1. Data-Based Target Selection for Aphasia Treatment
Dallin Bailey, PhD, CCC-SLP
Assistant Professor
Department of Communication Disorders
Auburn University
Auburn, AL
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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
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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
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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).
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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
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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)
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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)
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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
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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)…
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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
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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)
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12. A pre-corpus
Berger, 1968
Eavesdropping study—eavesdropping in a restaurant
Yielded words from adults’ conversations “of an unguarded and informal nature”—words tabulated in the appendix of the article
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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
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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
Sortable by part of speech.
Column to observe is SUBTLWF
Potentially good for core vocabulary
Frequency calculator for lists
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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
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16. Most common nouns in COCA
_v*
group by lemmas
display per million
sort by frequency
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17. Most common verbs in COCA
group by lemmas
display per million
sort by frequency
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18. Wikipedia corpus
Can create a virtual corpus 1.9 billion words from all Wikipedia articles on a day in 2014
Good for finding fringe vocab as specific as you like
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19. Other databases to use
MRC Psycholinguistics Database (Coltheart, 1981)
Lists at the end of Renvall et al., 2013b
Brysbaert concreteness ratings:
Other BYU corpora
For finding phonological neighbors
Source for some of these:
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20. Other ways to use COCA
You can also input lists and texts to find related words that may be useful
collocates or other related words may be good candidates for treatment
Example: Eaton & Newman, 2018
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21. References SHAA Convention 2019
Armstrong, E. (2005). Expressing opinions and feelings in aphasia: Linguistic options. Aphasiology, 19(3-5), doi: /
Balandin, S., & Iacono, T. (1998a). A few well-chosen words. Augmentative and Alternative Communication, 14(3), doi: /
Balandin, S., & Iacono, T. (1998b). Topics of meal-break conversations. Augmentative and Alternative Communication, 14(3), doi: /
Bastiaanse, R., & Jonkers, R. (1998). Verb retrieval in action naming and spontaneous speech in agrammatic and anomic aphasia. Aphasiology, 12(11), doi: /
Berger, K. (1968). The most common words used in conversations. Journal of Communication Disorders, 1(3), doi: / (68)
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),
Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), doi: /s
Coltheart, M. (1981). The MRC Psycholinguistic Database. Quarterly Journal of Experimental Psychology. Retrieved from
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), doi: /
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: /2018_AJSLP
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: /fnhum
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: / (2010/ )
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), e doi: /journal.pone
Renvall, K., Nickels, L., & Davidson, B. (2013a). Functionally relevant items in the treatment of aphasia (part I): Challenges for current practice. Aphasiology, 27(6), doi: /
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), doi: /
Stuart, S. (1997). Vocabulary use during extended conversations by two cohorts of older adults. Augmentative and Alternative Communication, 13(1),
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), doi: /j 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), doi: /
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), doi: /
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22. Questions?
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