Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, Indiana, United States of America.
Graduate Program in Bioengineering, University of California Berkeley-University of California San Francisco, San Francisco, California, United States of America.
PLoS Comput Biol. 2023 Jul 28;19(7):e1011244. doi: 10.1371/journal.pcbi.1011244. eCollection 2023 Jul.
Upon perceiving sensory errors during movements, the human sensorimotor system updates future movements to compensate for the errors, a phenomenon called sensorimotor adaptation. One component of this adaptation is thought to be driven by sensory prediction errors-discrepancies between predicted and actual sensory feedback. However, the mechanisms by which prediction errors drive adaptation remain unclear. Here, auditory prediction error-based mechanisms involved in speech auditory-motor adaptation were examined via the feedback aware control of tasks in speech (FACTS) model. Consistent with theoretical perspectives in both non-speech and speech motor control, the hierarchical architecture of FACTS relies on both the higher-level task (vocal tract constrictions) as well as lower-level articulatory state representations. Importantly, FACTS also computes sensory prediction errors as a part of its state feedback control mechanism, a well-established framework in the field of motor control. We explored potential adaptation mechanisms and found that adaptive behavior was present only when prediction errors updated the articulatory-to-task state transformation. In contrast, designs in which prediction errors updated forward sensory prediction models alone did not generate adaptation. Thus, FACTS demonstrated that 1) prediction errors can drive adaptation through task-level updates, and 2) adaptation is likely driven by updates to task-level control rather than (only) to forward predictive models. Additionally, simulating adaptation with FACTS generated a number of important hypotheses regarding previously reported phenomena such as identifying the source(s) of incomplete adaptation and driving factor(s) for changes in the second formant frequency during adaptation to the first formant perturbation. The proposed model design paves the way for a hierarchical state feedback control framework to be examined in the context of sensorimotor adaptation in both speech and non-speech effector systems.
当人类感知运动系统在运动中感知到误差时,它会更新未来的运动以补偿这些误差,这一现象被称为感知运动适应。这种适应的一个组成部分被认为是由感觉预测误差驱动的——预测和实际感觉反馈之间的差异。然而,预测误差驱动适应的机制仍不清楚。在这里,通过语音听觉-运动适应的反馈感知控制任务 (FACTS) 模型,研究了语音中基于听觉预测误差的机制。与非言语和言语运动控制的理论观点一致,FACTS 的层次结构依赖于较高层次的任务(声道收缩)以及较低层次的发音状态表示。重要的是,FACTS 还计算了感觉预测误差作为其状态反馈控制机制的一部分,这是运动控制领域的一个成熟框架。我们探索了潜在的适应机制,发现只有当预测误差更新发音到任务状态转换时,才会出现适应性行为。相比之下,当预测误差仅更新前向感觉预测模型时,设计不会产生适应性。因此,FACTS 表明:1)预测误差可以通过任务级别更新来驱动适应;2)适应很可能是通过任务级别控制的更新来驱动的,而不是(仅)通过向前预测模型来驱动。此外,使用 FACTS 模拟适应产生了许多关于以前报告的现象的重要假设,例如确定不完全适应的来源和在适应第一共振峰扰动时第二共振峰频率变化的驱动因素。拟议的模型设计为在言语和非言语效应器系统的感知运动适应中检查分层状态反馈控制框架铺平了道路。