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Re-starting a neural race: anti-saccade correction


  • Imran Noorani,

    1. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
    2. Division of Neurosurgery, Wessex Neurological Centre, University Hospital Southampton, Southampton, UK
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  • R. H. S. Carpenter

    Corresponding author
    1. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
    • Correspondence: Dr R. H. S. Carpenter, The Physiological Laboratory, Downing Site, University of Cambridge, Cambridge CB2 3EG, UK. E-mail:

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  • Both authors contributed equally to this work.


In the anti-saccade task, a subject must make a saccadic eye movement in the opposite direction from a suddenly-presented visual target. This sets up a conflict between the natural tendency to make a pro-saccade towards the target and the required anti-saccade. Consequently there is a tendency to make errors, usually corrected by a second movement in the correct anti-saccade direction. In a previous paper, we showed that a very simple model, with racing LATER (Linear Approach to Threshold at Ergodic Rate) units for the pro- and anti-directions, and a stop unit that inhibits the impending error response, could account precisely for the detailed distributions of reaction times both for correct and error responses. However, the occurrence and timing of these final corrections have not been studied. We propose a novel mechanism: the decision race re-starts after an error. Here we describe measurements of all the responses in an anti-saccade task, including corrections, in a group of human volunteers, and show that the timing of the corrections themselves can be predicted by the same model with one additional assumption, that initiation of an incorrect pro-saccade also resets and initiates a corrective anti-saccade. No extra parameters are needed to predict this complex aspect of behaviour, adding weight to our proposal that we correct our mistakes by re-starting a neural decision race. The concept of re-starting a decision race is potentially exciting because it implies that neural processing of one decision can influence the next, and may be a fruitful way of understanding the complex behaviour underlying sequential decisions.