LLM and Human Modes of Representation

2026-06-19

AI Summary

This study investigates the similarities and differences between large language models (LLMs) and humans in their cognitive mechanisms underlying linguistic knowledge representation and real-world reasoning. Integrating methods from cognitive science and artificial intelligence, the research systematically compares the two in terms of representational structure, learning efficiency, and generalization capabilities through behavioral experiments and model analyses. Findings reveal that, despite LLMs’ fluent performance on linguistic tasks, their internal representations and processing mechanisms differ substantially from those of humans. Moreover, LLMs exhibit markedly inferior learning efficiency and generalization in real-world reasoning tasks. This work challenges the prevailing paradigm of equating task performance with human-likeness and offers a novel perspective on the fundamental distinctions between artificial and human intelligence.

Abstract

Much work on the cognitive foundations of AI has focussed on comparisons between the ways in which Large Language Models (LLMs) and humans process information and represent it. One aspect of this comparison involves determining the extent to which LLMs can achieve or surpass human performance on a variety of cognitively interesting tasks. A second explores points of convergence and divergence between LLM and human systems for processing information. Here, I consider some recent research that has addressed both issues in two informational domains. The first is the representation of linguistic knowledge. The second is real world reasoning and planning. While LLMs frequently achieve impressive levels of performance and fluency on linguistic applications, they tend to handle linguistic content in ways that are distinct from human processing. They are also, for the most part, less efficient than humans in learning and generalisation for reasoning tasks.

Problem

• Large Language Models
• human cognition
• linguistic representation
• real-world reasoning
• information processing

Innovation

• Large Language Models
• human cognition
• linguistic representation
• real-world reasoning
• representation modes