United States

This study explores inter-subject neural synchronization, measured via inter-subject correlation (ISC), using EEG during fractal animation observation. Fractal animations, characterized by iterative self-similarity and visual complexity, provide a controlled stimulus devoid of semantic or emotional content, facilitating analysis of core sensory and predictive mechanisms. Fifteen participants watched fractal animations while their EEG was recorded. Following preprocessing, including artifact removal and spatial filtering, ISC was calculated using correlated component analysis. Results showed robust synchronization in occipital regions, linked to early visual processing, and frontal areas, associated with attentional control. Two control manipulations—phase randomization and temporal shuffling—reduced ISC by 65% and 70% (p &lt; 0.001), confirming coherence. Peak synchronization aligned with heightened visual complexity and abrupt transitions. A correlation between self-reported focus and ISC highlighted top-down modulation. These findings endorse fractal animations as a powerful paradigm for studying neural responses, yielding insights into multisensory integration and cognitive processing.

CogSci 2025

Exploring Neural Synchronization with EEG Using Fractal Animations

dynamic systems modeling

art and cognition

predictive processing

electroencephalography (eeg)

quantitative behavior

cognitive neuroscience

computational neuroscience

sensory processing

computational modeling

computer science

neuroscience

psychology

perception

artificial intelligence

vision

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

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#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

This study explores inter-subject neural synchronization, measured via inter-subject correlation (ISC), using EEG during fractal animation observation. Fractal animations, characterized by iterative self-similarity and visual complexity, provide a controlled stimulus devoid of semantic or emotional content, facilitating analysis of core sensory and predictive mechanisms. Fifteen participants watched fractal animations while their EEG was recorded. Following preprocessing, including artifact removal and spatial filtering, ISC was calculated using correlated component analysis. Results showed robust synchronization in occipital regions, linked to early visual processing, and frontal areas, associated with attentional control. Two control manipulations—phase randomization and temporal shuffling—reduced ISC by 65% and 70% (p < 0.001), confirming coherence. Peak synchronization aligned with heightened visual complexity and abrupt transitions. A correlation between self-reported focus and ISC highlighted top-down modulation. These findings endorse fractal animations as a powerful paradigm for studying neural responses, yielding insights into multisensory integration and cognitive processing.

poster

Learning motivation is a key variable in learning. Therefore, its assessment has consistently been a popular research topic. While traditional methods like self-report still dominate, methods integrating passive mobile sensing have emerged, using smartphones to collect behavioral data and assess learning motivation via statistical and machine learning techniques. Recent advances in large language models (LLMs) offer new perspectives for psychological measurements, yet their application in learning motivation assessment remains underexplored. To bridge this gap, we propose a novel approach that integrates LLMs with passive mobile sensing to assess and predict students’ learning motivation. We constructed our dataset using mobile sensing data and self-report measures, then designed zero-shot and few-shot tasks embedded in LLMs to evaluate the performance. Ultimately, our findings indicate the feasibility and highlight the potential of leveraging LLMs to predict learning motivation levels based on mobile sensing data.

An Explorative Investigation into Leveraging LLMs to Predict University Students' Learning Motivation

Normative concerns for ownership fundamentally regulate social interaction regarding resources in humans, but little is known about origins of ownership inferences and evaluations tin earliest human development. Here, we ask if normative evaluations of ownership respect are rooted in an early-developing protomoral dispreference for those who violate ownership. Across two studies (N = 66) infants saw puppet shows where an owner was approached by a non-owner who attempted to take the owner’s resource. Infants were subsequently allowed to choose between the two puppets. We controlled for the dominance implications of the outcomes of resource competition by systematically varying whether the non-owner or owner prevailed in attaining the resource. Both studies yielded clear Bayesian evidence that infants did not preferentially reach towards either the owner or ownership violator. These results suggest that infants do not hold strong negative evaluations of those who violate ownership.

Do infants prefer owners over thieves?

Accumulating knowledge and experience across the lifespan are bound to have an impact on the meaning of words. Here, we investigated this idea using primarily emotional valence of words as a test-case. We used French databases that gather psycholinguistic variables including emotional valence of words, in four groups of individuals including young (18-25; 26-39 years), middle-aged (40-59 years) and older (>60 years) adults. Following the hypothesis that words may display age-related differences in their psycholinguistic properties, we computed linear regressions over all individual words as a function of age-groups. Results revealed notably that between 5 and 10% of words show significant linear changes of emotional valence as a function of age. This pattern highlights the situated and flexible nature of word meanings and suggests that self-relevance of experience affects semantic memory.

From positive to negative "craziness": Changes in emotional valence of words across adulthood

Reinforcement learning (RL)—using past choices and outcomes to learn best policies—can model the effects of anxiety in human learning. RL mechanisms are commonly studied using game-like computer tasks; but real-life learning often happens in naturalistic settings, with artificial stimuli less involved than day-to-day situations and objects. We used an online “choose your own adventure” text game to test how modality of interaction—narrative versus gamified—impacts the effect of anxiety on RL. 211 participants completed five “chapters” (blocks) of seven choices, followed by informative feedback about underlying story structure. Higher anxiety scores linked to lower model-free accuracy and higher reaction times. An RL model with memory-decay and attention components showed comparable learning rates, but more decision noise and lower attention in anxious participants. This data shows that anxiety impacts RL in naturalistic processing contexts and holds potential insights into the use of narrative, RL-based gamification in educational settings.

