Evidence map›Paper›PMID 41775629›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2026

Mesocorticostriatal Reinforcement Learning of State Representation and Value with Implications for the Mechanisms of Schizophrenia.

Kenji Morita, Arvind Kumar

Abstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Kenji MoritaPhysical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan morita@p.u-tokyo.ac.jp.ORCID 0000-0003-2192-4248
Arvind KumarDivision of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm SE-100 44, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mesocorticostriatal dopamine projections are crucial for value learning, motivational control, and cognitive functions. However, while dopamine's role in value learning as reward-prediction-error (RPE) has been much understood, precise roles in motivational control and cognitive functions remain more elusive. Computationally, this corresponds to that while the operation of mesostriatal dopamine could be minimally described by simple reinforcement learning (RL) models with one-dimensional reward/RPE and fixed state representation, (1) how reward-specific motivational control can be achieved through heterogeneous dopamine responses, and (2) how sophisticated cortical state representation can be formed through mesocortical dopamine, cannot be captured by such simple models. To address both of these at once, we combined recent models for each of them: the "Reward Bases (RB)," which achieved reward-specific motivational control through multidimensional RPE (but with fixed cortical representation), and the "online value-recurrent-neutral-network (OVRNN)," which achieved state representation learning through training of RNN by RPE (but of one-dimensional). We show the combined model can achieve both functions simultaneously via double "feedback alignments" of the cortical and striatal downstream connections to the mesocorticostriatal dopamine projections. Crucially, cortical inhibition-dominance is a key for successful learning. Excessive excitation leads to aberrant persistent activity, which disrupts the alignments and impairs reward-specific motivational control and credit assignment. This implies how negative and positive symptoms of schizophrenia could emerge from excitation/inhibition imbalance, and we show how our model could explain altered brain activations in patients. Our model thus provides an integrated computational account for dopamine's functions, with implications on how its dysfunctions link to schizophrenia.

Indexed as

Cerebral CortexCorpus StriatumModels, NeurologicalReinforcement Machine LearningReinforcement, PsychologySchizophreniaAnimalsDopamineHumansMotivationRewardDopaminedopamineexcitation/inhibition balancefeedback alignmentrecurrent neural networksreinforcement learningschizophrenia

Identifiers

PMID41775629
PMCPMC13244656

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.