A laminar cortical model of stereopsis and three-dimensional surface perception

Grossberg, Stephen; Howe, Piers D. L.

doi:10.1016/s0042-6989(03)00011-7

Cited by 86 publications

(145 citation statements)

References 64 publications

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“…We suggest that interocular XOS would help solve the second problem by suppressing the weaker of the two features around the perceived intersection. However, the process is not automatic, but influenced by the matching of corresponding features across the eyes (McKee et al, 1994;Grossberg and Howe, 2003;Meese and Hess, 2005), as in the half-binocular condition here. Indeed, when this occurs (as for binocular masks), then it might be taken as evidence that corresponding retinal points are seeing a single object, in which case dichoptic XOS should be diminished, just as we found.…”

Section: Diplopia Suppression?mentioning

confidence: 97%

Psychophysical evidence for two routes to suppression before binocular summation of signals in human vision

2007

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Abstract-Visual mechanisms in primary visual cortex are suppressed by the superposition of gratings perpendicular to their preferred orientations. A clear picture of this process is needed to (i) inform functional architecture of image-processing models, (ii) identify the pathways available to support binocular rivalry, and (iii) generally advance our understanding of early vision. Here we use monoptic sine-wave gratings and cross-orientation masking (XOM) to reveal two crossoriented suppressive pathways in humans, both of which occur before full binocular summation of signals. One is a within-eye (ipsiocular) pathway that is spatially broadband, immune to contrast adaptation and has a suppressive weight that tends to decrease with stimulus duration. The other pathway operates between the eyes (interocular), is spatially tuned, desensitizes with contrast adaptation and has a suppressive weight that increases with stimulus duration. When cross-oriented masks are presented to both eyes, masking is enhanced or diminished for conditions in which either ipsiocular or interocular pathways dominate masking, respectively. We propose that ipsiocular suppression precedes the influence of interocular suppression and tentatively associate the two effects with the lateral geniculate nucleus (or retina) and the visual cortex respectively. The interocular route is a good candidate for the initial pathway involved in binocular rivalry and predicts that interocular cross-orientation suppression should be found in cortical cells with predominantly ipsiocular drive.

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Section: Diplopia Suppression?mentioning

confidence: 97%

Psychophysical evidence for two routes to suppression before binocular summation of signals in human vision

2007

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“…Grossberg generalized this hypothesis to predict in 1987 that 3D boundaries and surfaces are the units of 3D vision and figure-ground perception. He and colleagues have developed the FACADE (Form-And-Color-And-DEpth) model to explain and predict a wide range of perceptual and neurobiological data about 3D vision and figure-ground separation (Cao & Grossberg, 2005, Grossberg, 1987a, 1987b, 1994, Grossberg & Howe, 2003, Grossberg & Swaminathan, 2004, Grossberg & Yazdanbakhsh, 2005, Kelly & Grossberg, 2000, McLoughlin & Grossberg, 1998. See Grossberg (1994Grossberg ( , 2003 and Raizada and Grossberg (2003) for reviews.…”

Section: Boundary and Surface Representations Control Shrouds And Scansmentioning

confidence: 99%

View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds

Fazl

Grossberg

Mingolla

2009

Cognitive Psychology

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108

125

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“…The STARS model assumes that center-surround cells in MT derive their disparity selectivity from the V2-to-MT pathway (Maunsell & Van Essen, 1983a). The neural computation of depth from disparity is a complex problem (see, e.g., Cao & Grossberg, 2005;Grossberg & Howe, 2003;Grossberg & Swaminathan, 2004;Fang & Grossberg, in press;and Ponce & Born, in preparation), and requires a significant number of mechanisms not directly related to the problem of steering control. To keep the model as simple as possible, we do not attempt to simulate a robust binocular disparity mechanism in the V1-V2 network.…”

Section: Model Descriptionmentioning

confidence: 99%

A neural model of visually guided steering, obstacle avoidance, and route selection.

Elder¹,

Grossberg²,

Mingolla³

2009

Journal of Experimental Psychology: Human Perception and Perfor

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A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discontinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and VIP can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Key Words: Heading Perception, Steering, Optic Flow, Obstacle, Goal, Pursuit Eye Movement, Gain Fields, Peak Shift, V2, MT, MST, VIP, LIP 3 IntroductionMany important steering tasks are guided by visual information, including walking through a cluttered environment (Fajen & Warren, 2003), driving (Land & Horwood, 1995;Hildreth, Beusmans, Boer, & Royden, 2000;Wallis, Chatziastros, & Bülthoff, 2002), vehicle braking (Lee, 1976, piloting an aircraft (Gibson, Olum, & Rosenblatt, 1955;Beall & Loomis, 1997), and intercepting a moving target on foot (Fajen & Warren, 2004). Steering through a cluttered environment involves the selection of a path that avoids obstacles and simultaneously approaches the intended goal. Human steering is guided by visual information about the spatial layout of the environment and the direction of self-motion through the environment, as well as proprioceptive feedback and information from other sensory systems. In particular, the visual system provides information about the relative positions of the goal object and obstacles in the environment.Movement through the world creates a full-field pattern of motion on the retina, called optic flow, which contains information about the direction of self-motion, or heading (Gibson, 1950). In principle, optic flow can be used to compute heading (Longuet-Higgins & Prazdny, 1980). During translational movement of the eye, the optic flow field contains a singularity, called the focus of expansion, which specifies the direction of heading in the absence of an eye rotation.Figures 1 & 2 Whereas optic flow relates observer motion to the available visual stimuli, recent research clarifies how visual information governs the dynamics of human navigational behavior. Fajen and Warren (2003) studied the dynamics of human steering behavior in simple goal approach and obstacle avoidance tasks using an immersive virtual reality system. They found that human performa...

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A laminar cortical model of stereopsis and three-dimensional surface perception

Cited by 86 publications

References 64 publications

Psychophysical evidence for two routes to suppression before binocular summation of signals in human vision

Psychophysical evidence for two routes to suppression before binocular summation of signals in human vision

View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds

A neural model of visually guided steering, obstacle avoidance, and route selection.

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