Neural Models of Motion Integration, Segmentation, and Probabilistic Decision-Making

  • Stephen Grossberg


What brain mechanisms carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer’s motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer and its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the separation of occluding boundaries of the forest cover from boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feature tracking signals are amplified before they propagate across position and are integrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.


Motion Capture Motion Signal Motion Integration Boundary Signal Aperture Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Cognitive and Neural Systems, Center for Adaptive Systems, Center of Excellence for Learning in Education, Science and TechnologyBoston UniversityBostonUSA

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