Abstract
How does the brain maintain stable fusion of 3D scenes when the eyes move? Every eye movement causes each retinal position to process a different set of scenic features, and thus the brain needs to binocularly fuse new combinations of features at each position after an eye movement. Despite these breaks in retinotopic fusion due to each movement, previously fused representations of a scene in depth often appear stable. The 3D ARTSCAN neural model proposes how the brain does this by unifying concepts about how multiple cortical areas in the What and Where cortical streams interact to coordinate processes of 3D boundary and surface perception, spatial attention, invariant object category learning, predictive remapping, eye movement control, and learned coordinate transformations. The model explains data from single neuron and psychophysical studies of covert visual attention shifts prior to eye movements. The model further clarifies how perceptual, attentional, and cognitive interactions among multiple brain regions (LGN, V1, V2, V3A, V4, MT, MST, PPC, LIP, ITp, ITa, SC) may accomplish predictive remapping as part of the process whereby view-invariant object categories are learned. These results build upon earlier neural models of 3D vision and figure-ground separation and the learning of invariant object categories as the eyes freely scan a scene. A key process concerns how an object's surface representation generates a form-fitting distribution of spatial attention, or attentional shroud, in parietal cortex that helps maintain the stability of multiple perceptual and cognitive processes. Predictive eye movement signals maintain the stability of the shroud, as well as of binocularly fused perceptual boundaries and surface representations.
Highlights
Such a consciously seen surface percept in depth is maintained across eye movements due to the predictive remapping of their supporting boundaries by gain fields which occurs at several processing stages (Figure 4 and Equation 38)
The range of values of the allelotropic shift s, and the number of depth planes simultaneously represented in the 3D ARTSCAN model, are {+8o, +3o, 0o, −3o, −8o}
This article builds on the ARTSCAN and pARTSCAN models of how spatial attention in the Where stream modulates invariant object learning, recognition, and eye movement exploration of multiple object views in the What stream (Grossberg, 2007, 2009; Fazl et al, 2009; Cao et al, 2011; Foley et al, 2012; Chang et al, 2014)
Summary
Other modeling studies have demonstrated how the same retinotopic binocular mechanisms can process object features at multiple disparities (Grossberg and McLoughlin, 1997; Grossberg and Howe, 2003; Cao and Grossberg, 2005, 2012), including objects perceived from viewing stereograms (Fang and Grossberg, 2009) and natural 3D scenes (Cao and Grossberg, submitted), as well as objects that are slanted in depth (Grossberg and Swaminathan, 2004) All these results should be preserved under the action of predictive gain fields to convert their retinotopic boundary and surface representations into head-centered ones, since the gain fields merely predictively shift the representations that are created by the retinotopic mechanisms. Earlier models of the saccadic and smooth pursuit eye movement brain systems that are commanded by such positional representations can be used to augment the current model in future studies (e.g., Grossberg and Kuperstein, 1986; Grossberg et al, 1997, 2012; Gancarz and Grossberg, 1998, 1999; Srihasam et al, 2009; Silver et al, 2011)
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