Abstract
The brain's skill in estimating the 3-D orientation of viewed surfaces supports a range of behaviors, from placing an object on a nearby table, to planning the best route when hill walking. This ability relies on integrating depth signals across extensive regions of space that exceed the receptive fields of early sensory neurons. Although hierarchical selection and pooling is central to understanding of the ventral visual pathway, the successive operations in the dorsal stream are poorly understood. Here we use computational modeling of human fMRI signals to probe the computations that extract 3-D surface orientation from binocular disparity. To understand how representations evolve across the hierarchy, we developed an inference approach using a series of generative models to explain the empirical fMRI data in different cortical areas. Specifically, we simulated the responses of candidate visual processing algorithms and tested how well they explained fMRI responses. Thereby we demonstrate a hierarchical refinement of visual representations moving from the representation of edges and figure-ground segmentation (V1, V2) to spatially extensive disparity gradients in V3A. We show that responses in V3A are little affected by low-level image covariates, and have a partial tolerance to the overall depth position. Finally, we show that responses in V3A parallel perceptual judgments of slant. This reveals a relatively short computational hierarchy that captures key information about the 3-D structure of nearby surfaces, and more generally demonstrates an analysis approach that may be of merit in a diverse range of brain imaging domains.
Highlights
A fundamental challenge in visual neuroscience is to understand how the outputs of neurons responding to local, simple elements are progressively transformed to encode the critical features of spatially extensive objects
We presented parametric variations of slant, creating a family of surfaces that were slanted toward or away from the observer
PerhV4 V3v V2 right forming simulations at each of these noise levels (Fig. 7), we found that fMRI responses in V3A were consistently best execcentricity polar angle plained by the disparity gradient model
Summary
A fundamental challenge in visual neuroscience is to understand how the outputs of neurons responding to local, simple elements are progressively transformed to encode the critical features of spatially extensive objects. While models of the ventral visual stream detail the decoding transformations that support invariance (Riesenhuber and Poggio, 1999; Rust and DiCarlo, 2010), much less is known about the dorsal hierarchy. We test the processing of binocular depth signals that strongly engage dorsal visual cortex (Backus et al, 2001; Tsao et al, 2003; Neri et al, 2004; Tyler et al, 2006; Preston et al, 2008, 2009; Goncalves et al, 2015). Analyzed data; H.B. and A.E.W. wrote the paper
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