Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2013 May 23.
Published in final edited form as: Am J Speech Lang Pathol. 2013 May;22(2):S298–S309. doi: 10.1044/1058-0360(2013/12-0085)

Rehabilitation in bilingual aphasia: Evidence for within and between-language generalization

Swathi Kiran 1,2, Chaleece Sandberg 1, Teresa Gray 1, Elsa Ascenso 1, Ellen Kester 3
PMCID: PMC3662480  NIHMSID: NIHMS451399  PMID: 23695906

Abstract

Purpose

The goal of this project was to examine if there was a principled way to understand the nature of rehabilitation in bilingual aphasia such that patterns of acquisition and generalization are predictable and logical.

Methods

Seventeen Spanish-English bilinguals with aphasia participated in the therapy experiment. For each participant, three sets of stimuli were developed for each language: (a) English Set 1, (b) English Set 2 (semantically related to each item in English Set 1), (c) English Set 3 (unrelated control items), (d) Spanish Set 1 (translations of English Set 1), (e) Spanish Set 2 (translations of English Set 2; semantically related to each item in Spanish Set 1), and (f) Spanish Set 3 (translations of English Set 3; unrelated control items). A single subject experimental multiple baseline design across participants was implemented. Treatment was conducted in one language whereas generalization to within and between-language untrained items was examined.

Results

Treatment for naming on Set 1 items resulted in significant improvement (ES > 4.0) on the trained items in 14/17 participants. Of the 14 participants who showed improvement, within-language generalization to semantically related items was observed in 10 participants. Between-language generalization to the translations of trained items was observed for 5 participants; whereas between-language generalization to the translations of the untrained semantically related items was observed for 6 participants.

Conclusions

The results of this study demonstrate within and between-language patterns that are variable across participants, these differences are indicative of the interplay between facilitation (generalization) and inhibition.

Introduction

It is estimated that 60% of the world is bi/multi-lingual. Within the US, Spanish-English bilingualism is the largest growing bilingual population. Fifty-five million individuals (approximately 20%) in the United States are currently Spanish speakers (http://www.census.gov/prod/2010pubs/acs-12.pdf). Obviously, this translates to an increase in clinical need to address bilingual aphasia rehabilitation but no clear guidelines exist on how to do so. Although there is research that explores how bilingual individuals’ language systems are organized and function, there has been insufficient research on this topic in individuals with bilingual aphasia (Lorenzen & Murray, 2008) although attention to the topic is increasing due to practical demands of serving this clinical population (Kohnert, 2004; Laganaro, Di Pietro, & Schnider, 2006; see recent chapters in Gitterman, Obler & Goral, 2012). A recent review of 13 studies on bilingual aphasia rehabilitation (Faroqi-Shah, Frymark, Mullen, & Wang, 2010) was focused on the effectiveness of rehabilitation of language deficits in bilingual aphasia. Except for one study with 30 participants (Junque, Vendrell, Vendrell-Brucet, & Tobena, 1989) most studies were case studies. In general, Faroqi-Shah et al. observed that therapy provided in the L2 results in improved treatment outcomes in the treated language. Further, between-language transfer occurs in over half the participants. Interestingly, age of acquisition and language differences across studies do not specifically influence treatment outcomes. However, there was quite a bit of variability in treatment type and consequent treatment outcomes.

A few of the studies mentioned in Faroqi-Shah et al.’s review and some others not mentioned have specifically examined the issue of between-language generalization and will be briefly discussed here. For instance, cueing hierarchy treatment (i.e., when increasing/decreasing cues are systematically given to promote naming accuracy) in English or Spanish did not yield between-language generalization for one Spanish-English bilingual with transcortical motor aphasia, as measured by the Bilingual Aphasia Test (BAT, (Paradis, 1989)) naming subtest (Galvez & Hinckley, 2003; Hinckley, 2003). Overall improvement on the BAT was greater in Spanish than English, but naming improvement on the BAT was equal across languages. In another study, Kohnert (2004) examined the effect of cognitive-based treatment and lexical-based treatment on generalization in one Spanish-English bilingual participant with severe nonfluent aphasia. This patient showed between-language transfer for cognates but not for non-cognates. However, generalization to cognates is not necessarily a remarkable finding given the phonological and semantic overlap for cognates in the two languages. Recently Miller-Amberger (2011) examined one French-English individual who was French dominant and demonstrated greater impairment in English relative to French. This participant was trained in English and improved in the trained language but not in French, indicating a language-specific improvement as a function of treatment. In contrast, Miertsch and colleagues (Miertsch, Meisel, & Isel, 2009) trained a German, English and French speaking trilingual in his L3 (French) and found that both L3 (French) and L2 (English) improved as a function of treatment. Likewise, Goral and colleagues (Goral, Rosas, Conner, Maul, Obler, 2011) examined a multilingual Spanish, German, French and English speaking individual who received therapy in the weaker language, and found improvements in the trained language as well as some between-language generalization to the untrained languages. In another study, Goral et al. (2010) found selective generalization from trained L2 (English) to L3 (French) but not L1 (Hebrew) in a trilingual participant with agrammatic deficits.

When treatment is targeted towards naming deficits, there is a relatively strong theoretical foundation from bilingual lexical semantic processing, which allows specific predictions about between-language generalization to be generated. For instance, the revised hierarchical model (RHM; Kroll & Stewart, 1994; Kroll, Bobb, Misra, & Guo, 2008; Kroll & Stewart, 1994; Kroll, van Hell, Tokowicz, & Green, 2010) allows for language proficiency differences by proposing connections between both L1 and L2 and the semantic system; these connections differ in their strengths as a function of fluency in L1 relative to L2. In bilingual individuals with a dominant language, the lexicon of L1 is generally assumed to be larger than that of L2 because more words are known in the dominant language. Also, lexical associations from L2 to L1 are assumed to be stronger than those from L1 to L2. Conversely, the links between the semantic system and L1 are assumed to be stronger than from the semantic system to L2. With regards to activation of phonological representations from the semantic system, the prevailing theory suggests that activation flows from the semantic system to the phonological system of both languages simultaneously, indicating that lexical access is target language nonspecific (Costa, La Heij, 2006; Finkbeiner, Gollan & Carammazza, 2006). An alternate, but not neccessarily contradictory hypothesis is that in order for bilinguals to access the target language, the non-target language must be inhibited (Green, 1986, 1998). In other words, a speaker activates target language lemmas while simultaneously inhibiting the lemmas of the non-target language. Support for Green’s model comes from studies examining between-language translation in normal bilinguals, where translation from the stronger language to the weaker language occurs both in early bilinguals (e.g., Gollan, Forster, & Frost, 1997) and late bilinguals (e.g., Jiang, 1999; Williams, 1994). More recent studies have shown an asymmetric cost of translating from the stronger language to the weaker language (Costa, Santesban, & Ivanova, 2006; Grainger & Frenck-Mestre, 1998) because it takes more effort to inhibit the stronger language compared to the weaker language. In contrast, as the bilingual is more balanced, the asymmetry decreases (Costa, La Heij, et al., 2006).

