Evidence map›Paper›PMID 35581333›Full record

ArticleScientific reports2022

Gender differences in guilt aversion in Korea and the United Kingdom.

Tsuyoshi Nihonsugi, Toshiko Tanaka, Masahiko Haruno

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. 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
0.2field-weighted citation impact, top 52% of its field
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, 2 citations in OpenAlex.

  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

3 authors at 2 institutions in 1 country.

Tsuyoshi NihonsugiFaculty of Economics, Osaka University of Economics, Osaka, Japan. t.nihonsugi@gmail.com.
Toshiko TanakaCenter for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Japan.
Masahiko HarunoCenter for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Japan. mharuno@nict.go.jp.
National Institute of Information and Communications Technology · JPOsaka University of Economics · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Guilt aversion, which describes the tendency to reduce the discrepancy between a partner's expectation and his/her actual outcome, is a key driving force for cooperation in both the East and West. A recent study based on functional magnetic resonance imaging and online behavioral experiments reported that men show stronger guilt aversion than women and also suggested that men's predominance in guilt aversion arises from stronger sensitivity to social norms. However, since the participants of that study were all Japanese, it remains unaddressed how common the gender difference in guilt aversion is. Here, we conducted online behavioral studies on people from Korea and the UK (Korea; n = 294, UK; n = 347) using the same trust game. We confirmed that men exhibit stronger guilt aversion than women in both countries. Furthermore, consistent with the Japanese study, our Lasso regression analysis for UK participants revealed that Big Five Conscientiousness (rule-based decision) correlated with guilt aversion in men. In contrast, guilt aversion in Korean men correlated with Big Five Neuroticism. Thus, our results suggest that gender differences in guilt aversion are universal but the underlying cognitive processes may be influenced by cultural differences.

Indexed as

AffectGuiltFemaleHumansMagnetic Resonance ImagingMaleSex FactorsTrust

Identifiers

PMID35581333
PMCPMC9114390
OpenAlexW4280596551

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.