Local Constraints for the Perception of Binocular 3D Motion

Abstract

The authors discuss local constraints for the perception of three-dimensional (3D) binocular motion in a geometric-probabilistic framework. It is shown that Bayesian models of binocular 3D motion can explain perceptual bias under uncertainty and predict perceived velocity under ambiguity. The models exploit biologically plausible constraints of local motion and disparity processing in a binocular viewing geometry. Results from computer simulations and psychophysical experiments support the idea that local constraints of motion and disparity processing are combined late in the visual processing hierarchy to establish perceived 3D motion direction.

Systematic misperceptions of 3-D motion explained by Bayesian inference

“…Two groups have previously extended Bayesian motion-perception models to account for errors in the perception of 3-D motion based on binocular cues (Lages, 2006; Lages et al, 2013; Welchman et al, 2008). The model proposed by Welchman and colleagues provides an account for the lateral bias and predicts an effect of viewing distance.…”

“…For example, observers exhibit a lateral bias : They systematically overestimate angle of approach in 3-D, such that objects moving toward the head are perceived as moving along a path that is more lateral than the true trajectory (Harris & Dean, 2003; Harris & Drga, 2005; Lages, 2006; Rushton & Duke, 2007; Welchman et al, 2004; Welchman et al, 2008). Bayesian models of 3-D motion perception, assuming a slow motion prior, can account for this bias (Lages, 2006; Lages, Heron, & Wang, 2013; Wang, Heron, Moreland, & Lages, 2012; Welchman et al, 2008). However, existing models are restricted to specific viewing situations (stimuli in the midsagittal plane) and have been tested using tasks and stimuli that limit the kind of perceptual errors that can be observed.…”

On the Aperture Problem of Binocular 3D Motion Perception

“…The intersection of the left and right eye velocity constraint planes results in a binocular constraint line in 3D. Importantly, this intersection of constraint planes (ICP) can be derived from inverse projections of monocular motion constraints as well as binocular disparity constraints monitored over time [33,34]. In the following we assume that both inputs, monocular motion and binocular disparity over time [35], are integrated by a 3D motion system but have independent sources of noise (Equations (A10) and (A11)).…”

“…In other words, if the motion system does not take into account viewing distance between the moving object and the observer then Bayesian estimation would lead to implausible 3D motion predictions. A model with a 3D motion prior on the other hand makes ecologically plausible 3D velocity predictions [33,34,39].…”

Straight or curved? From deterministic to probabilistic models of 3D motion perception

“…If the stimulus has unique features such as moving endpoints, corners, or texture elements then their trajectories can be “tracked” in depth over time. If, however, there are only uncertain or ambiguous moving features then a weak prior favoring slow motion (or short displacement in 3D space) suggests a linear trajectory as the default solution (see Figure 1; Lages et al, 2013). Such a prior may reflect tuning characteristics of a population of binocular motion cells and can explain perceptual bias.…”