Integration of texture and disparity cues to surface slant in dorsal visual cortex

Abstract

Reliable estimation of three-dimensional (3D) surface orientation is critical for recognizing and interacting with complex 3D objects in our environment. Human observers maximize the reliability of their estimates of surface slant by integrating multiple depth cues. Texture and binocular disparity are two such cues, but they are qualitatively very different. Existing evidence suggests that representations of surface tilt from each of these cues coincide at the single-neuron level in higher cortical areas. However, the cortical circuits responsible for 1) integration of such qualitatively distinct cues and 2) encoding the slant component of surface orientation have not been assessed. We tested for cortical responses related to slanted plane stimuli that were defined independently by texture, disparity, and combinations of these two cues. We analyzed the discriminability of functional MRI responses to two slant angles using multivariate pattern classification. Responses in visual area V3B/KO to stimuli containing congruent cues were more discriminable than those elicited by single cues, in line with predictions based on the fusion of slant estimates from component cues. This improvement was specific to congruent combinations of cues: incongruent cues yielded lower decoding accuracies, which suggests the robust use of individual cues in cases of large cue conflicts. These data suggest that area V3B/KO is intricately involved in the integration of qualitatively dissimilar depth cues.

Emergence of symmetry selectivity in the visual areas of the human brain: fMRI responses to symmetry presented in both frontoparallel and slanted planes

“…If normalization does occur, then during engagement with symmetry we would expect to see greater feedback to e.g. V3 (for slanted compared to frontoparallel symmetry) from visual areas known to compute slant (Murphy, Ban, & Welchman, 2013; Tsutsui et al, 2001). However, if retinal structure is sufficient to extract symmetry from slanted planes then we would expect similar feedforward and feedback processes to be invoked independent of stimulus slant and task.…”

Differential patterns of 2D location versus depth decoding along the visual hierarchy

“…Most have focused on non-spatial aspects of depth, e.g., neural representations of 3D object structure (Backus et al, 2001; Durand et al, 2009), or the integration of different depth cues (Ban et al., 2012; Dekker et al, 2015; Murphy et al, 2013; Welchman et al, 2005), although a few recent studies have examined fMRI sensitivity to differences in depth from disparity, finding regions that are sensitive to absolute vs relative (Neri et al, 2004) or metric vs categorical (Preston et al, 2008) depth differences. Neurophysiology studies have also reported neurons with different depth preferences in various visual areas (DeAngelis & Newsome, 1999; Hubel et al, 2015; Tsao et al, 2003).…”

“…Interestingly, while binocular disparity signals are found as early as V1, these signals are not thought to correspond to perception of depth until later visual areas (Barendregt, Harvey, Rokers, & Dumoulin, 2015; Cumming & Parker, 1997, 1999; Preston et al, 2008). These later visual areas (including V3A, V3B, V7, IPS, MT+, LO) have been shown to be sensitive to 3D object structure (Backus et al, 2001; Durand et al, 2009), differences in perceived depth (Neri et al, 2004; Preston et al, 2008), and the integration of different depth cues (Ban et al, 2012; Dekker et al, 2015; Murphy, Ban, & Welchman, 2013; Welchman et al., 2005). However, the nature of position-in-depth (spatial) representations remains less explored.…”

Towards a unified perspective of object shape and motion processing in human dorsal cortex

“…Furthermore, temporary inactivation of area CIP in monkeys leads to impairments in perceiving depth structure and 3-D curvature, attesting to its critical role in representing those 3-D object properties (Van Dromme, Premereur, Verhoef, Vanduffel, & Janssen, 2016). Other studies suggest that CIP may also be a locus where different types of depth cues are integrated (Ban, Preston, Meeson, & Welchman, 2012; Murphy, Ban, & Welchman, 2013; Tsutsui, Jiang, Yara, Sakata, & Taira, 2001). CIP may be part of a larger object-processing network.…”