Our previous work examining a semantic-based treatment to improve naming in bilingual individuals with aphasia was based on these mutually overlapping theories with the specific prediction that training semantic attributes for targets in one language would improve naming in that language and facilitate generalization to untrained semantically related items in the trained language and translations of the trained and untrained items in the untrained language (Edmonds & Kiran, 2006). Three English-Spanish bilingual individuals with aphasia demonstrated a within- and between-language effect on generalization related to pre-stroke language proficiencies. In a follow-up study, Kiran and Roberts (2010) also administered the same semantic treatment to improve picture naming in two English-Spanish participants and two English-French participants and measured generalization to translations of the treated words and to words semantically related to the target words in each language. Results revealed that the performance in all four participants was highly variable but reflected both within- and between-language effects on generalization. Importantly, in addition to the previously identified factors including pre-stroke language proficiency and age of acquisition of each language, other factors such as post-stroke level of language impairment and type and severity of aphasia also influenced treatment outcome. Given the multi-dimensional factors that potentially influence naming impairment and recovery, it is still not clear whether treatment is effective in improving naming performance of the trained item s and/or language. Further, it is not clear if generalization occurs, when it occurs and under what circumstances it does not occur.

The goal of this project was to examine if there was a principled way to understand the nature of rehabilitation in bilingual aphasia such that patterns of acquisition and generalization are predictable and logical. In this study, we examine a large group of participants (N= 17) who have received therapy to improve naming in one language. Three questions were proposed in this work. First, what are the effects of treatment on trained items independent of the language in which training was given? Based on the meta-analytical review by Faroqi-Shah and colleagues and other work since, we hypothesized that irrespective of the language trained, treatment provided in one language should improve naming of items in that language. Second, what are the effects of treatment on generalization to translation items and untrained items independent of what language is trained? Given the extensive work in monolingual aphasia demonstrating that training semantic attributes results in improved naming of targets as wells as generalization to semantically related items (e.g., Kiran & Thompson, 2003; Kiran, Sandberg & Abbot, 2009), one main prediction of our work is that strengthening semantic features improves access to trained items within the language trained and to semantically related neighbors within that language (see Figure 1). Relatedly, a second prediction stemming from these models is that lexical-semantic connections between L1 and L2 are linked and lexical access is target language-nonspecific (e.g., Costa, La Heij et al., 2006; Hermans, Bongaerts, De Bot, & Schreuder, 1998). Therefore, between-language generalization is expected to occur as a function of treatment because repeated exposure to items in one language should result in improved access to the translations in the untreated language (see Figure 1). We, however, expect that patterns of generalization will vary across patients and will depend on individual patients’ language use and impairment profiles. Consequently, a final question of this study examined impairment and language use factors that may influence treatment outcomes. While individual case studies have interpreted their results both in terms of the level of impairment between the two languages and the nature of pre-morbid language use and proficiency, we expected to observe a systematic positive relationship between level of premorbid proficiency in a language and generalization to that language as well as a negative relationship between level of language impairment and improvements in each language.

Figure 1.

Figure 1

Open in a new tab

Schematic of hypothesized relationship between trained and untrained items as a function of treatment adapted from Costa, La Heij et al., (2006)’s framework of bilingual lexical access. Training one set (e.g., Celery, Set 1, highlighted in the open rectangle) should result in within-language generalization (e.g., cabbage, Set 2)(1). Training this set should also result in between-language generalization to the translation (e.g., apio, Set 1)(2) and to the translation of the semantically related item (e.g., repollo, Set 2)(3). Although a possible direct connection may exists between apio and cabbage in terms of word translation, we do not believe our study was set up to examine this potential connection.

Methods

Participants

Seventeen participants with bilingual aphasia (6 male, 11 female) participated in the therapy experiment. Five of these participants have been reported on previously (Edmonds & Kiran, 2006; Kiran & Roberts, 2010). All were at least five months post-onset from a left perisylvian area CVA (except one who had a gun-shot wound) and ranged in age from 33 to 87 years (M = 58.84, SD = 17.66). Thirteen participants were recruited from the Austin, TX, area, while the remaining four were recruited from the Boston, MA, area. All participants were native Spanish speakers and English was their second language. Participant education ranged from elementary school level to college level (M=10.78 years, SD=4.34).

Assessment of language impairment in English and Spanish

Participants were administered the Pyramids and Palm Trees Test (PPT) – Picture Version (Howard & Patterson, 1992) to measure language-independent semantic processing, the Boston Naming Test (BNT) (Kaplan, Goodglass, & Weintraub, 1983) in both Spanish and English to measure confrontation naming in both languages, and the Bilingual Aphasia Test (BAT) (Paradis, 1989) in both Spanish and English to determine the degree of overall language impairment in both Spanish and English. For the purpose of this paper, BAT-Comp-E and BAT-Comp-S are averages from the Pointing, Semi-Complex Commands and Complex Commands subtests in each language. BAT-Sem-E and BAT-Sem-S are averages from the Semantic Categories, Synonyms, Antonyms I and II, Semantic Acceptability and Semantic Opposites subtests. Finally, BAT-Trans S into E and BAT-Trans E into S are averages from the Translation of Words and Translation of Sentences subtests. Performance on these measures for each participant is listed in Table 1.

Table 1.

Demographic information, Spanish and English diagnostic scores for all participants. (PPT = Pyramids and Palm Tree, Howard & Patterson, 1992; BNT = Boston Naming Test, Kaplan, Goodglass, & Weintraub, 1983; BAT = Bilingual Aphasia Test, Paradis, 1989).

