Modelling Prosaccade Latencies across Multiple Decision-Making Tasks

Oculomotor decision making can be investigated by a simple step task, where a person decides whether a target has jumped to the left or the right. More complex tasks include the countermanding task (look at the jumped target, except when a subsequent signal instructs you not to) and the Wheeless task (where the jumped target sometimes then quickly jumps to a new location). Different instantiations of the LATER (Linear Approach to Threshold with Ergodic Rate) model have been shown to explain the saccadic latency data arising from these tasks, despite it being almost inconceivable that completely separate decision-making mechanisms exist for each. However, these models have an identical construction with regards to predicting prosaccadic responses (all step task trials, and control trials in countermanding and Wheeless tasks, where no countermanding signal is given or when the target does not make a second jump). We measured saccadic latencies for 23 human observers each performing the three tasks, and modelled prosaccade latencies with LATER to see if model parameters were usefully preserved across tasks. We found no significant difference in reaction times and model parameters between the step and Wheeless tasks (mean175 & 177ms, respectively; standard deviation, SD 22 & 24ms). In contrast, we identified prolonged latencies in the countermanding tasks (236ms; SD 37ms) explained by a slower rise and an elevated threshold of the decision making signal, suggesting elevated participant caution. Our findings support the idea that common machinery exists for oculomotor decision-making, which can be flexibly deployed depending upon task demands.