Evidence map›Paper›PMID 38433918›Full record

ArticleiScience2024

Deep learning reveals what facial expressions mean to people in different cultures.

Jeffrey A Brooks, Lauren Kim, Michael Opara, Dacher Keltner, Xia Fang, Maria Monroy, Rebecca Corona, Panagiotis Tzirakis, Alice Baird, Jacob Metrick and 3 more

Abstract read
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. 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

13 authors.

Jeffrey A BrooksResearch Division, Hume AI, New York, NY 10010, USA.
Lauren KimResearch Division, Hume AI, New York, NY 10010, USA.
Michael OparaResearch Division, Hume AI, New York, NY 10010, USA.
Dacher KeltnerResearch Division, Hume AI, New York, NY 10010, USA.
Xia FangDepartment of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang, China.
Maria MonroyDepartment of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA.
Rebecca CoronaDepartment of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA.
Panagiotis TzirakisResearch Division, Hume AI, New York, NY 10010, USA.
Alice BairdResearch Division, Hume AI, New York, NY 10010, USA.
Jacob MetrickResearch Division, Hume AI, New York, NY 10010, USA.
Nolawi TaddesseNode Survey Solutions, Addis Ababa, Ethiopia.
Kiflom ZegeyeNode Survey Solutions, Addis Ababa, Ethiopia.
Alan S CowenResearch Division, Hume AI, New York, NY 10010, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cross-cultural studies of the meaning of facial expressions have largely focused on judgments of small sets of stereotypical images by small numbers of people. Here, we used large-scale data collection and machine learning to map what facial expressions convey in six countries. Using a mimicry paradigm, 5,833 participants formed facial expressions found in 4,659 naturalistic images, resulting in 423,193 participant-generated facial expressions. In their own language, participants also rated each expression in terms of 48 emotions and mental states. A deep neural network tasked with predicting the culture-specific meanings people attributed to facial movements while ignoring physical appearance and context discovered 28 distinct dimensions of facial expression, with 21 dimensions showing strong evidence of universality and the remainder showing varying degrees of cultural specificity. These results capture the underlying dimensions of the meanings of facial expressions within and across cultures in unprecedented detail.

Indexed as

Artificial intelligencePsychologySocial sciences

Identifiers

PMID38433918
PMCPMC10906517

What OpenQuestion holds

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LicenceCC BY
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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.