识别自动化辅助信号检测中的低效策略
Lana Tikhomirov1, Megan L Bartlett2, Jackson Duncan-Reid3
1Australian Institute for Machine Learning, University of Adelaide.
Journal of experimental psychology. Applied
|July 20, 2023
概括
人类操作员使用自动诊断辅助工具效率低下. 一个混合模型最好地解释了他们的策略,表明他们有时遵循辅助工具,有时延迟,表明在信号检测任务中表现不佳.
科学领域:
- 人与计算机的交互
- 认知心理学 认知心理学
- 信号检测理论的理论
背景情况:
- 自动诊断辅助器协助人类操作员进行信号检测.
- 运营商经常不充分利用决策辅助工具,导致业绩低于最佳.
- 以前的研究表明,在人类援助互动中,缓慢的条件切断 (CC) 策略是缓慢的.
研究的目的:
- 为了测试运营商战略的缓慢条件切断 (CC) 模型.
- 在不同的任务难度下检查自动化辅助信号检测效率.
- 将CC模型与离散偏差 (DD) 和混合模型进行比较.
主要方法:
- 两个涉及数值决策任务的实验.
- 参与者根据概率读数判断信号或噪声.
- 任务难度是通过变化的平均读数值来操纵的.
主要成果:
- 用CC模型,DD模型和混合模型分析数据.
- 模型匹配表明混合模型最好地描述了操作者策略.
- 结果表明,在运营商如何使用信号检测辅助器件方面,存在多种低效.
结论:
- 操作员使用自动诊断辅助工具的策略各不相同,往往效率低下.
- 随机切断和尊重策略的混合解释了观察到的业绩.
- 需要进一步的研究来优化诊断任务中的人机交互.
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