United States

Blink synchronization may reflect a person’s internal states. An interested listener unconsciously synchronizes their blinks to a speaker; however, it is unclear if this is related to their comprehension of what is being said. This study examined the association of blink synchronization of a listener to a speaker based on the listener’s interest, comprehension, and empathy. Participants viewed a video clip explaining a research topic as a simulation of actual communication that requires comprehension, and their blinking patterns were measured using web cameras. Aspects of the internal states were assessed using a questionnaire. The findings revealed that participants with high comprehension synchronized their blinks to the speaker with a delay of a few hundred milliseconds. Additionally, levels of comprehension were significantly correlated with blink synchronization among the listeners. Therefore, blink synchronization may reflect comprehension levels in situations that require comprehension. Communication can be improved by utilizing these insights.

CogSci 2025

Relationship between comprehension and blink synchronization

empathy

statistics

eye tracking

human-computer interaction

emotion

poster

### 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.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### 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.

Infants' object play is closely tied to language learning and 3D object understanding.
How does this kind of embodied visual experience support infants' abilities to categorize objects ``in the wild?'' We address this question using domain adaptation, a machine learning framework for studying distribution shifts, i.e., how to transfer knowledge learned from one data distribution to a related but different distribution. We formalize a specific distribution shift problem inspired by infant visual learning---the VI-Shift problem---which mimics the tradeoff between object instances and viewpoints in these two regimes of visual experience. We study the VI-Shift problem through the lens of domain adaptation in deep learning architectures, in particular using novel metrics to demonstrate how the clusterability of learned features contributes to robust generalization.  We show that two classic domain adaptation methods do not perform well on the VI-Shift problem, and we demonstrate a novel loss function that improves performance by leveraging some of the distinctive visual characteristics of embodied object play experiences.  Our results illustrate one potential learning route through which the distinctive visual properties of embodied object experience can boost robust generalization.

How Infant-Like, Embodied Visual Experiences Can Support Generalization to In-The-Wild Images: Insights from Domain Adaptation

Human working memory (WM) is central to our complex cognitive capacities. Famously, it is limited, and much debate surrounds the nature of this limitation. Anatomical evidence reveals strongly-connected neural populations in PFC allowing robust maintenance. Past work implicates a basal ganglia circuit in WM management. There remain open questions about whether, aside from anatomical ‘hard’ limits, there are computational ‘soft’ limits that arise from learning/management bottlenecks. Here, we use computational modeling to tease apart these factors by considering them in isolation: we allow a transformer model trained to do a symbolic WM management task full access to past context, manipulating only the number of concurrent symbols it needs to learn to maintain, controlling for surface complexity. We find that despite having no ‘hard’ limits, the model shows difficulty in learning that scales with the computational demand, suggesting WM limits in humans may have arisen due to a learning bottleneck.

Learning imposes a bottleneck beyond anatomical constraints: a computational investigation into the nature of WM capacity limits

Behavioral adaptation in probabilistic environments requires learning through trial and error. While reinforcement learning (RL) models can describe the temporal development of preferences through error-driven learning, the diffusion decision model (DDM) allow for the mapping of state preferences on single response times. We present a Bayesian hierarchical RL-DDM integrating temporal-difference (TD) learning. Our implementation incorporates variants of TD learning, including SARSA, Q-Learning, and Actor-Critic models. We tested the model with data from N = 59 participants in a two-stage decision-making task. Participants exhibited learning over time, becoming both more accurate and faster. They also reflected a difficulty effect, with faster and more accurate responses for easier choices, as reflected by greater subjective value differences between available options. Model comparison demonstrated that the RL-DDM provided a better fit compared to standalone RL or DDM models. Notably, the RL-DDM captured both the temporal dynamics of learning and the difficulty effect in decision-making.

Temporal-Difference Learning in Uncertain Choice: A Reinforcement Learning-Diffusion Decision Model of Two-Stage Decision-Making

When people acquire a second language (L2), do they benefit from analogical reasoning?  Past research showed that people are likely to engage in analogical reasoning to support their L2 learning, yet it is unclear whether this process is explicit or only occurs automatically and implicitly. In the current study, English-speaking participants (N = 100) learned a miniature artificial language with grammatical markers that were either morphologically congruent or incongruent with English grammar. We then assessed participants’ acquisition of the artificial language and their explicit use of analogical reasoning. Acquisition was improved when the artificial language was structurally congruent with English, and was also better with participants who reported explicit analogical reasoning. This was especially pronounced for the ability to generate novel content. These findings provide evidence that learners acquiring a new language spontaneously leverage analogies with their existing languages, and this is especially beneficial when the analogies are recognized explicitly.

