Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 722

The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.

From reinforcement learning models to psychiatric and neurological disorders

Open access
Maia, Tiago V, Frank, Michael J·2011·Nature Neuroscience

Abstract

Over the last decade and a half, reinforcement learning models have fostered an increasingly sophisticated understanding of the functions of dopamine and cortico-basal ganglia-thalamo-cortical (CBGTC) circuits. More recently, these models, and the insights that they afford, have started to be used to understand important aspects of several psychiatric and neurological disorders that involve disturbances of the dopaminergic system and CBGTC circuits. We review this approach and its existing and potential applications to Parkinson's disease, Tourette's syndrome, attention-deficit/hyperactivity disorder, addiction, schizophrenia and preclinical animal models used to screen new antipsychotic drugs. The approach's proven explanatory and predictive power bodes well for the continued growth of computational psychiatry and computational neurology.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/nn.2723

Cites

106 of 106 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 106 references

Cited by

See all 722

Metadata

Authors
Maia, T. V., & Frank, M. J.
Venue
Nature Neuroscience
Published
2011-01-26
Volume
14
Issue
2
Pages
154-162
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
OpenAlex
W2058279623
Cited by
722
References
106
FWCI
17.39
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: From reinforcement learning models to psychiatric and neurological disorders. https://app.cochat.ai/papers/10.1038/nn.2723
The paper
Maia, T. V., & Frank, M. J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience. https://doi.org/10.1038/nn.2723

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.