning which features to prioritize requires extensive experience. Moreover, features that are irrelevant now may become relevant in the future. One way to address these issues is by sampling individual richly encoded experiences from episodic memory. Here we hypothesize that episodic memory is used to guide decisions based on multiple features of past events. We test this hypothesis using an experiment in which participants made choices about the value of features that were present in multiple past experiences. We find evidence suggesting that participants used episodic memories to flexibly access features of past events during decision making. Overall, these results suggest that episodic memory promotes adaptive decisions when knowledge of multiple features is necessary.

Independent pieces of corroborating evidence should provide stronger support to a hypothesis than dependent pieces of evidence. Overlooking the inferiority of dependent relative to independent items of evidence can lead to a chain reaction of double-counting evidence, over-estimating the probability that the fact under consideration is true, and making wrongful decisions. Within fictitious scenarios, we investigate people's sensitivity to the independency advantage. We assess their ability to integrate multiple items of evidence that come from (in)dependent sources who differ in reliability. We find that participants properly perceive dependencies when explicitly asked but fail to distinguish the probative value of dependent versus independent evidence in their belief updating. Still, individuals who perceive a strong dependence between sources treat the evidence as being more redundant. We find no dependency-related effects on participants' individual Bayesian network model predictions. Potential reasons why participants perceive (in)dependencies and yet (mostly) fail to discount for them are discussed.

This study investigates whether homophone competitors are activated during typewriting and to which extent such activation is modulated by syntactic category. In two experiments, we compared the typewriting of homophone pairs in high vs. low conflict sentences (i.e., both homophones vs. only one homophone in the sentence, respectively) in a sentence dictation task (Experiment 1) and in a question-answering task (Experiment 2). The homophone pairs either belonged to the same or different syntactic categories. In Experiment 1, we found a homophone interference effect in accuracy, independent of conflict and syntactic category. In Experiment 2, this effect was replicated, but in addition, participants were slower to type homophones in a high vs. a low conflict context. Our results show a robust, lexically-situated homophone interference effect, regardless of conflict and syntactic category, but when deeper processing of the sentence is involved, conflict starts to play a role.

Recent research indicates that paired comparisons can accelerate perceptual learning of challenging dermatological lesion categories. Here we investigated whether the role of object categories as targets or distractors differentially influences learning outcomes. The frequency with which a given category occupied the target position was manipulated across three learning conditions: Always-Never, where half of 10 categories were always shown as target and the other half never shown as target; Often-Rarely, where half of categories appeared 75% as targets and 25% as distractors, with reversed presentation frequency for the other half; and Equal Split learning, in which all categories appeared as targets or distractors equally often. After learning, transfer results indicated that all conditions yielded equivalent overall learning, but categories prioritized more often as targets exhibited greater learning gains. These findings implicate differential processing of images in comparisons, even when no information regarding target vs. distractor was given prior to feedback.

When adults estimate meaningful numbers their distribution of first-digits is strongly biased towards Benford's Law. Insight into why this bias emerges could be gained by examining when it emerges in children. Three hypotheses were formulated: the Representation Hypothesis predicted this distribution can be found in all grades; the Integration Hypothesis predicted a leap in Benford bias from Grade 3 to 4 due to increased mathematical knowledge; and the Distribution Hypothesis proposed a gradual increase across grades due to implicit learning. 151 children in Grades 2 to 4 were asked to estimate numbers based on images and questions. Results showed a strong Benford bias in all three grades but a significant leap from Grade 2 to 3. This was evidence for both the Representation and Integration Hypotheses. Therefore, Benford bias may develop in children due to how they represent numbers, or develop complex mathematical processes, or perhaps some combination of these.

Science can be viewed as a collective, epistemic endeavor. However, a variety of factors- such as the publish-or-perish culture, institutional incentives, and publishers who favor novel and positive findings- may challenge the ability of science to accurately aggregate information about the world. Evidence of the shortcomings in the current structure of science can be seen in the replication crisis that faces psychology and other disciplines. We analyze scientific publishing through the lens of cultural evolution, framing the scientific process as a multi-generational interplay between scientists and publishers in a multi-armed bandit setting. We examine the dynamics of this model through simulations, exploring the effect that different publication policies have on the accuracy of the published scientific record. Our findings highlight the need for replications and caution against behaviors that prioritize factors uncorrelated with result accuracy.

The study focuses on the mechanisms through which dance brings people together. We recorded 7 improvised dance duets and asked 5 skilled improvisers to rate the perceived togetherness in the recorded dances. Subsequently, we employed pose tracking techniques and developed a quantitative measure of the stability of interpersonal movement coordination between dancers, demonstrating that it strongly correlates with experts' togetherness ratings. Based on follow-up interviews, we revealed that experts' understanding of togetherness converges to a stable construct, involving a state of responsive, mindful attention. This construct can be grounded in the objective properties of movement coordination. These properties can be framed within the context of dynamical systems, suggesting potential systemic organization principles, such as moment-to-moment adaptation, that promote togetherness. Our mixed-methods research has implications for various fields, including psychology, cognitive science, and art studies.

