Evidence map›Paper›PMID 42053250›Full record

ArticlePerception2026

Recognition of real and artificial intelligence-generated faces.

Nicole Sulimani, Alexis van Hunenstijn, Andrea Albonico

Abstract read
In one paragraph

Article in Perception, 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.

Nicole SulimaniDepartment of Psychology, University of the Fraser Valley, Abbotsford, Canada.
Alexis van HunenstijnDepartment of Psychology, University of the Fraser Valley, Abbotsford, Canada.
Andrea AlbonicoDepartment of Psychology, University of the Fraser Valley, Abbotsford, Canada.ORCID 0000-0003-1975-0824

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is changing society in major ways. AI can create content that entertains, but it can also mislead people. One part of this change is the ability to produce human-like AI content, including voices and faces. In psychology, AI-generated faces have started to be used to study face perception, thanks to their flexibility and convenience. This choice appears to be supported by recent evidence indicating that AI-generated faces are indistinguishable from human faces. However, other findings suggest that AI- or computer-generated faces may be evaluated differently than real faces, with AI-generated faces even leading to hyperrealism. To address this, we conducted a study to investigate whether human and AI-generated faces are recognized similarly in a memory task, and whether their recognition is related to our evaluation of these faces. Results show that recognition accuracy was significantly higher for AI faces than for real faces. Critically, classification (i.e., the ability to correctly classify a face as human or AI-generated) accuracy was significantly better than chance level for both real and AI-generated faces, with no difference between the two. These results suggest that humans can correctly discriminate AI-generated and human faces, and that AI-faces might be recognized better than human ones, suggesting that AI-generated faces might engage human perceptual and memory systems differently from real faces. However, recognition and classification accuracy were not significantly correlated, suggesting that participants might be partially unaware of their increased performance with AI-generated faces.

Indexed as

Artificial IntelligenceFacial RecognitionRecognition, PsychologyAdultFemaleGenerative Artificial IntelligenceHumansMaleYoung Adultartificial intelligencecovert recognitionFace recognitiongenerative AIresponse bias

Identifiers

PMID42053250
PMCPMC13578642

What OpenQuestion holds

Textmetadata
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.