UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, California, United States of America.
Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, Indiana, United States of America.
PLoS Comput Biol. 2024 Oct 11;20(10):e1011986. doi: 10.1371/journal.pcbi.1011986. eCollection 2024 Oct.
Behavioral speech tasks have been widely used to understand the mechanisms of speech motor control in typical speakers as well as in various clinical populations. However, determining which neural functions differ between typical speakers and clinical populations based on behavioral data alone is difficult because multiple mechanisms may lead to the same behavioral differences. For example, individuals with cerebellar ataxia (CA) produce atypically large compensatory responses to pitch perturbations in their auditory feedback, compared to typical speakers, but this pattern could have many explanations. Here, computational modeling techniques were used to address this challenge. Bayesian inference was used to fit a state feedback control (SFC) model of voice fundamental frequency (fo) control to the behavioral pitch perturbation responses of speakers with CA and typical speakers. This fitting process resulted in estimates of posterior likelihood distributions for five model parameters (sensory feedback delays, absolute and relative levels of auditory and somatosensory feedback noise, and controller gain), which were compared between the two groups. Results suggest that the speakers with CA may proportionally weight auditory and somatosensory feedback differently from typical speakers. Specifically, the CA group showed a greater relative sensitivity to auditory feedback than the control group. There were also large group differences in the controller gain parameter, suggesting increased motor output responses to target errors in the CA group. These modeling results generate hypotheses about how CA may affect the speech motor system, which could help guide future empirical investigations in CA. This study also demonstrates the overall proof-of-principle of using this Bayesian inference approach to understand behavioral speech data in terms of interpretable parameters of speech motor control models.
行为言语任务已被广泛用于理解典型说话者和各种临床人群的言语运动控制机制。然而，仅基于行为数据确定典型说话者和临床人群之间的哪些神经功能存在差异是困难的，因为多种机制可能导致相同的行为差异。例如，与典型说话者相比，小脑共济失调（CA）患者在听觉反馈中的音高扰动产生异常大的补偿反应，但这种模式可能有多种解释。在这里，使用计算建模技术来解决这一挑战。贝叶斯推理用于拟合语音基频（fo）控制的状态反馈控制（SFC）模型，以适应 CA 患者和典型说话者的行为音高扰动反应。该拟合过程导致五个模型参数（感觉反馈延迟、听觉和躯体感觉反馈噪声的绝对和相对水平以及控制器增益）的后验似然分布的估计值，这些参数在两组之间进行比较。结果表明，CA 患者可能会与典型说话者不同比例地加权听觉和躯体感觉反馈。具体来说，CA 组对听觉反馈的敏感性大于对照组。控制器增益参数也存在较大的组间差异，这表明 CA 组对目标误差的运动输出反应增加。这些建模结果产生了关于 CA 如何影响言语运动系统的假设，这有助于指导未来对 CA 的实证研究。本研究还证明了使用这种贝叶斯推理方法根据言语运动控制模型的可解释参数来理解行为言语数据的总体原理。
