How do object reference frames and motion vector decomposition emerge in laminar cortical circuits?

Abstract

How do spatially disjoint and ambiguous local motion signals in multiple directions generate coherent and unambiguous representations of object motion? Various motion percepts, starting with those of Duncker (Induced motion, 1929(Induced motion, /1938 and Johansson (Configurations in event perception, 1950), obey a rule of vector decomposition, in which global motion appears to be subtracted from the true motion path of localized stimulus components, so that objects and their parts are seen as moving relative to a common reference frame. A neural model predicts how vector decomposition results from multiple-scale and multiple-depth interactions within and between the formand motion-processing streams in V1-V2 and V1-MST, which include form grouping, form-to-motion capture, figure-ground separation, and object motion capture mechanisms. Particular advantages of the model are that these mechanisms solve the aperture problem, group spatially disjoint moving objects via illusory contours, capture object motion direction signals on real and illusory contours, and use interdepth directional inhibition to cause a vector decomposition, whereby the motion directions of a moving frame at a nearer depth suppress those directions at a farther depth, and thereby cause a peak shift in the perceived directions of object parts moving with respect to the frame.Keywords Motion perception . Vector decomposition . Frames of reference . Peak shift . Complementary computing . V2 . MT . MST How do we make sense of the complex motions of multiple interacting objects and their parts? One required computational step is to represent the various motion paths in an appropriate reference frame. Various ways of defining a reference frame have been proposed, ranging from retinocentric, in which an object is coded relative to the location of the activity it induces on the retina, to geocentric, in which objects are represented independent of the observer's viewpoint (Wade & Swanston, 1987). According to an object-centered reference frame (Bremner, Bryant, & Mareschal, 2005;Wade & Swanston, 1996), objects are perceived relative to other objects. For example, on a cloudy night, the moon may appear to be moving in a direction opposite to that of the clouds. In a laboratory setting, this concept is well-illustrated by induced-motion experiments, wherein the motion of one object appears to cause opponent motion in another, otherwise static, object (Duncker, 1929(Duncker, /1938. Frames of referenceFrom a functional perspective, the creation of perceptual relative frames of reference may be one mechanism evolved by the brain to represent the motion of individual objects in a scene. This ability appears especially important when considering that the meaningfulness of the motion of a particular object can often be compromised by the motion of another object. For example, when looking at a person waving a hand from a moving train, the motion components of the hand and the train become mixed together. By representing the motion of the hand rel...

Visual crowding illustrates the inadequacy of local vs. global and feedforward vs. feedback distinctions in modeling visual perception

“…A combination of excitatory and inhibitory linear feedback can also preserve pattern representations even with large changes in overall intensity (Grossberg, 1973 ). Grossberg et al ( 2011 ), for example, used preservation to extend flickering stimuli long enough through time to allow their temporally integrated signals to generate smooth motion percepts.…”

“…The feedforward model of Riesenhuber and Poggio ( 1999 ), for example, has been used successfully for things like fast-feedforward object recognition or scene classification (e.g., Hung et al, 2005 ; Serre et al, 2005 , 2007a , b ; Poggio et al, 2013 ). Similarly, the feedback model of Grossberg (e.g., Grossberg and Mingolla, 1985 ) has spawned a multitude of subsequent publications (e.g., Grossberg and Todorovic, 1988 ; Grossberg and Rudd, 1989 ; Grossberg, 1990 ; Francis et al, 1994 ; Francis and Grossberg, 1995 ; Dresp and Grossberg, 1997 ; Grossberg, 2003 ; Grossberg and Howe, 2003 ; Grossberg and Yazdanbakhsh, 2003 ; Grossberg et al, 2011 ; Foley et al, 2012 ). Clearly there is an important role for both types of model architectures.…”

Localizing Non-Retinotopically Moving Objects

“…Non-retinotopic representation of motion has been depicted based on the idea of motion vector decomposition [20]–[23]. In general, a retinotopic motion vector can be decomposed into two orthogonal vector components (i.e.…”

Field-like interactions between motion-based reference frames

“…Hence, in general, learned figural configurations need also to be taken into account. Recently, Grossberg, Léveillé, and Versace (2011) proposed a neural-network model to explain how vector decomposition might occur by taking into account figural factors. According to their model, figure-ground separation and inhibition between neural populations, which represent motion at different depths, play the critical role; near-tofar inhibition and the resultant peak-shift in the population activity leads to vector decomposition.…”