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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Most common verbs in COCA group by lemmas display per million sort by frequency SHAA Convention 2019

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

Other databases to use MRC Psycholinguistics Database (Coltheart, 1981) Lists at the end of Renvall et al., 2013b Brysbaert concreteness 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

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

References SHAA Convention 2019 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

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