超越性能:基于POMDP的机器学习框架,用于专家认知
Hao He1, Yucheng Duan2,3
1Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, 100084, China.
Behavior research methods
|November 25, 2025
概括
体育方面的专业知识涉及到增强的感官精度 (SP) 和适应性先前信念 (pB) 校准,而不仅仅是技能. 这种认知重组使专家能够更好地预测不确定性下的行为.
科学领域:
- 认知科学 认知科学
- 运动科学 运动科学 运动科学
- 机器学习 机器学习
背景情况:
- 了解专家和新手之间的差异对于技能获取至关重要.
- 在不确定性下预期是一个重要的认知挑战.
研究的目的:
- 预先调查区分专家和新手的认知参数.
- 应用部分可观测的马尔科夫决策过程 (POMDP) 建模和机器学习来进行这项调查.
主要方法:
- 48名参与者 (24名专家,24名新手) 进行了篮球预测任务.
- 通过POMDP建模,提取了感官精度 (SP) 和先前信念 (pB) 参数.
- 机器学习分类器分析了专家和初学者参数配置文件的独特性.
主要成果:
- 与新手相比,专家显示出更高的SP和更中性的pB.
- 专家的认知参数与POMDP模型更加一致.
- 机器学习实现了>90%的分类准确度,根据SP和pB区分专家和新手.
结论:
- 专业知识包括增强的感知过 (SP) 和灵活的先前知识使用 (pB).
- 认知重组,而不仅仅是技能增长,定义了专业知识.
- 双参数方法提供了基于模型的专家认知的观点.
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