ArticleNeuron2023
The neural architecture of theory-based reinforcement learning.
Article in Neuron, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Neurocomputational modeling of rule abstraction and memorization during probabilistic stimulus-reward learning.iScience · 2026Article
- Beyond the dichotomy between controlled and naturalistic paradigms: modeling psychological constructs in cognitive neuroscience.Frontiers in psychology · 2026Article
- Dorsomedial striatum monitors unreliability of current action policy and probes alternative one via the indirect pathway.Science advances · 2025Article
- Electrical brain activations in preadolescents during a probabilistic reward-learning task reflect cognitive processes and behavior strategies.Frontiers in human neuroscience · 2025Article
- Advances in deep reinforcement learning enable better predictions of human behavior in time-continuous tasks.PloS one · 2025Article
- The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder.Entropy (Basel, Switzerland) · 2024Article
- Dynamic computational phenotyping of human cognition.Nature human behaviour · 2024Article
- Midbrain signaling of identity prediction errors depends on orbitofrontal cortex networks.Nature communications · 2024Article
- Naturalistic reinforcement learning.Trends in cognitive sciences · 2024Review
- Encoding neural representations of time-continuous stimulus-response transformations in the human brain with advanced deep neural networks.Imaging neuroscience (Cambridge, Mass.)Article
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5 authors.
Funding
Abstract
Humans learn internal models of the world that support planning and generalization in complex environments. Yet it remains unclear how such internal models are represented and learned in the brain. We approach this question using theory-based reinforcement learning, a strong form of model-based reinforcement learning in which the model is a kind of intuitive theory. We analyzed fMRI data from human participants learning to play Atari-style games. We found evidence of theory representations in prefrontal cortex and of theory updating in prefrontal cortex, occipital cortex, and fusiform gyrus. Theory updates coincided with transient strengthening of theory representations. Effective connectivity during theory updating suggests that information flows from prefrontal theory-coding regions to posterior theory-updating regions. Together, our results are consistent with a neural architecture in which top-down theory representations originating in prefrontal regions shape sensory predictions in visual areas, where factored theory prediction errors are computed and trigger bottom-up updates of the theory.
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