FMRI studies have revealed three scene-selective regions in human visual cortex (the Parahippocampal Place Area (PPA), Transverse Occipital Sulcus (TOS) and RetroSplenial Cortex (RSC)), which have been linked to higher-order functions such as navigation, scene perception/recognition, and contextual association. Here, we document corresponding (presumptively homologous) scene-selective regions in the awake macaque monkey, based on direct comparison to human maps, using identical stimuli and largely overlapping fMRI procedures. In humans, our results showed that the three scene-selective regions are centered near - but distinct from - the gyri/sulci for which they were originally named. In addition, all these regions are located within or adjacent to known retinotopic areas. Human RSC and PPA are located adjacent to the peripheral representation of primary and secondary visual cortex, respectively. Human TOS is located immediately anterior/ventral to retinotopic area V3A, within retinotopic regions LO-1, V3B, and/or V7. Mirroring the arrangement of human regions FFA and PPA (which are adjacent to each other in cortex), the presumptive monkey homologue of human PPA is located adjacent to the monkey homologue of human FFA, near the posterior superior temporal sulcus. Monkey TOS includes the region predicted from the human maps (macaque V4d), extending into retinotopically-defined V3A. A possible monkey homologue of human RSC lies in the medial bank, near peripheral V1. Overall, our findings suggest a homologous neural architecture for scene-selective regions in visual cortex of humans and non-human primates, analogous to the face-selective regions demonstrated earlier in these two species.

A parietal-frontal network in primates is thought to support many behaviors occurring in the space around the body, including interpersonal interactions and maintenance of a particular "comfort zone" or distance from other people ("personal space"). To better understand this network in humans, we used functional MRI to measure the responses to moving objects (faces, cars, simple spheres) and the functional connectivity of two regions in this network, the dorsal intraparietal sulcus (DIPS) and the ventral premotor cortex (PMv). We found that both areas responded more strongly to faces that were moving toward (vs away from) subjects, but did not show this bias in response to comparable motion in control stimuli (cars or spheres). Moreover, these two regions were functionally interconnected. Tests of activity-behavior associations revealed that the strength of DIPS-PMv connectivity was correlated with the preferred distance that subjects chose to stand from an unfamiliar person (personal space size). In addition, the magnitude of DIPS and PMv responses was correlated with the preferred level of social activity. Together, these findings suggest that this parietal-frontal network plays a role in everyday interactions with others.

What may be special about faces, compared to non-face objects, is that their neural representation may be fundamentally spatial, e.g., Gabor-like. Subjects matched a sequence of two filtered images, each containing every other combination of spatial frequency and orientation, of faces or non-face 3D blobs, judging whether the person or blob was the same or different. On a match trial, the images were either identical or complementary (containing the remaining spatial frequency and orientation content). Relative to an identical pair of images, a complementary pair of faces, but not blobs, reduced matching accuracy and released fMRI adaptation in the fusiform face area.

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Our visual environment abounds with curved features. Thus, the goal of understanding visual processing should include the processing of curved features. Using functional magnetic resonance imaging in behaving monkeys, we demonstrated a network of cortical areas selective for the processing of curved features. This network includes three distinct hierarchically organized regions within the ventral visual pathway: a posterior curvature-biased patch (PCP) located in the near-foveal representation of dorsal V4, a middle curvature-biased patch (MCP) located on the ventral lip of the posterior superior temporal sulcus (STS) in area TEO, and an anterior curvature-biased patch (ACP) located just below the STS in anterior area TE. Our results further indicate that the processing of curvature becomes increasingly complex from PCP to ACP. The proximity of the curvature-processing network to the well-known face-processing network suggests a possible functional link between them.curvature patches | face patches | curved Gabor filters D ecades of research have focused on understanding visual feature processing, particularly along the ventral visual pathway. Such studies have shown that neurons in lower-order visual areas (e.g., V1) respond strongly to simple oriented contours (1), whereas neurons in higher-order visual areas (e.g., inferior temporal cortex) respond selectively to more complex image features and/or visual categories (2-4), in ways that are not yet fully understood. To link these extremes in visual information processing, many studies have aimed to clarify the optimal "trigger" features at intermediate levels of the visual cortical hierarchy.Among these features, stimulus curvature has not been well studied. This is surprising because, strictly, all lines are curved to some extent, except for the single exception of a perfectly straight line. This ubiquity of curved shapes also extends to 3D surfaces (5). In nature, where much of our visual system presumably evolved, perfectly flat surfaces are rare. Even the flattest of natural features (e.g., oceans, sandy beaches) are often curved to some extent, due to wind, water motion, and even the curvature of the earth. Thus, it is important to understand curvature processing to fully unravel the steps in cortical visual processing.Among the few studies to test single neuron responses to curvature per se, Gallant et al. (6,7) reported that a significant percentage of neurons in macaque cortical area V4 is selective for curved stimuli. Intriguingly, these authors also noted that neurons preferring curved patterns were often anatomically clustered together. Subsequently, demonstrated that neurons in the parafoveal representation of dorsal V4 respond robustly to the curvature component of complex shapes. To our knowledge, there have been no systematic studies of curvature at levels below V4 in macaques.Intriguingly, some evidence suggests that the processing of curvature may interact selectively with the processing of faces. For instance, perceptual deficits in face reco...

