Vision Science Graduate Group, University of California, Berkeley, Berkeley, CA, USA.
Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
J Vis. 2022 Dec 1;22(13):3. doi: 10.1167/jov.22.13.3.
Human face recognition is robust even under conditions of extreme lighting and in situations where there is high noise and uncertainty. Mooney faces are a canonical example of this: Mooney faces are two-tone shadow-defined images that are readily and holistically recognized despite lacking easily segmented face features. Face perception in such impoverished situations-and Mooney face perception in particular-is often thought to be supported by comparing encountered faces to stored templates. Here, we used a classification image approach to measure the templates that observers use to recognize Mooney faces. Visualizing these templates reveals the regions and structures of the image that best predict individual observer recognition, and they reflect the underlying internal representation of faces. Using this approach, we tested whether there are classification images that are consistent from session to session, whether the classification images are observer-specific, and whether they allow for pattern completion of holistic representations even in the absence of an underlying signal. We found that classification images of Mooney faces were indeed non-random (i.e., consistent session from session) within each observer, but they were different between observers. This result is in line with previously proposed existence of face templates that support face recognition, and further suggests that these templates may be unique to each observer and could drive idiosyncratic individual differences in holistic face recognition. Moreover, we found classification images that reflected information within the blank regions of the original Mooney faces, suggesting that observers may fill in missing information using idiosyncratic internal information about faces.
人脸识别在极端光照条件下以及存在高噪声和不确定性的情况下也具有很强的鲁棒性。Mooney 面孔就是一个典型的例子：Mooney 面孔是由双色阴影定义的图像，尽管缺乏易于分割的面部特征，但它们很容易被整体识别。在这种贫困的情况下进行面部感知——尤其是 Mooney 面孔感知——通常被认为是通过将遇到的面孔与存储的模板进行比较来支持的。在这里，我们使用分类图像方法来测量观察者用于识别 Mooney 面孔的模板。可视化这些模板揭示了最佳预测个体观察者识别的图像区域和结构，并且它们反映了面孔的基础内部表示。使用这种方法，我们测试了是否存在从一个会话到另一个会话都一致的分类图像，是否分类图像是观察者特有的，以及它们是否允许即使在没有基础信号的情况下完成整体表示的模式完成。我们发现，Mooney 面孔的分类图像在每个观察者内确实是无随机的（即，会话之间一致），但在观察者之间是不同的。这一结果与先前提出的支持面部识别的面部模板的存在一致，并进一步表明这些模板可能对每个观察者都是独特的，并且可能导致整体面部识别的特殊个体差异。此外，我们发现分类图像反映了原始 Mooney 面孔空白区域内的信息，这表明观察者可能使用关于面孔的特殊内部信息来填补缺失的信息。
