4.2 Article

The effect of distractor modality and processing code on human-automation interaction

期刊

COGNITION TECHNOLOGY & WORK
卷 13, 期 4, 页码 233-244

出版社

SPRINGER LONDON LTD
DOI: 10.1007/s10111-010-0163-2

关键词

Distractor; Automation; Secondary task; Multiple Resource Theory

向作者/读者索取更多资源

We examined the effect of distractor characteristics (modality and processing code) on visual search performance and interaction with an automated decision aid. Multiple Resource Theory suggests that concurrent tasks that are processed similarly (e.g. two visual tasks) will cause greater interference than tasks that are not (e.g., a visual and auditory task). The impact of tasks that share processing and perceptual demands and their interaction with human-automation interaction is not established. In order to examine this, participants completed two blocks of a luggage screening simulation with or without the assistance of an automated aid. For one block, participants performed a concurrent distractor task drawn from one of four combinations of modality and processing code: auditory-verbal; auditory-spatial; visual-verbal; visual-spatial. We measured sensitivity, criterion setting, perceived workload, system trust, perceived system reliability, compliance, reliance, and confidence. Participants demonstrated highest sensitivity when performing with an auditory-spatial secondary task. Automation compliance was higher when the auditory-spatial distraction was present versus absent; however, system trust was highest in the auditory-verbal condition. Confidence (when disagreeing with the aid) was also highest when the distractor was auditory. This study indicates that some forms of auditory 'distractors' may actually help performance; these results further contribute to understanding how distractions influence performance when operators interact with automation and have implications for improved work environment and system design.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.2
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据