Horrey William J, Wickens Christopher D, Consalus Kyle P
Department of Psychology, University of Illinois at Urbana-Champaign, IL, USA.
J Exp Psychol Appl. 2006 Jun;12(2):67-78. doi: 10.1037/1076-898X.12.2.67.
In 2 experiments, the authors examined how characteristics of a simulated traffic environment and in-vehicle tasks impact driver performance and visual scanning and the extent to which a computational model of visual attention (SEEV model) could predict scanning behavior. In Experiment 1, the authors manipulated task-relevant information bandwidth and task priority. In Experiment 2, the authors examined task bandwidth and complexity, while introducing infrequent traffic hazards. Overall, task priority had a significant impact on scanning; however, the impact of increasing bandwidth was varied, depending on whether the relevant task was supported by focal (e.g., in-vehicle tasks; increased scanning) or ambient vision (e.g., lane keeping; no increase in scanning). The computational model accounted for approximately 95% of the variance in scanning across both experiments.
在两项实验中，作者研究了模拟交通环境和车内任务的特征如何影响驾驶员的表现和视觉扫描，以及视觉注意力计算模型（SEEV模型）能够在多大程度上预测扫描行为。在实验1中，作者操纵了与任务相关的信息带宽和任务优先级。在实验2中，作者研究了任务带宽和复杂性，同时引入了不常见的交通危险。总体而言，任务优先级对扫描有显著影响；然而，带宽增加的影响各不相同，这取决于相关任务是由焦点视觉（如车内任务；扫描增加）还是周边视觉（如车道保持；扫描无增加）支持。该计算模型解释了两项实验中扫描差异的约95%。
