Processing of binocular disparity is thought to be widespread throughout cortex, highlighting its importance for perception and action. Yet the computations and functional roles underlying this activity across areas remain largely unknown. Here, we trace the neural representations mediating depth perception across human brain areas using multivariate analysis methods and high-resolution imaging. Presenting disparity-defined planes, we determine functional magnetic resonance imaging (fMRI) selectivity to near versus far depth positions. First, we test the perceptual relevance of this selectivity, comparing the pattern-based decoding of fMRI responses evoked by random dot stereograms that support depth perception (correlated RDS) with the decoding of stimuli containing disparities to which the perceptual system is blind (anticorrelated RDS). Preferential disparity selectivity for correlated stimuli in dorsal (visual and parietal) areas and higher ventral area LO (lateral occipital area) suggests encoding of perceptually relevant information, in contrast to early (V1, V2) and intermediate ventral (V3v, V4) visual cortical areas that show similar selectivity for both correlated and anticorrelated stimuli. Second, manipulating disparity parametrically, we show that dorsal areas encode the metric disparity structure of the viewed stimuli (i.e., disparity magnitude), whereas ventral area LO appears to represent depth position in a categorical manner (i.e., disparity sign). Our findings suggest that activity in both visual streams is commensurate with the use of disparity for depth perception but the neural computations may differ. Intriguingly, perceptually relevant responses in the dorsal stream are tuned to disparity content and emerge at a comparatively earlier stage than categorical representations for depth position in the ventral stream.

Humans exploit a range of visual depth cues to estimate three-dimensional (3D) structure. For example, the slant of a nearby tabletop can be judged by combining information from binocular disparity, texture and perspective. Behavioral tests show humans combine cues near-optimally, a feat that could depend on: (i) discriminating the outputs from cue-specific mechanisms, or (ii) fusing signals into a common representation. While fusion is computationally attractive, it poses a significant challenge, requiring the integration of quantitatively different signals. We used functional magnetic resonance imaging (fMRI) to provide evidence that dorsal visual area V3B/KO meets this challenge. Specifically, we found that fMRI responses are more discriminable when two cues (binocular disparity and relative motion) concurrently signal depth, and that information provided by one cue is diagnostic of depth indicated by the other. This suggests a cortical node important when perceiving depth, and highlights computations based on fusion in the dorsal stream.

Our perception of the world's three-dimensional (3D) structure is critical for object recognition, navigation and planning actions. To accomplish this, the brain combines different types of visual information about depth structure, but at present, the neural architecture mediating this combination remains largely unknown. Here, we report neuroimaging correlates of human 3D shape perception from the combination of two depth cues. We measured fMRI responses while observers judged the 3D structure of two sequentially presented images of slanted planes defined by binocular disparity and perspective. We compared the behavioral and fMRI responses evoked by changes in one or both of the depth cues. fMRI responses in extrastriate areas (hMT+/V5 and lateral occipital complex), rather than responses in early retinotopic areas, reflected differences in perceived 3D shape, suggesting 'combined-cue' representations in higher visual areas. These findings provide insight into the neural circuits engaged when the human brain combines different information sources for unified 3D visual perception.

Expertise in recognizing objects in cluttered scenes is a critical skill for our interactions in complex environments and is thought to develop with learning. However, the neural implementation of object learning across stages of visual analysis in the human brain remains largely unknown. Using combined psychophysics and functional magnetic resonance imaging (fMRI), we show a link between shape-specific learning in cluttered scenes and distributed neuronal plasticity in the human visual cortex. We report stronger fMRI responses for trained than untrained shapes across early and higher visual areas when observers learned to detect low-salience shapes in noisy backgrounds. However, training with high-salience pop-out targets resulted in lower fMRI responses for trained than untrained shapes in higher occipitotemporal areas. These findings suggest that learning of camouflaged shapes is mediated by increasing neural sensitivity across visual areas to bolster target segmentation and feature integration. In contrast, learning of prominent pop-out shapes is mediated by associations at higher occipitotemporal areas that support sparser coding of the critical features for target recognition. We propose that the human brain learns novel objects in complex scenes by reorganizing shape processing across visual areas, while taking advantage of natural image correlations that determine the distinctiveness of target shapes.

The binocular disparity between the views of the world registered by the left and right eyes provides a powerful signal about the depth structure of the environment. Despite increasing knowledge of the cortical areas that process disparity from animal models, comparatively little is known about the local architecture of stereoscopic processing in the human brain. Here, we take advantage of the high spatial specificity and image contrast offered by 7 tesla fMRI to test for systematic organization of disparity representations in the human brain. Participants viewed random dot stereogram stimuli depicting different depth positions while we recorded fMRI responses from dorsomedial visual cortex. We repeated measurements across three separate imaging sessions. Using a series of computational modeling approaches, we report three main advances in understanding disparity organization in the human brain. First, we show that disparity preferences are clustered and that this organization persists across imaging sessions, particularly in area V3A. Second, we observe differences between the local distribution of voxel responses in early and dorsomedial visual areas, suggesting different cortical organization. Third, using modeling of voxel responses, we show that higher dorsal areas (V3A, V3B/KO) have properties that are characteristic of human depth judgments: a simple model that uses tuning parameters estimated from fMRI data captures known variations in human psychophysical performance. Together, these findings indicate that human dorsal visual cortex contains selective cortical structures for disparity that may support the neural computations that underlie depth perception.