PT Gender MPO Age at testing PPT BNT-E BNT-S BAT-Comp E BAT-Comp S BAT-Sem E BAT-Sem S BAT-Word Rec, E into S BAT-Word Rec, S into E BAT-Tran, E to S BAT-Tran, S to E
UT07 F 6 56.1 DNT 23 18 80 88 53 62 100 100 5 10
UT23 F 3.5 41.5 90 0 2 63 70 40 55 100 100 0 0
BU07 F 7.5 65.2 52 0 15 7 67 23 38 DNT DNT DNT DNT
UT19 M 50 75 75 3 47 17 75 DNT 32 40 60 8 11
UT16 F 16 56.11 75 5 5 63 60 75 48 100 100 67 89
UT01 M 8 53.8 DNT 0 0 38 15 28 40 20 40 0 5
UT11 F 9 53.1 DNT 8 5 52 45 23 15 100 100 5 33.5
UT09 F 6 87.9 DNT 57 10 97 75 67 42 DNT DNT DNT DNT
UT18 F 30 73.8 77 28 32 67 83 62 77 100 80 95 61
UT22 M 3.5 41.4 83 5 47 57 90 40 72 60 80 8 13
BU01 M 84 44.7 92 37 43 70 80 53 55 60 80 49 60
BU04 M 173 37 94 58 12 70 62 60 48 80 100 0 36
UT02 F 9 54.1 90 43 40 78 75 73 73 100 100 30 31.5
UT17 M 11 53.7 87 52 8 82 93 52 58 60 100 43 61
BU12 F 5 33.3 100 0 0 42 55 42 53 80 100 0 0
UT20 F 41 85.6 71 0 0 DNT 27 DNT 20 20 60 0 0
UT21 F 10 88 48 2 0 17 20 DNT DNT 0 0 0 0
Open in a new tab

Note: Pt = Participant; E = English; S = Spanish; Comp = Comprehension; Sem = Semantics; Trans = Translation; BAT Comp E and BAT Comp S are averages from subtests: Pointing, Semi-Complex Commands and Complex Commands; BAT-Sem E and BAT-Sem S are averages from subtests: Semantic Categories, Synonyms, Antonyms I and II, Semantic Acceptability and Semantic Opposites; BAT-Trans S into E and BAT-Trans E into S are averages from subtests: Translation of Words and Translation of Sentences; DNT = Did not test.

Assessment of language proficiency

For all participants, measures of language age of acquisition (AoA), use, and proficiency were obtained by administering a comprehensive questionnaire to each participant and/or his/her family members. The Language Use Questionnaire (LUQ) (Kiran, Pena, Bedore, & Sheng, 2010) covers the following information specific to each language: (a) age of acquisition; (b) amount of language exposure during the entire lifetime; (c) educational history in terms of both the language of instruction and language used by peers; (d) confidence of skill in each language; (e) time spent conversing in each language during his/her daily routine after the stroke (post-stroke exposure); (f) proficiency of immediate family members; and (g) a self-rating of pre- and post-stroke proficiency in each language. For pre-stroke language exposure, a weighted average of the proportion of exposure across the lifespan in hearing, speaking, and reading domains was obtained for each language. Likewise, a weighted average of the exposure in each language calculated hour by hour during a typical weekday and typical weekend reflected the proportion of post-stroke language exposure in each language. Finally, an average proportion score in terms of the participant’s ability to speak and understand the language in formal and informal situations in each language reflected participants’ perception of their own language proficiency. For the purpose of this paper, we calculated an average language use and proficiency value for the aforementioned factors in order to determine a composite picture of dominance in either of the languages. Details regarding the participants’ language background are listed in Table 2.

Table 2.

Language history and language ratings across languages for all participants.

Pts AoA, E LE, E LE, S Conf, E Conf, S Post-Stroke CE, E Post-Stroke CE, S Pre-Stroke LAR, E Pre-Stroke LAR, S Ed Hx, E Ed Hx, S Fam Prof E Fam Prof, S Ave E Ave S
UT07 0 DNT DNT DNT DNT DNT DNT 94 31 100 0 DNT DNT 97 16
UT23 9 33 67 42 100 29 71 66 94 22 78 33 100 38 85
BU07 45 10 90 5 100 2 98 32 100 0 100 0 100 8 98
UT19 27 16 84 13 76 15 85 20 100 0 100 0 100 11 91
UT16 0 62 38 99 94 62 38 94 74 67 33 100 100 81 63
UT01 0 75 25 100 83 94 6 100 40 100 0 83 83 92 40
UT11 11 DNT DNT DNT DNT DNT DNT 98 100 DNT DNT DNT DNT 98 100
UT09 5 DNT DNT DNT DNT DNT DNT 100 82 100 0 DNT DNT 100 41
UT18 17 40 60 80 100 0 100 100 100 25 75 58 100 51 89
UT22 18 10 90 11 92 38 63 34 94 0 100 17 100 18 90
BU01 19 28 72 42 94 22 78 89 89 0 100 33 100 36 89
BU04 7.5 74 26 81 100 66 34 100 49 100 0 67 100 81 52
UT02 21 31 69 DNT DNT DNT DNT 90 100 DNT DNT DNT DNT 60 85
UT17 6 66 34 96 98 55 45 100 100 58 42 75 100 75 70
BU12 12 28 72 54 100 46 54 80 100 28 72 65 100 50 83
UT20 69 5 95 2 100 12 88 DNT DNT 0 0 0 100 4 77
UT21 5 72 28 100 100 99 1 DNT DNT 100 0 100 100 94 46
Open in a new tab

Note: Pts = Participant; S = Spanish; E = English; AoA = age of acquisition in years; LE = lifetime exposure; Conf = confidence; CE = current exposure; LAR = language ability rating; Ed = education; Hx = history; Fam = family; Prof = proficiency; Ave: Average, DNT = did not test. All participants are native Spanish speakers so AoA–Spanish is 0 for everyone.

Stimuli

For all participants, target treatment items were selected from a corpus of 300 nouns gathered from our previous treatment studies for word finding in aphasia in both monolingual and bilingual populations (Edmonds & Kiran, 2006; Kiran & Bassetto, 2008; Kiran & Thompson, 2003; Kiran, 2008; Kiran & Johnson, 2008). Rather than proceeding with a pre-chosen set, target items for each participant were chosen based on a confrontation naming pre-test and hence, the number and the specific stimuli trained during treatment differed for each participant. For each participant, six individualized stimulus sets were created: English Set 1 (e.g., celery), Spanish Set 1 (e.g., apio), English Set 2 (e.g., cabbage), Spanish Set 2 (e.g., repollo), English control Set 3 (e.g., cow), Spanish control Set 3 (e.g., vaca). Thus, Set 1 and Set 2 consisted of semantically related items whereas Set 3 comprised an unrelated control set. See Table 3 for the number of trained items for each patient, All word pairs were category coordinates (e.g., horse and sheep). Cognates (e.g., elephant and elefante) and words with at least 50% phonetic similarity (e.g., cat and gato) were eliminated. The lists were balanced for average frequency (Bates et al., 2003; Frances & Kucera, 1982) and number of syllables. For each item to be trained, 12 semantic features were chosen from a database of 261 binary semantic features assembled for items across categories. For each target item (e.g., celery), six of the features were associated with the item, while six were not. Care was taken to ensure that each associated/non-associated pair belonged to one of six categories: category (e.g., is a vegetable), location (e.g., is found in a grocery store), physical (e.g., is green), function (e.g., is eaten), characteristic (e.g., is juicy), association (here the participant makes his/her own association, such as crunchy for the example celery).

