NEURAL DYNAMICS OF MOTION GROUPING - FROM APERTURE AMBIGUITY TO OBJECT SPEED AND DIRECTION

Citation
J. Chey et al., NEURAL DYNAMICS OF MOTION GROUPING - FROM APERTURE AMBIGUITY TO OBJECT SPEED AND DIRECTION, Journal of the Optical Society of America. A, Optics, image science,and vision., 14(10), 1997, pp. 2570-2594
Citations number
111
Categorie Soggetti
Optics
ISSN journal
10847529
Volume
14
Issue
10
Year of publication
1997
Pages
2570 - 2594
Database
ISI
SICI code
1084-7529(1997)14:10<2570:NDOMG->2.0.ZU;2-Y
Abstract
A neural network model of visual motion perception and speed discrimin ation is developed to simulate data concerning the conditions under wh ich components of moving stimuli cohere or not into a global direction of motion, as in barberpole and plaid patterns (both type 1 and type 2). The model also simulates how the perceived speed of lines moving i n a prescribed direction depends on their orientation, length, duratio n, and contrast. Motion direction and speed both emerge as part of an interactive motion grouping or segmentation process. The model propose s a solution to the global aperture problem by showing how information from feature tracking points, namely, locations from which unambiguou s motion directions can be computed, can propagate to ambiguous motion direction points and capture the motion signals there. The model does this without computing intersections of constraints or parallel Fouri er and non-Fourier pathways. Instead, the model uses orientationally u nselective cell responses to activate directionally tuned transient ce lls. These transient cells, in turn,activate spatially short-range fil ters and competitive mechanisms over multiple spatial scales to genera te speed-tuned and directionally tuned cells. Spatially long-range fil ters and top-down feedback from grouping cells are then used to track motion of featural points and to select and propagate correct motion d irections to ambiguous motion points. Top-down grouping can also prime the system to attend a particular motion direction. The model hereby links low-level automatic motion processing with attention-based motio n processing. Homologs of model mechanisms have been used in models of other brain systems to simulate data about visual grouping, figure-gr ound separation, and speech perception. Earlier versions of the model have simulated data about short-range and long-range apparent motion, second-order motion, and the effects of parvocellular and magnocellula r lateral geniculate nucleus lesions on motion perception. (C) 1997 Op tical Society of America.