ArticlePerception2025
Face to face: Comparing ChatGPT with human performance on face matching.
Article in Perception, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- AI-generated images of familiar faces are indistinguishable from real photographs.Cognitive research: principles and implications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ChatGPT's large language model, GPT-4V, has been trained on vast numbers of image-text pairs and is therefore capable of processing visual input. This model operates very differently from current state-of-the-art neural networks designed specifically for face perception and so I chose to investigate whether ChatGPT could also be applied to this domain. With this aim, I focussed on the task of face matching, that is, deciding whether two photographs showed the same person or not. Across six different tests, ChatGPT demonstrated performance that was comparable with human accuracies despite being a domain-general 'virtual assistant' rather than a specialised tool for face processing. This perhaps surprising result identifies a new avenue for exploration in this field, while further research should explore the boundaries of ChatGPT's ability, along with how its errors may relate to those made by humans.
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Registered trials
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.