Evidence map›Paper›PMID 40963797›Full record

ArticleFrontiers in psychology2025

Capturing variability in children's faces: an artificial, yet realistic, face stimulus set.

Sophia M Thierry, Stefan Uddenberg, Daniel Albohn, Alexander Todorov

Abstract read
In one paragraph

Article in Frontiers in psychology, 2025. 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
–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

1 citing paper in PubMed.

  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

4 authors.

Sophia M ThierryDepartment of Psychology, Brock University, St. Catharines, ON, Canada.
Stefan UddenbergDepartment of Psychology, The University of Illinois Urbana-Champaign, Champaign, IL, United States.
Daniel AlbohnBooth School of Business, The University of Chicago, Chicago, IL, United States.
Alexander TodorovBooth School of Business, The University of Chicago, Chicago, IL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Children's faces are underrepresented in face databases, and existing databases that do focus on children tend to have limitations in terms of the number of faces available and the diversity of ages and ethnicities represented. To improve the availability of children's faces for experimental research purposes, we created a novel face database that contains 500 artificial images of children that are diverse in terms of both age (ages 3 to 10) and ethnicity (representing 15 different racial or ethnic groups). Using deep neural networks, we produced a large collection of synthetic photographs that look like naturalistic, realistic faces of children. To assess the representativeness of the dataset, adult participants (

Indexed as

artificial face generationchildrenemotionsexpressionsface database

Identifiers

PMID40963797
PMCPMC12439430

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.