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.
Current efforts to understand Large Language Models (LLMs) are largely metaphorical. Researchers map LLMs onto familiar domains, from physics and neuroscience to psychology and sociology, each illuminating specific facets while obscuring others. We chart these metaphors across mechanistic, behavioral, and interactive scales and delineate their explanatory boundaries. Crucially, this metaphorical projection creates a recursive loop of anthropomorphism, fueling the “genuine understanding” versus “pattern matching” impasse. As an alternative approach, we propose machine experientialism, positing that LLMs build their own form of understanding from training corpora. The priority shifts from cataloging LLMs’ human-like traits to uncovering their distinct logic that emerges from this text-based world. This Perspective proposes machine experientialism, positing that LLMs build their own form of understanding from training corpora.
Lingyu Li, Yan Teng, Yingchun 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 primarily shape outputs after representations have been formed. They therefore cannot realise, using Iris Murdoch’s loving attention approach, a just, reality-sensitive orientation toward others that operates at the level of representation. Drawing on Murdoch’s moral philosophy, we identify three substrate-neutral features of loving attention, namely locational, representational, and dispositional, that survive translation from human moral phenomenology to computational systems. On this basis, we propose that moral processing in LLMs should be studied not only as a problem of output control but also as a problem of representational architecture. We further formulate the loving attention geometry hypothesis, suggesting that if morally improved perception involves a systematic shift from ego distorted to more just representations, this transformation may leave detectable structure in LLM embedding and activation spaces. With this focus on Murdoch, the paper contributes a novel philosophical technical approach that links moral attention, representation learning, and AI alignment. It clarifies why architectural considerations matter for moral AI, distinguishes between the tractability and validation of moral geometry, and outlines design constraints for responsible research. We argue that exploring representational forms of moral attention is a promising and necessary direction for AI ethics research. If transformer attention has been able to scale intelligence, it is worth inquiring if representations and architectures can scale morality.
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
Contemporary AI discourse attributes to language models properties they cannot bear: general intelligence as substrate-independent cognition, hallucination as cognitive failure, agency as autonomous goal-pursuit, sentience as emergent inner life, alignment as goal synchronization. This paper argues that these are instances of a single category mistake--properties constituted within human communicative practice are projected onto the machine side--and explains its structure. Human-LLM interaction constitutes a language game in which one side bears all normative activity. We call this configuration asymmetric communication since model outputs circulate communicatively, entering further exchanges, without the system undertaking commitments, bearing entitlements, or performing the assessment on which discursive standing depends. Three conditions define the asymmetry: (i) correctness is enforced exclusively by the receiver; (ii) accountability is borne by human participants alone; and (iii) the practical standing of any output depends entirely on human uptake. These conditions are structural, hold independently of capability, and remain unchanged as more powerful models raise the stakes of misattribution. The framework draws on Wittgenstein (meaning enacted in shared practices), Luhmann (communication completed on the receiver's side), Esposito (algorithmic contingency sufficient for uptake), and Brandom (normative scorekeeping as the source of discursive standing). Applied to all five, it reclassifies each as a receiver-side phenomenon, grounds guardrails as structural necessities rather than manifestations of machine moral agency, and yields an implication for AI governance. Alignment is institutional constraint engineering, not goal synchronization between agents, while responsibility remains with human institutions.
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, neuroscience and cognitive science suggest that biological intelligence is organized in the opposite way, where grounded world models acquired through interaction with the environment provide the semantic scaffold to which language is attached. Here, we illustrate five examples of neural circuits supporting grounded world modelling, which underlie navigation in physical and conceptual spaces, affordance-based perception and interaction with objects, active perception and exploratory learning, allostatic control and emotion, and the distinction between self- and world-generated outcomes. These examples highlight several features largely missing from current embodied AI, including the role of intrinsic dynamics as a foundation for learning, the centrality of action in aligning these dynamics with the external world, the prominence of autonomous experience and open-ended learning over passive assimilation of externally provided data, and the fact that early predictive and control mechanisms scaffold higher cognitive abilities such as reasoning, conceptual navigation, planning, imagination, understanding others'minds, and communication. Finally, we discuss whether and how principles derived from biological systems may inform future embodied AI, including training regimes based on social interaction to construct world models that are not only grounded but also socially shared and aligned with human norms and values.
Giovanni Pezzulo, D. Nuzzi, Marco D'Alessandro et al.· arXiv.org· 0 citations
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