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      Shared Representations Underlie Metaphonological Judgments and Speech Motor Control

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      Laboratory Phonology
      Ubiquity Press, Ltd.

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          Abstract

          Researchers often use metalinguistic judgments to investigate phonological representations. The representations are assumed to govern speech motor control and thereby shape articulatory and acoustic characteristics of speech. Yet little is known about the relationship between metalinguistic judgments, phonological representations, and motor control. This paper reports on an experiment that directly investigates the relation between metalinguistic judgments and articulatory control, hypothesizing that the two share a common representation. This hypothesis predicts that differences in judgments should be correlated with differences in the acoustic characteristics of responses. An experiment was conducted in which syllable count judgments and productions of words with tense vowel/diphthong nuclei and liquid codas were obtained from native speakers of English. A subset of these words have previously been shown to exhibit ­variation in syllable count judgments. Acoustic analyses of productions showed that rime ­durations and formant trajectories differed between words associated with monosyllabic vs. disyllabic syllable count judgments. These results support the hypothesis that a common representation is utilized by the processes responsible for metaphonological judgments of syllable count and speech motor control.

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          Multiple paired forward and inverse models for motor control.

          Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. In this paper, we propose a modular approach to such motor learning and control. We review the behavioral evidence and benefits of modularity, and propose a new architecture based on multiple pairs of inverse (controller) and forward (predictor) models. Within each pair, the inverse and forward models are tightly coupled both during their acquisition, through motor learning, and use, during which the forward models determine the contribution of each inverse model's output to the final motor command. This architecture can simultaneously learn the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given environment. Finally, we describe specific predictions of the model, which can be tested experimentally.
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            Exemplar dynamics

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              Forward modeling allows feedback control for fast reaching movements

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                Author and article information

                Journal
                Laboratory Phonology
                Ubiquity Press, Ltd.
                1868-6354
                1868-6354
                March 16 2016
                October 25 2016
                : 7
                : 1
                : 14
                Article
                10.5334/labphon.52
                9d3cb871-557b-4c5f-8669-a9760c0b71b0
                © 2016

                http://creativecommons.org/licenses/by/4.0

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