Part of the ventral temporal lobe is thought to be critical for face perception, but what determines this specialization remains unknown. We present evidence that expertise recruits the fusiform gyrus 'face area'. Functional magnetic resonance imaging (fMRI) was used to measure changes associated with increasing expertise in brain areas selected for their face preference. Acquisition of expertise with novel objects (greebles) led to increased activation in the right hemisphere face areas for matching of upright greebles as compared to matching inverted greebles. The same areas were also more activated in experts than in novices during passive viewing of greebles. Expertise seems to be one factor that leads to specialization in the face area.

Sensitivity to configural changes in face processing has been cited as evidence for face-exclusive mechanisms. Alternatively, general mechanisms could be fine-tuned by experience with homogeneous stimuli. We tested sensitivity to configural transformations for novices and experts with nonface stimuli ("Greebles"). Parts of transformed Greebles were identified via forced-choice recognition. Regardless of expertise level, the recognition of parts in the Studied configuration was better than in isolation, suggesting an object advantage. For experts, recognizing Greeble parts in a Transformed configuration was slower than in the Studied configuration, but only at upright. Thus, expertise with visually similar objects, not faces per se, may produce configural sensitivity.

How do we recognize objects despite differences in their retinal projections when they are seen at different orientations? Marr and Nishihara (1978) proposed that shapes are represented in memory as structural descriptions in objectcentered coordinate systems, so that an object is represented identically regardless of its orientation. An alternative hypothesis is that an object is represented in memory in a single representation corresponding to a canonical orientation, and a mental rotation operation transforms an input shape into that orientation before input and memory are compared. A third possibility is that shapes are stored in a set of representations, each corresponding to a different orientation. In four experiments, subjects studied several objects each at a single orientation, and were given extensive practice at naming them quickly, or at classifying them as normal or mirror-reversed, at several orientations. At first, response times increased with departure from the study orientation, with a slope similar to those obtained in classic mental rotation experiments. This suggests that subjects made both judgments by mentally transforming the orientation of the input shape to the one they had initially studied. With practice, subjects recognized the objects almost equally quickly at all the familiar orientations. At that point they were probed with the same objects appearing at novel orientations. Response times for these probes increased with increasing disparity from the previously trained orientations. This indicates that subjects had stored representations of the shapes at each of the practice orientations and recognized shapes at the new orientations by rotating them to one of the stored orientations. The results are consistent with a hybrid of the second (mental transformation) and third (multiple view) hypotheses of shape recognition: input shapes are transformed to a stored view, either the one at the nearest orientation or one at a canonical orientation. Interestingly, when mirrorimages of trained shapes were presented for naming, subjects took the same time at all orientations. This suggests that mental transformations of orientation can take the shortest path of rotation that will align an input shape and its memorized counterpart, in this case a rotation in depth about an axis in the picture plane.

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According to modular models of cortical organization, many areas of the extrastriate cortex are dedicated to object categories. These models often assume an early processing stage for the detection of category membership. Can functional imaging isolate areas responsible for detection of members of a category, such as faces or letters? We consider whether responses in three different areas (two selective for faces and one selective for letters) support category detection. Activity in these areas habituates to the repeated presentation of one exemplar more than to the presentation of different exemplars of the same category, but only for the category for which the area is selective. Thus, these areas appear to play computational roles more complex than detection, processing stimuli at the individual level. Drawing from prior work, we suggest that face-selective areas may be involved in the perception of faces at the individual level, whereas letter-selective regions may be tuning themselves to font information in order to recognize letters more efficiently.

Behavioral studies have shown that picture-plane inversion impacts face and object recognition differently, thereby suggesting face-specific processing mechanisms in the human brain. Here we used event-related potentials to investigate the time course of this behavioral inversion effect in both faces and novel objects. ERPs were recorded for 14 subjects presented with upright and inverted visual categories, including human faces and novel objects (Greebles). A N170 was obtained for all categories of stimuli, including Greebles. However, only inverted faces delayed and enhanced N170 (bilaterally). These observations indicate that the N170 is not specific to faces, as has been previously claimed. In addition, the amplitude difference between faces and objects does not reflect face-specific mechanisms since it can be smaller than between non-face object categories. There do exist some early differences in the time-course of categorization for faces and non-faces across inversion. This may be attributed either to stimulus category per se (e.g. face-specific mechanisms) or to differences in the level of expertise between these categories.

Successful object recognition is essential for finding food, identifying kin, and avoiding danger, as well as many other adaptive behaviors. To accomplish this feat, the visual system must reconstruct 3-D interpretations from 2-D "snapshots" falling on the retina. Theories of recognition address this process by focusing on the question of how object representations are encoded with respect to viewpoint. Although empirical evidence has been equivocal on this question, a growing body of surprising results, including those obtained in the experiments presented in this case study, indicates that recognition is often viewpoint dependent. Such findings reveal a prominent role for viewpointdependent mechanisms and provide support for the multiple-views approach, in which objects are encoded as a set of view-specific representations that are matched to percepts using normalization procedures.Just as you could not fully reconstruct a house from photos all taken from a single vantage point, "snapshots" at many angles must be combined to reconstruct a Burgess organism. Conway Morris told me that he managed to reconstruct the curious Wiwaxia-an animal with no modem relatives, and therefore no known prototype to use as a model-by passing countless hours "rotating the damned thing in my mind" from the position of one drawing to the different angle of another, until every specimen could be moved without contradiction from one stance to the next. Then he finally knew that nothing major was missing or out of place.-Stephen J. Gould, Wonderful Life (1989) The human ability to recognize objects is remarkable-under all but the most degraded conditions, we

Twelve participants were trained to be experts at identifying a set of 'Greebles', novel objects that, like faces, all share a common spatial configuration. Tests comparing expert with novice performance revealed: (1) a surprising mix of generalizability and specificity in expert object recognition processes; and (2) that expertise is a multi-faceted phenomenon, neither adequately described by a single term nor adequately assessed by a single task. Greeble recognition by a simple neural-network model is also evaluated, and the model is found to account surprisingly well for both generalization and individuation using a single set of processes and representations.