Anderson Andrew J, Smyrnis Nikolaos, Noorani Imran, Carpenter R H S
Department of Optometry and Vision Sciences, The University of Melbourne, Parkville 3010, Australia.
Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece; 2nd Psychiatry Department, National and Kapodistrian University of Athens, Medical School, University General Hospital "ATTIKON", Athens, Greece.
Neuroscience. 2021 Jan 1;452:345-353. doi: 10.1016/j.neuroscience.2020.11.022. Epub 2020 Nov 24.
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 (mean 175 and 177 ms, respectively; standard deviation, SD 22 and 24 ms). In contrast, we identified prolonged latencies in the countermanding tasks (236 ms; SD 37 ms) 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.
眼动决策可以通过一个简单的步进任务来研究，即一个人要决定目标是向左还是向右跳跃。更复杂的任务包括反指令任务（看向跳跃的目标，但后续信号指示不要看时除外）和无轮任务（跳跃的目标有时随后会迅速跳到一个新位置）。尽管几乎无法想象每个任务都存在完全独立的决策机制，但不同实例的LATER（具有遍历率的线性阈值方法）模型已被证明可以解释这些任务产生的扫视潜伏期数据。然而，这些模型在预测前扫视反应方面具有相同的结构（所有步进任务试验，以及反指令任务和无轮任务中的对照试验，其中没有给出反指令信号或目标没有进行第二次跳跃时）。我们测量了23名人类观察者在执行这三项任务时的扫视潜伏期，并用LATER对前扫视潜伏期进行建模，以查看模型参数在不同任务中是否能有效保留。我们发现步进任务和无轮任务之间的反应时间和模型参数没有显著差异（分别为平均175毫秒和177毫秒；标准差，SD分别为22毫秒和24毫秒）。相比之下，我们发现反指令任务中的潜伏期延长（为236毫秒；SD为37毫秒），这可以通过决策信号上升较慢和阈值升高来解释，这表明参与者更加谨慎。我们的研究结果支持这样一种观点，即存在用于眼动决策的通用机制，该机制可以根据任务需求灵活部署。
