GPT-4o reads the mind in the eyes
Humans possess a sophisticated ability to read the mind in the eyes of other people. Here we tested whether this ability is also present in GPT-4o, a multimodal LLM. Using two versions of a widely used theory of mind test, the Reading the Mind in Eyes Test and the Multiracial Reading the Mind in the Eyes Test, we found that GPT-4o outperformed humans in extracting mentalistic information from upright faces but underperformed humans when faces were inverted. GPT-4o errors were not random but revealed a highly consistent, yet incorrect, processing of mental-state information across trials, with an orientation-dependent error structure that qualitatively differed from that of humans for inverted faces but not for upright faces. These findings highlight how advanced mental state inference abilities coexist in GPT-4o alongside substantial differences in information processing compared to humans. • GPT-4o surpasses humans in reading mental states from the eye-region. • Face inversion severely reduces GPT-4o’s mindreading. • Unlike human errors, GPT-4o’ errors are not random. • Error analysis reveals GPT-4o’s highly structured error space.
Authors
- Chunzhi Yi (ORCID: https://orcid.org/0000-0002-4180-1109)
- Cristina Becchio (ORCID: https://orcid.org/0000-0002-6845-0521)
- Eugenio Scaliti (ORCID: https://orcid.org/0000-0002-4977-2197)
- Marco Celotto (ORCID: https://orcid.org/0000-0002-0890-0703)
- Guido Manzi
- Oriana Pansardi (ORCID: https://orcid.org/0000-0001-6092-1889)
- Alessandro Rufo (ORCID: https://orcid.org/0009-0003-8565-4192)
- Krati Saxena
- Stefano Panzeri
- Fabio Manzi
- James W.A. Strachan
- Michael S.A. Graziano
Institutions
- Universidad de Las Palmas de Gran Canaria (ES)
- Universität Hamburg (DE)
- Princeton University (US)
- Harbin Institute of Technology (CN)
- Italian Institute of Technology (IT)
- University Medical Center Hamburg-Eppendorf (DE)
- Heilongjiang Institute of Technology (CN)
- University of Turin (IT)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Computers in Human Behavior
- Published
- 2026-03-14
- DOI
- https://doi.org/10.1016/j.chb.2026.108992
- Primary Topic
- Face Recognition and Perception
- Type
- article
- Field-Weighted Citation Impact
- 0.00