United States

Motivated by the two contrasting forces in shaping linguistic knowledge—efficiency and redundancy, the present study examines sentence comprehension behaviour amongst Korean children aged three to six, focusing on verbs (relative to case markers) in interpreting transitive events. Through picture-selection experiments that systematically omit and obscure portions of transitive sentences, we find (1) a reduced role of verbs in sentence comprehension and (2) age-related variations in the application of case-marking knowledge. These findings suggest that verbs may sometimes become redundant during comprehension, which is attributable to early maturation and strong automatisation of verbs. This provides support for verb-periphery strategies so as to maximise efficiency in language activities amongst Korean preschoolers.

CogSci 2025

Verbs are sometimes redundant: Korean preschoolers’ comprehension of Korean active transitive construction

language comprehension

language acquisition

linguistics

syntax

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
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#### **CogSci 2023 Program Book**
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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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### 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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#### Code of Conduct

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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.

Anxiety and impulsivity can impact human decision making by changing our underlying uncertainty computations. In an online study of 109 participants, we tested how anxiety and impulsivity impact perceptual (“what am I seeing?”) and conceptual (“how does this fit into my knowledge of the world?”) uncertainty, when both types of uncertainty are presented together. participants were asked to classify and rate their confidence on 35 naturalistic hybrid animal images with varying degree of perceptual and conceptual uncertainty (e.g. 50% deer – 50% goat vs. 20% moose-80% penguin). Higher impulsivity scores correlated with more uncertainty in ratings under most uncertainty conditions. A Bayes-drift-diffusion (DDM) hybrid model predicting people’s task ratings and reaction times based on different types of uncertainty showed how impulsivity and anxiety impact uncertainty computations in mixed perceptual-and-conceptual uncertainty settings, by updating and leveraging conceptual beliefs to estimate the reliability of the estimated perceptual category response in the DDM.

A Computational Account of how Anxiety and Impulsivity Impact Uncertainty Computations

Human navigation is shaped by cognitive strategies, spatial awareness, and learned heuristics, yet existing models struggle to capture individual differences in wayfinding. To investigate the cognitive basis of navigational behavior, we conducted a virtual reality experiment where participants maneuvered around a human obstacle in a controlled, static environment. Using trajectory-based features, we classified participants with PartNet, a neural network that outperformed ElasticNet and Random Forest classifiers. While PartNet captured subtle yet consistent behavioral patterns, its interpretability was limited. To address this, we developed an analysis pipeline revealing key behavioral factors, showing that navigational styles differ primarily in midline adherence and speed. Clustering and embedding analyses further demonstrated participant separability, highlighting both individual distinctions and shared tendencies. By identifying structured variability in navigation, our work advances cognitive models of spatial decision-making, informing theories of wayfinding, predictive modeling of human movement, and applications in assistive navigation and urban design.

On the Separability of Human Navigational Behaviors in Virtual Reality

How humans process fractions is a topic of debate in numerical cognition research. While some studies suggest that humans process the holistic magnitude of fractions, others suggest we process only the fraction components (numerators and denominators). Two cognitive effects present in fraction processing data have shaped this debate: the distance effect (better performance with fractions separated by a far numerical distance) and the congruency effect (better performance when individual numbers are larger in the larger fraction). In a study with 160 young adults, we compared distance and congruency effects across two task formats of fraction comparisons, using simultaneous and sequential presentation of stimuli. Results revealed that the distance effect and the congruency effect were stronger in the simultaneous task than in the sequential task. These findings suggest that participants used both holistic and componential strategies when comparing fractions and highlight that task formats should not be used interchangeably.

Same, but Different: Sequential and Simultaneous Fraction Comparison Tasks Elicit Different Distance and Congruency Effects

Visual narrative comprehension is crucial in today’s visually driven world, yet research on aging and this ability remains inconclusive. The role of stress in this process is largely unexplored, despite stress becoming an ever-present factor in modern life. This online study investigated how acute stress affects visual narrative comprehension in younger (N = 203, 18-57 years) and older adults (N = 212, 60-85 years). Participants were assessed under stress and neutral conditions. Stress was induced through timed mathematical and logical tasks with social stress elements. Pictorial stories, consisting of three panels with the second intentionally left blank, were presented. On the next page, participants judged whether the given inference for the blank panel was correct. Results showed that acute stress impaired comprehension and confidence in younger adults, while older adults remained unaffected. These findings suggest that with age and experience, individuals develop more differentiated event schemas, enhancing resilience to stress.

Acute Stress Impairs Visual Narrative Comprehension in Younger but Not Older Adults

One of the cornerstones of sensory cognition is the ability to infer cause-and-effect relationships between entities in our sensory environment—a skill that depends on accurately perceiving the temporal order of events. Here, we asked whether early sensory input is critical for developing this proficiency in the visual domain by studying children born blind who gained sight late in childhood. Several years post-surgery, these late-sighted children performed on par with typically-sighted controls in determining the sequence in which two visual events occurred. However, this ability was not evident immediately after surgery but emerged over a protracted developmental period, underscoring the importance of subsequent visual experience. Our findings demonstrate that the neural resources necessary for this foundational aspect of sensory cognition remain accessible beyond infancy, offering fundamental insights into the role of early experience in sensory and cognitive development as well as practical guidance for clinical rehabilitation following sensory deprivation.

