Evidence map›Paper›PMID 41705896›Full record

ArticleBritish journal of psychology (London, England : 1953)2026

Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it.

James D Dunn, David White, Clare A M Sutherland, Elizabeth J Miller, Ben A Steward, Amy Dawel

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of psychology (London, England : 1953), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Training humans to detect AI-generated faces.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. 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

6 authors.

James D DunnSchool of Psychology, UNSW Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-6964-909X
David WhiteSchool of Psychology, UNSW Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-6366-2699
Clare A M SutherlandSchool of Psychology, King's College, University of Aberdeen, Aberdeen, UK.ORCID https://orcid.org/0000-0003-0443-3412
Elizabeth J MillerSchool of Medicine and Psychology, The Australian National University, Canberra, Australian Capital Territory, Australia.ORCID https://orcid.org/0000-0003-2572-6134
Ben A StewardSchool of Medicine and Psychology, The Australian National University, Canberra, Australian Capital Territory, Australia.ORCID https://orcid.org/0000-0002-7517-9215
Amy DawelSchool of Medicine and Psychology, The Australian National University, Canberra, Australian Capital Territory, Australia.ORCID https://orcid.org/0000-0001-6668-3121

Funding

Australian Research CouncilExperimental Psychology Society
6 · The paper itself

Abstract

The AI revolution has produced synthetic faces that often appear more human than photos of real people. We tested whether individual differences in human face recognition ability explain variation in discriminating AI from real faces. Super-recognizers - people with exceptional ability to recognize human faces (N = 36) - outperformed a typical sample by 15% and by 7% compared to a group of higher performing, motivated control participants (Cohen's d = 0.55; N = 89). Individual difference analysis revealed that this pattern reflected a positive association between human face recognition and AI face discrimination abilities. AI discrimination ability was also associated with individuals' sensitivity to the 'hyper-average' appearance of AI faces. Deep neural networks optimized for face identity processing confirmed a more central distribution of AI faces in face-space. Moreover, centrality was associated with a higher probability of super-recognizers judging the faces as AI, but this pattern was not observed for controls. Super-recognizers' correct interpretation of hyper-averageness as a cue to artificiality constitutes the first mechanistic link between evolved expertise in face processing and AI face detection and addresses a common misconception regarding the structure of human face space.

Indexed as

Artificial IntelligenceFacial RecognitionIndividualityRecognition, PsychologyAdultFemaleGenerative Adversarial NetworksGenerative Artificial IntelligenceHumansMaleYoung Adultartificial intelligenceface perceptiongenerative adversarial networkindividual differencesStyleGAN

Identifiers

PMID41705896
PMCPMC13655941

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.