arXiv AI August 7, 2026 By Nick Oh, Fernand Gobet

Small Foundation Models of Human Cognition and Behaviour

arXiv:2608. 05224v1 Announce Type: new Abstract: Large language models fine-tuned on human behavioural data have emerged as general-purpose cognitive proxies, but the scale this requires, and whether these models process task structure or exploit statistical shortcuts, remain open questions.

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arXiv Machine Learning

Sep 21

Causal Evidence that Language Models use Confidence to Drive Behavior

arXiv:2603.22161v3 Announce Type: replace Abstract: Metacognition -- assessing the quality of one's own cognitive performance -- guides adaptive behavior across species. Substantial research demonstr...

By Dharshan Kumaran, Nathaniel Daw, Simon Osindero, Petar Veli\v{c}kovi\'c, Viorica Patraucean

llms agents

arXiv Machine Learning

Sep 2

Pre-carved Niches: The Formation Dynamics of Modular Task Partitions in Early LLM Training

arXiv:2609.01170v1 Announce Type: new Abstract: Large language models exhibit a modular internal organization that mirrors well-studied functional networks of the human brain, but how this organizati...

By Guangqi Li, Yongxin Li

llms

arXiv AI

Jun 19

Apparent Psychological Profiles of Large Language Models are Largely a Measurement Artifact

arXiv:2606. 20205v1 Announce Type: new Abstract: Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research.

By Jelena Meyer, David Garcia, Dirk U. Wulff

llms safety

arXiv AI

Aug 18

Automatic or Controlled? Repetition Priming Reveals Divergent Processing in Base LLMs, Instruct LLMs, and Humans

arXiv:2608. 14681v1 Announce Type: cross Abstract: Words recur constantly in natural language use, yet it remains unclear whether language models reactivate prior representations or re-evaluate repeated words afresh, and whether post-training changes this default behavior.

By Jinglei Ren, Yuyue Wang

llms

arXiv AI

Aug 14

From Observation to Intervention: Memory in Brains and Large Language Models

arXiv:2608. 12377v1 Announce Type: cross Abstract: Brains and large language models (LLMs) are fundamentally different memory systems, but they can be compared through shared functional questions: where memory-related information is represented, how partial cues recover broader associations, how new information is written or updated, and how memory-related states can be perturbed.

By Morteza Salehjahromi, Shayan A. Zadegan, Amgad Muneer, Jia Wu

llms robotics

arXiv Machine Learning

Jul 30

Thinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models

arXiv:2607. 26845v1 Announce Type: new Abstract: Inference-time thinking improves the performance of large language models, but aggregate outcomes do not reveal whether models use available evidence more effectively or seek information that could improve future decisions.

By Hua-Dong Xiong, Xinyuan Yan, Ji-An Li, Jingming Xue, Marcelo G. Mattar, Robert C. Wilson

llms reinforcement-learning

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Related stories

Causal Evidence that Language Models use Confidence to Drive Behavior

Pre-carved Niches: The Formation Dynamics of Modular Task Partitions in Early LLM Training

Apparent Psychological Profiles of Large Language Models are Largely a Measurement Artifact

Automatic or Controlled? Repetition Priming Reveals Divergent Processing in Base LLMs, Instruct LLMs, and Humans

From Observation to Intervention: Memory in Brains and Large Language Models

Thinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models