在无人驾驶车辆控制任务中,利用认知模型和生理学指标对认知工作负载进行分析
Christopher A Stevens1, Megan B Morris1, Christopher R Fisher2
1Cognitive Models and Agents Branch, Air Force Research Laboratory Dayton, Ohio, USA.
概括
认知度量分析 (CMP) 有效地测量复杂任务中的认知负载,将工作负载与记忆需求联系起来. 这种方法为改善人机系统设计提供了对工作负载指标的统一视图.
科学领域:
- 人类因素 人类因素
- 认知心理学 认知心理学
- 系统工程 系统工程
背景情况:
- 评估认知负载对于优化人机交互至关重要.
- 现有的工作负载指标往往缺乏统一的理论框架.
- 了解认知资源需要帮助诊断性能限制.
研究的目的:
- 为了验证认知指标分析 (CMP) 与既定的工作负载指标相对应.
- 将复杂的任务工作负载分解为特定的认知资源需求.
- 建立认知模型和生理/行为指标之间的融合有效性.
主要方法:
- 开发了一个复杂的无人驾驶车辆控制任务的认知模型.
- 使用认知指标分析 (CMP) 来捕捉工作负载变异.
- 对行为表现,主观工作量和生理数据进行了模型预测的检查.
主要成果:
- CMP模型的复合工作负载准确地预测了生理指标,性能和主观工作负载.
- 模型分析显示程序,声明和时间记忆需求是关键的工作负载驱动因素.
- 初步证据支持CMP与生理工作负载指标的对应.
结论:
- 在复杂环境中,CMP在捕捉工作负载方面表现有前途.
- 这种方法可以弥合行为,生理和主观工作负载指标.
- CMP可以为减轻工作负载的策略和人员/自动化决策提供信息.
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