Adaptation to aphasia: grammar, prosody and interaction

Abstract

This paper investigates recurrent use of the phrase very good by a speaker with non-fluent agrammatic aphasia. Informal observation of the speaker's interaction reveals that she appears to be an effective conversational partner despite very severe word retrieval difficulties that result in extensive reliance on variants of the phrase very good. The question that this paper addresses using an essentially conversation analytic framework is: What is the speaker achieving through these variants of very good and what are the linguistic and interactional resources that she draws on to achieve these communicative effects? Tokens of very good in the corpus were first analyzed in a bottom-up fashion, attending to sequential position, structure and participant orientation. This revealed distinct uses that were subsequently subjected to detailed acoustic analysis in order to investigate specific prosodic characteristics within and across the interactional variants. We identified specific clusters of prosodic cues that were exploited by the speaker to differentiate interactional uses of very good. The analysis thus shows how, in the adaptation to aphasia, the speaker exploits the rich interface between prosody, grammar and interaction both to manage the interactional demands of conversation and to communicate propositional content.

Keywords:

• conversation analysis
• agrammatism
• linguistic interfaces

Acknowledgements

Thanks to Rosalind Rogers for making the data and clinical information available.

Declaration of Interest: The authors note no conflict of interest.

Notes

1. We used MANOVA instead of several ANOVAs to overcome the problems associated with multiple testing (e.g. reduced power of the tests). Moreover, it may be the case that the independent variables(IVs) (i.e. functions of the phrase “very good”) influence several dependent variables (prosodic measures) as a set, not each of them separately. ANOVAs will not be able to capture this; neither will they capture the interplay between DVs, if there is any. MANOVA creates one DV from several variables, and analyses whether the change in the IV makes the significant difference in this complex DV. Moreover, MANOVA is a valid alternative to any kind of within-subject design, and it is a much better alternative than carrying out the tests with corrections for multiple comparisons or for violations of necessary assumptions (see, for example, Max & Onghena, 1999).

2. We used Pillai's trace statistic because it is more robust to the unequal N values and small data samples (Tabachnik & Fidell, 2007), which is the case in our data set.

3. This means that all four prosodic parameters are important in discriminating between [±CLOSURE]. The correlation coefficient r shows their importance, so this reveals that f0 span is the most important and duration of very good is least important of the four parameters, yet all of them are important because all r coefficients are high.

4. We used Pillai's trace statistic because it is more robust to the unequal N values and small data samples (Tabachnik & Fidell, 2007), which is the case in our data set. This means that all four prosodic parameters are important in discriminating between [±CLOSURE]. The correlation coefficient r shows their importance, so this reveals that f0 span is the most important and duration of very good is least important of the four parameters, yet all of them are important because all r coefficients are high. That is f0 span; f0 mean; dur_g; dur_vg.