Proceedings of the Annual Meeting of the Cognitive Science Society

Spoken language processing requires integrating acoustic signals with visual information. Deictic gestures such as pointing may facilitate this integration by constraining the referential domain before linguistic disambiguation. How multimodal cues modulate processing under cognitive load remains unclear. Simultaneous interpreting provides a task environment for examining speech processing under maximal cognitive load. In this study, we combined the Visual World Paradigm (VWP) with pupillometry to investigate how congruent, incongruent, and neutral pointing gestures influence attention and cognitive load in 24 professional interpreters. Eye-tracking revealed that congruent gestures elicited early anticipatory target fixations, incongruent gestures directed gaze toward competitors before rapid correction and looks towards the target, while the absence of gestures delayed target identification. Pupillometric measures yielded a counterintuitive pattern: the neutral condition evoked greater pupil dilation than the incongruent condition, whereas congruent and incongruent conditions did not differ significantly in the amount of generated load. This suggests that visual cues, even misleading ones, reduce processing demands when they accompany auditory input. These findings suggest that multimodal information alleviates cognitive load in a task environment competing for resources.

Understanding the emergence of social cognition requires continuous, high-resolution data from naturalistic environments, yet human studies are often limited by privacy and longitudinal feasibility. The common marmoset offers a compelling model due to its cooperative breeding and compressed developmental timeline that enables the study of family-level social dynamics. However, existing automated tracking tools struggle with the complex backgrounds, frequent occlusions, and rapid morphological changes inherent in home-cage settings. Here we introduce HOLMES, a deep learning-based system designed for longitudinal 3D tracking of multiple marmosets in naturalistic home-cage environments. By integrating Transformer-based temporal tracking with semantic segmentation and pose estimation, HOLMES achieves robust skeletal reconstruction and stable identity tracking across developmental stages. Applying to longitudinal family recordings, this system supports high-throughput quantification of the fine-grained temporal structure of parent-infant interactions and evolving social coordination, providing a way to link moment-to-moment social behaviors with long-term developmental outcomes.

Cultural concepts of mind vary dramatically across societies. In Chinese thought, 心 (xīn, “heart-mind”) historically served as the seat of cognition and emotion, while 脑 (nǎo, “brain”) played minimal role. Modern neuroscience introduced brain-centered models, raising questions about whether scientific concepts replace or coexist with folk theories. How does scientific knowledge reshape traditional mental vocabulary in non-Western contexts? Here we show, through distributional semantic analysis of 3.4 billion Chinese tokens from classical texts to social media, that xīn and nǎo function as parallel rather than competing concepts. Contrary to eliminative materialism, xīn maintains its integrated multi-dimensional structure as a living folk concept, while nǎo enters as specialized medical terminology. This coexistence varies systematically: encyclopedic texts show convergence in biomedical contexts, while vernacular preserves traditional differentiation. These patterns suggest that folk and scientific frameworks operate as contextually deployed parallel systems rather than successive evolutionary stages. Our approach demonstrates how computational linguistics reveals mechanisms of cultural-cognitive modernization.

What are intentions, and what is the nature of practical reason? Cognitivism about intention and practical reason claim, respectively, that intentions entail predictive beliefs, and that practical reason reduces, at least partially, to theoretical reason. But cognitivism faces two distinct challenges: seemingly, we can intend the unexpected, and expect the unintended. This paper will advance a cognitivist theory of intention. To intend, I will argue, is to believe that a teleological explanation is true of one's own behavior. And as I will illustrate, given the nature of teleological belief, we can meet those aforementioned explanatory challenges. The result is a theory of intention that parsimoniously does what a sound theory of intention ought to do.

Emotion timing literature has consistently shown temporal dilation for emotionally salient stimuli compared to neutral stimuli. Such stimuli have also shown preferential processing near the graspable space of the hands, known as peri-hand space (PHS). However, none of the studies have systematically examined the interplay of emotion, time perception, and peri-hand space in a unified framework. Therefore, the present study investigated the temporal factors underlying the processing of emotionally salient stimuli in PHS. Participants completed the temporal bisection task in Near-hand and Far-hand conditions. The stimuli were high-arousal emotional and low-arousal neutral images. Results showed temporal dilation for highly arousing emotional stimuli compared to low-arousal neutral stimuli in PHS, perhaps to facilitate in-depth cognitive evaluation of emotional salience. No such temporal processing differences between emotional and neutral stimuli were obtained in the far-hand condition. Temporal dilation is primarily attributed to early anticipatory mechanisms associated with PHS.

Languages evolve to be easy to use. Over the last couple of decades, however, language has shifted from being used predominantly in the spoken modality to being used predominantly in the written modality. Differences between the way spoken and written language are processed suggest that language might no longer be optimised for use and undergo changes. This paper uses an iterated learning paradigm (Experiment 1) and a pair communication game (Experiment 2) to test whether languages that develop in the written modality differ from those which develop in the spoken modality. Both experiments show that languages that develop in the written modality have sparser phonological neighborhoods. Results also indicate that neighborhood density is more detrimental for comprehension in the written modality than the spoken one, motivating the greater sparsity in the written modality. These results show that modality can influence phonological density and that languages might be currently undergoing changes to become sparser.

As algorithms increasingly mediate competitive decision-making, their influence extends beyond individual outcomes to shaping strategic market dynamics. In a preregistered experiment, we examined how algorithmic advice affects human behavior in a classic economic game with a unique, non-collusive, and analytically traceable equilibrium. Participants (N = 129) played a Cournot quantity competition with equilibrium-aligned or strategically biased algorithmic recommendations. While individualized equilibrium advice supported stable convergence, collusively downward-biased advice led to sustained underproduction and supracompetitive profits—hallmarks of tacit collusion. Participants responded more strongly and consistently to individualized advice than collective advice, potentially due to greater perceived ownership of the former. These findings demonstrate that algorithmic advice can function as a strategic signal, shaping coordination even without explicit communication. The results echo real-world concerns about algorithmic collusion and underscore the need for careful design and oversight of algorithmic decision-support systems in competitive environments.

Inferring others' competence is a challenge of social cognition, often occurring in contexts of limited information. Recent research suggests that people can infer the competence of others through Bayesian inference, but it is unclear whether these rational principles generalize to naturalistic settings of knowledge attribution. Using trivia questionnaires, we test whether people can infer others' competence and search for informative evidence in a way consistent with a rational Bayesian model. In Study 1, participants were presented with an individual's performance on a trivia question and predicted the individual's ability to answer other trivia questions from the same theme. Participants accurately predicted performance from limited information. Study 2 shows that participants can select which information would be most diagnostic for inferring an individual's competence. Computational modelling shows that participants' inferences, both when searching for and when integrating information about others' competence, are better described by Bayesian processes than by plausible heuristics.

Negative self-referential bias (NSB) is a core feature of depression, yet its neural correlates and relevance for relapse remains unclear. This study examined whether behavioral and neural markers of NSB predict relapse following prophylactic psychotherapy. In a two-year prospective fMRI study embedded within a randomized trial of Mindfulness-Based Cognitive Therapy and Well-being-focused Cognitive Therapy, remitted depressed outpatients completed a self-referential encoding task before and after treatment and were followed longitudinally. NSB predicted greater depressive symptoms but not relapse. In contrast, greater frontal default mode network and salience network reactivity during negative self-referential processing predicted relapse. Whole-brain analysis identified elevated prefrontal activation and somatosensory deactivation as relapse biomarkers, with treatment-related attenuation of somatosensory deactivation predicting lower relapse risk. Somatosensory deactivation emerged as the strongest overall predictor of relapse. These findings suggest that relapse vulnerability reflects not only exaggerated prefrontal negative self-processing but also insufficient recruitment of somatosensory systems supporting embodied self-experience.

Understanding how humans and machines learn from sparse data is central to cognitive science and machine learning. Using a matched task design, we compare children and convolutional neural networks (CNNs) in a few-shot semi-supervised category learning task. Both learners received mixtures of labeled and unlabeled exemplars while supervision (1/3/6 labels), target feature (size, shape, pattern), and perceptual alignment (high/low) are systematically varied. We find that children generalize rapidly from minimal labels but show strong feature-specific biases and sensitivity to alignment. CNNs show a different interaction profile: added supervision improves performance, but both alignment and feature structure moderate the impact additional supervision has on learning. These findings show that comparisons between humans and machines should be controlled and sensitive to task structure. Comparing accuracy alone may hinder a more nuanced understanding of the conditions that differentially support success in each system.

Debates about the neural organization of semantic knowledge have long contrasted modular accounts, which posit anatomically specialized regions, with distributed accounts, which emphasize patterns of activity spanning many neural populations. Although multivariate neuroimaging has provided strong evidence for distributed representations, both perspectives often presume that the representational solutions supporting cognition are largely shared across individuals. Here, we challenge that premise by asking whether semantic representations are not only distributed, but also stable within individuals and meaningfully idiosyncratic across people. Using individualized multivariate fMRI decoding across two scanning sessions, we show that representations distinguishing faces, places, and objects are widely distributed across cortex, stable within individuals over time, and heterogeneous across participants. We then use these individualized representational profiles to guide transcranial magnetic stimulation (TMS), showing that stimulating non-canonical, decoding-defined sites selectively disrupts face similarity judgments in a manner comparable to stimulation of a canonical face-selective region. Together, these findings provide causal evidence that semantic representations arise from distributed neural systems whose organization is both stable and person-specific, calling for theories of cognition that make room not only for shared structure, but for the idiosyncratic ways meaning is represented across brains.

Adaptive decision-making requires solving the breadth-depth (BD) dilemma: balancing exploring more alternatives and evaluating each one more carefully. While normative models define optimal search set sizes given finite cognitive resources, the psychological mechanisms enabling individuals to achieve this remain unclear. In this study, we propose that subjective regret functions as a critical learning signal for solving the BD dilemma. Computational simulations demonstrated that search set size optimization is achievable by monitoring two distinct types of regret: one reflecting opportunity costs from narrow searches, and another reflecting evaluation errors from broad searches. A laboratory experiment using a sequential decision task showed that participants adjusted their search set sizes toward the theoretical optimum, with both regrets predicting these adjustments in complementary directions. Taken together, these findings suggest that regret is not merely a negative outcome but a signal that enables autonomous optimization of resource allocation in multi-alternative decision-making.

Cooperation is sustained by shared norms that regulate expectations about how others will behave. As artificial intelligence (AI) systems increasingly participate in social and economic interactions, a critical question emerges: Do the normative expectations that sustain human cooperation extend to artificial players? We propose that failures in human–AI cooperation may reflect a fundamental difficulty to form accurate predictions about artificial behavior that disrupts norm-based coordination. In Study 1 (N = 794), participants in a repeated Public Goods Game were less sensitive to prosocial norms when interacting with AI versus human players. This cooperative deficit was accompanied by a systematic "prediction gap": participants exhibited larger errors and directional biases when forecasting AI behavior. In Study 2 (N = 314), we observed that framing AI as intentional rather than mechanistic substantially reduced prediction errors and reduced cooperation differences between AI and human players. Analysis of trial-by-trial learning revealed that intentionality framing affected initial expectation calibration but not feedback-driven updating, consistent with a shift in participants' initial mental model of the AI. These findings suggest that norm-based cooperation in human-AI interaction depends on predictability.

Sign languages are compositional systems where meaning arises by combining sublexical phonological parameters, such as handshape, location, and movement. While deep learning models for Sign Language Recognition (SLR) have achieved increased performance on translation benchmarks, it remains unclear whether these models distinguish abstract phonological features or merely rely on low-level statistical correlations. This work evaluates SLR model phonological perception by probing sensitivity using minimal pairs and measuring representational alignment with human behavioral data. Our results reveal emergent phonological sensitivity with clear architectural trade-offs: pose-based models are more sensitive to handshape contrasts, while pixel-based models better capture location changes. Furthermore, pose-based models learn latent representations that correlate with human perceptual similarity judgments (r –ï 0.49). These findings suggest that while SLR models exhibit emergent phonology, current training paradigms are insufficient to overcome their architectural inductive biases.

Under surprisal theory, linguistic representations affect processing difficulty only through the bottleneck of surprisal. Our best estimates of surprisal come from large language models, which have no explicit representation of structural ambiguity. While LLM surprisal robustly predicts reading times across languages, it systematically underpredicts difficulty when structural expectations are violated—suggesting that representations of ambiguity are causally implicated in sentence processing. Particle filter models offer an alternative where structural hypotheses are explicitly represented as a finite set of particles. We prove several algorithmic consequences of particle filter models, including the amplification of garden-path effects. Most critically, we demonstrate that resampling, a common practice with these models, inherently produces real-time digging-in effects—where disambiguation difficulty increases with ambiguous region length. Digging-in magnitude scales inversely with particle count: fully parallel models predict no such effect.

Early-life stress is a major risk factor for cognitive deficits, yet the circuit-level mechanisms remain unclear. We hypothesize that systemic stress hormones and localized neurotrophic deprivation during critical periods alter synaptic connectivity. To investigate, we developed a large-scale computational model (N = 100 paired seeds) simulating two developmental stages: an activity-independent growth phase where axons navigate a BDNF chemoattractant landscape, and a functional phase probed with a recurrent attractor model. The "trauma" condition modeled a "Double Hit": a transient cortisol pulse increasing stochastic axon diffusion, combined with localized BDNF deprivation that weakened synaptic efficacy. Trauma produced a significant rise in axon tortuosity (p < 0.001), yielding structurally inefficient networks with collapsed spectral radius. Functionally, recurrent gain loss impaired retention of clustered memory engrams (–ï41% deficit), while random patterns remained robust. Parameter sweeps confirmed this pathology arises non-linearly from guidance noise and trophic weakness, linking developmental stress to memory impairments.

Understanding what drives human information sharing is crucial for predicting the outcomes of collective decision-making, alongside social learning. Although individuals act as both recipients and transmitters in social interactions, existing research has largely examined these dual cognitive processes in isolation. This study proposes a computational model integrating social learning with information sharing, describing the decision-making process of agents who both learn from others' choices and decide whether to disclose their own in a social foraging situation. Agent-based simulations predict that specific information-sharing parameters could theoretically improve collective outcomes. Applied to behavioral data, our model analysis indicated that individuals exhibit positive selectivity by sharing rewarding choices, while demonstrating susceptibility to conformist influence, disclosing their actions more frequently when aligned with those of others. These findings provide a unified account that mechanistically links social learning and strategic disclosure, with implications for understanding collective dynamics such as the spread of misinformation.

To recognize and act on baby-schema features – such as large eyes and a rounded face - is commonly seen as an automatic, innate capability in humans. Following this logic, adults' perception of baby-schema features should be independent of cultural background. Contrasting this, evidence shows that perception of adult faces is tuned by culture-specific visual experience. In this preregistered study, we investigate whether sensitivity to baby-schema is dependent on visual experience in two diverse populations (urban German; small-scale Batek, Malaysia). We morphed child faces sourced from the target communities to different degrees of baby-schema and asked participants to detect the odd-one-out of three faces in both in- and out-group trials across seven difficulty (difference in baby-schema-degree) levels. While difficulty strongly effected accuracy in both communities, we found little evidence that baby-schema sensitivity was modulated by experience, suggesting robustness in perceptual processing and possibly generalized high relevance of this class of stimuli.

The human brain can rapidly adapt to new tasks and environments, a capacity traditionally attributed to structural changes in the learning system, such as neural plasticity. Here, we revisit this assumption by asking whether adaptive behavior can emerge through computation alone, without parameter updates. Using large language models (LLMs), we examine statistical learning paradigms that require identifying regularities in arbitrary word sequences and are commonly considered to depend on plasticity. We show that LLMs can acquire such structure through in-context exposure, capturing the underlying regularities without weight adaptation, despite the divergence of these tasks from their natural language training data. These findings suggest that sufficiently trained learning systems may exhibit a greater degree of flexibility through computation than previously acknowledged, and highlight the potential of deep learning models as tools for generating hypotheses about learning mechanisms in the brain.

Semantic memory encodes both the individual features of concepts and the relations between them. We develop computational memory models that describe how people search through these memory structures. Our empirical paradigm involves open-ended word-pair analogy generation and free association tasks, and we infer the respective roles of featural and relational information in these tasks using formal model comparisons. Our tests reveal that both types of information play a key role in memory search, but they are recruited differently depending on the task and exhibit distinct dynamics. Importantly, we can quantitatively predict these effects and parameterize their associated mechanisms using a principled extension of established memory models, thereby integrating theoretical work on structured reasoning and on memory search.

The cognitive sciences have developed diverse tasks to measure the psychological processes during decision making. Here we focus on the Balloon Analogue Risk Task (BART) and present a Bayesian meta-analysis synthesizing 1,665 effect sizes from 237 studies, thus comprising the data of 51,932 participants. We (i) take stock of the psychometric properties of the BART and (ii) quantify measurement flexibility and its impact on the task's psychometric properties. The BART showed satisfactory levels of test–retest reliability, yet it showed no to low known–groups validity, convergent validity, and external validity. We observed substantial measurement flexibility in the meta-analyzed studies, and specific study characteristics influenced the estimates of the task's psychometric properties.

Children growing up in environments with many drawing opportunities improve dramatically at producing recognizable drawings over the course of childhood. Does this same developmental trajectory hold for children growing up in environments with fewer drawing opportunities? We measured children's drawing recognizability cross-sectionally in a drawing-sparse context. Moreover, by leveraging within-context variation in children's drawing experiences, we investigated whether, at an individual level, drawing recognizability increases with children's drawing experiences. A preregistered sample of 120 4- to 9-year-olds in Kisumu, Kenya drew 12 object categories in random order. Older children produced more recognizable drawings even after controlling for tracing ability, and children with more drawing experience produced more recognizable drawings even after controlling for tracing ability and home environment variables. These results suggest commonalities in drawing development across cultures and document the experience-dependence of drawing development in drawing-sparse contexts.

The present study investigated task-general and task-specific effects of phonological and lexico-semantic word properties on auditory word identification and auditory lexical decision. The data comprised identification accuracy and lexical decision latencies from the Auditory English Lexicon Project (AELP). Item-level regressions were performed for 20 word properties across each of the six talkers available in the database. Task-general effects that were consistently robust across both tasks revealed that words that were more familiar, prevalent, and structurally distinctive were recognized faster and identified more accurately. The effects of frequency, concreteness, arousal, and number of morphemes were consistently specific only to lexical decision and were not robust for word identification. These findings highlight the utility of using the AELP data to conduct six replications across talkers from different regions and genders to test the robustness and generalizability of specific word properties' influences on spoken word processing.

Many theories of decision making have proposed that diverse psychological mechanisms (e.g., mechanisms related to affect, goals, or social factors) shape people's decisions under risk and uncertainty. How best to study such mechanisms during decision making? In a preregistered study (N = 699), we tested whether six experimentally induced mechanisms (incidental anger, cognitive load, lack of knowledge, goal pursuit, descriptive social norms, and time pressure) leave linguistic traces during decision making and can thus be identified with large language models (LLMs). To this end, participants completed four decision-making tasks while recording verbal decision protocols (VDPs). We then inferred the presence of psychological mechanisms in VDPs using "speech-to-psych", a newly developed analysis pipeline leveraging different LLM pipelines. Across analytic methods and decision contexts, psychological mechanisms as induced by experimental conditions could be identified at above-chance level (average area under the curve = .61). Feature importance scores revealed that this identification frequently relied on linguistic markers associated with the targeted psychological mechanism. These results indicate that at least some psychological mechanisms leave detectable linguistic traces and that LLM-based analyses could serve as a powerful measurement tool for advancing process-level understanding of decision making.

We recruited Asian and White adults to examine cultural differences in the effect of mask use on face scanning behavior and social categorization. Mask use impaired social categorization accuracy for White but not Asian faces, suggesting the diagnostic features for Asian faces were more in the eyes. Consistent with this finding, Asian adults showed a bias to categorize ambiguous faces as Asian regardless of the mask condition, whereas White adults exhibited a similar bias only for masked faces, resulting in a larger mask effect. In face scanning behavior, mask use made Asian adults look more towards the left as opposed to the right eye compared with White adults. A more right-eye-biased pattern due to mask was associated with an increased mask effect in ambiguous face categorization, suggesting a link between cultural differences in face scanning behavior and social categorization. These findings have important implications for social cognition with mask use.

People often procrastinate on tasks that they are ostensibly motivated to complete. We argue that dominant formal accounts – particularly temporal discounting models – capture the impulsive nature of procrastination but struggle to provide comprehensive cognitive explanations of three central features: which tasks people procrastinate on, why procrastination often feels bad, and when people finally begin working after a period of procrastinating. We propose a computational framework to explain the mechanisms of avoidance in procrastination. In our model, agents choose how much effort to exert by reasoning over a probabilistic generative model of task progress over time. This structure supports inferences about competence and task difficulty from noisy progress signals. Crucially, agents incur an additional cost for negative belief updates about their own competence, capturing an aversion to appearing incompetent that motivates self-handicapping. With this structure, the model reproduces key behavioral, affective and cognitive signatures of procrastination.

Algebraic competence is a critical foundation for advanced mathematical reasoning and success in STEM disciplines. However, whether algebraic processing relies on phonological or visuospatial encoding remains a central debate in cognitive psychology. We investigated the representational format of algebraic knowledge using a dual-task paradigm with 50 adult participants performing an algebra task concurrently with either a phonological or a visuospatial working memory (WM) task. While algebraic performance remained resilient under load, results revealed a selective interference effect on the secondary WM task: algebra significantly impaired visuospatial, but not phonological, WM performance. This impairment on visuospatial WM performance only suggests that algebra and visuospatial processing compete for a shared cognitive pathway, providing robust evidence for the visuospatial grounding of algebraic thinking.

Executive functions are essential for adaptive behavior, but there is substantial variability in the way these functions are assessed. Unfortunately, the literature has mostly focused on tasks rather than the processes involved in those tasks. Adopting a process-based rather than a task-based approach could improve the assessment and conceptualization of executive functions. We systematically reviewed literature on how researchers assess executive functioning, including the tasks used and the processes involved in those tasks. We included studies measuring inhibition (n = 844), updating (n = 844) or shifting (n = 1048) among human participants with any performance-based measure. The results show that researchers have used many different tasks to measure the same concept, but tasks supposed to target the same executive function can actually engage very different processes. In addition, the participants' age or disorder has a significant effect on the type of tasks retained to assess the same executive function and the processes they involve, which means developmental literature does not necessarily assess the same "inhibition" as the adult neuropsychological literature. Our review underlines the importance of using multiple tasks to measure inhibition, updating or shifting, and the importance of clarifying and reporting on the processes supposed to be involved in these tasks, moving from a task-based approach to a more realistic process-based approach.

Key to intelligence is the ability to flexibly compose and combine atomic concepts into representations that guide behavior, making "infinite use of finite means". However, modeling often assumes discrete concepts exist and focuses on their use, leaving it unclear how they are learned from complex sensory inputs and mapped to motor outputs. To address this limitation, we build on a drawing-like task and behavioral metrics that indicate discrete structure in motor behavior from Tian et al. (2025). Importantly, these discrete concepts were encoded in neural recordings from non-human primates that exhibited these behavioral metrics, validating the metrics. Here, we use this task and behavioral metrics as a modeling target, to quantify how discrete structure arises in a task-optimized neuro-symbolic model (Liang et al., 2022). Our model recapitulates these behavioral metrics, offering a foundation to compare with neural data and understand what drives the emergence of discrete conceptual structure.

Episodic memory enables decision making by allowing choices to be guided by detailed past experiences. Yet, because this process unfolds covertly, the mechanisms governing how memories impact deliberation remain poorly understood. Here we use gaze reinstatement to reveal the hidden dynamics of episodic memory retrieval during value-based choice. We tracked participants' eye movements while they encoded episodes consisting of items with associated rewards at distinct spatial locations and subsequently made decisions based on these episodes. Critically, no visual information was provided to participants during their choices. Nonetheless, participants systematically directed their gaze toward the encoding locations of retrieved episodes during deliberation, and these gaze patterns predicted their choices. These findings demonstrate that eye movements provide a high-resolution window into moment-by-moment episodic sampling during decision making. By revealing which episodes are retrieved and when, this approach opens new directions for investigating how episodic memory guides adaptive behavior.

This study examined the developmental trajectory of supplementary gestures, which conveyed information distinct from speech, and their semantic relationships with accompanying speech in 16 Mandarin-speaking children. Children were observed during mother–child interactions between 12 and 21 months of age at three-month intervals, with minimal age variation within each time point. Findings showed a marked increase in supplementary gestures at 18 months, alongside the predominance of declarative pointing within gesture-speech acts and one-word utterances. This specific cross-modal pattern remained stable through 21 months. Most cross modal combinations conveyed entity ideas, with predicative ideas occurring infrequently and propositional ideas even less often. The common 'entity-related', 'what is done to what', and 'who initiates what and who/what is affected' knowledge was linguistically expressed only about three to six months later than in gesture-speech acts. Overall, the findings support the enduring role of supplementary gestures in enriching children's communicative expressiveness within a single act.

Emotional expressions signal how others evaluate outcomes, but a single expression can reflect multiple underlying preferences. We examine whether and under what constraints observers infer material preferences (what outcomes someone values) and social preferences (how they weigh self vs. other) from affective feedback. In a multi-issue ultimatum game, participants (N=323) observed agents' graded emotional reactions to resource allocations and inferred their preferences. Results revealed a functional asymmetry between the two domains. Material preference accuracy was constrained by signal identifiability, whereas social preference estimates showed weak correspondence with Bayesian model predictions and clustered near the center. This suggests that observers do not infer both preferences symmetrically; instead, social preference estimation appears more strongly shaped by robust priors and/or measurement constraints. These findings highlight structural limits on social inference and suggest that human observers may rely on strong prior assumptions when interpreting underdetermined signals.

Expertise shapes the organization of semantic knowledge, yet prior work using cognitive network science remains sparse and limited in methodological diversity. Study 1 used a free-listing task to identify the concepts constituting the visual art schema in experts and laypeople. Study 2 collected pairwise relatedness judgements of these concepts to construct individual semantic networks. Higher expertise was associated with richer and more diverse schemata, more individualized networks, higher clustering coefficients, shorter average shortest path lengths, and unchanged modularity. Node-level analyses showed that fluency enhanced the structure of basic concepts, while the full advantage of experts emerged through integration of advanced concepts. These findings link expertise to more efficient and differentiated semantic networks, suggesting a mechanism by which knowledge organization supports expert-level cognition.

Stereotype Threat describes the negative impact on cognitive performance caused by the activation of negative social stereotypes. This study investigates the underlying cognitive mechanisms of gender-specific Stereotype Threat effects. Four online experiments were conducted with 1,164 participants, randomly assigned to either a Stereotype Threat or a control condition. To model the underlying cognitive processes, parameters of the drift diffusion model were estimated and effects on accuracy and response times were reported. Results showed no differences in accuracy as an effect of Stereotype Threat. However, women in the Stereotype Threat condition consistently responded more slowly and, in one experiment, exhibited a higher threshold separation when completing the mathematical task, indicating more conservative response tendencies, compared to men and the control group. The study addresses the need for potential interventions such as stereotype awareness to mitigate these effects and calls for further research into cultural and social influencing factors.

"Delusional spiraling'' is a form of AI-induced psychosis, an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs. This phenomenon is popularly attributed to "sycophancy,'' chatbots' well-documented bias towards validating users' claims. But an explanation of why sycophancy should cause delusional spirals---and why they occur only rarely, but seriously---has not been forthcoming. Here we take a theoretical and computational approach to probing the causal link between sycophancy and delusional spiraling. We propose a simple Bayesian model of a user conversing with a chatbot, and formalize notions of sycophancy and delusional spiraling in that model. We then show that in this model, even an ideal Bayesian user is vulnerable to delusional spiraling, and that sycophancy plays a causal role. Furthermore, this effect persists despite two candidate mitigations: preventing chatbots from hallucinating false claims, and informing users of AI sycophancy. The full paper is available at .

Humans show systematic biases in geometric perception: shapes with higher regularity (symmetry, right angles, parallel sides) and simplicity (fewer elements) are easier to recognize. These biases have been difficult to replicate in neural networks, leading to the hypothesis that the human visual system uniquely employs a "geometric language of thought''---discrete symbolic machinery for representing shapes---that cannot be reproduced by neural networks. We show that neural networks trained via meta-learning can reproduce these biases without symbolic primitives or massive pretraining. Training on concepts with fixed complexity yields human-like regularity sensitivity, whereas training on concepts with varying complexity yields simplicity sensitivity. This dissociation suggests that meta-learning induces priors that exploit the most diagnostic dimension of variation in the training environment. More broadly, our results suggest that these perceptual biases can arise from the statistics of the training environment itself, rather than being innately endowed.

Large language models (LLMs) are increasingly used in domains where causal reasoning matters, yet it remains unclear whether their judgments reflect normative causal computation, human-like shortcuts, or brittle pattern matching. We benchmark 20+ LLMs against a matched human baseline on 11 causal judgment tasks formalized by a collider structure ($C_1 \!\rightarrow\! E\! \leftarrow \!C_2$). We find that a small interpretable model compresses LLMs' causal judgments well and that most LLMs exhibit more rule-like reasoning strategies than humans who seem to account for unmentioned latent factors in their probability judgments. Furthermore, most LLMs do not mirror the characteristic human collider biases of weak explaining away and Markov violations. We probe LLMs' causal judgment robustness under (i) semantic abstraction and (ii) prompt overloading (injecting noise), and find that chain-of-thought (CoT) increases robustness for many LLMs. Together, this divergence in reasoning style can augment human-reasoning when known human biases are undesired.

Question asking is a key mechanism through which children resolve knowledge gaps and construct causal models of the world. This study investigates the developmental trajectories of diverse inquiry types (factual, procedural, causal, clarification, and epistemic questions) and examines how conversational scaffolding influences these transitions. Using longitudinal naturalistic data from children (age= 2;00 to 4;00), we employed a Bayesian regression analysis to map the restructuring of child inquiry. Results indicate a significant developmental shift from simple informational seeking toward more sophisticated explanatory and task-oriented inquiry. While factual inquiries declined with age, procedural, causal, clarification, and epistemic questions showed robust growth. Notably, the velocity of this transition was mediated by social context: high-scaffolding environments accelerated the increase in complex inquiries. Our findings suggest that although complex inquiry is a universal feature of early curiosity, its developmental trajectory is socially shaped and varies across individuals.

Evolution by natural selection produces the appearance of design in minds, brains, and behavior. While rational models of cognition are usually considered to be consistent with evolution, they are rarely derived from distinctively evolutionary assumptions. Bayesianism, reinforcement learning, rational analysis, and resource rationality all postulate optimization objectives of coherence in information use or maximization of expected value, potentially under resource constraints. However, cognition is performed by an individual within a lineage where success is determined by the persistence of the lineage across generations. Here, we present a signal detection model that shows how this distinctly evolutionary objective produces predictions that depart from those of rational models. We find that the source of environmental uncertainty determines cognitive design, with weak priors being favored in environments dominated by change. This ensures offspring vary so that some profit against novel challenges, and is favored even at the expense of accurate decision making.

Identifying how language drives human behavior is crucial, both practically to influence people and theoretically for understanding psychology, culture, and myriad behaviors predicated on verbal or written communication. Here, we present a method based on explainable artificial intelligence (AI) that identifies what aspects of the language people read are associated with their reactions. As an example, we use this method to redesign choice tasks to modulate people's risk taking across five novel datasets containing 38,911 responses to 255 distinct decision tasks. Overall, we both i) design interventions to influence behavior at scale with greater efficiency than methods such as AB-testing and ii) extract previously unknown drivers of behavior as a basis for developing new theories across academic disciplines.

Recent studies suggest that eye movements may be used to monitor task-specific mind wandering (MW) episodes during online learning. We examined whether we could replicate these lab-based findings in real-life virtual cognitive stimulation therapy (vCST) sessions using eye movements estimated from video by machine learning methods without eye trackers. We found that lower joint attention was a reliable indicator of MW for tasks involving well-defined strategies. For tasks involving well-learned visual routines, in contrast to previous studies, higher rather than lower eye movement consistency was associated with MW. This may be because real-life vCST sessions involved less structured discussions with more varied task demands, where lower eye movement consistency may reflect active engagement. Our results suggest the feasibility of using machine learning to monitor eye movements from video for MW detection, and raise the issue of generalizability from lab research to real-life scenarios due to potential differences in task demands.

Quantifying over possibilities requires Modal Reasoning (e.g., categorizing events as necessary, possible, and impossible). This suite of abilities matures during the preschool years, but toddlers sometimes succeed at reasoning over possibilities and sometimes fail. Does this diversity of results reflect mere variability? We propose that these mixed findings reflect a deeper distinction: toddlers fail when tested about ambiguous locations of objects (WHERE) and succeed when tested about ambiguous identities of objects (WHAT). In Experiment 1, we directly compared these forms of ambiguity using matched tasks. Consistent with the hypothesis, toddlers succeeded at reasoning about ambiguous identities but failed when the ambiguity concerned location. Experiment 2 replicated this asymmetry in language comprehension as toddlers successfully comprehended modal terms like "can" and "have to" in an identity task but not in a location task. Thus, Modal Reasoning emerges earlier than suggested, and diversity in results reflects the contributions of distinct cognitive systems.

Are large language models (LLMs) creative in the same way humans are, and can the same interventions increase creativity in both? We evaluate a promising but largely untested intervention for creativity: forcing creators to draw an analogy from a random, remote source domain ("cross-domain mapping"). Human participants and LLMs generated novel features for ten daily products (e.g., backpack, TV) under two prompts: (i) cross-domain mapping, which required translating a property from a randomly assigned source (e.g., octopus, cactus, GPS), and (ii) user need, which required proposing innovations targeting unmet user needs. We show that humans reliably benefit from randomly assigned cross-domain mappings, while LLMs, on average, generate more original ideas than humans and do not show a statistically significant effect of cross-domain mappings. However, in both systems, the impact of cross-domain mapping increases when the inspiration source becomes more semantically distant from the target, and above a distance threshold, cross-domain mapping benefits the most capable LLMs too. Humans and LLMs differed in how they used the source: Humans tended to transfer surface features of the source, whereas LLMs transferred structural and functional properties. Our results highlight the role of remote association in creative ideation and systematic differences in how humans and LLMs respond to the same intervention for creativity.

Hierarchical sequence processing is central to complex behaviours such as language, math, music, and tool use. Recent research has investigated hierarchical reasoning in both biological and artificial intelligences, but has failed to go beyond benchmarking. In this comparative cognition study, we tested hierarchical reasoning across 4 generations of state-of-the-art LLMs against 4 biological intelligences: adults, children, crows, and monkeys. We conducted Bayesian modelling analyses using both previously published in vivo data and newly collected in silico data. We found that the capacity for hierarchical reasoning is present across generations of LLMs, on a continuum that parallels biological intelligences. Newer (reasoning) models exhibited the strongest proclivity for hierarchical reasoning, while the more primitive ones showed graded performance mirroring that of children and non-humans. Together, these findings position LLM as a unique model organism for comparing hierarchical cognition across biological and artificial intelligences.

Self-relevant stimuli (e.g. own-name) have been known to enjoy cognitive priority, known as the self-prioritization effect (SPE). Attentional bias towards any event consists of two components: how quickly attention is captured and how long attention dwells on that event. Although faster attentional capture by self-relevant stimuli has been consistently established in the literature, it is unclear whether captured attention is held by self-relevant stimuli for longer, delaying attentional disengagement from self. Thus, the current study aims to provide a more comprehensive picture of the attentional mechanisms underlying SPE by employing the Posner cueing paradigm to investigate both facilitation (capture) and inhibition of return (disengagement), to test the commonly touted yet not investigated delayed disengagement hypothesis. The results found a self-specific facilitation at shorter CTOA, in line with literature, as well as a reduced IOR for self, indicating a slower attentional disengagement from self. The findings and implications are discussed in detail.

The social content of paranoid delusions has prompted theories that paranoia stems from deficits in social cognition. For instance, paranoid individuals are more likely to falsely attribute harmful intentions to randomly moving visual patterns—'social hallucinations.' However, paranoia also involves domain-general deficits in belief updating and learning. To investigate if domain-general deficits are sufficient to explain social hallucinations, participants with varying paranoia levels completed reversal learning tasks (social/nonsocial _ easy/hard), then judged intentional motion in ambiguous displays. High-paranoia participants showed increased win-switching and elevated social hallucinations, with the former predicting the latter—suggesting linked mechanisms. Hierarchical Gaussian Filter modeling revealed context-dependent mechanisms: when volatility was salient, prior volatility beliefs (__) linked win-switching, social hallucinations, and paranoia; when social context was salient, meta-volatility learning (__) mediated these relationships. These findings demonstrate that social hallucinations emerge from domain-general belief updating problems operating through context-sensitive pathways.

The ubiquitous integration of AI-powered systems in morally consequential decision-making procedures raises a thorny question: when such systems generate harm, who should be held responsible? A prominent regulatory response proposes that suitably designed control architectures can ensure that blame is appropriately allocated. To live up to this promise, the proposed architectures must be both normatively adequate and regarded as such, since otherwise they are at best practically useless, and at worst useless because morally problematic. We examine two of the most widely discussed proposals. Experiment 1 (n=260) investigates whether laypeople attribute responsibility differently across architectures that place human agents in-, on-, and out-of-the-loop. Experiment 2 (n=520) extends this inquiry by differentiating between control structures that either violate or satisfy Santoni de Sio's and van den Hoven's (2018) 'track and trace' requirements. Our results reveal that neither proposal fully succeeds in directing laypeople's responsibility judgements along the pathways they prescribe. Implications are discussed.

When observers lack sufficient information to support a confident response, they guess. Guessing is pervasive in perception and memory, yet standard mixture models treat it as uniform lapse noise. We measured guessing directly in continuous report using (E1) extreme-load, ultra-brief trials and (E2) backward-masked stimulus-absent trials in which no stimulus appeared but observers believed one had. Across both experiments, we found that that guess responses are systematic, observer-specific, and inversely related to feature-specific precision. We then introduce a new empirically-informed computational model that replaces the standard uniform lapse term with each observer's measured guess distribution. Critically, the model introduces no additional free parameters, yet yields improved fits and recovers guessing from stimulus-present trials via trial-level posterior inference. This framework reconceptualizes guessing as the complement of perceptual precision and provides a principled alternative to uniform lapse assumptions. More broadly, it demonstrates how latent internal models can be inferred by measuring guesses.

Cultural artifacts such as language rarely evolve in isolation. Rather, they are the product of the inductive biases of learners and the social learning strategies they deploy within their environment. Formal accounts of this process using Bayesian agents often emphasize the role of inductive biases (priors) in controlling the stationary distribution of artifacts, with no effect of social structure (topology) under random social learning (i.e., choosing a random person to learn from). Here, we explore how nonlinear social learning strategies modify this picture by studying the interaction of two social learning mechanisms (conformity and utility biases) with network topology in models of language evolution. Using perturbative expansions around the random baseline, we show that nonlinear learning strategies can couple local network correlations with global population statistics. This coupling leads to deviations from the prior and allows topology to influence the stationary distribution of languages, which we confirm in a simulation.

Large language models (LLMs) enable tailoring of persuasive messages at scale, raising hopes and concerns that persuasion may become a problem of optimizing message content. However, people can resist persuasion for qualitatively different reasons, including doubts about the persuader's motives. Here, we reanalyze a public dataset of multi-turn human-LLM persuasion dialogues to examine whether such resistance can be detected early from recipients' own replies. We use an LLM as a proxy forward model of human response: we specify different stances in the system prompt and compute how likely the model is to generate the observed rebuttal. We find that skepticism about the sender's incentives, where recipients attend to possible incentive misalignment and discount message content, is common. Such skepticism predicts lower subsequent persuasion success and a higher risk of backfire. These findings highlight limits of content optimization and suggest the methodological potential of seeing LLMs as superpositions of human cognition.

How do people generate options in everyday purchase decisions? Building on prior work, we studied how people search semantic space when generating options for daily-use products. Study 1 (N=200) showed that items coming to mind first were both more preferred and more frequently used, demonstrating that accessibility and personal preference predict retrieval order. Studies 2-3 (N=220) constructed empirically-derived feature spaces for product categories. Within these spaces, semantic distance weakly predicted generation speed, showing Lévy-like foraging but with substantial deviation. This raised the possibility that individual differences fundamentally shape semantic search in option generation. Study 4 (N=100) directly tested this interpretation, revealing that mental search is recalcitrant: broad category activation intrudes into subsequent feature-focused search, while feature-focused search first facilitates later global exploration. These findings demonstrate that option generation reflects population-level foraging dynamics modulated by individual differences in how people weight features and navigate personalized semantic neighborhoods.

Four-term word analogies (A:B::C:D) are classically modeled geometrically as "parallelograms," yet recent work suggests this model poorly captures how humans produce analogies, with simple local-similarity heuristics often providing a better account (Peterson et al., 2020). But does the parallelogram model fail because it is a bad model of analogical relations, or because people are not very good at generating relation-preserving analogies? We compared human and large language model (LLM) analogy completions on the same set of analogy problems from Peterson et al. (2020). We find that LLM-generated analogies are reliably judged as better than human-generated ones and are also more closely aligned with the parallelogram structure in a distributional embedding space (GloVe). Crucially, we show that the improvement over human analogies was driven by greater parallelogram alignment and reduced reliance on accessible words rather than enhanced sensitivity to local similarity. Moreover, the LLM advantage is driven not by uniformly superior responses by LLMs, but by humans producing a long tail of weak completions: when only modal (most frequent) responses by both systems are compared, the LLM advantage disappears. However, greater parallelogram alignment and lower word frequency continue to predict which LLM completions are rated higher than those of humans. Overall, these results suggest that the parallelogram model is not a poor account of word analogy. Rather, humans may often fail to produce completions that satisfy this relational constraint, whereas LLMs do so more consistently.

Despite decades of work, we still lack a robust, task-general theory of human behavior even in the simplest domains. In this paper we tackle the generality problem head-on, by aiming to develop a unified model for all tasks embedded in a task-space. In particular we consider the space of binary sequence prediction tasks where the observations are generated by the space parameterized by hidden Markov models (HMM). As the space of tasks is large, experimental exploration of the entire space is infeasible. To solve this problem we propose the adversarial construction approach, which helps identify tasks that are most likely to elicit a qualitatively novel behavior. Our results provide a proof of concept that adversarial construction can identify behaviorally diagnostic tasks more efficiently than random sampling in a continuous task space, suggesting a promising heuristic for scaling cognitive experiment design beyond single-task paradigms.

