September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Evolution of decision weights and eye movements through learning in visual search
Author Affiliations & Notes
  • Ilmari Kurki
    Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki
  • Miguel P Eckstein
    Department of Psychological and Brain Sciences, University of California Santa Barbara
Journal of Vision September 2019, Vol.19, 26b. doi:https://doi.org/10.1167/19.10.26b
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      Ilmari Kurki, Miguel P Eckstein; Evolution of decision weights and eye movements through learning in visual search. Journal of Vision 2019;19(10):26b. doi: https://doi.org/10.1167/19.10.26b.

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

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Abstract

An important component of perceptual learning in complex visual environments is the dynamic optimization of eye movements to maximize the acquisition of visual information. Here, we investigate how the temporal evolution of eye movements and perceptual decision weights results in performance improvements. We used a new classification image - based method to estimate and visualize how observers dynamically vary the parts of the search stimulus used for perceptual decisions on each trial and compare it to eye movements across trials. Further, a Bayesian model observer that predicts the next saccade location from a history of previous stimuli and responses was used to analyze constrains in (1) memory and (2) spatial sampling. The stimuli were 16 Gabor patches on a virtual ring (radius 5.8 degrees). The contrast of each patch was randomly varied on each trial. Observers’ task was to detect a contrast increment (50% target present trials) in one patch. Stimulus duration was 300 ms, allowing for one or two saccades to the target. Observers knew that the target patch was at the same, randomized, location for a learning block of 300 trials. The landing points of the first saccade on every trial were extracted. Decision weights for each location were estimated by a maximum likelihood method that uses a history of stimulus values and responses. In the initial trials of the learning block observers used explorative search patterns. Then, after typically 50 – 100 trials, observers repetitively fixated to a single location that often but not always contained the target. The results show a close correspondence between estimated decision weights and saccade locations. A model observer with rather long trial memory and foveated vision could best predict subsequent saccade locations. Together this suggests dynamic and common representations mediating eye movement and perceptual decision learning.

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