United States

As AI tools become integral to daily life, evaluating their impact on human cognition and learning is essential. This study examines how AI-assisted writing (AIW) tools influence language development over six months. Participants were randomly assigned to either a control group, using basic auto-correction (e.g., Grammarly), or an experimental group, using dialog-based large language model (LLM) tools (e.g., ChatGPT). Each month, all participants will write an English essay and reported on tool usage frequency and strategies. Preliminary findings after 1 month suggest diverse usage behaviors with AIW tools which may lead to varied outcomes in linguistic performance. These results provide insights into AI’s role in fostering language growth and inform strategies for effective AI-enhanced writing practices. In July, we will be able to report 6 months of data.

CogSci 2025

Does using LLMs in daily life help or hinder learning a second language?

field studies

computer-based experiment

instruction and teaching

computer science

psychology

language acquisition

artificial intelligence

education

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

poster

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/amvr/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>

#### 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. 
<br><br>
* **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.
<br><br>
* **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.
<br><br>

#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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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.

Evaluating the cognitive abilities of Vision-Language Models (VLMs) is challenging due to their reliance on spurious correlations. To distinguish shortcut-taking from genuine reasoning, we introduce Concept Hacking, a paradigm manipulating concept-relevant information to flip the ground-truth but preserving concept-irrelevant confounds. For instance, in a perceptual constancy test, models must recognize that a uniformly wide bridge does not narrow in the distance; the manipulated condition using concept hacking altered the bridge to actually taper. We assessed 209 models across 45 experiment pairs spanning nine low-level cognitive abilities, encompassing all five core knowledge domains. Comparing performance on manipulated versus standard conditions revealed that models fell into shortcut-reliant or illusory understanding types, with none approaching human-level performance. Models of varying sizes appear in each category, indicating that scaling neither imparts core knowledge nor reduces shortcut reliance. These findings highlight fundamental limitations in current VLMs, reinforcing concerns about their ability to achieve genuine understanding.

Evaluating Vision Language Models Through Concept Hacking

Intrusive memories are disruptive to daily functioning and detrimental to well-being; unfortunately, the presence of these memories is a defining characteristic of post-traumatic stress disorder (PTSD). Although increasingly understood as a memory disorder, current trauma management and recovery strategies for PTSD do not often take principal memory theories into account. Many common practices, such as delayed processing after trauma exposure and spaced therapy sessions, might inadvertently strengthen the retention of intrusive memories in the long-term. In this paper, model simulations show that altering the timing of different presentations of emotional stimuli might affect subsequent intrusions. Experimentally, we demonstrated through a two-day within-subject image presentation task that when emotional images are presented in spaced intervals (as opposed to consecutive, “massed” presentation), the perceived frequency of intrusions for the mass-presented emotional images during the 24 hours after first exposure were significantly lower than spaced images. Our study presents a novel strategy that can potentially mitigate the frequency of intrusive post-traumatic memories, highlighting the advantages of translational applications of computational cognitive models to mental health.

Reducing Traumatic Memory Intrusions by Timing Their Re-Encoding: An Application of Computational Modeling to Mental Health

The role of mental simulation in human physical reasoning is widely acknowledged, but whether it is employed across scenarios with varying simulation costs and where its boundary lies remains unclear. Using a pouring-marble task, our human study revealed two distinct error patterns when predicting pouring angles, differentiated by simulation time. While mental simulation accurately captured human judgments in simpler scenarios, a linear heuristic model better matched human predictions when simulation time exceeded a certain boundary. Motivated by these observations, we propose a dual-process framework, Simulation-Heuristics Model (SHM), where intuitive physics employs simulation for short-time simulation but switches to heuristics when simulation becomes costly. By integrating computational methods previously viewed as separate into a unified model, SHM quantitatively captures their switching mechanism. The SHM aligns more precisely with human behavior and demonstrates consistent predictive performance across diverse scenarios, advancing our understanding of the adaptive nature of intuitive physical reasoning.

A simulation-heuristics dual-process model for intuitive physics

Problem-solving often requires both acting on and gathering information from the environment. Prior work in psychology proposes that people are intrinsically motivated to seek information (Ryan and Deci, 2000; Ruggeri et al., 2021). However, in realistic settings most available information has no utility, so optimal performance requires estimating its value. We introduce a model that applies the Bayesian framework of Lidayan et al. (2024), which formalizes the value of information as the resulting increase in expected rewards. Our model calculates the utility of information-gathering actions and treats humans as noisy utility-maximizers. We design a novel task in which information sources vary in their likelihood of enabling downstream rewards. Preliminary results suggest our model predicts human behavior more accurately than intrinsic motivation models, suggesting that humans learn to estimate the value of information from experience, and use it to make better decisions.

