Visual motion processing and human tracking behavior

TL;DR

This paper reviews how humans integrate retinal and extra-retinal cues for smooth pursuit eye movements, highlighting the importance of predictive information and probabilistic inference for improved tracking performance.

Contribution

It synthesizes recent research on human motion tracking, emphasizing the integration of sensory and predictive cues within a probabilistic framework and relating it to computer vision algorithms.

Findings

Predictive cues enhance tracking accuracy and reduce latency.

Integration of retinal and extra-retinal information is crucial for stable pursuit.

Probabilistic inference models can explain human tracking behavior.

Abstract

The accurate visual tracking of a moving object is a human fundamental skill that allows to reduce the relative slip and instability of the object's image on the retina, thus granting a stable, high-quality vision. In order to optimize tracking performance across time, a quick estimate of the object's global motion properties needs to be fed to the oculomotor system and dynamically updated. Concurrently, performance can be greatly improved in terms of latency and accuracy by taking into account predictive cues, especially under variable conditions of visibility and in presence of ambiguous retinal information. Here, we review several recent studies focusing on the integration of retinal and extra-retinal information for the control of human smooth pursuit.By dynamically probing the tracking performance with well established paradigms in the visual perception and oculomotor literature we provide the basis to test theoretical hypotheses within the framework of dynamic probabilistic inference. We will in particular present the applications of these results in light of state-of-the-art computer vision algorithms.