In:Cognitive Aphasiology – A Usage-Based Approach to Language in Aphasia
Rachel Hatchard
[Constructional Approaches to Language 31] 2021
► pp. xv–xv
Published online: 11 October 2021
https://doi.org/10.1075/cal.31.lof
https://doi.org/10.1075/cal.31.lof
List of figures
Figure 2.1Illustration of how it feels to have aphasia, by a person with aphasia resulting from stroke
Figure 2.2Transcribed speech extract from F.M., speaker with chronic non-fluent (Broca’s agrammatic) aphasia (Berndt, 2001, p. 382)
Figure 2.3Transcribed speech extract from M.L., speaker with chronic fluent (Wernicke’s) aphasia (Berndt, 2001, p. 382)
Figure 3.1Superimposition of [keep them ADJP] and [keep NP happy] (based on an example from Dąbrowska, 2014, p. 623)
Figure 7.1Predictions for verbs in relation to spoken language capability
Figure 7.2Total words per narrative
Figure 7.3Percentage of verbs per narrative
Figure 7.4Number of unique verb lemmas per 100 words
Figure 7.5Frequency ranks in the Spoken BNC (Davies, 2004-) of verb lemmas produced by the neurotypical speakers and PWA
Figure 8.1Mean length of strings (words)
Figure 8.2Mean number of clauses per string
Figure 8.3Mean number of verbs per string
Figure 8.4Percentage of well-formed strings per participant
Figure 8.5Percentage of acceptable strings and fluent strings per participant
Figure 8.6Percentage of well-formed strings with corpus attestation per participant
Figure 8.7Percentage of acceptable and of fluent strings with corpus attestation per participant
Figure 9.1Sample of DB’s narrative showing ‘filler’ and compositional uses of I don’t know
Figure II.i.Example of included and excluded items in a speech sample
Figure III.i.Pause duration as displayed in version 2.0.5 of Audacity(R) recording and editing software (Audacity Team, 2013–14)
