基于奖励预测错误的显式学习促进了敏捷的运动适应.
Tjasa Kunavar1,2, Xiaoxiao Cheng3, David W Franklin4,5,6
1Laboratory for Neuromechanics and Biorobotics, Department of Automatics, Biocybernetics, and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia.
PloS one
|December 6, 2023
概括
人类使用感官预测错误和奖励预测错误来进行运动学习. 虽然感觉运动适应修改了内部模型,但奖励学习使用了明确的策略,这两者对于适应新环境至关重要.
科学领域:
- 运动控制和学习.
- 计算神经科学是一种计算神经科学.
- 人类适应的人类适应
背景情况:
- 运动学习是由感官预测错误 (感官运动适应) 和奖励预测错误 (奖励学习) 驱动的.
- 了解这两种错误类型之间的相互作用对于理解运动技能获得至关重要.
研究的目的:
- 研究传感运动适应和奖励学习之间的独特特征和相互联系.
- 区分基于感官与奖励预测错误的基础上的适应的神经和计算机制.
主要方法:
- 采用视觉运动范式,参与者在扰乱条件下执行手臂运动.
- 试验对象收到了感官预测错误,终点错误或二进制奖励反.
- 开发了一个计算模型来区分传感运动和奖励学习过程.
主要成果:
- 参与者适应了新的扰动,不管错误类型如何,汇聚到类似的运动模式.
- 感官运动适应引起了显著的后果,而奖励学习导致后果较小,表明对内部模型的影响较小.
- 与感觉运动适应相比,在奖励学习 (明确的策略) 中,适应随机变化的扰动更快.
- 计算建模揭示了传感运动和奖励学习的独特适应过程.
结论:
- 感官运动适应和奖励学习是不同的过程,以不同的方式影响运动控制.
- 感官运动适应改变了内部模型,导致明显的后果.
- 奖励学习依赖于没有重大后果的明确策略,但能够更快地适应不断变化的环境.
- 人类整合了感觉运动和奖励学习,以有效地适应运动任务.
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