An intriguing region of human visual cortex (the fusiform face area; FFA) responds selectively to faces as a general higher-order stimulus category. However, the potential role of lower-order stimulus properties in FFA remains incompletely understood. To clarify those lower-level influences, we measured FFA responses to independent variation in 4 lower-level stimulus dimensions using standardized face stimuli and functional Magnetic Resonance Imaging (fMRI). These dimensions were size, position, contrast, and rotation in depth (viewpoint). We found that FFA responses were strongly influenced by variations in each of these image dimensions; that is, FFA responses were not “invariant” to any of them. Moreover, all FFA response functions were highly correlated with V1 responses (r = 0.95–0.99). As in V1, FFA responses could be accurately modeled as a combination of responses to 1) local contrast plus 2) the cortical magnification factor. In some measurements (e.g., face size or a combinations of multiple cues), the lower-level variations dominated the range of FFA responses. Manipulation of lower-level stimulus parameters could even change the category preference of FFA from “face selective” to “object selective.” Altogether, these results emphasize that a significant portion of the FFA response reflects lower-level visual responses.

What is the neural correlate of preference that governs our spontaneous selection of visual information? With a rapid, event-related functional magnetic resonance imaging design, we showed that the viewing of highly preferred compared to less preferred scenes (as assessed by participant ratings) was associated with greater blood-oxygen level dependent responses in the right parahippocampal cortex but not in the lateral occipital complex, ruling out feed forward and attentional effects. Highly preferred images also produced greater activation in the ventral striatum, suggesting that perceptual preference might engage the conventional reward system. These results are consistent with the hypothesis that high activity in the parahippocampal cortex, an area with a high density of cortical mu-opioid receptors, may be experienced as cognitively pleasurable.

Shape representation is accomplished by a series of cortical stages in which cells in the first stage (V1) have local receptive fields tuned to contrast at a particular scale and orientation, each well modeled as a Gabor filter. In succeeding stages, the representation becomes largely invariant to Gabor coding (Kobatake & Tanaka, 1994). Because of the non-Gabor tuning in these later stages, which must be engaged for a behavioral response (Tong, 2003; Tong et al., 1998), a V1-based measure of shape similarity based on Gabor filtering would not be expected to be highly correlated with human performance when discriminating complex shapes (faces and teeth-like blobs) that differ metrically on a two-choice, match-to-sample task. Here we show that human performance is highly correlated with Gabor-based image measures (Gabor simple and complex cells), with values often in the mid 0.90s, even without discounting the variability in the speed and accuracy of performance not associated with the similarity of the distractors. This high correlation is generally maintained through the stages of HMAX, a model that builds upon the Gabor metric and develops units for complex features and larger receptive fields. This is the first report of the psychophysical similarity of complex shapes being predictable from a biologically motivated, physical measure of similarity. As accurate as these measures were for accounting for metric variation, a simple demonstration showed that all were insensitive to viewpoint invariant (nonaccidental) differences in shape.