Bayes-Adaptive Information Gathering 

For some learning problems, information is readily available. For others, substantial time and effort is needed to acquire information. The present work tests whether this variation in “information accessibility” affects curiosity. In two experiments, we prompted adult participants to rate their curiosity about the answers to trivia questions. For each trivia question, participants were informed that information accessibility would be high—they would receive the answer with minimal time and effort—or low—they would receive the answer with substantial time and effort. We found that information accessibility affected decisions to seek information, but not self-reported curiosity. This suggests that curiosity is unhindered by the practical costs of information search.

Ease of Access to Information Does Not Impact Curiosity

Groupitizing, the ability to use perceptual grouping to facilitate enumeration, appears with schooling and predicts math achievement (Guillaume et al., 2023). Our study (Spring 2025) investigates the cognitive mechanisms underlying its development and enhancement during early education.

We propose that groupitizing among young children involves two core processes: (i) subitizing to identify the numerosity of small subgroups and (ii) using perceptual grouping to perform mental arithmetic, such as addition or multiplication.

To test this hypothesis, children from kindergarten through second grade will perform an enumeration task involving dot sets organized into various grouping structures (subgroups with equal or differing numerosity) and various grouping patterns (subgroups with consistent or varied shapes). Response times and error patterns will be analyzed, alongside children's counting knowledge, arithmetic skills and math achievement.

Our results aim to elucidate the cognitive mechanisms underlying groupitizing across development, and the links between foundational perceptual skills and higher-order mathematical reasoning.

Understanding the cognitive mechanisms behind groupitizing in early education

Previous research has proposed that bilinguals would rather be redundant than ambiguous. To test this hypothesis, we conducted an experiment examining lexical ambiguity in spoken Mandarin at the tonal, segmental, and orthographical levels. Using a picture naming task, we explored how L1 Mandarin L2 English speakers in the UK and more-monolingual speakers in China resolve ambiguity by analysing their verbal responses when naming pictures, manipulating whether the context in which a picture is named makes the preferred label ambiguous (e.g. do speakers avoid saying “fen3 si1” when describing a picture of glass noodles when it appears alongside a picture of fans which shares the same label? do bilinguals avoid this ambiguity more than more monolingual peers as claimed?). Our results do not support this hypothesis, as no reliable differences between groups were found. Despite the null results, we observed several interesting patterns worthy of further investigation.

Do bilinguals avoid ambiguity? An experimental study of lexical ambiguity in spoken Mandarin

Across four experiments, we identified two distinct mechanisms for learning sequential rules: one for capturing arbitrary structures (e.g., a yellow dot followed by a blue dot signals “get ready” then “go”), and another for recognizing intrinsic relationships (e.g., a small dot followed by a big dot signals “get ready” then “go”, perhaps represented by the rule “getting bigger means go”). Both mechanisms support abstract representations, as in Experiments 1 and 2, adults generalized sequential rules to novel combinations. However, Experiment 3 and 4 revealed that when both types of rules were present during learning, intrinsic rules disrupted the abstraction of arbitrary rules. This interference led to poor generalization performance. Overall, these findings suggest that intrinsic and arbitrary systems compete during rule learning, with intrinsic relationships imposing constraints on how sequential patterns are represented. These results are relevant for applications such as syntax learning, human factors, and graphic design.

Intrinsic relations impair abstract rule learning

Children born preterm are at increased risk of delays in early communicative development. However, studies often focus on extremely or very preterm children (< 32 weeks of gestation). To address this limitation, we use data from the Pebbles App, which reflects the gestational age distribution in the population. This app allows caregivers to document their child’s development and assesses the age of attainment of 14 early communicative milestones. Preliminary analyses include more than 4000 children. For most communicative milestones, we did not find significant differences between preterm and full-term children within the first two years using corrected age. However, preterm children acquired some milestones, such as babbling, earlier than full-term children. A possible explanation is the greater communicative experience during the extended time in the extrauterine environment. These findings challenge traditional assumptions about language delays in preterm children and highlight the importance of using more representative samples.

Assessing Early Communicative Milestones in Preterm and Full-Term Children Using the Pebbles App 

Counterfactual reasoning, the ability to reason about how events could have turned out differently, helps individuals understand the causes of events and prepare for the future. The simulation mindset hypothesis posits that exposure to counterfactual scenarios stimulates the generation of imaginary alternatives, enhancing planning, problem-solving, and behaviour adjustment. This study investigated whether  close-counterfactual scenarios prime a simulation mindset in children leading to better problem-solving abilities. Ninety six- and eight-year-olds were assigned to either a counterfactual condition, with storybooks featuring close-counterfactual events, or a control condition, with storybooks describing factual events. Participants then completed two problem-solving tasks requiring the generation of alternative solutions. Results showed that 8-year-olds exhibited better problem-solving abilities than 6-year-olds. Counterfactual scenarios did not significantly affect older children's problem-solving skills, however they showed benefits for the younger children. These findings provide emerging evidence that engaging in counterfactual reasoning can enhance divergent thinking and problem-solving skills in children.

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Evaluating Vision Language Models Through Concept Hacking

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Bridging the Skills Gap: A Course Model for Modern Generative AI Education

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