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Kathryn Koehler, Emre Akbas, Matthew Peterson, Miguel P. Eckstein; Human versus Bayesian Optimal Learning of Eye Movement Strategies During Visual Search. Journal of Vision 2012;12(9):1142. doi: https://doi.org/10.1167/12.9.1142.
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© ARVO (1962-2015); The Authors (2016-present)
There is a large literature investigating the effect of practice on the accuracy and speed of perceptual decisions. Little is known about how eye movement strategies are changed as organisms interact with perceptual stimuli and how learned oculomotor execution contributes to performance benefits. Here, we investigate changes in human eye movements given practice at a visual search task and compare them to those of a newly proposed foveated Bayesian ideal learner (FBIL) that uses posterior probabilities from previous trials as priors in subsequent trials to plan eye movements. Participants searched for a vertically aligned Gabor (8 cycles/deg) luminance signal (yes/no task with 50% probability of target presence) embedded in spatiotemporal white-noise. If present, the signal always appeared in the same location at an eccentricity of 5 degrees from initial fixation. In an "unknown" condition, observers were told the signal could be anywhere in the display, but did not know that the signal would always be in the same location. In a "known" condition, observers were told that the signal could be anywhere in the display, but it would never change locations. In each condition, trials were interleaved such that observers could either freely move their eyes or were required to hold their fixation in the center of the display. Results: Mean distance of saccade endpoints from the target location decreased across sessions in both conditions (unknown mean decrease = 4.2 ± 0.98 degrees; known = 3.0 ± 1.7 degrees) but occurred much slower than for the FBIL. Strategizing eye movements improved proportion correct (Pc) by an additional 0.23 ± .08 relative to the fixation condition paralleling the improvements by the FBIL. Together our results indicate that humans can learn to strategize eye movements to optimize perceptual performance, which can greatly contribute to performance improvement beyond learning mechanisms under steady fixation.
Meeting abstract presented at VSS 2012
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