We conduct tests of a hybrid-similarity exemplar model on its ability to account for the context-dependent memorability of items embedded in high-dimensional category spaces. According to the model, recognition judgments are based on the summed similarity of test items to studied exemplars. The model allows for the idea that “self-similarity” among objects differs due to matching on highly salient distinctive features. Participants viewed a study list of rock images belonging to geologically defined categories where the number of studied items from each category was manipulated, and their old-new recognition performance was then tested. With a minimum of parameter estimation, the model provided good accounts of changing levels of memorability due to contextual effects of category size, within- and between-category similarity, and the presence of distinctive features. We discuss future directions for improving upon the current predictions from the model.

Do children reason that people in close relationships accurately represent each other's minds? In two experiments (total N = 123), we found that 7- to 9-year-old children from the US (i) reason that people who are close will accurately represent each other's goals and desires and (ii) infer that people are socially close when they accurately predict each other's emotional states. These findings suggest that children reason flexibly about mental state attributions within close relationships.

Construal Level Theory (CLT) suggests that we represent objects close to us in a concrete and modal fashion, and that representations become more abstract and amodal with increasing distance from ourselves. Evidence for such an association of abstraction level and distance comes from the Implicit Association Test (IAT), where participants are faster when pressing one key for “near” and “concrete” and another key for “far” and “abstract” targets (congruent), than when “near” is paired with “abstract” and “far” with “concrete” (incongruent). However, previous experiments might have confounded distance and abstraction by employing inherently near and far targets (e.g., CHAIR vs. SUN) that might also differ in their abstractness. Here, we thus experimentally induced different distances in a learning phase before a subsequent IAT task. Even with this controlled distance manipulation, a pronounced congruency effect emerged, providing further support for an association of distance and abstraction level as suggested by CLT.

Dyadic social interaction is a complex coordination task involving a large number of interconnected variables. Previous research has shown that metastability -- persistence for an extended, but impermanent, period of time in a non-stable state of a system -- can be a useful lens for understanding what makes an interaction successful. However, this framework has thus far only been applied to para-conversational signals like heart rate and prosody -- not to the semantic content of a conversation. Here, we present pink noise analysis of semantic trajectories as a metric for conversational success and apply this technique to a large open conversation dataset. Our results demonstrate that pink noise in a conversation predicts a host of variables representing participants' perception of conversation quality. These results have implications for optimizing a whole host of difficult dyadic conversations -- like those between political partisans -- and human-computer interactions, with applications for improving large language models' adaptability.

We are able to learn from others through a combination of trust and vigilance: we trust and believe people who are reliable and have our interests at heart; we ignore those who are incompetent or self-interested. While past work has studied how others' competence influences social learning, relatively little attention has been paid to how others' motivations influence such processes. To address this gap, we develop a Bayesian model of vigilance that considers the speaker's instrumental self-interest, and test predictions of this model through an experiment. In accordance with our model, participants become more vigilant when informants stand to benefit from influencing their actions. When perceived self-interest is maximal, testimony can be discounted wholesale, rendering middle ground increasingly difficult, if not impossible, to find. Our results have implications for research on polarization, misinformation, and disagreement.

People often select only a few events when explaining what happened. What drives people's explanation selection? Prior research argued that people's explanation choices are affected by event normality and causal structure. Here, we propose a new model of these existing findings and test its predictions in a novel experiment. The model predicts that speakers value accuracy and relevance. They choose explanations that are true, and that communicate useful information to the listener. We test the model's predictions empirically by manipulating what goals a listener has and what actions they can take. Across twelve experimental conditions, we find that our model accurately predicts that people like to choose explanations that communicate optimal interventions

When a human and an AI agent collaborate to complete a task and something goes wrong, who is responsible? Prior work has developed theories to describe how people assign responsibility to individuals in teams. However, there has been little work studying the cognitive processes that underlie responsibility judgments in human-AI collaborations, especially for tasks comprising a sequence of interdependent actions. In this work, we take a step towards filling this gap. Using semi-autonomous driving as a paradigm, we develop an environment that simulates stylized cases of human-AI collaboration using a generative model of agent behavior. We propose a model of responsibility that considers how unexpected an agent's action was, and what would have happened had they acted differently. We test the model's predictions empirically and find that in addition to action expectations and counterfactual considerations, participants' responsibility judgments are also affected by how much each agent actually contributed to the outcome.

