ArticleFrontiers in human neuroscience2022
Quantitatively characterizing reflexive responses to pitch perturbations.
Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Active inference and speech motor control.Psychonomic bulletin & review · 2026Review
- An Integrated Approach to Concurrently Measure Corrective and Adaptive Responses to Auditory Errors.Journal of speech, language, and hearing research : JSLHR · 2025Article
- Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia.PLoS computational biology · 2024Article
- Do Not Cut Off Your Tail: A Mega-Analysis of Responses to Auditory Perturbation Experiments.Journal of speech, language, and hearing research : JSLHR · 2023Article
- DIVA Meets EEG: Model Validation Using Formant-Shift Reflex.Applied sciences (Basel, Switzerland) · 2023Article
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7 authors.
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Abstract
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.
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