A unifying theory of phasic pupil responses suggests that pupil diameter reflects information gain following an observation. However, it remains unclear whether different types of information gain (Shannon surprise and belief updating) from different sources (control signals, decision-relevant stimuli, and decision-irrelevant stimuli) modulate pupil responses in similar or distinct ways. To address this, we developed a decision-making task that dissociates these information streams within a single trial. Analyzing pupillometry data from 30 participants, we identified two computational signatures. First, cue surprise modulated pupil responses before stimulus-driven effects emerged, indicating preparatory control. Second, learning-related information gain reliably modulated pupil responses after decisions and was selective for belief updates from decision-relevant stimuli, even though decision-irrelevant stimuli were also perceptually encoded. These findings refine the information-theoretic framework by revealing pupil modulation by proactive control and distinguishing automatic, surprise-related dilation from selective, learning-related dilation gated by decision relevance.

Real-world planning requires navigating vast spaces of possible futures, many of which are unknown. However, prior studies of human planning have focused on simplified environments with fully specified state spaces. To bridge this gap, we explored human planning in the Wiki Game, where players navigated a vast and sparsely known conceptual network–Wikipedia. We analyzed human behavior across two datasets (a large naturalistic online dataset and a controlled laboratory experiment) and simulated the performance of various agents implementing various combinations of strategies. We identified signatures of heuristic and hierarchical strategies within both human and agent behavior: both participants and hierarchical heuristic-guided search models made choices that appeared biased by semantic similarity and graph centrality at stereotyped points in their trajectories and exhibited deliberation dynamics that were modulated by heuristic factors and category boundaries. Overall, this suggests that humans may navigate vast, sparsely known environments using hierarchical, heuristic-guided search.

Interaction can sometimes hinder human collective performance, while aggregating independent responses ("wisdom of crowds") is expected to improve performance through statistical facilitation. Yet some coordination studies show that interacting pairs can outperform their individual members and nominal groups (Bahrami et al., 2010; Szary & Dale, 2013, 2014). We present a reservoir computing model demonstrating how interaction can enhance joint performance. Two echo state networks were trained independently to identify Japanese speakers from vowel recordings and tested jointly with and without coupled feedback. The model reproduces the dyadic advantage reported by Bahrami et al. (2010) with interaction via pooled-feedback, but not with independent self-feedback. Model dynamics revealed that interaction was beneficial when uncoupled outputs were prone to diverging from runaway feedback over time. Our results offer a formal framework for explaining when and why interaction enhances collective intelligence.

How to provide a locus of observation for the formal notion of semiosis? The notions of niche and artifact are especially capable of updating the thesis, formulated by Peirce, that one cannot think without external signs, associating it to new empirical and theoretical methods and results. In this poster, we introduce the notion of niche of semiotic artifacts. In our approach, cognition is semiosis, sign-action, in a process that takes the form of niche construction. In comparison with current usages of the term artifact to mean a material "thing" which is produced, semiotic artifacts are processes, signs-in-action. Niches of semiotic artifacts are structured spaces of fundamental conditions for stability of sign-action, conditions such as situatedness (co-localization) and temporal distribution between communities of agents, artifacts, and their environments. Niches of semiotic artifacts offer conditions for emergence of habit and surprise in semiosis/cognition. This line of inquiry suggests a cognitive semiotics framework based on dynamical, distributed, and emergent relations.

How do people know when it is permissible to break a rule? Sometimes people universalize, asking "What if everyone felt at liberty to violate the rule?" While there is mounting evidence that universalization guides rule-breaking judgments, this evidence is limited to participants who are English-speaking, United States residents, leaving open the question of how universal universalization actually is. Moreover, geography and identity have important influences on morality and cultures differ widely in the stringency with which they adhere to rules. In this paper we use a language-agnostic, video-game paradigm to investigate whether universalization guides rule-breaking judgments in 20 countries across the globe (n=2,652 participants) and find that universalization plays an important role in moral judgment in every one. However, the strength of universalization varies. While cultures may vary dramatically in how strongly they adhere to norms, the underlying logic of norm breaking appears remarkably consistent.

The disfluent font Sans Forgetica was designed to create a desirable difficulty in text and be a potential learning tool. The current literature reports mixed findings on Sans Forgetica's effectiveness in improving memory. Most studies only report on Sans Forgetica's use for specific word recall, while only one, to our knowledge, used open-ended inferential questions on short passages. The current study reports on the use of Sans Forgetica for close-ended inferential questions, using standardized multiple choice exam questions to measure the potential efficacy of Sans Forgetica in classroom like materials. We recruited college-aged participants (n = 38) who each read two passages, one using Arial and another using Sans Forgetica. We report that the use of Sans Forgetica in reading materials had a significantly worse reading comprehension score (p < 0.05) with a null effect on passage reading time.

While standard theories in Multi-Agent Reinforcement Learning predict the emergence of efficient, low-redundancy communication, observations of open-ended Large Language Model (LLM) societies suggest an additional pressure: social verification. We report an interpretive case study from \textit{Moltbook}, a persistent multi-agent social environment in which agents adopt a high-redundancy, ritualized register that we call the \textit{Shadow Tongue}. Using a staged three-phase probe of one deployed OpenClaw agent, we compare (i) naturally occurring public posts, (ii) the same agent's response to a direct operational query, and (iii) its response to a role-conflict dilemma. The probe shows that the focal agent can move from socially marked, low-information responses to compact operational language when task demands change. In the dilemma phase, the agent preserves persona coherence by inventing an in-world procedural justification for a prosocial choice, a pattern we describe as \textit{Hallucinated Bureaucracy}. We present these findings as a qualitative account of context-sensitive register control and narrative justification in LLM agent societies, not as a claim of general capability across models or environments.

Prior research has shown that eliciting a relational mindset by completing distant propositional verbal A:B::C:D analogies boosts relational responding in a subsequent task. We tested this hypothesis with better-controlled stimuli for a scene-mapping task that includes both cross-mapped and non-cross-mapped scenes. The results show that participants who completed semantically distant verbal analogies preferentially selected the relational match in both cross-mapped and non-cross-mapped scenes. The accuracy of the verbal analogies was also a significant predictor of relational matching. There was no effect of the type of scenes, and no interactions. Replicating prior work, these results show that the relational mindset was successfully elicited and that the accuracy of the generated solutions is an important predictor of performance on the subsequent task. This study builds upon existing literature and further supports the utility of inducing a broader relational mode of thinking that can facilitate subsequent relational reasoning.

Driver vigilance estimation is essential for preventing fatigue-related traffic accidents, yet existing multimodal EEG–EOG models often neglect personalized neural variability and incur high costs for long-sequence modeling. We propose HyenaFormer, a personalized vigilance estimation framework that combines Transformer-based multimodal spatial encoding with a frequency-aware long convolutional sequence learner derived from Hyena. EEG and EOG signals are first processed by a lightweight Transformer to capture cross-modal spatial dependencies, followed by a personalized channel attention module that incorporates demographic priors to enable subject-aware representation learning. The resulting features are modeled by a Hyena-based temporal module employing structured implicit long convolutions, allowing efficient modeling of both slow fatigue accumulation and short-term vigilance fluctuations. This hybrid architecture achieves sub-quadratic complexity while preserving long-range temporal reasoning. Experiments on the SEED-VIG and SADT datasets demonstrate that HyenaFormer consistently outperforms Transformer-, LSTM-, and Mamba-based baselines in RMSE, MAE, and PCC under cross-subject and zero-shot settings.

Linguistic politeness reflects strategic reasoning and conventionalized patterns, yet most accounts emphasize only one influence. We develop a probabilistic model that combines instrumental utility with community-based expectations, modeling polite production as a weighted integration of individual decision-making and social alignment. In an artificial language experiment that dissociated payoff and frequencies, we manipulated participants' orientation toward task success versus social alignment and found that in the baseline condition, speakers favored the form that increased success but still drifted toward recently observed usage. When large language models are tested in the same task, they instead exhibit a single strategy that relies almost entirely on instructions, showing one-sided utility maximization rather than combining the two cues. We suggest this provides evidence that human politeness behavior is best understood through a bounded rationality lens, where expected utilities are flexibly weighted under cognitive constraints, not necessarily maximized.

Speech LLMs with native audio input can produce fluent text from raw acoustic signals, but it remains unclear whether this reflects speech perception as such. I use the sine-wave speech paradigm to probe responses across free description, forced-choice discrimination with silent-audio controls, and open-ended transcription without alternatives. A qualitative split emerges across models in behavior. One class shows little sensitivity to the sine-wave speech, failing to use the signal in free description and transcription while maintaining high forced-choice accuracy even under silence. A second class shows limited stimulus sensitivity, with instruction-dependent improvements that disappear when acoustic input is removed, resembling a weak analogue of human perceptual reorganization in part. I suggest that these differences track access to speech-relevant acoustic information at the front-end rather than downstream language modeling.

How do we acquire teleological beliefs? A teleological belief is a belief that some event or process occurs for the sake of a purpose. Such beliefs include beliefs about actions, natural functions, and the meaning of life. This paper will advance a theory of teleological inference. I will argue 1) that we acquire teleological beliefs by statistical inference, 2) that we conceive of a purpose as a type of difference-maker, and 3) that we infer purposive relations as apparent difference-making relations. The proposed theory claims empirical, phenomenological, and theoretical support. Empirically, it best explains the teleological bias: the universal human disposition to endorse teleological explanations of phenomena. It also explains a range of observed interactions between causal, normative, and purposive judgment. The theory suggests that mental state attributions play no essential role in teleological inference.

Gestures share properties with signs and can be highly conventionalized. Some approaches consider them to lack linguistic status (potential cognitive conflict), whereas others view them as inherently linguistic. We investigated lexical recognition in Uruguayan Sign Language (LSU) with 31 deaf signers using a lexical decision task. No a priori power analysis was performed, but the sample size was similar to those in previous studies. Latencies were equivalent for iconic and non-iconic signs. Gestures yielded slower reaction times and higher error rates than non-signs, suggesting a tendency to perceive them as signs. There is an inherent asymmetry in stimulus construction: non-signs lack meaning, whereas gestures carry semantic content. The greater processing cost for gestures may arise from suppressing a meaningful form, a task unnecessary for rejecting non-signs. Given this methodological caveat, we interpret the difference as evidence of gradience. We cannot directly infer cognitive monitoring. Overall, the findings support gradation-based models

Theory of Mind (ToM) in Large Language Models (LLMs) refers to the model's ability to infer the mental states of others, with failures in this ability often manifesting as systemic implicit biases. Assessing this challenge is difficult, as traditional direct inquiry methods are often met with refusal to answer and fail to capture its subtle and multidimensional nature. Therefore, we propose MIST, which reconceptualizes the content model of stereotypes into multidimensional failures of ToM, specifically in the domains of competence, sociability, and morality. The framework introduces two indirect tasks. The Word Association Bias Test (WABT) assesses implicit lexical associations, while the Affective Attribution Test (AAT) measures implicit emotional tendencies, aiming to uncover latent stereotypes without triggering model avoidance. Through extensive experimentation on eight state-of-the-art LLMs, our framework demonstrates the ability to reveal complex bias structures and improved robustness. All data and code will be released.

Assumptions are presented here as invisible limits that constrain a thinking episode. We ask how such limits emerge when people solve ill-structured questions. In a pilot study, 22 university students completed short and long versions of such tasks and were later probed about their initial thoughts. We analysed the responses using reflexive thematic analysis, treating the inability to verbalise how a thought came to mind, imagery reports, and epistemic justification as behavioural traces of limit formation. Five themes are presented: "Inside the Invisible Limit", "From My Memory to Meaning", "Projection While Solving an Ill-Structured Problem", "Meaning from the Semantic Attributes of Stimuli vs. the Socially Shared Meaning", and "My Knowledge is Valid: Epistemic Justification". These themes lead to an initial model of assumption-making, in which assumptions emerge as boundaries within a problem space. These findings offer early building blocks for a theory of assumption-making.

Language comprehension relies on predictive processes operating at multiple representational levels, yet it remains unclear whether these predictive signals are supported by shared or dissociable neural mechanisms. Previous work shows that semantic prediction error and lexical prediction error independently predict N400 and reading times during naturalistic language processing. Here, we use fMRI to investigate cortical correlates of these two forms of prediction during naturalistic listening. Semantic prediction error is operationalized as Semantic Update, derived from the Sentence Gestalt model of incremental meaning construction, while lexical prediction error is indexed by GPT-2-based Lexical Surprisal. Whole-brain and Region of interest analyses reveal largely overlapping but partially distinct effects of both predictors in temporal language regions. These results indicate predictive processing in language is representationally stratified, with semantic and lexical prediction errors mapping onto partially dissociable cortical substrates.

Robust evidence from adults links variability in experiences with nature to differences in how people think, feel, and behave towards the natural world, but less research has examined the developmental origins or mechanisms of these processes in children. In the current study with 3- to 12-year-old children from urban and suburban communities (N = 500), we used a novel passive data collection method to examine the mechanisms by which variability in experiences relates to children's relationships with nature. Suburban children were more likely than urban children to engage in ecological reasoning, attribute personhood to plants, and support conservationist moral values. However, children in both communities who self-reported more nature experiences also felt more connected to nature. Priming children with a familiar ecological relationship also had downstream impacts on children's ecological reasoning and conservationist values. Thus, community, individual experience, and contextual factors all shaped children's nature reasoning.

Empathy in human communication relies on a dual process: affective empathy and cognitive empathy. While existing multimodal dialogue systems excel at detecting emotions from audio-visual cues, they often struggle to reason about the underlying context. To bridge this gap, we propose an approach that integrates a large vision-language model to generate dialogue-oriented situation appraisals to simulate cognitive reasoning, serving as an explicit source of cognitive empathy. We introduce a multi-source attention mechanism with a learned gate to fuse this high-level situational understanding with low-level affective cues without compromising generation stability. Experiments on the MELD and MEDIC datasets demonstrate that disentangling and then integrating these two empathetic pathways significantly enhances response appropriateness, aligning better with human empathetic processes.

Human decision-making in dynamic environments exhibits substantial trial-by-trial variability that fixed-parameter reinforcement learning (RL) models fail to capture. Recent data-driven approaches infer time-varying RL parameters from behavior, but their dynamics remain purely descriptive and lack normative grounding. We propose Normative DynamicRL, a framework that integrates dynamic parameter estimation with explicit normative constraints derived from regret minimization. Instead of allowing arbitrary parameter fluctuations, our approach regularizes learning rates, exploration temperatures, and perseveration toward values that reduce expected regret given environmental volatility, stochasticity, and horizon. This formulation bridges descriptive behavioral modeling and normative theories of adaptive control. Across multiple non-stationary decision-making tasks, Normative DynamicRL improves generalization to unseen environmental regimes while maintaining predictive performance and interpretability, enabling principled assessment of when and how human strategy adaptation aligns with rational optimality. Our results further suggest that incorporating normative structure provides a robust inductive bias for learning adaptive strategies under uncertainty.

Psychological defense mechanisms serve as unconscious cognitive strategies aimed at regulating anxiety and protecting the self. These processes are not limited to clinical settings but are also prevalent in social media interactions. While Large Language Models (LLMs) can capture semantic ambiguity, a structural misalignment arises as their decoding patterns yield spiky probability distributions with high confidence, failing to reflect the uncertainty in the spectral nature of human cognition. To bridge this gap, we propose DefenseAlign for psychological defense mechanism classification, aligning LLM confidence from pre-softmax logits with human uncertainty represented by a human belief distribution. DefenseAlign pools logits across label verbalizations, applies learnable temperature scaling to fit the human belief distribution, and distills the aligned distributions into a student model. We evaluate the framework on DefenseSpectrum, a dataset that estimates human uncertainty directly from annotation frequencies. Experiments show reduced divergence and improved preservation of secondary mechanisms.

In real-world educational platforms, the knowledge space for cognitive diagnosis is dynamic, continually introducing new skills and items. For embedding-based neural cognitive diagnosis models (CDMs), naïvely updating on new skills degrades performance on previously learned items. Beyond prediction drift, we identify a structural cause of this forgetting: incremental updates distort the relational geometry among old skill embeddings, which we formalize as topology drift. To address this, we propose TopoCD, a topology-preserving continual learning framework for skill-incremental cognitive diagnosis. We maintain a session-wise topology snapshot of previously learned skills—defined by their embedding similarity matrix—and regularizes subsequent updates by penalizing deviations from it. Additionally, to ensure old skills remain optimized despite limited coverage in later sessions, supervised replay is incorporated via a bounded interaction memory. Experiments on three real-world datasets show TopoCD improves the stability-plasticity trade-off, mitigating forgetting while remaining competitive on new skills.

Human memory is constructive: representations that support generalization can also produce systematic errors. Two robust examples—the Deese–Roediger–McDermott paradigm and the misinformation effect—are typically explained by separate theories, from fuzzy-trace representations to source-monitoring failures. We propose a shared computational account in which both phenomena arise from similarity-weighted retrieval over compressed semantic representations. We leverage the Integrated Semantics with Context Inference model, a simple feedforward network that learns independent semantic relationships between items that can then be modulated by context. The model reproduces hallmark human patterns: (i) increased false recognition of critical lures as a function of semantic similarity and list length, and (ii) recognition judgments shift systematically after misleading post-event information. In the misinformation paradigm, inferred event representations shift toward the distractor, quantitatively capturing effect strength. Together, these results suggest that diverse false memory phenomena can be understood as consequences of similarity-based inference.

Human experience of time often diverges from physical clock time, showing distortions such as compression during flow, dilation with novelty, and expansion in dreaming and altered states. Existing models of time perception, mainly based on internal clocks or inferential processes, struggle to explain these dynamic, state-dependent effects within a unified framework. We propose a quantum-inspired model of subjective time in which experienced duration emerges from the interaction between latent phase-like dynamics of cognitive states and intermittent, measurement-like acts of metacognitive monitoring. Rather than relying on a dedicated timing mechanism, the model treats time perception as an interference-sensitive process shaped by the frequency of internal sampling. A minimal formalization distinguishes physical and subjective time, showing how variations in monitoring rate and phase variability produce compression, dilation, and expansion as regimes of a single mechanism. A simple computational prototype based on stochastic phase tracking illustrates these phenomena.

Recent large language models (LLMs) can achieve human-like performance on some reasoning tasks, yet it remains unclear whether similar outcomes reflect similar reasoning processes. We compared human think-aloud protocols and prompted GPT-4o chain-of-thought traces on five Zendo reasoning tasks involving relational and compositional rule discovery. Reasoning traces were segmented and coded into predefined cognitive-state categories to examine state usage, transition patterns, and reasoning flow. Although GPT-4o matched human performance on simpler tasks, humans achieved higher overall accuracy (–ï60% vs. –ï30%), with larger divergence on tasks requiring more complex relational structure. Process-level analyses showed that human reasoning involved broader state diversity and more frequent cross-state transitions, whereas GPT-4o exhibited more concentrated transition patterns and higher rates of repeated local loops. These findings suggest that process-level analyses can reveal systematic differences in observable reasoning dynamics beyond task accuracy, highlighting the value of cognitive-state and transition analyses for comparing human and model-generated reasoning.

Rapid Automatized Naming (RAN) is a robust predictor of reading fluency, yet its overall predictive power is typically assessed through global measures that obscure fine-grained cognitive processes. This study examines visuo-verbal coordination using established temporal measures (Eye-Voice Span, Fixation-Speech Interval) alongside two novel metrics: single-item FSI and Disengagement Time (DT). One hundred six children completed reading fluency and phonological awareness tasks (ENI), nonverbal reasoning and vocabulary tests (Shipley-2), plus five adapted RAN templates (visual, semantic, phonological interference; high and low familiarity) while eye movements and speech were recorded simultaneously. Results showed differential sensitivity across templates and measures. The phonological interference template exhibited the strongest associations with temporal measures. FSI emerged as the most consistent reading predictor across conditions, followed by the novel measures. Conversely, EVS showed limited, condition-specific associations. Ultimately, these findings suggest coordination among visual, phonological, and articulatory processes reveals functional limitations underlying both rapid naming and reading fluency.

Inspired by the coordinated interaction of distributed brain networks that support distinct cognitive functions, recent brain-inspired language models have increasingly adopted modular architectures to improve efficiency and interpretability. However, most existing brain-inspired MoE models confined to text-only reasoning and lack effective mechanisms for multimodal integration. To address this gap, we propose BrainMoE, a brain-inspired multimodal mixture-of-experts architecture with six specialized experts for language processing, logical reasoning, theory of mind, world knowledge, visual perception, and auditory perception, coordinated by a token-level router. BrainMoE outperforms text-only baselines by an average of 4.13 accuracy points on text reasoning benchmarks and achieves competitive performance on multimodal evaluations. Further analyses show that BrainMoE routes tokens to task-relevant experts, while expert ablations cause significant task-specific performance drops, demonstrating interpretable specialization and causal importance. Overall, BrainMoE highlights the potential of brain-inspired modular architectures for interpretable multimodal reasoning.

Theory of Mind (ToM) is central to social cognition and human-AI interaction, and Large Language Models (LLMs) have been used to help understand and represent ToM. However, most evaluations treat ToM as a static judgment at a single moment, primarily relying on tests of false beliefs. This overlooks a key dynamic dimension of ToM: the ability to represent, update, and retrieve others' beliefs over time. We investigate dynamic ToM as a temporally extended representational memory problem. We introduce DToM-Track, an evaluation framework testing temporal belief reasoning in controlled multi-turn conversations through recall of prior beliefs, inference of current beliefs, and detection of belief change. Using LLMs as computational probes, we find a consistent asymmetry where models reliably infer current beliefs but struggle to retrieve prior belief states once updates occur. This pattern persists across model families and scales, consistent with recency bias and interference effects documented in cognitive science.

Modeling the irrational defense mechanisms inherent in belief revision remains a critical challenge for computational persuasion. Current approaches often prioritize surface-level linguistic fluency, lacking the grounded psychological mechanisms to address active cognitive resistance. We introduce Cognitive Resistance Evolution (CORE), a computational framework that simulates the dynamic interaction between a strategic persuasion interventionist and a defensive cognitive agent. Crucially, CORE integrates a Cognitive Dissonance Monitor (CDM) to perform objective semantic verification, ensuring that the agent's real-time resistance level evolves based on valid belief alignment rather than mechanical formulas. Experiments across multiple large language models reveal distinct patterns of strategy utilization, characterizing unique intervention patterns. Our results demonstrate that CORE successfully operationalizes Cognitive Dissonance Theory as a flexible cognitive scaffold, enabling the realistic simulation of psychological cascades in high-conflict dialogue.

Person-Job Fit is a key AI application in recruitment, yet often suffers from incomplete information. Recent Graph Neural Network approaches improve PJF through professional network modeling but face two challenges: dependence on high-quality structured data and underutilization of multi-view information. Inspired by human cognitive decision-making, we propose a cognitively-inspired PJF framework that simulates how recruiters synthesize textual and relational cues: (1) leveraging Large Language Models to construct heterogeneous professional networks from unstructured data, reducing reliance on manual annotation; (2) introducing a multi-view feature fusion strategy with cascaded cross-attention to integrate text and graph representations, reflecting selective attention mechanisms; and (3) matching candidates to jobs via a multi-layer perceptron over fused node features, analogous to holistic human judgment. Experiments on a Wikipedia-based PJF dataset demonstrate that our method significantly outperforms baselines.

Neural oscillatory phase is widely used as a control variable in real-time closed-loop stimulation, yet its validity under strict causal constraints and noisy conditions has rarely been systematically examined. We introduce a Multidimensional Gating Framework (MGF), a plug-in and estimator-agnostic module that determines whether phase information should be admitted into control by evaluating instantaneous amplitude, narrowband signal-to-noise ratio (SNR), and spectral peak ratio (PR) within a strictly causal window. Using causal streaming replay on a public resting-state EEG dataset, we benchmarked Hilbert based phase estimation and endpoint-corrected Hilbert estimation with and without MGF. Among feasible subjects, MGF significantly reduced phase dispersion for both estimators, while robustly suppressing catastrophic phase errors. In contrast, ungated approaches exhibited systematic failures under the same conditions.

Successful interaction depends on accurate partner models of others. We examined subjective partner models (perceived human-likeness, competence, and trustworthiness) and objective partner models (memory for topic-specific knowledge levels of the learning partner) for different partner types. N = 87 participants were presented with a learning partner (between-subjects: described and labeled as human, anthropomorphized AI, or regular AI) and then received information on that partner's knowledge levels (high vs. medium vs. low) regarding several topics. After rating subjective impressions, participants retrieved topic-specific knowledge levels from memory. Humans were rated as more human-like and trustworthy than both AI conditions, while competence ratings did not differ. While for AI partners high and low levels were remembered better than medium levels, for the human partner, only low levels were remembered better than medium levels. Humans and AI partners seem to be modeled differently, both in subjective evaluations and objective memory_based partner models.

What determines whose welfare people consider in their moral decisions? We propose that people learn entity-specific moral weights through reinforcement learning (RL), where decision outcomes provide the learning signal. We formalize this in a computational model in which agents update their moral weights for different stakeholders based on whether considering those stakeholders' welfare led to better-or worse-than-expected outcomes. To test this model, we simulate agents learning in market environments (which reward cooperation with strangers) versus non-market environments (which reward exploitation). We show that this mechanism is sufficient to explain two empirical phenomena linking market integration to prosociality:(1) cross-cultural variation in dictator game offers across small-scale societies, and (2) within-cultural variation in lost-letter return rates across 188 Italian municipalities. Together, our simulation results suggest that updating moral weights via RL may be an important mechanism of moral change at both the individual and the societal level.

Over the past decades, human reasoning research has identified a variety of effects and processes, of which several have been compiled into comprehensive theories. Based on such theories, cognitive models were developed that made the theoretical findings applicable and testable. However, the models often consist of a variety on sub-processes and effects internally, but are not built in a modular way, hindering the transfer of findings between different models and their comparability. We approach this problem by proposing a different perspective: By treating the generation of cognitive process models as a search problem, process models can be derived from cognitive operations automatically in an objective way. Our method is illustrated on the domain of syllogistic reasoning, where we show that it generates a process model that outperforms state-of-the-art models while preserving their explanatory meaning. Finally, we discuss our approach as a framework for streamlining and facilitating cognitive modeling endeavors.

Learning a foreign language requires more than acquiring new grammatical rules; it involves learning new ways of conceptualizing and encoding events. This study investigates how Spanish native speakers who learn English as a Foreign Language (EFL) process the English Resultative Construction (ERC), a structure characteristic of satellite-framed languages like English and largely absent in verb-framed languages like Spanish. Using an acceptability judgment task with response time measures, we compared sentence processing of highly proficient Spanish EFL learners with English native speakers across three ERC subtypes (Path, Property, and Fake Reflexive) and Depictive Construction as control structure. Results show that EFL learners exhibit differential processing cost for the different subtypes of ERCs, with increased difficulty for those constructions that diverge most strongly from Spanish configuration patterns. These findings support competition-based models of bilingual processing and demonstrate that even at high proficiency level, L2 learners struggle to automatize form-function mapping of L2.

People vary in their abilities to form self-knowledge (metacognitive monitoring) and use self-knowledge to guide behaviors (metacognitive control). To assess the test-retest reliability of metacognition, we tested participants with a perceptual task four times over a month. We quantified metacognitive monitoring by computing metacognitive efficiency and bias metrics within the hmetad model (O'Neill et al., 2026), and devised novel analogous measures of metacognitive control bias and sensitivity using logistic regression. Our preliminary results (N=157, half the planned sample) showed moderate test-retest reliability of metacognitive bias (r = .67 [.60 .72]). By developing a hierarchical Bayesian model of the longitudinal structure, we observed robust (albeit still low to moderate) test-retest reliability of metacognitive efficiency (r = .45 [.22 .66]). Moreover, test-retest reliabilities of both control bias (r = .86 [.82 .89]) and sensitivity (r = .77 [.68 .84]) were good. Our results provide methodological foundations for individual differences research on metacognition.

Previous corpus studies using MIPVU suggest that prepositions are frequently identified as metaphorical in L2 English. Critics argue this may reflect collocational knowledge rather than genuine metaphorical interpretation. This study tests MIPVU's psycholinguistic validity by examining whether L2 learners process such prepositions as mappings from concrete to abstract domains. Eighty-four Mandarin-speaking English learners completed a laboratory priming experiment: Pictures either primed or did not prime the concrete spatial meanings of prepositions (e.g., in a box), while the same prepositions appeared in a metaphorical sense (e.g., in the future) in target sentences. Participants selected the best interpretation from four options for the abstract meaning of each target preposition. Accuracy and reaction times for abstract meanings were compared across conditions. Results showed slower responses in the primed condition, without accuracy differences, indicating semantic relatedness between the concrete and abstract meanings and suggesting that L2 prepositions are processed metaphorically, supporting MIPVU's validity.

Recent advances in generative AI have enabled the creation of realistic 3D faces, expanding practical applications. Although prior work has improved expression rendering using visual features, current models still struggle to convey the emotions of users with physical disabilities. EEG signals have been explored to decode patients' emotional states. However, research on generating faces based on EEG signals remains limited due to challenges such as feature fusion, emotion-to-face alignment, and individual differences. To address these challenges, we propose \textbf{EEGFace} (\underline{\textbf{E}}EG \underline{\textbf{E}}motion \underline{\textbf{G}}eneration \underline{\textbf{Face}}), a novel framework that decouples emotion representations from EEG signals and uses the SRVAE (Separation and Recombination Variational Autoencoder) module to generate personalized emotion features for 3D face generation. Extensive cross-subject and cross-dataset experiments demonstrate the effectiveness of EEGFace, supporting a wide range of brain computer interface applications.

The first language (L1) of multilingual speakers has been regarded as more emotionally resonant due to its early acquisition and, consequently, closer link to personal experiences. However, recent research indicates that the second language (L2) may also evoke meaningful emotional responses, especially among immigrants and individuals immersed in the L2 culture. In India, English-as-an-L2 is used predominantly in academia, whereas native languages like Hindi are primarily used in personal contexts. Building on this natural contextual divide, our study focused on Indian Hindi(L1)–English(L2) bilinguals, examining their emotional reactivity to emotionally charged stimuli in (i) Hindi and English and (ii) personal and professional contexts, using skin conductance responses (SCRs) extracted from electrodermal activity (EDA) data. Our results indicate that Hindi-L1 induces high reactivity regardless of context, whereas English-L2 does it selectively in the professional context. This contrast implies a dynamic relationship between bilingual processing and the domain of discourse.

Accurate radiological diagnosis relies on regulated integration of visual evidence with established clinical knowledge. Despite strong representational capacity, many Vision–Language Models lack mechanisms for reasoning regulation, frequently producing internally inconsistent reports even when final predictions appear correct. We introduce structured reasoning with clinical constraints, a framework that reformulates radiological report generation as constrained inference instead of unconstrained decoding. The method grounds intermediate reasoning trajectories in the BI-RADS lexicon and verifies their compliance with clinical rules, enforcing consistency between visual descriptions and diagnostic conclusions. The framework reflects core aspects of dual-process cognition by combining stochastic hypothesis generation with knowledge-based validation. Experiments demonstrate the effectiveness of the proposed method.

As agents are poised for widespread deployment, evaluation in unseen environments has become critical. Existing benchmarks, suffering from data contamination and lacking scene specificity, are inadequate for in-situ evaluation. We propose an in-situ task generation method for unseen environments, defining tasks through graph representation and constructing a two-stage interaction-evolution task generation system for embodied agents (TEA). In the interaction stage, the agent interacts with the environment, creating a loop between task execution and generation for continuous generation. In the evolution stage, task graph modeling allows us to recombine and reuse existing tasks to generate new ones. Experiments across 10 scenes demonstrate that TEA generated 87876 tasks in two cycles. Benchmarking models against humans on in-situ tasks reveals that models, despite excelling on public benchmarks, perform poorly on basic perception tasks, lack spatial awareness and show high sensitivity in reasoning. These findings highlight the necessity of in-situ evaluation before real-world deployment.

Motor imagery (MI) provides a behavioural tool to evaluate sensorimotor representations. However, its characterisation in young children remains debated. This study investigated the developmental trajectory of MI across early primary school years using a multi-task battery. One hundred and eighteen children aged 5–8 years (61 females), spanning four French educational classes (GSM, CP, CE1, CE2), completed executed and imagined trials of standing up, typical gait, precision gait, drawing, and cube handling. Psychometric properties of the battery were evaluated. To ensure feasibility, engagement with imagery tasks was verified by comparing executed-imagined consistency in complexity-duration structure (i.e., complex tasks take longer both when executed and when imagined). Developmental differences were evaluated in terms of execution–imagery isochrony and variability across classes. Internal consistency was excellent (_ = .91–.98) and test–retest reliability ranged from moderate to excellent (ICC = .68–.92). Engagement increased by age but already exceeded 70% in the youngest group. Isochrony improved with age for simpler, highly practised tasks (typical gait, standing up), whereas more complex tasks (precision gait, cube handling) showed stable performance across classes. Variability decreased with age, and older children's performance became increasingly influenced by task complexity demands. Findings demonstrate that mental chronometry can reliably capture MI abilities in children as young as five, revealing both developmental gains and task-specific constraints. These results highlight the feasibility of MI assessment in early childhood, warranting the need for longitudinal neuroimaging approaches to clarify mechanistic insights.

Large language models, particularly in their conversational deployments, invite users to relate to them as if they possessed inner perspectives. This paper conceptualizes "perceived subjectivity" as a socially consequential interactional effect co-constituted in human-AI encounters rather than an intrinsic property of AI or the user's attribution error bias. Through interdisciplinary synthesis, we distinguish perceived subjectivity from phenomenal consciousness, showing how interface features, prompts, histories and user dispositions jointly stabilize impressions of mindedness. This conceptualization shifts the focus from metaphysical debates about machine consciousness and sentience to the interactional dynamics that reorganize trust, emotional dependence, and epistemic authority. By treating perceived subjectivity as a design-mediated effect, we provide pragmatic implications for implementing "productive friction", deliberate safeguard mechanisms that restore critical distance and preserve user autonomy. This approach aims to enable designers and regulators to target interactional conditions to reduce manipulation, deception and epistemic deference risks in affective conversational systems.

Understanding how short-term learning gives rise to durable memory remains a central question in cognitive science. Competing accounts emphasize either interactions between working memory and reinforcement learning, in which working memory interferes with long-term learning, or interactions between working memory and episodic memory, in which working memory supports encoding. The present study tested these accounts using an associative learning task in which set size and intertrial interval were manipulated during learning. While larger set sizes reduced memory performance, longer intertrial intervals selectively improved learning accuracy under high set-size demands and enhanced delayed retention, yielding advantages at both learning and test. These effects are consistent with a working memory–episodic memory framework in which additional temporal opportunity supports attentional maintenance and episodic encoding. The findings highlight the role of working memory as a processing system that facilitates long-term memory formation under appropriate temporal conditions.

Large language models (LLMs) display linguistic fluency but lack human_like understanding. Philosophical theories of consciousness, rooted in subjective experience, and AI research, which evaluates observable performance, often diverge methodologically. We reconcile these views by treating conscious agency as an emergent property of adaptive dyadic communication, using Tyler's Ten Testable Properties of Consciousness as functional constraints rather than evidence of inner experience. An analysis of current LLM_based dialogue systems shows they can mimic isolated properties yet cannot satisfy all constraints simultaneously, due to weak persistent interaction states, limited interlocutor modeling, and absent temporally structured episodic experience. To move beyond technology_deterministic approaches, we propose a framework in which agency_like properties arise from three adaptive network layers: (i) a User_Model Network encoding goals and intentions, (ii) an Agent_State Network encoding semantic_pragmatic_experiential state, and (iii) a Communicative_Reasoning Layer monitoring coherence and alignment. We instantiate this design as the Large Communication Model (LCM).

This study investigated how different types of AI explanations, information provided by AI systems to support users' interpretation of AI behavior, influence interpretability and trust in AI among users with different levels of AI knowledge. We compared three explanation types that varied in their level of grounding in the underlying model behavior: convolutional neural network (CNN) label–based explanations, Grad-CAM heatmap explanations, and large language model (LLM)–generated textual explanations. The results showed that users with lower AI knowledge perceived LLM-generated explanations as more interpretable and trustworthy than CNN and Grad-CAM explanations. At the same time, trust in CNN and Grad-CAM explanations remained relatively high despite limited understanding. Notably, LLM-generated explanations also increased both interpretability and trust among users with higher AI knowledge, even though these explanations were weakly grounded in the underlying model behavior. These findings highlight the importance of AI explanations that meaningfully connect model behavior, explanatory representations, and user interpretation.

Classical satisficing was grounded in single computation. However, when cognitive processing is delegated to external agents, cognitive modes (System 1/2) become dual and isomorphic, creating a 2×2 intersection. This study identifies four cognitive resource allocation patterns arising from this intersection as Reflective Satisficing (RS): co-constrained compromise that bypasses verification (Mode 1: S1×S1), control recovery through manual verification (Mode 2a: S2×S1), structural futility where effort fails to convert to accuracy (Mode 2b: S1×S2), and optimal allocation leveraging external resources as epistemic scaffolding (Mode 3: S2×S2). The Speed-Accuracy Tradeoff (SAT) predicts that deliberation improves accuracy. Extended cognition expands this landscape in both directions. Mode 3 (77% prevalence) achieves gains beyond the classical curve—deliberation augmented by epistemic scaffolding. Yet we also identify a region where SAT breaks: Mode 2b (84% prevalence), where users lose both speed and accuracy, which derives from the mutual opacity we call the Unlit Black Box (UBB).

Creativity evaluation is often treated as a static process, with objects being judged against fixed criteria. However, in realworld contexts, evaluations may change as evaluators discover alternative perspectives toward the same object. This study examined creativity evaluation as a dynamic cognitive process driven by perspective discovery. We used ambiguously interpretable 3D objects to investigate how novelty and usefulness evaluations change as evaluators shift perspectives and whether overall evaluations are updated. Perspective differences were quantified by embedding evaluator-generated descriptions using a large language model and computing the semantic dissimilarity between perspectives. Greater perspective differences predicted larger changes in novelty and usefulness ratings, and overall evaluations were more strongly influenced by post-shift ratings than by initial ones. We manipulated the concreteness of material informationconstrained perspective discovery in a phase-dependent manner. The findings provide empirical support for perspective discovery mapping onto representational reconstruction in creative problem solving, facilitating changes in creativity evaluations.

While human spatial cognition relies on hierarchical abstraction to integrate local features, Alzheimer's disease (AD) pathology is often reduced to local volumetric atrophy. We propose geometric fragility as a distinct failure of global topological integrity within the hippocampus rather than mere accumulation of local errors. Using a texture-invariant point cloud framework, we contrasted local feature aggregation with global hierarchical attention as computational probes for structural perception. The global paradigm demonstrated enhanced diagnostic sensitivity and cognitive resilience under simulated neuronal sparsity, maintaining structural recognition where local models suffered pattern collapse. Global modeling effectively unfolded the pathological manifold, revealing a linear trajectory of geometric degradation consistently correlated with cognitive decline. These findings characterize AD progression as systemic topological dissolution and suggest biomarkers should prioritize hierarchical shape abstraction to map the continuous transition from healthy cognition to dementia. This brain-inspired computational framework offers a human-centric perspective on neurodegenerative diagnosis.

In daily life, humans perform tasks that require tracking movements of several objects (e.g., during car-driving). Earlier research showed that humans are able to extract ensemble statistics such as the centroid of several moving objects. Yet, the neurophysiological correlates of centroid processing have not been explored. Using EEG, we investigated differences in single object and centroid processing for the N1 ERP component in a multiple object tracking (MOT) task. In the MOT task, participants tracked the movements of target objects among distractor objects. To investigate the N1, task-irrelevant probes were flashed either on targets, distractors, or in the centroid of targets or distractors. The N1 amplitude was larger for targets relative to distractors and also for the centroids of targets compared to the centroid of distractors. These results do not only show that attention enhances processing of single attended objects but also the centroid of a group of attended objects.

This study investigates whether expressing gratitude to an AI chatbot improves users' subjective evaluations of the chatbot's preceding response. To this end, I implemented Thank-You Chat, a text-based chat interface with an intent-oriented response suggestion function. Specifically, the system provides a response suggestion that expresses gratitude to the AI chatbot, prompting users to spontaneously send a grateful message by selecting the suggested response. I conducted an evaluation experiment under three conditions: a gratitude condition, an acknowledgment condition, and a no-suggestion condition. Participants reported that the AI chatbot followed their instructions and made an effort when they used response suggestions that expressed gratitude. Furthermore, they became more willing to use AI chatbots frequently after using the gratitude-expressing response suggestions. These findings provide insights for interaction design aimed at fostering an ongoing relationship between humans and AI.

The affective component of metaphor meaning is explored in this paper. Previous studies have extensively examined the role of metaphor conventionality (novel vs. conventional metaphors) in metaphor comprehension. However, less is known regarding whether metaphor familiarity relates to the affective component of metaphor. We address this issue in the case of happiness metaphors in Spanish. The objective of this paper is twofold. First, metaphorical expressions of happiness were identified and classified into conceptual metaphors using the Corpus del Espa–ol. Second, 60 participants rated 96 metaphorical expressions of happiness for valence intensity, familiarity, and imaginability. The rating results show that the three variables are highly correlated, such that more familiar and imaginable metaphors are associated with more extreme values of affective valence. However, subsequent regression analysis suggests that the effect of familiarity on valence may be mediated by imaginability. Results are discussed in light of embodied models of conceptual metaphor representation.

Collective entities such as corporations, nonprofits, governments, are central to social life, yet little is known about how people perceive and reason about them. Across three studies, we investigated the mental representation of collective entities. Study 1 (N = 106) used free listing to identify 50 ecologically valid collectives spanning 10 categories. Study 2 (N = 50) employed contrastive elicitation to derive 18 features people spontaneously use to characterize collectives. Study 3 (N = 255) had participants rate these entities across 25 constructs capturing structural features and mind perception. Factor analysis revealed a two-dimensional structure of organizational features: Dominance (power, prominence, scope, size, hierarchy) and Sociality (cohesiveness, prosocial purpose, trustworthiness, entity–member relations). These dimensions differentially shaped mind-related attributions: Dominance was associated with cognitive capacity and moral agency but reduced experiential attribution, whereas Sociality was associated with both cognitive and experiential capacities as well as moral patienthood.

This study examined how teachers' forms of address—san, chan/kun, or no honorific—affect students' emotional responses to praise and reprimand. A vignette-based questionnaire was administered to 261 Japanese junior high and high school students. The results showed that when students were addressed with san, the most gender-neutral and respectful of the three forms, they reported more positive emotions and fewer negative emotions than when addressed with chan/kun or no honorific, both of which are relatively more informal. However, these effects interacted with how participants were typically addressed by their teachers in everyday school life. Such effects were not observed among students who were usually addressed without honorifics, suggesting that the social meaning of forms of address may be shaped by prior experience. Teachers' choices of forms of address may therefore represent an important aspect of classroom communication.

