Mechanisms of sensorimotor adaptation in a hierarchical state feedback control model of speech.
Mechanisms of sensorimotor adaptation in a hierarchical state feedback control model of speech.
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DOI：
10.1371/journal.pcbi.1011244
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发表时间：
2023-07
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
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在运动过程中感知到感觉错误时，人类感觉运动系统会更新未来的运动以补偿错误，这种现象称为感觉运动适应。这种适应的一个组成部分被认为是由感官预测误差-预测和实际感官反馈之间的差异驱动的。然而，预测误差驱动适应的机制仍不清楚。本文采用反馈感知言语任务控制（FACTS）模型，研究了言语认知-运动适应中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的分层结构依赖于更高级别的任务（声道收缩）以及更低级别的发音状态表征。重要的是，FACTS还计算感觉预测误差，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，发现只有当预测错误更新了关节到任务的状态转换时，才存在适应行为。相比之下，预测误差单独更新前向感觉预测模型的设计不会产生适应。因此，事实表明：1）预测误差可以通过任务级更新来驱动适应，2）适应可能是由任务级控制的更新驱动的，而不是（仅）向前预测模型。此外，模拟适应与FACTS产生了一些重要的假设，关于以前报道的现象，如识别源（S）的不完全适应和驱动因素（S）的变化，在第二共振峰频率适应第一共振峰扰动。所提出的模型设计铺平了道路的分层状态反馈控制框架进行检查的背景下，在语音和非语音效应器系统的感觉运动适应。当我们移动时，我们的大脑会预测运动产生的感觉反馈，并根据任何感觉预测错误（预测与实际感觉反馈之间的差异）快速调整未来的运动。这种学习过程，感觉运动适应，已经在许多运动中被广泛研究（例如，行走、伸手、说话），但其潜在机制仍不清楚。在这里，我们研究了使用FACTS模型响应于改变的听觉反馈而驱动语音适应的机制，FACTS模型是语音的分层状态反馈控制模型，其中高级控制器实现语音目标（例如，声道的收缩）通过引导移动发音器官的低级控制器（例如，（1）舌头和下巴的位置。我们证明了预测误差可以通过高水平控制的变化来驱动适应，但不仅仅是通过运动结果或低水平控制的预测变化。除了复制语音中感觉运动适应的多个关键特征外，我们的模拟还为目前知之甚少的现象产生了潜在的新解释。重要的是，鉴于我们的模型设计与广泛接受的言语之外的运动控制框架密切相关，这些结果也有可能广泛适用于非言语运动系统。
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. When we move, our brain predicts the sensory feedback that would result from the movement, and can quickly adjust future movements based on any sensory prediction errors—differences between the predictions and actual sensory feedback. This learning process, sensorimotor adaptation, has been extensively studied in many movements (e.g., walking, reaching, speaking), but its underlying mechanisms remain largely unclear. Here, we examined mechanisms driving speech adaptation in response to altered auditory feedback using the FACTS model, a hierarchical state feedback control model of speech in which a high-level controller achieves speech goals (e.g., constrictions of the vocal tract) by directing a low-level controller that moves the speech articulators (e.g., positions of the jaw and the tongue). We demonstrated that prediction errors can drive adaptation through changes in high-level control, but not solely through changes in predictions of movement outcomes or low-level control. In addition to replicating multiple key features of sensorimotor adaptation in speech, our simulations also generated potential new explanations for phenomena that are currently poorly understood. Importantly, given that our model design is closely aligned with widely accepted motor control frameworks outside of speech, these results have the potential to be broadly applicable to non-speech motor systems as well.
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