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Data-Based Target Selection for Aphasia Treatment

Data-Based Target Selection for Aphasia Treatment. Dallin Bailey, PhD, CCC-SLP Assistant Professor Department of Communication Disorders Auburn University Auburn, AL dallinbailey@auburn.edu. Scan code to download presentation. Or send me an email.

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Data-Based Target Selection for Aphasia Treatment

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  1. Data-Based Target Selection for Aphasia Treatment Dallin Bailey, PhD, CCC-SLP Assistant Professor Department of Communication Disorders Auburn University Auburn, AL dallinbailey@auburn.edu Scan code to download presentation. Or send me an email. I have no relevant financial or nonfinancial relationship(s) within the products or services described, reviewed, evaluated or compared in this presentation.

  2. Learning Outcomes • As a result of participation, the attendee will be able to describe the rationale for data-based target selection vs traditional target selection • As a result of participation, the attendee will be able to describe major principles for implementing data-based target selection methods for aphasia treatment • As a result of participation, the attendee will be able to list specific resources for use in data-based target selection SHAA Convention 2019

  3. Rationale for data-based target selection • Personal relevance a worthwhile guiding principle • Varying definitions and strategies • Problematic: • Conversation analysis • “Strategy of the blank page” • Frequent focus on concrete stimuli • Clinician-selected stimuli SHAA Convention 2019

  4. Objective word-level data are rarely referenced in selecting treatment stimuli, despite their intuitive connection with the constructs of usefulness and functionality (Renvall, Nickels, & Davidson, 2013). • People with aphasia want to be able to express basic needs, as well as opinions on topics like politics and religion (Worrall et al, 2011). SHAA Convention 2019

  5. Concreteness • How easily the thing a word represents can be experienced with the five senses or through • High concreteness: • Can be explained by pointing to it or demonstrating it • E.g., apple, dig • Low concreteness: • Best explained by using other words • E.g., theory, decide • Concreteness Ratings for 40 Thousand Generally Know English Word Lemmas • Brysbaert, Warriner & Cuperman SHAA Convention 2019

  6. Tools for communication of basic needs and for social interaction likely includes a mix of high and low concreteness words • Common topics of conversation (Balandin & Iacono, 1998): • Food, transportation, sports, health, family life • Judgments, social relations, finances • Tools for expressing opinions and feelings include low concreteness verbs with low and high frequencies. • Mental verbs (Halliday, 1994) • despise (low frequency) • believe (high frequency) SHAA Convention 2019

  7. Rationale for data-based target selection • Data-based strategies: • Get a list from data from actual language use • Shortcut/hybrid method (combine corpus-based list with client preference) SHAA Convention 2019

  8. Borrowing from AAC research • Yorkston, Dowden, Honsinger, Marriner, & Smith, 1988 • Compiled 11 vocabulary lists from different sources (built-in AAC vocabularies, individual AAC users’ word repertoires, ELL dictionaries, lists of words from conversations, etc. • Made lists of words in common to most of the lists and words that were unique • Conclusion: no one list was perfect, but the composite lists are likely useful • Note: list not provided in the article SHAA Convention 2019

  9. Studies of typical speakers • Stuart, 1997 • Most common words and 2-3 word sequences from conversations of healthy older adults (60-74 and 75-85), Caucasian, Lincoln, NE. • Wide variety of words: • Want, take, I, down, day, keep, know, make, time, need, about, pretty • Balandin & Iacono, 1998b • Most common topics of conversation for working adults at mealtime at four locations in Australia • Work, fact-finding, food, family life, judgments (gossip)… SHAA Convention 2019

  10. Hard to predict • But, even when just predicting words (not choosing stimuli to apply to treatment), difficulty in predicting (Balandin & Iacono, 1998a) • SLPs and related professionals had to predict the topics of meal-time conversations of working adults, and give five key words for each topic; predicted topics then compared with recorded conversations • Fairly good prediction of topics. However, 33% of the keywords predicted did not appear in the conversations. • In other words, clinicians not perfect at predicting word usage SHAA Convention 2019

  11. Corpus research on frequency • Corpus: large, searchable body of text • Could make clinicians’ search for targets more systematic and objective (reducing bias) (Renvall, Nickels, & Davidson, 2013b) SHAA Convention 2019

  12. A pre-corpus • Berger, 1968 • Eavesdropping study—eavesdropping in a restaurant • Yielded 25000 words from adults’ conversations “of an unguarded and informal nature”—words tabulated in the appendix of the article SHAA Convention 2019

  13. Another natural language corpus • Stuart, 1997, as mentioned before • most common words in conversations from older adults • wide variety of words • Not searchable • Size not clear SHAA Convention 2019

  14. SUBTLEX-US • Corpus of spoken language from American films and tv series (Brysbaert & New, 2009) • 51 million words • Note: not naturally occurring language • Free spreadsheets • https://www.ugent.be/pp/experimentele-psychologie/en/research/documents/subtlexus • Sortable by part of speech. • Column to observe is SUBTLWF • Potentially good for core vocabulary • Frequency calculator for lists • http://subtlexus.lexique.org/moteur2/index.php SHAA Convention 2019

  15. Corpus of Contemporary American English (COCA) • Multiple genres of text, updated every few months • Academic, fiction, newspaper, and spoken • Mostly news shows in the spoken section, so topics biased towards politics and news • 560 million words • Probably more naturally occurring than video • Demonstration—most common nouns, verbs • Also potentially good for core vocabulary • https://corpus.byu.edu/coca/ SHAA Convention 2019

