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.
This Perspective proposes machine experientialism, positing that LLMs build their own form of understanding from training corpora, and shifts from cataloging LLMs’ human-like traits to uncovering their distinct logic that emerges from this text-based world.
Lingyu Li, Yan Teng, Ying-Chun Wang et al.· Communications Psychology· 1 citation
Large language models (LLMs) have achieved remarkable gains in cognitive performance through attention mechanisms functionally inspired by human attention. This paper asks philosophically whether a comparable architectural insight could technically advance moral processing. We argue that current alignment techniques pr...
G. Bombaerts, Bram Delisse, U. Kaymak· AI and Ethics· 0 citations
If AI is to support human cognitive growth, design must move beyond answer provision and efficiency maximization toward the organization of productive human-AI relations: relations that challenge users’ initial assumptions while providing support appropriate to the task and the user's level of expertise.
Xiaokun Wu, Min Chen, Giancarlo Fortino· Big Data and Cognitive Compu...· 0 citations
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.
Samuel A. Nastase, Zaid Zada, A. Goldberg et al.· Neuron· 1 citation
This paper argues that human-LLM interaction constitutes a language game in which one side bears all normative activity, and draws on Wittgenstein, Luhmann, Esposito, and Brandom to reclassify each as a receiver-side phenomenon, grounds guardrails as structural necessities rather than manifestations of machine moral ag...
Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data. However, the resulting systems remain largely based on passive training regimes where linguistic regularities create the scaffold onto which information from other modalities is attached. Conversely,...
Giovanni Pezzulo, D. Nuzzi, Marco D'Alessandro et al.· arXiv.org· 0 citations
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