Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Working Memory Modulates Auditory Perceptual Sensitivity During Speech Planning.

Journal of speech, language, and hearing research : JSLHR·2026
Same author

The complex impact of auditory error direction, magnitude, and exposure on corrective responses.

Frontiers in human neuroscience·2026
Same author

Individuals with aphasia generate larger adaptive and corrective responses to suddenly introduced auditory perturbations.

Frontiers in human neuroscience·2025
Same author

Neural mechanisms of articulatory motor speech deficit in post-stroke aphasia: An ERP study.

NeuroImage·2025
Same author

Delayed auditory feedback increases speech production variability in typically fluent adults but has the opposite effect in stuttering adults.

Frontiers in human neuroscience·2025
Same author

An Integrated Approach to Concurrently Measure Corrective and Adaptive Responses to Auditory Errors.

Journal of speech, language, and hearing research : JSLHR·2025

Related Experiment Video

Updated: Nov 4, 2025

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
10:13

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds

Published on: November 26, 2012

14.5K

A Computational Model for Estimating the Speech Motor System's Sensitivity to Auditory Prediction Errors.

Ayoub Daliri1

  • 1College of Health Solutions, Arizona State University, Tempe.

Journal of Speech, Language, and Hearing Research : JSLHR
|May 27, 2021
PubMed
Summary
This summary is machine-generated.

This study developed a state-space model to assess speech motor control, finding that feedforward and feedback systems operate independently and their error sensitivity can be accurately predicted by the model.

More Related Videos

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

736
Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation
12:09

Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation

Published on: June 14, 2014

19.3K

Related Experiment Videos

Last Updated: Nov 4, 2025

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
10:13

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds

Published on: November 26, 2012

14.5K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

736
Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation
12:09

Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation

Published on: June 14, 2014

19.3K

Area of Science:

  • Speech Motor Control
  • Auditory Neuroscience
  • Computational Linguistics

Background:

  • Speech motor control relies on feedforward and feedback mechanisms.
  • Both control systems are sensitive to prediction errors.
  • Quantifying the error sensitivity of these systems is crucial for understanding speech production.

Purpose of the Study:

  • To develop and validate a state-space model for estimating the error sensitivity of speech motor control systems.
  • To investigate whether the developed model accurately reflects the error sensitivity of feedforward and feedback control.
  • To determine if the feedforward and feedback systems exhibit similar error sensitivity.

Main Methods:

  • A state-space model was developed to estimate error sensitivity.
  • Fifty participants underwent a speech adaptation paradigm with formant perturbations.
  • Adaptive responses were measured at early and late time points to derive data-driven estimates of error sensitivity.

Main Results:

  • Late adaptive responses were significantly larger than early responses.
  • Model-based estimates of error sensitivity strongly correlated with data-driven estimates.
  • Error sensitivity estimates for feedforward and feedback systems did not correlate, suggesting independent function.

Conclusions:

  • The state-space model accurately predicts the dynamics and error sensitivity of speech motor control.
  • The feedforward and feedback control systems for speech production function independently.
  • This model provides a valuable tool for analyzing speech motor control mechanisms.