Individual differences in classification images of Mooney faces.

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.