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      Successful auditory motor adaptation requires task-relevant auditory errors

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      Journal of Neurophysiology
      American Physiological Society

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          Abstract

          When we produce speech movements, we also predict the auditory consequences of the movements. We use discrepancies between our predictions and incoming auditory information to modify our future movements (adapt). Although auditory errors are crucial for speech motor learning, not all perceived auditory errors are consequences of our own actions. Therefore, the brain needs to evaluate the relevance of perceived auditory errors. In this study, we examined error assessment processes involved in auditory motor adaptation by systematically manipulating the correspondence between speech motor outputs and their auditory consequences during speaking. Participants ( n = 30) produced speech while they received perturbed auditory feedback (e.g., produced “head” but heard a word that sounded like “had”). In one condition, auditory errors were related to participants’ productions (task-relevant errors). In another condition, auditory errors were defined by the experimenter and had no correspondence with participants’ speech output (task-irrelevant errors). We found that the extent of adaptation and error sensitivity (derived from a state-space model) were greater in the condition with task-relevant auditory errors compared with those in the condition with task-irrelevant auditory errors. Additionally, participants with smaller perceptual targets (derived from a categorical perception task) adapted more to auditory perturbations, and participants with larger perceptual targets adapted less. Similarly, participants with smaller perceptual targets were more sensitive to errors in the condition with task-relevant auditory errors. Together, our results highlight the intricate mechanisms, involving both perception and production systems, that the brain uses to optimally integrate auditory errors for successful speech motor learning.

          NEW & NOTEWORTHY Feedback monitoring is essential for accurate speech production. By providing empirical results and a computational framework, we show that 1) the brain evaluates relevance of auditory errors and responds more to relevant errors, and 2) smaller perceptual targets are associated with more sensitivity to errors and more auditory motor adaptation.

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          Most cited references26

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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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            Computational neuroanatomy of speech production.

            Speech production has been studied predominantly from within two traditions, psycholinguistics and motor control. These traditions have rarely interacted, and the resulting chasm between these approaches seems to reflect a level of analysis difference: whereas motor control is concerned with lower-level articulatory control, psycholinguistics focuses on higher-level linguistic processing. However, closer examination of both approaches reveals a substantial convergence of ideas. The goal of this article is to integrate psycholinguistic and motor control approaches to speech production. The result of this synthesis is a neuroanatomically grounded, hierarchical state feedback control model of speech production.
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              Relevance of error: what drives motor adaptation?

              During motor adaptation the nervous system constantly uses error information to improve future movements. Today's mainstream models simply assume that the nervous system adapts linearly and proportionally to errors. However, not all movement errors are relevant to our own action. The environment may transiently disturb the movement production-for example, a gust of wind blows the tennis ball away from its intended trajectory. Apparently the nervous system should not adapt its motor plan in the subsequent tennis strokes based on this irrelevant movement error. We hypothesize that the nervous system estimates the relevance of each observed error and adapts strongly only to relevant errors. Here we present a Bayesian treatment of this problem. The model calculates how likely an error is relevant to the motor plant and derives an ideal adaptation strategy that leads to the most precise movements. This model predicts that adaptation should be a nonlinear function of the size of an error. In reaching experiments we found strong evidence for the predicted nonlinear strategy. The model also explains published data on saccadic gain adaptation, adaptation to visuomotor rotations, and force perturbations. Our study suggests that the nervous system constantly and effortlessly estimates the relevance of observed movement errors for successful motor adaptation.
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                Author and article information

                Journal
                Journal of Neurophysiology
                Journal of Neurophysiology
                American Physiological Society
                0022-3077
                1522-1598
                August 01 2019
                August 01 2019
                : 122
                : 2
                : 552-562
                Affiliations
                [1 ]College of Health Solutions, Arizona State University, Tempe, Arizona
                Article
                10.1152/jn.00662.2018
                31215301
                12fd3385-50e7-4095-8203-e9a0aa69937f
                © 2019
                History

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