What aspects of facial information do we use to recognize individuals? One way to address this fundamental question is to study image transformations that compromise facial recognizability. The goal would be to identify factors that underlie the recognition decrement and, by extension, are likely constituents of facial encoding. To this end, we focus here on the contrast negation transformation. Contrast negated faces are remarkably difficult to recognize for reasons that are currently unclear. The dominant proposals so far are based either on negative faces' seemingly unusual pigmentation, or incorrectly computed 3D shape. Both of these explanations have been challenged by recent results. Here, we propose an alternative account based on 2D ordinal relationships, which encode local contrast polarity between a few regions of the face. Using a novel set of facial stimuli that incorporate both positive and negative contrast, we demonstrate that ordinal relationships around the eyes are major determinants of facial recognizability. Our behavioral studies suggest that destruction of these relationships in negatives likely underlies the observed recognition impairments, and our neuro-imaging data show that these relationships strongly modulate brain responses to facial images. Besides offering a potential explanation for why negative faces are hard to recognize, these results have implications for the representational vocabulary the visual system uses to encode faces.contrast negation ͉ face perception ͉ fMRI ͉ neural representation ͉ object recognition I n principle, a contrast negated image is exactly as informative as its positive counterpart; negation perfectly preserves an image's 2D geometric and spectral structure. However, as anyone who has had to search through a roll of negatives for a snapshot of a particular person knows, this simple operation has dramatically adverse consequences on our ability to identify faces, as illustrated in Fig. 1 (1-6). Exploring the causes of this phenomenon is important for understanding the broader issue of the nature of information the visual system uses for face identification.Several researchers have hypothesized that negated face-images are hard to recognize because of the unnatural shading cues in negatives, which compromise shape from shading processes (7-11). The resulting problems in recovering veridical 3D facial shape are believed to impair recognition performance. Although plausible, it is unclear whether this explanation is a sufficient one, especially in light of experimental results showing preserved recognition performance in the absence of shading gradients (12), and theories of face recognition that are based on the use of 2D intensity patterns rather than recovered 3D shapes (13,14). Another prominent hypothesis is that negation causes faces to have unusual pigmentation (15, 16). However, the adequacy of this ''pigmentation hypothesis'' has been challenged by data showing that hue negation, which also results in unnatural pigmentation (making the entire face l...

Understanding how the human visual system recognizes objects is one of the key challenges in neuroscience. Inspired by a large body of physiological evidence, a general class of recognition models has emerged, which is based on a hierarchical organization of visual processing, with succeeding stages being sensitive to image features of increasing complexity. However, these models appear to be incompatible with some well-known psychophysical results. Prominent among these are experiments investigating recognition impairments caused by vertical inversion of images, especially those of faces. It has been reported that faces that differ 'featurally' are much easier to distinguish when inverted than those that differ 'conf igurally'; a f inding that is difficult to reconcile with the physiological models. Here, we show that after controlling for subjects' expectations, there is no difference between 'featurally' and 'conf igurally' transformed faces in terms of inversion effect. This result reinforces the plausibility of simple hierarchical models of object representation and recognition in the cortex.

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