| Performance detriments in the antisaccade task have been linked to numerous psychiatric and neurological disorders yet, there is no consensus as to how healthy individuals perform the task. Most computational models of the antisaccade task assume that cue onset automatically triggers programming of a prosaccade towards the cue and that successfully performing an antisaccade away from the cue requires top-down inhibition of the erroneous prosaccade before a correct antisaccade can be made. However, a recent body of research on oculomotor control suggests that humans have much less control over their eye movements. 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, like that which is needed in the antisaccade task? In order to test this idea, a new model called ABS(Attention Biased Salience)-CRISP was created which builds upon the CRISP (Nuthmann, Smith, Engbert, & Henderson, 2010) model of saccade generation (which models the automatic saccade timer as a random walk process) by adding a spatial component that computes the saccade target location as the weighted sum of a bottom-up saliency map and a top-down attentional map. The CRISP and ABS-CRISP models were evaluated and compared to human performance in a mixed-block antisaccade task. The ABS-CRISP model was able to replicate individual distributions of saccade latencies that were indistinguishable from a majority of the subjects data. The results support the idea that the initiation of saccade timing is not tied to cognitive events that occur during fixations but instead, are triggered by a random timer. The results also support the idea that inter-individual, intra-individual and inter-task differences in performance can be explained, in large, by changes in the bias between bottom-up and top-down information in the spatial component of saccade programming. |
