Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.

ABSTRACT: The human visual system possesses a remarkable ability to detect and process faces across diverse contexts, including the phenomenon of face pareidolia--seeing faces in inanimate objects. Despite extensive research, it remains unclear why the visual system employs such broadly tuned face detection capabilities. We hypothesized that face pareidolia results from the visual system's optimization for recognizing both faces and objects. To test this hypothesis, we used task-optimized deep convolutional neural networks (CNNs) and evaluated their alignment with human behavioral signatures and neural responses, measured via magnetoencephalography (MEG), related to pareidolia processing. Specifically, we trained CNNs on tasks involving combinations of face identification, face detection, object cat

SUBMITTER: Gupta P 

PROVIDER: S-EPMC11790231 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature