在感官运动学习中的因果推断,预测和状态估计
Hyosub E Kim1, Romeo Chua2, Davin Hu2
1Kinesiology; Neuroscience, The University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada.
Proceedings. Biological sciences
|August 12, 2025
概括
大脑通过区分内部运动噪音和外部视觉干扰来从运动错误中学习. 一个新的贝叶斯模型,PIECE,解释了我们如何适应外部错误,同时忽略自我生成的错误.
科学领域:
- 神经科学是一个神经科学.
- 发动机控制器的控制器
- 计算理论 计算理论
背景情况:
- 传感运动系统必须区分自我产生的错误和外部干扰,以有效地学习和适应运动.
- 之前的研究表明,人类忽略了内部产生的达到错误 (机器噪声),但适应外部错误 (视觉干扰).
研究的目的:
- 将大脑如何将运动错误解析成内部和外部组件的理解正式化.
- 提出和验证一个新的贝叶斯决策模型,用于传感运动适应.
主要方法:
- 用16个神经型成年人复制之前的发现.
- 内部和外部错误原因分析 (PIECE) 模型的开发,这是一个贝叶斯决策框架.
- 将PIECE模型与三种已建立的手对目标对齐模型进行比较.
主要成果:
- PIECE模型准确地捕获了在人类行为中观察到的内部与外部错误的精确解析.
- 运动纠正被证明反映了状态估计和对外部干扰的信念.
- 在解释观察到的错误解析时,PIECE模型的性能优于手对目标对齐模型.
结论:
- 感官运动适应涉及对错误源的因果推断,挑战专注于手对目标对齐的理论.
- 神经系统有效地抵消内在的运动噪声,同时适应外部干扰.
- PIECE模型为保持精细校准的运动提供了规范性的解释.
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