United States

Learning to read requires the integration of top-down and bottom-up processing of orthographic and phonological information. This study investigates how the dorsal and ventral inferior frontal gyrus (dIFG and vIFG) interact with posterior regions, including the ventral occipitotemporal cortex (vOT) and temporoparietal cortex (pSTG-SMG), during a visual word rhyming task. Using Dynamic Causal Modeling (DCM) on fMRI data from children aged 10 to 17 years, we examine directional influences among these regions under four conditions involving phonological and orthographic conflict and non-conflict. We hypothesize that dIFG exerts stronger top-down influence than vIFG, particularly under conditions of conflict. Additional hypotheses address the balance between top-down and bottom-up influences, region-specific effects of phonological and orthographic conflict, and the relationship between top-down modulation and reading skills. Data collection is complete, with 72 participants assessed, and analyses are underway. This pre-registered study aims to advance understanding of the neural mechanisms underlying reading development and inform interventions for reading difficulties.

CogSci 2025

Effective connectivity analysis in children: exploring the impact of the dorsal and ventral part of inferior frontal gyrus on phonological and orthographic processing 

cognitive neuroscience

computational modeling

fmri

phonology

reading

### 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

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.

poster

Memory binding is the ability to bind together multiple components in a memory trace. Previous studies have shown that one way to improve memory binding is to integrate multiple components into the same item in a meaningful way – a strategy known as unitization. To further investigate unitization as a source of development of memory binding, in terms of whether a unitization strategy has different effects on different memory structures and different age groups, the current study incorporated a semantic unitization strategy into a memory binding task and recruited multiple age groups for the experiment. Results showed that unitization led to a marked improvement in three-way binding for 7-year-olds and adults, but not for 5-year-olds, suggesting that semantic unitization could be a driving force for the development of complex memory binding in childhood and even beyond adulthood, but its effect in young children might still be limited due to their immaturity.

An Effective Strategy to Reduce Interference: Effects of Unitization on the Development of Memory Binding

Do neural network models that fail to behave human-like reflect a fundamental divergence from human cognition, or do they mirror earlier developmental or evolutionary stages? We propose that such models may, in fact, offer insights into the origins of human abstraction. We evaluated over 200 pretrained neural networks alongside macaques, Tsimane natives, US adults and children on three visual match-to-sample tasks targeting increasing levels of abstraction: visual-semantic similarity, shape regularity, and relational reasoning. As task demands grow more abstract, just like monkey's, model decisions increasingly diverge from adult human behavior. However, representational similarity analyses reveal shared internal structure with all human groups, suggesting overlapping cognitive strategy. We further show that model alignment depends on specific design choices—architecture, scale, training regime, and language supervision—highlighting which inductive biases support human-like abstraction.

Artificial Neural Networks Reveal a Cognitive Continuum Toward Human Abstraction

Despite substantial charitable giving, donations often fail to maximize impact. While a variety of persuasive strategies can increase donations to effective charities, their success depends on individual differences. Large Language Models (LLMs) offer a powerful solution to this problem by dynamically personalizing persuasive strategies. In a pre-registered experiment (N=1952), we tested whether personalized LLM conversations could increase donations to the Against Malaria Foundation (AMF), rated one of the world’s most effective charities. Participants allocated $1 between their favorite charity and AMF after being assigned to either: (1) a personalized persuasive LLM conversation, (2) a static LLM-generated persuasive message, and (3) a control conversation. Personalized LLM conversations significantly increased donations to AMF by 46.6%, outperforming the static message (28.7% increase). Personalized LLMs also shifted moral attitudes about charitable giving. Our findings highlight the potential of AI-driven personalization to enhance effective giving and provide new insights into the psychology of charitable persuasion.

Increasing effective charitable giving with personalized LLM conversations

Research on individual decision-making often finds a positivity bias, where people weight positive outcomes more strongly than negative ones during learning. This can be beneficial when rewards are rare, by amplifying relative value differences. Yet, we know very little about learning rate biases in social settings, where a key advantage is being able to vicariously learn from the negative experiences of others. This would imply a benefit for focusing on negative outcomes when learning socially, but is at odds with the seemingly inflexible positivity bias found in individual learning. Here, we examine learning rate biases across both individual and social settings, testing for adaptivity versus generally stable biases. Overall, participants appear more flexible in their learning rate biases when learning socially than when learning individually. This implies that human social learning may be more flexible and closer to normatively optimal behavior than individual learning.

Flexibly biased learning rates in social learning

Cognitive scientists have long debated whether human learners are rational decision-makers. Much work has found that adults and children tend to use probability matching strategies in probability learning tasks despite probability maximizing being the optimal strategy. However, other work provides conflicting findings on what decision-making strategies are used and under what circumstances. Unlike previous studies that employed a typical design with a single individual making decisions (where probability maximizing is the optimal strategy), we investigate decision-making strategies in a group foraging context where probability matching is the optimal strategy. In the current study, we tested 14- to 20-month-old infants’ ability to (1) distinguish rates of reward distribution in a group foraging scenario and (2) their expectations for probability matching based on these rates. Our results are the first to suggest infants are capable of quantitative reasoning involving rates and they form expectations for optimal decision-making strategies based on rate information.

