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Updated: Feb 20, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
Published on: June 3, 2013
Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it
James D Dunn1, David White1, Clare A M Sutherland2,3
1School of Psychology, UNSW Sydney, Sydney, New South Wales, Australia.
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.
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