Do Not Cut Off Your Tail: A Mega-Analysis of Responses to Auditory Perturbation Experiments.

PURPOSE: The practice of removing "following" responses from speech perturbation analyses is increasingly common, despite no clear evidence as to whether these responses represent a unique response type. This study aimed to determine if the distribution of responses to auditory perturbation paradigms represents a bimodal distribution, consisting of two distinct response types, or a unimodal distribution. METHOD: This mega-analysis pooled data from 22 previous studies to examine the distribution and magnitude of responses to auditory perturbations across four tasks: adaptive pitch, adaptive formant, reflexive pitch, and reflexive formant. Data included at least 150 unique participants for each task, with studies comprising younger adult, older adult, and Parkinson's disease populations. A Silverman's unimodality test followed by a smoothed bootstrap resampling technique was performed for each task to evaluate the number of modes in each distribution. Wilcoxon signed-ranks tests were also performed for each distribution to confirm significant compensation in response to the perturbation. RESULTS: Modality analyses were not significant (p > .05) for any group or task, indicating unimodal distributions. Our analyses also confirmed compensatory reflexive responses to pitch and formant perturbations across all groups, as well as adaptive responses to sustained formant perturbations. However, analyses of sustained pitch perturbations only revealed evidence of adaptation in studies with younger adults. CONCLUSION: The demonstration of a clear unimodal distribution across all tasks suggests that following responses do not represent a distinct response pattern, but rather the tail of a unimodal distribution. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24282676.

Quantitatively characterizing reflexive responses to pitch perturbations.

Background: Reflexive pitch perturbation experiments are commonly used to investigate the neural mechanisms underlying vocal motor control. In these experiments, the fundamental frequency-the acoustic correlate of pitch-of a speech signal is shifted unexpectedly and played back to the speaker via headphones in near real-time. In response to the shift, speakers increase or decrease their fundamental frequency in the direction opposing the shift so that their perceived pitch is closer to what they intended. The goal of the current work is to develop a quantitative model of responses to reflexive perturbations that can be interpreted in terms of the physiological mechanisms underlying the response and that captures both group-mean data and individual subject responses. Methods: A model framework was established that allowed the specification of several models based on Proportional-Integral-Derivative and State-Space/Directions Into Velocities of Articulators (DIVA) model classes. The performance of 19 models was compared in fitting experimental data from two published studies. The models were evaluated in terms of their ability to capture both population-level responses and individual differences in sensorimotor control processes. Results: A three-parameter DIVA model performed best when fitting group-mean data from both studies; this model is equivalent to a single-rate state-space model and a first-order low pass filter model. The same model also provided stable estimates of parameters across samples from individual subject data and performed among the best models to differentiate between subjects. The three parameters correspond to gains in the auditory feedback controller's response to a perceived error, the delay of this response, and the gain of the somatosensory feedback controller's "resistance" to this correction. Excellent fits were also obtained from a four-parameter model with an additional auditory velocity error term; this model was better able to capture multi-component reflexive responses seen in some individual subjects. Conclusion: Our results demonstrate the stereotyped nature of an individual's responses to pitch perturbations. Further, we identified a model that captures population responses to pitch perturbations and characterizes individual differences in a stable manner with parameters that relate to underlying motor control capabilities. Future work will evaluate the model in characterizing responses from individuals with communication disorders.

A Simple 3-Parameter Model for Examining Adaptation in Speech and Voice Production.

Sensorimotor adaptation experiments are commonly used to examine motor learning behavior and to uncover information about the underlying control mechanisms of many motor behaviors, including speech production. In the speech and voice domains, aspects of the acoustic signal are shifted/perturbed over time via auditory feedback manipulations. In response, speakers alter their production in the opposite direction of the shift so that their perceived production is closer to what they intended. This process relies on a combination of feedback and feedforward control mechanisms that are difficult to disentangle. The current study describes and tests a simple 3-parameter mathematical model that quantifies the relative contribution of feedback and feedforward control mechanisms to sensorimotor adaptation. The model is a simplified version of the DIVA model, an adaptive neural network model of speech motor control. The three fitting parameters of SimpleDIVA are associated with the three key subsystems involved in speech motor control, namely auditory feedback control, somatosensory feedback control, and feedforward control. The model is tested through computer simulations that identify optimal model fits to six existing sensorimotor adaptation datasets. We show its utility in (1) interpreting the results of adaptation experiments involving the first and second formant frequencies as well as fundamental frequency; (2) assessing the effects of masking noise in adaptation paradigms; (3) fitting more than one perturbation dimension simultaneously; (4) examining sensorimotor adaptation at different timepoints in the production signal; and (5) quantitatively predicting responses in one experiment using parameters derived from another experiment. The model simulations produce excellent fits to real data across different types of perturbations and experimental paradigms (mean correlation between data and model fits across all six studies = 0.95 ± 0.02). The model parameters provide a mechanistic explanation for the behavioral responses to the adaptation paradigm that are not readily available from the behavioral responses alone. Overall, SimpleDIVA offers new insights into speech and voice motor control and has the potential to inform future directions of speech rehabilitation research in disordered populations. Simulation software, including an easy-to-use graphical user interface, is publicly available to facilitate the use of the model in future studies.