Division of Psychology, Department of Life Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York 10027.
J Neurosci. 2021 Mar 3;41(9):1952-1969. doi: 10.1523/JNEUROSCI.1449-20.2020. Epub 2021 Jan 15.
Faces of different people elicit distinct fMRI patterns in several face-selective regions of the human brain. Here we used representational similarity analysis to investigate what type of identity-distinguishing information is encoded in three face-selective regions: fusiform face area (FFA), occipital face area (OFA), and posterior superior temporal sulcus (pSTS). In a sample of 30 human participants (22 females, 8 males), we used fMRI to measure brain activity patterns elicited by naturalistic videos of famous face identities, and compared their representational distances in each region with models of the differences between identities. We built diverse candidate models, ranging from low-level image-computable properties (pixel-wise, GIST, and Gabor-Jet dissimilarities), through higher-level image-computable descriptions (OpenFace deep neural network, trained to cluster faces by identity), to complex human-rated properties (perceived similarity, social traits, and gender). We found marked differences in the information represented by the FFA and OFA. Dissimilarities between face identities in FFA were accounted for by differences in perceived similarity, Social Traits, Gender, and by the OpenFace network. In contrast, representational distances in OFA were mainly driven by differences in low-level image-based properties (pixel-wise and Gabor-Jet dissimilarities). Our results suggest that, although FFA and OFA can both discriminate between identities, the FFA representation is further removed from the image, encoding higher-level perceptual and social face information. Recent studies using fMRI have shown that several face-responsive brain regions can distinguish between different face identities. It is however unclear whether these different face-responsive regions distinguish between identities in similar or different ways. We used representational similarity analysis to investigate the computations within three brain regions in response to naturalistically varying videos of face identities. Our results revealed that two regions, the fusiform face area and the occipital face area, encode distinct identity information about faces. Although identity can be decoded from both regions, identity representations in fusiform face area primarily contained information about social traits, gender, and high-level visual features, whereas occipital face area primarily represented lower-level image features.
不同人的面孔在人类大脑的几个面孔选择性区域中会引起不同的 fMRI 模式。在这里,我们使用表示相似性分析来研究三个面孔选择性区域(梭状回面孔区(FFA)、枕叶面孔区(OFA)和后上颞沟(pSTS))中编码的身份区分信息的类型。在一个由 30 名人类参与者(22 名女性,8 名男性)组成的样本中,我们使用 fMRI 测量了由著名面孔身份的自然视频引发的大脑活动模式,并将每个区域中的表示距离与身份之间差异的模型进行了比较。我们构建了各种候选模型,范围从低水平的图像可计算属性(像素、GIST 和 Gabor-Jet 差异),到更高水平的图像可计算描述(OpenFace 深度神经网络,经过训练可按身份对人脸进行聚类),再到复杂的人类评分属性(感知相似性、社会特征和性别)。我们发现 FFA 和 OFA 所代表的信息存在明显差异。FFA 中面孔身份的差异由感知相似性、社会特征、性别以及 OpenFace 网络的差异来解释。相比之下,OFA 中的表示距离主要由基于图像的低级属性(像素和 Gabor-Jet 差异)的差异驱动。我们的结果表明,尽管 FFA 和 OFA 都可以区分身份,但 FFA 的表示形式与图像进一步分离,编码了更高层次的感知和社会面孔信息。最近使用 fMRI 的研究表明,几个面孔反应性脑区可以区分不同的面孔身份。然而,尚不清楚这些不同的面孔反应性区域是否以相似或不同的方式区分身份。我们使用表示相似性分析来研究三个大脑区域对自然变化的面孔身份视频的反应中的计算。我们的结果表明,两个区域,即梭状回面孔区和枕叶面孔区,对面孔的身份信息进行了区分。虽然可以从两个区域解码身份,但梭状回面孔区的身份表示主要包含社会特征、性别和高级视觉特征的信息,而枕叶面孔区主要表示较低级别的图像特征。