Integrated models of learning and decision making in complex tasks

复杂任务中学习和决策的集成模型

• 批准号: DE230100171
• 负责人: Dr Luke Strickland
• 金额: $ 30.9万
• 依托单位: Curtin University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-07-13 至 2026-07-12
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE230100171
• 关键词: Integrated; models; learning; decision; making

How do people learn to make decisions in complex work systems when assisted by automation? This project will develop computational models of human learning and decision making that explain and predict complex decisions relevant to industries such as aviation and defence. It will examine how humans learn to use automated advice, how learning affects remembering to perform planned (deferred) actions, and factors that pose a risk to learning and adaptation. The expected outcome is a significant theoretical advance in human factors and cognitive psychology, and a tool for informing work design (e.g., computer interface, task allocation) and training, with the potential to reduce human error in safety-critical workplaces.

在自动化的帮助下，人们如何学会在复杂的工作系统中做出决策？该项目将开发人类学习和决策的计算模型，以解释和预测与航空和国防等行业相关的复杂决策。它将研究人类如何学习使用自动建议，学习如何影响记忆以执行计划（延迟）行动，以及对学习和适应构成风险的因素。预期的结果是在人为因素和认知心理学方面取得重大理论进展，并为工作设计（例如，计算机界面，任务分配）和培训提供信息工具，有可能减少安全关键工作场所的人为错误。