ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2025
Neurophysiological Evidence of Sensory Prediction Errors Driving Speech Sensorimotor Adaptation.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Split-belt treadmill training improves gait symmetry and lower limb function in patients with stroke.Scientific reports · 2025Trial
- Sensorimotor Adaptation of Vocal Pitch Is Impaired in Cerebellar Ataxia.Journal of cognitive neuroscience · 2026Article
- Modeling framework disentangles cerebellar mechanisms in speech feedback control, revealing trade-offs reshaped by degeneration.bioRxiv : the preprint server for biology · 2026Article
- Audiomotor prediction errors drive speech adaptation even in the absence of overt movement.PLoS biology · 2026Article
- Perceptual prediction error supports implicit process in motor learning.PLoS computational biology · 2026Article
- Enhanced pitch centering in individuals with laryngeal dystonia.Frontiers in human neuroscience · 2026Article
- Predictive interception across speed profiles.Frontiers in neural circuits · 2026Article
- Formant variability is related to vowel duration across speakers.JASA express letters · 2025Article
- Increased vowel contrast and intelligibility in connected speech induced by sensorimotor adaptation.bioRxiv : the preprint server for biology · 2025Article
- Adaptation response to pitch shift during speech is attenuated upon relearning.Frontiers in human neuroscience · 2025Article
- Pars Opercularis Underlies Efferent Predictions and Successful Auditory Feedback Processing in Speech: Evidence From Left-Hemisphere Stroke.Neurobiology of language (Cambridge, Mass.) · 2024Article
- A narrative review of AI-driven stroke rehabilitation systems through the lens of human motor learning.Frontiers in neurologyReview
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9 authors.
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Abstract
The human sensorimotor system has a remarkable ability to learn movements from sensory experience. A prominent example is sensorimotor adaptation, learning that characterizes the sensorimotor system's response to persistent sensory errors by adjusting future movements to compensate for those errors. A component of sensorimotor adaptation is implicit (i.e., the learner is unaware of the learning) which has been suggested to result from sensory prediction errors-discrepancies between predicted sensory consequences of motor commands and actual sensory feedback. However, neurophysiological evidence that sensory prediction errors drive adaptation has never been directly demonstrated. Here, we examined prediction errors via magnetoencephalography imaging of the auditory cortex during sensorimotor adaptation of speech to altered auditory feedback, an entirely implicit adaptation task. Specifically, we measured how speaking-induced suppression (SIS)-a neural representation of auditory prediction errors-changed over the trials of the adaptation experiment. In both male and female speakers, reduction in SIS (reflecting larger prediction errors) during the early learning phase compared with the initial unaltered feedback phase positively correlated with behavioral adaptation extents, suggesting larger prediction errors were associated with more learning. In contrast, such a reduction in SIS was not found in a control experiment in which participants heard unaltered feedback and thus did not adapt. In addition, in some participants who reached a plateau in the late learning phase, SIS increased, demonstrating that prediction errors were minimal when there was no further adaptation. Together, these findings provide the first direct neurophysiological evidence for the hypothesis that prediction errors drive sensorimotor adaptation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.