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Learning Similar Actions by Reinforcement or Sensory-Prediction Errors Rely on Distinct Physiological Mechanisms

机译:通过加固或感觉预测误差依赖于不同的生理机制学习类似的行动

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摘要

Humans can acquire knowledge of new motor behavior via different forms of learning. The two forms most commonly studied have been the development of internal models based on sensory-prediction errors (error-based learning) and success-based feedback (reinforcement learning). Human behavioral studies suggest these are distinct learning processes, though the neurophysiological mechanisms that are involved have not been characterized. Here, we evaluated physiological markers from the cerebellum and the primary motor cortex (M1) using noninvasive brain stimulations while healthy participants trained finger-reaching tasks. We manipulated the extent to which subjects rely on error-based or reinforcement by providing either vector or binary feedback about task performance. Our results demonstrated a double dissociation where learning the task mainly via error-based mechanisms leads to cerebellar plasticity modifications but not long-term potentiation (LTP)-like plasticity changes in M1; while learning a similar action via reinforcement mechanisms elicited M1 LTP-like plasticity but not cerebellar plasticity changes. Our findings indicate that learning complex motor behavior is mediated by the interplay of different forms of learning, weighing distinct neural mechanisms in M1 and the cerebellum. Our study provides insights for designing effective interventions to enhance human motor learning.
机译:人类可以通过不同形式的学习获得新的电机行为的知识。最常见的两种形式最常见的是基于感官预测误差(基于错误的学习)和基于成功的反馈(强化学习)的内部模型的开发。人类行为研究表明,这些是不同的学习过程,尽管所涉及的神经生理机制尚未表征。在此,我们使用非侵入性脑刺激评估来自小脑和主要电机皮层(M1)的生理标志物,而健康参与者培养了手指达到的任务。我们通过提供关于任务性能的矢量或二进制反馈来操纵受试者依赖基于误差或加强的程度。我们的结果表明,双解离,主要通过基于误差的机制学习任务导致小脑可塑性修饰,但不长期增强(LTP) - M1中的可塑性变化;在通过加固机制学习类似的作用,引发了M1 LTP样可塑性,但不是小脑塑性变化。我们的研究结果表明,学习复杂的电机行为是由不同形式的学习的相互作用介导的,称重M1和小脑中的不同神经机制。我们的研究提供了设计有效干预措施来增强人类运动学习的见解。

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