隐藏状态推断或在动态视觉运动技能期间不断更新的信念
1School of Public Health and Sport Sciences, Medical School, University of Exeter, Exeter, EX1 2LU, UK. D.J.Harris@exeter.ac.uk.
概括
人类在感觉运动任务中不断更新信念,而不是做出突然的状态推断. 这种连续的贝叶斯更新支持适应性行动,即使不是完美的最佳.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
- 人类运动控制人体运动控制
背景情况:
- 适应性感官运动行为依赖于随着环境变化更新信念.
- 预测处理理论表明,等级推理驱动这种灵活性.
- 这项研究调查了持续的信念更新与离散状态推断在行动中的关系.
研究的目的:
- 为了确定连续的信念更新或离散状态推断是否更好地解释了拦截任务中的行为.
- 使用贝叶斯式和强化学习框架建模感知推理.
- 为了比较不同计算模型对人类行为的预测能力.
主要方法:
- 一个基于虚拟现实的拦截任务,具有概率线索和周期性的上下文反转.
- 记录球弹之前的参与者凝视位置作为感知推断的索引.
- 应用贝叶斯学 (层次高斯学过器) 和关联式学习模型来分析行为.
主要成果:
- 持续的信念更新贝叶斯模型在预测行为方面超过了关联式学习模型.
- 持续的信念更新比离散状态推断更好地解释了凝视行为.
- 尽管能够更好地预测任务结构,但状态推理模型的表现优于状态推理模型.
结论:
- 人类的感觉运动行为以不断更新贝叶斯信念为特征.
- 在动态任务中,行为倾向于灵活的实时更新,而不是在动态任务中明确的结构推理.
- 认知过程显示了规范性最佳性和现实世界的适应性行动之间的差距.
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