United States

This study investigates how stress and anxiety influence the interplay between visual working memory and decision-making in human participants. Using the Socially Evaluated Cold Pressor Test (SECPT) to induce acute stress, we examined cognitive performance on a computerized behavioral paradigm (the Marble Jar Task) requiring the storage, manipulation, and utilization of visual information. Results revealed that while experimentally induced stress did not significantly affect overall accuracy, higher self-reported state anxiety was correlated with both lower decision-making accuracy and poorer visual memory performance. Interestingly, higher state anxiety was also correlated with increased attention towards high-value outcomes in decision-making. This work highlights the importance of understanding how stress and anxiety affect the interaction between interconnected cognitive functions, rather than studying isolated cognitive phenomena.

CogSci 2025

Examining the Influence of Stress and Anxiety on Visual Working Memory and Decision-Making

bayesian modeling

decision making

psychology

representation

memory

### 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.

poster

Grammaticalization is the process by which a lexical item (e.g., noun) acquires a more functional role (e.g., preposition) over time. Grammaticalization is considered largely unidirectional, that is, change from functional to lexical is far less common (Hopper & Traugott, 2003). What is the cause of this unidirectionality? Our experiment tests whether individuals have a preference in the direction of grammaticalization when performing semantic extension in communication. We focus on the phenomenon of using body part nouns as a source of spatial prepositions. We predicted that participants extending body parts to use as prepositions would find the task easier and more intuitive than participants extending prepositions to use as body parts. However, our results show no directional bias, indicating that the historical unidirectional tendency for body parts to be used as spatial relations does not originate in a bias that individuals have for using one to refer to the other.

No directional preference for grammaticalization in semantic extension game

Primates’ social interactions are grounded in temporal and spatial
relationships, with physical proximity commonly used to assess
affiliation, dominance, and tolerance. Yet proximity is often treated
as a static, categorical measure rather than a dynamic, continuous
process. Here, we combine computer vision and environmental
markers to precisely quantify short-range social distances in two
groups of Japanese macaques housed in large outdoor enclosures.
Our social tolerance test results show that, when entering a
food-baited circle, macaques positioned themselves at
greater-than-chance distances from conspecifics, particularly to
dominants. Furthermore, lower-ranking individuals tended to
follow more indirect paths before approaching the food resource,
suggesting they weigh social risks alongside physical positioning.
By treating social proximity as a dynamic process, our study
provides new insights into how primates navigate social and
physical environments. This illustrates the potential of our method
for more nuanced measures of group organization, tolerance, and
decision-making.

Socially Situated Navigation: Social Rank and Sex Influence Spatial Navigation Strategies in Japanese Macaques

Expectations, or the prior probability of a choice outcome, are powerful sources of evidence for improving decision making under uncertainty. Most expectations in the real world are learned implicitly on the basis of statistical properties of observers’ environments. However, most studies investigating effects of expectations on perceptual decisions explicitly instruct observers on prior probabilities within the experiment, and thus fail to capture the experience-dependent uncertainty of real-world expectation learning. Here, we report data from a novel expectation-guided perceptual decision making task specifically designed to address this gap. Human observers (n=21) learned, through experience, probabilistic relationships between cues and images. Then, they explicitly reported both an estimate of each cue’s prediction and a confidence rating in that estimate before performing a cued perceptual decision task. We find that, although these measurements are highly correlated, confidence in an explicit report is the primary factor that interacts with implicit expectations to shape perceptual decisions.

Investigating implicit and explicit expectations in perceptual decision making

Statistical learning (SL) mechanisms drive learning across several domains, including language acquisition. Can SL mechanisms like cross-situational word learning, which rely on the accumulation of statistical evidence, also account for the rapid acceleration in children’s word learning? This study examined whether learners could track and integrate several statistical regularities concurrently and use this information to map words to objects that never co-occurred during training—a behavior known as zero-shot learning. Experiment 1 showed that learners leveraged their acquired knowledge of word and object categories to map words to objects that did not co-occur during training. Experiment 2 extended this finding by demonstrating that learners integrated their newly acquired statistics with prior knowledge to map novel words to perceptually similar, yet previously unseen, objects. These findings suggest that integrating several types of statistical relationships between words and objects can accelerate new learning, making SL an efficient mechanism for early word learning.

Zero-Shot Cross-Situational Learning for Building Word-Referent Mappings 

Making sense of the world and acting in it relies on building simplified mental representations that abstract away aspects of reality. This principle of cognitive mapping is universal to agents with limited resources. Living organisms, people, and algorithms all face the problem of forming functional representations of their world under various computing constraints. In this work, we explore the hypothesis that human resource-efficient planning may arise from representing the world as predictably structured. Building on the metaphor of concepts as programs, we propose that cognitive maps can take the form of generative programs that exploit predictability and redundancy, in contrast to directly encoding spatial layouts. We use a behavioral experiment to show that people who navigate in structured spaces rely on modular planning strategies that align with programmatic map representations. We describe a computational model that predicts human behavior in a variety of structured scenarios. This model infers a small distribution over possible programmatic cognitive maps conditioned on human prior knowledge of the world, and uses this distribution to generate resource-efficient plans. Our models leverages a Large Language Model as an embedding of human priors, implicitly learned through training on a vast corpus of human data. Our model demonstrates improved computational efficiency, requires drastically less memory, and outperforms unstructured planning algorithms with cognitive constraints at predicting human behavior, suggesting that human planning strategies rely on programmatic cognitive maps.

