Paper Detail
Patrick J. Mineault, Thomas L. Griffiths, Sean Escola
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.
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@article{mineault2026cognitive,
title = {Cognitive Dark Matter: Measuring What AI Misses},
author = {Patrick J. Mineault and Thomas L. Griffiths and Sean Escola},
year = {2026},
abstract = {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 neuros},
url = {https://arxiv.org/abs/2603.03414},
keywords = {q-bio.NC},
eprint = {2603.03414},
archiveprefix = {arXiv},
}
{}