Evidence map›Paper›PMID 41730946›Full record

ArticleScientific reports2026

Enforcing a high success percentage interferes with reward-based motor learning.

Katinka van der Kooij, Nina M van Mastrigt, Moira van Leeuwen, Maaike R van der Horst, Joyce E Burger, Jeroen B J Smeets

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

6 authors.

Katinka van der KooijDepartment of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. k.vander.kooij@vu.nl.
Nina M van MastrigtDepartment of Psychology, Justus Liebig Universität Giessen, Giessen, Germany.
Moira van LeeuwenDepartment of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Maaike R van der HorstDepartment of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Joyce E BurgerDepartment of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Jeroen B J SmeetsDepartment of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Humans can adapt their movements based on binary reward feedback about success and failure. To engage in such ‘reward-based’ motor learning, the learner must encounter at least some failures, but it is unclear what percentage of failures is optimal. For learning, we hypothesize that a success percentage of 50% is optimal, as it provides the most information. For motivation, in contrast, we hypothesize that a success percentage of 80% is optimal, since too many failures can reduce motivation. In this study, we simultaneously test the hypotheses on learning and motivation in participants of a wide age range (7 to 58 years) who performed a brief circle-drawing task. The participant’s goal in this task was to double the size of the baseline circles drawn with the unseen hand. We assigned participants to a reward scheme that targets either 50% success (moderate success group) or 80% success (high success group). In line with our hypothesis on learning, the results show more motor learning in the moderate success group compared to the high success group. In contrast to our hypothesis on motivation, motivation was not higher in the high success group.

Indexed as

LearningMotivationMotor SkillsRewardAdolescentAdultChildFemaleHumansMaleMiddle AgedYoung AdultLearningMotivationReward-based motor learningSuccess

Identifiers

PMID41730946
PMCPMC13031413

What OpenQuestion holds

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Registered trials

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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.