Cognitive maps are generative programs

Knowing the intended use of an artifact impairs people’s ability to think of alternative uses. Here we ask whether children consider not just “what“ a tool is used for, but also “who“ uses the tool in that way. We focused on gender roles since children are sensitive to these early in development and adapted a classic insight learning task (Defeyter and German 2003). Success on the task requires inserting a stick into a tube to remove a ball. We compared children’s (N = 112; 27 children/condition) latency to solve the problem at Baseline and three Demonstration conditions. In all the Demonstration conditions, the long stick was used as a magnet wand to brush away iron filings. In the Researcher Demonstration condition– a direct replication of Defeyter and German 2003– this function was demonstrated by a single individual – the experimenter; in the Same and Different Gender Peer Group conditions, the function was demonstrated by a group of  children whose gender matched or differed from the participant’s. Both Peer Group Demonstration conditions induced functional fixedness comparable to the Researcher Demonstration, and children were slower to solve the problem in all Demonstration conditions than Baseline. Critically however, children were faster to solve the problem in the Different Gender Peer Group condition than in the Same Gender Peer Group Condition, suggesting that children encode attributes of a function’s typical user in their representations of artifacts, and that functional fixedness is affected by children’s identification with a social role.

Functional fixedness and cooties: Children solve insight problems faster when they learn functions from peers of a different gender

Children are adept at learning the principles and properties governing their environment. However, this environment is often highly inconsistent: causes do not always bring about their effects; people do not always act according to their preferences. Past research shows that young causal learners readily reason from probabilistic evidence, but little is known as to how they reason about that evidence. This study presented preschoolers (N=114) with the behavior of three different causes—one consistently effective, one consistently ineffective, and one inconsistent—from one of three domains (social, mechanical, biological) and asked children to predict the future behavior of each. Children’s predictions not only captured the different degrees of inconsistency observed in the evidence but also reflected differences in prior knowledge and expectations about inconsistency between domains. These results offer a novel, more nuanced look into early causal cognition and often-overlooked complexities of causal learning and reasoning in the real world.

Maybe She’ll Say Yes: How Young Learners Acquire and Apply Knowledge about Inconsistent Causal Relationships from Different Domains

Counterfactual (CF) reasoning – the process of considering alternative events and their outcomes – plays a vital role in understanding causation in fields like cognitive psychology and philosophy of science. In this paper, I develop a theoretical framework of Structural Causal Stacks (SCS) that provides a conceptual structure to describe the relationships between related causal and counterfactual analyses. Then, I explore its useability for observing human reasoning by running 500 pilot simulations of causal stack agents. My simulation modelled Gerstenberg et al. (2013)’s experiment design, which measured whether people’s judgements about the consequence of a counterfactual state changes depended on the order they considered the events. According to my preliminary results, the stack model replicated the asymmetry in backwards versus forward counterfactual reasoning, aligning with the established consensus in a cognitive psychology literature while extending a persistent explanation for successive analyses.

Causal Stacks: A Theoretical Framework for Recurrent and Hierarchical Counterfactual Reasoning

Statistical learning (SL), or the ability to unintentionally extract patterns underlying sensory information, allows the human mind to acquire various regularities in written languages. However, we are unclear about its potential in learning multiple probabilistic regularities in a complex language, such as the sub-lexical mappings between orthography (form), phonology (sound), and semantics (meaning) in Chinese characters. We tested (implicit) SL and its explicit form in acquiring the Chinese sub-lexical mappings as a randomized controlled trial. Ninety-five 1st-4th graders with or at risk for dyslexia were randomly assigned to an implicit-SL group that was exposed to a set of characters with the sub-lexical form-sound-meaning mappings, an explicit-SL group that was exposed to the same set of characters with explicit instruction on the form-sound mappings, or a no-SL control group. Only the explicit-SL group showed abstraction of form-sound mappings, while only the implicit-SL group showed optimized reading processes across phonology and semantics.

Abstraction and Optimization in Statistical Learning: A Randomized Controlled Trial of Implicit and Explicit Reading Intervention for Students with Dyslexia

The Abstraction and Reasoning Corpus (ARC) is a visual program synthesis benchmark designed to test out-of-distribution generalization in machines. Although recent advancements have led to human-level performance on ARC, it is still unclear how people solve ARC tasks. Other work has demonstrated that people often make errors when reasoning about these problems, and sometimes fail to infer the true underlying program. In this work, we explore the hypothesis that the cognitive mechanisms supporting reasoning are more approximate, graded and resource-limited compared to those suggested by purely discrete, program-induction models. Taking inspiration from previous work on program sketches and partial programs, we model human reasoning in the ARC domain using a neurosymbolic, meta-learned model that interleaves symbolic operations with approximate, statistical pattern completion. We then evaluate the model against human errors from H-ARC, a recent, comprehensive behavioral dataset on the training and evaluation sets of ARC.

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No directional preference for grammaticalization in semantic extension game

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The Impact of Acute Social Stress on Working Memory Updating in Social Anxiety Disorder

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