Evidence map›Paper›PMID 42546046›Full record

ArticlePLoS computational biology2026

Successful reinforcement history suppresses explicit and implicit error corrections.

John Buggeln, Nicholas Muscara, Seth R Sullivan, Jan A Calalo, Truc T Ngo, Matthew Short, Adam M Roth, Michael J Carter, Joshua G A Cashaback

Abstract read
In one paragraph

Article in PLoS computational biology, 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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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

John BuggelnDepartment of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America.ORCID 0000-0002-4276-8806
Nicholas MuscaraDepartment of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America.
Seth R SullivanDepartment of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America.
Jan A CalaloDepartment of Mechanical Engineering, University of Delaware, Newark, Delaware, United States of America.
Truc T NgoDepartment of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America.
Matthew ShortDepartment of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America.
Adam M RothDepartment of Mechanical Engineering, University of Delaware, Newark, Delaware, United States of America.
Michael J CarterDepartment of Kinesiology, McMaster University, Hamilton, Ontario, Canada.ORCID 0000-0002-0675-4271
Joshua G A CashabackDepartment of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America.

Funding

National Science Foundation NSF 2234748Natural Sciences and Engineering Research Council of Canada
6 · The paper itself

Abstract

A skilled basketball player expects to make many shots. After a miss, they could make a larger correction because the miss was "surprising." Alternatively, they could make only a small correction since their average past performance was successful. Despite its relevance, past work has not elucidated whether or how a history of reinforcement influences explicit (cognitive) and implicit (involuntary) error corrections. Across three reaching experiments, we compared two competing hypotheses: 1) reward prediction errors ("surprise") modulate explicit and implicit error corrections, or 2) expected value (average reward) modulates explicit and implicit error corrections. Our experimental results and computational modelling support the idea that the expected value of a long-history of reinforcement modulates explicit error corrections. Further, we find that an immediate-history of reinforcement directly modulates implicit error corrections, which could not be explained by an independent task error model. Collectively, we find that a successful reinforcement history suppresses explicit and implicit error corrections.

Indexed as

Psychomotor PerformanceReinforcement, PsychologyBasketballComputational BiologyComputer SimulationHumansMaleReward

Identifiers

PMID42546046
PMCPMC13432137

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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.