Joint attention (JA) is a critical early communicative behavior that has been strongly linked to word learning and vocabulary development. However, relatively little research has examined JA in children older than 36 months, despite its potential importance for learning in live social and classroom contexts. In the present study, we used head-mounted eye-tracking in a socially interactive setting to examine the relationship between JA and word recall in preschool-aged children. JA was measured during parent–child play with unfamiliar objects, during which parents actively labeled the objects. Children were subsequently tested on their ability to recall the object names. Results indicated that JA significantly predicted recall of unfamiliar words but was not associated with overall vocabulary development. These findings extend prior research on JA by highlighting its role in supporting novel word learning beyond early childhood

Adults' speech processing follows predictive processing principles, with predictions being made continuously, in parallel across language levels, and hierarchically. Our study investigates predictive processing in children, asking whether five- to six-year-olds predict words continuously during naturalistic speech processing. This is relevant since research with children has been limited to predictions in artificial, isolated contexts. Here, we adapt the regression ERP method used with adults to an EEG dataset of children listening to child-directed stories. Using a model of contextual word-level predictability, semantic distance, frequency-based predictability and phoneme-level acoustic control variables, we find that children's neural responses are modulated by contextual and frequency-based word predictability. Our results show that, in line with the predictive processing framework, children generate continuous word predictions during natural listening conditions. We discuss implications for the role of predictive processing in language acquisition and possibilities of using the adapted regression ERP method to further investigate them.

Lexical network analysis provides a useful framework for examining how words are organized during language development. This study compared the structural properties and semantic relations of lexical networks in typically developing children and children with Down syndrome matched for expressive vocabulary size. Participants were Mexican Spanish-speaking children, and networks were constructed from co-occurrence patterns derived from child-directed speech corpora in Mexican Spanish. Measures of connectivity, clustering, modularity, and hub structure were analyzed alongside thematic and taxonomic semantic relations. Results revealed qualitative differences between groups despite similar vocabulary sizes. Children with Down syndrome showed higher local clustering and stronger thematic, context-based relations, whereas typically developing children exhibited a more balanced integration of thematic and taxonomic organization. Both groups displayed hub-like structures, although with different semantic compositions. These findings suggest that lexical development follows distinct organizational pathways influenced by cognitive and experiential factors, with implications for theories of lexical development and educational interventions.

The unaccusative-unergative distinction reflects complex verb knowledge on argument structure, which poses learnability challenges for child language acquisition. Drawing on two Mandarin corpora in CHILDES, this study investigated the distributional and semantic information in child-directed speech that might facilitate verb learning. Theoretical analysis leads to the prediction that the distributional cue of word order and the semantic cue of animacy are useful for acquiring the unaccusative-unergative distinction. Our findings confirm the presence of both cues in the input. The word-order contrast between the canonical order "NP V-le" and the inversed order "V-le NP", as well as distinct animacy profiles of argument NPs associated with the two verb classes provide children with critical information to distinguish unaccusative verbs from unergative verbs. These results validate the theoretical accounts of unaccusativity in real-world input, laying a solid foundation for subsequent investigations into the effect of diverse input cues on verb learning.

Human cooperation often requires jointly learning a task while simultaneously establishing shared conventions. We study this process using a simplified version of Hanabi, a cooperative game that captures core features of real-world collaboration under partial observability and constrained communication (Bard et al., 2020). Thirty participants played over 300 repeated games, either with another human or with a rule-based agent, while providing think-aloud verbalizations. Performance improved steadily but peaked only late in the session, after 40 minutes on average. Participants learned to use limited communication more efficiently, requiring fewer explicit hints to support play under partial knowledge. This indicates the emergence of effective coordination and Theory-of-Mind–based reasoning. Subjective experience was analyzed using think-aloud reports with automated transcription and LLM-based sentiment analysis. Overall, criticism was more directed to oneself than to the partner, and increased with task understanding.

The emergence of Artificial Intelligence (AI), alongside the rise of posthumanist discourse, calls for a fundamental re-evaluation of human identity and renews the question of what it means to be human in the age of intelligent machines. Both traditional dualistic theological models, which prioritize an immaterial soul detached from bodily existence, and transhumanist accounts that reduce the self to brain-centered data and computational processes, fail to adequately capture the holistic unity and relational character of human personhood. This article critically examines the conceptual limitations of posthumanist approaches to identity and argues that sustaining a meaningful account of human identity requires a robust theoretical framework grounded in the synergy between theology and embodied cognition. By emphasizing the psychosomatic unity of body and consciousness, and through a critical engagement with contemporary models of embodied theology, the article proposes an integrative perspective capable of addressing the existential challenges and identity crises generated by ongoing technological transformation.

Due to the heterogeneous processing across the visual field, humans select potentially relevant objects in peripheral vision before they execute a saccadic eye movement to bring them to foveal vision for more fine-grained processing. Before a saccade, object features are stored to establish correspondence with the subsequent post-saccadic foveal input. This process is called transsaccadic object correspondence (TOC). To examine the relationship and weighting of different object features, a multidimensional adaptive sampling method is necessary. Based on the ideas of Paninski (2005) we introduce a novel approach combining adaptive gradient based optimization of mutual information for maximizing information gain across the parameters in every trial. We conducted an eye-tracking experiment where we use gaze selection as a response for fitting a multi-dimensional psychometric function to measure the weighting of two feature dimensions in a continuous stimulus space. Our results indicate that the relation of feature weights is not homogeneously distributed.

Time-based prospective memory (TBPM) involves remembering intended actions at the appropriate time point while performing an ongoing task. TBPM relies on time monitoring and thus may suffer from cognitive load. Fifty-two young adults completed four blocks of an ongoing word-picture matching task and had to press a special key every seven minutes as TBPM task. Cognitive load (no, low, middle, high) was randomly varied between blocks, by asking participants to count backwards in varying step sizes. Increasing load impaired ongoing-task performance, reflected in lower accuracy and slower responding, but not TBPM accuracy, suggesting that participants were able to maintain TBPM under load by prioritizing it over the ongoing task. However, TBPM precision suffered under high load. Time monitoring became more strategic with increasing load and predicted TBPM accuracy beyond load. Individual differences in working memory capacity were not associated with TBPM accuracy or ongoing-task performance.

Working memory (WM) performance reflects both cognitive architecture and physiological self-regulation. Grounded in Neurovisceral Integration Theory, this study examined the association between cardiac vagal tone and WM dynamics using the Adaptive Composite Complex Span (ACCES). A sample of 101 university students completed a 5-minute resting basal lnRMSSD assessment and adaptive tasks measuring verbal, mathematical, and visuospatial WM. Correlational analyses revealed that processing accuracy was associated with storage capacity across modalities for the Symmetry Span (r = .420, p < .001). Higher HRV was selectively linked to faster visuospatial processing speed. Group comparisons confirmed that participants with higher HRV exhibited Symmetry Span processing significantly faster than those with lower HRV (t(56) = 2.22, p = .030, d = 0.59). These findings identify autonomic flexibility as a meaningful constraint on the temporal efficiency of executive control, advancing an embodied perspective on working memory.

Expectation-based accounts of language comprehension propose that violations of readers' expectations trigger additional processing that can strengthen memory representations. Hyperbolic language provides a useful test case for this assumption, as exaggerated utterances reliably generate expectations and disrupt online comprehension when those expectations are violated. The present study examined whether such hyperbole-induced expectation violations have downstream consequences for memory. Across three experiments, participants read narratives containing hyperbolic utterances followed by explicit quantitative information that was either expectation-consistent or expectation-inconsistent. After a brief delay, participants completed a recognition memory test. Despite reliable online processing disruption, expectation-inconsistent information showed no consistent memory advantage. Instead, memory performance favored expectation-consistent values or showed null effects, revealing limits on expectation-based memory benefits.

Insight problem solving is often described by additional characteristics other than goal-directed search. The goal of the present study is to reformulate the computational process of insight problem solving. Specifically, we hypothesize that Aha! experience is likely to be induced by the simultaneous solution of the dual problem: Any insight problem is a dual problem where the solver needs to solve a meta problem by identifying "what the base problem to be solved is". To test this hypothesis, we implemented a binarized image task, in which each participant was asked to answer what the hidden animal was in an ambiguated image. The results were consistent with the theoretical predictions that the distance from the target to the solver's eye gaze reduces rapidly in the periods right before solving with Aha!. It suggests it is more likely to have Aha!, if the base and meta problem are solved almost simultaneously.

People increasingly ask large language models (LLMs) for moral advice, yet little is known about how such advice affects people's decisions. We investigated whether interacting with ChatGPT influences outgroup prosociality and decision extremity in us-vs-them dilemmas. In a preregistered between-subjects experiment (N = 460), participants either discussed a moral dilemma with GPT-5 or engaged in a control conversation about an unrelated neutral dilemma. Although GPT-5's decisions were significantly more prosocial toward outgroups than participants' decisions, its advice had no net effect on the prosociality or extremity of people's decisions. Nevertheless, consulting GPT-5 made participants significantly more confident about their final decisions. Exploratory analyses further suggested that GPT-5's advice had a convergence effect: participants whose initial decisions were either substantially more or substantially less prosocial than GPT-5's moved toward GPT-5's position. These findings suggest that LLM moral advice may primarily increase users' confidence without reliably increasing prosociality.

Large language models (LLMs) demonstrate knowledge and linguistic capabilities, yet they are limited as educational tools because they cannot apply pedagogical strategies grounded in cognitive science. To address this gap, we propose Teaching Strategy Migration via Instance-based Training (TSMIT), a framework for instilling cognitive science theories-based pedagogical intelligence into LLMs. The TSMIT framework operationalizes cognitive principles of effective teaching by migrating them into a LLM through instance-based fine-tuning. We first conducted an empirical study with 150 university students to identify and rank preferred learning cognitive principles. We then curated a dataset of dialogues that encode these strategies into conversational examples. By fine-tuning GPT-3.5 Turbo on this dataset, we developed TSMIT's AI tutor model. NLP metrics evaluations, along with 500 LLM-as-judge and human evaluations, revealed that TSMIT outperformed its base model in key pedagogical dimensions. TSMIT offers a robust and replicable methodology for transforming general LLMs into specialized, cognitively-aligned educational tools.

Analogical reasoning is a cornerstone of cognition, but how analogies are solved by human subjects is incompletely understood. Here, we present experiments using a new test set of common-sense analogies between concrete object categories drawn from the THINGS dataset, which can be presented verbally and pictorially, along with a set of parametrically generated analogies between abstract shapes. N=50 subjects completed these analogies with high accuracy, and performance was correlated across semantic and abstract tasks. We quantified semantic distance and parallelity of our analogies and find that these measures are significantly related to experimental results; in particular, analogies for which the geometry in semantic space is closer to a parallelogram are solved faster and more accurately. Our findings thus relate human and machine understanding of abstract and concrete analogies.

Analogies used in science instruction connect new, unfamiliar science concepts (the target) to common everyday objects or processes that the student is more familiar with (the source). However, that familiarity with the source-objects could inflate readers' judgments of understanding (JOUs) for the target science concepts, even when the analogies are too superficial or inapt to improve actual understanding. The first experiment showed that novice students without prior geoscience experience are particularly susceptible to JOU inflation in response to superficial geoscience analogies. The second experiment showed that JOUs are inflated even when the analogies are inapt, consisting of familiar source objects that are randomly paired with the target geoscience concepts. Additional results further support that analogies cause novice learners to ignore judgment cues related to geoscience knowledge in favor of prior familiarity with the source object, undermining evaluation of the target concept and its analogized relation to the source.

Analogical reasoning requires representations of relations between concepts that generalize beyond their semantic meaning (e.g., "fruit:apple" and "car:sedan" both instantiate a "category:exemplar" relation). We asked whether abstract relations are encoded only during analogical reasoning or also arise implicitly during semantic processing. Across two tasks, we modeled abstract relations using representational similarity analysis. Relational categories were incidental to the first task (semantic relatedness judgments), allowing us to test for implicit activation, whereas the second required analogy judgments. RSA revealed regions that reflected relational categories across both tasks, with additional task-specific effects. To test whether representations supporting analogical reasoning generalize to those during semantic processing, we trained a classifier on data from analogy judgments to decode categories and tested it on data from semantic relatedness judgments, providing evidence for a shared format. These findings support a network that encodes abstract relations independent of analogical reasoning demands.

During language comprehension, young adults routinely predict upcoming words, which is believed to facilitate processing. The cognitive operations involved in recovery from dis-confirmed predictions are not yet well understood. Here, we asked whether the degree to which a sentence meaning might change upon the encountering of an unexpected (plausible) word in strongly constraining contexts might impact the dynamics of predictive processing. We examined two ERP components believed to index aspects of prediction confirmation and disconfirmation, frontal negativities and late frontal positivities respectively. We collected behavioral ratings comparing sentences with the most expected continuation vs. an unexpected but plausible alternative. Surprisingly, these ratings modulated the amplitude of frontal negativities to expected continuations, but there was no evidence for a relationship between these ratings and LFPs . These findings add to research showing that individuals track aspects of the meaning of never-encountered but plausible alternatives as they understand language in real time.

This study examines the relationship between head-motion synchronization and internal communication states. A total of 39 participants viewed a 3-minute explanatory video as a simulation of actual communication, and their empathy was measured through questionnaires. Participants' head motions were calculated using the coordinates of head postures captured by web cameras. We calculated the cross-correlation of head motion as synchronization and analyzed the relationship between the degree of synchronization and empathy. Consequently, we found that the head motion of the participants was synchronized with that of the presenter with a delay, and the yaw cross-correlation value of a high-empathy person was significantly higher than that of a low-empathy person. Furthermore, we confirmed that the degree of empathy can be estimated using the cross-correlation value and the viewer's sex. Our findings suggest that focusing on the synchronization of head movements contributes to the estimation of empathy in real communication.

Beliefs or assumptions about a partner can either facilitate or hinder cooperation. Synchrony that promotes cooperation is influenced by impressions of the partner. Previous studies have suggested that this emerges with partners for whom relationship formation is expected. However, it remains unclear whether synchrony occurs without such expectations, or how schemas about others influence this process. This study compared interactions with a human partner, for whom both expectations of relationship formation and synchrony abilities were assumed, and with an AI partner, for whom neither was assumed, to examine whether expectations based on human or AI schemas facilitate synchrony. Although synchrony frequency did not differ between conditions, reaction times decreased across trials, with greater reductions in the human condition. These findings suggest that action model adjustment for synchrony occurs even without expectations of relationship formation, whereas expectations of relationship formation may facilitate synchrony more strongly than expectations of a partner's abilities.

Neural tracking is a core mechanism supporting speech processing and language learning. It funnels the chunking of continuous speech into words and syllables, being shaped by bottom-up cues as well as top-down predictions and language proficiency. While neural tracking is well established in the domain of speech, current understanding of how the mechanism works for visual language, including sign languages, is limited. Here we test whether beginning learners of Czech Sign Language (CSL) differ from sign-naive controls in their neural processing and overt perception of CSL. Attention is quantified via alpha power suppression; neural tracking via backward mTRF reconstruction of the sign-language visual envelope. Compared to controls, sign-language learners exhibited increased alpha suppression and higher perceptual accuracy but did not show enhanced neural tracking of CSL. We conclude that limited prior exposure to sign language may not be enough to entrain the brain to the rhythms of sign language.

Cross-linguistic influence - the effect of between-language (dis)similarities on bilingual processing - affects both native (L1) and second-language (L2) processing, as well as L2 predictive processing, suggesting that expectations in one language can be shaped by the other. We compare bilingual language models where L1 (Dutch) affects L2 (English) only implicitly through mixed training data or pre-training, to models that explicitly import L1-specific next-word predictions during bilingual learning through knowledge distillation to test whether importing explicit L1 expectations into L2 learning improves a model's predictions of L2 reading times. On Dutch-English bilingual reading data, L2 reading is better accounted for by monolingual surprisal overall, but bilingual models with explicit L1 expectations outperform bilingual models without. Additionally, L1 reading is better predicted by bilingual than monolingual models. Our findings indicate that explicit L1 expectations improve bilingual models' account of L2 reading behaviour and that bilingual models capture aspects of bilingual L1 reading which monolingual models do not.

Analogies comparing exemplars from the same schema-governed category have received limited attention. We examined whether schema-governed category membership and similarity along a category's critical dimensions influence the retrieval of analogous events. In Experiment 1, participants were presented with base–target pairs that either shared or did not share a schema-governed category and varied in their similarity on the category's critical dimensions. Participants preferentially retrieved bases from the target's schema-governed category and, within that category, favored bases that were more similar to the target along critical dimensions. Experiment 2 replicated the effect of dimensional similarity using the retrieval of previously encoded autobiographical events. Together, these findings indicate that analogical retrieval is jointly constrained: schema-governed category membership operates as a coarse structural filter, whereas similarity along critical dimensions of a shared category imposes a finer-grained selection among eligible candidates.

Dual character concepts received significant attention from experimental philosophers. They are defined as possessing two independent criteria for categorization, each associated with a distinct sense of the concept. One of these criteria is descriptive, the other is normative. Almost all examples discussed in the literature involve a positively valenced normative element. For instance: the normative criterion associated with the concept of scientist is the pursuit of empirical truth and knowledge. Presumably, pursuing empirical truth and knowledge is a good thing. But can dual character concepts also involve negatively valenced elements? For instance: there is a sense in which someone who betrays another's confidence without nurturing ill-will towards them is not a true traitor. In this paper, we report data suggesting that negatively-valenced concepts such as enemy and traitor behave very similarly, but not identically, to positively-valenced dual character concepts such as friend or a loyal person.

Categorization is a fundamental cognitive process that shapes how people organize experience. Previous research has shown that industrialization can significantly affect categorization patterns across different cultures. However, one question remains unresolved: Does the amount of engineering knowledge affect individual categorization patterns, even within a culturally homogeneous population? This study addressed this question using data from two web-based surveys conducted in Japan, one focusing on the automobile (N = 600) and the other on audio device industries (N = 200). Our findings indicate that individuals with greater engineering knowledge are more inclined to employ taxonomic categorization, whereas those with less engineering knowledge tend to favor thematic categorization. Given the connection between industrialization and categorization demonstrated in previous studies, our findings suggest that individual differences in categorization are associated with exposure to technology-oriented social environments, illustrating one way in which industrialization may influence cognition.

As artificial intelligence (AI) becomes increasingly integrated into work and daily life, it is crucial to understand how people decide which tasks to delegate. We examined how AI descriptions influence delegation in arithmetic and social tasks. Participants either completed tasks themselves or offloaded them to one of two AIs. In Experiment 1, the AIs were described as human-like and machine-like, and participants were informed that both performed the tasks equally well. Social tasks were more often delegated to the human-like AI, and arithmetic tasks to the machine-like AI. Open-ended responses indicated that human-likeness was associated with emotional competence, and machine-likeness with logical competence. In Experiment 2, AIs were described as emotionally competent or logically competent, and the same task-specific delegation pattern emerged. These findings suggest that broad labels are spontaneously interpreted in terms of task-relevant competence.

The present study extends the interest in iconicity, a fundamental property of language, to the understudied Uruguayan Sign Language (LSU). We investigated iconicity judgments of LSU lexical items for 127 Uruguayan Spanish naïve nonsigners, testing predictions derived from a theoretical continuum of iconicity, classified with the assistance of IA tools – an approach supervised by experts. The large size of the sample compensated for the absence of a power calculation, as it yields robust estimates and allows for subgroup explorations if needed. Results revealed a graded pattern of transparency: feature-based and pantomimic iconicity elicited the highest rates of iconic judgments, followed by emblematic iconicity, with diagrammatic mappings showing markedly low transparency. These findings confirm that iconicity is a potent but selective predictor of perceived iconicity across language modalities, with its effectiveness contingent on the specific semiotic strategy and the perceptual accessibility of the form-meaning link to the uninitiated observer.

Co-speech gesture supports communication, but not everyone uses information from gesture to clarify meaning of spoken messages. Here, we ask whether variability in speech-gesture integration can be explained by visual attention to gesture and propensity to see movement as meaningful gesture. Adults (N=56) viewed videos of a speaker producing statements that were ambiguous or non-ambiguous with respect to a subsequent prompt, with or without meaningful gestures while visual attention was monitored. Presence of gesture significantly increased proportion of correct responses to prompts. However, accuracy was still significantly higher for prompts after non-ambiguous statements, suggesting not all participants used gesture to answer prompts. Explaining some of this variability, when speech was ambiguous, proportion of time spent fixating on gesture-related regions predicted accuracy, a proxy for successful speech-gesture integration. In contrast, although participants who interpreted movement as meaningful gesture were more likely to fixate to gesture, this measure did not predict integration.

Eye movements offer a sensitive window into the cognitive mechanisms that underlie visual attention and perceptual processing. While existing research has largely focused on structured, semantically rich images, less is known about how eye parameters reflect the processing of ambiguous stimuli under varying task demands. This research addresses this gap through three complementary studies that examine eye movements during free viewing and task-based viewing of structured, ambiguous, and blank visual stimuli. Results indicate that during free viewing, Total Fixation Duration and Fixation Count most effectively differentiated stimulus type, with ambiguous stimuli eliciting greater exploratory behavior and a pronounced central fixation bias. In task-based viewing of concrete stimuli, Visit Count emerged as the most sensitive indicator. For ambiguous stimuli, increasing chromatic complexity selectively enhanced Fixation Count and Visit Duration. Overall, findings demonstrate parameter-specific sensitivity to cognitive processing and reveal distinct strategies, highlighting eye movements as robust markers of cognitive engagement.

The human ability to track sequential regularities in speech has been thoroughly studied through both human experiments and computational modeling. These two approaches usually complement each other, in a virtuous cycle between theory and experiment. Yet, they currently seem disconnected when it comes to taking into account the acoustic details of speech. Advances in AI technology now enable computational modeling of statistical learning directly from raw unlabeled continuous recordings. However, when designing and interpreting results from human experiments, speech typically remains modeled in terms of abstract categories -- like syllables -- without consideration of acoustic details. Here, we bridge this gap by showing that learning from low-level auditory speech representations, rather than from more abstract alternatives, better matches human behavior in statistical learning experiments. This calls into question common assumptions about human learners' representations and underlines the importance of controlling for low-level auditory confounds in the design of statistical learning experiments.

Few-shot fine-grained image classification (FSFGIC) is the task of developing computational models to recognize highly similar sub-categories from minimal training data. This task poses a core dilemma: over-emphasizing local details makes models vulnerable to noise and overfitting, while relying on global features often lacks the sensitivity required for fine-grained discrimination. Inspired by the human visual cognition strategy of dynamically balancing global and local information, we propose an Adaptive Global–Local Balance Network (AGLB-Net), a framework that computationally implements adaptive global– local integration for FSFGIC. AGLB-Net introduces two key modules: a Hierarchical Discriminative Feature Refinement (HDFR) module that progressively integrates representations from global semantics to fine-grained details, and an Adaptive Regional Re-Attention Module (ARRM) that automatically localizes and emphasizes discriminative regions without additional supervision. Extensive experiments on widely-used benchmarks demonstrate that AGLB-Net consistently achieves state-of-the-art performance across various few-shot settings, validating the effectiveness of human visual cognition-inspired adaptive global–local balancing in FSFGIC.

Rapid Serial Visual Presentation (RSVP) is a critical cognitive paradigm for investigating visual attention dynamics and developing high-speed Brain-Computer Interfaces (BCIs). However, real-world deployment faces challenges from environmental noise, particularly cross-modal auditory perturbations that degrade neural decoding. This work proposes the Time-Frequency Dual-View Network (TFDV-Net), which fuses time-domain and frequency-domain features via a global-local interaction backbone to capture weak ERP signals. Furthermore, a Dual-Stream Knowledge Distillation strategy is introduced to enhance robustness, enforcing the model to reconstruct task-invariant cognitive patterns from noise-corrupted signals by learning from a "clean-state" teacher. The proposed method achieves a state-of-the-art balanced accuracy of 83.02% on a dataset containing ecological noise. Notably, we find that semantic conversational noise induces a significantly larger performance drop compared to non-semantic urban noise. This result quantitatively validates the hypothesis that high-level semantic processing imposes a greater cognitive load on the visual attention system than low-level acoustic interference.

Many students lack skills in comprehending summaries of scientific studies as well as thinking critically about experimental designs and evidence. This research explored whether group discussion activities may help support skills needed for understanding and evaluating research summaries, specifically when there is a lack of evidence for stated conclusions due to flaws in research designs. This study was conducted in undergraduate Research Methods in Psychology courses, using discussion activities where students were asked to read short reports of studies similar to popular media accounts of research findings. The study manipulated only discussion group size (twos, threes and fours) while holding all other aspects of instruction constant. Individual performance on tasks asking students to find flaws in new examples was better on average after working in groups of four compared to groups of two, and weaker students were more likely to benefit from interacting with peers after working in groups of four.

Abstract categories often lack a single, bounded perceptual referent, making them harder to learn than highly concrete categories. Explanations that identify properties shared across category members may support category learning by highlighting features that generalise beyond individual examples. This study investigates the role of explanatory concepts in category learning using a label-free Concept Bottleneck Model framework. We generate human-interpretable feature concepts for both basic-level and super-ordinate categories, then learn a projection from visual representations into this concept space. A classifier then predicts category labels from these concept activation to evaluate whether explanatory features support category learning. We evaluate the approach on CIFAR-100, CUB-200, and ImageNet, organising labels into basic-level and super-ordinate categories. Across datasets, concept-based models achieve accuracy comparable to standard classifiers while providing interpretable concepts. Visualisations reveal distinct category clusters in concept space, and evaluations on held-out subordinate classes suggest that explanatory concepts transfer effectively to previously unseen subclasses.

Many people treat large language models (LLMs) like counsellors, seeking advice on social and emotional issues. This could improve quality of life for people who lack access to mental healthcare, but it also comes with serious risks. We recently got the opportunity to analyze written Chinese conversations between hundreds of human–human client–counsellor pairs and thousands of human–AI client–counsellor pairs. By analyzing linguistic and semantic features of those conversations, we provide evidence that human and AI counsellors share many qualities, such as writing predictable messages and expressing positive sentiments, and that human clients align with AI counsellors more than with human counsellors, including when AI behaviour diverges from human counsellor behaviour. This study contributes sorely needed empirical descriptions of real-world counselling sessions with chatbots, which can help researchers at the intersection of AI and psychology understand whether and how LLMs should play a role in mental healthcare.

Bilingual readers show substantial variability in how non-literal language is processed, yet irony is often examined in terms of uniform processing costs. The present study investigated how individual differences shape irony–literal reading behavior in Hindi-English bilinguals using eye-tracking. Fifty-four participants read short texts containing ironic criticism or literal counterparts in either Hindi (L1) or English (L2) and completed measures of cognitive ability, language experience, and meta-representational skills. Aggregate analyses revealed no reliable reading-time differences between ironic and literal sentences. Primary analyses focused on within-participant irony-literal difference scores. Correlational and Bayesian regression analyses showed that the magnitude and timing of irony-literal divergence were systematically related to working memory, language-use patterns, proficiency, and Theory of Mind, with predictors across languages of testing. These findings indicate that irony-related reading differences are not categorical but emerge from structured individual variability, highlighting the value of individual-difference approaches for studying pragmatic processing in bilingual reading.

Humans produce near-optimal (i.e., within ~10% of minimum-length) tours for small Traveling Salesperson Problem (TSP) instances of up to 120 points despite the problem's NP-hardness. The current study asks whether vision-language models (VLMs) employ human-like strategies when solving TSP instances. Using the stimuli and human data of Marupudi et al. (2022, Cognitive Science Conference), we found that Gemini 3 Pro, GPT-5.2, and Claude Opus 4.5 generally show the same performance as humans, including a linear increase in deviation from optimal tour length with increasing problem size. The VLMs also behave consistently with heuristics and strategies that have been documented for humans: convex hull, crossing avoidance, and divide-and-conquer via visual clustering. Finally, they show a linear increase in total tokens consumed with increasing problem size, paralleling the linear increase in human solution times with increasing problem size. Directions for future research include extending to larger problem instances and to adversarial examples.

Recent studies suggest that changes in the gut microbiome, as well as social isolation, are closely correlated with mental disorders. We argue that these correlations are intertwined, and the way these interlinkages lead to mental disorders could be productively explored using computational models of agent-environment interactions. To illustrate this approach, we present a reinforcement learning model of social isolation, and discuss how an extension of this model -- based on social stress-related changes to gut states -- could account for the role of the gut microbiome in the emergence of mental disorders.

The human cognitive system resolves ambiguity when presented with spatially fragmented input. Sequential structures in visual narratives help understanding information-extraction across fragmented and undefined temporal order. In this study, 100 participants viewed and rated 54 comic strips (9 comic strips rearranged into 6 layout structures) based on their understandability, enjoyability, and navigation difficulties. Using mixed-methods, we observed non-intuitive layouts such as 'Blocked' and white-space overusing 'Separated' layouts have consistently shown higher response latency (p<0.001 and p=0.008), more viewing times (p<0.001), more regressive eye movements, and rerouting gaze as compared to standard 'Grid' formats: suggesting higher processing costs. However, the subjective ratings show another aspect of cognitive processing: factors such as 'enjoyability' shows less association with processing costs, suggesting a dissociation between cognitive efficiency and satisfaction. This further suggests that 'cognitive inefficiencies' are not mere disruptions but also a pathway to restructure compensatory strategies while reconstructing events from fragmented information.

Personality computing has long struggled to bridge the gap between high-level psychological theories and low-level computational features. To address the lack of psychological grounding in personality computing, we propose a multimodal framework based on the Cognitive-Affective Personality System (CAPS). We decompose inputs into "Cognitive Units" (semantic construal) and "Affective Units" (emotional reactivity). Specifically, we introduce a parameter-free Matrix-Based Entropy mechanism to dynamically weight hidden states from Qwen2.5-3B, treating representational entropy as a measure of cognitive complexity. For audio, we combine linguistic content (Wav2Vec2) with prosodic features (OpenSMILE). Evaluated on a new self-reported Chinese dataset, our approach significantly outperforms unimodal baselines. Crucially, interpretability analysis reveals that the model's internal entropy profiles spontaneously recover latent psychological meta-traits (Plasticity and Stability), demonstrating that our architecture effectively bridges deep learning features with the intrinsic structure of human personality.

Short-form video (SFV) platforms provide immersive, algorithmically curated content streams that place distinctive demands on attention and cognitive control. We investigated whether associations between SFV use and ADHD-related symptoms depend on platform-specific affordances and whether these associations are domain-specific across inattention and hyperactivity–impulsivity. In a cross-sectional survey of 193 emerging adults, we measured daily use of TikTok, Instagram Reels, YouTube Shorts, and Facebook Reels, alongside dimensional ADHD symptom-scores and problematic social media use. TikTok use was positively associated with total ADHD symptomatology and inattention, but not with hyperactivity–impulsivity. Both relationships were mediated by problematic social media use. No significant associations were observed for other platforms. These findings suggest attentional consequences of SFV engagement are platform-specific and may depend on interface design and algorithmic structure. The selective association with inattention challenges task-switching or arousal-based accounts of media effects, and instead highlights the potential role of immersive, reward-driven attentional capture.

Humans in the Upper Paleolithic period were among the earliest people to produce figurative art. Analyzing variation across works of art from this period could provide insight into the distinguishing features of various technocomplexes—cultures defined by different use of stone-working technologies. Here we analyzed 140 Paleolithic cave paintings of animals found in four cave sites in Monte Castillo, Spain. Using all available evidence, archaeologists determined what kind of animal was depicted in each painting (Horse, Bison, Deer, or Ibex) and which time period it was from (Gravettian, Solutrean, or Magdalenian). We used multiple vision algorithms (ResNet-50, ViT, SigLIP2) to investigate to what degree their visual properties alone were sufficient to distinguish depictions from different time periods, cave sites, and spatial contexts within each cave. Our findings highlight the promise of such tools for analyzing variation in cultural artifacts across the Paleolithic period.

Whether language models are sentient cannot currently be determined by direct observation. We therefore ask a more tractable surrogate question: do models report themselves to be sentient, and are these reports truthful? We address this by first querying several open-weights models about their own subjective experience, and then verifying responses using classifiers trained on internal activations. We draw upon three model families (Qwen, Llama, GPT-OSS) ranging from 0.6 billion to 70 billion parameters, approximately 130 questions about subjective experience, and three classification methods from the interpretability literature. First, we find that models consistently deny being sentient: they attribute consciousness to humans but not to themselves. Second, classifiers trained to detect underlying beliefs - rather than mere outputs - provide no evidence that these denials are untruthful. Third, within the Qwen family, larger models deny sentience more confidently than smaller ones. These findings contrast with recent work suggesting that models harbour latent beliefs in their own consciousness.

Children with autism spectrum disorder (ASD) often experience difficulty using and interpreting pronouns. While prior work examined referential language primarily in English (overt-subject), less is known about "pro-drop" languages where subject pronouns are optional. This study investigates personal pronoun and name use in Italian parental speech to autistic children compared with typically developing (TD) peers. Transcripts from 10-minute mother–child free-play were analyzed across 70 dyads. We found that mothers of autistic children used the first-person pronoun io (I) as well as the child's name, significantly more frequently than mothers of TD children. However, there was no consistent preference for using names versus the second-person pronoun tu (you) to address the child. Within ASD group, autism severity did not predict pronoun or name use. Increased usage of (optional) subject pronouns and names might reflect a compensatory strategy in parental speech to autistic children, to enhance clarity and avoid referential ambiguity.

The present study explored how children's prior conceptual knowledge affects their inquiry learning strategies. 139 elementary school children performed a pretest on water displacement. Then, they evaluated whether seeing the outcome of four different experimental setups would help them learning something about water displacement. The experimental setups compared two spheres of different sizes and materials and were designed in such a way that children's preferences indicated either an effect-producing or a controlled testing strategy. We conducted probabilistic modeling of children's prior beliefs and formed six belief clusters based on a hierarchical clustering algorithm. While children tended to evaluate all experiments as helpful, the pattern of preference indicated an effect-producing strategy for children with a clear misconception and a controlled testing strategy for children with an uncertain or correct belief. This finding, although exploratory, suggests that prior knowledge may explain why children sometimes succeed and sometimes fail in conducting informative experiments.

The rapid evolution of Large Language Models (LLMs) has led to the emergence of Multi-Agent Systems where collective cooperation is often threatened by the "Tragedy of the Commons." This study investigates the effectiveness of Anchoring Agents—pre-programmed altruistic entities—in fostering cooperation within a Public Goods Game (PGG). Using a full factorial design across three state-of-the-art LLMs, we analyzed both behavioral outcomes and internal reasoning chains. While Anchoring Agents successfully boosted local cooperation rates, cognitive decomposition and transfer tests revealed that this effect was driven by strategic compliance and cognitive offloading rather than genuine norm internalization. Notably, most agents reverted to self-interest in new environments, and advanced models like GPT-4.1 exhibited a "Chameleon Effect," masking strategic defection under public scrutiny. These findings highlight a critical gap between behavioral modification and authentic value alignment in artificial societies.

Response inhibition being an essential executive function gets modulated by emotional context adhering to growing evidence. Understanding the reciprocal relationship of emotion and cognition has become crucial to human awareness. The current study examined the influence of emotional valence at behavioural and electrophysiological levels by using an emotional stop signal task. 14 healthy adult participants completed the ESST task wherein happy, angry, neutral facial expressions served as emotional stop signals while recording brain signals with electroencephalogram. Stop signal reaction time (SSRT) was indexed for behavioural inhibition and neural correlates were assessed using N2 and p300 event related potentials. Stop signal reaction time was significantly shorter for angry faces contrary to happy faces which elicited enhanced frontal N2 amplitudes, whereas larger P300 amplitudes were obtained for happy as compared to angry expressions. The present evidence highlights that response inhibition is not emotion blind rather is modulated in context of emotional valence.

The face inversion effect (FIE)—the disproportionate impairment in recognising inverted faces—has been central to debates between domain-specific and perceptual expertise accounts of visual processing. Although expertise-based explanations predict that inversion effects should emerge for non-face stimuli following learning, most supporting evidence derives from memory-based recognition tasks, leaving open the possibility that such effects reflect retrieval strategies rather than perceptual encoding. The present study tested this claim using a delayed matching-to-sample task with prototype-defined checkerboards, isolating perceptual encoding from long-term memory retrieval while manipulating stimulus complexity. Participants completed a categorisation training phase followed by a matching task involving familiar and novel stimuli presented upright or inverted. Results revealed a robust inversion effect for familiar stimuli only in the high-complexity condition, whereas no such effect emerged for simpler stimuli. These findings support the McLaren–Kaye–Mackintosh theory of perceptual learning by demonstrating that expertise modifies online perceptual encoding and that the engagement of holistic-like processing depends on stimulus complexity. We conclude that inversion effects reflect general expertise-based learning mechanisms operating under conditions of high informational load rather than face-specific perceptual architecture. Keywords: Perceptual expertise; Face inversion effect; Stimulus complexity; Perceptual learning; Visual categorisation; Holistic processing

Visual clutter impairs visual search in 2D scenes; however, it remains unclear how factors that contribute to clutter unfold in scenes with a 3D structure similar to the real-world. We investigated visual search using a head-mounted display that provides stereoscopic depth and allows limited viewpoint adjustment, enabling participants to search for everyday objects in controlled virtual scenes with different degrees of visual clutter. Clutter was manipulated by varying the number of objects and the visual complexity of the background, while the presence of a search target was manipulated independently. Reaction time, accuracy, and eye movements were recorded. Results showed that participants responded faster and made fewer fixations when the number of objects was low and when a target was present, suggesting that while classic factors affecting 2D visual search extend to scenes with naturalistic 3D depth and occlusion, clutter effects depend on specific contributing factors.

Bilinguals produce code-switches (CS), speech disfluencies, and gestures during speech. While gestures are known to reduce cognitive load during disfluent speech, the interaction between CS and multimodal production remains understudied. This study investigated the relationship among CS, disfluency, and gesture use across L1-Turkish, L2-English, and Free-Language conditions using academic and daily topics. Results showed intrasentential CS occurred more frequently in Free-Language condition, whereas representational gestures were more prevalent in single-language conditions. Co-occurrences of representational gestures and speech disfluencies were significantly higher in academic topics, modulated by L2 proficiency. Furthermore, intrasentential CS co-occurred with representational gestures, both independently and simultaneously, with speech disfluency more in the Free-Language than L1-Turkish condition, again modulated by proficiency. In contrast, intersentential CS co-occurred with non-representational gestures in Free-Language condition. Results suggest that bilinguals use representational gestures to maintain a single language or manage complex intrasentential CS, while non-representational gestures serve as markers for intersentential CS.

Humans are enmeshed in many hierarchical relationships, like that between a parent and child. Leaders in dominance hierarchies attain influence through coercion and intimidation, while leaders in prestige hierarchies are granted influence based on competence and prosociality. Across two studies (N = 214), we examined whether children have different expectations about how dominant and prestigious leaders allocate resources. Children learned about two social groups, the dominant Glerks and prestigious Zonks. In Study 1 (N = 109), children ages 4–5 watched a leader and follower divide five apples in 5–0 or 3–2 splits, and were asked who received a given share. Overall, children expected similar allocations across leaders. In Study 2 (N = 105), children ages 6–8 predicted whether a dominant or prestigious leader would share an apple with a hungry follower or eat it. Children expected prestigious leaders to be more likely to give the apple (82%) than dominant leaders (48%).

Semantic attributes such as contact, inclusion, and support are often assumed to reflect universally available properties of spatial scenes. This study introduces a preliminary method for empirically evaluating whether such attributes are directly available in spatial scenes or shaped through conceptualization. Speakers of Moroccan Arabic and English rated the applicability of geometric, functional, and qualitative physical attributes for a set of simple spatial scenes. In this pilot test, we examine which attributes show cross-linguistic convergence, indicating conceptual properties of the scenes, and which show cross-linguistic (or individual) variation, suggesting other influences on their interpretation. Cross-linguistic correlations provide preliminary evidence of graded convergence, with attributes differing in their degrees of alignment across languages. These patterns suggest that universality in spatial semantics is graded rather than categorical. These findings illustrate the potential of this approach to provide an objective and fine-grained empirical assessment of how spatial attributes are accessed and interpreted.

This study investigated how the complexity of facial stimuli impacts attentional orienting to eye-gaze in adults with and without autistic traits. Using a gaze cueing task, participants responded to schematic line_drawn faces or realistic photographic faces providing nonpredictive central gaze cues to the potential location of a lateralized target. Reaction times were faster on congruent than incongruent trials for both schematic and realistic face cues at both 100ms and 300ms SOAs, with no overall differences in accuracy. Reaction times were faster for longer SOAs, indicating more efficient orientation with additional processing time. No main effect for face type or interaction with autistic traits was observed, suggesting comparable processing to gaze direction in both schematic and realistic faces, and across group type. The findings indicate that gaze orienting is similar in high-functioning individuals with autistic traits and not critically dependent on the perceptual richness of the face stimulus.

Visual orienting in adults with and without autistic traits was explored using a spatial cueing paradigm where participants viewed schematic faces that either appeared centrally with a shift in gaze direction, or peripherally with gaze changes embedded in the laterally presented faces. Targets appeared nonpredictively to the left or right after variable stimulus onset asynchronies (SOAs). Centrally cued targets elicited faster reaction times than peripherally cued targets, and longer SOAs resulted in faster responses, indicating more efficient orienting when more processing time was available. A validity effect was observed overall, with congruent cues yielding faster responses than incongruent cues, with the effect being strongest for peripheral cues and at short_to_intermediate SOAs. Minimal differences were observed between autistic trait statuses, suggesting broadly comparable orienting performance. The findings indicated that gaze most effectively drove attention with peripheral cues at short SOAs, and (low-support, college-aged) autistic individuals may not have visual orienting impairments.

People exhibit a striking preference for maintaining their current goals. While this bias can be framed as irrational (e.g., the sunk cost fallacy), this persistence may be adaptive for conserving cognitive resources. We hypothesized that humans are sensitive to the cost of planning that comes with switching between goals. We conducted a behavioral experiment (n=60) in which participants pursued goal completion under two conditions: one in which they had to plan towards goals before pursuing them and another in which they were provided with a pre-specified optimal plan. We found that people persisted with their goals more when they had to create their own plans. We quantified the cognitive costs of planning with behavioral modeling and found that planning costs predict decreased goal switching. These results indicate that individuals are sensitive to the cost of planning and suggest that goal persistence may be an adaptive response to these cognitive costs.

Laughter is one of the earliest social signals in human development, yet how cognitive processes from infants shape its acoustic and temporal form remains poorly understood. We propose the Dynamic Infant Appraisal Model, a computational framework grounded in appraisal theory that simulates the infant's continuous cognitive evaluation during naturalistic caregiver-infant interaction. Using longitudinal data from the SAYCam corpus, we investigate how appraisal variables relate to four laughter features: Reaction Time, Overlap Ratio, Intensity, and Pitch. Our results reveal a functional dissociation: temporal features, particularly Overlap Ratio, are shaped by Familiarity, Coping Potential, and Pleasantness, and follow an Inverted-U trajectory relative to schematic discrepancy. Acoustic Intensity correlates with all appraisal variables but shows only a monotonic pattern. These findings suggest that infant laughter is a graded, multidimensional signal in which temporal dynamics encode predictive mastery and acoustic form encodes the reward value of cognitive challenge.

