scholarly journals Training verb argument structure production in agrammatic aphasia: Behavioral and neural recovery patterns

Cortex ◽  
2013 ◽  
Vol 49 (9) ◽  
pp. 2358-2376 ◽  
Author(s):  
Cynthia K. Thompson ◽  
Ellyn A. Riley ◽  
Dirk-Bart den Ouden ◽  
Aya Meltzer-Asscher ◽  
Sladjana Lukic
2018 ◽  
Vol 61 (2) ◽  
pp. 373-385 ◽  
Author(s):  
Chien-Ju Hsu ◽  
Cynthia K. Thompson

Purpose The purpose of this study is to compare the outcomes of the manually coded Northwestern Narrative Language Analysis (NNLA) system, which was developed for characterizing agrammatic production patterns, and the automated Computerized Language Analysis (CLAN) system, which has recently been adopted to analyze speech samples of individuals with aphasia (a) for reliability purposes to ascertain whether they yield similar results and (b) to evaluate CLAN for its ability to automatically identify language variables important for detailing agrammatic production patterns. Method The same set of Cinderella narrative samples from 8 participants with a clinical diagnosis of agrammatic aphasia and 10 cognitively healthy control participants were transcribed and coded using NNLA and CLAN. Both coding systems were utilized to quantify and characterize speech production patterns across several microsyntactic levels: utterance, sentence, lexical, morphological, and verb argument structure levels. Agreement between the 2 coding systems was computed for variables coded by both. Results Comparison of the 2 systems revealed high agreement for most, but not all, lexical-level and morphological-level variables. However, NNLA elucidated utterance-level, sentence-level, and verb argument structure–level impairments, important for assessment and treatment of agrammatism, which are not automatically coded by CLAN. Conclusions CLAN automatically and reliably codes most lexical and morphological variables but does not automatically quantify variables important for detailing production deficits in agrammatic aphasia, although conventions for manually coding some of these variables in Codes for the Human Analysis of Transcripts are possible. Suggestions for combining automated programs and manual coding to capture these variables or revising CLAN to automate coding of these variables are discussed.


2015 ◽  
Vol 62 (1) ◽  
Author(s):  
Hillary K. Sang

Background: The spontaneous and narrative language of Kiswahili agrammatic aphasic and non-brain-damaged speakers was analysed. The bilingual participants were also tested in English to enable comparisons of verb production in the two languages. The significance of this study was to characterise bilingual Kiswahili-English spontaneous agrammatic output. This was done by describing Kiswahili-English bilingual output data with a specific focus on the production of verbs. The description involves comparison of verb and argument production in Kiswahili and English. Methods and procedures: The participants recruited for this study were drawn from two groups of participants (six non-fluent aphasic/agrammatic speakers and six non-braindamaged). From each participant, a sample of spontaneous output was tape-recorded in English and Kiswahili based on the description and narration of the Flood rescue picture’ and the ‘Cookie theft picture’. The data elicited were compared for each subject and between the participants and relevant verb parameters have been analysed. The variables that were studied included mean length of utterance (MLU), inflectional errors, verb tokens and types, copulas and auxiliaries. Further, all verbs produced were classified as per their argument structure. Results: The results from English data supported previous findings on agrammatic output. The agrammatic participants produced utterances with shorter MLU and simpler sentence structure. However, Kiswahili data surprisingly showed reversed results, with agrammatic speakers producing longer utterances than non-brain-damaged (NBD) controls. The results also revealed selective impairment in some agrammatic speakers who made inflectional errors. The verb argument structure showed contrasting results, with agrammatic speakers preferring transitive verbs whilst the NBD speakers used more intransitive verbs.Conclusions: The study attempts for the first time to characterise English-Kiswahili bilingual spontaneous and narrative output. A quantitative analysis of verb and argument production is conducted. The results of the English data are consistent with those in the literature; agrammatic speakers produce utterances with shorter MLU and simpler sentence structure. However, Kiswahili data reveals a surprisingly reversed pattern most notably with respect to MLU with agrammatics producing longer utterances than NBD controls. Argument structure analysis revealed that agrammatics used more transitive verbs than intransitives.


2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Jona Sassenhagen ◽  
Ryan Blything ◽  
Elena V. M. Lieven ◽  
Ben Ambridge

How are verb-argument structure preferences acquired? Children typically receive very little negative evidence, raising the question of how they come to understand the restrictions on grammatical constructions. Statistical learning theories propose stochastic patterns in the input contain sufficient clues. For example, if a verb is very common, but never observed in transitive constructions, this would indicate that transitive usage of that verb is illegal. Ambridge et al. (2008) have shown that in offline grammaticality judgements of intransitive verbs used in transitive constructions, low-frequency verbs elicit higher acceptability ratings than high-frequency verbs, as predicted if relative frequency is a cue during statistical learning. Here, we investigate if the same pattern also emerges in on-line processing of English sentences. EEG was recorded while healthy adults listened to sentences featuring transitive uses of semantically matched verb pairs of differing frequencies. We replicate the finding of higher acceptabilities of transitive uses of low- vs. high-frequency intransitive verbs. Event-Related Potentials indicate a similar result: early electrophysiological signals distinguish between misuse of high- vs low-frequency verbs. This indicates online processing shows a similar sensitivity to frequency as off-line judgements, consistent with a parser that reflects an original acquisition of grammatical constructions via statistical cues. However, the nature of the observed neural responses was not of the expected, or an easily interpretable, form, motivating further work into neural correlates of online processing of syntactic constructions.


PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0123723 ◽  
Author(s):  
Ben Ambridge ◽  
Amy Bidgood ◽  
Katherine E. Twomey ◽  
Julian M. Pine ◽  
Caroline F. Rowland ◽  
...  

2001 ◽  
Vol 28 (1) ◽  
pp. 127-152 ◽  
Author(s):  
ANNA L. THEAKSTON ◽  
ELENA V. M. LIEVEN ◽  
JULIAN M. PINE ◽  
CAROLINE F. ROWLAND

1993 ◽  
Vol 45 (3) ◽  
pp. 423-447 ◽  
Author(s):  
L.P. Shapiro ◽  
B. Gordon ◽  
N. Hack ◽  
J. Killackey

Cognition ◽  
2004 ◽  
Vol 91 (3) ◽  
pp. 191-219 ◽  
Author(s):  
Stefan Frisch ◽  
Anja Hahne ◽  
Angela D Friederici

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