September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Using Eye Tracking to Develop Classification Images for Perceptual Learning
Author Affiliations
  • Theodore Jacques
    University of California, Riverside
  • Aaron Seitz
    University of California, Riverside
Journal of Vision September 2018, Vol.18, 1071. doi:10.1167/18.10.1071
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      Theodore Jacques, Aaron Seitz; Using Eye Tracking to Develop Classification Images for Perceptual Learning. Journal of Vision 2018;18(10):1071. doi: 10.1167/18.10.1071.

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

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Abstract

A key question in perceptual learning is what template do participants learn as they train to conduct a given task. While there are a number of approaches that speak to this question, the typical is to generate a classification image based upon noise added to the stimulus that people are trying to discriminate and examining how different noise components contribute to performance. Here we examine a complementary approach where participants search for a low-contrast target on a noisy background. In this way we are able to determine as people learn the extent that their template can be described from the pattern of fixations made as they search for the target and how this changes as they learning the task. The paradigm begins with a pre-test to establish detection thresholds for gabor patches in noise in a predictable location. In the training phase of the experiment, subjects complete a free-viewing search for a near-threshold gabor embedded at an unpredictable location in visual noise using an eye tracker. Subjects are rewarded for finding the target within a set timeframe, and an adaptive procedure maintains an appropriate level of difficulty throughout the training period. We present data to validate the training task, also using pre and post tests to examine transfer. We find reduced thresholds over time for the free-search training task, indicating task-specific learning, but no stimulus-specific improvements in the transfer at post-test. We will also present data showing the extent to which different individuals change how they prioritize noise regions that contain information similar to the task-targets.

Meeting abstract presented at VSS 2018

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