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      Quantitatively characterizing reflexive responses to pitch perturbations

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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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          Most cited references59

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          A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.

          Intraclass correlation coefficient (ICC) is a widely used reliability index in test-retest, intrarater, and interrater reliability analyses. This article introduces the basic concept of ICC in the content of reliability analysis.
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            An approximate distribution of estimates of variance components.

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              Learning of action through adaptive combination of motor primitives.

              Understanding how the brain constructs movements remains a fundamental challenge in neuroscience. The brain may control complex movements through flexible combination of motor primitives, where each primitive is an element of computation in the sensorimotor map that transforms desired limb trajectories into motor commands. Theoretical studies have shown that a system's ability to learn action depends on the shape of its primitives. Using a time-series analysis of error patterns, here we show that humans learn the dynamics of reaching movements through a flexible combination of primitives that have gaussian-like tuning functions encoding hand velocity. The wide tuning of the inferred primitives predicts limitations on the brain's ability to represent viscous dynamics. We find close agreement between the predicted limitations and the subjects' adaptation to new force fields. The mathematical properties of the derived primitives resemble the tuning curves of Purkinje cells in the cerebellum. The activity of these cells may encode primitives that underlie the learning of dynamics.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                02 November 2022
                2022
                : 16
                : 929687
                Affiliations
                [1] 1Department of Speech, Language, and Hearing Sciences, Boston University , Boston, MA, United States
                [2] 2The McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, MA, United States
                [3] 3College of Health Solutions, Arizona State University , Tempe, AZ, United States
                [4] 4Department of Communication Sciences and Disorders, Temple University , Philadelphia, PA, United States
                [5] 5Gradutate Program for Neuroscience, Boston University , Boston, MA, United States
                [6] 6Department of Biomedical Engineering, Boston University , Boston, MA, United States
                [7] 7The Picower Institute for Learning and Memory, Massachusetts Institute of Technology , Cambridge, MA, United States
                Author notes

                Edited by: Jeffery A. Jones, Wilfrid Laurier University, Canada

                Reviewed by: David Jackson Morris, University of Copenhagen, Denmark; Nishant Rao, Haskins Laboratories, United States

                *Correspondence: Elaine Kearney, ekearney@ 123456bu.edu

                This article was submitted to Speech and Language, a section of the journal Frontiers in Human Neuroscience

                Article
                10.3389/fnhum.2022.929687
                9666385
                36405080
                72f9bb1e-c120-413c-ba75-76a301bc3094
                Copyright © 2022 Kearney, Nieto-Castañón, Falsini, Daliri, Heller Murray, Smith and Guenther.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 27 April 2022
                : 04 October 2022
                Page count
                Figures: 4, Tables: 4, Equations: 21, References: 61, Pages: 20, Words: 13095
                Funding
                Funded by: National Institutes of Health, doi 10.13039/100000002;
                Award ID: R01 DC002852
                Award ID: R01 DC016270
                Award ID: F31 DC016197
                Categories
                Human Neuroscience
                Original Research

                Neurosciences
                computational modeling,motor control,speech production,pitch,auditory feedback
                Neurosciences
                computational modeling, motor control, speech production, pitch, auditory feedback

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