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A S H A 2 0 1 2
T E R E S A G R A Y & S W A T H I K I R A N
D E P A R T M E N T O F S P E E C H A N D L A N G U A G EH E A R I N G S C I E N C E S , B O S T O N U N I V E R S I T Y
Bilingual Aphasia: What is the roleof proficiency and impairment?
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Disclaimer
The authors have no relevant financial or nonfinancialrelationships in the products or services described,reviewed, evaluated or compared in this presentation.
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Project Goals
1. Determine patterns of lexical and semanticprocessing deficits in bilingual aphasia andconceptualize a theoretical framework that
accounts for language deficits;2. Identify language proficiency measures that predict
post-stroke language deficits.
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Bilingual Language Processing: receptive language
Bilingual Interactive
Activation Model +
(Dijkstra & van Heuven,
2002)
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Bilingual Language Processing: expressive language
Revised Hierarchical
Model
(Kroll & Stewart,
1994)
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Monolingual Language Processing
(Ellis and Young, 1988)
(Annotated version)
Auditory analysis
system
Auditory input
lexicon
Semantic
system
Speech output
lexicon
Phoneme Level
Speech
Heard Word
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ExpressionSpanish
ExpressionEnglish
Comprehension Spanish
ComprehensionEnglish
WordRecognitionSp Eng
SemanticsEnglish
Semantics
Spanish
TranslationSp Eng
TranslationEng Sp
WordRecognitionEng Sp
SemanticsNon-
linguistic
Gray & Kiran (under revision)
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Methods
19 Spanish-English bilingual aphasic patients (meanage 63.1, SD 17.82); 11 females
Bilingual Aphasia Test (BAT): English and Spanish +Part C
Boston Naming Test (BNT): English and Spanish
Pyramids and Palm Trees (PPT): Picture Version
Language Use Questionnaire (Kiran, Pena, Bedore, &Sheng, 2010)
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Language UseQuestionnaire
Variables
Age of Acquisition
Pre- StrokeLanguage
Ability Rating
Post-StrokeCurrent
Exposure
EducationHistory
LifetimeExposure
Post- StrokeLanguage
Ability Rating
Confidence
FamilyProficiency
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Theoretical framework that accounts for languagedeficits
Conceptualize a framework for bilingual languageprocessing
Foundation based on psycholinguistic models
Connections validated with patient data
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Expression
Spanish
Expression
English
Comprehension Spanish
ComprehensionEnglish
WordRecognitionSp Eng
SemanticsEnglish
SemanticsSpanish
TranslationSp Eng
TranslationEng Sp
WordRecognitionEng Sp
SemanticsNon-
linguistic
Semantics Non-Linguistics: PPT-
Picture Version
Semantics English/Spanish: average
of 6 BAT subtests: Semantic
Categories, Synonyms, Antonyms I &
II, Semantic Acceptability, and
Semantic Opposites
Comprehension English/Spanish:
average of 3 BAT subtests: Pointing,
Semi-Complex Commands, and
Complex Commands
Word Recognit ion (Spanish into
English and vice versa): BAT Part C
subtest Word Recognition
Expression English/Spanish: BNT
Translation (Spanish into English
and vice versa): average of 2 BAT
Part C subtests, Word and SentenceTranslation
Gray & Kiran (under revision)
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Figure 1: Schematic of Bilingual Language Processing. All p-values < 0.05.
Expression
Spanish
Expression
English
Comprehension Spanish
ComprehensionEnglish
WordRecognitionSp Eng
Semantics
English
Semantics
Spanish
TranslationSp Eng
TranslationEng Sp
WordRecognitionEng Sp
SemanticsNon-linguistic
.51
.62
.48
.58
.57.68 .59
.80
.67
.57
.65.68
.86
.65.67
.47
.74
.58
.55
.71
.67
Gray & Kiran (under revision)
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Figure 1: Schematic of Bilingual Language Processing. All p-values < 0.05.
Expression
Spanish
Expression
English
Comprehension Spanish
ComprehensionEnglish
WordRecognitionSp Eng
Semantics
English
Semantics
Spanish
TranslationSp Eng
TranslationEng Sp
WordRecognitionEng Sp
SemanticsNon-linguistic
.51
.62
.48
.58
.57.68 .59
.80
.67
.57
.65.68
.86
.65.67
.47
.74
.58
.55
.71
.67
Gray & Kiran (under revision)
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Identify language proficiency measures that predict post-stroke language deficits
1. Determine language metrics
2. Decide LUQ metrics
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Organization of Data Variables
Comprehension English/Spanish
Average of 3 BAT subtests: Pointing, Semi-Complex Commands, andComplex Commands
Semantics English/Spanish
Average of 6 BAT subtests: Semantic Categories, Synonyms, Antonyms I& II, Semantic Acceptability, and Semantic Opposites
Expression English/Spanish
BNT
Word Recognition (Spanish into English and vice versa)
BAT Part C subtest Word Recognition
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Language UseQuestionnaire
Variables
Age of Acquisition
Pre- StrokeLanguage
Ability Rating
Post-StrokeCurrent
Exposure
EducationHistory
LifetimeExposure
Post- StrokeLanguage
Ability Rating
Confidence
FamilyProficiency
Dependent Measures:BAT Comprehension, BAT Semantics, BNT and BAT-Word Recognition(Categorical Variable: Language)
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Identify language proficiency measures that predict post-
stroke language deficits
BAT Comprehension
BAT Semantics
BNT
BAT Word Recognition
Confidence
Current Exposure
Pre-Stroke LAR
( R2= .499, F (3, 22) = 7.33, p ≤ 0.01)
( R2= .33, F (3, 21) = 3.57, p ≤ 0.05)
( R2= .25, F (3, 22) = 2.52, p = 0.08)
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LAR
β = 0.55, t = 2.33, p = 0.02
β = 0.65, t = 2.43, p = 0.023
β = 0.47, t = 1.64, p = 0.11
β = 0.58, t = 2.01, p = 0.058
BAT Comprehension
BAT Semantics
BAT Word Recognition
BNT
Pre-Stroke
Identify language proficiency measures that predict post-
stroke language deficits
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Take Home for Clinicians
1. The benefit of using a framework to interpretpatient data:
Where is language breaking down?