Table 3.

Treatment results for all participants. Table provides Effect sizes for Trained Language (Set 1, Set 2, Set 3 control) and Untrained Language (Set 1, Set 2, Set 3 contro1).

Participant Number of items trained Language trained Trained Language Set 1 Trained Language Set 2 Trained Language Set 3 (Control) Untrained Language Set 1 Untrained Language Set 2 Untrained Language Set 3 (Control)
UT07 10 Spanish 12.41 0.94 1.50 3.11 2.83 4.91
UT23 15 Spanish 13.84 13.47 1.39 10.68 6.35 0.58
BU07 15 English 2.89 2.02 0.35 4.08 1.83 2.30
UT19 17 English 4.55 1.73 0.00 0.99 4.89 0.00
UT16 15 English 6.82 6.83 6.63 0.83 0.17 2.83
UT01* 10 English 14.90 5.15 1.15 1.15 −0.58 −0.58
UT11* 15 English 12.70 7.51 0.58 0.58 −0.58 −0.58
UT09* 10 Spanish 10.97 2.64 0.00 2.07 1.92 5.07
UT18 15 Spanish 15.17 −0.29 3.46 1.73 0.87 3.46
UT22 15 Spanish 12.73 0.24 2.83 1.89 1.18 −1.41
BU01 15 English 4.92 3.57 1.57 1.42 2.28 1.28
UT02 10 Spanish 11.08 6.36 2.12 4.95 6.84 2.12
BU04 10 Spanish 16.50 4.33 0.83 2.52 2.39 0.61
UT17* 15 English 5.32 0.43 −5.43 1.19 −0.63 −0.56
BU12 15 English 8.16 0.00 0.00 0.00 0.00 0.00
UT20 15 Spanish 0.00 0.00 0.00 0.00 0.00 0.00
UT21 15 English 0.00 −0.70 0.00 0.00 0.00 0.00
Open in a new tab

Note

*

indicates participants who underwent two phases of treatment (i.e., trained in both languages) and only ES for the first phase is discussed in this paper.

Treatment Procedures

To facilitate access to naming of trained items, a semantic treatment was implemented. These procedures have been described in detail previously and are briefly summarized here (Edmonds & Kiran, 2006; Kiran & Roberts, 2010). All participants received treatment two times per week, for two hours each session. For each target item, participants performed five treatment steps that emphasize semantic feature attributes of that particular item. First, they were required to label/name a picture of the item. Then, the participant was asked to choose five features (from a field of 10) that belong to that item. Each feature belonged to a different feature class: (a) a superordinate label (e.g., belongs to), (b) a function (e.g., is used for), (c) a characteristic (e.g., has/is), (d) a physical attribute (e.g., is made of/appears), and (e) a location (e.g., is found). After these were chosen, the participant was asked to generate an association and a non-association (e.g., reminds me of/doesn’t remind me of). Following this, the participant was asked yes/no questions about the relationship of the semantic features with the target item and was required to accept or reject these and other features as being applicable to the target example. Finally, the participant was asked to name the picture again. The average number of treatment sessions was 10.5 weeks (range of 7–13 weeks). Treatment was discontinued when naming accuracy met 80% for the trained items on two consecutive weekly picture-naming probes or when 20 sessions were completed. All participants were trained in one language during the course of treatment. Naming probes were administered in both languages, comprised the same stimuli as those presented during baseline and always preceded every alternate treatment session.

Data Analysis

Prior to treatment, three, four or five naming probes were given to establish a baseline; the specific number of baseline probes was varied across participants. Following treatment, two or three post treatment probes were administered in 11/17 participants. Four participants (UT11, UT01, UT09, UT17) received therapy in the second language after completion of the first treatment, however only the first phase of therapy is reported here. The extent to which changes from baseline to the post-treatment phase are statistically reliable was determined by calculating effect size (ES). Effect size was calculated by comparing the mean of all data points in the post-treatment phase relative to the baseline mean divided by the standard deviation of the baseline data points. For the six participants who were not administered post-treatment probes, ES was calculated from the final three treatment probes. The benchmarks set for the present study were 4.0 (small ES) and 10.0 (large ES) (Beeson & Robey, 2006).

Results

Results for the participants in this study are presented according to the questions posed. Approximately half of the participants (N = 9) were trained in English and the remaining participants were trained in Spanish. Thus, instead of discussing the results in terms of English/Spanish, the results are discussed in terms of Trained Language Set 1, Set 2, and Set 3 and Untrained Language Set1, Set 2 and Set 3.

Q1. What are the effects of treatment on acquisition of trained items?

When examining the effects of treatment on the trained language, independent of what language was trained, 14 of 17 participants show an ES greater than 4.0 (82% of participants) and nine of the 17 participants show an ES greater than 10.0 (52% of participants). Indeed, most participants improved on the trained items relative to the unrelated control items (see Figure 2). Only one participant (UT16) showed improvements in set 3 control items (trained language) with ES greater than 4.0. Additionally, a one way ANOVA 1on the trained effect size with language as the independent variable revealed that training in Spanish results in higher ES than training in English ( F(1, 15) = 5.18, p = .03) indicating that overall, participants showed greater gains in Spanish than in English.

Figure 2.

Figure 2

Open in a new tab

Effect sizes for participants on the trained language Set 1 relative to Trained language Set 3 (control items). Participants marked with an asterisk indicate that these participants received a second phase of therapy but only the first phase is reported here.

Q2. What is the nature of generalization to untrained items within and between-language?

Using the same criterion for ES (greater than 4.0) for generalization, Table 3 shows that six participants met that criterion for semantically related items within the trained language (set 1 to set 2), three participants met that criterion for between-language generalization from trained language set 1 to their translations (untrained language set 1), two participants met criterion for between-language generalization from trained language set 1 to untrained language set 2 and two participants met that criterion for control items in the untrained language. To corroborate these relatively subjective criteria for generalization, we performed cross-correlation function analyses using the autoregressive integrated moving average (ARIMA) procedure in SPSS. For each time series, a regression line is fit to the actual data and the residuals are calculated for that data. Then cross-correlations are calculated on the residuals and averaged over time (Box, Jenkins & Reinsel, 1994). In this study, for each participant, we correlated the time series between (a) trained items and untrained items in the same language and (b) trained items and untrained items in the other language at 10 lag points (−5 to 5). Correlations that exceeded .50 and exceeded two standard errors were deemed statistically significant and are represented in Figure 3. Three participants (UT20, UT21, BU12) did not have enough nonzero data points to include in the analysis. For the remaining 14 participants, we first examined cross-correlation coefficients for the items in the trained set versus Set 2 of the corresponding language (i.e., within-language generalization). Ten of the 14 participants exhibited a significant relationship between trained Set 1 and Set 2 of the same language. Note that items from each set are semantically related to each other (e.g., celery in English Set 1, cabbage in English Set 2). Next, we examined cross-correlation coefficients for the items in the trained language Set 1 versus Set 1 of the untrained language (i.e., between-language generalization). Five of the 14 participants exhibited a significant relationship. Note that these sets of words are translations of each other (e.g., celery in English Set 1, apio in Spanish Set 1). Finally, we examined cross-correlation coefficients for the items in the trained Set 1 versus Set 2 of the untrained language (i.e., between-language generalization), which revealed a significant relationship for six of the 14 participants. Note that these sets of words are semantically related words in different languages (e.g., celery in English Set 1, repollo in Spanish Set 2).

