Performance Monitoring in Eye Movement Fields of the Frontal and Medial Frontal Cortices

62 | 2010 | VOLUME 1 VANDERBILT REVIEWS | NEUROSCIENCE ©2010 Vanderbilt Brain Institute. All rights reserved. THE STOP SIGNAL TASK AND OCULOMOTOR CONTROL The stop-signal task (SST) was developed to investigate response inhibition. In a typical version, subjects are required to discriminate between stimuli by making speeded manual responses. On a subset of trials, the stimulus to be discriminated is quickly followed by a second stimulus (the stop-signal) instructing subjects to cancel their prepared response. Logan and Cowan proposed a race model of the SST which provides a crucial estimate of the timing of the hidden, response inhibition process. The ability to measure stop-signal reaction time (or SSRT) proved invaluable to the physiological investigation of cancelling action. It was subsequently demonstrated that the race model could fit behavior in a saccade version of the SST , and this version has been used with great success to investigate oculomotor control in monkeys . Specifically, the logic of the race model identifies activation and timing criteria necessary for neurons to participate in preparing or cancelling eye movements. This framework allowed Hanes and colleagues to identify neural populations in the frontal eye field (FEF) and superior colliculus (SC) with activity necessary to produce or inhibit saccades during the SST. These findings led to the hypothesis that movement and fixation related cells in the FEF and SC may implement a process similar to the race model during the SST. By relaxing an assumption of independence, Boucher and colleagues provided an elegant link, showing that the race model can be extended in neurally plausible terms which fit both behavioral and physiological data. Thus, a large body of established work provides a great deal of theoretical leverage for investigation of the oculomotor system using the SST. Special attributes of the SST which will be referred to in italics throughout the discussion also make it uniquely suited to investigate error detection and performance monitoring (Figure 1).