Infants’ understanding of rates and probability matching in a foraging task

Repetitive habitual behaviour can persist even when it conflicts with goals. This is termed an action slip. Wood et al. (2023) demonstrated this effect using a novel procedure, where participants’ performance was strong on congruent trials but comparatively poor on incongruent trials. The current study extends their work by exploring a devaluation version of the experiment, followed by a further experiment that manipulates training duration. The devaluation approach eliminated the congruency effect, while reinstating standard testing with varied training led to its reappearance—even after a short amount of training. These findings challenge the Stimulus-Response (S-R) account and dual-process theory. We discuss the question of whether the congruency effect seen in these experiments is evidence to support a dual-process account of habitual behaviour.

Action Slips: The Effects of Devaluation and Amount of Training

This study reports a counterintuitive reversal of Thorndike’s Law of Effect in human sequential decision-making. Participants in a parlay gambling task chose between banking their current wager or betting it on a risky gamble, where winning would increase the next wager while losing or banking would reset it. We observed improving payoffs across runs, indicating learning. However, contradictory to the Law of Effect, participants were more likely to choose betting after losses rather than wins. We further developed computational models incorporating prospect theory and reinforcement learning. Consistent with our model-free analyses, models incorporating reverse updating of subjective probabilities (negative learning rate) not only significantly outperformed traditional learning models in fitting human data, but also led to higher payoff than models with positive learning rate. These findings highlights the complexity and adaptability of human learning, despite not fitting within the framework of the Law of Effect.

Reverse Law of Effect in Sequential Parlay Gambling

Switching from one task to another typically induces performance costs, but it remains unclear whether switching strategies within the same task also induces similar switching costs. In this study, the Building Sticks task, a simple problem-solving task, was used to investigate whether strategy switching produces costs comparable to task switching. By using a task-switching paradigm, participants completed pure blocks (using a single strategy) and mixed blocks (switching between two strategies), allowing direct comparison of switch and nonswitch trials. We measured both reaction time (RT) and a Linear Integrated Speed-Accuracy Score (LISAS). Results showed that switch trials were significantly higher than nonswitch trials for both RT and LISAS measures. Moreover, splitting participants at the median accuracy revealed that high-accuracy individuals consistently showed smaller switch costs (RT and LISAS) than low-accuracy individuals. We conclude that strategy switching within a single task triggers robust performance costs, partially mitigated by stronger baseline accuracy.

Is there a strategy switch cost when switching strategies within one task?

In perceptual decision-making tasks, humans integrate noisy sensory evidence over time to guide their choices. The optimal integration process assumes that all evidence is weighted equally within a trial and that different trials are independent. However, humans exhibit systematic deviations from optimality, including uneven weighting of evidence within trials and influences from previous trials. Prior studies have demonstrated that biological constraints can account for this suboptimality. In this study, we present evidence that humans adapt their evidence integration strategies over time in response to task demands, and that the suboptimal uneven weighting is gradually learned over the course of the task. By explicitly modeling this adaptation through online gradient-based learning, our model outperforms existing approaches in capturing human behavior and unifies both observed forms of suboptimality in the Click task: dependence across trials emerges from an error-driven learning process that also gives rise to uneven integration weights within trials. We further propose a bounded-rational adaptation account to explain why humans progressively learn to weight evidence unevenly within a trial.

Our modeling framework provides a general approach of resource-rational adaptation. It captures how initially uninformed agents can gradually update their strategies through error-driven learning and is applicable to a broad range of learning and decision-making scenarios.

Humans Learn to Weight Evidence Unevenly Over Time

The rise of ChatGPT has sparked interest among educators in integrating it into teaching and learning. However, effective teaching requires a deep understanding that allows instructors to use multiple representations to support comprehension and address students’ misconceptions. Before relying on ChatGPT as a teaching tool, it is crucial to assess its ability to interpret multiple representations. This study evaluates ChatGPT’s understanding of the fundamental statistical concept “data = model + error,” which underpins a number of statistical analyses in introductory statistics courses. Through tasks involving graphical representations, we qualitatively examined GPT models’ understanding and compared their strengths and limitations to those of students. The results showed that while ChatGPT demonstrated competence in certain areas, it also exhibited misunderstandings—some resembling students’ and others unique to the models themselves.

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An Effective Strategy to Reduce Interference: Effects of Unitization on the Development of Memory Binding

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Next from CogSci 2025

An Effective Strategy to Reduce Interference: Effects of Unitization on the Development of Memory Binding

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Exploring Vector Representations for Phonological Similarity

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PRESENTATIONS

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Downloads