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.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.