2. Our framework can be used with languagecombinations other than Spanish-English
Our framework is meant to explain a diverse set of test results.
3. The importance of pre- and post- morbid languageuse data to interpret bilingual aphasic data:
Language impairment vs pre-morbid proficiency
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Figure 1: Schematic of Bilingual Language Processing. All p-values < 0.05.
Expression
Spanish
Expression
English
Comprehension Spanish
ComprehensionEnglish
WordRecognitionSp Eng
Semantics
English
Semantics
Spanish
TranslationSp Eng
TranslationEng Sp
WordRecognitionEng Sp
SemanticsNon-linguistic
.51
.62
.48
.58
.57.68 .59
.80
.67
.57
.65.68
.86
.65.67
.47
.74
.58
.55
.71
.67
Gray & Kiran (under revision)
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Future Directions
1. Expand our sample size to include more patients.
2. Evaluate the data of other language combinations
3. Use pre-stroke LAR as a means to identify
impairment patterns within our patients.
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A C K N O W L E D G M E N T S : O U R P A T I E N T S A N D
T H E I R F A M I L I E S ; M E M B E R S O F T H E B U A P H A S I A R E S E A R C H L A B O R A T O R Y
C O N T A C T I N F O R M A T I O N :
T E R E S A G R A Y : T G R A Y @ B U . E D U
THANK YOU
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References
de Bot, K. (1992). A bilingual processing model: Levelt’s ‘speaking’model adapted. Applied Linguistics, 13, 1-23.
Dijkstra, T., van Heuven, W. J. B., & Grainger, J. (1998). Simulating cross-language competition with the bilingual interactive activation model. Psychologica Belgica, 38, 177-196.
Dijkstra, T., & van Heuven, W. J. B. (2002). The architecture of the bilingual word recognitionsystem: From identification to decision. Bilingualism: Language and Cognition, 5 (03).
Ellis, A. & and Young, A. (1988). Human cognitive neuropsychology. East Sussex, UK:
Lawrence Erlbaum Associates Ltd., Publishers.
Howard, D., Patterson, K. (1992). Pyramids and Palm Trees test. San Antonio: Pearson.
Kiran, S., Peña, E., Bedore, L., & Sheng, L. (2010). Evaluating the relationship betweencategory generation and language use and proficiency. Paper presented at the Donostia
Workshop on Neurobilingualism, San Sebastian, Spain.Kaplan, E., Goodglass, H., and Weinraub, S. (2001). The Boston Naming Test. 2nd edition. Baltimore: Lippincott, Williams and Wilkins.
Kroll, S. & Stewart, E. (1994). Category interference in translation and picture naming:Evidence for asymmetric connections between bilingual memory representations.
Journal of Memory and Language, 33, 149-174.
Muñoz, M., & Marquardt, T. (2003). Picture naming and identification in bilingual speakers ofSpanish and English with and without aphasia. Aphasiology, 17 (12), 1115-1132.
Paradis, M. (1989). The Bilingual Aphasia Test. Hillsdale, NJ: Lawrence Erlbaum Associates(out of print). Online at http://www.mcgill.ca/linguistics/research/bat/.
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Results: Are there distinct subgroups into which patients with bilingual aphasia can be categorized?
Determine the relationship between language proficiency and post-stroke language deficits.
Pre-Stroke Language Proficiency:
pre-stroke LARPost-Stroke Comprehension/single word naming:
BAT Comprehension (average of subtests Pointing, Semi-Complex and Complex Commands)
BNT
We established three subgroups of post-stroke languageimpairment presentations among our patients (N=17).
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Results: Are there distinct subgroups into which patients with bilingual aphasia can be categorized? (N=17)
Group 1: Parallel (n=5).
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Results: Are there distinct subgroups into which patients with bilingual aphasia can be categorized? (N=17)
Group 2: Parallel (n = 4).
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Results: Are there distinct subgroups into which patients with bilingual aphasia can be categorized? (N=17)
Group 3: Differential (n=8)
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