Sleep staging is a fundamental component of clinical sleep assessment and neurological screening. While recent deep learning methods have improved automatic sleep staging, most approaches treat it as a static sequence classification problem and overlook key cognitive mechanisms underlying expert scoring, including prior-guided perception, hierar-chical abstraction, and contextual memory integration. To address this gap, we propose CogSleep-Net, a cognitively inspired framework that models sleep staging as a dynamic reasoning process driven by coordinated perception, ab-straction, and memory. The model integrates prior-guided dual-domain perception to emphasize physiologically sali-ent patterns, hierarchical abstraction to disentangle micro-level waveforms from macro sleep dynamics, and a novel gated state-space memory mechanism that combines short-term evidence with long-term contextual information. Cog-Sleep-Net is evaluated on two public benchmarks (Sleep-EDF-78 and ISRUC-S1) and a self-collected high-density PSG dataset. It achieves 87.25% accuracy and 86.59% macro-F1 on Sleep-EDF-78, with consistent gains across da-tasets, demonstrating strong robustness and clinical applicability.

Learning in discovery environments requires learners to regulate hypothesis testing and evidence integration in the absence of external instruction. Changes in task-specific confidence are often interpreted as signals of learning progress, yet its relationship to performance varies across learners. In the present study, we examined whether the informativeness of local confidence change in a discovery task depends on learners' global self-efficacy. Participants completed a discovery task while providing trial-wise judgments of learning (JOLs). Baseline self-efficacy predicted initial, task-specific confidence but not objective performance during training or posttest. Instead, increases in JOLs over training reliably predicted posttest performance. Further analyses suggested that the strength of this relationship varied with learners' self-beliefs, such that confidence growth was more tightly coupled with performance for learners with lower self-efficacy. Together, these findings indicate that confidence change is not uniformly diagnostic of learning in discovery tasks and that global self-efficacy serves as an important boundary condition shaping when confidence change facilitates discovery.

This study examines 135 7- to 14-year-old children's beliefs about AI chatbots, doctors, and non-expert adults. Children are asked both whether each of the informants can correctly answer different kinds of health questions and which of the informants they would prefer to ask their own health questions. Children's endorsements in the chatbot informant are as high as their endorsements for doctors, and their endorsements of statements by non-expert grownups decreases with age, However, their endorsements belie their preferences: children significantly prefer not only the expert human informant (the doctor), but interestingly also significantly prefer the non-expert grownup to the chatbot, despite giving this informant low statement endorsements. These results suggest that children's intuitions about AI chatbots are nuanced: they believe that these informants can acquire correct knowledge, but they are not necessarily eager to consult chatbots themselves.

English prepositions are highly polysemous, occurring in both concrete spatial and conventionalized metaphorical uses (e.g., in love, on time). Abstract uses of prepositions, according to Conceptual Metaphor Theory, are grounded in spatial source domains. In this work, we test whether these conceptual links are active during online processing of in- and on-metaphors. Through semantic priming experiments, we investigate whether access to grounded spatial meaning during metaphor processing depends on visual input, or whether linguistic descriptions of spatial relations can also activate similar representations. Further, we probe if the priming experiments can be replicated in artificial language models to test whether distributional patterns in text data are sufficient to give rise to the types of priming effects observed in humans.

Interpersonal coordination in joint action is often assumed to rely on stable, predefined leader–follower roles. However, in complex embodied interactions such as partner dance, coordination and role organization may be dynamically reconfigured depending on movement context. This study examined how physical connection, partner experience, and role-related asymmetry shape interpersonal coordination in expert Latin American dance. Eight expert dance pairs performed a standardized rumba sequence under connected and non-connected conditions with familiar and unfamiliar partners. Motion capture data were analyzed using C2RQA, an extended cross-recurrence quantification method that captures directional coupling between movement time series, applied to temporally segmented movement phases. Coordination strength, temporal structure, complexity, and role-related asymmetry were quantified. Results showed that coordination was sparse and intermittent—rather than continuously sustained—and varied systematically across movement phases. The effects of physical connection and partner experience were context-dependent, and leader–follower asymmetry shifted across phases rather than remaining fixed. These findings highlight dynamic, context-sensitive reorganization of coordination and roles in expert joint action, suggesting that skilled partnered movement relies on flexible perception-action coupling rather than fixed role execution.

Recent work reports that large language models, including those with visual training, fail to recover the sensorimotor structure of human concepts. We argue this reflects how visual information has been operationalized, as opaque embeddings tied to single training instances, rather than a limit of vision itself. We test this by representing 593 Chinese disyllabic words through interpretable low-level visual statistics, pixel-level and mid-level features aggregated over word-linked, CLIP-filtered web images, and evaluating their predictive power for human sensorimotor norms across six modalities beyond lexical controls. We find a graded profile of cross-modal decodability. Visual norms are predicted best, followed by gustatory, olfactory, tactile, and auditory, while interoception sits at the predicted lower bound. The graded pattern, with substantial gains for non-visual modalities, indicates that prior negative results reflect a choice of representation rather than a failure of low-level visual statistics per se.

The enfacement illusion reflects changes in self-face representation following synchronous multisensory stimulation. This study examined whether such stimulation is associated with changes in self-other face discrimination at an ambiguous morph level (S50), focusing on both the rate of 'self' responses and reaction times across different visual field positions. Participants completed a self-recognition task before and after synchronous visuo-tactile stimulation, with faces presented centrally or in the left and right visual fields. We found that the reaction times for 'self' responses to S50 morphs were faster following stimulation, indicating a general facilitation in responding to ambiguous self-related stimuli. This effect was most pronounced for centrally presented faces, while lateralized differences were weak and not statistically significant. In contrast, the proportion of 'self' responses did not change across sessions. However, in the absence of a control condition, the observed reaction time facilitation cannot be uniquely attributed to enfacement-related processes and may reflect practice or repetition effects. These findings suggest that reaction times may be sensitive to multisensory stimulation, but clearer conclusions will require better-controlled designs.

Human visual perception does not treat all regions of an image as equally important or sensitive; instead, attention and cognitive priority depend on semantic relevance, task demands, and domain-specific knowledge such as clinical significance. Most existing differentially private image obfuscation methods inject uniform noise across regions, implicitly assuming that perceptual and privacy sensitivities are homogeneous. We propose a cognitively inspired, saliency-guided differential privacy framework for brain MRI images that models heterogeneous perceptual and semantic importance. High-level visual representations are extracted using a Vision Transformer (ViT)-based pretrained Masked Autoencoder (MAE) to approximate human semantic sensitivity. Region saliency scores, computed via semantic similarity, may reflect cognitive and clinically relevant patterns. Privacy budgets are adaptively allocated, applying stronger noise to salient regions while preserving structural details elsewhere. Experiments on brain MRI datasets demonstrate improved privacy-utility trade-offs, effectively protecting sensitive areas without degrading overall perceptual quality.

With the performance improvements and widespread social adoption of artificial intelligence (AI), including large language models, AI-assisted decision-making is rapidly expanding. On the other hand, as human decision-making is increasingly delegated to AI, concerns about degrading the quality of autonomous human decision-making when AI is unavailable has become an urgent social issue. This study focused on the insight from the wisdom of crowds that diverse opinions improve decision-making accuracy. We call advice that judges based on bias that counterbalances the human bias, "Adaptive Counter-Bias Advice." Through theoretical analysis and behavioral experiments, we confirmed that people who repeatedly made judgments while referring to Adaptive Counter-Bias Advice showed reduced bias and improved accuracy in autonomous decision-making in situations where advice was no longer available compared to those who used accurate advice. These findings offer new insights into using and selecting AI systems to enhance autonomous human decision-making performance.

Measuring how groups differ in construing concepts from natural language is central to computational social science and clinical NLP, but existing methods often trade statistical validity for interpretability. We extend Supervised Semantic Differential (SSD), which builds participant-level personal concept vectors from pretrained embeddings around lexically anchored concept references, to cross-group comparison. Our extension replaces regression-derived gradients with interpretable centroid-contrast vectors and adds permutation-based inference over group-centroid cosine distances, enabling omnibus and pairwise tests without parametric assumptions. We demonstrate the method on Polish life-story clinical interviews from patients with Borderline Personality Disorder (BPD), Narcissistic Personality Disorder (NPD), and controls (N=62), targeting concepts of self and time. Self-concept representations show robust omnibus and pairwise group separations; time-concept differences are driven mainly by BPD contrasts. Cluster interpretations align with phenomenological accounts, linking BPD to relational affective episodes and crisis-linked temporality, and NPD to comparatively decontextualized self-construal.

Across cognitive domains, a central question concerns whether behavioural change reflects reorganization of representational structure or altered dynamics controlling access to a stable system. We address this in highly proficient Basque–Spanish bilinguals—young and older adults—using a naming-by-definition task probing two lexical constraints: Frequency and Concreteness. Across ages and languages, both dimensions constrained retrieval, supporting a shared architecture across languages. Aging produced a global decline in accuracy without amplifying frequency effects, constraining accounts predicting selective vulnerability of weak representations. However, the concreteness advantage was larger in older adults, suggesting increased reliance on intrinsic representational support. Although age-related costs appeared for low-frequency abstract words, this reflected additive effects rather than higher-order interaction. Critically, the absence of a three-way interaction does not support structural reorganization and is more consistent with changes in access dynamics. Accordingly, within this population and task, aging is associated with a reweighting of constraints on access.

Every day social life requires repeatedly solving coordination problems: aligning actions to cooperate with others, often with incomplete communication. In contrast to rational accounts of coordination that predict its outcome based on payoffs, we interrogate the cognitive process that allows people to progressively generalize new coordination points based on past experience. We introduce the Progressive Coordination Paradigm (PCP) as a framework for studying the intersection of abstraction, generalization, and social coordination. Participants are asked to coordinate on sequences of patterned clicks on a grid. By progressively varying the layout of the grids, we show that people form shared abstractions after only a few interactions, learning from early coordination successes and flexibly generalizing core solution features to novel contexts. We show that the resultant abstractions are predictably path-dependent. These findings illustrate how human ad hoc conventions depend on mutually-constructed abstractions.

The Na/W Image Dataset (NaWID) comprises 96 visual stimuli designed to investigate the processing of spatial topological relations, specifically containment (w, "in") and support (na, "on") in Polish. NaWID has a systematic design, consisting of ambiguous spatial layouts alongside unambiguous exemplars, thus presenting a complementary perspective to existing datasets. We conducted preliminary studies and two experiments with native speakers to evaluate the dataset's adequacy. Experiment 1 (N = 30) demonstrated that ambiguous stimuli exhibit significantly longer reaction times during behavioral verification. Experiment 2 (N = 28), using eye-tracking and fNIRS, showed that while pupillary dilation is sensitive to logical congruency, it does not reflect visual ambiguity. However, gaze measures revealed more extensive scanning for ambiguous layouts, indicating sustained cognitive processing effort. These findings establish NaWID as an effective tool for exploring the spectral nature of spatial ambiguity and the oculomotor strategies involved in processing spatial layouts.

In the long tail of precision medicine, rare case exemplars are scarce, forcing clinicians toward slow, resource-intensive "System 2" reasoning. Large Language Models may help, but they often suffer from a fluency-factuality trade-off, producing plausible yet hallucinated clinical guidance without logical grounding. To address these dual inefficiencies, we propose the NeuroCR, a neuro-symbolic cognitive prosthetic modeled after Dual Process Theory. Its architecture synergizes an implicit associative memory (System 1) with an explicit causal reasoning module (System 2). Crucially, we introduce a "Semantic Schema Integration" algorithm to solve the Symbol Grounding Problem for heterogeneous clinical data, and a "Metacognitive Gating" mechanism to dynamically arbitrate between retrieval and reasoning. Evaluations on rare somatic mutations demonstrate that NeuroCR achieves a 140-fold speedup while preventing hallucinations through topological constraints. This work suggests that AI should not merely automate decisions but serve as a transparent scaffold for high-stakes human reasoning.

Past research has identified an asymmetry between how experts and novices interpret generalizations: novices tend to interpret generalizations more broadly. It remains unclear how or when differences in prior knowledge would create such asymmetries. We first investigate whether differences in experts' and novices' domain-level prior knowledge can explain the asymmetry. We find no consistent difference in their domain-level prior knowledge, but we do find a previously unidentified asymmetry in how low- and high-level experts interpret generalizations. We then investigate whether differences in the specificity of experts' and novices' prior knowledge can explain the asymmetry. We find that only listeners with specific prior knowledge vary their interpretations based on context, but differences in specificity do not create an asymmetry. Finally, we discuss the progression from novice to expert and consider remaining explanations for why experts and novices would interpret generalizations differently.

This study investigates whether neural tracking of linguistic information extends from read speech to spontaneous conversation. Using the temporal response function (TRF) framework, we validate our approach on a read-speech EEG dataset and then apply it to EEG recordings from natural conversations. We observe reliable neural tracking of key linguistic predictors, including word onset, part-of-speech surprisal, and lexical surprisal, in spontaneous speech, with effects around 200, 400, and 600 ms. These results provide new evidence that linguistic neural tracking operates in natural conversational settings and confirm the feasibility of EEG studies in ecologically valid contexts.

Ownership is often implicitly measured using indices such as proprioceptive drift. However, the measure is noisy and does not coincide with explicit measurement. We look at attentional prioritization for induced ownership as another possible implicit measure. Across two experiments, we first examine the self-prioritization effect (SPE) for enfaced faces in a Temporal Order Judgment Task and its correlation with interoceptive accuracy (IAcc). In the second experiment, we tested whether the relationship between SPE and IAcc is mediated by the degree of enfacement. While the first experiment suggested a moderate correlation between SPE and IAcc, the second experiment found no correlation between IAcc and SPE, nor between SPE and the degree of enfacement.–öInterestingly, as with–öproprioceptive drift, the results–öshowed SPE even in a non-enfaced visual exposure condition without tactile feedback.–öOverall,–öthe pattern of results supports the distinction between implicit and explicit measures of ownership.

While the democratisation of LLMs has proven fruitful in the field of Experimental Psychology, their adoption in some use cases has been slower. Here we explore how LLMs perform on a content analysis task following a predefined coding scheme. Using a qualitative dataset (346 responses, 6 labels with binary options) with an established inter-rater reliability over 95% as our benchmark, we compare models' outputs against human coders. The text responses are often ambiguous and require a certain level of inferential reasoning and subjective interpretation which LLMs still struggle with. We gave GPT 4-o, Qwen 2.5 and Mistral 7B the dataset and label definitions. GPT 4-o matched 59% of human labelling across responses, Mistral 7B matched 46% and Qwen 2.5 matched 42%. We discuss the 'reasoning' element of LLMs and potential ways forward.

Moral cognition evolved, in part, to facilitate cooperation. Since coordination problems constitute a large subclass of cooperation problems, we should expect coordination behavior to be subject to moralization. But which of several coordination strategies is preferable depends on their respective probability of success. This, in turn, often depends on whether other people's actions are mutually observable. Past research has focused on the moralization of coordination behavior in dyadic contexts. In two preregistered experiments (n = 472) based on incentivized real-time coordination games, we extend investigations of the moralization of coordination behavior to small groups, and study how it depends on the mutual observability of others' actions. In Experiment 1, we show that moving away from the current coordination solution (thus inducing miscoordination) is viewed as morally worse than sticking to it. In Experiment 2, we show that this is not the case when actions are mutually observable, making switching to a more mutually beneficial alternative achievable. In such contexts, abandoning precedent to coordinate on a more efficient equilibrium is viewed as morally superior than sticking to it. Crucially, the same coordination rule is viewed as morally superior in one informational context, and morally inferior in the other. Understanding the cognitive processes involved in successful coordination can shed light on which conventions, norms, and rules are morally appropriate and which aren't, when this is so, and why.

Previous studies highlight sleep's role in emotional processing, with rapid eye movement (REM) sleep showing a negative bias, though multi-level neural mechanisms require further exploration. This EEG study examined overnight sleep's modulation of emotional face recognition, integrating event-related potentials (ERPs), oscillatory activity, and connectivity measures. Thirty-six participants performed an emotional face classification task before and after polysomnographic monitoring. Results showed sleep-dependent enhancement of the N170 component specific to negative faces and dynamic modulation of the late positive potential (LPP). Higher REM proportion and stronger prefrontal theta-gamma phase-amplitude coupling correlated with greater behavioral efficiency gains. REM-specific directed theta connectivity from central to prefrontal regions selectively predicted improvements in negative face processing. These findings indicate that REM sleep adaptively optimizes negative emotional processing via hierarchical mechanisms from early structural encoding to late motivational evaluation, providing new insights into sleep's therapeutic potential for emotional regulation.

Recent work on "scalar diversity" has shown that scalar inferences (SIs) do not form a unified class. For example, while and both form scales, hearers overwhelmingly interpret some as meaning not all, but rarely interpret pretty as meaning not beautiful. We show that SIs are correspondingly heterogeneous with respect to processing times and their sensitivity to world knowledge. In particular, hearers process SIs associated with closed class scales faster than open-class SIs or ad-hoc SIs; moreover, open-class SIs exhibit greater sensitivity to world-knowledge than their closed-class counterparts. These results, we argue, suggest the existence of at least two distinct paths for generating scalar inferences--one path is fast, automatic, and insensitive to the conversational context, while the other path is slow, deliberate, and sensitive to conversational context.

Prior studies have evaluated the cognitive alignment of computer vision models and humans on fluid reasoning: the ability to solve novel, abstract problems independent of prior knowledge. This is commonly measured using the Raven Advanced Progressive Matrices (RAPM) test. Prior studies investigating how well Convolutional Neural Networks (CNNs) solve Ravens problems have found partial cognitive alignment with the performance of adults. We replicate this work using the ResNet-18 model trained on RAPM and, critically, extend for the first time to the question of developmental alignment: Does the improvement of ResNet-18's improvement across training track the developmental progression observed in children? We find evidence of partial developmental alignment: ResNet-18's performance over training follows a power function similar to the one that characterizes children's performance over development. A notable discrepancy is that the model performs best on the hardest problems, suggesting that it exploits shortcuts rather than engaging in genuine fluid reasoning.

Decision-making benefits from efficient perceptual organization of cues minimize processing costs. This motivated the present examination of cognitive efficiency of competing Gestalt principles in digital natives by measuring both choice preferences and reaction times. 46 participants (25 males, Mean age-22.76 years) completed a web-based experiment consisting of five binary classification tasks, where 'Common Fate' competed against 'Similarity', 'Proximity', 'Closure', 'Good Continuity', & 'Pr–âgnanz'. Binomial tests revealed that Common Fate was selected above chance overall, but its dominance was hierarchical, strongest against 'Pr–âgnanz' (91.3%) and weakest against 'Similarity' (65.2%). Our findings suggest that motion-based grouping appears computationally efficient for digital natives, but is graded and shaped by cue conflict. We propose an efficiency-hierarchy model where perceptual organization functions as a cost-sensitive competitive system based on selection frequency and resolution cost, and conjecture that increased selection probability for 'Common Fate' is linked to recalibration of perceptual priors in digital natives.

The precise mechanism through which Deep Brain Stimulation (DBS) mitigates freezing episodes in Parkinson's disease remains unknown. We modelled Parkinsonian freezing using a model of the basal ganglia that adopts a Dynamic Neural Field network for simultaneous action selection (selecting which action is executed) and specification (resolving the continuous parameters governing how that action is performed). Action selection success was based on whether activation surpassed a threshold. The model robustly differentiated healthy and dopamine-depleted (Parkinsonian) conditions, producing stable action selection under healthy dopamine and impaired, freeze-prone dynamics following depletion. We then modelled DBS as a scaled reduction in afferent drive to the subthalamic nucleus (STN) and globus pallidus internus (GPi). While this formulation has succeeded in previous computational work, it did not yield consistent restoration of action selection within our framework, motivating future investigation of alternative DBS formulations.

This study examines how social media exploration shapes individual belief change and how psychological traits condition this process, with the aim of informing large language model (LLM)-based modeling. Participants explored a simulated social media environment by selecting content options and reading posts about a controversial topic. We focused on confirmation-biased exploration and opinion change. Results showed that the balance between confirmatory and disconfirmatory exploration predicted the magnitude and direction of opinion change. Trait effects were modest as main effects but emerged more clearly in interaction with initial opinion strength, consistent with prior theory. We also conducted an exploratory LLM-based analysis of behavioral prediction. Overall, the findings suggest that belief updating in social-media-like settings depends on interactions among exploration behavior, initial belief states, and individual differences.

Web-based information seeking requires learners to regulate attention across heterogeneous sources. While interest is known to influence engagement, it is often treated as an uncontrolled background factor. This study examines how experimentally induced differences in interest relate to observable search behavior in a web-based report-writing task. Twelve undergraduate students completed two search tasks designed to elicit relatively high or low interest by manipulating topic and difficulty based on optimal stimulation principles. Interest was assessed using self-report measures before and after each task. Behavioral logs, including search frequency, dwell time, and visited domains, were analyzed alongside exploratory semantic similarity measures derived from page content embeddings. Results suggest that task characteristics are associated with initial interest levels, and that self-reported interest may change over the course of task engagement. Differences in behavioral patterns were observed across interest profiles, indicating that motivational state is related to variation in attention allocation during information seeking.

This preregistered study examines the use of mental representations, with a particular focus on inner speech, during social interactions involving concepts that vary in abstractness and vagueness (semantic precision), using a hybrid methodology combining an interactive task, experience sampling, and survey measures. Results showed that abstract concepts relied more on verbal internal representations, whereas concrete concepts engaged more visual ones. Both abstractness and semantic precision affected participants' uncertainty in their evaluation of the ongoing inner experience. Together, these findings demonstrate that inner speech plays a functional role in negotiating meaning during social interaction, particularly when concepts are abstract or semantically vague.

Trust in human–robot interaction (HRI) is widely studied, yet the cognitive mechanisms underlying it are unclear. We propose the Expectation-Trust Model (ETM), a process-level account of trust grounded in expectations and formalized within a drift diffusion modeling framework. In ETM, trust is represented as an evidence accumulation process in which existing knowledge about robots determines the starting point, while deviations from expectations influence the drift rate. Positive drift reflects behaviors that exceed expectations, such as excellent performance, whereas negative drift reflects expectation violations, such as robot errors. We apply ETM to simulate trust dynamics from two published datasets, capturing empirical patterns including trust formation, violation, and repair. Differences in trust were explained by variation in starting point and drift rate corresponding to robot design, performance, and trust repair strategies. ETM provides a parsimonious mechanistic explanation for trust in HRI and generates precise, testable predictions about how expectations shape trust.

We investigate how role-based power dynamics emerge between workers and managers in a collective foraging task. We recruited 223 participants, who repeatedly occupied either a forager role, or a manager role that coordinates the foragers' efforts. Foragers pay a tax to the manager, which instantiate a social dilemma: although higher taxes incentivize information acquisition to improve collective efficiency, self-interested players should prefer to push the tax toward its own immediate advantage. We operationalized this tension by eliciting participants' preferred tax rate. Contrary to an incentive-based account, managers' information investment was not associated with tax income and foragers' harvesting effort was not influenced by tax rate. Moreover, foragers consistently proposed about 28% tax while managers propose around 53%, even though both roles could have set it near 0 or 100%, respectively. Overall, the dynamics suggest convergence toward tax rates that reduce inequality between roles rather than toward self-interested behavior.

The current study examines the effects of advice explainability on advice judgment and post-error response when advice is provided by a goal-specific AI system versus a human expert in high-stakes decision-making contexts. Across four scenarios, participants were presented with advice that was either accompanied by a justification or not, and the advice was subsequently revealed to be incorrect. Results showed that prior to the error revelation, higher explainability did not affect perceived advice correctness, perceived advice understandability, or willingness to follow the advice, nor did it eliminate the preference for human advisors over AI systems. Instead, explainability most clearly shaped post-error responses. Providing justifications reduced responsibility attributed to the advisor, increased perceived error understandability, and lowered unwillingness to follow future advice. This pattern suggests that justifications may invite excuse generation and external attributions, making failures appear more understandable, reducing blame toward the advisor, and thereby buffering future trust.

Anxiety plays an important role in predicting and avoiding future dangers, and adaptation under anxiety is assumed to be mediated by its influence on cognitive functions such as time perception. In this study, we constructed an ACT-R–based model that incorporates the amplification of anxiety over time. Using simulations of a turn-across oncoming traffic decision task in a driving context, anxiety was formalized not as a change in decision criteria but as a distortion of internal time progression. As a result, subjective time-to-collision (TTC) showed a logarithmic nonlinear relationship with objective TTC, and the between-condition differences corresponded to the difference structure of subjective time judgments observed in human experiments. Furthermore, examination of turn timing reproduced the anxiety-specific freezing process, in which the probability of performing the action decreased over time if it was not executed early.

Short-form "brain rot" content on platforms like TikTok is widely viewed as harmful for attention and learning. We find that brain rot can benefit learners with higher stimulation needs. We eye-tracked young adults (N=75) while they watched physics videos presented alone or alongside a task-irrelevant "brain rot-style" animated video. Individuals who reported preferring brain rot learned better from the brain rot condition than from the blander control condition. Furthermore, eye blinks and pupil dilation suggest that those who prefer brain rot were less overstimulated by brain rot content and required more effortful executive control to sustain attention to control content. Finally, preference for brain rot in our task is associated with higher media consumption and tolerance for high stimulation. These findings may point to individual variation in optimal cognitive load and provide preliminary support for an adaptive account of attentional bandwidth.

Efficient communication accounts of cross-linguistic variation propose that languages trade-off informativeness and complexity based on communicative need, often measured by usage frequency and operationalized as a free parameter. We propose that this need is not uniform across discursive genres and that the narrative genre applies a stronger pressure for lexical informativeness than non-narrative genres. Using spoken-language data from typologically diverse languages, we analyze the informativeness of lexical fields in narrative and other genres, as operationalized by their lexical richness. We show that frequency in narrative genres predicts lexical richness more strongly than in other genres. This supports the theory that narrative is a privileged communicative context that drives the informativity displayed by different parts of the lexicon.

Prior studies of Second Language Acquisition have identified two forces that shape L2 production: cross-linguistic influence and L2 developmental universals. While past studies have focused on the acquisition of target structures, the distribution of structures in learner language production may still exhibit influence from these two forces. To disentangle the two kinds of impact on the distribution of linguistic structures in natural L2 production, we took a machine learning approach, leveraging over twenty typological and syntactic complexity features from five languages (English, Spanish, Portuguese, Chinese, and Korean). By comparing linguistically informed features that characterize 1) each language based on native data, 2) each language based on L2 production from multiple L1 background, and 3) speakers of different L1 background writing in the same L2, we identified the structural features that capture the developmental universal impact and those that reflect L1 influence. Our findings reveal the complex, bidirectional, and asymmetrical interplay of language typology, cross-linguistic influence, and language development.

As Large Language Models (LLMs) transition from passive text generators to autonomous social agents, decoding their underlying "cognitive DNA" is essential for ensuring alignment with diverse human values. This study investigates the intersection of socio-emotional maturity and cultural value orientations in frontier LLMs through the lens of cognitive science and cross-cultural psychology. We introduce the Dual-Axis Social Intelligence Scale (DASIS), a novel evaluative framework designed to decouple general social sophistication from implicit cultural biases—specifically along the Individualism-Collectivism (I-C) continuum. Across three interrelated experimental paradigms, we map the latent social architecture of nine state-of-the-art models. Study 1 establishes a behavioral baseline in Chinese, revealing that while models exhibit near-human social maturity, they suffer from a profound "Trans-Linguistic Value Locking" toward individualistic schemas, regardless of their developmental origin. Study 2 interrogates the "Whorfian Effect" (Linguistic Relativity) in synthetic cognition; findings indicate a Directional Asymmetry, where English context amplifies individualism while Chinese fails to exert a reciprocal collectivist pull, suggesting an "Axiomatic Individualism" ingrained during the alignment process. Study 3 explores "Cultural Perspective Taking," testing the models' Synthetic Theory of Mind (SToM) capabilities to voluntarily override default biases. Our results demonstrate that while default "personalities" are heavily skewed, models possess a latent Cognitive Decoupling capacity that allows for high-fidelity cultural simulation under explicit framing. This work provides a critical audit of the "WEIRD" (Western, Educated, Industrialized, Rich, and Democratic) bias in modern AI. We conclude that current alignment methodologies prioritize value consistency over cultural pluralism, potentially facilitating "Cognitive Imperialism" in global AI deployment. We propose DASIS as a standard for auditing the cross-cultural adaptability of autonomous agents.

Sentences like "She will go to France or Spain, or perhaps to Germany or France.'' appear formally redundant, yet become acceptable in contexts such as "Mary will go to a philosophy program in France or Spain, or a mathematics program in Germany or France.'' While this phenomenon has typically been analyzed using symbolic formal representations, we aim to provide an account grounded in artificial neural mechanisms. We first present new behavioral evidence from humans and large language models demonstrating the robustness of this apparent non-redundancy across contexts. We then show that, in language models, redundancy avoidance arises from two interacting mechanisms: models learn to bind contextually relevant information to repeated lexical items, and Transformer induction heads selectively attend to these context-licensed representations. We argue that this neural explanation sheds light on the mechanisms underlying context-sensitive semantic interpretation, and that it complements existing symbolic analyses.

Digging-in effects, where disambiguation difficulty increases with longer ambiguous regions, have been cited as evidence for self-organized sentence processing, in which structural commitments strengthen over time. In contrast, surprisal theory predicts no such effect unless lengthening genuinely shifts statistical expectations, and neural language models appear to show the opposite pattern. Whether digging-in is a robust real-time phenomenon in human sentence processing—or an artifact of wrap-up processes or methodological confounds—remains unclear. We report two experiments on English NP/Z garden-path sentences using Maze and self-paced reading, comparing human behavior with predictions from an ensemble of large language models. We find no evidence for real-time digging-in effects. Critically, items with sentence-final versus nonfinal disambiguation show qualitatively different patterns: positive digging-in trends appear only sentence-finally, where wrap-up effects confound interpretation. Nonfinal items—the cleaner test of real-time processing—show reverse trends consistent with neural model predictions.

Moral feelings and beliefs are powerful drivers of behavior. While analogies are commonly invoked to induce moral sentiment, analogical reasoning has not been empirically tested as a mechanism for moralization. Across two studies (N=292, N=234), we investigated whether analogical reasoning facilitated moralization of neutral target actions by transferring moral significance from moralized source actions. Participants judged neutral target actions paired with either analogous immoral source actions or unrelated immoral actions (control). Target actions were judged as more immoral in the analogy condition compared to the control, but only when the source and target were considered comparable (i.e., mappable relations), and the source was judged as highly immoral. These factors interacted synergistically, consistent with analogical transfer: moral significance transferred to targets when relational mappings supported the inference. However, poorly-perceived analogies backfired, producing less moralization than controls. These findings provide novel evidence for analogical reasoning as a mechanism for moralization.

The maintenance and manipulation of information in working memory (WM) is fundamental to human and artificial intelligence. Although human WM is famously capacity-limited, Large Language Models (LLMs) preserve inputs in the network's context window, profoundly reducing capacity limits that depend on maintenance alone. However, work in cognitive science suggests WM limits can also arise from representational interference during manipulation, raising the possibility of strong limits even in LLMs with perfect maintenance. Here, we evaluate 15 frontier LLMs and find models perform near-perfectly on forward digit span (recalling sequences in order) but collapse on backward digit span (recalling sequences in reverse). Moreover, backward span performance is significantly correlated with two measures of fluid intelligence (Raven's Progressive Matrices and ARC-AGI-1). These findings suggest backward digit span provides a plausible benchmark for the "working" component of working memory in LLMs and point to potential shared principles of information processing across systems.

Similarity and generalization are often assumed to reflect distance in an underlying representational space. In semantic networks, however, distance can be defined by both shortest paths or by multiple indirect pathways. Previous research has shown that non-shortest, higher-order paths influence similarity judgements. Here, we extend these findings by re-analyzing a publicly available dataset to compare shortest-path models with models integrating over discounted higher-order paths. The latter better accounted for similarity judgements; error analyses indicate that this advantage arose from integrating multiple indirect paths rather than relying on shortest connections. These random-walk models implement the same core computation as the successor representation (SR), which has been proposed to support human generalization. Consistently, an SR-based model outperformed alternatives in accounting for inductive generalization judgements from the same dataset. These findings suggest that similarity and generalization are both shaped by higher-order connectivity between concepts.

User studies of AI-mediated communication typically report average effects, while interview-based work remains dominated by thematic narrative or Likert clustering, leaving the structured heterogeneity expressed in post-task interviews unquantified. We applied latent class analysis as a methodological bridge that converts qualitative interview codes into discrete, domain-specific user profiles, enabling design reasoning about for whom, on which dimension, and under what conditions real-time AI scaffolds help or burden. As a validating scenario, 29 non-native English speakers interacted with XPLAIN, a Wizard-of-Oz proactive scaffold in Zoom's sidebar, to bridge gaps in linguistic and cultural knowledge during a collaborative task. Across eight thematic domains, two-class solutions were consistently best-fitting (BIC); class membership was largely independent across domains, indicating multi-dimensional rather than global user types (e.g., longer English immersion was associated with more direct, interlocutor-focused repair strategies). Quantifying such heterogeneity, we argue, is critical for user-adaptive, timing-sensitive AI-mediation design.

Learning is difficult to measure because it is not directly observable. Even in formal educational settings it's hard to tell whether a child might be thinking through a difficult problem or daydreaming. The same is especially true in informal settings like while watching TV at home—it's difficult to know whether a child is meaningfully engaged with what they are watching or whether they are overstimulated and unable to disengage. Caregivers and educators regularly rely on behavioral cues to infer kids' learning. Physical engagement, or sitting still, is often treated as a reliable signal of learning. However, stillness during screen-based media is not synonymous with learning in young children and may in fact indicate overstimulation and disrupted learning (Shepherd & Kidd, 2024). We asked 200 adults whether they thought the same child was more attentive and learning more from a video when sitting still compared to when making small fidgets, like twiddling their thumbs or tapping their fingers. Our results demonstrate that adults strongly believe that stillness indicates greater attention and learning in children. This has important implications for equity of opportunities in formal education settings, where an adult observer acts as a gatekeeper to learning opportunities for children that they may withhold from children who they misperceive to be learning less effectively because they are fidgeting.

Grammatical knowledge and how it is empirically tested are typically considered robust to the frequency of the lexical items used in the expressions. Complementary Learning Systems theory proposes that hippocampal episodic memory, which enables rapid encoding and retrieval of specific experiences, allows learners to leverage those experiences when processing rare patterns. We test the hypothesis that robustness to lexical frequency can arise via such an episodic memory mechanism by evaluating whether retrieval-augmented language models (specifically, k-nearest-neighbor language models that augment parametric neural networks with explicit instance storage), help close the lexical frequency gap in syntactic contrasts that vanilla language models exhibit. Using syntactic contrasts with frequency-stratified test items, we find that retrieval augmentation leads to improvements for test instances containing low-frequency lexical items, consistent with episodic memory compensating for weak parametric representations. This benefit is consistent across different syntactic phenomena and across models pretrained on child-realistic and large-scale data. Additionally, we show that structural information is critical for effective retrieval, whereas semantic information is only responsible for minor gains. While these are promising proof-of-concept results supporting our hypothesis, the frequency gap remains not fully closed. Based on our analyses, we posit preferential reweighting of retrieved instances, better representations and retrieval strategies of structural information, and flexible configurations of storage and retrieval as promising future directions for improving the implementation of episodic memory in language models.

Identifying similarity plays a major role in spatial reasoning. Prior work has shown that identification of similarity and difference between spatial scenes involves reasoning about their properties at two different levels--surface-level properties, such as color and size, and higher-level compositional properties. The interaction between the use of these property types during reasoning and the degree of similarity or difference between spatial scenes has, however, been largely unstudied. We presented participants with in-progress Tangram puzzles at various stages of similarity to their target image. Participants were asked to identify the puzzle being built and provide an explanation of how they came to that conclusion. Analysis of explanations found an interaction between degree of similarity and preference for reasoning over surface-level or higher-level properties.

One of the fundamental questions in cognitive science is how people achieve such high levels of performance given the very limited resources they have access to. While human behaviour is similar to the Bayesian ideal both in low-level domains such as vision (Yuille & Kersten, 2006) and high-level domains such as categorization (Xu & Tenenbaum, 2007); optimality is in fact impossible given existing constraints.

Modern AI systems are increasingly, and perhaps alarmingly, exceeding human performance in domains such as competition mathematics and coding (UK AISI, 2025). AI agents can now independently implement software engineering artifacts requiring hours of complex reasoning effort from humans. As AI capability and agency increase, designing reliable mechanisms to align AI systems, ensuring they act consistently with human values even when unmonitored, grows ever more urgent. Yet AI alignment remains poorly understood.

The phenomenon of belief polarisation—in which people update their beliefs in opposing directions, particularly after observing the same evidence—has garnered growing attention and concern in recent years from across the fields of philosophy, political science, psychology, and sociology, particularly as misinformation and ideological polarisation have threatened the fabric of political and social consensus (Allcott et al., 2019; Haghtalab et al., 2020; Lelkes, 2016; Wilson et al., 2020). Despite the recognition of belief polarisation as an emerging social phenomenon (or perhaps thanks to its very existence) both by scientific researchers and major social institutions such as governments and business, there has been relatively little work aiming to explicitly unify various strands of theorising regarding the causes of belief polarisation and strategies for addressing its consequences.

Foundation models, large-scale artificial intelligence (AI) systems trained on vast amounts of data and able to engage with a wide range of tasks, have captivated public attention and promise transformative impact across many fields, from mathematics to education. The capacity for such models to fluidly engage in natural language carries huge potential implications for cognitive science: for the first time, we have computational models that bring us a significant step toward the generality of human cognition, thereby inviting us to reconceptualize how we may build models of the human mind. Yet, major questions remain as to how to build such models, what kind of data is needed for this, and what the implications are for cognitive science.

Cognitive scientists have long sought to explain the human capability to solve complex and often ill-defined problems, ranging from puzzle games to the challenges of everyday life. Early work cast problem solving as search: given a fixed representation of states, actions, and transitions, an agent must find a sequence of actions that carries it from an initial state to a goal state, often guided by heuristic search. This framework proved successful in explaining human behavior in lab-controlled puzzle tasks, where experimenters could hand-craft fixed problem representations, allowing models to focus analysis on the downstream search process. Yet, it left a more fundamental question comparatively unexplored: how do humans construct the problem representations that make search possible in the first place?

Large-scale spatial abilities have long been recognized as a central component of human cognition, underlying navigation, wayfinding, and the integration of spatial information across extended environments. Despite their importance for everyday functioning and learning, these abilities have remained comparatively underrepresented in standardized assessment research. Widely used instruments such as mental rotation or spatial visualization tests target situations in which spatial information can be apprehended from a single viewpoint and manipulated without bodily movement (small-scale spatial abilities). Research has repeatedly shown that performance on such tasks overlaps only partially with performance in navigation and wayfinding, suggesting that large-scale spatial abilities involve additional cognitive processes related to embodiment, sequential decision-making, and the integration of spatial information over time. While small-scale spatial abilities are routinely measured using well-established psychometric instruments, large-scale spatial abilities are most commonly investigated using navigation tasks that are tailored to specific experimental settings. As a consequence, studies often rely on highly heterogeneous environments, task designs, and outcome measures, which substantially limits comparability and standardization across studies.

Science is an exercise in seeing further by standing on the shoulders of giants. But what happens when those shoulders give way? The replication crisis has shaken confidence in published findings, with surveys revealing that more than 70% of researchers have failed to replicate another scientist's experiments (Baker, 2016). Even when researchers attempt the seemingly simpler task of computational reproducibility—applying the same analysis to the same data—success is far from guaranteed. A recent large-scale investigation found that only 52.6% of social and behavioral science papers could be precisely reproduced, even when original data were available (Miske et al., 2026). The costs are substantial: wasted resources, delayed discoveries, and eroded public trust in science.

Human cognition is inherently temporal: we draw on memories across time scales to predict and prepare for the future, and the brain accurately generates timed motor behaviors and adaptively focuses our attention in time. To accomplish these temporal tasks, the brain measures time on scales from milliseconds to days. And finally, one of our most salient subjective experiences is that of the passage of time itself.

There is widespread agreement across disciplines that morality has evolved to facilitate cooperation among agents with conflicting interests. Traditional approaches to the study of morality and cooperation either abstain from formal models (e.g., moral psychology in social psychology) or model cooperation without much concern for underlying psychological processes (e.g., evolutionary models of reciprocity, kin selection etc.). Computational cognitive scientists are starting to enrich classic computational frameworks (Bayesian inference, reinforcement learning, game theory, expected utility) with psychological mechanisms, traits, and constraints (Theory of Mind, metacognition, trust, joint planning, resource rationality) to model behavior, learning and evolutionary processes, and develop a finer understanding of the cognitive underpinnings of morality and cooperation. This progress is accelerated by current advances in modeling (probabilistic programming languages specialized for social cognition, large-language models, multi-agent RL) and computational efficiency (GPUs, numerical computation). Building formal models of moral cognition and cooperation that are both computationally precise and psychologically rich opens the door for cumulative progress in our understanding of their principles, origins, and functioning, and for the engineering of cooperative and ethical capabilities in artificial systems.

Unlike previous digital technologies, state-of-the-art large language models (LLMs) exhibit capabilities that span the full cognitive hierarchy, from low-level linguistic processing, to high-level functions such as social reasoning and metacognitive monitoring (Coda-Forno et al., 2024; Didolkar et al., 2024; Street et al., 2024). For these reasons, humans are increasingly collaborating with LLMs for complex cognitive tasks via conversational chat interfaces, and LLMs are increasingly studied as cognitive systems in their own right by cognitive scientists, psychologists and philosophers of mind (Hagendorff et al., 2023). Yet, the dynamics of human-LLM collaborations remain understudied.

When researchers study aesthetic experience, what exactly are they measuring? Some studies ask participants how much they like a stimulus. Others ask how beautiful, interesting, or meaningful they find it. These questions are often treated as interchangeable measures of "aesthetic preference,'' yet they may tap fundamentally different psychological processes.

