Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands.
Cereb Cortex. 2011 Dec;21(12):2893-9. doi: 10.1093/cercor/bhr093. Epub 2011 May 12.
The identification of a face comprises processing of both visual features and conceptual knowledge. Studies showing that the fusiform face area (FFA) is sensitive to face identity generally neglect this dissociation. The present study is the first that isolates conceptual face processing by using words presented in a person context instead of faces. The design consisted of 2 different conditions. In one condition, participants were presented with blocks of words related to each other at the categorical level (e.g., brands of cars, European cities). The second condition consisted of blocks of words linked to the personality features of a specific face. Both conditions were created from the same 8 × 8 word matrix, thereby controlling for visual input across conditions. Univariate statistical contrasts did not yield any significant differences between the 2 conditions in FFA. However, a machine learning classification algorithm was able to successfully learn the functional relationship between the 2 contexts and their underlying response patterns in FFA, suggesting that these activation patterns can code for different semantic contexts. These results suggest that the level of processing in FFA goes beyond facial features. This has strong implications for the debate about the role of FFA in face identification.
面孔识别包括视觉特征和概念知识的处理。表明梭状回面孔区（FFA）对面孔身份敏感的研究通常忽略了这种分离。本研究首次使用以人物语境呈现的单词而不是面孔来分离概念性面孔处理。该设计包括 2 种不同的条件。在一种条件下，参与者呈现与类别水平相关的单词块（例如，汽车品牌，欧洲城市）。第二种条件由与特定面孔的个性特征相关的单词块组成。这两种条件都是从相同的 8×8 单词矩阵创建的，从而控制了跨条件的视觉输入。单变量统计对比在 FFA 中没有发现这两种条件之间有任何显著差异。然而，机器学习分类算法能够成功地学习这两种语境及其在 FFA 中的潜在反应模式之间的功能关系，这表明这些激活模式可以为不同的语义语境编码。这些结果表明，FFA 的处理水平超出了面部特征。这对关于 FFA 在面孔识别中的作用的争论具有重要意义。
