Evidence map›Paper›PMID 32726592›Full record

ArticleNeuroscience letters2020

The neural basis of feedback-guided behavioral adjustment.

Sai Sun, Shuo Wang

Abstract read
In one paragraph

Article in Neuroscience letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Sai SunCenter for Studies of Psychological Application, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, China. Electronic address: sunsai1215@gmail.com.
Shuo WangDepartment of Chemical and Biomedical Engineering and Rockefeller Neuroscience Institute, West Virginia University, Morgantown, WV 26506, USA. Electronic address: wangshuo45@gmail.com.

Funding

West Virginia IDEA-CTRU54GM104942 · NIGMS · WEST VIRGINIA UNIVERSITY · PI Laura F. Gibson · 2012 to 2026
$81.0M
NIGMS NIH HHS U54 GM104942
6 · The paper itself

Abstract

Given feedback on the outcomes of our choices, humans can then make adjustments to future decisions. This is how we learn. However, how knowing the outcome of one's decisions influences behavioral changes, and especially the neural basis of those behavioral changes, still remains unclear. To investigate these questions, we employed a simple gambling task, in which participants chose between two alternative cards and received trial-by-trial feedback of their choices. In different sessions, we emphasized either utility (win or loss) or performance (whether the choice was correct [better than the alternative] or incorrect), making one of the two aspects more salient to participants. We found that trial-by-trial feedback and the saliency of the feedback modulated behavioral adjustments and subjective evaluations of the outcomes. With simultaneous electroencephalogram (EEG) recording, we found that the feedback-related negativity (FRN), P300, and late positive potential (LPP) served as the neural substrates for behavioral decision switching. Together, our findings reveal the neural basis of behavioral adjustment based on outcome evaluation and highlight the key role of feedback evaluation in future action selection and flexible adaptation.

Indexed as

BrainChoice BehaviorElectroencephalographyEvoked PotentialsFeedback, PsychologicalFemaleHumansMaleYoung AdultDecision makingFeedback-related negativity (FRN)Late positive potential (LPP)P300SaliencyTheta-band power

Identifiers

PMID32726592
PMCPMC8389058

What OpenQuestion holds

Textmetadata
LicenceTDM
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.