How do humans decide what to look at and when to stop looking? The Rational Action, Noisy Choice for Habituation (RANCH) model formulates looking behaviors as a rational information acquisition process. RANCH instantiates a hypothesis about the perceptual encoding process using a neural network-derived embedding space, which allows it to operate on raw images. In this paper, we show that the model not only captures key looking time patterns such as habituation and dishabituation, but also makes fine-grained, out-of-sample predictions about magnitudes of dishabituation to previously unseen stimuli. We validated those predictions experimentally with a self-paced looking time task in adults (N = 468). We also show that model fits are robust across parameters, but that assumptions about the perceptual encoding process, the learning process and the decision process are all critical for predicting human performance.

The control-of-variables strategy is often considered to be the superior strategy when children learn from experiments. However, by simulating Bayesian likelihoods of outcomes from a water displacement task, we show that certain confounded comparisons may support belief revision better than controlled comparisons. We tested this assumption by experimentally varying the types of comparisons that participants observed in a learning task involving balls of different sizes and materials (N = 90, age range 6- to 9-yrs). In the Size, Material, and Mixed conditions we presented controlled comparisons. In the Confounded Incongruent Condition, we presented confounded comparisons in which the larger ball was made of the heavier material. In line with our hypotheses, children in the Confounded Incongruent Condition revised their beliefs more than children in the other conditions, as indicated by higher transfer test scores. These findings suggest that confounded comparisons may in fact sometimes provide more optimal information for learning.

Metaphors are often used to intuitively communicate about movement. Here, expert pianists played two melodies while keeping eight different metaphors in mind, contrasting arousal level, valence direction, and metaphor type (action-related and emotion-related metaphors). Measures of keystroke timing and velocity were analyzed to assess the relative contribution of metaphor content and melodic note sequence to motor performance, alongside ratings of semantic similarity between metaphors. Using Bayesian multilevel models, results indicate that the arousal level of the metaphor has the most influence on keystroke force, average tempo, and tempo variability. Additionally, interactions with valence are seen for the timing measures, and for both valence and type in force. No effects of the melody sequence were found. Similarity ratings of metaphor pairs indicate that mental similarities largely mirror performance similarities. These findings show the potential effects of mental imagery on motor performance and have implications for teaching complex movements in practical settings.

There is considerable debate about the origin, mechanism, and extent of humans' capacity for recursive mindreading: the ability to represent beliefs about beliefs about beliefs (and so on). Here we quantify the extent to which language exposure could support this ability, using a Large Language Model (LLM) as an operationalization of distributional language knowledge. We replicate and extend O'Grady, et al. (2015)'s finding that humans can mindread up to 7 levels of embedding using both their original method and a stricter measure. In Experiment 2, we find that GPT-3, an LLM, performs comparably to humans up to 4 levels of embedding, but falters on higher levels, despite being near ceiling on 7th-order non-mental control questions. The results suggest that distributional information (and the transformer architecture in particular) can be used to track complex recursive concepts (including mental states), but that human mentalizing likely draws on resources beyond distributional likelihood.

This paper uses phylogenetic modeling to investigate the evolutionary mechanisms responsible for the maintenance of sound symbolism in the world's languages. Applying our model to sound-meaning correspondences reported in the literature, we find that many previously established associations are weaker than expected when analyzed using our framework. This is possibly because certain sound-meaning associations are artifacts of slow-changing vocabulary items rather than specific preferences for certain sounds in words with certain meanings. For sound-meaning associations for which we find evidence, the maintenance of sound symbolism appears to be due to a tendency to preserve words in certain meanings if certain sounds are present.

Attribution theory aims to explain people's judgments about the cause of some behavior or outcome, often involving other people. The theory has proven to be broadly applicable and points towards important aspects of human cognition. This relevance is perhaps unsurprising given that attribution theory is a type of causal inference. However, there has been relatively little work on attribution theory in relation to causal learning. More specifically, previous literature has mostly examined attributions and their behavioral and motivational outcomes following a single observation, rather than capturing the dynamics of causal attribution (i.e., how those judgments shift as people observe more vignettes and thereby learn about the situation). We thus ran an exploratory study using a vignette design to investigate whether attributions and their outcomes change across multiple instances of observation and behavior adaptation.

The amount of resources someone has can influence their emotional responses to events. Two preregistered experiments investigated whether adults and children consider others' resource quantities when inferring their emotions. Sixty adults (Experiment 1) and 135 8-10-year-olds (Experiment 2) saw stories about people wanting an item but differing in the number of items they have enough money to buy (ranging from 1 to 5). Participants rated how these people felt both when buying the item and when losing it. Both adults and children judged that the fewer resources someone has, the sadder they felt when the item was lost, and the bigger emotional change they experienced (relative to when buying the item). Adults also judged that the impact of resource scarcity on emotion was most significant when the person had depleted all their resources, as opposed to still retaining some to influence the negative outcome, and this pattern is emerging in children. These findings suggest that even when the same negative event occurs, adults and children as young as 8 consider others' available resources when inferring their emotional responses to the event.