Evidence map›Paper›PMID 30761043›Full record

ArticleFrontiers in psychology2019

Personality Counts More Than Appearance for Men Making Affective Judgments of Verbal Comments.

Shan Gao, Lizhu Luo, Wanyu Zhang, Yuxin Lan, Ting Gou, Xun Li

Abstract read
In one paragraph

Article in Frontiers in psychology, 2019. 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

6 authors.

Shan GaoSchool of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, China.
Lizhu LuoThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China.
Wanyu ZhangSchool of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, China.
Yuxin LanSchool of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, China.
Ting GouSchool of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, China.
Xun LiSchool of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Previous research has shown that that evaluative verbal information (praise and criticism) conveys different affective values: criticism is perceived as unpleasant while praise is generally considered pleasant. Here, using praise and criticism in Chinese, we investigated how affective value is modulated in men and women, depending on the particular attribute (personality vs. appearance) targeted by social comments. Results showed that whereas praise was rated as pleasant and criticism as unpleasant overall, criticizing personality reduced pleasantness more than criticizing appearance. In men, moreover, criticism of personality was deemed more unpleasant than criticism of appearance while personality-targeted praise was rated more pleasant than appearance-targeted praise. This effect was absent in women and consistent with men's higher arousal ratings for personality- relative to appearance-targeted comments. Our findings suggest that men are more concerned about external perception of their personality than that of their appearance whereas women's affective judgment is more balanced. These gender-specific results may have implications for topic selection in evaluative social communication.

Indexed as

appearancecriticismgenderpersonalitypraise

Identifiers

PMID30761043
PMCPMC6362430

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.