Face-likeness and image variability drive responses in human face-selective ventral regions.

Abstract

The human ventral visual stream contains regions that respond selectively to faces over objects. However, it is unknown whether responses in these regions correlate with how face-like stimuli appear. Here, we use parameterized face silhouettes to manipulate the perceived face-likeness of stimuli and measure responses in face- and object-selective ventral regions with high-resolution fMRI. We first use "concentric hyper-sphere" (CH) sampling to define face silhouettes at different distances from the prototype face. Observers rate the stimuli as progressively more face-like the closer they are to the prototype face. Paradoxically, responses in both face- and object-selective regions decrease as face-likeness ratings increase. Because CH sampling produces blocks of stimuli whose variability is negatively correlated with face-likeness, this effect may be driven by more adaptation during high face-likeness (low-variability) blocks than during low face-likeness (high-variability) blocks. We tested this hypothesis by measuring responses to matched-variability (MV) blocks of stimuli with similar face-likeness ratings as with CH sampling. Critically, under MV sampling, we find a face-specific effect: responses in face-selective regions gradually increase with perceived face-likeness, but responses in object-selective regions are unchanged. Our studies provide novel evidence that face-selective responses correlate with the perceived face-likeness of stimuli, but this effect is revealed only when image variability is controlled across conditions. Finally, our data show that variability is a powerful factor that drives responses across the ventral stream. This indicates that controlling variability across conditions should be a critical tool in future neuroimaging studies of face and object representation.