从简单的实验室任务到虚拟球场:贝叶斯集成在网球中的贝叶斯集成
Damian Beck1, Stephan Zahno1, Ralf Kredel1
1Department of Movement and Exercise Science, Institute of Sport Science, University of Bern, Bern, Switzerland.
Journal of neurophysiology
|June 25, 2025
概括
人类行为使用贝叶斯原则整合感官数据和期望,即使在复杂的任务中,如返回网球服务. 这项研究表明,目光模式与贝叶斯预测一致,随着时间的推移适应经验和感官输入.
科学领域:
- 认知神经科学 认知神经科学
- 发动机控制器的控制器
- 人与计算机的交互
背景情况:
- 人类的行为往往依赖于将感官信息与先前的期望整合起来,这是贝叶斯推理模型的过程.
- 对贝叶斯集成的现有证据主要来自简化的实验室任务,限制了对其在复杂的现实场景中的应用的理解.
研究的目的:
- 为了调查人类在复杂的感觉运动任务中的预测性目光行为,返回网球服务是否与贝叶斯集成原则保持一致.
- 检查先前的预期和感官证据是如何动态加权基于可靠性在不同的时间尺度.
主要方法:
- 使用扩展现实 (XR) 设置进行了两项实验,模拟了真正的网球发球回报与不受约束的运动.
- 参与者 (n=64) 面对有着不同的发球位置分布的对手,并记录了他们的预测性目光行为和球跳位置的明确判断.
- 在第二次实验中,视觉不确定性通过变化的球速度来操纵.
主要成果:
- 预测性凝视行为表明对手偏好服务位置的偏见,与贝叶斯预测一致.
- 这种先前驱动的偏差在增加的视觉不确定性 (更高的球速度) 下更加明显.
- 在两个时间尺度上观察到可靠性加权的整合:先前的影响随着匹配经验的增加而增加,而单次服务估计则通过传入的感觉数据进行更新.
结论:
- 贝叶斯理论为理解复杂,高性能任务中的感觉运动适应提供了一个强大的框架.
- 扩展现实技术可以在受控的实验条件下测试现实世界的电机控制原理.
- 这些发现强调了先前知识和感官输入在指导人类行为的动态,基于可靠性的整合.
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