ArticleProceedings of the National Academy of Sciences of the United States of America2026
Understanding human metacontrol and its pathologies using deep neural networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Understanding human metacontrol and its pathologies using deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
Error monitoring is crucial for inferring how controllable an environment is, and thus for estimating the value of control processes (metacontrol). In this study, we use computational simulations with deep neural networks to investigate its behavioral and neural correlates. We trained both humans and deep reinforcement learning (RL) agents to perform a reward-guided learning task that required adaptation to changes in action controllability. Deep RL agents could only solve the task when designed to explicitly predict action prediction errors that fire in the medial prefrontal cortex. When trained this way, they displayed signatures of metacontrol that closely resembled those observed in humans. Moreover, when deep RL agents were trained to over- or underestimate controllability, they developed behavioral pathologies partially matching those of humans who reported depressive, anxious, or compulsive traits on transdiagnostic questionnaires. These findings open up avenues for studying metacontrol using deep neural networks.
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Registered trials
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