Anxiety Impacts Reinforcement Learning Subprocesses In a Choose-Your-Own-Adventure Text-Based Game

Online public discourse faces many threats such as human and bot networks spreading disinformation or harassment campaigns aimed at excluding certain voices. One such threat is the strategy of 'flooding the zone': intentionally pumping into the discourse information that is irrelevant to, or distracting from, an important issue. This technique is employed by both individual and state actors with seeming success. How and why that technique is successful, by contrast, is less well understood. In this paper we use agent-based modelling to help elucidate the disruptive impact of flooding the zone on communication itself. Specifically, we probe the ways in which flooding hampers the spread of relevant information and show consequences of this even for idealized, rational, actors.

Flooding the Zone: An Agent-based Exploration

This study investigates real-time processing and interpretation of quantified sentences in English and Chinese, focusing on cases where an existential quantifier precedes a universal quantifier. Using a self-paced reading (SPR) task and comprehension questions, we found that surface scope was processed faster than inverse scope, with differences emerging in later processing regions, aligning with the Processing Scope Economy principle. Cross-linguistic differences revealed that inverse scope was less accessible in Chinese than in English, confirming scope rigidity in Chinese. Working memory influenced offline interpretation but not online processing. Additionally, large language models only partially resembled human performance, with BERT-based models aligning with human data in English but not in Chinese, likely due to training biases. These findings contribute to understanding scope processing mechanisms, cross-linguistic variation, and cognitive constraints, while also informing the limitations of LLMs in modeling human sentence comprehension.

Scope Interpretation: Evidence from Human and Large Language Models

Facial expression recognition lies at the intersection of computer science and cognitive psychology, yet the cognitive structure underlying facial action unit (AU) and emotion processing remains unclear. Are AUs and emotions processed in parallel or sequentially? Does gender influence this process? We constructed a 3D face dataset annotated with AU amplitudes and emotion labels. To model cognitive processing hypotheses, we implemented parallel and sequential architectures via multi-task learning and pipelined CNNs. Gender-specific models were compared using representational similarity analysis (RSA) with theoretical emotion spaces. The parallel model (F1 = 42.9%) outperformed the sequential one (F1 = 17.1%), supporting the parallel processing hypothesis. RSA revealed that females’ emotion recognition aligned with social distance between emotions, while males’ performance was selectively influenced by anger representations. These findings suggest sex-specific representational structures in emotion processing and support parallelism as a plausible cognitive mechanism in facial expression recognition.

CoEmo: Modeling Cognitive Processes in Facial Expression Recognition through Action Units and Gender Perspectives

While research has explored cross-cultural variation in child-directed speech (CDS), little is known about if and how it may have changed over time. We explore whether CDS has undergone historical change by analyzing prosodic features in child-directed (CD) broadcasts from a German children’s bedtime program (1959–present) and comparing them to adult-directed (AD) weather forecasts from the same period. The program originated in East Germany and continued after German reunification in 1990, potentially reflecting a socio-cultural shift toward more child-centric attitudes characteristic of Western liberal democracies. Pitch variation in CD broadcasts, although higher than in AD broadcasts, remained stable over time. In contrast, articulation rates showed no register difference pre-1990; only after 1990 did CD broadcasts exhibit the slower articulation rates typical of CDS. This suggests that some features of CDS may be subject to cultural evolution over historical time, which can be accelerated by major historical events.

Is the Past a Different Culture? Tracking Changes in Prosodic Features of Child-Directed Broadcasting Across Six Decades

Recently, the field of Multi-Agent Systems (MAS) has gained popularity as researchers are trying to develop artificial intelligence capable of efficient collective reasoning. Agents based on Large Language Models (LLMs) perform well in isolated tasks, yet struggle with higher-order cognition required for adaptive collaboration. Human teams achieve synergy not only through knowledge sharing, but also through recursive reasoning, structured critique, and the ability to infer others’ mental states. Current artificial systems lack these essential mechanisms, limiting their ability to engage in sophisticated collective reasoning. This work explores cognitive processes that enable effective collaboration, focusing on adaptive theory of mind (ToM) and systematic critical evaluation. We investigate three key questions. First, how does the ability to model others’ perspectives enhance coordination and reduce redundant reasoning? Second, to what extent does structured critique improve reasoning quality by identifying logical gaps and mitigating biases? Third, the interplay of these mechanisms can lead to emergent cognitive synergy, where the collective intelligence of the system exceeds the sum of its parts. Through an empirical case study on complex decision making, we show that the integration of these cognitive mechanisms leads to more coherent, adaptive, and rigorous agent interactions. This article contributes to the field of cognitive science and AI research by presenting a structured framework that emulates human-like collaborative reasoning MAS. It highlights the significance of dynamic ToM and critical evaluation in advancing multi-agent systems’ ability to tackle complex, real-world challenges.

Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluation

It is now well-established that counterfactual reasoning takes place when people make moral judgments. Less is known about which counterfactuals lead to stronger moral judgment, especially when judging agents who unknowingly produce negative consequences. We explored the relationship between counterfactual salience and responsibility ascription in two experiments. In Experiment 1, we asked people to produce counterfactual alternatives to a vignette they read spontaneously. We manipulated whether agents who produced harm knew the relevant information beforehand and what the reasons for the possible ignorance were. The counterfactual type that people first came up with (e.g., related to external factors or agent's actions) mediated the relationship between the condition and responsibility ratings. Experiment 2 investigated the causal connection between certain counterfactual types and responsibility ascription. We show that guiding people to consider alternative perpetrator's actions leads to a higher tendency to ascribe responsibility than considering victim's actions.

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