Experience Alone Can Generate Human Face Specialization: Evidence From Deep Learning Models.

Overview

1. School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel
2. Sagol School of Brain Sciences, Tel Aviv University, Tel Aviv, Israel

Abstract

The specialization of human face recognition for upright own-race faces is well-established. While experience is thought to play a key role in face specialization, establishing its direct causal contribution in humans is difficult, as natural experience cannot be systematically controlled. Recent advances in deep learning algorithms offer a solution: these algorithms were shown to generate human-like face specialization effects, including the face inversion and other-race effects. Critically, deep neural networks allow precise manipulation of their training experience, allowing us to test its sole contribution to human-like face expertise in artificial systems. In the present study, we systematically manipulated the amount of face experience provided to deep neural networks and examined its effect on the face inversion, the other-race and other-age effects. Mirroring human development, the magnitude of the other-group and face inversion effects increased with greater own-group upright face experience. These effects were primarily driven by a steep improvement in recognition of upright, own-group faces, with much shallower gains for other-group or inverted faces. These findings demonstrate that increased exposure to upright own-group faces selectively improves performance for this category, establishing that experience alone is sufficient to produce human-like face specialization effects in artificial systems.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data availability statement

The AUC values and optimal-threshold accuracy scores analyzed in this study are available on the Open Science Framework (OSF) at https://osf.io/htmaq/?view_only=4d05a21142af4782abfef2b052912f3d. Training code and analysis scripts are available upon request from the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 4 keywords, 1 funder, 63 references.

Cite

This paper

Guy, N., Rosemblaum, M., & Yovel, G. (2026). Experience Alone Can Generate Human Face Specialization: Evidence From Deep Learning Models. Open mind : discoveries in cognitive science, 10, 908-922. https://doi.org/10.1162/opmi.a.363

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