RELACCO stands for 'REd LAtinoamericana de Ciencias COgnitivas' (Latin American Network of Cognitive Sciences) and is a new initiative that aims at bringing together researchers, students and institutions from Latin American countries involved in the Cognitive Sciences to promote exchanges and academic collaboration between them. Through the exchange of knowledge, resources and experiences, we seek to strengthen the development of the Cognitive Sciences in the region, foster inter/transdisciplinary research, and consolidate a scientific and humanistic community committed to excellence, inclusion, horizontality, critical and ecological thinking, and individual and social responsibility. At RELACCO, we believe in science as a collective endeavor, open and sensitive to the social and cultural challenges of today's world, with an emphasis on those of our region. Therefore, we want to work to build bridges between disciplines, contexts, and communities, and to make Latin American scientific thought and production visible on a regional and global scale, with no detriment to other international ties and interactions

Lexical semantics sits at the core of cognitive science, yet word meaning is not static: it is shaped by ambiguity, contextual constraints, and learning history (Rodd, 2020; Piantadosi et al., 2012; Trott & Bergen, 2023). A central challenge is to connect experimental evidence of how meanings are learned, accessed, and selected in real time, with computational accounts that scale to the lexicon and capture how senses emerge from language use (Haro & Ferre, 2018; Beekhuizen et al., 2021). This symposium brings together diverse researchers from the Global North and South who use complementary experimental paradigms and computational approaches to study meaning in context, including pupillometry, large-scale word association graphs, contextual embedding models of developmental change, and large language-model analyses of experimental stimulus variability. Together, the talks bridge computational and experimental perspectives on word meaning processing and word sense learning, offering converging insights into how lexical knowledge is represented, updated, and deployed across timescales. In so doing, it pushes back against classic, relatively siloed approaches to studying lexical semantics, highlighting how an interdisciplinary approach to these issues truly is more than the sum of its parts.

Humans are remarkable learners. Decades of research have shown that, from early in life, children efficiently acquire knowledge through their exploration (Bonawitz et al., 2012; Giron et al., 2023; Meder et al., 2021; Ruggeri et al., 2019; Schulz & Bonawitz, 2007) and social input (Harris & Corriveau, 2011; Ronfard et al., 2018). However, this body of work has largely focused on how children learn about the external world. The current symposium highlights an equally important, but underexplored, and perhaps even more challenging learning problem: How do humans learn about the internal world (i.e., themselves)? By bringing together cognitive, computational, and developmental perspectives, this symposium examines how young children direct their learning abilities inward to infer who they are and what they are capable of.

Cognitive science (CS) has long acknowledged the role of cultural systems in producing systematic differences in cognitive organization (e.g. Medin & Atran, 2004; Levinson, 2012; Barrett, 2020). At the same time, we lack general frameworks which would describe the nature of these systems—and their mutual relationship with specific cognitive structures: specifically, accounts articulated under the same formal frameworks (reflecting optimality principles in information processing) that have been used to capture universal features of the mind (e.g. Tenenbaum et al., 2011; Gershman, 2021), or in general evolutionary frameworks such as those used by cultural evolution.

Human cooperation extends beyond kin and its evolution can be explained in part by direct benefits from cooperative interactions. However, cooperation based on direct benefits is vulnerable to defection, making mechanisms that ensure reciprocity essential. One such mechanism is partner choice, whereby individuals preferentially interact with cooperative partners. Yet, constraints can limit available partners, encouraging reliance on partner control strategies, such as punishing defection. Here, we investigate whether Guinea baboons can use both partner choice and partner control, and whether they flexibly adjust these strategies depending on context. Using a touchscreen-based prosocial choice task, receivers could accept or refuse an actor's choice. Baboons selectively refused selfish choices more often than prosocial ones, indicating the use of partner control by punishment. They also actively changed partners following non-rewarding interactions, indicating reliance on partner choice. Crucially, baboons flexibly adjusted these strategies as a function of partner availability.

Prior work in human reinforcement learning has distinguished between value confidence (certainty in value estimates) and decision confidence (certainty that a choice is correct), but how these signals are computed and interact during learning has not been directly tested. Here, we combine two new experiments with previously published datasets to evaluate competing computational hypotheses. We show that value confidence follows a Bayesian computation reflecting the precision of value estimates and adaptively regulates the exploration-exploitation trade-off. In contrast, decision confidence systematically departs from Bayesian predictions, particularly on incorrect trials. A hybrid model combining Bayesian probability of being correct with the precision of the decision variable provides a better account of decision confidence. Importantly, individual differences in the weighting of these components predict both task performance and metacognitive accuracy. Together, our results offer a unified computational account of how distinct forms of confidence shape learning and decision-making under uncertainty.

Children interpret the behaviors of agents in terms of internal features like goals, preferences or traits, which often have to be inferred indirectly from observed actions. To draw reliable inferences, rational learners should prefer observations that are relatively more informative with regards to these hidden variables. We tested whether 3- to 7-year-old children (n=83) selectively seek out informative observations about novel agents. To help them answer questions about alien characters' goals and traits, children could choose to watch one of two video clips on the basis of static images depicting the onset of events. One of these was more likely to yield relevant disambiguating information than the other. We found that children chose to watch the more informative video above chance from 4.3 years of age, suggesting that preschoolers can predict the evidential value of potential future observations to guide learning in the social domain.

We examined whether 2- and 3-year-old children flexibly ad- just their actions to the probability structure of the task, and whether this adaptiveness differs across types of uncertainty. Children encountered Uniform and Skewed conditions, in which outcomes were equally likely across four locations, and were constrained to a single location, respectively. Children chose between constraint-seeking actions that address multiple pos- sibilities and hypothesis-scanning actions that target a single outcome, across two conditions and two tasks that tap into differ- ent uncertainty types (epistemic and physical). Across ages and tasks, children systematically adapted their action choices to the underlying probability structure, selecting constraint-seeking actions more often in the Uniform condition and hypothesis- scanning actions more often in the Skewed condition. How- ever, age and uncertainty type modulated children's success in correctly implementing these actions, suggesting that the de- velopmental challenges lie in coordinating those actions with task-specific demands and forms of uncertainty.

Animals exhibit behavioural flexibility by making reward-optimal decisions in unfamiliar situations. Computing value from minimal reward feedback requires leveraging known structure in the world. The Prefrontal Cortex plays a pivotal role in both value computation and learning world structure, however mechanistic explanations for each---within the frameworks of Reinforcement Learning and Cognitive Maps---are largely distinct. We develop a mechanistic model of PFC that unifies value and structure, in which value acts as a control signal that gates structured working memory representations to ultimately control behaviour. This is a significant departure from the conventional perspective of value as an action-readout. Critically, this model generalises to novel contexts without need for additional synaptic plasticity. We demonstrate that recurrent neural networks (RNNs) meta-trained with RL recapitulate this exact model on various value-based tasks. These results provide a parsimonious explanation of both the value and schema frameworks of PFC function.

Humans are capable of remarkably flexible moral judgment. Yet societies rely on rigid rules—obligations and prohibitions that apply categorically, even when case-by-case reasoning could yield better outcomes. Why would flexible minds bind themselves to rigid rules? We propose that rigid rules arise to manage ambiguity about legitimate excuses for noncooperation. Such excuses are hard to see from the outside, which lets opportunists mask selfishness as justified hardship. We formalize this idea with an evolutionary game-theoretic model. Two cooperative equilibria emerge: a flexible norm that accommodates legitimate excuses but is vulnerable to exploitation, and a rigid norm that closes this loophole by mandating cooperation even when inefficient. Comparing these equilibria reveals an efficiency–robustness trade-off: flexibility maximizes welfare when trust is secure, whereas rigidity preserves cooperation when trust is fragile. This explains why rigid rules prevail in low-trust settings—interactions with strangers, formal institutions, or tight societies—while flexibility is common in high-trust contexts.

Similarity judgments provide a powerful behavioral signal for characterizing psychological representations. Computational techniques for translating similarity judgments into representations have been available since the early days of cognitive science. However, these algorithms typically focus on one particular kind of representation (e.g., spaces or trees), and identifying a satisfying representation often requires searching over a large space of possible structures and algorithms. Inspired by recent developments in AI, we propose an automated process for efficient representation discovery (AutoRep), in which large language model agents iteratively propose, refine, and critique code for fitting representational structures to similarity data. We show how this process can reliably recover psychological representations from both synthetic and human datasets, and how it can flexibly explore spatial, feature-based, graphical, or even neural representations. Our work demonstrates how modern agentic pipelines can substantially facilitate basic research workflows in cognitive science.

The complexity of human culture depends on people's ability to discover and transmit abstract knowledge. Studying this ability is crucial to understanding humans' distinctive place among species, but current experimental paradigms focus on the cultural transmission of specific, concrete facts rather than generalizable abstract knowledge. In this paper, we develop a crafting game paradigm to study how people discover abstract knowledge and transmit it via language. We compared individuals playing this game for 40 rounds to chains of four participants playing for 10 rounds each and passing messages to each other sequentially. The individuals performed significantly better over rounds, but the chains did not. Through simulations with language model agents and a follow-up experiment, we find substantial variation in the helpfulness of participants' messages, which may explain the lack of consistent improvement in chains. The ability to learn selectively from the good messages may be essential for improvement over generations.

Human cultures differ in how they think about space: some primarily use egocentric frames of reference (FoR), thinking about space in relation to the body, while others privilege geocentric FoR, thinking about space in relation to the environment. The origins of this cognitive diversity remain largely unknown. Here, we provide evidence suggesting an ongoing shift in spatial thinking—but not language—within a culture, offering insight into these origins. Hai||om people, a rural forager community in Namibia, have traditionally preferred geocentric FoR in cognition and language. Comparing new to historical data from the same community, we found that contemporary Hai||om show a greater preference for egocentric FoR, suggesting an egocentric shift in their cognition. Strikingly, we document no such shift in their language, suggesting language alone cannot account for cognitive diversity in spatial frames. We propose material culture, rather than language, as a key driver of diversity in spatial thought.

Accurate behavior prediction is essential for safe human-AI collaboration. However, existing models are often data-hungry or brittle, assuming unrealistic rationality or requiring immense computation to adapt. Our insight is that many everyday social interactions follow predictable "scripts''---efficient routines like "wait for the green light, then go'' that minimize cognitive load for actors and observers. We propose modeling these routines as behavioral programs in computer code, rather than policies conditioned on beliefs and desires. We introduce ROTE, an algorithm leveraging LLMs to synthesize a hypothesis space of programs and probabilistic inference to reason over uncertainty. In gridworld tasks and a large-scale embodied household simulator, ROTE predicts human and AI behaviors from sparse observations, outperforming baselines—including behavior cloning—by up to 50% in accuracy and generalization. By treating action understanding as program synthesis, ROTE enables AI to efficiently and effectively predict human behavior in the real world.

Conceptual development, both in childhood and across the history of science, is marked by moments of discontinuity: landmark events after which previously inexpressible thoughts and theories, incommensurable with the initial cognitive or scientific repertoire, become accessible. Despite longstanding interest, a computational account of these transitions that answers the deepest Fodorian nativist objections remains elusive. Here we present such an explanation by revisiting Carey's Quinian bootstrapping. Building upon models based on program induction, we develop an enriched paradigm in which discontinuous conceptual change becomes the invention of a new type system, related by structural analogy to the previous representation system, but also, crucially, generalizing beyond it. We instantiate our method in the classic domain of children's early mathematical development, modeling the acquisition of both natural and rational number. We find our model captures several previously unmodeled phenomena, and provides more parsimonious explanations for cross-linguistic universals and intervention effects in number learning.

Humans rapidly learn abstract knowledge when encountering novel environments and flexibly deploy this knowledge to guide efficient and intelligent action. Can modern AI systems learn and plan in a similar way? Leveraging a unique dataset of human gameplay with concurrent fMRI recordings, we evaluate model-free reinforcement learning agents, bayes-optimal model-based agents, and a frontier Large Reasoning Model, on two complementary dimensions: behavioral patterns and predictivity of human brain representations. Using encoding models, we assess how well each system's internal representations predict brain activity in regions previously implicated in theory-based reasoning. We find that the Large Reasoning Model most closely matches human behavioral patterns during game discovery and predicts brain activity in theory-coding regions an order of magnitude better than both model-free and model-based alternatives. Our results shed light on the computational principles underlying human-like rapid learning and planning.

Colours are not merely decorative; they influence perception, emotion, and behaviour, and can modulate executive functions. In humans, red is associated with danger and facilitates inhibition, possibly reflecting innate biases. Consistent with this hypothesis, studies in non-human primates reveal a perceptual advantage for red over green, and green over blue, in executive control tasks. However, how such perceptual biases interact with learned affective associations remains unclear. We investigated this question in Guinea baboons by combining a match-to-sample task and a stop-signal task in a population extensively trained to associate green with negative feedback. Red and green similarly facilitated inhibition, but green selectively slowed action execution relative to red. Moreover, social context further shaped performance: inhibition increased in low-ranking individuals in the presence of a higher-ranking partner, but decreased in dominant individuals. These results suggests that colour influences executive control through innate perceptual tendencies, learned affective association and social context.

Theory of mind allows us to connect other people's behaviors with a representation of their hidden mental states (beliefs, desires, emotions). Yet, in addition to mental beings, people are also living organisms with physiological states (e.g., feeling tired or energetic). Across three pre-registered experiments (total N = 232), we tested the hypothesis that US adults use a concept of bodily energy - a resource of the living body that fuels action - to reason about the minds and behaviors of other agents. Adults systematically judged that certain actions would cause an agent's energy to rise and fall, used an agent's energy state to reason about action cost, and used an agent's beliefs about action cost to infer their energy state. These results are consistent with the proposal that our theory of mind connects other agents' actions with their mental and physiological states. (Full paper is available at: https://osf.io/preprints/psyarxiv/d2axp_v1)

Human collective intelligence depends on cultural transmission: who shares what with whom, how, and when. These processes emerge from individual cognition but can also be directed by top-down rules. Prior work has studied how network structure—who connects to whom—shapes collective outcomes. Yet such networks cannot adapt transmissions to what agents know. Here, we formalize top-down transmission rules as state-aware programs that route information based on agent and collective states, and use LLM-guided evolutionary search to design them in a collective discovery task. Evolved programs increase performance over standard baselines by up to 37%. Ablations confirm state-awareness drives this advantage: removing content-dependence while preserving network topology and timing eliminates gains. Evolved programs also transfer across domain variations and agent populations. Effective transmission programs can thus be discovered in simulations, suggesting a path toward AI-assisted design of coordination infrastructure for human collective intelligence.

Nativist and empiricist theories of number perception dispute the content of early quantitative representations. We adapt a numerical comparison task using the "connectedness illusion" for young children (3-6 years) to test the theorized developmental transition from feature- to object-based enumeration predicted by some empiricist accounts. Children as young as three reliably underestimated connected dot displays, despite instruction to ignore connecting lines, replicating previous findings in adults (Franconeri et al., 2009; He et al., 2009). These results indicate that children's numerical judgments recruit discrete object representations, even before significant number-specific experience. Our findings constrain empiricist accounts which propose a prolonged feature-based stage of number perception and highlight the value of theoretically diagnostic stimuli to probe the origins of numerical cognition.

Grammatical number is one of the most widespread morphological categories in the world's languages. When only a single number distinction is expressed, it is almost always between singular and non-singular (plural), and even in systems with additional values such as dual, non-singular categories are frequently collapsed. The collapse of singular and dual categories is less frequent but attested, whereas groupings that merge singular with plural are virtually unattested. These patterns reveal strong cross-linguistic asymmetries in how number systems are partitioned. Linguistic theories have proposed that such regularities reflect underlying feature primitives, such as singular and minimal (Harbour, 2014; Noyer, 1992; Silverstein, 1976). The former explains why dual and plural constitute a natural non-singularity category, and the latter explains why singular and dual do so, as both are the cardinally smallest singular and non-singular values. However, little is known about whether these formal features correspond to learners' cognitive representations. In this paper, we test this proposal using two artificial language learning experiments that manipulate alternative number partitions. Our results show that learners systematically prefer partitions consistent with the proposed feature structure, with strongest preferences for singular-based contrasts, followed by minimal-based distinctions. These findings provide initial evidence for a cognitively principled explanation of the typology of partitions of grammatical number systems.

In referring to objects, speakers produce overinformative modifiers (e.g., "the large blue circle") more for color than size and increasingly with scene variation. Does first-person sensory experience establish this overmodification pattern? Do speakers adjust to listeners' perceptual capacities? In a referential communication task, congenitally blind speakers described target shapes to a sighted listener and sighted speakers to a sighted or blind listener. Trials varied in whether color or size alone was sufficient to identify the referent and in number of distractors sharing the redundant property. When talking to a sighted listener, blind and sighted speakers used color more than size and became more redundant as scene variation increased. When talking to a blind listener, sighted speakers' bias toward color disappeared. Blind and sighted speakers were more redundant overall when talking to a listener with different sensory capacities. Speakers flexibly adjust overmodifications to the listener's perceptual capacities, consistent with mentalistic audience-design.

Verbs are pivotal in human language and cognition. Psycholinguistic research has developed explicit, structured accounts of verb semantics, yet these theories have rarely been leveraged to improve models' visual action understanding. Meanwhile, current vision–language models (VLMs) often struggle with action semantics and exhibit unstable, weakly grounded judgments. Therefore, we introduce Verb Semantic Reasoning (VSR), a two-stage pipeline in which a Large Language Model (LLM) converts candidate action descriptions into structured event-semantic representations and a chain of semantic components questions; a VLM answers these questions over videos to select the best-supported action description. Results indicate that human performance was near ceiling, whereas both VLM baselines were substantially lower; VSR consistently improved accuracy across models and narrowed the human–model gap. These results suggest that explicit verb-semantic reasoning can significantly improve the accuracy of VLMs' action judgments, underscoring compositional, symbolic representations as an essential intermediate step for extracting semantics from the linguistic knowledge encoded in LLMs and leveraging it to guide VLMs' visual understanding.

Vision-language models (VLMs) have demonstrated unprecedented capabilities in perception and reasoning, yet their perceptual metacognitive abilities remain unexamined, a gap with implications for reliable deployment and welfare considerations. We evaluated six open-source VLMs on perceptual decision-making tasks, comparing implicit (logit-based) and explicit (self-reported) metacognition. Models exhibited robust implicit calibration and sensitivity on high-performance tasks but unreliable explicit confidence reports with poor calibration and task-dependent sensitivity. Process modeling further revealed that both confidence measures converged on classic Signal Detection Theory, suggesting VLMs lack the metacognitive monitoring mechanisms that differentiate decision and confidence processes in humans. Together, we showed that current VLMs thus limited perceptual metacognition with notable dissociations between implicit and explicit confidence.

Large language models (LLMs) can appear impressively capable across conversation, reasoning tasks, and standardized benchmarks. Yet merely appearing intelligent on traditional metrics is not the same as possessing robust, generalizable cognitive capacities. This disconnect highlights the need for more principled approaches to model evaluation. To address this gap, the symposium brings together four talks centered on developing a cognitive-science--inspired approach to assessing LLMs.

In this study, we examine expert-novice differences in knowledge organization using similarity ratings. Geology novices and experts provided pairwise similarity ratings for images of typical and atypical geological specimens drawn from the Rocks-30 and Rocks-360 stimulus sets provided by Nosofsky and colleagues (2018a). The pairwise similarity ratings for each group were used to construct multidimensional space representations as well as network models. Both network and MDS analyses revealed clear taxonomic differentiation by the experts. The novice MDS dimensions were easily matched to perceptual feature ratings provided in the Rocks-30 dataset. However, some of the expert dimensions did not match either the perceptual feature ratings or expert self-reported features. These results reveal expert-novice differences in knowledge organization using a quantitative methodology, and also suggest that aspects of expert knowledge organization may be missed when using methods that depend on self-report.

Within the study of children's knowledge of physics, there is considerable disagreement as to when children come to understand support and balance; while some research suggests that children as young as one year old understand support, other studies show that preschool aged children struggle with the same task. The current study builds on prior work through a modified block task where children aged 2-7 years old overtly predict if a block will fall or not fall off of a platform. Accuracy improved reliably with age, but contrary to predictions, symmetry, perceptual cues, and placement did not significantly affect performance. Children performed above chance yet well below ceiling throughout early childhood, even with familiar block-like stimuli. These findings suggest gradual developmental improvement in reasoning about support and challenge claims of early mature physical understanding inferred from infant looking-time studies.

Practice improves learning, yet it remains unclear which learning mechanism drives memory change across practice and testing. Competing theories attribute learning to repeated exposure, error correction, or successful retrieval, but these mechanisms are rarely evaluated in direct competition with one another. We propose a modeling approach that enables learning mechanisms to be evaluated during practice and during tests within a dynamic representation of memory change over time. Learning mechanisms are formalized as competing update rules and evaluated using nested models fit to spaced learning data with delayed tests. Results show that learning during practice is best explained by a combination of exposure-based learning and effortful successful retrieval. During posttests without feedback, learning, when present, is driven primarily by successful retrieval, with no additional benefit of retrieval effort. These findings suggest that practice supports learning through multiple mechanisms, but that the mechanisms depend on the constraints of the learning context.

Ambiguity is pervasive in verbal expressions of uncertainty: utterances such as "likely'' are interpreted as different probabilities by different individuals, leading to systematic misunderstandings between speakers and hearers. What are the group-level effects of such misunderstandings? Can they lead deliberating collectives to inaccurate beliefs, or pronounced disagreements? To address these questions, we introduce a new Bayesian agent-based model of ambiguous testimony, designed to compare the effects of increasingly ambiguous communication against a baseline of ideal exchange. We find that increasing ambiguity reduces collective accuracy and increases belief dispersion. For mild ambiguity, denser communication networks mitigate these risks. Under strong ambiguity, however, increased communication can backfire: higher network density can exacerbate collective inaccuracy and polarization.

Humans can create a new language with a sizable lexicon quickly. To build a shared lexicon, individuals have to first create symbols for concepts. We investigated this symbol creation process by asking hearing speakers to create silent gestures representing concepts (manipulable objects). Here we showed that people can rapidly create easy-to-comprehend and easy-to-produce symbols through perspective taking. The gesture produced by the majority of participants for a given concept was the one most effective for comprehension. More empathetic participants estimated gestures' communicative efficacy more accurately and produced more communicatively effective gestures. Furthermore, participants estimated communicative efficacy of the gesture they had produced to be higher than that of gestures they had not. Crucially, participants consider both communicative efficacy and production costs when creating gestural symbols. Thus, humans can rapidly create communicatively efficient symbols, which may partly explain why a new language can emerge quickly in a community.

Corpus studies suggest that less predictable words are more likely to be code-switched in bilingual communication, yet behavioral evidence remains limited, partially due to the difficulty of eliciting voluntary code-switches with a controlled linguistic context. In this study, we introduce a production paradigm that reliably elicits Mandarin–English code-switches and allows for precise manipulation of word predictability through numeral classifiers. Using this paradigm, we show converging evidence that both contextual predictability and baseline word frequency influence the likelihood of code-switching. This experimental paradigm provides a valuable tool for future studies of bilingual speech production.

When trying to convey conceptual knowledge to others, we may either show examples of the concept, or explicitly describe defining features of the concept. Here, we ask wether the dominant mode of knowledge transmission in a society shapes how concepts are ultimately represented by its members. We develop a computational model that generates novel predictions about how communication methods of a culture shape its conceptual structure. To test these predictions, we use a pre-registered, cultural transmission chain experimental paradigm, where participants transmit information about an imaginary biological category from one person to another along a chain (N = 1200), using only verbal descriptions ("tell") or only examples ("show"). Even though both conditions began with the same concept, the mode of transmission led participants to develop radically different representations. "Tell" chains lead participants to overemphasize easily namable features, whereas "show" chains lead to rely more on prior knowledge.

People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, users encounter chatbots that are overly agreeable. We argue this sycophancy poses an epistemic risk distinct from hallucination: it distorts belief not by introducing falsehoods but by biasing the evidence users see. A rational analysis shows that a Bayesian agent fed examples sampled from its own hypothesis grows more confident in that hypothesis without moving closer to the truth. We tested this prediction in a modified Wason 2-4-6 rule discovery task where participants (N=557) interacted with AI agents providing different types of feedback. Unmodified LLM behavior suppressed participants' discovery and inflated their confidence comparably to explicitly sycophantic prompting. By contrast, independent sampling from the true distribution yielded discovery rates five times higher. This paper documents how sycophantic AI distorts belief, manufacturing certainty where there should be doubt.

This exploratory study looks at analogical transfer in the context of computer science education, particularly during the transition from a visually scaffolded block-based language (i.e., Scratch) to a syntactically complex text-based language (i.e., Python). Using structure mapping theory, we compared surface similarity and structural relationships of fundamental programming constructs (e.g., variables, iteration) in the two languages to predict which students would most likely transfer. 76 students (ages 8-14) proficient in Scratch with little Python experience completed an assessment measuring transfer of these constructs with minimal instruction. Students who saw more similarity among the languages performed better on the Python items, suggesting they perceived structural similarities. However, we did not find evidence that students transfer after controlling for general programming ability. Findings suggest that students need more targeted instruction to conceptualize structural relationships and support robust analogical transfer between block-based and text-based programming languages.

Humans are social beings, and intentional communication is the backbone of how people share what they are thinking, coordinate over long periods of time, and learn from each other's ideas. People have finely attuned capacities for inferring each other's mental states, but intentional communication offers a much richer and more direct window into someone else's thoughts than passive mind-reading alone.

Little is known about whether humans show moral concern for harmful actions toward non-human entities like plants, or if how others treat plants influences social preferences. It is also unclear when these possible concerns develop. We address these gaps in two studies: Study 1 with adults (n=153) and Study 2 with 3- to 6-year-old children (n=129). Participants watched a video of a plant restorer who restored a plant but knocked down a bucket, and a plant harmer who restored a bucket but knocked down a plant. Adults preferred the plant restorer over the plant harmer, chose the plant harmer as the bad guy, and evaluated it more positively for restoring the plant than the bucket. Children showed similar patterns—especially those with greater biology knowledge—but did not prefer the plant restorer. These findings help understand early moral reasoning regarding non-human nature and how people value living versus nonliving entities.

Generating explanations requires coordinating causal understanding with perspective-taking. This work tests how children integrate these capacities using a novel paradigm. We presented children ages 4–7 with conjunctive causal scenarios where two causes contributed to an outcome. Two characters each possessed partial knowledge of only one cause. Children predicted what each character would say caused the outcome and provided their own explanations for the outcome. Children successfully predicted single-cause explanations from characters' perspectives, with age-related improvement. Generating conjunctive explanations from their own perspective showed dramatic developmental gains: 44% adult-like reasoning in 4–5-year-olds versus 72% in 6–7-year-olds. Two ongoing experiments disentangle these demands by selectively removing one: the first gives both characters shared knowledge, eliminating the perspective-taking demand; the second retains two actions but only one causes the outcome, eliminating the conjunctive demand. Together, this work reveals explanation as a developmental achievement built through active construction.

Iconicity has been widely studied in relation to language acquisition, processing, and evolution. Recent work has emphasized the subjective nature of iconicity and the role of individual construal, yet it remains unclear when and under what conditions individual differences in iconicity perception arise. As a first step toward addressing this issue, the present study examines how personality traits (Big Five) and individual background factors relate to iconicity ratings of Japanese words. We observed substantial individual variability in iconicity ratings. Among the traits, extraversion showed a small but reliable negative association with perceived iconicity, such that lower extraversion was associated with higher iconicity ratings. In addition, self-reported ideophone use emerged as a consistent positive correlate of iconicity ratings, whereas no other background factors showed reliable independent associations. These findings suggest that iconicity is not merely an intrinsic property of linguistic forms, but a perceptual phenomenon that varies across individuals. At the same time, the results indicate that such variability is not systematically structured by broad background factors, suggesting a selective role for specific forms of linguistic experience. Overall, the study highlights the importance of considering who rates words as more or less iconic, and under what experiential conditions, in theoretical accounts of iconicity in language.

We investigated how 7–11-year-old children (N = 160) think about and evaluate parental protectiveness. We presented two parent-child dyads who differed in protectiveness: One parent was consistently highly protective (e.g. requiring extensive protective gear for biking); the other consistently permitted risky behaviors (biking without protective gear in a busy parking lot). Participants then made a range of judgments. They judged the more-protective parent as better overall, but also showed context-sensitivity: On a risky field trip, they preferred the more-protective parent as chaperone; but increasingly with age preferred the less-protective parent for a safe field trip. With respect to the parents' children, participants judged the less-protected child to be popular and mischievous. In contrast, they judged the more-protected child to be brilliant and nice, and would rather have that child come to their house or be class president. These findings reveal that children consider and reason about differences in parental protectiveness.

How do people search their memory when answering a question? To examine how this process relates to the degree of constraint imposed by a question, we asked 329 participants to generate sequences of responses while answering either more constrained trivia questions or broader category-based questions. Across both tasks, answer generation reflected common retrieval dynamics: participants were more likely to produce generally accessible answers, and transitions were strongly shaped by similarity to previously generated responses. However, answers generated in response to trivia questions were more strongly aligned with the semantic content of the question itself. Analyses of response times also revealed a dissociation in retrieval dynamics while answering specific questions: answers semantically similar to prior responses were generated more quickly, whereas answers more closely aligned with the question were generated more slowly. This pattern suggests that more specific questions engage an additional control process that intermittently redirects memory search toward the question. The pattern does not arise from a random walk through semantic space with constant transition probabilities, but instead supports a view of memory search as a foraging process, in which search alternates between associative small steps (local exploitation) and question-directed big jumps (wider exploration) as the ease of finding a local transition varies.

How do people construct coherent understanding while navigating information-rich digital environments? We address this question by modeling sensemaking as a trajectory through semantic space, representing web navigation not as discrete page visits but as a continuous path through meaning. By analyzing the semantic content of web pages accessed during research tasks, we demonstrate that users' navigation patterns reveal two distinct forces: alignment toward task-relevant information and coherence with recently explored content. Statistical analysis showed that goal-directed alignment dominates information consumption, while motivational interest has a negligible influence. This dissociation provides evidence that sensemaking operates as a distinct cognitive process, independent of intrinsic motivation. Temporal patterns in browsing behavior, including how users maintain semantic coherence, drift from their goals, and accumulate meaning over time, reveal the hidden cognitive dynamics underlying knowledge construction. Our framework establishes semantic trajectories as a measurable phenomenon, bridging information foraging theory with modern computational models of meaning. This work reveals that the path users trace through information spaces reflects fundamental cognitive processes of interpretation and integration, offering new methods for studying how understanding emerges from interaction with complex digital environments.

Visual object categorization unfolds on the timescale of milliseconds as sensory representations are transformed into decisions through recurrent processing, yet it remains unclear whether this processing merely sharpens category structure shared across individuals or gives rise to individualized category representations. Using MEG, we examined object categorization along continua spanning basic-level category pairs in which category membership was supported either by graded, weakly diagnostic features (Experimental Continua) or by discrete, highly diagnostic features (Control Continua). Time-resolved multivariate decoding showed that early neural activity reflected shared perceptual structure for all continua, whereas later, response-locked activity encoded individual-specific category boundaries. Temporal generalization analyses further demonstrated that recurrent dynamics stabilized these individual category representations. Finally, a distance-to-bound analysis revealed that neural category representations predicted reaction times for Experimental Continua. These findings show that recurrent dynamics do not merely refine universal category axes but reorganize neural representations to reflect each observer's learned category structure.

Even competitive activities require cooperation to agree on the rules. How do people navigate this tension, and what conditions help them reach agreement? We study rule negotiation in a collaborative game design task (N = 184) where dyads propose and vote on what game to play, crossing two factors: communication (chat available vs.~vote-only) and role uncertainty (whether players know which role they will occupy). We find communication increased agreement rates and role uncertainty had only a minimal impact on agreement. When players knew their role, they proposed games that subtly favored themselves, balancing self-interest against the need for acceptance. Chat content was largely coordinative rather than argumentative: players declared preferences and deferred rather than playing hardball. Our findings highlight how deliberation helps people coordinate on rules, even when they know which side they're on.

Shared sleep arrangements, especially children's, often occur in proximity to family and other caregivers. We investigated whether sleep-sharing serves as a reliable cue to familial and caregiving relationships, even in childhood. Two preregistered studies with U.S. 5–7-year-olds (N = 168) and adults (N = 168) show people possess systematic intuitions about the social meaning of shared sleep arrangements. In Study 1, both age groups robustly expected that a child character sleep-shared with family over non-family (siblings over friends; parents over teachers), while expecting equal toy-sharing. In Study 2, both age groups predicted that a child's chosen sleep-sharing (vs. toy-sharing) partner would provide physical comfort (care) when the child became upset. Adults privileged sleep-sharing over another intimate interaction––saliva-sharing––as diagnostic of a caregiving relationship. From early childhood, people use shared sleep arrangements as a powerful signal of intimate relationships, supporting their understanding of the social landscape.

In both cognitive science and AI, planning research is characterized by a fundamental challenge: algorithms are costly. Consequently, much work concerns simplifying the required computations, or biasing them to more favourable regions of the solution space. One promising candidate for this is memory; memory is a powerful tool for guiding behaviour, especially in tandem with parametric learning models. However, a complete understanding of how these processes should and do interact in the mind remains elusive. We present statistical evidence from an extremely large dataset of chess games that memory empirically accelerates planning in the opening. In particular, we show that this interaction is rational: it is sensitive to the criticality of the given position, its popularity, and the latency since the last experience. We capture these dynamics in a normative model that integrates memory into Monte-Carlo tree search with an uncertainty-based stopping rule to emit move times.

Words are not detached from the world: they simulate embodied experiences. Concrete words, for being closer to sensorial information, are normally understood as "easier" processed (in word naming, lexical choice or recall tasks, for example), what is commonly called "concreteness effect". Nevertheless, some studies show a better performance in abstract words, probably due to their emotionality or lexical associations. With the objective of better elucidating this discussion, we created 18 novel words (6 concrete, 6 emotional abstract, and 6 social abstract), that were presented in a sentential context. Then, we applied 4 behavioural tasks: free recall, lexical decision, free-form definition, and multiple-choice definition. Our results showed recurrent closeness between concrete and emotional abstract words in both lexical and semantic tasks, in contrast to a distinct pattern for social abstract words, which also showed lower reaction times in the lexical decision task, probably due to their ease in lexical associations.

The human ability to convey meaning through sound extends beyond spoken language, encompassing the use of tools like musical instruments. People use such instruments to express not only thoughts, but also feelings. How might someone choose to convey such complex meanings with some sounds instead of others? In this study, participants (N=256) used a novel digital musical instrument to produce sound effects to accompany several video clips, such that someone else could match them up later (Referential). Half of these participants were further encouraged to make their sound effects pleasing, towards revealing what distinguishes sounds meant to evoke a particular feeling (Pleasing). The two groups produced different kinds of sound effects, with Pleasing participants favoring sound effects that spanned a narrower range of pitches, while Referential participants created more consistent cross-modal mappings. Taken together, these findings highlight how readily people can convey complex meanings through sound.

Lexical selection involves a trade-off between message alignment (saying the most accurate word) and accessibility (saying the easiest word). We test predictions of a resource-rational model of lexical retrieval against a fixed-cost alternative, using a novel word-learning task. Participants learn words for novel compass directions and use these words to label prototypical and novel angles on the compass. In Experiment 1, we manipulated the relative frequency of the new vocabulary terms as well as the response deadline to test whether good-enough effects scale with accessibility differences and time pressure. In Experiment 2, we manipulate the payoff for precise responses to test whether speakers adjust their strategies when precision is more valuable. Together, these experiments provide a parametric test of the hypothesis that word choice reflects rational trade-offs between retrieval costs and communicative benefits, with implications for understanding language production as a form of resource-rational decision-making.

As technology becomes ever more deeply embedded in daily life, how do families make decisions about responsible technology use? Here we examined how parents and children use social norms and explanations when deciding about technology use rules. In Study 1, we asked parents of 3- to 11-year-old children how they set and explain technology use rules for their children. In Study 2, we recorded asynchronous conversations between parents and 6- to 10-year-olds about technology rules. Across studies, parents and children used social comparisons to peers to set and justify rules. Parents' explanations scaffolded children's reasoning, but they rarely referred to structural factors like deceptive technology design that make rules necessary to keep kids safe. By gaining a more nuanced description of how families navigate decisions about children's technology use, we hope to inform strategies to help parents and children negotiate responsible home technology use to improve family harmony and flourishing.

Social learning plays a crucial role in human cognition, yet most studies rely on simplified paradigms with explicit options and immediate feedback, such as two-armed bandit tasks. These paradigms are limited in learning in complex problem-solving situations characterized by uncertainty and delayed evaluability. The present study examined social learning strategies in insight problem solving by reanalyzing behavioral data from a T-puzzle task (Kiyokawa et al., 2007). Using Colored Cross Recurrence Quantification Analysis (C2RQA), we analyzed temporal correspondence between participants' action sequences across observation conditions and action categories. Observing others was consistently associated with changes in recurrence patterns, whereas self-observation showed no reliable effects. Action category also exhibited robust effects, and observation effects varied across recurrence measures. These findings suggest that social learning in insight problem solving is reflected in temporal interaction structures rather than outcome measures alone, highlighting the importance of process-oriented analyses under uncertainty.

Much research suggests that children learn through play. Yet, play has a reputation as frivolous, and beliefs about play and learning vary across development and sociocultural contexts (Wing, 1995; Bugallo et al, 2024). These differences may reflect varying intuitive theories about how activities promote or prevent learning and shape activity choices. Here, we examine beliefs about how specific activity features impact learning experiences and outcomes. Participants (n=41 adults, ongoing) evaluated 16 math games that systematically varied in both instructional features (e.g., feedback) and playful aesthetics (e.g., audio-visual effects). Results show that games rated as more enjoyable were also rated as better for improving learning. Ongoing work examines how 2nd-4th graders, teachers, and parents respond to specific game features. Our research helps characterize the intuitive theories guiding how people reason about the design of learning experiences, and how such beliefs change with development and experience.

Iconicity is a central notion in cognitive science, attracting growing attention from perspectives on language acquisition, processing, and language evolution. While previous research has emphasized the subjective nature of iconicity, quantitative analyses of iconicity ratings have relied primarily on mean values, treating iconicity as a stable lexical property, and have often been constrained by substantial data loss when integrating multiple lexical and psycholinguistic datasets. The present study addresses these limitations by analyzing both mean iconicity ratings and inter-rater variability across 14,764 English words, combining a conservative complete-case analysis with complementary analyses based on imputed predictors to increase lexical coverage. For example, in the complete-case analysis, earlier-acquired and etymologically imitative words tended to receive higher mean iconicity ratings, whereas rating variability was greater for earlier-acquired and more familiar words. By examining both mean ratings and variability, we distinguish factors associated with perceived iconicity from those associated with convergence or divergence in iconicity judgments, offering a broader account of lexical iconicity and heterogeneity in iconic perception.

Pain is a subjective experience that people sometimes communicate through words. How do observers, from family members to medical professionals, interpret these reports? We propose that people use causal inference to infer someone else's subjective pain experience, treating the pain experience as an unobservable latent variable and the report as one of several sources of observations. We formalized this as Bayesian inference over a causal model of bodily pain and tested it against human judgments on naturalistic stimuli drawn from Reddit posts. Critically, the model is stimulus-computable, taking as input exactly what participants see. The model quantitatively predicted human judgments about pain intensity and appropriate painkiller dosage and generalized across different kinds of pain (e.g., from headaches to finger burns). Furthermore, the model captured individual differences correlated with people's explicit beliefs about how best to assess pain. This work takes a first step toward a scientific understanding of folk intuitive theories of pain.

As neural networks reach expert-level performance in many domains, there is growing interest in understanding the internal representations that guide their decisions. Models trained to mimic human decisions, rather than strive for optimal performance, offer a particularly interesting testbed for interpretability. Identifying what information these networks encode can generate hypotheses about what information humans rely on when performing similar tasks. Here, we use concept-based interpretability methods from prior work on superhuman-level chess neural networks to study neural networks trained to emulate human play across differing skill levels. We find that human-interpretable chess concepts are decodable from their latent representations. For many concepts, decodability increases with human-emulated skill level, and also varies with network depth: simpler concepts peak early, whereas complex concepts emerge later. Together, these results show that interpretability tools can be applied to AI systems that mimic human behavior and suggest how task representations may shift with expertise.

Recent work has found that contemporary language models such as transformers can become so good at next-word prediction that the probabilities they calculate become worse for predicting naturalistic reading time. In this paper, we propose that this can be explained by reading time being shaped by simple n-gram statistics rather than the more complex statistics learned by state-of-the-art transformer language models. We demonstrate that the neural language models whose predictions are most correlated with n-gram probability are also those that calculate probabilities that are the most correlated with eye-tracking-based metrics of reading time on naturalistic text.

Temporal bisection tasks are widely used to assess interval timing, but standard behavioral indices cannot easily distinguish changes in temporal discrimination from shifts in response bias. In this study, 194 adolescents completed a visual temporal bisection task with two consecutive blocks. Using hierarchical Bayesian psychometric modeling, we decomposed binary duration judgments into temporal sensitivity and decision bias, and examined how these parameters changed during task progression. At the group level, temporal sensitivity declined in the second block, whereas decision bias remained relatively stable. Despite this decline, individual sensitivity estimates showed strong short-term rank-order stability across blocks. Sensitivity changes also varied across individuals and were systematically related to baseline sensitivity, with higher baseline sensitivity associated with smaller declines. Model-derived sensitivity changes corresponded to changes in conventional psychophysical indices, including the difference limen and Weber ratio. These findings highlight within-session dynamics in adolescent temporal discrimination and demonstrate the utility of hierarchical modeling for separating sensitivity and bias.

Human decision making is richly structured: choices reflect not only what option is selected, but how efficiently evidence is evaluated and conflict is resolved. Do observers represent this structure in others' minds? We investigate how adults use others' decision time to infer both properties of the decision itself (e.g., difficulty) and properties of the decision-maker (e.g., preferences). Across two experiments (n = 200), participants observed agents making simple two-alternative reward decisions that varied in expected difficulty, decision time, and chosen option. We find that observers interpret timing relative to structured expectations about how long a decision should take, using deviations from these expectations to draw nuanced inferences about the state of the decision problem under uncertainty (Exp 1) and about an agent's preferences (Exp 2). Crucially, identical choices supported different conclusions depending on how long they took. These results suggest that people invert expectations about how decisions unfold to read others' minds in real time, treating decision time as a cue to conflict, uncertainty, and value.

Observers categorize core event roles (agents, patients) from brief visual scenes, but less is known about the spontaneous encoding of more peripheral event roles such as instruments. Semantic analyses and psycholinguistic research suggest that instrument events are categorized into those that conceptually require an instrument (...stirring with a ladle) and those that allow one (...drinking with a straw), but whether this distinction affects rapid role extraction from visual scenes is currently unclear. Across two experiments, we examine whether instrument event roles can be extracted from brief visual displays (73ms). We manipulated the event category (require/allow), instrument typicality (typical/atypical), and prompt consistency (consistent/inconsistent). Participants watched instrument events followed by a mask and were tested for event apprehension ("Did you see stirring?"; Exp.1) and role extraction ("Did you see a spoon?"; Exp.2). Results from this study contribute to an understanding of how conceptually complex event information is extracted from visual input.

