人脸刺激自动设计的贝叶斯优化
Bayesian optimization for automatic design of face stimuli
摘要：
研究面孔加工的认知和神经机制是现代神经科学和心理学的一项基本任务。迄今为止，大多数此类研究都集中在使用预先选定的刺激。个性化刺激的缺乏带来了严重的局限性，因为它无法解释每个人的面部处理系统是如何适应文化嵌入的，或者它是如何在疾病中被破坏的。在这项工作中，我们提出了一个新的框架，该框架将生成对抗网络（GANs）与贝叶斯优化相结合，以识别对许多不同面孔的个体反应模式。形式上，我们采用贝叶斯优化来有效地搜索最先进的GAN模型的潜在空间，目的是自动生成新的面孔，以最大限度地提高个体受试者的响应。我们提出了基于网络的原理证明研究的结果，参与者通过对GAN的潜在空间执行贝叶斯优化来对自己的图像进行评级。我们展示了该算法如何有效地定位一个人的最佳面孔，同时映射出他们的反应在不同的语义转换的脸;个体间的分析表明，该方法可以提供丰富的信息，在面部处理的个体差异。
英文摘要：
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.
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