Abstract
Face perception is a critical social ability and identifying its neural correlates is important from both basic and applied perspectives. In EEG recordings, faces elicit a distinct electrophysiological signature, the N170, which has a larger amplitude and shorter latency in response to faces compared to other objects. However, determining the face specificity of any neural marker for face perception hinges on finding an appropriate control stimulus. We used a novel stimulus set consisting of 300 images that spanned a continuum between random patches of natural scenes and genuine faces, in order to explore the selectivity of face-sensitive ERP responses with a model-based parametric stimulus set. Critically, our database contained "false alarm" images that were misclassified as face by computational face-detection system and varied in their image-level similarity to real faces. High-density (128-channel) event-related potentials (ERPs) were recorded while 23 adult subjects viewed all 300 images in random order, and determined whether each image was a face or non-face. The goal of our analyses was to determine the extent to which a gradient of sensitivity to face-like structure was evident in the ERP signal. Traditional waveform analyses revealed that the N170 component over occipitotemporal electrodes was larger in amplitude for faces compared to all non-faces, even those that were high in image similarity to faces, suggesting strict selectivity for veridical face stimuli. By contrast, single-trial classification of the entire waveform measured at the same sensors revealed that misclassifications of non-face patterns as faces increased with image-level similarity to faces. These results suggest that individual components may exhibit steep selectivity, but integration of multiple waveform features may afford graded information regarding stimulus appearance.