Reasoning aligns language models to human cognition

• URL: http://arxiv.org/abs/2602.08693v1
• Date: Mon, 09 Feb 2026 14:13:39 GMT
• Title: Reasoning aligns language models to human cognition
• Authors: Gonçalo Guiomar, Elia Torre, Pehuen Moure, Victoria Shavina, Mario Giulianelli, Shih-Chii Liu, Valerio Mante,
• Abstract summary: We introduce an active probabilistic reasoning task that cleanly separates sampling (actively acquiring evidence) from inference (integrating evidence toward a decision)<n> Benchmarking humans and a broad set of contemporary large language models against near-optimal reference policies reveals a consistent pattern.<n>This model places humans and models in a shared low-dimensional cognitive space, reproduces behavioral signatures across agents, and shows how chain-of-thought shifts language models toward human-like regimes of evidence accumulation and belief-to-choice mapping.
• Score: 12.07126784684808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Do language models make decisions under uncertainty like humans do, and what role does chain-of-thought (CoT) reasoning play in the underlying decision process? We introduce an active probabilistic reasoning task that cleanly separates sampling (actively acquiring evidence) from inference (integrating evidence toward a decision). Benchmarking humans and a broad set of contemporary large language models against near-optimal reference policies reveals a consistent pattern: extended reasoning is the key determinant of strong performance, driving large gains in inference and producing belief trajectories that become strikingly human-like, while yielding only modest improvements in active sampling. To explain these differences, we fit a mechanistic model that captures systematic deviations from optimal behavior via four interpretable latent variables: memory, strategy, choice bias, and occlusion awareness. This model places humans and models in a shared low-dimensional cognitive space, reproduces behavioral signatures across agents, and shows how chain-of-thought shifts language models toward human-like regimes of evidence accumulation and belief-to-choice mapping, tightening alignment in inference while leaving a persistent gap in information acquisition.

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