Bayesian optimization for automatic design of face stimuli

TL;DR

This paper introduces a novel framework combining GANs and Bayesian optimization to generate personalized face stimuli, enabling efficient identification of individual response patterns and advancing understanding of face processing variability.

Contribution

The work presents a new method that uses Bayesian optimization over GAN latent spaces to automatically generate personalized face stimuli for individual response analysis.

Findings

Efficiently locates individuals' optimal face stimuli.

Maps responses across semantic face transformations.

Reveals individual differences in face processing.

Abstract

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.