Figure 3.

Figure 3

Open in a new tab

Summary representation of cross correlation functions between (a) trained language Set 1 and untrained Set 2 (within-language generalization)(blue), (b) trained language Set 1 and untrained language Set 1 (between-language generalization(red), and (c) trained language Set 1 and untrained language Set 2 (between-language)(green). Participants are organized into subgroups: (1) participants who show between and within- language generalization, (2) participants who only show within-language generalization, (3) participants who only show between-language generalization to translations, (4) participants who only show within and between-language generalization to semantically related items, and (5) participants who do not show any generalization.

As shown in Figures 1 and 3, different participants showed different patterns of within and between-language generalization. Three participants (UT07, UT23, & BU07) show both between and within-language generalization (Figure 1: 1, 2, and 3). Five participants (UT19, UT16, UT01, UT11, UT09) show only within-language generalization (Figure 1: 1). Two participants (UT18, UT22) show between-language generalization only to translations in the untrained language (Figure 1: 2) and three participants (UT02, BU01, BU04) showed within-language generalization and between-language generalization only to semantically related untrained items in the untrained language (Figure 1: 1,3). Importantly, every participant who shows between-language generalization to the semantically related items also shows within-language generalization to those items.

Q3. What impairment and language use factors influence treatment outcomes?

Given the limited number of participants (N = 17) in the study, we chose to compute a non-parametric Spearman correlation for language impairment factors such as PPT (language independent semantic processing), BNT-E and BNT-S (language specific lexical access), BAT-comp-E and BAT-comp-S (language specific comprehension), BAT-Sem-E, BAT-Sem-S (language specific semantic processing), Ave-E and Ave-S (average pre-stroke language use in each language), AoA-English (there was no variance in AoA-Spanish) and trained language effect size for Set 1. The results of the correlation matrix are provided in Table 4 and show several significant positive relationships between language impairment variables such as (a) BNT in English and Spanish, (b) BNT and BAT comprehension, BAT comprehension and BAT semantic indicating significant relationships between impairments in naming, comprehension and semantic processing in the two languages. Of note, AoA in English negatively correlated with an average composite of the various language use and proficiency variables in English (i.e., the later English was learned, the less proficient the individual was in English) but positively correlated with the average use and proficiency in Spanish. A moderate correlation was observed between trained language set 1 effect sizes and PPT and BAT-Semantic scores in Spanish. Likewise, a moderate correlation was observed between untrained language set 1 effect sizes and BNT-Spanish and BAT-Comprehension in Spanish.

Table 4.

Nonparametric Spearman correlation matrix for language impairment variables, averaged premorbid language use, Age of Acquisition, and effect sizes for trained language (TL) Set 1. All correlations marked with an asterisk are significant at p <.05

PPT BNT-E BNT-S BAT-Comp E BAT-Comp S BAT-Sem E BAT-Sem S Trained Language Set 1 Unrained Language Set 1 AoA, E Average English Composite Average Spanish Composite
PPT 1.00 0.37 0.09 0.52 0.26 0.00 0.42 **0.56 0.33 −0.17 0.18 −0.08
BNT-E 1.00 **0.49 **0.83 **0.55 **0.61 0.40 0.29 0.38 −0.19 0.45 −0.17
BNT-S 1.00 0.23 **0.76 0.28 0.43 0.27 **0.52 0.39 −0.27 0.43
BAT-Comp E 1.00 **0.59 **0.70 **0.53 0.32 0.38 −0.30 0.40 −0.41
BAT-Comp S 1.00 0.26 **0.72 0.30 **0.52 0.12 −0.14 0.18
BAT-Sem E 1.00 0.43 0.06 0.03 −0.20 0.26 −0.43
BAT-Sem S 1.00 **0.56 0.42 −0.13 0.03 −0.13
Trained Language Set 1 1.00 0.44 −0.16 0.29 0.12
Unrained Language Set 1 1.00 0.06 −0.01 0.14
AoA, E 1.00 **−0.81 **0.74
Average English Composite 1.00 **−0.58
Average Spanish Composite 1.00
Open in a new tab

Note: Pts = Participant; S = Spanish; E = English; AoA = age of acquisition in years, Comp = Comprehension; Sem = Semantics.

Discussion

The goal of this project was to examine the nature of within- and between-language generalization following a semantic based naming therapy in individuals with Spanish-English bilingual aphasia. Our hypothesis was that all participants would improve on the items that were trained and will likely show both between and within-language generalization but that these patterns will vary based on individual language profiles. In general, results of this study showed that training naming resulted in improvements on trained items irrespective of language trained, although, training in Spanish which was the native language for all speakers resulted in greater outcomes than in English. It is not immediately clear why this would be the case; of the patients trained in Spanish, half of them were Spanish-dominant and half of them were English-dominant speakers. Interestingly, participant scores on the three picture version of the PPT and the BAT semantic scores in Spanish was significantly associated with higher treatment outcomes, indicating patients who showed the most improvements also had higher semantic processing abilities.

With regards to generalization, data from the seventeen patients were segregated into five subgroups. Three participants (UT07, UT23, & BU07) showed both between- and within-language generalization, suggesting that strengthening semantic features improves access to (a) trained items (e.g., ballena) within the trained language, (b) semantically related neighbors (e.g., tiburon) within the trained language, and (c) translations of these items in the untrained language. In the second subgroup, five participants (UT19, UT16, UT01, UT11, UT09) showed only within-language generalization indicating that for these participants, the impact of the semantic feature training was limited to semantically related items within-language. Improvements in the semantically related untrained items within the trained language indicated that therapy targeted at emphasizing semantic features improved access to trained items as well as semantically related items irrespective of which language is trained (Kiran & Bassetto, 2008). Two participants (UT18, UT22) show between-language generalization only to translations in the untrained language but no within-language generalization to semantically related items. This is a surprising finding, and unfortunately, since there are only two participants who showed this pattern, it is hard to draw any meaningful interpretations. In the fourth subgroup, three participants (UT02, BU01, BU04) showed within-language generalization and between-language generalization only to semantically related untrained items in the untrained language. Importantly, every participant who showed between-language generalization to the semantically related items also showed within-language generalization to those items. Notably, both the first group of three participants who show within and between-language generalization and the fourth group of three participants show relatively minor differences between English and Spanish BNT and BAT scores.

