Abstract
Face recognition is supported by selective neural mechanisms that are sensitive to various aspects of facial appearance. These include event-related potential (ERP) components like the P100 and the N170 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 extrastriate cortex and determine what critical properties an image must possess to be considered face-like. Robot faces are a particularly interesting stimulus class to examine 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 the current study, we thus chose to investigate how ERP responses to robot faces may differ from the response to human faces and non-face objects. In two experiments, we examined how the P100 and N170 responded to human faces, robot faces, and non-face objects (clocks). In Experiment 1, we found that robot faces elicit intermediate responses from face-sensitive components relative to non-face objects (clocks) and both real human faces and artificial human faces (computer-generated faces and dolls). These results suggest that while human-like inanimate faces (CG faces and dolls) are processed much like real faces, robot faces are dissimilar enough to human faces to be processed differently. In Experiment 2 we found that the face inversion effect was only partly evident in robot faces. 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.
Highlights
Face recognition is supported by neural mechanisms that are selective for different aspects of face structure
An important property of face-sensitive neural responses to consider is their generality: If event-related potential (ERP) components like the P100 and N170 are large for face images, how broad is the class of face images that will elicit a strong response? By presenting stimuli that possess or lack critical visual features or that belong to varying categories we may define the scope of face processing with regard to a specific neural response
We find differential effects at the P100 and N170 that suggest that the status of robot faces as faces may vary depending on what stage of processing we consider
Summary
Face recognition is supported by neural mechanisms that are selective for different aspects of face structure. Paras and W ebster[15] found that while placing symmetric eye spots in 1/f “totem pole” images elicited pareidolia, this was not typically sufficient to elicit a robust N170 response This kind of demonstration of what does and does not allow an image to cross the boundary from face to non-face offers useful information about the nature of face tuning at specific stages of processing. In terms of the N170, robot faces elicit an intermediate response that suggests they tend to share some critical features with human faces, and lack some important image structure that the N170 is tuned to. We close by discussing the potential for more targeted analysis of critical image features that may lead robot faces to look more-or-less face-like, and the possibility that social interactions between robots and humans may affect the status of robot faces within the extended face network
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