Motion-based prediction is sufficient to solve the aperture problem

TL;DR

This paper demonstrates that motion-based predictive coding alone can effectively solve the aperture problem in motion perception, without the need for additional specialized mechanisms, through a probabilistic, context-dependent diffusion model.

Contribution

It introduces a novel model showing that predictive coding suffices for solving the aperture problem, eliminating the need for ad hoc mechanisms used in prior models.

Findings

Progressive solution to the aperture problem observed in simulations

Formation of a tracking behavior independent of texture

Incoherent features are explained away, coherent information diffuses globally

Abstract

In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent global percept. This is well demonstrated for the detection of motion in the aperture problem: as luminance of an elongated line is symmetrical along its axis, tangential velocity is ambiguous when measured locally. Here, we develop the hypothesis that motion-based predictive coding is sufficient to infer global motion. Our implementation is based on a context-dependent diffusion of a probabilistic representation of motion. We observe in simulations a progressive solution to the aperture problem similar to physio-logy and behavior. We demonstrate that this solution is the result of two underlying mechanisms. First, we demonstrate the formation of a tracking behavior favoring temporally coherent features independent of their texture. Second, we observe that incoherent features are explained away, while coherent information diffuses progressively to the global scale. Most previous models included ad hoc mechanisms such as end-stopped cells or a selection layer to track specific luminance-based features as necessary conditions to solve the aperture problem. Here, we have proved that motion-based predictive coding, as it is implemented in this functional model, is sufficient to solve the aperture problem. This solution may give insights into the role of prediction underlying a large class of sensory computations.