September 2015
Volume 15, Issue 12
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Modeling Task Control of Gaze
Author Affiliations
  • Matthew Tong
    Center for Perceptual Systems, University of Texas at Austin
  • Shun Zhang
    Department of Computer Science, University of Texas at Austin
  • Leif Johnson
    Department of Computer Science, University of Texas at Austin
  • Dana Ballard
    Department of Computer Science, University of Texas at Austin
  • Mary Hayhoe
    Center for Perceptual Systems, University of Texas at Austin
Journal of Vision September 2015, Vol.15, 784. doi:
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      Matthew Tong, Shun Zhang, Leif Johnson, Dana Ballard, Mary Hayhoe; Modeling Task Control of Gaze. Journal of Vision 2015;15(12):784.

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

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Natural behavior involves sequences of gaze changes that serve behavioral goals. A body of evidence suggests that eye-movement targeting is controlled by a priority map that is influenced by the stimulus and a variety of top down factors, including subjective value. However, it is not known how such maps evolve over time to guide attention and gaze from one target to the next. We take the approach of decomposing behavior into a sequence of sub-tasks, where gaze is allocated to gather specific information for a sub-task, such as location of an obstacle to be avoided. We examined behavior in a virtual environment where subjects walk along a path, collect targets, and avoid obstacles. We manipulated relative importance of the tasks using different instructions, and manipulated uncertainty about object location by adding random motion to the objects (Tong & Hayhoe, 2013). We adapted a soft barrier model previously developed by Johnson et al (2014). This model is similar to a random walk, with two parameters that reflect the rate of growth of uncertainty and the priority of a particular sub-task. Different sub-tasks compete for gaze, and a location is likely to be chosen as a gaze target if it important and its location is very uncertain. We used estimates of the priority values that were consistent with subjective values of different sub-tasks recovered from walking behavior using Inverse Reinforcement Learning, and estimated the growth of uncertainty over time. We were able to predict the proportion of time spent on the path, obstacles, and targets in the environment, as well as the effect of added uncertainty about object location. This supports the claim that, in natural behavior, the next target for gaze is determined by both the subjective value of the behavior, and by its information needs.

Meeting abstract presented at VSS 2015


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