Does Explicit Analogical Reasoning Help Second Language Acquisition? Evidence From Artificial Language Learning

We use a computer vision model to examine inequities in the quality of videos shown to children from different socioeconomic backgrounds. This work is foundational to understanding the origin of divergence in children’s cognitive development. We use our model to quantify visual salience across three categories of children’s media: ad supported, paid, and educational public television. We find that ad-supported media contains significantly higher levels of flicker, a feature of visual salience linked to disrupted processing and worse learning outcomes (Essex et al., 2022; Shepherd & Kidd, 2024). These results represent the first to quantitatively demonstrate a difference in the quality of media shown to low- versus high-income children. These findings confirm that children from low-income families are watching more visually salient content and thus more at risk for the potential harms this type of content poses.

Bright shiny garbage: Video content shown to low-income children is characterized by higher flicker

Generative AI is shaping an increasingly hybrid society, where ideas and cultural artefacs are created both by humans and intelligent machines. Human creativity is influenced in complex, nonlinear ways by the actions of AI-driven agents within their social networks, but these influences are difficult to measure using traditional methods. This study examines how human-AI interactions shape the evolution of collective creation within large-scale social network experiments,  where human and AI participants collectively create stories. Participants (either humans or AI) joined 5×5 grid-based networks in which stories were selected, modified, and shared over many iterations. Initially, AI-only networks showed greater creativity (rated by a separate group of human raters) and collective diversity of stories than human-only and human-AI networks. However, over time, hybrid human-AI networks became more diverse in their creations than AI-only networks. In part, this is because AI agents retained little from the original stories, while human-only networks preserved continuity. These findings highlight the value of experimental social networks in understanding human-AI hybrid societies.

The Dynamics of Collective Creativity in Human-AI Hybrid Societies

This study proposes an analytical method to visualize and quantify the process of collaborative creativity. While many studies have theoretically emphasized the importance of process in creativity, its complex nature—characterized by emergence and revisitability, representation and embodiment, and conscious and unconscious aspects—has made it difficult to quantify in a standardized way. We introduce an extended version of cross-recurrence quantification analysis, C2RQA, as a suitable method. C2RQA is applicable to various data types, including continuous, categorical, and binary, and can visualize correspondences between two time series, thereby revealing interaction dynamics. We applied C2RQA to two creative activities: an idea generation task and an insight problem. The results suggest that C2RQA effectively captures broad dynamic transitions in ideas and the underlying subconscious processes involved in collaborative creativity.

Method for Quantification of the Process of Collaborative Creativity: Visualization of the Dynamics by C2RQA

A pedagogical tool in undergraduate oceanography and atmospheric science education is a water filled, rotating, tank used to model geophysical fluid flow on Earth. We used a typical classroom rotating tank to investigate students’ mental models of fluids in rotation. In two experiments, we conducted semi-structured interviews with participants (N=59) who predicted the behavior of water in rotation, explained their predictions, and attempted to make sense of observed demonstrations. We found participants had accessible mental models of fluid behavior based on analogies, however these mental models were wrong. Our results suggest that the behavior of rotating fluids is highly unintuitive and that without tangible opportunities for mental model formation, the mind adopts mental models representative of experiences with fluids to fill this gap. We discuss implications for education and suggest a better understanding of how humans reason about fluids can inform cognitive science while improving oceanography and atmospheric science instruction.

Mental models of fluids in solid body rotation: students’ reasoning about fluid processes modeled during oceanography and atmospheric science instruction

It has previously been shown that by using reinforcement learning (RL), agents can derive simple approximate and exact-restricted numeral systems that are similar to human ones (Carlsson, 21). However, it is a major challenge to show how more complex recursive numeral systems, similar to for example English, could arise via a simple learning mechanism such as RL.
Here, we introduce an approach towards deriving a mechanistic explanation of the emergence of efficient recursive number systems. We consider pairs of agents learning how to communicate about numerical quantities through a meta-grammar that can be gradually modified throughout the interactions. 
Utilising a slightly modified version of the meta-grammar of Hurford (1975), we demonstrate that our RL agents, shaped by the pressures for efficient communication, can effectively modify their lexicon towards Pareto-optimal configurations which are comparable to those observed within human numeral systems in terms of their efficiency.

Learning Efficient Recursive Numeral Systems via Reinforcement Learning

Previous work has shown that people’s arousal states become synchronized during natural communication. The present study examined the functional role of such synchrony. We recorded 10 personal stories, half happy, half sad, from a set of storytellers while continuously measuring electrodermal activity (EDA). A separate set of participants listened to the recordings while EDA was measured, and they completed a state-empathy questionnaire following each story. We predicted listeners would empathize more with storytellers when EDA synchrony was higher. Results revealed that synchrony did modulate empathy, but this depended on the valence of the story and the trait empathy level of the listener. Among people with low trait empathy, as synchrony increased, so did state empathy. Among people with high trait empathy, this correlation was negative. These relationships obtained only for sad stories. The findings point to intriguing individual differences in the functional role of arousal synchrony on empathy.

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Next from CogSci 2025

How Infant-Like, Embodied Visual Experiences Can Support Generalization to In-The-Wild Images: Insights from Domain Adaptation