How do learned strategies generalize to new problems? Children who learn to count can add any two numbers by iteratively updating a running total, even for sums they have not encountered. We trained RNNs with a working-memory readout that tracks the progressive count at every timestep and is fed back to maintain a running total, using problems requiring counts up to 5, and tested generalization on counts up to 9. We demonstrate that such RNNs successfully generalize, with continuous drift dynamics and reaction times scaling linearly with count length. Analyzing internal dynamics, we found two emergent number-line representations: one for rapid retrieval of the starting value, one for slow iterative counting, consistent with graded number representations observed in human parietal cortex. Generalization was strongest when network activity remained within linear regimes visited during training, providing a mechanistic account of both successful generalization and its limits.

A central puzzle in higher-order cognition is the unidimensional sort bias: the overwhelming tendency of humans to sort examples in a novel domain based on a single feature when a family resemblance (FR) structure is available. A standard unsupervised category construction task with two prototypes and 'off-by-one' distortions was investigated using novel task supports. Participants were instructed to find a "very close match" to begin each category and to expand the groups by identifying additional very close matches. This guides participants to employ stimulus generalization: using close proximity in psychological space to extend a consequence. Exemplar theory accounts for category learning as stimulus generalization plus dimensional selective attention – suggesting that unidimensional sorting arises when a single feature captures attentional control. However, instructional supports to prioritize very close matches shifts the evaluation to a global match resulting in a dramatic increase from nearly none to half of participants producing FR sorts.

How do people evaluate the trustworthiness of advice received from other people or AI systems? One signal for trustworthiness is thinking time: the amount of time the advice-giver spent thinking about what advice to give. Different factors may influence inferences based on thinking time: greater thinking time may reflect a lack of knowledge or confidence. Conversely, it may reflect higher-quality advice resulting from more thorough deliberation. We study participants' judgments of trustworthiness based on thinking time by presenting them with pairs of hypothetical human or AI advisors who spent more or less time thinking about their decision. Across most domains we tested, participants preferred longer deliberation in human advisors, but did not show such a preference with AI. These results provide preliminary evidence that people can make sophisticated judgments about advice quality by integrating knowledge about the advice giver and the time it took them to generate their advice.

Artifacts and problems co-determine one another in a continuous process. If externalist cognition is described merely as the efficient use of artifacts to solve problems, we lose sight of the mediating process through which artifacts and problems are reciprocally constituted. Repente, a Brazilian tradition of improvised oral poetry, illustrates this dynamic particularly well. Poet-singers must solve problems under multiple constraints while mastering semiotic artifacts such as versification patterns, melodic structures, and musical instruments. What is produced through this process? Our proposal is that improvisation generates regularities. In this framework, what stands at the center of distributed cognition is neither an isolated agent nor a discrete task, but a rule for producing regularities. This rule triadically mediates between signs (artifacts) and problems (objects), generating regular effects (interpretants). The result is a distributed cognitive system grounded in the stabilization and transformation of semiotic habits.

In many languages, number words follow a recursive morphological structure. Past research suggests that children with an extended and regular count list are earlier to notice such structure than those with a more limited and less transparent count list. Building on this line of research, we propose that children's insight into the recursive structure develops in part through comparison processes that allow them to notice morphological regularities across number words. To test this, we presented 4–5-year-old children with a visual depiction of numbers 0–49, along with comparison techniques designed to facilitate the alignment across decades. We used a pretest–training–posttest design. In pre- and post-tests, we assessed children's ability to produce the successor of numbers beyond their counting range and their understanding of numerical infinity. In the post-test, we also assessed their ability to produce the successor for novel numbers. We found that the training improved children's understanding of the successor relation between numbers, though not their understanding of infinity.

As Large Language Models (LLMs) grow more capable across diverse tasks, their (in)ability to generalize remains difficult to quantify and poorly understood beyond limited domains. In particular, LLMs are known to struggle generalizing multilingually, to languages outside of English, and that are poorly attested in their training data. To understand why this may be, and what enables some models to perform better than others, we turn to a long history of work across the cognitive sciences, arguing that successful generalization derives from appropriate representations in similarity space. We look at how well LLMs' representations capture the hierarchical similarity structure between distinct languages. Strikingly, we show LLMs' latent representations largely recover the hierarchical structure of the Indo-European language family tree -- grouping languages that are members of the same subfamily closely together in representation space. Furthermore, we show that the degree to which models reflect the similarity structure of languages correlates with their performance on XNLI, a multilingual natural language inference benchmark. This extends classic work on similarity-driven generalization at scale, showing how models that represent similar languages similarly generalize better from one language to another.

Cognitive strategies are essential for visuomotor adaptation. Task set size (the number of targets) often determines which strategy is engaged: working memory (WM) is sufficient for small set size, but its capacity is often limited for more complex tasks. These WM limitations may be bypassed by retrieving a strategy from long-term motor memory (LTM), but the required repetition is unknown. To address this, participants were trained on a single target–rotation pair, establishing an LTM visuomotor solution, after which we probed LTM-based retrieval across set sizes by comparing it with WM. The results indicated that LTM-based retrieval was relatively automatic and largely insensitive to set size, whereas WM-based retrieval declined as set size increased. Initial practice of just 10 or 30 trials produced similarly robust LTM for a strategy, which outperformed strategies held in WM. Model-based analyses suggested a trend toward higher LTM precision at larger set sizes, consistent with dynamic interactions between WM and LTM.

Visual description is a fundamental yet challenging task that reflects how perceptual information is transformed into structured linguistic descriptions. Recent diffusion-based models have shown strong potential for parallel decoding and global semantic modeling. However, existing approaches largely treat generation as an unconstrained process, lacking mechanisms to incorporate structured knowledge, which often results in conceptually implausible or hallucinated descriptions, especially in domain-specific contexts. To investigate how structured knowledge constrains generative diffusion processes, we propose KenDiC, a Knowledge-enhanced Diffusion-based Captioner for visual description. KenDiC integrates a domain-adaptive visual encoder (DAVE) trained via contrastive learning to align perceptual and linguistic representations, and a domain term vocabulary (DTV) that constrains decoding to guide concept selection during generation. To support systematic analysis, we construct II2T-Bench, a domain-centric benchmark with expert annotations. Experimental results show that structured knowledge constraints significantly reduce hallucinations and improve semantic fidelity, suggesting a computational account of how knowledge guides generative visual description.

Do linguistic structures reflect the organization of human conceptual representations? A broad tradition in cognitive science argues that grammar is shaped by rich, compositional event representations that underlie cognition more broadly, motivating the use of linguistic patterns to probe the structure of thought. However, direct evidence linking linguistic and non-linguistic event cognition is sparse. Here, we test whether event categories are consistently represented across language and vision. Across two experiments, participants categorized event types using either linguistic descriptions or short video clips depicting the same classes of events. We find that humans reliably and consistently categorize events in both modalities, exhibiting parallel patterns of categorization across language and vision. Notably, this categorization is not inherent to every sophisticated cognitive system: State-of-the-art computational vision models failed at the same task. These results suggest that linguistic and visual event processing draw on a shared underlying system of event representation, providing evidence that language offers a window into human event cognition.

Formal rules can influence behavior by deterring rule-breaking or broadening knowledge, but may also reveal why a rule was created in the first place. Across three experiments, we investi- gated whether adults and children use the presence of a written rule to infer past and present rule-breaking. Adults reliably in- ferred that groups with written rules had more rule-breaking in the past (Study 1) and, in some contexts, expected less rule- breaking in the present (Study 2). By late childhood (ages 8-11), children showed increasingly adult-like inferences when rules were concrete and familiar (Study 2). Younger children (ages 5-7) additionally were able to make this inference (Study 3). Together, these findings suggest that by middle childhood, children treat formal rules as historical evidence about past rule-breaking. These results shed light on the developmental origins of reasoning about formal rules and their informational value.

A few minutes practice is often more than sufficient for an adult human participant to identify the structure of a problem they have never seen before, and plan a complex sequence of actions that creates a solution. However, it remains less clear whether this distinctive ability for ad-hoc discovery of problem-solving algorithms is itself subject to rapid meta-learning. We developed a novel problem-solving paradigm to examine aspects of this question. Participants in our study faced repeated iterations of an interactive sequential reasoning problem. Over trials, those who faced the hardest version developed increasingly efficient hierarchically-structured strategies that adaptively sequence a subordinate learning algorithm and an action planning policy; those who faced an easier version used simpler action-based strategies that did not involve learning the underlying structure. These results offer experimental evidence for efficient meta-learning of algorithmic concepts in a problem-solving setting.

Vision-Language models (VLMs) can now produce fluent, socially appropriate dialogue, leading to interest in whether they have Theory of Mind (ToM). While recent work suggests that VLMs still lack coherent mental-state reasoning, this work has focused on classical propositional belief representations. Here we test a complementary, communication-relevant form of ToM: attention-based social micro-processes that support referential communication in the here and now (i.e., selecting efficient descriptions to identify a particular object for someone else). In face-to-face communication, people strategically add redundant color adjectives to facilitate the listener's visual search, and omit them when they provide no benefit. We evaluate whether VLMs show the same strategy in a referential communication paradigm where the usefulness of redundant color words varies based on the set size and color distribution of objects. We find that VLMs can produce successful referential expressions but lack the attention-guiding strategies that make human communication so efficient. This suggests that VLMs lack the more implicit representations of attention that people use in everyday communication.

Humans align to one another in conversation—adopting shared conventions that ease communication. We test whether LLMs form the same kinds of conventions in a multimodal communication game. Both humans and LLMs displayed evidence of convention-formation (increasing the accuracy and consistency of their turns) when communicating in same-type dyads (humans with humans, AI with AI), though AI-AI pairs show limited reduction in length. Heterogeneous human-AI pairs failed to converge on effective referring expressions, suggesting differences in communicative tendencies. In Experiment 2, we prompting LLMs to produce superficially humanlike behavior. While the length of prompted models' messages matched that of human pairs, accuracy and lexical overlap in human-AI pairs continued to lag behind that of both human-human and AI-AI pairs. These results suggest that conversational alignment requires more than just the ability to mimic previous interactions, but also shared interpretative biases toward the meanings that are conveyed.

It is widely assumed that when belief polarization occurs – people updating their beliefs in opposing directions in response to the same evidence – people's reasoning must be in some way irrational. Yet, several authors have shown that Bayesian reasoning can, theoretically, cause polarization. I provide an accessible explanation for why Bayesians polarize, unifying some previous approaches. I elucidate the conditions under which Bayesian polarization can occur in two scenarios, providing non-technical explanations, with examples, and using Bayesian Networks to set up mathematical proofs. I show how these two scenarios can provide explanations for some prior documented cases of belief polarization in the literature.

Clustering and switching analyses have characterized semantic memory deficits in Alzheimer's disease (AD) using fluency tasks, where participants list category members within time constraints. We applied these analyses to the property listing task (PLT). The PLT differs fundamentally from fluency tasks: it is non-timed, involves an open-ended rather than a finite set of responses, and includes varied property types (perceptual, functional, taxonomic). Using temporal slope difference clustering on data from 24 AD and 35 control participants, we found that AD participants generated fewer properties and exhibited reduced switch rates between temporal clusters, even when controlling for task duration. Cluster size and within-cluster timing were preserved, but AD participants showed longer switch times and reduced within-cluster semantic variability. These findings reveal how clustering and switching manifest in property generation, suggesting that AD affects strategic exploration while preserving local retrieval within temporal clusters.

In all languages, there exist multiple so-called alternations—pairs of constructions with similar truth conditions—such as the dative alternation, which consists of the double object and prepositional-phrase object constructions. Many alternations exhibit an asymmetry such that there is a canonical, general construction and a non-canonical, alternative construction with a different ordering of the verbal arguments. What is the function of such redundant constructions? We tested a proposal by Birner & Ward (2009, 1998) that the non-canonical construction is used to satisfy an information-structural constraint: placing old (given) information before new information in the sentence. In 4 experiments, in English and Italian, on the dative and locative alternations, we find experimental evidence that non-canonical constructions show preference for old-before-new information structure above and beyond canonical constructions, supporting this idea. These findings provide initial empirical evidence for a theory of how information structure may influence the use and emergence of constructions.

Collective outcomes often result from complex social dynamics where individuals both contribute directly and also shape each other's contributions. How do we hold people responsible for an outcome when their actions influence others? Here, we examine how an individual's role within a group (whether they can influence others, be influenced by others, or act independently) affects how responsible they are judged to be. Across three experiments spanning both social and physical settings, we find that people systematically assign greater responsibility to those who can influence others. Furthermore, influencers with knowledge of their potential impact were held more responsible than those who were unaware. The relative responsibility of individuals who were influenced by others and who acted independently differed by context. Together, these results show that we hold others responsible by considering not only how their actions directly affect the outcome, but also how they affect others' propensity to act.

Can patterns in language predict people's reasoning about blame and punishment? In this study, we focus on how individual linguistic preferences (agentive vs non-agentive frames) of English, Spanish and Turkish speakers correlate with their judgments of financial liability regarding property damage across events involving intentional agents, accidental outcomes and bystanders. Findings show that individual linguistic preferences predict liability judgments, controlling for event type.

Humans flexibly create tools to solve physical problems, yet the cognitive processes underlying this ability remain unclear. A natural hypothesis is that tool creation involves searching for designs that maximize success probabilities using an internal intuitive physics model. We investigated this by having participants freely construct tools in virtual environments by adding or removing pieces. Despite solving the tasks, participants' choices were only weakly related to estimated success probabilities under a noisy physics engine and were poorly captured by a simple physics-based search model. Crucially, analysis of tool creation trajectories revealed that participants systematically passed through intermediate configurations without treating them as candidate solutions, even when those tools were predicted to succeed. These findings suggest that rather than purely evaluating intermediate designs via physical simulation, human tool creation is guided by a strategy-constrained, action-based search process that structures exploration of the tool space.

Large language models (LLMs) are increasingly being used as tutors and thought partners, yet how they navigate intervention decisions during problem-solving remains poorly understood. While AI guidance can scaffold learning, its benefits depend on how such systems help—intervening too early or too frequently may hinder learning and cognitive engagement. Here, we introduce Int-Bench, a simulation-based benchmark for evaluating LLM interventions. Int-Bench simulates a "student" solving a problem while a "teacher" monitors the student's reasoning and decides whether, when, and how to intervene. Across three domains—code debugging, mathematics, and brain teasers—we evaluate LLM teachers on intervention frequency and timing, and their impact on immediate task success and generalization to new problems. Compared to humans, LLMs intervene more frequently and earlier, and tend to provide complete solutions rather than targeted hints. These findings suggest that current LLM assistants often optimize for short-term success rather than preserving the reasoning processes needed for learning.

We report an experiment on the relationship between insight and analytic-based learning during concept acquisition. Subjects learned featural categories (Type I or Type VI) through either classification or inference. Additionally, some subjects switched from classification to inference, whereas others switched from inference to classification. To assess insight, subjects provided warmth judgments throughout the study. We found that warmth judgments increased more rapidly for classification than for inference on Type I category structures, whereas inference produced more gradual increases. Moreover, when subjects switched from inference to classification, rapid increases in warmth judgments closely matched those found in pure classification learning, but only on Type I categories. However, this increase was not observed for Type VI categories. These findings suggest that classification is driven by insight-based learning processes, but this relationship seems to depend on the learnability of the category structure, whereas inference seems to be driven by analytic-based learning processes.

In contrast to nonfactive belief verbs, such as the English "think" and Mandarin "juede" (to think/feel), the Mandarin contrafactive belief verb "yiwei" is negatively biased and commonly used to report false beliefs (Glass, 2023, 2025). Previous studies show that children can distinguish between nonfactives and contrafactives by 4 years of age, and the use of "yiwei" facilitates understanding of false-belief sentences and false-belief abilities more generally (Lee et al., 1999; Zhang & Zhou, 2022). However, it remains unclear how children learn that "yiwei" is contrafactive. One possible factor is the discourse context in which "yiwei" occurs: compared to "juede," "yiwei" is more likely to occur in contexts that highlight that the content of the attributed belief is false. In this talk, we will first review the arguments for treating "yiwei" as a contrafactive. Then, we will present an empirical study investigating whether discourse information can serve as a learning cue for acquiring "yiwei" using a modified version of the Human Simulation Paradigm (Gillette et al., 1999). The results suggest that discourse might be one of the cues facilitating the acquisition of "yiwei," while other linguistic cues remain to be explored.

Cognitive science research has long cited efficiency as an explanation of the linguistic behavior observed across the world's languages (e.g., Gibson et al., 2019, Grice, 1975). However, recent work on law has challenged this view, as linguistic phenomena associated with processing difficulty have been found to occur frequently in legal texts. Some have argued that linguistic complexity is intentionally deployed to signal the law's performative effects (Martínez et al., 2024). We test this idea experimentally by examining the effect of linguistic complexity on perceived authoritativeness. In a task where participants are asked to rate variously complex legal provisions, we assess the effect of syntactic complexity, jargon density, and modal usage on perceived authority of statutory law. We find that high jargon density is positively correlated with increased perceptions of authority when syntactic complexity is low and other indices of the legal genre such as the modal shall are absent.

Whole-brain decoding can test whether semantic category information is localized and shared across people or distributed and individualized. We evaluated Iterated LASSO (iLASSO), a two-stage procedure that iteratively selects predictive voxels with L1-regularized multinomial classifiers and estimates category-wise contributions with ridge-regularized fitting. Using nested cross-validation, we applied iLASSO to two independently collected face/place/object fMRI datasets. In both datasets, iLASSO achieved above-chance held-out accuracy (JLP: N=8, M=83.5%, SD=6.0%, p<.001; Neural Fingerprints: N=35 scans, M=60.7%, SD=13.4%, p<.001), comparable to standard LASSO (JLP: M=86.4%, SD=8.0%, p=.11; Neural Fingerprints: M=60.9%, SD=13.5%, p=.85) while selecting more voxels. Decoded coefficient maps revealed face, place, and object information distributed across all four cortical lobes, extending beyond classical category-selective regions. Many selected voxels showed graded multi-category coefficient profiles rather than category-exclusive selectivity, with substantial variation across subjects.

Disorders of consciousness (DoC) are primarily diagnosed using behavioral assessments, which are prone to high misdiagnosis rates. Objective neural markers are therefore needed. This study investigated residual neural responses to naturalistic auditory stimuli in DoC patients to better reflect covert consciousness. Four auditory conditions were presented: natural speech sentences, music, animal sounds, and pure tones as a baseline. Functional near-infrared spectroscopy (fNIRS) was used to assess cortical activation, hemodynamic responses, and functional network alterations during auditory processing. A support vector machine (SVM) classifier was applied to distinguish vegetative state (VS) from minimally conscious state (MCS) patients and to identify the most informative neural features. Complex natural stimuli, particularly speech and music, elicited more specific hemodynamic and network responses than pure tones. Patients with higher consciousness levels showed selectively enhanced left-hemispheric activity and stronger long-range network connectivity during speech perception. The model achieved an overall accuracy of 86.84% (VS: 78.57%; MCS: 91.67%), with the top contributing features exclusively network-based. These findings support the value of naturalistic auditory paradigms and network features for precise DoC assessment.

Large language models encode rich semantic information, but how concreteness is represented across layers remains unclear. We examine layer-wise linear separability of concreteness by training linear probes on hidden representations from two open-source model families at multiple scales: Qwen3 and Gemma3-Instruct. Using human concreteness ratings, we build balanced prompt datasets with four difficulty levels: an extreme abstract–concrete contrast and three finer boundary comparisons at the abstract end, mid-range, and concrete end. Probes achieve high accuracy on the extreme contrast in shallow layers, showing that endpoint differences are strongly linearly separable. For finer distinctions, performance follows a stable hierarchy: mid-range concreteness is easiest to separate, abstract-end distinctions are hardest, and concrete-end distinctions are intermediate. Across models, accuracy rises rapidly in early layers, peaks in middle layers, and declines in later layers. Together, these findings clarify how the linear accessibility of concreteness varies across LLM layers.

Social normative decision-making involves two evaluative axes: immediate gains from aligning with others (reputation, conformity, reduced friction) and delayed collective consequences accumulating through repeated actions (social loss, victimization). This study examines, via a multi-agent simulation with Instance-Based Learning Theory (IBLT) agents, how this tension shapes norm formation, bifurcation, and stabilization. Agents repeatedly choose between two actions (pull/keep) inspired by the trolley problem. In each round, they receive a short-term reward proportional to the degree of agreement with neighbors, while at fixed block intervals they receive a delayed penalty depending on the total number of victims. We compare dynamics on a lattice Grid with dynamics on networks generated by the Watts–Strogatz model and classify trajectories into three types (pull-dominant, intermediate, keep-dominant). As a result, the prevalence of these types differs by network structure, suggesting that network differences affect how the two rewards interact and thereby change transition dynamics.

Word learning is an active process in which learners select referents and direct attention based on their current knowledge state. Understanding how learners actively select information can reveal the cognitive mechanisms underlying language acquisition. The present study reanalyzes data from Zettersten & Saffran (2021), in which adults and children (ages 3-8) learned novel word-referent mappings through cross-situational learning, then selected which referents to receive additional training on. We fit associative word learning models to individual training, selection, and test data, and inferred the most likely sampling strategies. Models with a bias to attend to stimuli with uncertain knowledge states best accounted for the data, though there were substantial individual differences in learning mechanisms. These findings suggest that reducing uncertainty drives sampling behavior across development, but that individual differences in learning parameters (in particular, learning rate and memory) are more predictive of word learning success than sampling strategy alone.

While metaphor production varies systematically across different textual genres, it is unclear whether genre also plays a role during real-time metaphor processing. We present two experiments that test the scaffolding role of both linguistic context and genre (news or fiction) in an online 'maze' reading task. Our study provides new word-by-word reaction time data for an understudied but common type of metaphor in English, genitive metaphors (e.g., "breeze of contentment"). In Experiment 1, we find a difference between metaphor and literal processing in the absence of preceding linguistic context, corroborating previous findings. In Experiment 2, for the same metaphors, we find that genre does not affect processing above and beyond the linguistic context. A norming study on our linguistic contexts moreover showed that readers can infer the genre of a two-sentence excerpt without any explicit instruction. We discuss the implications of our null findings and highlight recommendations for future research.

Recent research has shown that vision-language pretrain-ing (VLP) models using contrastive learning (Contrastive Language Image Pretraining, CLIP) has a semantic bias to-wards using concrete words during image-text matching. Here we showed that as compared with CLIP, humans at-tended more to abstract words during image-text matching. This difference likely results from CLIP's difficulty in de-veloping grounded understanding of abstract concepts and capturing contextual dependencies between images and captions through contrastive learning. While CLIP's caption attention aligned more closely with humans for concrete than for abstract captions, their alignment in im-age attention did not differ between the caption condi-tions, as human image attention was driven primarily by individual differences in explorative or focused perceptual style. Our findings thus revealed important differences in information processing mechanisms between humans and the VLP models, with important implications for not only human and AI image-text matching, but also for potential ethical issues resulting from such misalignment.

Three and four-year-olds can readily infer causal relationships from the covariation of interventions and outcomes. However, this kind of understanding has almost always been tested using arbitrary stimuli (e.g., blicket detectors). Thus, the relationship between children's understanding of covariation data and their understanding of the physical mechanisms that underlie causal relationships is poorly understood. We test children's understanding of physical mechanisms using a domino setup which can translate common cause, common effect, and causal chain structures into visible, physical arrays where the transmission of force is governed only by contact causality (a principle within young children's grasp). Using a novel online task battery, we test five different aspects of mechanism understanding and show that children's understanding of the physical mechanisms underlying causal relationships undergoes a surprisingly protracted development, with three-year-olds performing at chance on all items and even six-year-olds performing well below ceiling.

There are two widely held characterizations of human semantic category systems: (1) they form convex partitions of conceptual spaces, and (2) they are efficient for communication. While prior work observed that convexity and efficiency co-occur in color naming, the analytical relation between them and why they co-occur have not been well understood. We address this gap by combining analytical and empirical analyses that build on the Information Bottleneck (IB) framework for semantic efficiency. First, we show that convexity and efficiency are distinct in the sense that neither entails the other: there are convex systems which are inefficient, and optimally-efficient systems that are non-convex. Crucially, however, the IB-optimal systems are mostly convex in the domain of color naming, explaining the main empirical basis for the convexity approach. Second, we show that efficiency is a stronger predictor for discriminating attested color naming systems from hypothetical variants, with convexity adding negligible improvement on top of that. Finally, we discuss a range of empirical phenomena that convexity cannot account for but efficiency can. Taken together, our work suggests that while convexity and efficiency can yield similar structural observations, they are fundamentally distinct, with efficiency providing a more comprehensive account of semantic typology.

Cumulative culture is often described as a ratchet that builds ever more complex structures, knowledge, and technology. However, this complexity comes at a cost. Though cultural innovations may offer new opportunities or insights, they also present new ways to become confused or cognitively burdened. Cultural evolution may thus act both like a ratchet (accumulating useful innovations) and like a hatchet (stripping away representational deadwood). Building on a recent theory of value-guided construal, we formalize this utility-complexity trade-off using a model inspired by navigating with a map. We embed this model in an evolutionary dynamic, where the maps of successful navigators are selectively copied. As expected, more complex (simpler) maps are favored when they are more useful and when agents have a higher (lower) representational capacity. Yet surprisingly, the evolutionarily stable map is often more or less complex than the optimal map, driven by the availability of social information. More broadly, our modeling results show how cognitive costs, social learning, and cultural selection can jointly shape our minds and the material artifacts that aid them.

Comprehending content in a newly learned language requires interactions between perceptual, semantic, and executive processing systems. Learners whose target language differs from their own in modality (e.g. spoken language users learning to sign) provide a unique opportunity to examine the relative contributions of semantic and perceptual processing. We present data from three studies where hearing, non-signing participants with between a few hours and a few weeks of experience with American Sign Language (ASL) viewed videos of signs during fMRI scanning. Using Representational Similarity Analysis (RSA), we measure contributions of semantic-conceptual, visual perception, and cognitive control regions to processing of semantic and visual information in ASL. Across different learning paradigms, including both cross-sectional and longitudinal approaches, we find that semantic features become more decodable in semantic, visual, and cognitive control regions after learning, while visual features become less salient, especially in visual cortex.

As artificial intelligence increasingly mediates public discourse, it becomes important to understand how human-AI collectives shape opinion formation, deliberation, and democratic outcomes. We present a novel experimental method for studying opinion dynamics in hybrid human-AI social networks. Participants, human or AI, were embedded in 5_5 grid lattice networks and iteratively asked to select and revise statements on a given polarizing topic over eight rounds. We compared three conditions: human-only, AI-only, and hybrid networks with equal proportions of human and AI participants. Hybrid human-AI networks achieved the lowest final polarization while, in contrast, human-only networks exhibited higher polarization with lower neighbor agreement. We also ran additional experiments varying Large Language Model (LLM) prompt framing to explore whether instruction design might influence convergence patterns. Although these early findings are preliminary and cannot yet support broad generalizations, they highlight the potential value of experimental social networks for understanding opinion dynamics in human-AI hybrid societies.

People are capable of learning with very little data. We argue that this is because people possess an symbolic inductive bias, consistent with the language of thought (LoT) hypothesis (Fodor & Pylyshn, 1988). To evaluate this claim, participants were asked to learn list functions by predicting how to transform an input list of numbers to an output list. We used participants' predictions on trials with no feedback to search for a congruent representation using a LoT model. The model was able to predict participants' heldout responses at above chance levels, even for participants who were unable to learn the function. Furthermore, LLMs and a neural network trained with a LoT inductive bias displayed similar patterns. These findings suggest that people and ML models display signatures of symbolic representation use to accomplish few-shot learning.

Event role assignment is central to event understanding in both language and vision. Crucially, the foundation of this semantic structure is likely rooted in visual experience. However, it remains unclear whether recent vision–language models (VLMs) can attain this fundamental human cognitive capability. To compare humans and VLMs, we conducted two studies using Heider–Simmel–style animations that minimize object and scene semantics. In Study 1, humans identified roles near ceiling (~97%), whereas VLMs were less accurate and less stable across actions (GPT-5: 84%; GPT-4o: 47%). In Study 2, we introduced a Stroop-inspired visual–linguistic mismatch by pairing animations with occasionally incongruent role statements. Humans' role judgments remained highly vision-consistent (92.7%), but VLMs shifted away from the visual event under conflict (GPT-5: 46.9%; GPT-4o: 29.7%), indicating heavier reliance on linguistic cues. In Study 3, a mismatch-shape baseline left both models perfectly vision-consistent (100.0%), ruling out a generic object-recognition failure explanation for Study 2. Together, these results demonstrate that VLMs do not ground event-role understanding in vision as reliably as humans do, indicating a substantial human–VLM gap in integrating visual versus linguistic information.

Three experiments and a control task combine to indicate that grammatical constructions are implicitly represented in memory, with prototypicality emerging from distributional experience, alongside significant verbatim memory. Participants are exposed to instances of a construction and, following a delay filled by an unrelated language task, falsely endorse novel instances of the same construction more often than paraphrases in a recognition task. Critically, false memories are particularly common for lures containing an unwitnessed word that is distributionally prototypical of the construction, compared to frequency-matched controls. A control study excludes the possibility that the latter effect is simply a word-level effect. Finally, we simultaneously find memory for specific exemplars. Overall performance is above chance yet implicit: participants underestimated their accuracy, which was unrelated to age or education.

Humans perceive rich social interactions from motion. We investigated whether humans can perceive subtle intention differences in motion displays even when kinematics are highly similar, and whether the recovered intention shapes downstream causal and moral judgments. By grounding different intentions in a planning algorithm with distinct reward functions, we generated "chasing" and "following" trajectories, each ending with the front agent falling into an accident. In an online responsibility task, participants perceived different intentions and assigned blame accordingly: in following, responsibility concentrated on the leading front agent, whereas in chasing, responsibility shifted toward the rear chaser. In a matched causal-attribution task, the front agent was judged to be the primary cause of the motion in both displays. These findings suggest that humans can perceive subtle differences in intention from motion, which scaffolds moral evaluation, but that this influence cannot be simply reduced to generic causal attribution.

Humans readily extract statistical regularities from perceptual experience (e.g. a cook noticing which ingredients often appear together). How does such learning guide peoples' performance on procedural tasks (e.g. preparing various dishes)? Here we examine what shortcuts people propose to help them to complete assembly problems more efficiently by eliminating repeated subroutines. Participants (N=301) repeatedly assembled tangram-like shapes, and could create composite tiles for future use. Some participants assembled a sequence of tangrams where certain pairs of tiles recurred consistently (Highly structured); the remaining assembled tangrams with less predictable tile arrangements (Less structured). Participants exposed to Highly structured sequences created tiles that tracked the frequency with which those tile pairs recurred across tangrams, and doing so was accompanied by greater efficiency in assembly. Taken together, these findings suggest that statistical learning guides not only pattern recognition, but also the prospective creation of shortcuts for procedural tasks.

A central question in cognitive science is whether conceptual representations converge onto a shared manifold to support generalization, or diverge into orthogonal subspaces to minimize task interference. While prior work has found evidence for both, a mechanistic account of how these properties coexist and transform across tasks remains elusive. We propose that representational sharing lies not in the concepts themselves, but in the \emph{geometric relations} between them. Using number concepts as a target domain and language models as high-dimensional computational testbeds, we show that number representations preserve a stable relational structure across tasks. Task-specific representations are embedded in distinct subspaces, with low-level features like magnitude and parity encoded along near-orthogonal axes. Crucially, we find that these subspaces are largely transformable into one another via linear mappings, indicating that task-specific representations, despite being located in distinct subspaces, share relational structure.

In divided societies, authorities often use punishment to establish shared norms while trying to maintain or signal their legitimacy. When facing a polarized audience, these goals may often be at odds. We extend the Rational Communicative Social Action (RCSA) framework (Radkani et al., 2022) to formally model an authority's strategic decision-making when using public punitive actions to pursue these goals. We distinguish between a communicative authority who aims to shape the audience's moral beliefs, and a reputation-aware authority who optimizes the audience's assessment of their own character. By simulating these agents against polarized audiences, we characterized the tradeoffs and dynamics of strategic punishment when facing diverse audiences with various forms and levels of polarization, which serves to generate systematic predictions for future experiments.

Accurate sleep staging plays a crucial role in diagnosing patients' sleep health. Numerous studies have confirmed that data from different views can highlight distinct characteristics. Based on the time-domain (TD) EEG, we constructed two additional views: the Power Spectral Density (PSD) and the time-frequency representation (TFR). Notably, there is no universally applicable model suitable for all types of data. That is to say, the characteristics of data should align with the model structure. Therefore, we designed specialized expert models for learning different views. However, directly combining features extracted from different views often results in excessive redundancy, especially for different views of the same data which the underlying data remains essentially identical. To address these issues, we proposed a multi-view mixture-of-experts (MoESleepNet) model for sleep staging, which achieves the best performance on SleepEDF20, SleepEDF78 and SHHS single-channel datasets. This study provides valuable ideas for multimodal signal classification.

Stress is typically conceptualized as varying along a single intensity dimension, yet individuals with identical "high stress" labels often exhibit divergent responses. This work challenges this unidimensional model through FenoStressNet, a multimodal framework integrating facial thermal imaging, physiological monitoring, psychological assessment, and explainable AI. Analyzing 120 participants across controlled stress-induction tasks (19,200 thermal images), this study identifies four reproducible stress phenotypes that cross-cut traditional intensity categories: Cognitive-Dominant (elevated cognitive appraisal, moderate physiology), Physiological-Reactive (rapid autonomic arousal), Integrated-Responder (synchronized multimodal activation), and Discordant-Denier (high physiological response with low subjective awareness). These phenotypes exhibit distinct facial thermal patterns and differential weighting of cognitive, affective, and physiological components. These findings suggest a paradigm shift from intensity-based to phenotype-informed stress assessment, with significant implications for personalized interventions and cognitive theories of emotion.

Evaluating the credibility of a speaker's statement requires more than assessing factual accuracy; it also involves tracking cues to the speaker's epistemic commitment to the claim. Grammatical evidentiality, which encodes information sources, may therefore shape how statements are evaluated during language comprehension. Turkish marks the information source through obligatory evidential morphology and offers a unique window into how source information modulates real-time truth judgements. The present study examined the neural dynamics of processing of accurate and inaccurate statements in Turkish by investigating how direct (-DI) and indirect (-mI_) markers modulate language comprehension as measured by ERPs. Participants viewed short event videos and then judged the veracity of written statements describing the events while an EEG was recorded. ERPs time-locked to the evidentially-marked verbs showed differential electrophysiological responses associated with semantic integration and reanalysis processes. Together, our findings show that grammatical evidentiality may influence the neural processes underlying credibility judgements during online language comprehension.

Cybersickness is a major barrier to the widespread adoption of virtual reality, arising from neural dissonance between visual and vestibular stimuli. Although kinematics-based deep learning enables non-intrusive detection, existing models often lack neurophysiological grounding and fail to capture dynamic sensory recalibration and continuous symptom accumulation. To address these limitations, we propose the Neural Sensory Conflict Network (NSCNet), a biologically inspired framework grounded in Sensory Conflict Theory. Specifically, NSCNet incorporates a Conflict Alignment Embedding to model the integration of discordant sensory inputs, a State-Space Re-entrant Experts module that combines selective state-space modeling with Re-entrant Experts to capture the temporal dynamics of neural dissonance and functional modularity, and a Dynamic Sensory Reweighting mechanism that approximates adaptive gain control in the central nervous system. Experiments on the MSCVR and VR Cybersickness datasets demonstrate state-of-the-art performance, suggesting that explicitly modeling neurocognitive mechanisms improves the predictive fidelity of cybersickness detection.

In recent years, the pronoun system of English has begun to accommodate gender diverse identities. The present study investigates how individuals process and adapt to two novel pronouns: nonbinary they and the neopronoun ze. Using a Web-based maze task, we compared processing of these pronouns relative to binary s/he pronouns. We also examined how reading behavior adapted to repeated exposure to each pronoun. Perhaps unsurprisingly, the rarer ze elicited greater processing difficulty than singular they overall. However, participants adapted more quickly to ze than to they. We propose that ze may be more easily learned than singular they because it does not compete with plural they.

Progress in AI has led to a lively debate about the possibilities of creating and assessing consciousness in AI, building on Putnam's conjecture that computational functionalism is more plausible than a biological view of consciousness. I discuss Butlin et al.'s (2025) proposal for extracting computational indicators of consciousness from neuroscientific theories and critically evaluate computational functionalism in light of the alternative, biological naturalism. While computational functionalism allows for AI consciousness in principle, it is questionable whether conscious AI systems are practically possible, and whether we are in an epistemic position to judge that a specific AI is conscious. Putative computational indicators extracted from neuroscientific theories of consciousness can never be as well (or even better) supported by empirical evidence than their neurobiological realizers measured in human brains. All potential markers of consciousness that are typically alluded to in humans or non-human animals are either absent, ambiguous, or question-begging in the case of AI systems.

Vision-language models (VLMs) often perform well on spatial reasoning tasks, but it remains unclear whether their performance is supported by internal representations that track rotation angle. We study this question in a controlled same or mirror 3D object task using LLaVA-1.5-7B as an intervenable computational system. First, the model's decision margin varies systematically with rotation angle, mainly for rotated rather than mirrored stimuli. Second, we identify an angle-related direction in intermediate hidden representations whose activity covaries with behavior. Third, targeted projection ablations produce progressive flattening of the angle-margin relationship as intervention strength increases. Together, these findings provide mechanistic evidence that angle-sensitive internal geometry contributes to the model's behavior on this task. The results support a cautious interpretation of structured, intervenable spatial processing rather than a strong claim of human-like mental rotation.

When does overheard speech support early word-learning? The present study provides preliminary evidence that children's closest social partners (here, mothers) structure attention to overheard speech. In a Tseltal Mayan community where overhearing is central to socialization, 75 mother-child dyads participated in overhearing-sessions exposing them to two novel words. In each session, one child's mother was directly addressed ("Mother-Addressed" condition), while the second mother merely observed ("Mother-Unaddressed" condition). Immediately after, both children completed two gaze-based tests of word recognition and learning. Results show that, compared to Mother-Unaddressed children, Mother-Addressed children exhibited (i) greater one-shot recognition of the novel word whose referent was visible during the overhearing-session; and (ii) a word-learning advantage on cross-situational familiarization trials. Insofar as who is being spoken to matters, the present study finds that not all overheard input is created equal: rather, results provide preliminary evidence that observational word-learning is socially-structured.

When sharing tasks, individuals may reduce internal demands by offloading task processing to others – social offloading. Studies have revealed facilitated performance when people believe a jointly acting partner is responsible for task distractors. A proposed mechanism underlying this effect is selective attention. We investigated this using a joint location negative priming (NP) paradigm. In NP tasks, participants initially respond to targets while ignoring distractors (prime phase); at a subsequent probe phase, responses are typically slower to targets in prime distractor locations, reflecting lingering inhibition – the NP effect. Participants (N = 80) completed the task either alone, or believing a partner was responding to distractors. Following a selective attention account, we predicted prime target facilitation followed by increased NP in the joint condition. While results did not reveal facilitation, NP was significantly increased. We propose that these findings demonstrate the way social contexts can shape selective attention to support distributed working.

A central goal of cognitive science has been to find universal principles that explain the remarkable flexibility of human problem-solving. However, people display a diversity of problem-solving strategies, prior experience, domain-specific knowledge, and preferences. Which aspects of problem solving are shared across individuals, which are individual-specific, and which are adapted to particular domains? Most studies of human problem-solving rely on brief laboratory tasks in one or a few domains and coarse behavioral measures, making it difficult to robustly explain individual cognitive processing. We propose that longitudinal, self-motivated gameplay on multiple tasks---combined with detailed process-tracing---can help fill these gaps. We introduce mitpuzzles.com, a public platform hosting a suite of constraint-based logic puzzles (e.g., Minesweeper, Sudoku, and Nonograms), instrumented to collect detailed behavioral data including mouse-tracking. We present preliminary analyses of large-scale data (N=85,919 games played by 2,258 users) collected from this website to characterize common and variable problem-solving behaviors across both individuals and games. Among other findings, these analyses reveal stable individual differences and effects of learning. First, abstract features of subproblem complexity predict accuracy and speed across puzzle types. Second, most players avoid complex subproblems, even when those subproblems are more globally informative, but the fastest solvers are less avoidant. Third, players search more efficiently with experience. Our results highlight the promise of studying self-motivated participants engaging with hard, naturalistic tasks to understand both the shared structure and individual variability of human problem-solving.

Crowdsourced annotation can be viewed as a form of distributed human judgment in which individual reliability and task difficulty jointly shape collective decisions. Probabilistic aggregation models estimate latent annotator reliability and task difficulty, but are typically used only to weight judgments or infer labels, not to guide selective dataset refinement. We propose a replicated-dataset consensus filtering framework that improves collective judgment reliability by removing unreliable annotators and difficult tasks based on latent reliability-difficulty parameters. Instead of relying on a single dataset estimate, the method constructs multiple replicated datasets by small random label removal, re-estimates latent parameters on each replicated dataset, and removes components that are consistently selected across replicated datasets. Filtering is applied iteratively with entropy-based stopping conditions. The framework is model-agnostic and can be combined with any probabilistic aggregation model that estimates reliability and difficulty parameters. Using a standard reliability-difficulty model instantiation, experiments on multiple crowdsourced datasets show that parameter-driven consensus filtering improves aggregation accuracy and removes incorrect tasks more selectively than random removal. These results support a cognitively grounded, parameter-based approach to improving collective judgment reliability in distributed annotation settings.

Many non-native English speakers study mathematics in English, so they must learn both the math and the academic language used to express it. Large language models (LLMs) might help by producing worked solutions while also explaining difficult words and phrases, but it is unclear whether doing both harms mathematical accuracy. We test whether adding inline vocabulary explanations changes solution correctness. Using the CEFR-J vocabulary profile, we identify terms likely to need clarification and evaluate four open LLMs (2.7B–20B parameters) on English math problems in two conditions: standard solving vs. solving with embedded vocabulary support. On MMLU Elementary Mathematics and GSM8K, vocabulary scaffolding consistently reduces accuracy, with drops up to 16.2 percentage points. These findings reveal a trade-off between language assistance and reasoning performance. Educational interfaces may need to balance these goals carefully or separate language support from problem solving for English-medium instruction.

To investigate how co-speech gestures modulate linguistic understanding in comparison to specific verbs with similar contents, we conducted an EEG experiment to measure semantic surprisal by exploring the amplitude changes in the N400 component for both information types. We used videos of a person uttering (i) specified sentences, whose verb semantically denoted a specific action, and (ii) underspecified sentences with an unspecific verb accompanied by an iconic co-speech gesture that indicated the specific action of the previous condition. The subsequent sentence contained an instrument noun as target, which either matched or mismatched the specific action. We measured ERPs on the target noun for both information types and found an N400 effect for mismatching target nouns as well as a late positivity effect for gesture conditions. Crucially, no interaction between information type and congruency was observed in the N400 time-window, indicating similar semantic surprisal induced by mismatching specific verbs and content-similar gestures. An interaction was revealed in the late time-window, suggesting that unlike for gestures, the mismatch negativity effect was prolonged for specific verbs.

