Cognitive Dark Matter: Measuring What AI Misses
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2026
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| author | Mineault, Patrick J. Griffiths, Thomas L. Escola, Sean |
| author_facet | Mineault, Patrick J. Griffiths, Thomas L. Escola, Sean |
| contents | We propose that the jagged intelligence landscape of modern AI systems arises from a missing training signal that we call "cognitive dark matter" (CDM): brain functions that meaningfully shape behavior yet are hard to infer from behavior alone. We identify key CDM domains-metacognition, cognitive flexibility, episodic memory, lifelong learning, abductive reasoning, social and common-sense reasoning, and emotional intelligence-and present evidence that current AI benchmarks and large-scale neuroscience datasets are both heavily skewed toward already-mastered capabilities, with CDM-loaded functions largely unmeasured. We then outline a research program centered on three complementary data types designed to surface CDM for model training: (i) latent variables from large-scale cognitive models, (ii) process-tracing data such as eye-tracking and think-aloud protocols, and (iii) paired neural-behavioral data. These data will enable AI training on cognitive process rather than behavioral outcome alone, producing models with more general, less jagged intelligence. As a dual benefit, the same data will advance our understanding of human intelligence itself. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03414 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cognitive Dark Matter: Measuring What AI Misses Mineault, Patrick J. Griffiths, Thomas L. Escola, Sean Neurons and Cognition We propose that the jagged intelligence landscape of modern AI systems arises from a missing training signal that we call "cognitive dark matter" (CDM): brain functions that meaningfully shape behavior yet are hard to infer from behavior alone. We identify key CDM domains-metacognition, cognitive flexibility, episodic memory, lifelong learning, abductive reasoning, social and common-sense reasoning, and emotional intelligence-and present evidence that current AI benchmarks and large-scale neuroscience datasets are both heavily skewed toward already-mastered capabilities, with CDM-loaded functions largely unmeasured. We then outline a research program centered on three complementary data types designed to surface CDM for model training: (i) latent variables from large-scale cognitive models, (ii) process-tracing data such as eye-tracking and think-aloud protocols, and (iii) paired neural-behavioral data. These data will enable AI training on cognitive process rather than behavioral outcome alone, producing models with more general, less jagged intelligence. As a dual benefit, the same data will advance our understanding of human intelligence itself. |
| title | Cognitive Dark Matter: Measuring What AI Misses |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2603.03414 |