PLoS BiologyNeurosciencePeer-reviewed journal article
Audiomotor prediction errors drive speech adaptation even in the absence of overt movement
Benjamin ParrelliD, Minju BaeiD, Chris NaberiD, Olivia A. KimiD, Caroline A. NiziolekiD, Samuel D. McDougleiD
Abstract
Observed outcomes of our movements sometimes differ from our expectations. These sensory prediction errors recalibrate the brains internal models for motor control, reflected in alterations to subsequent movements that counteract these errors (motor adaptation). While leading theories suggest that all forms of motor adaptation are driven by learning from sensory prediction errors, dominant models of speech adaptation argue that adaptation results from integrating time-advanced copies of corrective feedback commands into feedforward motor programs. Here, we tested these competing theories of speech adaptation by inducing planned, but not executed, speech. Human speakers were prompted to speak a word and, on a subset of trials, were rapidly cued to withhold the prompted speech. On standard trials, speakers were exposed to real-time playback of their own speech with an auditory perturbation of the first formant to induce single-trial speech adaptation. Speakers experienced a similar sensory error on movement cancellation trials, hearing a perturbation applied to a recording of their speech from a previous trial at the time they would have spoken. Speakers adapted to auditory prediction errors in both contexts, altering the spectral content of spoken vowels to counteract formant perturbations even when no actual movement coincided with the perturbed feedback. Such adaptation was not observed when participants passively listened to perturbed feedback without the intention to speak, ruling out observational learning as the cause of adaptation in movement cancellation trials. These results build upon recent findings in reaching, and suggest that prediction errors, rather than corrective motor commands, drive audiomotor adaptation in speech.
- Citations
- 1
- FWCI
- 4.13
- Field percentile
- top 13%
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