Evidence map›Paper›PMID 33914891›Full record

ArticleSchizophrenia bulletin2021

Reliability and Replicability of Implicit and Explicit Reinforcement Learning Paradigms in People With Psychotic Disorders.

Danielle N Pratt, Deanna M Barch, Cameron S Carter, James M Gold, John D Ragland, Steven M Silverstein, Angus W MacDonald

Open access · bronzeAbstract read
In one paragraph

Article in Schizophrenia bulletin, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.8field-weighted citation impact, top 9% 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

19 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Belief Updating and Paranoia in Individuals With Schizophrenia.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2022
    Article
  17. Review
  18. Article
  19. Article
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

7 authors at 5 institutions in 1 country.

Danielle N PrattDepartment of Psychology, University of Minnesota, Minneapolis, MN.
Deanna M BarchDepartment of Psychology, Washington University, St. Louis, MO.
Cameron S CarterDepartment of Psychiatry, University of California at Davis, Davis, CA.
James M GoldMaryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD.
John D RaglandDepartment of Psychiatry, University of California at Davis, Davis, CA.
Steven M SilversteinDepartment of Psychiatry, University of Rochester, Rochester, NY.
Angus W MacDonaldDepartment of Psychology, University of Minnesota, Minneapolis, MN.
University of California, Davis · USUniversity of Minnesota · USUniversity of Maryland, Baltimore · USUniversity of Rochester · USWashington University in St. Louis · US

Funding

Cognitive Neurocomputational Task Reliability and Clinical Applications ConsortiumR01MH084840 · NIMH · WASHINGTON UNIVERSITY · PI Deanna Barch, Cameron S. Carter · 2008 to 2026
$8.5M
4/5-Cognitive Neuroscience Task Reliability & Clinical Applications ConsortiumR01MH084821 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI GOLD, JAMES M. · 2008 to 2023
$4.2M
2/5-Cognitive Neuroscience Task Reliability & Clinical Applications ConsortiumR01MH084826 · NIMH · UNIVERSITY OF CALIFORNIA-IRVINE · PI CARTER, CAMERON S. · 2008 to 2023
$4.0M
5/5-Cognitive Neuroscience Task Reliability & Clinical Applications ConsortiumR01MH084861 · NIMH · UNIVERSITY OF MINNESOTA · PI MACDONALD, ANGUS W · 2008 to 2024
$3.8M
Cognitive Neurocomputational Task Reliability & Clinical Applications ConsortiumR01MH084828 · NIMH · UNIV OF MED/DENT NJ-R W JOHNSON MED SCH · PI SILVERSTEIN, STEVEN M · 2008 to 2024
$3.3M
NIMH NIH HHS R01 MH084821NIMH NIH HHS R01 MH084826NIMH NIH HHS R01 MH084828NIMH NIH HHS R01 MH084840NIMH NIH HHS R01 MH084861
6 · The paper itself

Abstract

backgroundMotivational deficits in people with psychosis may be a result of impairments in reinforcement learning (RL). Therefore, behavioral paradigms that can accurately measure these impairments and their change over time are essential.

methodsWe examined the reliability and replicability of 2 RL paradigms (1 implicit and 1 explicit, each with positive and negative reinforcement components) given at 2 time points to healthy controls (n = 75), and people with bipolar disorder (n = 62), schizoaffective disorder (n = 60), and schizophrenia (n = 68).

resultsInternal consistency was acceptable (mean α = 0.78 ± 0.15), but test-retest reliability was fair to low (mean intraclass correlation = 0.33 ± 0.25) for both implicit and explicit RL. There were no clear effects of practice for these tasks. Largely, performance on these tasks shows intact implicit and impaired explicit RL in psychosis. Symptom presentation did not relate to performance in any robust way.

conclusionsOur findings replicate previous literature showing spared implicit RL and impaired explicit reinforcement in psychosis. This suggests typical basal ganglia dopamine release, but atypical recruitment of the orbitofrontal and dorsolateral prefrontal cortices. However, we found that these tasks have only fair to low test-retest reliability and thus may not be useful for assessing change over time in clinical trials.

Indexed as

Reinforcement, PsychologyAdultBipolar DisorderFemaleHumansMaleMiddle AgedNeuropsychological TestsProbability LearningPsychomotor PerformancePsychotic DisordersReproducibility of ResultsSchizophreniapositive and negative reinforcementpractice effectsschizophrenia

Identifiers

PMID33914891
PMCPMC8084427
OpenAlexW3107856989

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.