Evidence map›Paper›PMID 35143543›Full record

ArticlePloS one2022

Culture-free perceptual invariant for trustworthiness.

Ce Mo, Irene Cristofori, Guillaume Lio, Alice Gomez, Jean-René Duhamel, Chen Qu, Angela Sirigu

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Perception of facial trustworthiness in mild cognitive impairment.Journal of Alzheimer's disease : JAD · 2026
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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

7 authors.

Ce MoDepartment of Psychology, South China Normal University, Guangzhou, China.
Irene CristoforiInstitute of Cognitive Sciences Marc Jeannerod CNRS, UMR 5229, Bron, France.ORCID 0000-0003-2339-1457
Guillaume LioInstitute of Cognitive Sciences Marc Jeannerod CNRS, UMR 5229, Bron, France.
Alice GomezInstitute of Cognitive Sciences Marc Jeannerod CNRS, UMR 5229, Bron, France.
Jean-René DuhamelInstitute of Cognitive Sciences Marc Jeannerod CNRS, UMR 5229, Bron, France.
Chen QuDepartment of Psychology, South China Normal University, Guangzhou, China.
Angela SiriguInstitute of Cognitive Sciences Marc Jeannerod CNRS, UMR 5229, Bron, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Humans beings decide to trust others selectively, often based on the appearance of a face. But how do observers deal with the wide variety of facial morphologies and, in particular, those outside their own familiar cultural group? Using reverse correlation, a data-driven approach to explore how individuals create internal representations without external biases, we studied the generation of trustworthy faces by French and Chinese participants (N = 160) within and outside their own cultural group. Participants selected the most trustworthy or attractive (control condition) face from two identical European or Asian descent faces that had been modified by different noise masks. A conjunction analysis to reveal facial features common to both cultures showed that Chinese and French participants unconsciously increased the contrast of the "pupil-iris area" to make the face appear more trustworthy. No significant effects common to both groups were found for the attraction condition suggesting that attraction judgements are dependent on cultural processes. These results suggest the presence of universal cross-cultural mechanisms for the construction of implicit first impressions of trust, and highlight the importance of the eyes area in this process.

Indexed as

TrustAdultAsian PeopleCross-Cultural ComparisonFaceFemaleHumansMaleYoung Adult

Identifiers

PMID35143543
PMCPMC8830731

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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.