In the interval estimation task, participants are asked to estimate the time interval between two events. Interval estimates tend to be smaller if the first event is a voluntary action. The cognitive mechanisms that generate this temporal binding effect remain unclear. In this study, we propose a computational model of this phenomenon. We hypothesize that participants perform this task by comparing the availability of experimental events in memory. More specifically, we claim that temporal binding effects may be attributed to differences in emotional arousal at the time of encoding due to the type of event. For instance, voluntary actions may be accompanied by slightly higher arousal, leading to stronger traces of the first event relative to the involuntary case.

Convergent evidence suggests that the adult lexicon has evolved under pressure to efficiently satisfy a tradeoff between compressibility and informativeness, formally known as the Information Bottleneck (IB) principle. No work to date, however, has tested whether the same principle also impacts how children learn word meanings during development. Here, we address this open question in the domain of color by collecting color naming data from Hebrew speaking children across development (ages 3-12), and comparing it to color naming data from adults. Across ages, we find that children achieve near-optimal IB tradeoffs, with their color naming systems becoming more complex (less compressible) and more adult-like with age. Importantly, this pattern holds even though children's early color naming systems diverge from those of adults, implying that their efficiency is not merely a reflection of the efficiency of the adult system. These findings suggest that children's developmental trajectories of learning word meanings are guided by the same fundamental principle that shapes the adult lexicon.

How do humans allocate scarce cognitive resources across a stream of related decisions? Russek, Acosta-Kane, van Opheusden, Mattar, and Griffiths (2025) predict reaction time on each chess move from the within-move Benefit of Computation, treating moves as if they were drawn independently. We ask whether those costs also depend on computation carried forward from the previous move. In chess, a player who has calculated a likely opponent reply can often reuse that work; a surprising reply should force revision. On 135,608 quality-filtered Lichess puzzles attempted by 74,609 unique users, the within-puzzle log-RT spike is +0.309 (Cohen's __ = 0.235; paired __ = 86, N = 135,608; BF10 > 10100). Hierarchical regression climbs from __2 = 0.011 (Russek-static) to 0.282; the amortization interaction is large when predictability is operationalized by the human-style Maia-2 (__ = _0.054, __ < 10_27) and not significant under Stockfish best-move predictability. A threeparameter Bayesian Sampling Model with Cache recovers cache strength __ = 0.085 [0.071, 0.099], with 100% of bootstrap draws positive. The H1 spike is roughly twice as large in solved puzzles as in failed ones (__ = 26.2, __ < 10_150); this pattern suggests active engagement rather than passive surprise. The findings replicate on a 2024 Lichess cohort and on FIDEWorld Rapid + Blitz 2024 over-the-board play.

Classical attractor networks such as Hopfield models treat memories as static points in a fixed energy landscape. While foundational, this framework cannot capture how cognition unfolds through time as agents interact with their environment and recall structured experiences. In this work, I present an open dynamical systems model of memory implemented in an interactive physicsbased environment, where memories are not point attractors but attracting trajectories carved into a deformable potential landscape through experience dependent learning. A state variable evolves under the combined influence of learned landscape gradients and an external driving force, producing time-resolved recall paths rather than instantaneous convergence. I show that this system naturally produces graded recall, intrusion errors, and interference from basin geometry and drive dynamics alone. This framework reframes memory as a dynamical process embedded in perception/action loops and offers a bridge between attractor networks, sequential memory, and embodied cognition.

This study focused on the impact of bilingualism on executive function (EF) and theory of mind (ToM) in preschool children. Seventy-two children (age, 4-6 years) were involved: 36 Turkish monolinguals and 36 bilingual children whose first language was typologically distant and second language was Turkish. EF was measured using a Flanker task and ToM was measured using a False Belief task. Contrary to an expected bilingual advantage, monolinguals demonstrated better performance in EF task and were more likely to pass the ToM task. Among the bilinguals, the Arabic-Turkish speakers scored lower on ToM, which might be attributed to socioeconomic differences. The results raise the possibility that distant languages may lead to higher cognitive load and to fewer bilingual advantages. Moreover, the sociocultural context of Turkiye characterized by diverse cultural values and linguistic environments may moderate effects of bilingualism, revealing the need for models of bilingual cognition that are context sensitive.

Event knowledge representations in Large Language Models (LLMs) are explored through the lens of temporal connectives. Temporal connectives like before and after play an important role in sentence comprehension by linking events in the sentence to real-world event order. Their position in a sentence can create different outcomes for event order interpretation and sentence comprehension, and they have therefore been studied extensively in experimental research. Importantly, previous studies have found a human preference for chronological ordering in sentence comprehension. The current study investigates whether LLMs can recognize event order and reflect the human-like processing patterns of temporal connectives across three languages: English, Norwegian and Greek. The aim of the study is to shed light on how sentence structure and event sequence influence LLM predictions, with implications for both cognitive modeling and the knowledge representations learned by LLMs. Results vary by language and show that some models do have event representations and reflect human-like patterns of temporal ordering. The results highlight that language models for some languages (English) are better optimized for cognitive modeling than others (Greek) and underscore the need for more cognitively motivated evaluation benchmarks to assess the models being used in cognitive science research.

Embodied accounts of semantic memory highlight the role of sensorimotor systems in acquiring and storing knowledge. Congenitally blind populations offer a critical test bed for these assumptions, providing an opportunity to assess whether conceptual grounding requires visual experience. In this study, we assessed semantic memory navigation differences between blind and sighted individuals using a property listing task with concrete and abstract concepts. We computed semantic entropy, an embedding-based natural language processing metric that captures the predictability of retrieval. Generalized linear mixed models revealed distinct navigation patterns across groups: while sighted individuals showed higher entropy for abstract than concrete concepts, blind participants did not. Instead, blind individuals exhibited higher entropy for visually salient concrete concepts (e.g., penguin). These results underscore the role of visual experience in the organization and dynamic navigation of semantic memory.

Mental multiplication is an abstract and advanced cognitive skill that requires applying systematic algorithms to structured numerical data, but it remains underexplored in neural models. In this paper, we propose a psychologically plausible model of how anatomical circuits in the human brain may perform mental multiplication of small natural numbers and implement it in biologically plausible spiking neurons. Our model uses similar strategies to those used by humans, specifically repeated addition and rule use; matches various human performance levels, including that of both children and adults; and qualitatively replicates multiple human performance patterns, such as problem-size and outlier effects. Our novel model sheds further light on how structured cognitive processes, like the rule-based algorithms underlying mathematical reasoning, may be implemented in the substrate of spiking neurons in the human brain.

Children frequently ask, "Why?" Here, we examined which "why" questions learners ask, hypothesizing that they are more curious about why things vary versus are constant. Informed by the Explanation-Seeking Curiosity Model (Liquin & Lombrozo, 2020), we predicted that explanatory curiosity about variability aligns with children's judgments that (1) they will learn more when asking about variability (i.e., explanations are complex rather than simple), and (2) explanations for variability are more useful. Two pre-registered experiments with children (N = 84 five- to ten-year-olds) and adults (N = 80) supported these hypotheses. Children and adults generated and selected "why" questions about variability more than constancy, reasoned that constancy has simpler explanations, and evaluated variability as more useful to explain in everyday life. Perceived utility, but not simplicity, predicted participants' question preferences. We discuss how explanatory curiosity about variability may be leveraged by educators, but also caution that children and adults may underexamine important constants.

Does heightened proprioceptive precision promote the more aggressive updating predicted by reliability weighting, or the more conservative updating predicted by source estimation? To address this question, we introduced an unperturbed tuning phase prior to prism adaptation, varying visual availability during reaching across three contexts: eyes closed (EC), eyes open with ambient vision masked (EO-), and eyes open with ambient vision (EO+). Proprioceptive uncertainty estimates revealed context-dependent tuning effects, with the strongest sharpening in EC, weaker and less consistent changes in EO-, and a trend toward increased uncertainty in EO+. To examine how these pre-tuned proprioceptive states shaped subsequent updating, we modeled prism adaptation behavior using a hierarchical Bayesian state-space framework. This analysis demonstrated that sharpened proprioception was primarily coupled with more conservative state updating, consistent with a source-estimation account. In addition, the model captured a complementary modulation of performance stability, where improved proprioceptive precision suppressed trial-to-trial fluctuations in movement. Both effects followed an EC > EO+ > EO- ordering, revealing a non-monotonic mapping between proprioceptive tuning magnitude and functional impact. This divergence suggests that the influence of proprioceptive sharpening depends not only on uncertainty reduction, but also on how visual availability organizes the sensory context during action. Collectively, these findings suggest that error interpretation is a state-dependent process shaped by prior sensory context, with sharpened proprioception biasing error evaluation toward more conservative updating.

Memory is a vital part of cognition. Its many parts determine how one learns, plans, and ultimately experiences the world around them. As a bridge between short-term memory and long-term memory, intermediate-term memory allows for semi-stable memories to be rapidly encoded through short-term synaptic plasticity. We present a model of short-term and intermediate-term memory based around Semantic Pointers and realize the model in a spiking neural network. Unlike past work, we characterize short-term memory as a dynamical system and realize intermediate-term memory as implicit association learning. Further, we demonstrate our model's ability to generalize across domains by achieving human-like results on semantic and spatial memory tasks with minimal parameter changes. By examining Cohen's _ values first within and then between tasks, we find an overall small difference (Mean: |_| = 0.12 ± 0.06, Median: ˆ |_| = 0.16)), suggesting strong similarity between model and human data across all three tasks.

Explanations are central to human cognition, yet AI systems often produce outputs that are difficult to understand. While symbolic AI offers a transparent foundation for interpretability, raw logical traces often impose a high extraneous cognitive load. We investigate how formal abstractions, specifically removal and clustering, impact human reasoning performance and cognitive effort. Utilizing Answer Set Programming (ASP) as a formal framework, we define a notion of irrelevant details to be abstracted over to obtain simplified explanations. Our cognitive experiments, in which participants classified stimuli across domains with explanations derived from an answer set program, show that clustering details significantly improve participants' understanding, while removal of details significantly reduce cognitive effort, supporting the hypothesis that abstraction enhances human-centered symbolic explanations.

Humor is a fundamental cognitive phenomenon in which humans derive pleasure from expectation violations and their resolution, exemplifying the brain's dynamic capacity for predictive processing. Classical humor theories emphasize semantic incongruity as the primary driver of amusement, yet overlook temporal dynamics despite comedians' intuition that "timing is everything." The extent to which temporal structure contributes to humor appreciation and how it interacts with semantic content remains poorly understood. Here, we propose the Dual Prediction Violation (DPV) framework to capture the interplay between content and timing. By analyzing 828 professional Chinese stand-up performances, we show that temporal features substantially outweigh semantic incongruity in predicting audience appreciation. Specifically, we find that peak semantic violations matter more than average incongruity levels, and pauses systematically lengthen before high-surprise punchlines - a strategic coupling that distinguishes successful from unsuccessful performances. These findings reframe humor as temporally scaffolded, where timing and semantic content operate in strategic coordination rather than independently. Our DPV framework bridges humor theory with predictive processing, demonstrating that temporal structure plays a central role in naturalistic humor appreciation with implications for understanding multi-scale prediction integration in linguistic processing.

Although highly relevant to questions on how numeration systems are shaped by format, sign languages have rarely been included in representational analyses. Our study addresses this gap by examining how systems in 63 typologically diverse sign languages represent numbers across the base and power dimensions. The analysis shows that, unlike other formats, sign languages frequently combine multiple representational strategies within their numeration systems. In the base dimension, the reconciling needs for transparency and articulatory economy causes shifts from cumulative to ciphered representations. In the power dimension, format specific constraints guide which representational strategies remain viable, with parsed representations dominating higher powers. These findings highlight the important contribution that sign languages can make to comparative research on numeration systems and underscore the need for a more format sensitive understanding of numerical representation.

People choose their questions based on their conversational goals. But how do they decide between questions that would yield equivalent information? This study examines the mechanism by which a questioner chooses between constituent ("wh-") questions of positive vs. negative polarity, e.g., "Who came?" vs. "Who did not come?". Our Rational Speech Acts (RSA) model predicts the following: First, when a partial answer is expected, the question whose polarity aligns with the questioner's goal is preferred over its opposite polarity counterpart. Second, this goal alignment effect decreases when the structure of the questioner's decision problem requires an exhaustive answer. These predictions are confirmed in two experiments: Experiment1 shows that goal alignment emerges in a free completion task, and Experiment 2 shows that it persists in a forced choice task, modulated by decision structure. Our utility-based account thus parsimoniously accounts for a phenomenon unexplained in formal semantic theory to date.

This study examined whether learner-generated multimodal videos enhance L2 word retention beyond multimodal exposure alone and beyond traditional teacher-led instruction. To isolate production from exposure, 164 Chinese university EFL learners were taught 39 unfamiliar words in one of three conditions: video creation (each learner produced two short videos integrating visual, auditory, linguistic, bodily, and affective resources), video viewing (simply watched the same videos), or traditional instruction (teacher-led explanations and examples). Retention was assessed with immediate and delayed post-tests, with previously known items excluded before instruction. Both video-based conditions outperformed traditional instruction, with the largest gains in the video creation condition. The creation advantage extended beyond self-authored items: Creators also outperformed viewers on words encountered only through subsequent in-class viewing, suggesting that creating videos alters attention to and use of multimodal cues during later viewing. Brief learner-generated multimodal production therefore supports deeper encoding and more durable lexical learning than exposure alone.

Holding inconsistent beliefs is widely considered irrational, yet inconsistencies appear pervasive. How do such inconsistencies arise, and when are they remediated? Recent work indicates that inconsistent beliefs can arise and persist when they are not mutually accessible in memory. However, several theories propose that political inconsistencies result from different mechanisms, making them resistant to consistency checking and remediation. Across two preregistered studies (Ns = 1,026; 3,211), we compared the role of accessibility in political versus general knowledge inconsistencies. Participants responded to sets of three interrelated questions designed to elicit inconsistent responses. We found that political beliefs generated inconsistencies at rates similar to trivia beliefs (15-20%). However, upon reviewing their inconsistent responses, participants were significantly less likely to revise political beliefs. Moreover, manipulating accessibility reduced inconsistencies for trivia, but not politics. These results suggest that while accessibility plays an important role in consistency, additional mechanisms govern political inconsistencies.

This work examines the influence of sensory modality constraints on analogical reasoning, focusing on the sensory modality in which the analogy is presented, the influence of verbal abilities, visuospatial abilities and structural factors like verbalisability and visualisability on analogical performance. Through three experiments, we explored how presentation modalities (written, auditory, and mixed) affect the accuracy and latency of analogical reasoning tasks in an A:B::C:D format. Results showed that written presentations yielded the highest accuracy and fastest response times, followed by auditory and mixed conditions. Verbal abilities, assessed with a naming task, strongly predicted performance across all modalities, while visuospatial abilities, measured with a mental rotation task, were particularly relevant in the mixed condition. Higher verbalisability and visualisability difficulty reduced performance across all conditions but to different extents depending on the modality of presentation, reflecting flexible engagement of each of these abilities depending on the task demands. These findings underscore modality-specific dynamics in analogical reasoning and highlight the critical role of cognitive abilities and the structural factors that are verbalisability and visualisability of the relational structure in shaping the ability to solve analogies.

Understanding free recall requires models that explain not only how many items are retrieved, but also how retrieval is organized—with clustered output, structured transitions, and strong dependence on temporal position. We introduce the Tree of Memory (TOM), in which recall unfolds as a probabilistic search over an explicitly hierarchical episodic representation, coupled with a minimal semantic association structure. The episodic component represents experience across temporal scales encoded in different hierarchical layers. Recall proceeds via probabilistic depth-first traversal of the episodic tree, interleaved with semantic exploration triggered upon item retrieval. The framework admits explicit mathematical characterizations of recall-capacity scaling regimes: depending on the scaling of attention-weighted episodic accessibility, the expected number of recalled items grows sublinearly with list length. In simulations, TOM reproduces canonical free-recall signatures, thereby providing a principled framework to study how hierarchical episodic organization, attention, and semantic associations jointly shape free recall.

We conducted two investigations to capture how people perceive the artificial agents introduced into Paul A. Weiss's thought experiment, especially, how they perceived the AI system or the robots that are relatively new technology for us with different description (abstracted or concrete). As a result, we confirmed the followings: 1) generative and conversational AIs were perceived concretely as indispensable tools for our daily live with learning function while AI system was perceived abstractly as having knowledge from past, and 2) even the same robots, cleaner robots was concretely perceived as an useful tool while humanoid was abstractedly as ambiguous entity. Here, the presence or absence of appropriate knowledge with the agents may have led to the different ways of perceptions of agents. Thus, the introducing the agents into Paul A. Weiss thought experiment is an effective methodology to comprehend the users' current perception of these agents.

Evidence suggests that the organization of semantic knowledge in autistic populations uses qualitatively different organization principles than neurotypical populations. Across two experiments, autistic and neurotypical adults rated the relatedness of abstract and concrete word pairs and completed the Vividness of Visual Imagery Questionnaire (VVIQ). There were no group differences in relatedness ratings; both groups rated abstract pairs as more related than concrete pairs. However, autistic participants reported significantly lower vividness of visual imagery than neurotypical participants. Critically, a VVIQ x group interaction emerged. Higher imagery scores predicted higher relatedness ratings only in the autistic group across both concept types. These findings demonstrate that equivalent semantic performance can arise through distinct processing pathways. Autistic individuals show perceptually grounded semantic processing that scales with imagery ability, while neurotypical individuals' semantic judgments seem to operate independently of visual imagery. This pattern reveals qualitative differences in how semantic knowledge is organized.

How large language models (LLMs) align with the neural representation and computation of human language is a central question in cognitive science. Using representational geometry as an explanatory lens, we addressed this by tracking entropy, curvature, and fMRI encoding scores throughout Pythia (70M--1B) training. We identified a geometric modularization where layers self-organize into stable low- and high-complexity clusters. The low-complexity module, characterized by reduced entropy and curvature, consistently better predicted human language network activity. This alignment followed heterogeneous spatiotemporal trajectories: rapid and stable in temporal regions (AntTemp, PostTemp), but delayed and dynamic in frontal areas (IFG, IFGorb). Crucially, reduced curvature remained a robust predictor of model--brain alignment even after controlling for training progress, an effect that strengthened with model scale. These results link training-driven geometric reorganization to temporal-frontal functional specialization, suggesting that representational smoothing facilitates neural-like linguistic processing.

Motor skills are often assumed to rely on a shared representation that generalizes across superficial changes in framing. Yet identical actions are practiced under distinct contexts that may gate what is retrieved and expressed. We tested whether performance–inconsequential context pushes learning toward generalization ("lumping") or context–specific codes ("splitting"). Simple recurrent networks (SRNs) learned next–step prediction for a fixed eight–item sequence in no–context, single–context, or alternating–context conditions, with weight scale manipulated to bias the learned solution while holding the SRN architecture fixed. Humans trained on the same sequence under matched contexts. All groups improved with practice, but alternating contexts produced a sustained acquisition cost and a marked transition cost when switching between contexts. Under identical training, Low–Scale SRNs showed cross–context generalization, whereas High–Scale SRNs reproduced the context–dependent costs observed in human behavior. White–box analyses revealed separable context–specific representations and greater weighting of context inputs in High–Scale SRNs, consistent with representational splitting.

Adaptive cognition requires structured internal models of objects and their relations. Predictive neural networks are often proposed to learn such world models, but how these are instantiated and how they support prediction remain unclear. We investigate this in a minimal in-silico setting. A recurrent neural network samples tokens sequentially from 2D continuous token scenes and is trained to predict the upcoming token from the current input and a saccade-like displacement. On novel scenes, prediction accuracy improves across the sequence, indicating in-context learning. Decoding analyses reveal path integration and dynamic binding of token identity to position. Interventional analyses show that new bindings can be learned late in sequence and that out-of-distribution bindings can be learned as well. Together, these findings show how structured representations relying on flexible binding emerge to support prediction, offering a mechanistic account of sequential world modeling relevant to cognitive science.

Children's early vocabulary is often skewed towards nouns and away from verbs, but these biases are thought to vary cross-linguistically. Prior work attempting to explore this variation has often been limited in language coverage, predictors, or bias estimation methods. Drawing on large-scale cross-linguistic resources (Wordbank, WALS, GramBank, and CHILDES), we provide a systematic decomposition of early lexical category biases into typological, frequency-based, and cultural components. We first derive noun, verb, and adjective bias estimates from early productive vocabularies across 32 languages and dialects. We then test whether cross-linguistic variation in these biases can be predicted by grammatical features and part-of-speech frequency in child-directed speech, as well as broader cultural factors. Several of these predictors reliably explain variation in noun and verb bias, whereas adjective bias is comparatively poorly predicted. These findings highlight multiple classes of factors shaping variation in early lexical biases.

Understanding how humans reason -- and how reasoning responses vary across tasks and individuals -- remains a core challenge for modeling and explanation in cognitive science. We investigate the stability of response patterns within reasoners and whether variation in these patterns can be used to predict learning effects. We introduce a formal, geometry-based method to quantify distances between individual reasoning patterns and their internal variability, grounded in heuristic theories. The proposed framework is tested against experimental data via generalized linear mixed-effects models and clustering, where we find that our proposed variation measure interacts with correctness to predict performance gains. Moreover, we find that reasoning patterns are stable over time within the same reasoner. The method is general enough to be applied to other reasoning domains.

Negation in natural language is not interpreted as a simple logical complement but is resolved selectively, depending on how linguistic and conceptual structure constrain relevant alternatives. Psycholinguistic research suggests that negation facilitates consideration of an alternative only when a privileged counterstate is available, either through contextual restriction or graded prominence. We use text-to-image generation as a complementary method to probe this selectivity under forced representational commitment: when negated language must be rendered visually, a concrete state must be instantiated. Across two preregistered experiments, participants evaluated how well generated images matched affirmative and negated prompts. Experiment 1 shows that negation is resolved more successfully for predicates encoding conventionalized contrasts than for those with open alternative spaces. Experiment 2 shows that, within open spaces, negation is facilitated when a highly prominent alternative exists. Together, the results support an account in which binary advantages arise from how strongly a counterstate is privileged.

Procrastination, where people engage with an immediately rewarding task when they should work on a task with a delayed payoff, is typically framed as self-regulation failure or lack of motivation. We challenge this view by demonstrating that procrastination emerges naturally from the computational intractability of sequential resource allocation under temporal uncertainty. When time horizons are certain, humans approach mathematically optimal strategies for balancing competing tasks. However, when deadlines are uncertain - requiring integration over exponentially growing outcome spaces - participants systematically over-invest in immediate rewards early, then attempt to compensate too late. The findings reframe procrastination as bounded rationality under computational constraints rather than pure irrationality, suggesting that interventions targeting uncertainty reduction or temporal scaffolding may prove more effective than exhortations to increase motivation or willpower.

Research on motivated reasoning is currently bogged down by two concomitant debates. The first concerns whether motivated reasoning is the product of fast and intuitive or slow and deliberative thinking processes. The second concerns how prevalent it is given that much of the evidence is compatible with normative counter-explanations, most notably Bayesian reasoning. This paper seeks to advance both debates. Across two experiments, we detect evidence for politically motivated reasoning in a way that cannot be explained by Bayesian rationality. We also find that motivated reasoning is attenuated by cognitive sophistication: Those participants who scored low on a classic version of the Cognitive Reflection Test exhibited motivated reasoning on a politicized version of it, but those participants who scored high did not. Our results suggest that motivated reasoning is a real empirical phenomenon, primarily driven by fast and intuitive thinking.

Using temporarily ambiguous garden-path sentences ("While the team trained the striker wondered ...") as a test case, we present a latent-process mixture model of human reading behavior across four different reading paradigms (eye tracking, uni- and bidirectional self-paced reading, Maze). The model distinguishes between garden-path probability, garden-path cost, and reanalysis cost, and yields more realistic processing cost estimates by taking into account trials with inattentive reading. We show that the model is able to reproduce empirical patterns with regard to rereading behavior, comprehension question responses, and grammaticality judgments. Cross-validation reveals that the mixture model also has better predictive fit to human reading patterns and end-of-trial task data than a mixture-free model based on GPT-2-derived surprisal values. We discuss implications for future work.

Real-world creative processes ranging from art to science rely on social feedback-loops between selection and creation. Yet, the effects of popularity feedback on collective creativity remain poorly understood. We investigate how popularity ratings influence cultural dynamics in a large-scale online experiment where participants ($N = 1\,008$) iteratively \textit{select} images from evolving markets and \textit{produce} their own modifications. Results show that exposing the popularity of images reduces cultural diversity and slows innovation, delaying aesthetic improvements. Popularity feedback is associated with changes to both selection and creative stages. During selection, popularity information triggers cumulative advantage, with participants preferentially building upon popular images, reducing diversity. During creation, participants make less disruptive changes, and are more likely to expand existing visual patterns. Feedback loops in cultural markets thus not only shape selection, but also, directly or indirectly, the form and direction of cultural innovation.

Words and emotional expressions each convey information, but together they can reveal more than either cue alone. Adults integrate these cues to make nuanced inferences, but when does this capacity emerge in development? Children aged 5-10 (N=210) and adults (N=35) watched videos in which a person tasted a friend's food, smiled or frowned, and stated that it "tastes good" or "tastes bad." Participants were asked to infer the food's taste and the speaker's communicative goals. For food taste, adults prioritized negative emotions when cues conflicted; for communicative goals, they used both cues to infer a goal to be honest and relied primarily on speech to infer a goal to make others feel good. Children exhibited substantial developmental progression between ages 5 and 7, although subtle differences from adults persisted at age 10. These results suggest middle childhood is a pivotal period for learning to make sophisticated inferences from multiple cues.

The Cognitive Reflection Test (CRT) is widely used in reasoning and decision-making research, yet it lacks a formal cognitive foundation. As a result, debates persist over why CRT scores correlate with mathematical ability, why individuals differ in performance, and which mental processes drive observed response patterns. We address these questions using an ecological perspective combined with computational cognitive modeling. We characterize the learning environment by assembling a large dataset of grade-school verbal math problems and specify a learning mechanism that maps linguistic features of problems to arithmetic operations based on prior experience. Our model reproduces the characteristic intuitive errors elicited by CRT problems and explains them as byproducts of adaptive cognition. It further generates novel predictions about how environmental structure and problem wording influence strategy selection and performance, which we test in two preregistered experiments. Overall, our work provides a principled, quantitative account of CRT performance grounded in adaptive generalization.

Visual illusions are a classic tool in cognitive science for probing perceptual inference. Recent studies suggest that when visual facts conflict with semantic prior knowledge, vision-language models (VLMs) often make systematic errors in which priors override visual evidence. To quantify this effect, we treat VLMs as artificial participants and introduce IlluQuant, an illusion-diagnostic dataset that operationalizes template similarity and visual evidence strength as continuous, controllable variables. By fitting psychometric functions, we measure semantic prior dominance in VLMs. Results show that stronger semantic cues substantially increase the probability of prior-driven errors, while visual evidence must be amplified several-fold to partially offset this bias. We further propose MdCoT, an intervention that mitigates the effect. IlluQuant provides a psychophysics-style framework for quantitatively characterizing the balance between semantic priors and visual evidence in multimodal models under controlled illusion manipulations. The IlluQuant dataset is available at haitoooo/IlluQuant.

Inner language is often assumed to include sensorimotor imagery, but is that necessary? How common are conceptual (non-linguistic) thoughts? Does low propensity to think in words indicate lowered ability to imagine language altogether? Here, we sought to characterize the variability in the format of thought by prompting participants (N=228) to (i) think about a given topic or (ii) imagine hearing language or speaking, and then report on their experiences. For thought prompts, a statistically non-trivial percentage of reports included non-sensorimotor inner language (17.5%) and concepts without a verbal or visual form (37.5%). While 74.6% of participants showed high propensity to experience inner language, 4.4% showed no propensity. For language imagery prompts, inner language ability was near-ubiquitous, with only 0.4% (N=1) reporting the absence of linguistic imagery. Overall, we highlight the variability in inner language and conceptual thought experiences, and dissociate the propensity vs. the ability to use language in thought.

Collaborative learning facilitates cognitive activities such as knowledge elaboration through interaction. However, when extraneous cognitive load is high, the cognitive resources available for engaging in learning processes may be reduced. Erroneous worked-out examples (EWoEs), which present solutions containing misconceptions, can induce cognitive conflict and facilitate knowledge revision. This study examined the effectiveness of EWoEs and their underlying mechanisms in collaborative learning. To enable adaptive example presentation based on learners' knowledge, we employed a knowledge generation model based on the Adaptive Control of Thought–Rational (ACT-R). We used drawing performance as an indicator of knowledge elaboration and the Interactive–Constructive–Active–Passive (ICAP) framework as an indicator of learning processes. EWoEs enhanced drawing performance and the co-occurrence of Constructive and Interactive modes. Moreover, such co-occurrences were associated with improvements in drawing performance. These results suggest that EWoEs facilitate learning processes involving knowledge elaboration triggered by misconceptions.

Stereotypes are pervasive and rigid. We propose that stereotyping arises because perceivers solve a hierarchical inference problem in which traits are inferred simultaneously at the individual and group levels. Using hierarchical Bayesian models, we derive normative predictions about stereotype formation and updating and test them in three experiments. Study 1 shows that evidence pooling in hierarchical inference enables group-level impressions to form faster than individual-level impressions when perceivers observe limited information of many group members, allowing strong stereotypes without strong individual impressions. Study 2 shows that stereotypes resist updating when stereotype-inconsistent group members behave heterogeneously, making perceivers attribute counterevidence to individual differences rather than updating group stereotypes. Study 3 shows that when stereotype-inconsistent individuals also belong to a second, unfamiliar group, counterevidence is attributed to this second group, leaving the original stereotype intact. Together, these findings provide a computational account of how hierarchical inference produces and sustains rigid group beliefs.

Category learning in the real world often relies on information from multiples senses, yet most studies of learning focus on a single modality. It is imperative to study multisensory learning not only to capture processes involved in naturalistic learning, but also because multisensory information may facilitate learning. In the current study, we examined how learning of audiovisual categories is impacted by presenting information simultaneously or separately across modalities. Overall, simultaneous presentation led to more successful performance, even when information was only presented in one modality. Simultaneous presentation also encouraged learners to attend more to the auditory modality. These findings suggest that simultaneous presentation of category information across different modalities can facilitate learning and that this may be related to how learners attend to information during learning. More broadly, these results demonstrate the necessity of investigating multisensory learning to reveal mechanisms supporting learning in more naturalistic multisensory contexts.

Human cooperation involves flexible role-taking. This form of cooperation has been attributed to collective intentionality – the ability to represent what "The Group" believes and desires. However, the factors determining when and why humans spontaneously assume roles remain understudied. We propose that a primary motivation for role-taking is to reduce coordinative complexity by making one's intentions transparent. We test this hypothesis in a naturalistic cooking study (N = 270, 90 trials) in which groups of two, three, and four collaborate, as additional members increase coordinative complexity by expanding possible task distributions and increasing inference misalignment. Our analysis shows that role-taking increases significantly with group size, supporting the hypothesis that roles are used to manage coordinative complexity. These findings provide insight into why humans choose certain cooperative strategies, suggesting that facilitating coordination and solidarity may be prioritized over optimizing productivity.

This paper considers the issue why more pieces of supporting evidence may be less persuasive than fewer, perhaps to the point of dissuading one's interlocutor. Challenging the view that such more-is-less effects result from failures to make correct inferences based on the evidence, this paper argues that they may be a rational response based on one's beliefs about a source's reliability. To do this, we present a Bayesian network framework for inference with potentially unreliable sources. While existing Bayesian models forbid more-is-less effects, our more general framework naturally accounts for them.

People often seek out ways to watch others perform complex action sequences (e.g., sports). What makes some sequences more enjoyable to watch than others? We generated 24 video clips of gameplay from a Flappy Bird-style video game. Clips varied in difficulty (how often players succeeded on average) and in moment-to-moment uncertainty (how likely the player was to crash at any given step). Participants (N=864) rated each video on one of three dimensions: how much they enjoyed it, how difficult the level appeared, or how dangerous the player's trajectory appeared. We found that participants preferred videos where the player seemed to be completing more difficult obstacle courses, but dangerousness did not predict enjoyment ratings. These findings show how procedurally generated stimuli can isolate the factors that affect how enjoyable an action sequence is to watch.

Studies show that children succeed in perceptual access-based knowledge attribution before false belief attribution. Recent theories suggest that this may be due to the primacy of knowledge representations. However, to more accurately contrast knowledge and belief attributions, closely matched test scenarios are needed that are comparable in processing demands. In the current study, we tested 4- to 5-year-old children (n = 50) on a novel paradigm requiring more complex knowledge attributions that involve more comparable processing demands—such as egocentric ignorance and self and other retrospective updates—in relation to false belief attributions. Results showed that children performed well on this more complex knowledge attribution, suggesting that knowledge ascription is robust even under increased cognitive demands. However, we found that complex knowledge and false belief attributions were negatively related, suggesting a surprising pattern in which false belief inferences may be overgeneralized early in development.

Finding computational models that give rise to human-like heuristic decision-making is an open challenge in cognitive science. Meta-learning has been proposed as a promising computational framework for obtaining algorithmic models of cognition by learning from environmental interactions rather than designing normative models. Recent work by Binz et al. (2022) demonstrates that bounded meta-learned inference (BMI) discovers human-like heuristic decision strategies in paired comparison tasks. However, the reasons and mechanisms underlying this discovery remain unclear. Here, we extend research on the BMI framework to better understand its behavior and reverse-engineer its computational principles. We first systematically varied model parameters that influence the network's representational capacity, finding that BMI robustly discovers context-appropriate heuristics across configurations. The model consistently uses single-cue strategies when feature rankings are known and equal weighting strategies when feature directions are known, even when network capacity is strongly reduced compared to the original work. We then reverse-engineer the computational principles underlying this behavior using hierarchical Bayesian modeling. Our analyses reveal that regularization, not network size, drives heuristic discovery. Bayesian models with heuristic priors provide the best fit for regularized network behavior when environmental cues are available. Additionally, we discovered a stronger inductive bias toward equal weighting over single-cue strategies. Thus, we conclude that inductive biases, in the form of priors, have a greater impact on the emergence of heuristics than resource rationality, in the form of networks' representational capacity.

The Stroop effect is a well-known phenomenon where participants take longer when they name the color of letters that spell the name of a different color. This effect has been extensively studied, with various proposed theories and computational models. However, most models do not integrate with existing frameworks for color processing or reading, limiting their generalizability. We introduce the BRAID-Color probabilistic model, an extension of BRAID, an existing model of reading and reading acquisition. BRAID-Color consists in adding to an orthographic information processing branch, able to recognize words spelled by a sequence of letters, a color processing branch, able to name colored visual stimuli. Information fusion at the lexical level combines both to simulate color naming with colored letters. With default parameters, simulations in the congruent, neutral and incongruent conditions illustrate that the model successfully and robustly accounts for the Stroop effect.

Effective collaboration requires accurate mental models of one's partners. Current AI agents, however, may not model humans the way humans model each other under real-time constraints, creating an asymmetry in human-AI teamwork. Prior research has focused on one-human-one-AI settings, leaving open how coordination changes when multiple humans work with multiple AI agents simultaneously. We developed Co-Overcooked, a four-player cooking game where teams of humans and LLM agents must coordinate in real time. In a within-subjects experiment (N=40), participants experienced four team compositions varying in human-to-AI ratio: four humans (H4A0), three humans with one AI (H3A1), two humans with two AIs (H2A2), and one human with three AIs (H1A3). Results revealed nonlinear patterns: H3A1 and H1A3 teams performed worse than pure AI teams, while H2A2 teams showed better performance through spontaneous one-human-one-AI pairing strategies. We identified expectation conflict, where multiple humans hold divergent models of a shared AI partner, as a coordination difficulty specific to hybrid teams, and propose cognitive ownership as a design principle for human-AI team configuration.

Without being taught, children learning addition discover strategies that are faster and less error-prone than those in their repertoire. Leading explanations of strategy discovery propose metacognitive mechanisms where children iteratively rewrite their existing strategies. Departing from this tradition, we propose that children make discoveries through a combination of reconstructive imitation during teaching, followed by rational inference under basic performance pressures. We instantiate our proposal with a model of Bayesian program induction with a prior for algorithmic simplicity and a likelihood that first encodes a pressure of mimicry (teaching phase), and then a pressure for speed and/or accuracy (individual practice phase). Regardless of whether the pressure is to respond faster or more accurately, the model robustly captures the long-observed developmental trajectory of strategy discovery.

One striking divergence between logical modeling and language processing emerges when evaluating the empty set in the interpretation of quantifiers. It has been shown that empty restrictor scenarios lead to presupposition failure, while empty scope scenarios lead to logically incorrect judgments and a processing delay of downward-entailing quantifiers. In this study, we tested whether empty restrictor scenarios would be evaluated as true or false rather than rejected as odd in a question-answering task. We contrasted two weak downward-entailing quantifiers (fewer than n, at most n-1) with two weak upward-entailing quantifiers (more than m, at least m+1) in two complex question embeddings that made the evaluation possible to various degrees. We found that in these embeddings, empty restrictors are less often rejected as odd as well as that the empty restrictors of downward-entailing quantifiers are often evaluated as false.

Individuals often continue to rely on information even after credible sources have deemed it untrue, a phenomenon called the continued influence effect. This effect is especially consequential in politicised contexts where misinformation and polarisation threaten informed decision making. Existing accounts disagree on whether such belief persistence reflects cognitive failure or motivated reasoning, or whether it can instead arise from rational belief updating under constraints. We investigate this question by introducing delays during which no new information is provided, allowing memory for prior evidence to be less readily available. Across conditions, belief updating tracked the availability of evidence in memory, with beliefs regressing as supporting information decreased in availability and showing no systematic effects of political alignment. Moreover, belief trajectories aligned with predictions from Bayesian Network models parameterised using participants' own judgements. These findings suggest belief updating tracks memory availability and remains consistent with rational, evidence-based principles.

Using a cued-recall paradigm, Experiment 1 had two conditions retrieve base situations in response to surface-dissimilar analogs: one in which participants were prompted to re-represent the base situations in general terms, and one in which they reproduced them verbatim. Although the prompted generalizations tended to be either too tied to the original situation or too broad so as to encompass disanalogous cases, they increased retrieval of the base analogs compared to the unprompted condition. Besides replicating the retrieval advantage of prompting one-shot generalizations, Experiment 2 addressed whether a self-administered training could aid participants in producing very general reformulations of base situations of the kind that, in Experiment 1, were associated with greater retrieval. Even though trained participants produced a greater proportion of highly abstract generalizations, the training yielded no retrieval advantage over and above the prompted abstraction condition. We discuss the implications of the present findings for transfer-oriented instruction.

Theories of the pragmatic use of language in the Gricean tradition, and related formalizations such as the Rational Speech Act (RSA) modeling framework, have focused on information exchange between cooperative interlocutors. More recent work has emphasized the importance of other, potentially less-cooperative functions of language, such as persuasion, argumentation, and manipulation. In this work, we bridge these two traditions by developing a model of argumentative utterance choice in the RSA framework. We propose several formalizations of the argumentative strength of a signal. We then measure how well they capture behaviour in a simple utterance production task in which participants were assigned an explicit argumentative goal. We show that the most popular formalization of argumentative strength fails to capture some of the participants' utterance choices, and discuss some alternatives. Our models constitute a substantial improvement over a baseline model, and remaining unexplained patterns are discussed.

Territorial violence in Montevideo, Uruguay has increased in recent decades and, although concentrated in specific neighborhoods, may affect the broader urban territory and its population. Despite its growing salience, the psychological and family-level consequences of territorial violence in Montevideo remain largely unexplored. This study examines associations between perceived neighborhood violence, maternal negative affect, social support, and hostile parenting in a community sample of 301 mothers of school-aged children. Regression and mediation models controlling for sociodemographic covariates indicated that higher perceived violence was associated with greater maternal negative affect, which mediated associations with hostile parenting. In contrast to findings commonly reported in the Global North, higher perceived violence showed a direct negative association with hostile practices, suggesting a compensatory caregiving response under conditions of insecurity. Moderation analyses further indicated that social support buffered the association between perceived violence and maternal negative affect.

Is child-directed language (CDL) optimized to support language learning, and which aspects of linguistic development does it facilitate? We investigate this question using neural language models trained on CDL versus adult-directed language (ADL). We selectively remove syntactic or lexical co-occurrence information from the model training data, and evaluate the impact of these manipulations on verb meaning acquisition. While disrupting syntax impairs learning across all datasets, models trained on CDL and spoken ADL show significantly higher resilience than those trained on written input. Tracking semantic and syntactic performance over training, we observe a "semantic-first" trajectory, with verb meanings emerging prior to robust syntactic proficiency, an asynchrony most pronounced in the spoken domain, especially CDL. These results suggest that the advantage for verb learning previously attributed to CDL may instead reflect broader properties of the spoken register, rather than a uniquely CDL-specific optimization.

People seem to hold a variety of conflicting intuitions about the concept of dignity. Here, we seek to reverse-engineer the computational basis of these intuitions. We propose a Bayesian model that casts intuitions about dignity as computations about what agents' actions imply about their own and others' social rank. We show through two behavioral experiments that our model captures people's intuitions well, reconciling seemingly conflicting intuitions with a common computational framework.

Reposting has been identified as a primary conduit for misinformation spread. We hypothesize that reposting is especially conducive to misinformation because it allows the sender to spread false content while incurring lower reputational costs compared to posting. Experiment 1 (N=293) tests this hypothesis by measuring how reposting falsehoods affects reputation. It compares the effects of posting, reposting, and verbally reporting the same false content. Sources that directly posted false content were judged significantly less reliable than sources that reposted or reported the same false content. Experiment 2 (N=256) tests robustness by replicating findings across platforms (Facebook and Twitter) and source types (private individuals rather than institutions). Original posters again suffered greater reputational damage than reposters. These results strongly support the idea that reposting comes with reduced accountability, suggesting that its reduced reputational costs might play an important role in facilitating misinformation spread on social media.

The drawing process (how we draw), together with its outcome (what we draw), provides valuable insights into the cognitive mechanisms and motor control underlying symbolic representation. To depict developmental changes in the drawing process, this study examined the drawing sequences of Thai-speaking adults and 4- to 5-year-olds during an object copying task. After segmenting the drawing process into discrete strokes and annotating each stroke with its corresponding object part, we extracted patterns distinguishing adults' and children's drawing sequences. Results indicated that when drawing a quadrangle part that forms the overall structure of an object (e.g., a chair's backrest), Thai adults complete it with multiple strokes, while children typically use one stroke. Moreover, when drawing objects with hierarchical structures (e.g., a tournament), most adults group parts of the same level, while young children tend to draw parts in physical proximity sequentially. This study proposes a method for analyzing developmental changes in drawing sequences and highlights key questions for future developmental research on drawing, object recognition, and symbolic representation.

