AI Models Can Predict and Collaboratively Modulate Human Memory Search

Cognitive Convergence: Deep Similarities Between Large Language Models and Human Cognition

LLMs are widely regarded as alien intelligences, systems whose cognitive operations are fundamentally unlike our own. Apparent similarities to human cognition are therefore often seen as the result of anthropomorphic projection. We argue that this framing is mistaken. LLMs clearly differ from humans in important respects, including their physical substrate, learning history, and the environments with which they interact. These differences make it all the more striking that contemporary LLM-based systems converge with human cognition on a number of principles of cognitive organization with longstanding support in cognitive science. We identify structural correspondences across five dimensions: inferential organization, computational architecture, representational structure, prediction-driven learning, and reinforcement-learning-like mechanisms supporting goal-directed action. These correspondences support a broader model of intelligent cognition in which core principles long used to explain human intelligence also characterize contemporary LLM-based systems.

Assessing mentalization in humans and large language models

Mentalization - the ability to infer others'beliefs and intentions to guide one's own choices - is a key cognitive function underlying human social interactions. Large language models (LLMs) demonstrate behaviour consistent with humans on theory-of-mind tasks, yet whether these models can guide adaptive behaviour through mentalization is unknown. Here we use two economic games with cognitive computational modeling to uncover the latent strategies underlying mentalization in LLMs. We tested individual LLM agents across four model families, DeepSeek, GPT-4.1, GPT-5 and Gemini 2.0 Flash (N = 2,099), against opponents of varying sophistication and examined whether a prompting strategy designed to elicit strategic reasoning improved performance. We benchmarked results against human participants (N = 251) as a comparative measure. Across both games, LLMs showed clear behavioural and computational signatures of mentalizing that differed markedly by model provider and size. Strategic prompting generally improved performance by inducing more sophisticated reasoning, yet the extent of the benefit differed across the two tasks. Last, GPT-5 agents flexibly adapted their recursive depth of reasoning to increasingly sophisticated opponents, demonstrating superior performance to human participants. Collectively, we demonstrate different capacities for mentalization across LLMs, and highlight cognitive computational modeling as a formal method for assessing comparative intelligence across humans and machines.

Are LLMs tools to understand human neurocognition during abstract reasoning?

Human-Centered AI and Cognitive Adaptation: An Empirical Study of Human Responses to AIMediated Information Systems

Artificial intelligence now shapes much of how information reaches people, who read it, and what they make of it. The environments it creates are probabilistic, often fluent without being grounded, and curated by systems whose workings stay hidden. Most research on this shift has gone to AI literacy, explainability, and the mechanics of human–AI interaction. Far less has gone to a prior question: how does human cognition itself change to cope? The paper addresses that question with the Human Cognitive Adaptation Framework (HCAF). One hundred thirty-one adults who use AI-generated and algorithmically curated content daily completed a 25-item, six-point inventory covering five facets—orientation, dimensional literacy, ambiguity tolerance, coherence recognition and critique, and relational navigation. The research examined reliability, the correlations among facets, and dimensionality. The full scale was highly reliable (α = .902) and the data factored cleanly (KMO = .841). One factor dominated: its eigenvalue of 7.93 carried 31.7% of the variance, and parallel analysis retained a single factor. The facets correlated strongly with one another (r = .46 to .69), which reads as one adaptive response rather than five separate skills. Within that single capacity, people recognized synthetic coherence more readily than they tolerated the uncertainty recognition exposes, so adaptation is not uniform across its parts. The research used these results to develop the HCAF, which treats adaptation as the work of staying oriented, judging coherence, and holding up under instability, and draws out what follows for human-centered AI, AI literacy, education, and the design of AI-supported decision environments.

Artificial Intelligence and Psycholinguistics: How Do Large Language Models Represent Human Language Processes? A Systematic Review

The rapid development of large language models (LLMs) has fundamentally transformed contemporary perspectives on language representation and processing. Models such as GPT, Gemini, Claude, Llama, and DeepSeek demonstrate remarkable capabilities in language comprehension, generation, translation, summarization, and reasoning, raising important questions regarding their relationship to human language processing. While psycholinguistic theories traditionally explain language through cognitive mechanisms involving perception, memory, attention, semantic representation, and executive control, LLMs rely on statistical learning, neural network architectures, and large-scale pattern recognition. The present study synthesizes contemporary research examining the similarities and differences between human language processing and LLM-based language representation. Employing a qualitative systematic review, the study integrates empirical findings published between 2018 and 2025 from psycholinguistics, cognitive science, neuroscience, computational linguistics, and artificial intelligence. The findings indicate that LLMs successfully approximate numerous observable characteristics of human language behavior, including syntactic processing, contextual prediction, semantic association, discourse coherence, and pragmatic inference. However, important differences remain regarding grounding, intentionality, episodic memory, emotional experience, and embodied cognition. Although LLMs provide valuable computational models for investigating psycholinguistic theories, they cannot currently be considered cognitive equivalents of human language users. Instead, they represent sophisticated probabilistic systems that emulate linguistic behavior without reproducing the complete cognitive architecture underlying human communication. The review discusses theoretical implications for psycholinguistics, cognitive science, and artificial intelligence while identifying future research directions for integrating computational and cognitive approaches to language.

Unifying the structures of language in a neural population code.

It is concluded that explaining how language can emerge from neural population codes, in both biological and artificial systems, will not be achieved through the incremental refinement of algebraic-symbolic theories but will demand new theoretical paradigms.