The Fusiform Face Area responds automatically to statistical regularities optimal for face categorization

Abstract

Statistical regularities pervade our perceptual world. Assuming that the human brain is tuned for satisfying the constraints of the visual environment, visual system computations should be optimized for processing such regularities. A socially relevant and highly recurrent homogenous pattern for which the brain has developed sensitivity is certainly the human face. Yet, for which statistical regularities the face sensitive regions are tuned for, and to what extent their detection occurs automatically is largely unexplored. Using fMRI we measured activations within the face sensitive areas for nonface symmetrical and asymmetrical curvilinear patterns with either more high-contrast elements in the upper or in the lower part. Faceness evaluation performed outside of the scanner showed that these patterns were not perceived as schematic faces. Noticeably, symmetry violations disrupted perception of faceness, despite objective image similarity measures showing high faceness values for those patterns. Among the faces sensitive regions, only the right Fusiform Face Area (FFA) showed sensitivity to symmetry. This region showed also greater responses to patterns with more elements in the upper part. Critically, the FFA's responses were more strongly correlated with the physical objective faceness properties of the stimuli than the perceived subjective faceness ratings of the observers. These findings provide direct evidence that the neural computations of the right FFA are tuned to curvilinear symmetrical patterns with high-contrasted elements in the upper part, which fit best with the physical structure of human faces. Such low-level geometrical regularities might be used by the FFA to automatically categorize visual shapes as faces.

Is there a genuine advantage to the upper part of words during lexical access? Evidence from the Stroop task

“…When this factor is controlled, the apparent bias for the upper part of words vanishes completely, as we revealed in Experiment 2. Hence, the bias for the upper part of objects that occurs in the visual perception of particular features in faces (e.g., Caldara & Seghier, 2009) and objects (Thomas & Elias, 2011) does not seem to affect the automatic detection of letter features during visual-word recognition.…”

Event‐related potential and functional MRI measures of face‐selectivity are highly correlated: A simultaneous ERP‐fMRI investigation

“…The right hemisphere has been long considered predominant for human face recognition [Levy et al, 1972;Rhodes, 1985]. The asymmetrical processing of human faces is often attributed to the role played by the right hemisphere in global or holistic processing [Jiang et al, 2009; as well as its sensitivity to specific stimulus regularities that characterize human faces [Caldara and Seghier, 2009]. This right hemisphere dominance is further supported by patient studies showing that deprivation of visual information to the right hemisphere results in face recognition impairment, but not so in left hemisphere deprivation [Le Grand et al, 2003] and that acquired prosopagnosia is most often caused by lesions to the right (or bilateral) cortex [Barton et al, 2002;De Renzi, 1997;Rossion, 2008;.…”

Looking away from faces: Influence of high-level visual processes on saccade programming

“…Recent neuroimaging findings have shown that brain regions dedicated to face processing are tuned to process intrinsic visual regularities present in human faces (i.e., a topheavy vertical bias constituted by the eyes, eyebrows, hairs toward the mouth). Neurons in the right middle fusiform gyrus respond to non-face curvilinear shapes containing more high-contrast elements in the upper compared to the lower part (Caldara & Seghier, 2009;Caldara et al, 2006), indicating that such low-level global properties might be used by those neurons to automatically categorize visual shapes as human faces.…”