Evidence map›Paper›PMID 26685155›Full record

ArticleBrain : a journal of neurology2016

Dopamine selectively remediates 'model-based' reward learning: a computational approach.

Madeleine E Sharp, Karin Foerde, Nathaniel D Daw, Daphna Shohamy

Open access · bronzeAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed, 1 pooled it
10.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

70 citing papers in PubMed, 1 synthesis or guideline pooled it, 147 citations in OpenAlex.

  1. Pooled it
  2. Dopamine and Temporal Discounting: Revisiting Pharmacology and Individual Differences.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Trial
  3. Trial
  4. Trial
  5. Article
  6. Activation by Alcohol of Prefrontal Layer 5 Pyramidal Neurons Depends on Ascending Dopaminergic Input.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  7. Article
  8. Review
  9. Review
  10. Differentiating Reinforcement Learning and Episodic Memory in Value-Based Decisions in Parkinson's Disease.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  11. Review
  12. Article
  13. Proactive and reactive construction of memory-based preferences.bioRxiv : the preprint server for biology · 2024
    Article
  14. A dopaminergic basis of behavioral control.bioRxiv : the preprint server for biology · 2024
    Article
  15. Article
  16. Article
  17. Article
  18. Dopamine Alters the Effect of Brain Stimulation on Decision-Making.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
    Article
  19. Review
  20. Article

10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Madeleine E Sharp1 Department of Neurology, Columbia University Medical Centre, New York NY, USA.
Karin Foerde2 Department of Psychology, New York University, New York NY, USA.
Nathaniel D Daw3 Center for Neural Science, New York University, New York NY, USA.
Daphna Shohamy4 Department of Psychology and Kavli Center for Brain Sciences, Columbia University, New York NY, USA shohamy@psych.columbia.edu.
New York University · USColumbia University · USColumbia University Irving Medical Center · US

Funding

CRCNS: Computational and neural mechanisms of memory-guided decisionsR01DA038891 · NIDA · PRINCETON UNIVERSITY · PI DAW, NATHANIEL DOUGLASS, SHOHAMY, DAPHNA · 2014 to 2018
$1.7M
NIDA NIH HHS R01 DA038891NIDA NIH HHS R01DA038891
6 · The paper itself

Abstract

Patients with loss of dopamine due to Parkinson's disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from 'model-free' learning. The other, 'model-based' learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson's disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson's disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson's disease may be related to an inability to pursue reward based on complete representations of the environment.

Indexed as

Patient-Specific ModelingRewardAgedDopamineFemaleHumansLearningMaleMiddle AgedParkinson DiseasePhotic StimulationPsychomotor PerformanceDopaminedopaminemodel-basedmodel-freeParkinson’s diseasereinforcement learning

Identifiers

PMID26685155
PMCPMC5868097
OpenAlexW2222166274

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.