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

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.