title: A psychometric analysis of functional category production in English agrammatic narratives.
abstract: Hierarchical models of agrammatism propose that sentence production deficits can be accounted for in terms of clausal syntactic structure [Friedmann, N., & Grodzinsky, Y. (1997). Tense and agreement in agrammatic production: Pruning the syntactic tree. Brain and Language, 56, 397-425; Hagiwara, H. (1995). The breakdown of functional categories and the economy of derivation. Brain and Language, 50, 92-116]. Such theories predict that morpho-syntactic elements associated with higher nodes in the syntactic tree (complementizers and verb inflections) will be more impaired than elements associated with lower structural positions (negation markers and aspectual verb forms). While this hypothesis has been supported by the results of several studies [Benedet, M. J., Christiansen, J. A., & Goodglass, H. (1998). A cross-linguistic study of grammatical morphology in Spanish- and English-speaking agrammatic patients. Cortex, 34, 309-336; Friedmann, N. (2001). Agrammatism and the psychological reality of the syntactic tree. Journal of Psycholinguistic Research, 30, 71-88; Friedmann, N. (2002). Question production in agrammatism: The tree pruning hypothesis. Brain and Language, 80, 160-187], it has also been challenged on several grounds [Burchert, F., Swoboda-Moll, M., & De Bleser, R. (2005a). Tense and agreement dissociations in German agrammatic speakers: Underspecification vs. hierarchy. Brain and Language, 94, 188-199; Lee, M. (2003). Dissociations among functional categories in Korean agrammatism. Brain and Language, 84, 170-188; Lee, J., Milman, L. H., & Thompson, C. K. (2005). Functional category production in agrammatic speech. Brain and Language, 95, 123-124]. In this paper the question of hierarchical structure was re-examined within the framework of Item Response Theory [IRT, Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests (Expanded ed.). Chicago: University of Chicago Press]. IRT is a probabilistic model widely used in the field of psychometrics to model behavioral constructs as numeric variables. In this study we examined production of functional categories (complementizers, verb inflections, negation markers, and aspectual verb forms) in narrative samples elicited from 18 individuals diagnosed with nonfluent aphasia and 18 matched controls. Data from the aphasic participants were entered into an IRT analysis to test (1) whether production of clausal functional categories can be represented as a variable on a numeric scale; and (2) whether production patterns were consistent with hierarchical syntactic structure. Pearson r correlation coefficients were also computed to determine whether there was a relation between functional category production and other indices of language performance. Results indicate that functional category production can be modeled as a numeric variable using IRT. Furthermore, although variability was observed across individuals, consistent patterns were evident when the data were interpreted within a probabilistic framework. Although functional category production was moderately correlated with a second measure of clausal structure (clause length), it was not correlated with more distant language constructs (noun/verb ratio and WAB A.Q.). These results suggest that functional category production is related to some, but not all, measures of agrammatic language performance.
authors.affiliation: Department of Speech and Hearing Science, Ohio State University, 1070 Carmack Road, Columbus, OH 43210, USA. milman.3@osu.edu
authors.forename: Lisa H
authors.initials: LH
authors.lastname: Milman
authors.forename: Michael Walsh
authors.initials: MW
authors.lastname: Dickey
authors.forename: Cynthia K
authors.initials: CK
authors.lastname: Thompson
mesh_terms: D000328:Adult; D000368:Aged; D001039:Aphasia, Broca / Q000175:diagnosis*; D002544:Cerebral Infarction / Q000175:diagnosis; D004292:Dominance, Cerebral; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D033262:Narration; D011594:Psychometrics / Q000706:statistics & numerical data; D012016:Reference Values; D012660:Semantics; D013068:Speech Production Measurement / Q000706:statistics & numerical data*