Evidence map›Paper›PMID 41034260›Full record

ArticleScientific data2025

A generated image repository of aging faces.

Anna Pot, Laura L Carstensen

Abstract readDataset
In one paragraph

Article in Scientific data, 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

2 authors.

Anna PotDepartment of Psychology, Stanford University, Stanford, USA. annapot@stanford.edu.
Laura L CarstensenDepartment of Psychology, Stanford University, Stanford, USA.

Funding

U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) R37AG00881630
6 · The paper itself

Abstract

Faces are a rich source of information for humans and a substantial amount of behavioral science research uses face stimuli to assess person perception. Unfortunately, this body of research is limited by an overreliance on young, predominantly white faces normed on young adult perceivers. To address these limitations, we created an open-access database of AI-generated faces that represents the same individuals at three life stages (young adulthood, middle age, and older adulthood) including equal numbers of males and females. Using advanced generative algorithms, the approach digitally aged 62 young individuals, thus preserving identity-specific features while realistically portraying age-related changes. The resulting database comprises 186 images. Each image has been age-normed and validated for authenticity. Although the database will be useful for many research questions, the stimuli are especially well-suited for research on age comparisons because the same individuals can be presented at different ages.

Indexed as

AgingDatabases, FactualFaceAdolescentAdultAgedAlgorithmsFemaleHumansMaleMiddle AgedYoung Adult

Identifiers

PMID41034260
PMCPMC12489049

What OpenQuestion holds

Textmetadata
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.