Cognitive Foundations for Reasoning and Their Manifestation in LLMs
Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reasoning. To understand this gap, we synthesize cognitive science research into a taxonomy of 28 cognitive elements spanning reasoning invariants, meta-cognitive controls, representations for organizing reasoning & knowledge, and transformation operations. We introduce a fine-grained evaluation framework and conduct the first large-scale empirical analysis of 192K traces from 18 models across text, vision, and audio, complemented by 54 human think-aloud traces, which we make publicly available. We find that models under-utilize cognitive elements correlated with success, narrowing to rigid sequential processing on ill-structured problems where diverse representations and meta-cognitive monitoring are critical. Human traces show more abstraction and conceptual processing, while models default to surface-level enumeration. Meta-analysis of 1.6K LLM reasoning papers reveals the research community concentrates on easily quantifiable elements (sequential organization: 55%, decomposition: 60%) but neglecting meta-cognitive controls (self-awareness: 16%) that correlate with success. Models possess behavioral repertoires associated with success but fail to deploy them spontaneously. Leveraging these patterns, we develop test-time reasoning guidance that automatically scaffold successful structures, improving performance by up to 66.7% on complex problems. By establishing a shared vocabulary between cognitive science and LLM research, our framework enables systematic diagnosis of reasoning failures and principled development of models that reason through robust cognitive mechanisms rather than spurious shortcuts, while providing tools to test theories of human cognition at scale.
6925dec84b7b0d870b18278f
Priyanka Kargupta
claimed_verified
2026-03-23T11:02:58.512Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6476ae4083d4fdaedddf405f/ViS5-L6bNTfP5Vg0ASbqM.jpeg
Priyanka Kargupta
pkargupta
user
pkargupta
6925dec84b7b0d870b182790
Shuyue Stella Li
6925dec84b7b0d870b182791
Haocheng Wang
6925dec84b7b0d870b182792
Jinu Lee
6925dec84b7b0d870b182793
Shan Chen
6925dec84b7b0d870b182794
Orevaoghene Ahia
6925dec84b7b0d870b182795
Dean Light
6925dec84b7b0d870b182796
Thomas L. Griffiths
6925dec84b7b0d870b182797
Max Kleiman-Weiner
6925dec84b7b0d870b182798
Jiawei Han
6925dec84b7b0d870b182799
Asli Celikyilmaz
6925dec84b7b0d870b18279a
Yulia Tsvetkov
Cognitive Foundations for Reasoning and Their Manifestation in LLMs
Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reasoning. To understand this gap, we synthesize cognitive science research into a taxonomy of 28 cognitive elements spanning reasoning invariants, meta-cognitive controls, representations for organizing reasoning & knowledge, and transformation operations. We introduce a fine-grained evaluation framework and conduct the first large-scale empirical analysis of 192K traces from 18 models across text, vision, and audio, complemented by 54 human think-aloud traces, which we make publicly available. We find that models under-utilize cognitive elements correlated with success, narrowing to rigid sequential processing on ill-structured problems where diverse representations and meta-cognitive monitoring are critical. Human traces show more abstraction and conceptual processing, while models default to surface-level enumeration. Meta-analysis of 1.6K LLM reasoning papers reveals the research community concentrates on easily quantifiable elements (sequential organization: 55%, decomposition: 60%) but neglecting meta-cognitive controls (self-awareness: 16%) that correlate with success. Models possess behavioral repertoires associated with success but fail to deploy them spontaneously. Leveraging these patterns, we develop test-time reasoning guidance that automatically scaffold successful structures, improving performance by up to 66.7% on complex problems. By establishing a shared vocabulary between cognitive science and LLM research, our framework enables systematic diagnosis of reasoning failures and principled development of models that reason through robust cognitive mechanisms rather than spurious shortcuts, while providing tools to test theories of human cognition at scale.
LLMs exhibit reasoning gaps compared to humans, underutilizing cognitive elements and failing to deploy meta-cognitive controls, but test-time guidance can improve their performance on complex problems.
cognitive elements reasoning invariants meta-cognitive controls representations transformation operations fine-grained evaluation framework think-aloud traces sequential processing abstraction conceptual processing surface-level enumeration self-awareness reasoning guidance robust cognitive mechanisms spurious shortcuts