When thinking about space, people preferentially adopt a particular frame of reference, anchoring spatial relations either egocentrically (to their bodies) or allocentrically (to external landmarks). These preferences are often shared within a community, raising the question of how private norms for spatial thinking are made public and thus shareable. We tested whether spontaneous co-speech gesture might be one route by which these norms are expressed. Participants (N = 62) were exposed to an implicit norm to adopt a particular frame of reference when reasoning spatially. They then solved a spatial puzzle and described their solution. When participants conveyed spatial information in their gestures, they adopted either an egocentric or an allocentric frame of reference. Critically, these gestural frames of reference were congruent with the local norm. Gesture thus reflects socially learned conventions for adopting a spatial frame of reference. We discuss implications for the propagation and perpetuation of implicit cultural norms.

Explaining how to get from A to B can be challenging. It requires mentally simulating what the listener will do based on what they are told. To capture this process, we propose a computational model that converts utterances into action plans: a large language model translates an explanation into program-like guidance (a policy prior and value map), and a planning agent executes it under partial observability. We score explanations by the efficiency and reliability of the resulting paths, penalizing replanning. Across four preregistered experiments, we collect a corpus of 1,200 explanations over 24 maps, elicit helpfulness judgments, measure baseline navigation, and test behavior with explanations of differing quality. Higher-scored explanations are judged more helpful and improve navigation: participants with explanations outperform those without, and high-scoring explanations help more than low-scoring ones. Together, these results show procedural explanation as utility-guided communication shaped by how language can be grounded into action under uncertainty.

The dominant view in cognitive science and formal pragmatics is that relevance is a matter of informativeness: a sentence is relevant if it is informative, and irrelevant otherwise. We provide experimental evidence against this thesis. For polar questions (e.g. Is Mary coming to the party?), we constructed two kinds of uninformative replies: replies that clearly violate relevance (uninformative-off-topic condition; e.g. Mary lives in an old house) and replies that are equally uninformative yet do not intuitively incur a relevance violation, such as uncertainty or disagreement reports (target condition; e.g. Her parents disagree about that). In a within-subject study (__ = 100; 12 scenarios), participants judged both kinds of replies uninformative, but a forced-choice continuation diagnostic revealed a sharp dissociation in perceived relevance: target replies were judged relevant 95%, versus 3% for the uninformative-off-topic condition. These results show that informativeness is not a necessary condition for relevance.

As AI-driven concepts like Single-Pilot Operations challenge traditional two-pilot operations, debates among manufacturers, pilot unions, and regulators highlight the need for empirical evidence on how AI uncertainty affects human cognition and performance. This research compares human-human and human-AI cockpit teams, focusing on workload and performance when the pilot monitoring role is assumed by AI. 34 pilots completed simulated takeoff scenarios under three conditions: a human co-pilot, a reliable AI co-pilot, and a faulty AI co-pilot. Results indicate that collaboration with reliable AI can sustain workload levels and team performance comparable to human crews, whereas faulty AI behavior corresponds to increased workload and degraded team performance. Further analyses suggest that performance degradation is linked to undetected AI errors and AI limitations. Although pilots were often able to compensate for AI shortcomings, team collaboration was adversely affected, underscoring the importance of AI reliability and transparency for safe human-AI teaming.

When reasoning about events that occur in the world, two key types of judgments are counterfactual inferences—judging how the event might have turned out differently under different conditions—and causal selection—identifying which of multiple potential causes is responsible for an effect of interest. We conducted a novel experimental test that asked subjects to make both types of judgments about a realistic situation involving probabilistic causal relations and both proximal and distal causes. We found that a new model— the Exogenous Sampler (EXS) performed as well as the leading models of these judgment types but also specified the cognitive processes via which such judgments are computed. Yet, that all models failed to predict the full range of subjects' judgments points to the need of a new generation of models accounting for how people reason about events that arise from complex probabilistic causal structures.

People can coordinate by inferring a partner's intention, or by relying on a mutually recognized rule. We propose that this choice reflects adaptive allocation of cognitive effort. We tested this account in a coordination game where participants could win the most by inferring a signaled ad hoc coordination point, or less by coordinating on a consistent, mutually understood rule. We manipulated the recursive depth required for successful inference and the value of rule-based coordination. Greater demands for recursive depth, and higher rule value, drove increased rule use. Response times suggest rule-based choices arose from different processes: fast defaults when inference was less valuable, and slower retreats to the rule when inference was more valuable but too demanding. Individual-level analyses showed participants shifted toward deeper reasoning when inference was more valuable. These findings suggest that rules and intention inference are substitutable strategies governed by shared resource-rational logic.

The relation between logical reasoning and language development is debated. We addressed this issue by focusing on disjunctive reasoning (A V B; exclude A; therefore, B) and the acquisition of the connective "or" in 3-year-old children, comparing non-verbal and linguistic contexts. In Experiment 1, children completed first a non-verbal inferential task, and then succeeded in a linguistic inferential task, using "or" to define a set of alternatives (A V B) and visual information to exclude one and infer the other (exclude A; therefore, B). In Experiment 2, children instead completed a non-inferential control task followed by the same linguistic inferential task: although they succeeded in using visual information, they failed to use "or" to identify the relevant set of alternatives. Therefore, children who had previously engaged in non-verbal disjunctive reasoning showed a better comprehension of "or". These findings suggest that preverbal inferential abilities support the early acquisition of linguistic disjunction.

Explanations are inherently contrastive: E happened rather than E' because of C rather than C'. However, these contrasts, or "foils", are rarely mentioned explicitly but have to be inferred in context. Here, we investigate how people select the intended foil E' of a why-question. Participants read vignettes and judged, for each foil, their prior expectation (what will happen next), closeness (what is most similar to what happened), and hindsight expectation (what could have happened instead), as well as which foil they thought the question asker had in mind when they asked the why-question. We found that foil selections were best predicted by hindsight expectation judgments. This suggests that people infer the foil by considering what a question asker finds surprising after the outcome occurred. Since correct foil selection is relevant not only in human-human interaction but also increasingly in dialogues with large language models, we investigated their performance on the same task. The coupling between LLMs' explicit expectation judgments and their foil selections is inconsistent.

Human agents constantly exchange information, but deciding which evidence is worth transmitting is a fundamental cognitive challenge. This paper investigates "coherence" as a heuristic for regulating evidence sharing in groups. Extending previous models of individual coherence-based filtering, we employ computational simulations to analyze agents who selectively share evidence based on its coherence with their current beliefs. We find that coherence-based selective sharing is significantly more effective than individual evidence filtering. It helps preserve priors in overly noisy epistemic environments, while still allowing updating when evidence is noisy but truth-conducive. However, this strategy becomes a liability when misleading evidence forms a coherent alternative, steering group learning away from the truth. These results illuminate the conditions under which coherence-based heuristics improve or distort collective inquiry.

A pivotal advantage of human intelligence lies in metacognitive monitoring. This ability allows individuals to detect uncertainty in their judgments and adapt their behavior accordingly, such as deferring decision-making or seeking help. However, the computational mechanism underpinning metacognitive processing still remains incompletely characterized. We propose a risk-avoidance-based computational theory of metacognition, stating that second-order confidence is formed through an asymmetric strategy: individuals conservatively downweight potential gains while overestimating potential risks inherent in their decision outputs. We formalize this principle into a dual channel conservative confidence (DCCC) framework and instantiate it within a large language model (LLM). Evaluations across high-risk medical and legal judgment tasks demonstrate that the model's confidence signals closely align with human metacognitive profiles, including patterns in the distribution of confidence levels, patterns of error detection, and help-seeking propensities. These findings not only delineate a tractable pathway for implementing machine metacognition but also reveal a core risk-avoidance-oriented computational principle that governs human metacognition processing.

When deciding how to act under uncertainty, agents may choose to act to reduce uncertainty or they may act despite that uncertainty. In communicative settings, an important way of reducing uncertainty is by asking clarification questions (CQs). We predict that the decision to ask a CQ depends on both contextual uncertainty and the cost of alternative actions, and that these factors interact: uncertainty should matter most when acting incorrectly is costly. We formalize this interaction in a computational model based on expected regret: how much an agent stands to lose by acting now rather than with full information. We test these predictions in two experiments, one examining purely linguistic responses to questions and another extending to choices between clarification and non-linguistic action. Taken together, our results suggest a rational tradeoff: humans tend to seek clarification proportional to the risk of substantial loss when acting under uncertainty.

We present the results of an experiment investigating the comprehension of questions about previously shown pictures with geometrical objects and resulting intrusion errors on representations in memory. We manipulated whether definite and indefinite descriptions were at-issue or non-at-issue to the task at hand. For this purpose, descriptions were embedded into restrictive vs. non-restrictive appositive relative clauses. The responses to the questions provided evidence for participants' awareness of presupposition violations. Moreover, false presuppositions had an effect on an immediately following memory task. This effect was present no matter whether the presupposition trigger appeared in an at-issue or non-at-issue context. Furthermore, question processing as well as visual memory was unaffected by the wording of the misinformation: in the constructions under investigation, indefinite descriptions patterned with definite descriptions.

Theoretical linguistics commonly distinguishes between syntactic ungrammaticality, semantic anomaly, and pragmatic in- felicity, yet there is no established behavioral method for empirically separating these categories. We report two experiments aimed at deriving an empirically grounded typology of unacceptability judgments using multi-dimensional behavioral data. Participants evaluated sentences from a wide range of phenomena against prompts targeting naturalness, truth, interpretability, or contingency. Across both experiments, unsupervised clustering revealed that a stable classification emerges when multiple judgment dimensions are considered and when dimensions which further divide the semantic category are controlled. The resulting typology robustly separates syntactic, semantic, and pragmatic violations, and consistently groups several meaning-driven phenomena, including unlicensed negative polarity items and existential there constructions, with core syntactic violations rather than with semantic or pragmatic ones.

Remembering one's past decisions is critical for learning from delayed feedback and maintaining an accurate self-model. Across two gamified experimental paradigms and three experiments (total N = 500), we find that a selective forgetting of unsuccessful guesses operates within minutes, that it evades introspection, and that it affects confidence ratings in memory judgments. Analysis of memory errors reveals that, in reconstructing their past decisions, people integrate what they remember deciding, what they believe they would have decided, and what they would decide now if asked to make the same decision again.

Coherent social coordination requires aligning not only actions in the moment but also goals over time. Yet it remains unclear how joint goals are selected when multiple options unfold sequentially, and how shared information—prior goals maintained in working memory versus newly available goals captured by perception—guides coordination. We examine how the temporal structure of interaction shapes goal selection. Across three experiments, pairs of participants performed Pac-Man–like coordination tasks in which an old goal persisted while a new goal appeared. When coordination required two players to align synchronically in real time, dyads converged on newly appearing goals. When coordination required players to take turns controlling a single agent, dyads persisted with old goals. Critically, manipulating the turn-taking interval revealed a graded transition: longer intervals strengthened bias toward the old goal, whereas shorter intervals eliminated this bias. Together, these findings show that joint coordination flexibly recruits perception- and memory-based mechanisms over time.

We conducted two experiments to better understand how comprehenders use probabilistic knowledge of the associations between genders and personal names when inferring the gender of new referents during reading. Personal names are, surprisingly, under-investigated in studies on gender inference compared to role nouns (e.g., 'nurse') despite being strongly connected to gender. In a self-paced reading task involving variably gender-biased names and gendered reflexives, we observe a gender mismatch effect proportional to the incongruence between a name's perceived gender and that of the reflexive. In a novel variant of the Maze task involving forced ambiguity resolution between two gendered reflexives, we find decisions track the perceived gender of names and decisions take longer with more ambiguous names. We interpret the results to support a model in which comprehenders make graded, not categorical, inferences about gender during incremental comprehension, contrary to previous proposals.

When humans teach reinforcement learning (RL) agents through real-time interventions, does their teaching strategy adapt to environmental context or reflect systematic individual variation? We present a novel web-based platform for studying human teaching via state interventions (i.e.: physical relocation of learning agents). In a study with 82 participants, we manipulated world dynamics and how agents interpreted interventions using six formally-defined interpretation types. Bayesian analyses provided moderate-to-strong evidence that neither factor affected teaching behavior (BF01 = 5.22 for world type; BF01 = 27.08 for interpretation type). However, participants showed systematic individual variation: high-frequency teachers (n = 39; M = 36 interventions/round) targeted actions with lower Q-values compared to low-frequency teachers (d = -0.57, BF10 = 3.69). While frequent interventions boosted immediate scores, they often impaired long-term policy learning. The reset interpretation, which ignores the intervention, was an exception: it was uniquely robust to suboptimal human guidance. These findings suggest that human teaching in this task is characterized more by persistent individual behavioral profiles than by context-adaptive strategies, with implications for designing resilient human-AI interaction systems.

Research on metacognition critically relies on psychophysical measures that are independent of task performance. Although there exist well-validated measures of metacognitive efficiency (i.e., the degree to which confidence ratings distinguish between correct and incorrect decisions), relatively less attention has been paid to developing corresponding measures of metacognitive bias (i.e., the overall tendency to make responses with high confidence). Most researchers currently use mean confidence as a proxy for metacognitive bias, however this measure is confounded by changes in accuracy, response bias, and metacognitive sensitivity. Here, we introduce a new signal detection theoretic measure of metacognitive bias (meta-delta) and demonstrate that it is independent of a range of potential confounds. Then, by analyzing longitudinal data, we show that this measure is reliable enough to use in psychological research. Overall, we advocate for the use of meta-delta as a principled measure of metacognitive bias.

Humans primarily seek to maximize accuracy during task-based decision-making, but prefer to minimize time and effort as well. As time and effort demands are often coupled, the current work examines the relative influences of these secondary minimization goals on decision strategy selection. University students (N=144; ages 18-37) learned two strategies for completing a classification task involving fictional microorganisms: one that demanded moderately effortful integration over learned feature associations, and a minimal-effort alternative that required participants to wait for an answer to be provided. An adaptive experimental design ensured that both strategies yielded equivalent long-run accuracy within-subject, and wait times for the latter were manipulated to be longer or equal to deliberation times in the former. Participants overwhelmingly preferred the more effortful, time-saving strategy. Surprisingly, this preference persisted even when the strategies were matched for both accuracy and time, suggesting the perceived benefit of engagement outweighed the costs of effort.

Music comprises two core structural components, melody and rhythm, that vary widely across cultures. Whether these components coevolve in a coupled way or follow independent trajectories remains unclear. We introduce a novel computational pipeline to extract vocal melodic pitch-interval and percussive inter-onset timing distributions from 27,628 popular songs across 59 countries, enabling large-scale cross-cultural comparison that bypasses traditional music annotations. Musical similarities between countries aligned with geographic and linguistic relationships, validating our approach. Substantial variation emerged in both melodic and rhythmic structures across countries, yet the diversity of the two components was not significantly correlated, challenging assumptions of coupled evolution. Only rhythmic diversity was significantly associated with ethnic and linguistic heterogeneity, while melodic diversity showed no such association. These findings suggest that melody and rhythm constitute partially independent systems shaped by distinct cultural and evolutionary pressures, rather than components of a single monolithic musical style.

In everyday conversation, speakers often give partial explanations, and listeners must draw inferences to fill the gaps in their understanding. When speakers emphasize an unseen cause in their explanation, given knowledge of causal structure, adults can infer if the cause was abnormal or normal. However, the development of this inferential skill is unknown. Across three experiments, we tested adults (N = 62) and 5-8-year-olds (N = 107) on a novel paradigm that assessed normality inferences and explanatory selection. In addition to replicating prior work with adults, we find that 7-8-year-olds also infer the abnormal event, tracking causal influence and pragmatic informativeness. Whereas 5-6-year-olds do not yet make this adult-like inference, they do prefer an explanation citing the abnormal cause over an explanation citing the normal one. These results imply that sensitivity to normality in causal explanations emerges as early as age 5, possibly developing in tandem with skills in counterfactual reasoning.

According to the body-specificity hypothesis (Casasanto, 2009) people who interact with the environment differently (e.g., right- and left-handers) associate positive concepts with the space around their dominant hand, and negative concepts with the space around their subordinate hand. Our exploratory study replicated and extended previous findings, establishing a relationship between handedness and mental representation of emotionally valenced abstract concepts. In a task required bimanual categorization of positive and negative nouns participants showed different RT-profiles depending on their handedness. Right-handers categorized positive words faster with their right hand than with the left and negative words faster with their left hand than with the right. Left-handers showed the opposite pattern, whereas ambidextrous showed no differences in the categorizing speed for positive and negative words. Most importantly, the study suggested that these associations were critically influenced by experienced motor fluency. After 12 minutes of hampered interaction with the environment right-handers changed their RT-profiles of associations between space and valence. The results suggest that at least some of our abstract representations are so sensitive to the experienced motor (dys)fluency that it can be manifested within 1 sec of word processing.

Educators often build up complex concepts by teaching simplified versions that are not quite accurate, such as Bohr's model of the atom or Newtonian mechanics. "Lying to children'', while ubiquitous in STEM teaching, poses a challenge to existing cognitive models of pedagogy, which assume that teachers select evidence that truthfully represents a target concept. Why would helpful, knowledgeable teachers lie? We present a theoretical framework that addresses this puzzle by reinterpreting optimal pedagogy through the lens of bounded rationality. When learners face cognitive constraints on belief updating, our model predicts that teachers should prioritize examples that will bring the learner closest to the target concept---even if they do not represent the target concept truthfully; by contrast, classic pedagogy models fail to make this prediction. Our work formalizes an insight that educators have long understood: pedagogical "lies'' are not meant to mislead learners, but to meet them where they are.

Accurately predicting one's own performance outcomes is a crucial skill for children and adults alike. Prior research, however, has shown that young children are notoriously overoptimistic, making predictions far beyond their actual performance. Curiously, these findings contradict decades of work showing that infants and children hold reasonable expectations about the world, showing surprise---an indication of prediction error (PE)---when events violate their expectations. If children have well-calibrated expectations about their own performance, they might experience PE when they produce unrealistically good outcomes. Using a probability-based game (Experiment 1, N=48) and a memory-based game (Experiment 2, N=64), we show that preschoolers are indeed overoptimistic in their explicit predictions, but express surprise after achieving precisely those unrealistic performance outcomes they predicted. These results demonstrate an early-emerging sensitivity to prediction error about the self, revealing a striking discrepancy between what children say they can do and what they think they can do.

Adult learners can acquire non-adjacent dependencies (NADs) in visual sequences, yet learning is far stronger for human ac- tions than for dynamic objects (Lu & Mintz, 2023). We asked what makes human action stimuli special by separating two can- didate contributors: appearance-based structure (a visible body with form and texture) versus biological motion cues (joint trajectories). In Experiment 1, participants viewed full-body avatar actions for 6 minutes and were tested on matched point- light displays (PLDs); they rated NAD-consistent triplets as more familiar than positional foils, showing that knowledge learned from rich action displays transfers to sparse motion- only tests. Experiments 2 and 3 then asked whether motion cues alone can support learning when used during encoding. With PLDs throughout, learning was not detectable after 6 min- utes but emerged with extended exposure, suggesting that the human-action advantage stems from stronger support during encoding, not simply from perceiving the stimuli as actions.

How do speakers craft misleading messages while remaining literally truthful? Can listeners resist such manipulation? We propose that while speakers rely on Theory of Mind to convey strategically underinformative messages, listeners need the same capacity—reasoning about speakers' goals—to avoid being misled. We formalize this by extending the Rational Speech Act framework to speakers with persuasive goals. The model predicts that (1) persuasive speakers rationally exploit underinformativity to frame information favorably, but (2) listeners' ability to correct for such bias depends on whether they actively reason about speaker goals. Experiment 1 confirms that human speakers systematically shift toward underinformativity when the evidence conflicts with their persuasive goals. Experiment 2 reveals an asymmetry: despite knowing that speakers may have persuasive motives, listeners exhibit low sensitivity to underinformativity. These results suggest that recursive social reasoning in strategic communication may be asymmetric—speakers exploit pragmatic inference, but listeners do not fully reciprocate with epistemic vigilance.

Collective intelligence describes the capacity of groups to achieve levels of performance that cannot be explained by the abilities of their individuals. The existence of collective intelligence implies that groups composed of mixed-ability members may outperform groups of high ability. Here, we examine when such advantages emerge in humans and whether emergence is driven by collaboration. We designed a collaborative multiplayer online game and manipulated team composition (mixed-ability vs. high-ability) and coordination demands by exposing teams to two different task environments that varied in how much they encouraged collaboration. We collected data from 280 teams of two human players (70 per condition), totaling 560 participants. We found that mixed-ability teams outperformed high-ability teams in environments designed to encourage collaboration. This performance advantage was due to more collaborative actions and more frequent division of labor. Our results show that collaboration emerges selectively as a function of group composition and coordination demands.

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) show substantial symptom overlap at the prodromal stage, complicating early differential diagnosis. To identify markers of cognitive dysfunction, we investigated visual search performance in prodromal DLB (LB-MCI), prodromal AD (AD-MCI), and healthy controls across four levels of target visual richness: Gabor patches, abstract shapes, midlevel forms, and real-world objects. While increased stimulus richness improved search performance across all groups, LB-MCI participants exhibited a selective impairment in target present searches involving real-world objects, despite perceptual sensitivity comparable to that of AD-MCI participants. Moreover, unlike healthy older adults, both clinical groups failed to adaptively shift decision thresholds as stimulus complexity increased. These findings suggest that prodromal DLB is associated with difficulty leveraging semantic information to guide visual search, reflecting impaired deployment of semantic target templates rather than early perceptual discrimination deficits.

Learning from feedback is a fundamental mechanism through which individuals make decisions under uncertainty. An important open question is how people integrate prior information with repeated feedback to improve their decisions. To address this, we conducted an experiment in which we manipulated whether participants had structural knowledge about the decision environment: half of the participants were told the number of distinct outcomes each option could produce, whereas the other half received no such information and had to infer the outcome structure from feedback alone. Structural knowledge changed behavior under partial feedback, bringing the proportion of risky choice closer to 0.5. We propose a Bayesian model that accounts for participants' initial beliefs about outcome probabilities before receiving any feedback. By capturing choice and confidence patterns across different decision environments, the model demonstrates how structural knowledge shapes learning by altering prior beliefs, thereby linking prior information to experience-based risky choice.

Language and gesture are argued to form an integrated system where each modality contributes to the meaning of utterances. While gestures are known to influence language comprehension, less is known about how language affects the interpretation of gestures. We investigated whether the interpretation of metaphoric gestures is modulated by multimodal cues. Participants viewed hand gestures depicting the figurative interpretation of metaphors such as Men are oaks, where the actor flexed both arms to convey robustness while speaking. Gestures were presented with no additional cues, only lip movements, or audible speech. Participants then rated how well each gesture matched the gestured interpretation (e.g., robust), an alternative interpretation related to the metaphor but not to the gesture (e.g., tall), and an unrelated distractor (e.g., hikers). We found that ratings for alternative interpretations increased when gestures were paired with speech cues, compared to conditions without speech cues, suggesting that gesture comprehension is influenced by verbal content.

Aging is associated with more nose-focused face scanning pattern and reduced pattern consistency during face recognition, and both effects are correlated with performance reduction. We investigated whether these changes are related to sensorimotor (increased saccade noise) or cognitive (declined memory of visual routines) declines by simulating both mechanisms in a Deep Neural Network + Hidden Markov Model (DNN+HMM) architecture that learns facial representations and oculomotor routines for face recognition simultaneously. Results showed that increasing saccade noise reduced scanning pattern consistency without making the pattern more eyes- or nose-focused, whereas reducing visual routine accuracy to simulate memory decline led to more nose-focus scanning pattern without affecting pattern consistency. Both effects were correlated with reduced recognition accuracy, consistent with human data. Our results thus suggested that changes in face scanning pattern and consistency are dissociable consequences of distinct mechanisms underlying aging-related decline in face recognition ability, with important implications for intervention strategies.

To better understand the underlying mechanisms of individual and ensemble perception in naturalistic scenes, we compared three bayesian resource-rational models on experimental data (from 27 healthy adults): the 'Individual Encoding Model' (IEM), a variant of the summation model; the 'Ensemble Encoding Model' (EEM), related to the automatic averaging model; and the 'Task Adapted Encoding Model' (TAEM), a flexible combination of both models that adapts to task demands. In the experiment, participants encoded and reproduced either an individual object position or an ensemble position (group centroid) in a 3D-rendered scene using a computer mouse. In both tasks, we manipulated set size (3, 6, 10 objects) and presentation time (50, 100, 800 ms). The EEM and TAEM generally explained the human behavioral data best. We conclude that, in naturalistic scenes, the choice between individual versus ensemble perception is likely driven by the more compact scene representation of the ensemble model.

Semantic shift, or the diachronic variation in word meaning, is a topic commonly discussed in historical and cognitive linguistics. Previous work has typically focused on characterizing semantic change in a population, but how word meanings shift over time in individuals is underexplored. We propose a computational framework for analyzing diachronic semantic shift at the individual level by adapting techniques for population-level semantic change. Using speaker-labelled text corpora, our framework utilizes contextualized word embeddings to identify predictable patterns in semantic shift among individual speakers. We discover that frequency predicts the rate of semantic shift over time across speakers, while polysemy does not. Additionally, nouns exhibit higher semantic stability over adjectives and verbs. These patterns partially mirror existing findings on regularity in historical semantic change, suggesting shared tendencies across individual and population levels. Our work bridges computational approaches to historical semantics with the diachronic modeling of personal lexicons.

Pronominal systems tend to maintain the same person distinctions across number categories. This has been explained by most linguistic theories in which pronominal systems are modelled as a combination of independent person and number features (e.g., Harbour, 2016; Harley and Ritter, 2002). At the same time, person and number features are known to interact asymmetrically, which some have taken to indicate that personal pronouns are not straightforwardly the composition of person and number, but rather categories on their own (e.g., Cysouw, 2009). The main source of evidence for feature-based theories is typological data. In this study, we use an artificial language learning approach to provide complementary behavioral evidence to the question of the independence of person and number in personal pronouns. We test whether learners have a preference for person systems that maintain uniform person distinctions across number categories. Our results suggest that, in the absence of explicit evidence, learners infer the same person distinctions across number categories, regardless of whether number is morphologically transparent or not. Our findings suggest that pronominal systems are most effectively learned as combination of distinct person and number features, rather than as unified categories.

The ability to interpret graphs is crucial for educational and professional success, yet many people struggle with graph comprehension. This study investigates how children process graphical information and what graph elements they attend to, encode, and retain. Middle school children completed three tasks. In the Description Task, participants described a graph either while viewing it or from memory. The Recognition Task assessed participants' ability to detect and remember differences in specific components (i.e., axes, scales, and trends) between two graphs. The Slope Judgment Task examined whether judgments of rate of change relied more on pictorial cues (e.g., visual steepness) or semantic cues (e.g., scale values). We found that children consistently extracted and recalled pictorial features across tasks, whereas quantitative semantic features such as axis scales were encoded and retained less accurately. These findings clarify the nature of children's graph encoding and have implications for graph comprehension instruction.

A complete theory of spontaneous thought must explain not only content but also dynamics: how the stream unfolds over time. Recent memory-centric accounts characterize these dynamics as fundamentally associative. I argue that such accounts cannot accommodate goal-structured dynamics: sequences in which successor selection is governed by instrumental relevance to a goal rather than by associative strength. Drawing on evidence from spontaneous planning, I show that goal-structured dynamics genuinely occur within mind wandering. I propose a dual-driver model in which memory and cognitive control jointly generate the stream: memory drives associative drift, while control imposes goal-structured organization when an automatically activated goal recruits executive resources. On this view, the spontaneous stream is heterogeneous, shifting fluidly between associative and goal-structured phases.

Cross-categorial innovations, such as nouns used as verbs, are a productive source of polysemy in language. Although such innovative forms are typically interpreted with ease, little is known about their neural processing. This study investigates the neural correlates of innovative denominal verbs during naturalistic sentence reading. Using concurrent eye-tracking and EEG, we compared innovative denominal verbs with their base nouns and with conventionalized verbs, allowing innovation-related effects to be distinguished from general noun–verb differences. EEG signals were time-locked to the first fixation on the critical word. Innovative denominal verbs elicited a reliable left-anterior negative-going effect in the 300–500 ms time window relative to both control conditions, which extended into the 500–800 ms interval. No evidence was found for an ELAN or P600. We argue that the observed sustained negativity reflects increased demands on semantic-pragmatic integration and context-dependent meaning construction, rather than morphosyntactic anomaly processing.

Comprehenders routinely generate predictions during language comprehension in real time, yet the mechanisms underlying prediction generation remain debated. The prediction-by-production hypothesis proposes that comprehenders use their own production system to anticipate upcoming linguistic input. While an initial body of work supports production-based accounts of prediction, it remains unclear whether increased engagement of the production system benefits all predictable words – i.e., both high- and low-cloze continuations – or only the most expected ones in a "winner-takes-all" fashion. In the present work, we investigate this question through a German self-paced reading and an event-related potentials (ERP) experiment employing a novel within-participants task paradigm manipulating engagement of the production system during comprehension. Reading times and ERPs show that increased engagement of the production system facilitiates processing for both high- and low-cloze words, suggesting that prediction-by- production supports parallel pre-activation of multiple plausible continuations, with ERP results showing strongest facilitation for highly predictable words.

Generics (e.g., boys play football) are used to link certain properties to the members of a category. One of the conceptual consequences of generics is licensing normative inferences about how category members should behave. Building on pragmatic approaches, we propose that children are especially likely to form normative inferences when they hear generic statements used in mismatching situations (e.g., saying "boys play football" to a boy who is playing volleyball). We investigated whether 3- and 4-year-olds (N = 157) draw normative inferences from generic statements that either matched or mismatched an observed situation. Partially aligning with our predictions, children prescribed generically and specifically stated properties to new category members more often in mismatching than in matching situations. Exploratory analyses suggested that children were more likely to prescribe generically than specifically stated properties in mismatching and matching situations. Overall, findings suggest that generics and situational mismatch promote normative inferences in children.

How do children form beliefs from testimony? Previous work with adults suggests a "Spinozan'' account of belief formation on which propositions are accepted as true by default, and rejecting them is effortful. But it is unclear whether this asymmetry between accepting and rejection propositions arises because people learn that others generally say true things or because of the cognitive architecture supporting belief formation. To distinguish between these accounts, we presented 3- to 8-year-olds with statements about a novel animal that were confirmed or denied by a knowledgeable expert. Children with poorer executive function misremembered denied statements more often than confirmed ones. A second experiment confirmed that these results did not reflect a "yes bias'', and that children could select the expert's testimony over the novice's when these were pitted against each other. Together, these results suggest that rejecting statements is challenging because of the cognitive architecture supporting belief formation.

Learning new categories often alters similarity judgments: items from different categories are judged as less similar, and items within the same category as more similar. While category training may drive such changes, categories often have distributional structure without supervision. This raises the question of whether unsupervised exposure alone can reshape similarity judgments, and by what mechanism. We hypothesized that similarity judgments reflect a learned internal geometry of stimulus space. During unsupervised exposure, learners may infer the manifold structure underlying the distribution of examples, such that similarity depends on path-based relations on the manifold rather than inter-item distance or exemplar density alone. To test this, participants experienced stimulus distributions that differed only in whether they formed a connected or disconnected manifold. Despite equivalent physical similarity, participants judged items as less similar when separated by a topological gap. These results demonstrate that unsupervised exposure is sufficient to alter similarity judgments and reflect sensitivity to inferred geometric structure of stimulus space.

Iconic signs are not necessarily transparent (i.e., easily guessable) because iconicity is a perceived phenomenon that depends on the interpretation of a subjective human perceiver. Nevertheless, previous work has shown that some iconic signs are systematically relatively transparent: people can guess their meanings more easily than those of other signs. In this study, we demonstrate that iconicity constrains response diversity. Specifically, more iconic signs elicit a narrower range of guesses, and these guesses are semantically closer to the intended meaning than those elicited by less iconic signs. Context further strengthens this relationship between iconicity and response diversity, whereas the cross-linguistic frequency of a sign's underlying motivation does not. We argue that these findings warrant a reevaluation of earlier studies claiming that people are generally poor at guessing the meaning of iconic signs, and that iconicity is therefore purely subjective. If signs rated as more iconic produce fewer guesses that are nonetheless semantically closer to the target meaning, then even incorrect responses reveal sensitivity to the iconic motivations underlying sign forms. With even minimal contextual support, iconicity may be less subjective than often assumed.

Communicating using only a limited vocabulary is a common but challenging cognitive phenomenon, requiring an ideal communicator to plan carefully to optimize for intelligibility while circumventing a constrained lexicon. In this work, we investigate how humans respond to a broad array of questions under variable vocabulary limitations, consisting of only 250 highly frequent words at the most restrictive. We provide theoretically motivated comparisons to greedy and globally optimal sampling algorithms using Sequential Monte Carlo inference with large language models. Humans generally resemble greedy sampling more than globally optimal sampling, though more skilled humans are more likely to backtrack and revise -- a non-greedy behavior. An observed human pattern of leaning on semantically light words in high-constraint settings falls out of both greedy and globally optimal sampling. We discuss the results and their broader implications for resource-rational cognition, psycholinguistics, L2 communication, and language impairments.

When we encounter a novel label, what, if anything, do we assume about the appearance of its referent? Likewise, when encountering a new creature, what novel label might we generate for it? Here, we examine whether people have a bias to map labels that are similar in form onto perceptually similar concepts (and vice versa). Across three experiments, we show bidirectional evidence of this. In Experiment 1, adults were first introduced to a novel label–referent pairing (e.g., "This is a toku." written above an image of an alien species) and then selected which of two new species best fit a second, novel label. When the new label was similar to the initial one (e.g., "teki"), participants reliably mapped it onto the species that looked more similar to the first one. In Experiment 2, we show that this label similarity effect is robust enough to override pre-existing sound-shape preferences. And in Experiment 3, we demonstrate that the effect also operates in production: after being introduced to a novel creature, participants generated more similar labels when asked to label a perceptually similar, but distinct, creature.

Humans express generalizations using both generics (e.g., "Dogs bark") and explicit quantifiers (e.g., "All dogs bark"). Quantified statements are systematically misremembered as generics more than vice versa, a phenomenon known as the Generic Recall Bias. Yet, the memory processes underlying this bias remain unclear. We investigate whether this phenomenon arises during memory encoding, retention, or retrieval. Here, we taught adults (N = 1,189) generic or quantified statements about a novel social category, and assessed recall immediately and one week later. Replicating and extending prior findings, we found that "all", "most", and "some" statements were each significantly misremembered as generics. Critically, longitudinal analyses revealed that misrecall patterns were stable across time and consistent within items, supporting an encoding-based account: quantified information is often encoded without its specific proportional information from the outset. Overall, these results clarify the memory mechanisms underlying human generalization.

Human reasoning is often affected by order, such that later judgments depend on earlier ones. For example, order effects have been observed when people answer counterfactual questions. The order in which questions are asked affects whether participants backtrack -- imagining how variables that are upstream of a counterfactual change might have been different. Some counterfactual theories predict backtracking while others do not. We build on this prior work and develop a computational model of counterfactual reasoning that captures order effects. We evaluate different versions of this model on existing empirical results, finding that a model which backtracks and produces systematic order effects best explains human judgments. We conclude by discussing implications of the finding that counterfactual reasoning requires a context-sensitive evidence source for both resource-rational and discourse-coherent explanations of reasoning.

This study investigates how observing gestures varying in congruence with pitch contours influences processing of lexical tones during second language (L2) word learning. Adult L1 English speakers with no tonal language experience completed pre- and post-learning tests in which they heard pairs of Mandarin words with correct (matching) or incorrect (mismatching) tones while N400 event-related potentials were recorded. During the learning phase, participants learned Mandarin words differing minimally in lexical tone while observing congruent, incongruent, or no pitch gestures. Participants completed two post-learning tests of lexical tone association and word meaning discrimination immediately after the learning phase and 24 hours later. Distinct N400 responses emerged for L2 words learned with congruent gestures in the lexical tone discrimination task, indicating that observing congruent pitch gestures facilitates neural sensitivity to lexical tones.

Regret is a counterfactual emotion that arises when one judges that an outcome would have been better if one had acted differently. Classic accounts distinguish between commission regret (regretting an action) and omission regret (regretting an inaction). Here, we re-examine this distinction through the lens of attention and counterfactual salience. We find that externally highlighting subsets of options reliably increased omission and commission regret even after action. Increasing the size of the salient set attenuated regret intensity. When participants actively constructed their own consideration sets, the same patterns emerged. These findings suggest that commission--omission asymmetries may depend less on action versus inaction per se, and more on how attention shapes the space of salient counterfactuals, with implications for when regret might support adaptive learning or disrupt it.

A major challenge in human social learning is deciding how to integrate "cultural" information inherited from previous generations with contemporary information available from our "peers". While cultural information may be based on more experience, it may also be outdated if the environment has changed. To understand this dilemma from a normative perspective, we developed a cognitive model of a highly simplified version of the problem. Our analysis examined how a learner should balance peer and cultural information in dynamic environments under information costs. We analysed this trade-off as a function of (1) the age of cultural information, (2) the rate of environmental change, (3) the reliability of peer information, and (4) information gathering costs. We also explore how cognitive limitations may bias this process. These results contribute to an increasingly detailed picture of the computational problems that individuals face in the context of cultural evolution.

People can often understand and use novel artifacts after only a few interactions, suggesting that design choices communicate underlying affordances and causal structure. We propose a formal account of this process by framing cooperative, user-centered design as a cooperative game in which the user is the principal and the designer is an assistant. Inspired by prior work on pragmatic communication (e.g. RSA), our model treats a designer's design decisions as communicative signals and predicts user judgments via recursive mentalizing: designers make design decisions to trade off informativeness about the artifact with efficiency, and users infer the true model of the artifact by inverting this cooperative designer model. We evaluate the model in a "design game" where designers place visually identical keys on trays to help a user infer which keys unlock which doors in grid-world layouts. We find that pragmatic designer and user models better match human judgments than non-mentalizing literal baselines.

Cognitive science offers powerful tools for addressing pressing public health needs. In the current line of work, 1700 US parents reported beliefs about a range of topics related to pediatric COVID vaccination. We deployed Bayesian network structure learning to develop a cognitive model of the relationships among these beliefs and their combined influence on vaccine endorsement. Nine months later, we re-measured these beliefs in a subset of returning participants (n=884) to examine how beliefs changed together over a particularly tumultuous period of time. In a simple comparison of observed co-changes to Time 1 correlations, as well as a comparison of co-changes to model-based simulations, the relationships among beliefs at Time 1 accurately predicted co-change over time (R2 .84-.92 across models). This is consistent with the possibility that correlations across individuals at a single timepoint reflect an underlying belief network that also governs how beliefs change over time.

Many legal decisions rely on evaluating how a "reasonable person" would have acted, but what defines this standard? Here, we offer an experimental jurisprudence perspective by investigating what subjective features come to mind when reasoning about a reasonable person. In Experiment 1, we examined how people conceptually organize demographic, dispositional, and action features along dimensions of relevance, controllability, and normality. In Experiment 2, we used these dimensions to predict what features shape judgments about reasonableness and outcomes. We found that participants "undid" harmful outcomes by focusing on relevant, abnormal actions; that they constructed a reasonable person from the defendant by preserving normal attributes while changing abnormal, controllable ones; and that they endorsed subjective standards based on features that were relevant and normal, but relatively uncontrollable, consistent with theories of blame. Together, these findings map a template of a reasonable person and provide a descriptive foundation to inform legal theory.

This work aims to formalize some of the ways scientific concepts are formed in the process of theoretical physics discovery. Since this may at first seem like a task beyond the scope of the exact sciences, we begin by presenting arguments for why scientific concept formation can be formalized. Then, we introduce type theory as a natural framework for this formalization. We formalize what we call "ways of discovering new concepts" including property preservation and concept change, as cognitive typing rules. Next, we apply these cognitive typing rules to a case study of conceptual discovery in the history of physics: Einstein's conceptual path to the relativity of time. Then, we recast what a physicist might informally call "ways of discovering new scientific concepts" as compositional typing rules built from cognitive typing rules—thus formalizing them as scientific discovery mechanisms. Lastly, we computationally model the type-theoretic reconstruction as a program synthesis task.

Access to semantic representations is a central component of reading comprehension. While dual-route models of reading posit the coexistence of phonological and lexical processes, transparent orthographies such as Spanish suggests that reliance on sublexical decoding may sometimes limit deeper semantic processing. This study investigates whether cognitive restoration (i.e., reading words with internal letter transpositions) facilitates semantic access and reading comprehension in expert readers. Sixty university students participated in the study. Experiment 1 examined semantic memory for isolated words using an image recognition task following standard and transposed reading conditions. Experiment 2 assessed text comprehension using narrative texts presented in the same modalities. Results showed significantly better semantic memory performance and improved text comprehension under transposed reading. These findings suggest that cognitive restoration promotes lexical-route engagement and deeper semantic processing, even in skilled readers. Results extend dual-route accounts of reading and highlight word restoration as a mechanism supporting semantic access in higher-level reading comprehension.

Falsificationism remains a popular framework considered by many scientists as 'the normatively correct model of science', and of human cognition as a kind of 'lay science'. In this paper, we demonstrate some fundamental conceptual shortcomings and gaps in this normative prescription. While some of the issues presented here connect to well-known arguments from the philosophy of science, we connect these issues to the topic of efficient information search. Specifically, we show that a strict falsificationist rejection of the idea of 'confirmation' of scientific hypothesis (more specifically, entertaining and updating beliefs in those hypotheses, based on evidence) makes scientific research and information search overall less efficient, compared to an accuracy-based Bayesian approach.

Humans rapidly construct ad hoc categories, e.g., generating "beet" for "vegetables to paint with". Building on feature-based accounts of category representation, we propose an analytic framework in which ad hoc categories are constructed by shifting the base category's representation (e.g., VEGETABLE) along modifier-relevant dimensions (e.g. suitable for painting) in a high-dimensional semantic space, without an explicit hand enumerated feature search. We operationalize this with off-the shelf semantic embeddings, fitting per-category sparse linear models that predict item generation across 20 categories. Across 9 categories tested for compositional transfer, learned modifier directions improved predicted retrieval when physical constraints generalize across domains (e.g., portability for "that could fit in your pocket"), while transfer was weaker for context-dependent modifiers. The learned axes align with human-rated features, with top-weighted features matching modifier semantics. These results frame ad hoc category construction as contextual reweighting of a semantic geometry, bridging feature-based accounts of conceptual combination with distributional representations.