PDF(Pubmed)
Abstract：
The unpredictable nature of our world can introduce a variety of errors in our actions, including sensory prediction errors (SPEs) and task performance errors (TPEs). SPEs arise when our existing internal models of limb-environment properties and interactions become miscalibrated due to changes in the environment, while TPEs occur when environmental perturbations hinder achievement of task goals. The precise mechanisms employed by the sensorimotor system to learn from such limb- and task-related errors and improve future performance are not comprehensively understood. To gain insight into these mechanisms, we performed a series of learning experiments wherein the location and size of a reach target were varied, the visual feedback of the motion was perturbed in different ways, and instructions were carefully manipulated. Our findings indicate that the mechanisms employed to compensate SPEs and TPEs are dissociable. Specifically, our results fail to support theories that suggest that TPEs trigger implicit refinement of reach plans or that their occurrence automatically modulates SPE-mediated learning. Rather, TPEs drive improved action selection, that is, the selection of verbally sensitive, volitional strategies that reduce future errors. Moreover, we find that exposure to SPEs is necessary and sufficient to trigger implicit recalibration. When SPE-mediated implicit learning and TPE-driven improved action selection combine, performance gains are larger. However, when actions are always successful and strategies are not employed, refinement in behavior is smaller. Flexibly weighting strategic action selection and implicit recalibration could thus be a way of controlling how much, and how quickly, we learn from errors.
摘要：
我们世界的不可预测的性质会在我们的行为中引入各种错误，包括感官预测错误(SPE)和任务性能错误(TPE)。当我们现有的肢体环境属性和相互作用的内部模型由于环境的变化而被错误校准时，SPE就会出现，而当环境扰动阻碍任务目标的实现时，就会发生TPE。感觉运动系统用于从此类与肢体和任务相关的错误中学习并改善未来性能的精确机制尚未得到全面理解。为了深入了解这些机制，我们进行了一系列学习实验，其中范围目标的位置和大小是不同的，运动的视觉反馈以不同的方式受到干扰，和指令被仔细地操纵。我们的发现表明，用于补偿SPE和TPE的机制是可分离的。具体来说,我们的结果不能支持这样的理论,即TPE触发了对到达计划的内隐细化,或者TPE的发生会自动调节SPE介导的学习.相反，TPE推动改进的动作选择，也就是说,口头敏感的选择，减少未来错误的自愿策略。此外，我们发现暴露于SPE对于触发隐式重新校准是必要且足够的。当SPE介导的内隐学习和TPE驱动的改进的动作选择相结合时，性能增益更大。然而,当行动总是成功的，而战略没有被采用时，行为的细化较小。因此，灵活地加权战略行动选择和隐式重新校准可以是一种控制多少，有多快，我们从错误中学习。
