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      Tutorial: Speech Motor Chaining Treatment for School-Age Children With Speech Sound Disorders

      1 , 1 , 1
      Language, Speech, and Hearing Services in Schools
      American Speech Language Hearing Association

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          Abstract

          Purpose

          Operationalized treatments for school-age children with speech sound disorders may result in more replicable and evidence-based interventions. This tutorial describes Speech Motor Chaining (SMC) procedures, which are designed to build complex speech around core movements by incorporating several principles of motor learning. The procedures systematically manipulate factors such as feedback type and frequency, practice variability, and stimulus complexity based on the child's performance.

          Method

          The rationale and procedures for SMC are described. Examples are presented of how to design stimuli, deliver feedback, and adapt the approach. Free resources are provided to guide clinicians through implementation of the procedure. Data on fidelity of implementation and dose per session are presented. Clinical and research evidence is provided to illustrate likely outcomes with the procedure.

          Results

          SMC is a method that can result in successful acquisition of target speech patterns and generalization to untrained words. Most clinicians can implement the procedure with over 90% fidelity, and most children can achieve over 200 trials per session.

          Conclusion

          Clinicians and researchers can use or adapt the operationally defined SMC procedures to incorporate several principles of motor learning into treatment for school-age children with speech sound disorders.

          Supplemental Material

          https://osf.io/5jmf9/

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          Most cited references31

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          Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning.

          The authors describe the effects of practice conditions in motor learning (e.g., contextual interference, knowledge of results) within the constraints of 2 experimental variables: skill level and task difficulty. They use a research framework to conceptualize the interaction of those variables on the basis of concepts from information theory and information processing. The fundamental idea is that motor tasks represent different challenges for performers of different abilities. The authors propose that learning is related to the information arising from performance, which should be optimized along functions relating the difficulty of the task to the skill level of the performer. Specific testable hypotheses arising from the framework are also described.
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            The DIVA model: A neural theory of speech acquisition and production.

            The DIVA model of speech production provides a computationally and neuroanatomically explicit account of the network of brain regions involved in speech acquisition and production. An overview of the model is provided along with descriptions of the computations performed in the different brain regions represented in the model. The latest version of the model, which contains a new right-lateralized feedback control map in ventral premotor cortex, will be described, and experimental results that motivated this new model component will be discussed. Application of the model to the study and treatment of communication disorders will also be briefly described.
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              Neural representations and mechanisms for the performance of simple speech sequences.

              Speakers plan the phonological content of their utterances before their release as speech motor acts. Using a finite alphabet of learned phonemes and a relatively small number of syllable structures, speakers are able to rapidly plan and produce arbitrary syllable sequences that fall within the rules of their language. The class of computational models of sequence planning and performance termed competitive queuing models have followed K. S. Lashley [The problem of serial order in behavior. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior (pp. 112-136). New York: Wiley, 1951] in assuming that inherently parallel neural representations underlie serial action, and this idea is increasingly supported by experimental evidence. In this article, we developed a neural model that extends the existing DIVA model of speech production in two complementary ways. The new model includes paired structure and content subsystems [cf. MacNeilage, P. F. The frame/content theory of evolution of speech production. Behavioral and Brain Sciences, 21, 499-511, 1998 ] that provide parallel representations of a forthcoming speech plan as well as mechanisms for interfacing these phonological planning representations with learned sensorimotor programs to enable stepping through multisyllabic speech plans. On the basis of previous reports, the model's components are hypothesized to be localized to specific cortical and subcortical structures, including the left inferior frontal sulcus, the medial premotor cortex, the basal ganglia, and the thalamus. The new model, called gradient order DIVA, thus fills a void in current speech research by providing formal mechanistic hypotheses about both phonological and phonetic processes that are grounded by neuroanatomy and physiology. This framework also generates predictions that can be tested in future neuroimaging and clinical case studies.
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                Author and article information

                Journal
                Language, Speech, and Hearing Services in Schools
                LSHSS
                American Speech Language Hearing Association
                0161-1461
                1558-9129
                July 12 2019
                July 12 2019
                : 50
                : 3
                : 343-355
                Affiliations
                [1 ]Department of Communication Sciences and Disorders, Syracuse University, NY
                Article
                10.1044/2018_LSHSS-18-0081
                6802861
                31051085
                9e51807b-a799-4ac5-9036-829e9815a91d
                © 2019
                History

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