A motion pooling model of visually guided navigation explains human behavior in the presence of independently moving objects

Abstract

Humans accurately judge their direction of heading when translating in a rigid environment, unless independently moving objects (IMOs) cross the observer's focus of expansion (FoE). Studies show that an IMO on a laterally moving path that maintains a fixed distance with respect to the observer (non-approaching; C. S. Royden & E. C. Hildreth, 1996) biases human heading estimates differently from an IMO on a lateral path that gets closer to the observer (approaching; W. H. Warren & J. A. Saunders, 1995). C. S. Royden (2002) argued that differential motion operators in primate brain area MT explained both data sets, concluding that differential motion was critical to human heading estimation. However, neurophysiological studies show that motion pooling cells, but not differential motion cells, in MT project to heading-sensitive cells in MST (V. K. Berezovskii & R. T. Born, 2000). It is difficult to reconcile differential motion heading models with these neurophysiological data. We generate motion sequences that mimic those viewed by human subjects. Model MT pools over V1; units in model MST perform distance-weighted template matching and compete in a recurrent heading representation layer. Our model produces heading biases of the same direction and magnitude as humans through a peak shift in model MSTd without using differential motion operators, maintaining consistency with known primate neurophysiology.

A Neural Model of MST and MT Explains Perceived Object Motion during Self-Motion

“…However, the influence is modest for the objects considered here that occupy a small portion of the visual field. Objects that occupy larger areas may exert a larger influence and may even shift the most active cells in MSTd, consistent with biases in human heading perception (Layton andFajen, 2016a, 2016b).…”

“…Each feedforward template integrates MT responses with greater weight near the preferred singularity position (x 0 , y 0 ), and the weights decrease exponentially with distance (Layton et al, 2012;Layton and Browning, 2014).…”

A Neural Circuit for Robust Time-to-Contact Estimation Based on Primate MST

“…The dViSTARS model is based on the dynamical systems models described in Browning et al (2009aBrowning et al ( , 2009b and Layton et al (2012).…”

“…Each template defines the organization of inputs from MT that the MST cell responds optimally to. Template models of MSTd are able to explain human heading perception data in static environments (Browning, Grossberg, & Mingolla, 2009a;Perrone & Stone, 1994 and in the presence of independently moving objects (Layton, Mingolla, & Browning, 2012). Template models of MSTd can explain rotation data through the use of extraretinal signals to remove the effects of rotation before the template is applied (Beintema & van den Berg, 1998;Elder, Grossberg, & Mingolla, 2009).…”

Monocular heading estimation in non-stationary urban environment

“…One of the early algorithms has been implemented in hardware [11]. The need for dealing with outliers was only recognized later, partly due to results on human motion perception ( [12]), which is still an active field of research ( [13], [14]). Since then, many different methods have been applied to FoE estimation [15], including state-of-the-art statistical methods [5].…”