Evidence map›Paper›PMID 41904210›Full record

ArticleScientific reports2026

Exploring the importance of shape on dynamic recognition of self-face or friend-face.

Sogo Yumura, Karen Lander, Miyuki G Kamachi

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Sogo YumuraInformatics Program, Graduate School of Engineering, Kogakuin University, 2665-1 Nakano, Hachioji, Tokyo, 192-0015, Japan. ed24003@ns.kogakuin.ac.jp.
Karen LanderDivision of Psychology, Communication and Human Neuroscience, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
Miyuki G KamachiFaculty of Informatics, Kogakuin University, 1-24-2 Nishi-Shinjuku, Shinjuku-ku, Tokyo, 163-8677, Japan.

Funding

Japan Society for the Promotion of Science JP20H00608Japan Society for the Promotion of Science JP25KJ2090
6 · The paper itself

Abstract

Our own face is well-known to us, but it is unclear whether we perceive it in the same way as other familiar faces. Unlike others’ faces, self-faces provide limited opportunities to observe facial motion. This study investigated the importance of shape when recognizing self or friend from dynamic face clips. Dynamic sequences were created using Deepfake, manipulating face shape and motion independently. In both experiments, participants observed a visually presented face and identified whether the face motion was self or friend. In Experiment 1, face shape was manipulated to match or mismatch the dynamic parameters of the observed person. In Experiment 2, the contribution of self- and friend- shape was manipulated in a series of stages to match or mismatch the dynamic parameters. The results showed the identification of the friends’ face motion was independent of the observed face shape. However, self-face motion could not be clearly identified until the face shape was judged to be self-face. These results support the prediction that self-face motion identification is more dependent on face shape, compared to another familiar friend face. We propose that self-faces may have specific perceptual characteristics that are distinct from the recognition of other familiar faces.

Indexed as

FaceFacial RecognitionFriendsPattern Recognition, VisualRecognition, PsychologyAdultFemaleHumansMalePhotic StimulationYoung AdultDeepfakeDynamic facial informationFace recognitionIdentificationSelf-face

Identifiers

PMID41904210
PMCPMC13039733

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.