Evidence map›Paper›PMID 40730242›Full record

ArticleProceedings. Biological sciences2025

Indirect feedback hinders explicit sensorimotor adaptation.

Yifei Chen, Sabrina Abram, Richard B Ivry, Jonathan S Tsay

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Indirect feedback hinders explicit sensorimotor adaptation.Proceedings. Biological sciences · 2025
    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

4 authors.

Yifei ChenDepartment of Psychology, Princeton University, Princeton, NJ, USA.ORCID 0009-0009-9271-5890
Sabrina AbramDepartment of Psychology, University of California, Berkeley, CA, USA.
Richard B Ivry *Department of Psychology, University of California, Berkeley, CA, USA.
Jonathan S Tsay *Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.ORCID 0000-0002-3992-9023

Funding

Human Cerebellar Function in Multiple Task DomainsR35NS116883 · NINDS · UNIVERSITY OF CALIFORNIA BERKELEY · PI RICHARD IVRY · 2020 to 2026
$5.3M
Human Frontier Science ProgramNIHNINDS NIH HHS R35 NS116883
6 · The paper itself

Abstract

Motor adaptation-the process of reducing motor errors through feedback-is an essential feature of human competence, allowing us to move accurately in dynamic and novel environments. Adaptation typically results from direct sensory feedback, with most learning driven by visual and proprioceptive feedback that arises with the movement. In humans, motor adaptation can also be driven by indirect numerical feedback. In the present study, we examine how implicit and explicit components of motor adaptation are modulated by indirect numerical feedback. We conducted three reaching experiments involving over 400 human participants to compare direct sensory feedback and indirect numerical feedback using a task in which both types of learning processes could be present (experiment 1) or tasks in which learning was expected to be limited to only an explicit process (experiments 2 and 3). Adaptation with indirect feedback was dominated by explicit strategy use, with minimal evidence of implicit recalibration. When matched for information content, adaptation to both rotational and mirror-reversal perturbations was slower with indirect feedback than with direct feedback, due to increased random and systematic exploration. These results suggest that the nature of feedback shapes strategic discovery, offering new insights into how feedback type influences the mechanisms of sensorimotor learning.

Indexed as

Adaptation, PhysiologicalFeedback, SensoryLearningPsychomotor PerformanceAdolescentAdultFemaleHumansMaleYoung Adultdeclarative memoryexplicit learningimplicit learningmotor learningsymbolic feedback

Identifiers

PMID40730242
PMCPMC12307068

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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.