Competitive Dynamics in MSTd: A Mechanism for Robust Heading Perception Based on Optic Flow

Abstract

Human heading perception based on optic flow is not only accurate, it is also remarkably robust and stable. These qualities are especially apparent when observers move through environments containing other moving objects, which introduce optic flow that is inconsistent with observer self-motion and therefore uninformative about heading direction. Moving objects may also occupy large portions of the visual field and occlude regions of the background optic flow that are most informative about heading perception. The fact that heading perception is biased by no more than a few degrees under such conditions attests to the robustness of the visual system and warrants further investigation. The aim of the present study was to investigate whether recurrent, competitive dynamics among MSTd neurons that serve to reduce uncertainty about heading over time offer a plausible mechanism for capturing the robustness of human heading perception. Simulations of existing heading models that do not contain competitive dynamics yield heading estimates that are far more erratic and unstable than human judgments. We present a dynamical model of primate visual areas V1, MT, and MSTd based on that of Layton, Mingolla, and Browning that is similar to the other models, except that the model includes recurrent interactions among model MSTd neurons. Competitive dynamics stabilize the model’s heading estimate over time, even when a moving object crosses the future path. Soft winner-take-all dynamics enhance units that code a heading direction consistent with the time history and suppress responses to transient changes to the optic flow field. Our findings support recurrent competitive temporal dynamics as a crucial mechanism underlying the robustness and stability of perception of heading.

Causal inference accounts for heading perception in the presence of object motion

“…Isolating the effect of object speed on heading judgments is difficult because varying the speed of an isolated object will change the duration and timing with which it moves through specific regions of the visual field. Biases in heading perception caused by object motion are well known to depend on starting and ending object position and the temporal profile of the object motion relative to optic flow (19,20,30). Thus, our design employed a multipart object such that it filled the display at all times throughout the trial.…”

“…In general, two broad classes of models have been developed to account for heading perception in the presence of moving objects. First, neurally inspired models mimic the response properties of neurons in visual cortex to implement mechanisms such as motion sampling (20), differential motion detection (19,28,29), and competitive dynamics among heading template units (30). The second class of models utilizes features of the visual image to extract heading information.…”

“…Conversely, an observer's self-motion can also bias perception of object trajectory (24)(25)(26)(27). However, these interactions have generally been interpreted using empirical models based on visual motion processing mechanisms, and no attempt has been made to cast the problem of judging object and self-motion within a normative framework that can be generalized across sensory domains (28)(29)(30)(31)(32).…”

Heading perception from optic flow in the presence of biological motion

“…Previous studies found that independently moving objects affected heading judgments only when they obscured or were in close proximity to the FOE of the background optic flow (Layton & Fajen, 2015, 2016a, 2016bRoyden & Hildreth, 1996;Warren & Saunders, 1995). In contrast, Li et al (2018) showed that independently moving objects can affect heading judgments even when they are not close to the FOE of the background optic flow.…”

“…Layton and Fajen (2016b) have thus proposed that heading bias produced by object motion may arise from the discrepant optic flow generated within the object and the pseudo-FOE produced at the object's trailing edge. Layton et al have modeled the bias as a peak shift produced by competitive interactions between neural representations of heading in MSTd (Layton & Fajen, 2016a;Layton, Mingolla, & Browning, 2012).…”