Computational Cognitive Modeling

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The field builds computational accounts of mental processes, from memory and language to decision-making, emotion, and social reasoning. Researchers use Bayesian and reinforcement-learning models, resource-rational algorithms, cognitive architectures, neural networks, and program induction, testing them against behavioral, neural, and real-world data. Newer work also uses language models and automated experiments to generate and compare theories. A central challenge is distinguishing genuine explanatory mechanisms from systems that merely match behavior, while ensuring models generalize beyond the tasks and data on which they were developed.

Latest papers

126 papers7 papers in the last four weeks, down 56% on the four weeks before. 0.0% of all new papers.

Unraveling the cognitive patterns of Large Language Models through module communities
Aug 25, 2025Kushal Raj Bhandari, Pin-Yu Chen, Jianxi GaoComputational Cognitive Modeling LLM Interpretability
Department of Computer Science Rensselaer Polytechnic Institute Troy, NY, USA · IBM Research Yorktown Heights, NY, USA
How Causal Abstraction Underpins Computational Explanation
Aug 15, 2025Atticus Geiger, Jacqueline Harding, Thomas IcardComputational Cognitive Modeling Causal Abstraction
A memory-based active inference model of DishBrain-like adaptive behaviour
Aug 9, 2025Aswin Paul, Moein Khajehnejad, Forough Habibollahi +2Computational Cognitive Modeling Active Inference
Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton 3800, Australia · VERSES, Los Angeles, California, USA · Cortical Labs Pty Ltd, Melbourne 3056, Australia +1
State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions
Jul 18, 2025Sen Lu, Xiaoyu Zhang, Mingtao Hu +3State Space Models Computational Cognitive Modeling
Electrical Engineering and Computing Science, University of Michigan
In Dialogue with Intelligence: Toward Insightful Co-Augmentation
May 28, 2025Eleni VasilakiComputational Cognitive Modeling Human-AI Interaction
Emergence of psychopathological computations in large language models
Apr 10, 2025Soo Yong Lee, Hyunjin Hwang, Taekwan Kim +5Computational Cognitive Modeling
KAIST, Kim Jaechul Graudate School of AI · UCL, Mental Health Neuroscience Department · UvA, Informatics Institute +2

Aug 25, 2025J/K move · Enter open · S save

Unraveling the cognitive patterns of Large Language Models through module communities

Kushal Raj Bhandari, Pin-Yu Chen, Jianxi Gao

Department of Computer Science Rensselaer Polytechnic Institute Troy, NY, USA · IBM Research Yorktown Heights, NY, USA

Computational Cognitive Modeling LLM Interpretability Modular Neural Networks

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Large Language Models (LLMs) have reshaped our world with significant advancements in science, engineering, and society through applications ranging from scientific discoveries and medical diagnostics to Chatbots. Despite their ubiquity and utility, the underlying mechanisms of LLM remain concealed within billions of parameters and complex structures, making their inner architecture and cognitive processes challenging to comprehend. We address this gap by adopting approaches to understanding emerging cognition in biology and developing a network-based framework that links cognitive skills, LLM architectures, and datasets, ushering in a paradigm shift in foundation model analysis. The skill distribution in the module communities demonstrates that while LLMs do not strictly parallel the focalized specialization observed in specific biological systems, they exhibit unique communities of modules whose emergent skill patterns partially mirror the distributed yet interconnected cognitive organization seen in avian and small mammalian brains. Our numerical results highlight a key divergence from biological systems to LLMs, where skill acquisition benefits substantially from dynamic, cross-regional interactions and neural plasticity. By integrating cognitive science principles with machine learning, our framework provides new insights into LLM interpretability and suggests that effective fine-tuning strategies should leverage distributed learning dynamics rather than rigid modular interventions.

Latest papers

Unraveling the cognitive patterns of Large Language Models through module communities

How Causal Abstraction Underpins Computational Explanation

A memory-based active inference model of DishBrain-like adaptive behaviour

State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions

In Dialogue with Intelligence: Toward Insightful Co-Augmentation

Emergence of psychopathological computations in large language models