Dissociating oral motor capabilities: Evidence from patients with movement disorders. (27th January 2017) - Staiger, Anja; Schölderle, Theresa; Brendel, Bettina; Ziegler, Wolfram
Abstract: Abnormal articulation rate is a frequent symptom in neurogenic speech disorders. Performance rates in speech-like or nonspeech tasks involving the vocal motor apparatus are commonly accepted predictors of speech motor function in general and of articulation rate in particular. However, theoretical arguments and behavioral observations in populations with disordered speech indicate that different oral motor behaviors may be governed by distinct mechanisms. The objective of the present study was to expand our knowledge of the relationship between speech movements, on the one hand, and speech-like and nonspeech oral motor behaviors, on the other, by using a rate paradigm. 130 patients with neurological movement disorders of different origins and 130 neurologically healthy subjects participated in the study. Rate data was collected in a speech task (oral reading/repetition), in speech-like tasks (rapid syllable repetitions), and in nonspeech tasks (rapid single articulator movements of the tongue/lips). The main analyses involved a multiple single-case method, by which we tested for differences among each patient's performance rates on the three task types. The results disclosed statistically significant (classical and strong) dissociations between movement rates obtained from the speech task and those obtained from speech-like and nonspeech oral motor tasks in a number of patients. The findings can be interpreted as reflecting major differences in task demands and underlying control mechanisms. The validity of diagnostic indices for speech obtained from speech-like or nonspeech tasks must thus be called into question. Highlights: Speech is governed by task-specific control mechanisms. Articulation rate cannot be reliably predicted by speech-like or nonspeech rate indices. Dysarthria assessments should include articulation rate measures based on real speech.