A final, fifth group consisted of four patients, UT17 and BU12 (who was not entered into the cross-correlation analysis) who did not show any generalization patterns and no changes beyond the trained group; and UT20 and UT21, who did not show any improvements in the treatment. For the former two patients there is nothing apparent from the test results that can be construed as a possible explanation for the results. Also, they are the only ones who show a pattern that sometimes occurs in aphasia treatment, of no generalization beyond the treated items. The latter two patients were very severely impaired in their output as evidenced by their very low scores on the various tasks reported in Table 1.

In order to account for the generalization mechanisms across these subgroups of patients, we propose an integrative framework comprising two mutually overlapping mechanisms mentioned in the introduction that may influence the treatment effects. One mechanism is that of spreading activation, which is a generalized mechanism of increasing activation as a function of treatment of both target words and their semantically related affiliates in both the trained language and the untrained language (Kiran & Bassetto, 2008; Kiran & Sandberg, 2011). The second mechanism is one of inhibitory control. In the context of the present study, there seem to be at least two forms of inhibitory control at play, one between semantically related items within one language, which has been reported extensively in studies of monolingual lexical semantic processing (Belke, Meyer, & Damian, 2005; Bloem, van den Boogaard, & La Heij, 2004; Damian & Martin, 1999 Starreveld & La Heij, 1995) and in studies of blocked cyclic naming in aphasia (Hsiao, Schwartz, Schnur, & Dell, 2009). The second form of inhibition pertains to bilingual inhibitory control, as proposed by Green’s (1986; 1998) model to address the issue of language control during language production.

Returning to our data, the ideal case scenario for positive between- and within-language generalization is when increased activation due to the general effects of therapy outweighs the inhibition/interference of specific items during lexical selection. The first subgroup of three patients who show both within- and between-language generalization appear to show this equilibrium between the general facilitative effects of increased activation as a function of treatment and optimal inhibitory control such that during the weekly naming probes, these participants show increased naming accuracy across the sets of stimuli. In addition, inspection of the first subgroup of patients’ language impairment and language use profiles does not reveal any trends in terms of language use, but in general these participants show relatively minor differences between English and Spanish BNT and BAT scores suggesting that equal levels of language impairment post-stroke may have some influence on the extent of generalization. In the second group of five patients who show only within-language generalization, the between-language inhibition mechanisms (Green’s IC model) may be stronger than the generalized increased activation as a function of treatment. Of note, four of the five participants in this subgroup were trained in English and three of these four participants had higher average pre-stroke language use and/or post-stroke language impairment scores in English relative to Spanish. Two of these patients were provided therapy in their weaker language and also only showed within-language generalization. What may be the precise mechanisms driving the interaction between facilitation and inhibition for these patients remain unclear, however, as will be discussed below, the generalization patterns are influenced by language use, language dominance and language impairment. Future work will need to carefully contrast language inhibition and control with non-linguistic control tasks as was conducted by Green and colleagues in a case study (Green, Grogan, Crinion, Ali, Sutton, & Price, 2010), in the context of rehabilitation.

In the next subgroup of two patients who only show generalization to the between-language translations, it appears that the within-language interference precludes increased activation to semantically related targets, although the between-language inhibitory control works in positive tandem with increased activation. Both these participants were trained in Spanish, which was also the stronger language pre-stroke (higher average composite scores in Spanish relative to English). Patterns of generalization for these two patients are at odds with patients like UT16 and UT01 from the previous subgroup, who were also trained in their stronger language but show within- and not between-language generalization. One way to resolve this apparent discrepancy in the data is to examine each of these four participants individually. Both UT16 and UT01 learned English early in life and reported stronger English language use and proficiency relative to Spanish. When these two individuals were trained in English, perhaps the lack of continuous exposure to Spanish (e.g., attrition) and the language of the environment (English) may have resulted in within-language generalization to untrained targets in English. On the other hand, UT18 and UT22 learned English later in life and reported stronger Spanish language use and proficiency relative to English. The combination of the later AoA of English and the language of the environment (and perhaps the native language of the clinicians) may have facilitated easier access to untrained targets in English (hence the between-language generalization).

These observations raise interesting questions about the presumed benefits of early bilingualism in terms of novel word learning (Kaushanskaya, & Marian, 2009; Kaushanskaya, & Rechtzigel, 2012) and by extension, facilitation of word retrieval after rehabilitation. Also, it appears that the language of the environment and that of the treating clinicians also likely plays a role in the extent of between-language generalization. These observations are speculative at this point and require further study. A few more observations can be made about the influence of individual participants’ language use and backgrounds and how they may have influenced treatment outcomes. Table 2, Table 3 and Figure 3 show that of the 17 patients, seven (UT07, BU07, UT19, UT09, BU01, BU04, BU12) were trained in their weaker language and of these, four of them show some form of within- and between-language generalization.

A final note about the factors influencing treatment outcomes that emerged from the correlational analysis. Not surprisingly, several significant correlations were observed between naming, comprehension and semantic processing in English and naming and semantic processing in Spanish. These findings underscore the relationship between receptive and expressive language impairments in each of the two languages in bilingual individuals with aphasia (Gray & Kiran, in press). Treatment outcome in the trained language was associated with semantic scores on the PPT, indicating that patients with better semantic processing abilities improved more in treatment. Interestingly, effect sizes for the trained language items and their translations correlated with language assessments in Spanish (BNT, BAT-Comp and BAT-Sem) and may be related to another observation that effect sizes were higher when training was provided in Spanish than in English. Importantly, the average language use measure did not correlate with treatment outcomes or impairment measures, but was associated with age of acquisition of English. Given these results, it is possible that creating a composite/average number to capture an individual’s level of language proficiency may not be ideal or meaningful (Kiran & Roberts, 2012) in terms of interpreting behavioral impairment or treatment outcomes. Given that there are 17 patients in this study, we can begin to address the potential, albeit complicated influence of several facets of language proficiency, language impairment and use on the treatment outcomes, a notable contribution of this study. That said, we acknowledge that no strong conclusions can be drawn regarding the potential influence of one or more of the abovementioned variables on the treatment outcome.

