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Investigating the cognitive and neural mechanisms involved with faceprocessing is a fundamental task in modern neuroscience and psychology. Todate, the majority of such studies have focused on the use of pre-selectedstimuli. The absence of personalized stimuli presents a serious limitation asit fails to account for how each individual face processing system is tuned tocultural embeddings or how it is disrupted in disease. In this work, we proposea novel framework which combines generative adversarial networks (GANs) withBayesian optimization to identify individual response patterns to manydifferent faces. Formally, we employ Bayesian optimization to efficientlysearch the latent space of state-of-the-art GAN models, with the aim toautomatically generate novel faces, to maximize an individual subject'sresponse. We present results from a web-based proof-of-principle study, whereparticipants rated images of themselves generated via performing Bayesianoptimization over the latent space of a GAN. We show how the algorithm canefficiently locate an individual's optimal face while mapping out theirresponse across different semantic transformations of a face; inter-individualanalyses suggest how the approach can provide rich information about individualdifferences in face processing.

Bayesian optimization for automatic design of face stimuli