  Investigating the cognitive and neural mechanisms involved with face
processing is a fundamental task in modern neuroscience and psychology. To
date, the majority of such studies have focused on the use of pre-selected
stimuli. The absence of personalized stimuli presents a serious limitation as
it fails to account for how each individual face processing system is tuned to
cultural embeddings or how it is disrupted in disease. In this work, we propose
a novel framework which combines generative adversarial networks (GANs) with
Bayesian optimization to identify individual response patterns to many
different faces. Formally, we employ Bayesian optimization to efficiently
search the latent space of state-of-the-art GAN models, with the aim to
automatically generate novel faces, to maximize an individual subject's
response. We present results from a web-based proof-of-principle study, where
participants rated images of themselves generated via performing Bayesian
optimization over the latent space of a GAN. We show how the algorithm can
efficiently locate an individual's optimal face while mapping out their
response across different semantic transformations of a face; inter-individual
analyses suggest how the approach can provide rich information about individual
differences in face processing.