Conclusions

To conclude, the results of this study demonstrate the beneficial effects of a semantic based naming treatment for individuals with bilingual aphasia. In addition, within- and between-language patterns are variable across participants and these differences are indicative of the interplay between facilitation (generalization) and inhibition and appear to be influenced by language proficiency, use, and the patient’s current language environment. In general, these results have implications for theoretical models of bilingual language processing and rehabilitation of bilingual aphasia.

Acknowledgments

A portion of this research was supported by NIDCD # R21DC009446 and Clinical Research Grant from American Speech Language Hearing Foundation to the first author. The authors would like thank Danielle Tsibulsky, Anne Alvarez and Rajani Sebastian for their assistance in data collection and analysis. The authors would also like to thank all their participants for their time and cooperation.

Footnotes

1

We conducted the Shapiro-Wilks test for normality on trained effect size data and the results showed that this assumption of normality was satisfied (Shapiro-Wilks W=.96117, p=.65360).

References

  1. Bates E, D’Amico S, Jacobsen T, Szekely A, Andonova E, Devescovi A, Herron D, Lu CC, Pechmann T, Pleh C, Wicha N, Federmeier K, Gerdjikova I, Gutierrez G, Hung D, Hsu J, Iyer G, Kohnert K, Mehotcheva T, Orozco-Figueroa A, Tzeng A, Tzeng O. Timed picture naming in seven languages. Psychonomic Bulletin Review. 2003;10(2):344–380. doi: 10.3758/bf03196494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Beeson PM, Robey RR. Evaluating single-subject treatment research: lessons learned from the aphasia literature. Neuropsychological Review. 2006;16(4):161–169. doi: 10.1007/s11065-006-9013-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Belke E, Meyer AS, Damian MF. Refractory effects in picture naming as assessed in a semantic blocking paradigm. Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology. 2005;58:667–692. doi: 10.1080/02724980443000142. [DOI] [PubMed] [Google Scholar]
  4. Bloem I, van den Boogaard S, LaHeij W. Semantic facilitation and semantic interference in language production: Further evidence for the conceptual selection model of lexical access. Journal of Memory and Language. 2004;51:307–323. [Google Scholar]
  5. Box G, Jenkins GM, Reinsel G. Time Series Analysis: Forecasting & Control. Prentice Hall: New Jersey; 1994. [Google Scholar]
  6. Costa A, Santesteban M, Ivanova I. How Do Highly Proficient Bilinguals Control Their Lexicalization Process? Inhibitory and Language-Specific Selection Mechanisms Are Both Functional. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2006;32(5):1057–1074. doi: 10.1037/0278-7393.32.5.1057. [DOI] [PubMed] [Google Scholar]
  7. Costa A, La Heij W, Navarrete E. The dynamics of bilingual lexical access. Bilingualism: Language and Cognition. 2006;9(2):137–151. [Google Scholar]
  8. Damian MF, Martin RC. Semantic and phonological codes interact in single word production. Journal of Experimental Psychology: Learning, Memory, and Cognition. 1999;25:345–361. doi: 10.1037//0278-7393.25.2.345. [DOI] [PubMed] [Google Scholar]
  9. de Groot A. Determinants of word translation. Journal of Experimental Psychology: Learning, Memory and Cognition. 1992;18:1001–1018. [Google Scholar]
  10. Dijkstra Ton, Van Heuven Walter JB. The architecture of the bilingual word recognition system: From identification to decision. Bilingualism: Language and Cognition. 2002;5(3):175–197. [Google Scholar]
  11. Edmonds LA, Kiran S. Effect of semantic naming treatment on crosslinguistic generalization in bilingual aphasia. Journal of Speech Language and Hearing Research. 2006;49(4):729–748. doi: 10.1044/1092-4388(2006/053). [DOI] [PubMed] [Google Scholar]
  12. Faroqi-Shah Y, Frymark T, Mullen R, Wang B. Effect of treatment for bilingual individuals with aphasia: A systematic review of the evidence. Journal of Neurolinguistics. 2010;23(4):319–341. [Google Scholar]
  13. Finkbeiner M, Gollan TH, Caramazza A. Lexical access in bilingual speakers: What’s the (hard) problem? Bilingualism: Language and Cognition. 2006;9(02):153–166. [Google Scholar]
  14. Frances N, Kucera H. Frequency analysis of English usage. Boston, MA: Houghton Mifflin; 1982. [Google Scholar]
  15. Galvez A, Hinckley JJ. Transfer patterns of naming treatment in a case of bilingual aphasia. Brain and Language. 2003;87:173–174. [Google Scholar]
  16. Gitterman M, Goral M, Obler LK. Aspects of Multilingual Aphasia. Vol. 1. Clevedon, UK: Multilingual Matters; 2012. [Google Scholar]
  17. Gollan TH, Forster KI, Frost R. Translation priming with different scripts: Masked priming with cognates and noncognates in Hebrew-English bilinguals. Journal of Experimental Psychology: Learning, Memory, and Cognition. 1997;23(5):1122–1139. doi: 10.1037//0278-7393.23.5.1122. [DOI] [PubMed] [Google Scholar]
  18. Goral M, Levy E, Kastl R. Cross-language treatment generalization: a case of trilingual aphasia. Aphasiology. 2010;24(2):170–187. doi: 10.1016/j.bandl.2007.07.116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Goral M, Rosas J, Conner PS, Maul KK, Obler LK. Effects of language proficiency and language of the environment on aphasia therapy in a multilingual. Journal of Neurolinguistics. 2012;25(6):538–551. doi: 10.1016/j.jneuroling.2011.06.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Grainger J, Frenck-Mestre C. Masked priming by translation equivalents in proficient bilinguals. Language and Cognitive Processes. 1998;13(6):601–623. [Google Scholar]
  21. Gray T, Kiran S. A theoretical account of lexical semantic deficits in bilingual aphasia. Journal of Speech Language and Hearing Research. doi: 10.1044/1092-4388(2012/12-0091). in press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Green DW. Control, activation, and resource: a framework and a model for the control of speech in bilinguals. Brain and Language. 1986;27(2):210–223. doi: 10.1016/0093-934x(86)90016-7. [DOI] [PubMed] [Google Scholar]
  23. Green DW. Mental control of the bilingual lexico-semantic system. Bilingualism: Language & Cognition. 1998;1:67–81. [Google Scholar]
