Department of Psychology.
Department of Psychology, University of Amsterdam.
J Exp Psychol Hum Percept Perform. 2019 Jun;45(6):826-839. doi: 10.1037/xhp0000638. Epub 2019 Apr 18.
Motorists often engage in secondary tasks unrelated to driving that increase cognitive workload, resulting in fatal crashes and injuries. An International Standards Organization method for measuring a driver's cognitive workload, the detection response task (DRT), correlates well with driving outcomes, but investigation of its putative theoretical basis in terms of finite attention capacity remains limited. We address this knowledge gap using evidence-accumulation modeling of simple and choice versions of the DRT in a driving scenario. Our experiments demonstrate how dual-task load affects the parameters of evidence-accumulation models. We found that the cognitive workload induced by a secondary task (counting backward by 3s) reduced the rate of evidence accumulation, consistent with rates being sensitive to limited-capacity attention. We also found a compensatory increase in the amount of evidence required for a response and a small speeding in the time for nondecision processes. The International Standards Organization version of the DRT was found to be most sensitive to cognitive workload. A Wald-distributed evidence-accumulation model augmented with a parameter measuring response omissions provided a parsimonious measure of the underlying causes of cognitive workload in this task. This work demonstrates that evidence-accumulation modeling can accurately represent data produced by cognitive workload measurements, reproduce the data through simulation, and provide supporting evidence for the cognitive processes underlying cognitive workload. Our results provide converging evidence that the DRT method is sensitive to dynamic fluctuations in limited-capacity attention. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
驾驶者在驾驶过程中经常会从事与驾驶无关的次要任务，这些任务会增加认知工作量，从而导致致命的车祸和伤害。国际标准化组织（ISO）有一种衡量驾驶员认知工作量的方法，即检测响应任务（DRT），它与驾驶结果密切相关，但对于其在有限注意能力方面的潜在理论基础的研究仍然有限。我们在驾驶场景中使用简单和选择版本的 DRT 的证据积累模型来解决这一知识空白。我们的实验证明了双任务负荷如何影响证据积累模型的参数。我们发现，次要任务（从 3 开始倒数）引起的认知工作量会降低证据积累的速度，这与注意力容量有限的速度一致。我们还发现，响应所需的证据量增加，非决策过程的时间略有加快。发现 ISO 版本的 DRT 对认知工作量最敏感。一种带有测量响应遗漏参数的 Wald 分布证据积累模型提供了对该任务中认知工作量的潜在原因的简洁衡量标准。这项工作表明，证据积累模型可以准确地表示认知工作量测量产生的数据，通过模拟再现数据，并为认知工作量背后的认知过程提供支持证据。我们的结果提供了一致的证据，证明 DRT 方法对有限容量注意力的动态波动敏感。（PsycINFO 数据库记录（c）2019 APA，保留所有权利）。
