Not quite human, not quite machine: Electrophysiological responses to robot faces.

Abstract

Face recognition is supported by selective neural mechanisms that are sensitive to various aspects of facial appearance. These include ERP components like the P100, N170, and P200 which exhibit different patterns of selectivity for various aspects of facial appearance. Examining the boundary between faces and non-faces using these responses is one way to develop a more robust understanding of the representation of faces in visual cortex and determine what critical properties an image must possess to be considered face-like. Here, we probe this boundary by examining how face-sensitive ERP components respond to robot faces. Robot faces are an interesting stimulus class because they can differ markedly from human faces in terms of shape, surface properties, and the configuration of facial features, but are also interpreted as social agents in a range of settings. In two experiments, we examined how the P100 and N170 responded to human faces, robot faces, and non-face objects (clocks). We found that robot faces elicit intermediate responses from face-sensitive components relative to non-face objects and both real and artificial human faces (Exp. 1), and also that the face inversion effect was only partly evident in robot faces (Exp. 2). We conclude that robot faces are an intermediate stimulus class that offers insight into the perceptual and cognitive factors that affect how social agents are identified and categorized.