  24. Green DW, Grogan A, Crinion J, Ali N, Sutton C, Price CJ. Language control and parallel recovery of language in individuals with aphasia. Aphasiology. 2010;24(2):188–209. doi: 10.1080/02687030902958316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Herman D, Bongarts T, De Bot K, Schreuder R. Producing words in a foreign language: Can speakers prevent interference from their first language? Bilingualism: Language and Cognition. 1998;1:213–229. [Google Scholar]
  26. Hinckley JJ. Picture naming treatment in aphasia yields greater improvement in L1. Brain and Language. 2003;87:171–172. [Google Scholar]
  27. Howard D, Patterson K. Pyramids and Palm Trees. London, England: Harcourt Assessment; 1992. [Google Scholar]
  28. Hsiao EY, Schwartz MF, Schnur TT, Dell GS. Temporal characteristics of semantic perseverations induced by blocked-cyclic picture naming. Brain and Language. 2009;108(3):133–144. doi: 10.1016/j.bandl.2008.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Jiang N. Testing processing explanations for the asymmetry in masked cross-language priming. Bilingualism: Language and Cognition. 1999;2:59–75. [Google Scholar]
  30. Junque C, Vendrell P, Vendrell-Brucet JM, Tobena A. Differential recovery in naming in bilingual aphasics. Brain and Language. 1989;36(1):16–22. doi: 10.1016/0093-934x(89)90049-7. [DOI] [PubMed] [Google Scholar]
  31. Kaplan E, Goodglass H, Weintraub S. Boston Naming Test. Philadelphia, PA: Lea and Febiger; 1983. [Google Scholar]
  32. Kaushanskaya M, Marian V. The bilingual advantage in novel word learning. Psychonomic Bulletin & Review. 2009;16(4):705–710. doi: 10.3758/PBR.16.4.705. [DOI] [PubMed] [Google Scholar]
  33. Kaushanskaya M, Rechtzigel K. Concreteness effects in bilingual and monolingual word learning. Psychonomic Bulletin & Review. 2012 doi: 10.3758/s13423-012-0271-5. [DOI] [PubMed] [Google Scholar]
  34. Kiran S. Typicality of Inanimate Category Exemplars in Aphasia Treatment: Further Evidence for Semantic Complexity. Journal of Speech Language and Hearing Research. 2008;51(6):1550–1568. doi: 10.1044/1092-4388(2008/07-0038). [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kiran S, Thompson CK. The role of semantic complexity in treatment of naming deficits: Training semantic categories in fluent aphasia by controlling exemplar typicality. Journal of Speech Language and Hearing Research. 2003;46(3):608–622. doi: 10.1044/1092-4388(2003/048). [DOI] [PubMed] [Google Scholar]
  36. Kiran S, Johnson L. Semantic Complexity in Treatment of Naming Deficits in Aphasia: Evidence From Well-Defined Categories. American Journal of Speech Language Pathology. 2008;17(4):389–400. doi: 10.1044/1058-0360(2008/06-0085). [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kiran S, Bassetto G. Evaluating the effectiveness of semantic-based treatment for naming deficits in aphasia: what works? Seminars in Speech and Language. 2008;29(1):71–82. doi: 10.1055/s-2008-1061626. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Kiran S, Sandberg C, Abbott K. Treatment for lexical retrieval using abstract and concrete words in patients with aphasia: Effect of complexity. Aphasiology. 2009;23(7–8):835–853. doi: 10.1080/02687030802588866. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kiran S, Pena E, Bedore L, Sheng L. Evaluating the relationship between category generation and language use and proficiency. Paper presented at the Donostia Workshop on Neurobilingualism; San Sebastian Spain. 2010. [Google Scholar]
  40. Kiran S, Roberts PM. Semantic feature analysis treatment in Spanish-English and French-English bilingual aphasia. Aphasiology. 2010;24(2):231–261. [Google Scholar]
  41. Kiran S, Sandberg C. Treating communication problems in individuals with disordered language. In: Peach R, Shapiro L, editors. Cognition and Acquired Language Disorders: A Process-Oriented Approach. London, England: Elselvier; 2011. pp. 298–326. [Google Scholar]
  42. Kiran S, Roberts P. What do we know about assessing language impairment in bilingual aphasia? In: Gitterman M, Goral M, Obler LK, editors. Aspects of Multilingual Aphasia. Clevedon, UK: Multilingual Matters; 2012. pp. 35–51. [Google Scholar]
  43. Kohnert K. Cognitive and cognate-based treatments for bilingual aphasia: a case study. Brain and Language. 2004;91(3):294–302. doi: 10.1016/j.bandl.2004.04.001. [DOI] [PubMed] [Google Scholar]
  44. Kroll JF, Stewart E. Category interference in translation and picture naming: Evidence for asymmetric connection between bilingual memory representations. Journal of Memory and Language. 1994;33(2):149–174. [Google Scholar]
  45. Kroll JF, Bobb SC, Misra M, Guo T. Language selection in bilingual speech: evidence for inhibitory processes. Acta Psychologica (Amsterdam) 2008;128(3):416–430. doi: 10.1016/j.actpsy.2008.02.001. S0001-6918(08)00022-X [pii] [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Kroll JF, van Hell JG, Tokowicz N, Green DW. The Revised Hierarchical Model: A critical review and assessment. Bilingualism, Language and Cognition. 2010;13(3):373–381. doi: 10.1017/S136672891000009X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Laganaro M, Di Pietro M, Schnider A. What does recovery from anomia tell us about the underlying impairment: the case of similar anomic patterns and different recovery. Neuropsychologia. 2006;44(4):534–545. doi: 10.1016/j.neuropsychologia.2005.07.005. [DOI] [PubMed] [Google Scholar]
  48. Lorenzen B, Murray LL. Bilingual aphasia: A theoretilcal and clinical review. American Journal of Speech-Language Pathology. 2008;17(3):299–317. doi: 10.1044/1058-0360(2008/026). [DOI] [PubMed] [Google Scholar]
  49. Miertsch B, Meisel JM, Isel F. Non-treated languages in aphasia therapy of polyglots benefit from improvement in the treated language. Journal of Neurolinguistics. 2009;22(2):135–150. doi: 10.1016/j.jneuroling.2008.07.003. [DOI] [Google Scholar]
  50. Miller Amberber A. Language intervention in French–English bilingual aphasia: Evidence of limited therapy transfer. Journal of Neurolinguistics. 2012;25(6):588–614. [Google Scholar]
  51. Paradis M. Bilingual Aphasia Test. New Jersey: Lawrence Erlbaum Associates, Inc; 1989. [Google Scholar]
  52. Starreveld PA, La Heij W. Semantic interference, orthographic facilitation, and their interaction in naming tasks. Journal of Experimental Psychology: Learning, Memory, and Cognition. 1995;21:686–698. [Google Scholar]
  53. Williams JN. The relationship between word meanings in the first and second language: Evidence for a common, but restricted, semantic code. European Journal of Cognitive Psychology. 1994;6(2):195–220. [Google Scholar]

RESOURCES