August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Using CRISP to model saccade parameters and error rates in the antisaccade task
Author Affiliations
  • Ryan Hope
    Cognitive Science, Rensselaer Polytechnic Institute
  • Wayne Gray
    Cognitive Science, Rensselaer Polytechnic Institute
Journal of Vision September 2016, Vol.16, 932. doi:10.1167/16.12.932
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      Ryan Hope, Wayne Gray; Using CRISP to model saccade parameters and error rates in the antisaccade task. Journal of Vision 2016;16(12):932. doi: 10.1167/16.12.932.

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

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Abstract

A growing body of research on oculomotor control suggests that humans have much less control over their eye movements than typically assumed. Eye trackers have revealed that the eyes are in constant motion, even when fixating, and that these fixational eye movements are possibly functional. The growing consensus is that saccades are initiated automatically by a rhythmic trigger from the brainstem. An important question now is how does a system based on automatic (involuntary) saccade timing still allow for top-down (voluntary) control? The CRISP model of saccade generation, which models the saccade timer as a random walk process, proposes two mechanisms for control; cognitive processes can affect the rate of the saccade timer and potentially cancel ongoing programming. The present work tests whether these two mechanisms are sufficient for capturing the complex saccadic behavior observed from humans performing the antisaccade task, a task specifically designed to elicit eye movements that are incompatible with top-down goals. We hypothesize that these two mechanism will not be sufficient to reproduce human behavior and propose that a spatial mechanism needed. In order to test this hypothesis, the parameter space of the baseline CRISP model as well as two variant models (one with a bottom-up saliency driven saccade target map and one with a top-down attention driven saccade target map) were explored to see if the distribution of saccade parameters (e.g. latency and amplitude) could be generated that were consistent with those generated by humans performing the antisaccade task. As predicted, the spatial models were able to replicate the distributions of saccade parameters and error rates for 18 human subjects who performed a mixed-block antisaccade task. These results suggest that top-down control with automatic saccade timing is accomplished by influencing the spatial component of the eye movement.

Meeting abstract presented at VSS 2016

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