Evidence map›Paper›PMID 40799702›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2023

The neural dynamics of loss aversion.

Shaozhi Nie, Muzhi Wang, Jian Li, Huan Luo, Hang Zhang

Abstract read
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2023. 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

5 authors.

Shaozhi NieSchool of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.
Muzhi WangSchool of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.
Jian LiSchool of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.
Huan LuoSchool of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.
Hang ZhangSchool of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In human decision-making under risk, loss is typically valued more than the same amount of gain, a behavioral phenomenon known as loss aversion, which suggests that gain and loss are evaluated differently in the brain. Most previous neuroimaging studies focused on the brain regions that show differential responses to losses relative to gains. What is still largely unknown is how the neural processing of gain and loss may unfold in time and drives loss aversion. Here, we designed a gambling task ideal for investigating the temporal course of the valuation process and used magnetoencephalography (MEG) to track human participants' brain activities for valuating gain and loss. Computational modeling of participants' behaviors implies that the gain and loss presented simultaneously can compete for cognitive resources, during which loss signals dominate the valuation process, resulting in loss aversion. Indeed, time-resolved MEG analysis reveals that the evaluation process of loss terminated later for participants with higher loss aversion than those with lower loss aversion, though the gain valuation had similar temporal courses for different participants. These results suggest that the origin of loss aversion may lie in the neural dynamics of loss processing.

Indexed as

decision under riskloss aversionMEGProspect Theory

Identifiers

PMID40799702
PMCPMC12007549

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.