  16. Most common nouns in COCA • _v* • group by lemmas • display per million • sort by frequency SHAA Convention 2019

  17. Most common verbs in COCA • _v* • group by lemmas • display per million • sort by frequency SHAA Convention 2019

  18. Wikipedia corpus • Can create a virtual corpus 1.9 billion words from all Wikipedia articles on a day in 2014 • https://corpus.byu.edu/wiki/ • Good for finding fringe vocab as specific as you like SHAA Convention 2019

  19. Other databases to use • MRC Psycholinguistics Database (Coltheart, 1981) • Lists at the end of Renvallet al., 2013b • Brysbaertconcreteness ratings: • http://crr.ugent.be/archives/1330 • Other BYU corpora • http://corpus.byu.edu • For finding phonological neighbors • http://www.iphod.com/calculator/V2CalcWords.html • Source for some of these: https://www.reilly-coglab.com/data/ SHAA Convention 2019

  20. Other ways to use COCA • You can also input lists and texts to find related words that may be useful • https://www.wordandphrase.info/analyzeText.asp • collocates or other related words may be good candidates for treatment • Example: Eaton & Newman, 2018 SHAA Convention 2019

  21. References • Armstrong, E. (2005). Expressing opinions and feelings in aphasia: Linguistic options. Aphasiology, 19(3-5), 285-295. doi:10.1080/02687030444000750 • Balandin, S., & Iacono, T. (1998a). A few well-chosen words. Augmentative and Alternative Communication, 14(3), 147-161. doi:10.1080/07434619812331278326 • Balandin, S., & Iacono, T. (1998b). Topics of meal-break conversations. Augmentative and Alternative Communication, 14(3), 131-146. doi:10.1080/07434619812331278316 • Bastiaanse, R., & Jonkers, R. (1998). Verb retrieval in action naming and spontaneous speech in agrammatic and anomic aphasia. Aphasiology, 12(11), 951-969. doi:10.1080/02687039808249463 • Berger, K. (1968). The most common words used in conversations. Journal of Communication Disorders, 1(3), 201-214. doi:10.1016/0021-9924(68)90032-4 • Brysbaert, M., & New, B. (2009). Moving beyond Kucera and Francis: A Critical Evaluation of Current Word Frequency Norms and the Introduction of a New and Improved Word Frequency Measure for American English. Behavior Research Methods, 41(4), 977-990. • Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), 904-911. doi:10.3758/s13428-013-0403-5 • Coltheart, M. (1981). The MRC Psycholinguistic Database. Quarterly Journal of Experimental Psychology. Retrieved from http://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm • Conroy, P., Sage, K., & Lambon Ralph, M. A. (2006). Towards theory‐driven therapies for aphasic verb impairments: A review of current theory and practice. Aphasiology, 20(12), 1159-1185. doi:10.1080/02687030600792009 • Eaton, C. T., & Newman, R. S. (2018). Heart and ____ or Give and ____? An Exploration of Variables That Influence Binomial Completion for Individuals With and Without Aphasia. Am J Speech Lang Pathol, 1-8. doi:10.1044/2018_AJSLP-17-0071 • Evans, W. S., Quimby, M., Dickey, M. W., & Dickerson, B. C. (2016). Relearning and Retaining Personally-Relevant Words using Computer-Based Flashcard Software in Primary Progressive Aphasia. Front Hum Neurosci, 10, 561. doi:10.3389/fnhum.2016.00561 • Holland, A. L., Halper, A. S., & Cherney, L. R. (2010). Tell Me Your Story: Analysis of Script Topics Selected by Persons With Aphasia. American Journal of Speech-Language Pathology, 19(3), 198. doi:10.1044/1058-0360(2010/09-0095) • Palmer, R., Hughes, H., & Chater, T. (2017). What do people with aphasia want to be able to say? A content analysis of words identified as personally relevant by people with aphasia. PloS one, 12(3), e0174065. doi:10.1371/journal.pone.0174065 • Renvall, K., Nickels, L., & Davidson, B. (2013a). Functionally relevant items in the treatment of aphasia (part I): Challenges for current practice. Aphasiology, 27(6), 636-650. doi:10.1080/02687038.2013.786804 • Renvall, K., Nickels, L., & Davidson, B. (2013b). Functionally relevant items in the treatment of aphasia (part II): Further perspectives and specific tools. Aphasiology, 27(6), 651-677. doi:10.1080/02687038.2013.796507 • Stuart, S. (1997). Vocabulary use during extended conversations by two cohorts of older adults. Augmentative and Alternative Communication, 13(1), 40-47. • Webster, J., & Whitworth, A. (2012). Treating verbs in aphasia: exploring the impact of therapy at the single word and sentence levels. International Journal of Language & Communication Disorders, 47(6), 619-636. doi:10.1111/j.1460-6984.2012.00174.x • Worrall, L., Sherratt, S., Rogers, P., Howe, T., Hersh, D., Ferguson, A., & Davidson, B. (2011). What people with aphasia want: Their goals according to the ICF. Aphasiology, 25(3), 309-322. doi:10.1080/02687038.2010.508530 • Yorkston, K., Dowden, P., Honsinger, M., Marriner, N., & Smith, K. (1988). A comparison of standard and user vocabulary lists. Augmentative and Alternative Communication, 4(4), 189-210. doi:10.1080/07434618812331274807 SHAA Convention 2019

  22. Questions? • Email: dallinbailey@auburn.edu SHAA Convention 2019

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