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.

本研究提出了一种新的框架将生成对抗网络和贝叶斯优化结合，以识别个体响应模式，研究面孔加工的认知和神经机制，通过网络中寻找潜在空间以有效地生成新面孔，给出了一个基于网络的原型实验，揭示了算法如何在不同语义转换的面孔上映射人们的响应，以及如何提供有关个体差异的丰富信息。

自动设计面孔刺激的贝叶斯优化