The Role of Early Experience in Judging the Temporal Order of Visual Events: Insights from Late-Sighted Children

How does linguistic telicity map onto mental representations of events? Recent work suggests considerable flexibility in how people mentally represent temporal event structure, yet we know little about how linguistic cues modulate these representations. We investigated how different forms of quantization correspond to event construal using a novel experimental paradigm that bridges event perception and linguistic processing. Participants first learned to distinguish bounded from unbounded events, then categorized sentences varying in quantization strength. Our results revealed a systematic relationship between linguistic form and event representation: strongly quantized expressions ("drink one beer") reliably corresponded to bounded event construal and activity descriptions ("did some writing") to unbounded interpretations, while bare plurals ("drink beers") showed genuine flexibility in interpretation. This graded pattern indicates that temporal boundedness in cognition operates along a continuum, with linguistic cues providing weighted probabilistic constraints on event representations. The findings demonstrate how different linguistic forms correspond to varying degrees of flexibility in event understanding, contributing to our knowledge of how language interfaces with event cognition.

Mapping between Telicity and Event Representations

In daily life, humans perform tasks in teams, in which the labor division is decided by one individual in the team and not jointly by the team members (e.g., if an employer delegates a task to an employee). In the present study, we tested how the labor is divided if one team member decides the labor division in a joint multiple object tracking (MOT) task. We found that humans preferred to equally split the number of tracked targets in the joint MOT task. When comparing the data with our previous study, in which participants performed the same task with a computer partner, we found that this preference for an equal labor division is specific to interactions with a human but not with a computer partner. Moreover, participants also tended to take into account the tracking difficulty of the delegated targets more with a human compared to a computer partner.

Play fair: Humans prefer an equal division of labor in a joint multiple object tracking task

Advances in deep multi-agent reinforcement learning learning (MARL) enable sequential decision making for a range of exciting multi-agent applications.
The black-box characteristic of MARL restricts the safe and scalable application of decision models in practical deployment.
However, existing interpretability methods for deep reinforcement learning models are are not suitable for addressing challenges posed by multi-agent environments and often inadequate in generating logical sequential decisions.
We present an innovative framework called BT4MARL, which introduces the behavior tree structure to explainable MARL.
The proposed method clusters state space by aggregating temporally related states and divides agents into several groups in the new state.
Based on these clustered states and agents, we constructs behavior tree structures.
In this way, we use an exploration technique based on pairing a combined behavior tree with the target model.
We empirically show that our framework is effective in four benchmark MARL domains. Moreover, the results of a user study show that the generated explanations significantly improve performance and satisfaction.
This work represents a significant stride towards addressing the challenges of explainability and performance in MARL applications.

 A Framework for Modeling Cognitive Processes in Intelligent Agents Using Behavior Trees

With the rapid improvement of machine learning (ML) models, cognitive scientists are increasingly asking about their alignment with how humans think. Here, we ask this question for computer vision models and human sensitivity to geometric and topological (GT) concepts. Under the core knowledge account, these concepts are innate and supported by dedicated neural circuitry.
In this work, we investigate an alternative explanation, that GT concepts are learned ``for free'' through everyday interaction with the environment. We do so using computer visions models, which are trained on large image datasets. We build on prior studies to investigate the overall performance and human alignment of three classes of models -- convolutional neural networks (CNNs), transformer-based models, and vision-language models -- on an odd-one-out task testing 43 GT concepts spanning seven classes. Transformer-based models achieve the highest overall accuracy, surpassing that of young children. They also show strong alignment with children's performance, finding the same classes of concepts easy vs. difficult. By contrast, vision-language models underperform their vision-only counterparts and deviate further from human profiles, indicating that naïve multimodality might compromise abstract geometric sensitivity. These findings support the use of computer vision models to evaluate the sufficiency of the learning account for explaining human sensitivity to GT concepts, while also suggesting that integrating linguistic and visual representations might have unpredicted deleterious consequences.

Computer Vision Models Show Human-Like Sensitivity to Geometric and Topological Concepts

The rising prevalence of adolescent depression is a major public health concern. Current diagnostic methods, often burdensome and lacking objective biomarkers, hinder early detection. This paper proposes a novel multi-task framework integrating attention and resting EEG signals using Biosignal Learning and Agent Transformer (BLAT) for depression detection. EEG data is segmented, channeled, and positionally encoded, followed by feature extraction and classification via Agent Transformer. Experiments with 50 depressed adolescents and 50 controls achieved 85% accuracy using BLAT, offering a promising method for early adolescent depression screening.

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A Computational Account of how Anxiety and Impulsivity Impact Uncertainty Computations

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