September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Predictive eye movements in natural vision
Author Affiliations
  • Mary Hayhoe
    Center for Perceptual Systems, University of Texas Austin, USA
Journal of Vision September 2018, Vol.18, 1355. doi:10.1167/18.10.1355
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      Mary Hayhoe; Predictive eye movements in natural vision. Journal of Vision 2018;18(10):1355. doi: 10.1167/18.10.1355.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Natural behavior can be described as a sequence of sensory motor decisions that serve behavioral goals. To make action decisions the visual system must estimate current world state. However, sensory-motor delays present a problem to a reactive organism in a dynamically changing environment. Consequently it is advantageous to predict future state as well. This requires some kind of experience-based model of how the current state is likely to change over time. It is commonly accepted that the proprioceptive consequences of a planned movement are predicted ahead of time using stored internal models of the body's dynamics. It is also commonly assumed that prediction is a fundamental aspect of visual perception, but the existence of visual prediction and the particular mechanisms underlying such prediction are unclear. Some of the best evidence for prediction in vision comes from the oculomotor system. In this case, both smooth pursuit and saccadic eye movements reveal prediction of the future visual stimulus. I will review evidence for prediction in interception actions in both real and virtual environments. Subjects make accurate predictions of visual target motion, even when targets follow trajectories determined by the complex dynamics of physical interactions, and the head and body are unrestrained. These predictions appear to be used in common by both eye and arm movements. Predictive eye movements reveal that the observer's best guess at the future state of the environment is based on image data in combination with representations that reflect learnt statistical properties of dynamic visual environments.

Meeting abstract presented at VSS 2018

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