Effect of language proficiency and executive control on verbal fluency performance in bilinguals

Effect of language proficiency and executive control on verbal fluency performance in bilinguals

Cognition 114 (2010) 29–41 Contents lists available at ScienceDirect Cognition journal homepage: www.elsevier.com/locate/COGNIT Effect of language ...

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Cognition 114 (2010) 29–41

Contents lists available at ScienceDirect

Cognition journal homepage: www.elsevier.com/locate/COGNIT

Effect of language proficiency and executive control on verbal fluency performance in bilinguals Lin Luo a,*, Gigi Luk b, Ellen Bialystok a,b a b

Department of Psychology, York University, Toronto, ON, Canada M3J 1P3 Rotman Research Institute of Baycrest, Toronto, ON, Canada

a r t i c l e

i n f o

Article history: Received 12 November 2008 Revised 24 August 2009 Accepted 28 August 2009

Keywords: Bilingualism Verbal fluency Executive control Vocabulary Language proficiency

a b s t r a c t We use a time-course analysis to examine the roles of vocabulary size and executive control in bilinguals’ verbal fluency performance. Two groups of bilinguals and a group of monolingual adults were tested in English with verbal fluency subtests from the Delis– Kaplan Executive Function System. The two bilingual groups were equivalent in their self-rated English proficiency but differed in levels of receptive and expressive vocabulary. We hypothesized that the difference between the two bilingual groups in vocabulary and between the monolingual and bilingual groups in executive control would lead to differences in performance on the category and letter fluency tests and dissociate the roles of vocabulary knowledge and executive control in verbal production. Bilinguals and monolinguals performed equivalently in category fluency, but the high-vocabulary bilingual group outperformed both monolinguals and low-vocabulary bilinguals in letter fluency. An analysis of the retrieval time-course functions in letter fluency showed dissociable effects of resources available at the initiation of the trial, considered to reflect vocabulary size, and ability to monitor and retrieve new items using a novel phonemic-based word searching strategy, considered to reflect executive control. The difference in slope of the best-fitting curves reflected enhanced executive control for both bilingual groups compared to monolinguals, whereas the difference in the starting point of the logarithmic functions reflected higher levels of vocabulary for high-vocabulary bilinguals and monolinguals compared to low-vocabulary bilinguals. The results are discussed in terms of the contributions of linguistic resources and executive control to verbal performance. Ó 2009 Elsevier B.V. All rights reserved.

1. Introduction Recent studies have demonstrated the consequences of life-long bilingualism both within and beyond the language domain. Two patterns have emerged from this research: disadvantages in tasks assessing linguistic processing, such as rapid verbal production or picture naming (e.g., Costa, 2005; Michael & Gollan, 2005), and advantages in tasks that rely heavily on executive control, such as conflict resolution or control of attention (e.g., Bia-

* Corresponding author. Address: Department of Psychology, York University, 4700 Keele St., Toronto, ON, Canada M3J 1P3. Tel.: +1 416 736 2100x22754. E-mail address: [email protected] (L. Luo). 0010-0277/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.cognition.2009.08.014

lystok, 2005, 2007). Both effects are a consequence of the co-existence of two language systems in bilinguals but reflect opposite outcomes of that situation. The central question, therefore, is how a linguistic experience can be simultaneously detrimental to linguistic performance but advantageous to nonverbal cognitive ability. The present article addresses this question by analyzing the effect of bilingualism on performance in a verbal fluency task that incorporates both types of effect. 1.1. Consequences of bilingualism within and beyond the language domain Despite the ability to communicate effectively in both languages, bilinguals typically show lower levels of

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performance than monolinguals in simple, highly constrained verbal tasks. Delays in the vocabulary development of bilingual children have been reported in standardized receptive and expressive vocabulary tests (e.g., Bialystok & Feng, in press; Oller, Pearson, & Cobo-Lewis, 2007). Some studies have found that vocabulary deficits persist into at least early adulthood (Portocarrero, Burright, & Donovick, 2007), whereas other studies have reported equivalent vocabulary scores in young and older bilingual adults relative to their monolingual counterparts (Bialystok, Craik, Klein, & Viswanathan, 2004). Therefore, it is common for bilingual children to have lower vocabulary scores than monolinguals, but such a disadvantage is not inevitable in adulthood. In addition to lower vocabulary scores, bilinguals also show poorer performance in tasks that require lexical access, such as picture naming tasks. For example, Gollan, Montoya, Fennema-Notestine, and Morris (2005) reported that bilinguals named pictures more slowly than monolinguals did, even though the naming task was done in their dominant language. Ivanova and Costa (2008) replicated the finding with bilinguals who named pictures in their dominant and first language. They showed that the slower lexical access persisted in bilinguals, even when their performance was as accurate as monolinguals (Gollan et al., 2005). Therefore, slower word retrieval seems to be specific to bilingualism and is independent of factors mediating language proficiency for bilinguals such as order of acquisition and language dominance (Ivanova & Costa, 2008). In contrast, recent studies have reported bilingual advantages in a variety of nonverbal cognitive tasks of executive functioning (see Bialystok, 2005, 2007 for reviews). These tasks typically involve the need to resolve conflict (Bialystok et al., 2004; Carlson & Meltzoff, 2008; Costa, Hernandez, & Sebastián-Gallés, 2008), to suppress distracting information (Bialystok, Craik, & Ruocco, 2006; Colzato et al., 2008), or to switch between multiple rules (Bialystok & Martin, 2004; Bialystok & Viswanathan, 2004). The effects are especially strong in children and older adults (e.g., Bialystok & Martin, 2004; Bialystok et al., 2004), and are smaller but still reliable in younger adults (Costa et al., 2008). To summarize, bilingual disadvantages have been found in verbal tasks based on lexical access but bilingual advantages have been observed in nonverbal tasks requiring executive control. Yet verbal performance sometimes requires executive control processes. Therefore, the relevant case for understanding the relation between the advantages and disadvantages of bilingualism is a linguistic task that involves executive functioning. One such instance is verbal memory performance. The roles for controlled processing in these tasks include engaging in effective searching strategies (Tulving, 1983), monitoring memory output to attain accuracy (Mitchell & Johnson, 2000), and disregarding irrelevant and misleading information (Hasher, Lustig, & Zacks, 2007). Early studies on verbal memory performance in bilinguals have pointed in the direction of a disadvantage; bilinguals recalled fewer words from both semantic knowledge (Gollan, Montoya, & Werner, 2002) and newly

acquired information (Fernandes, Craik, Bialystok, & Kreuger, 2007) in free recall and in a proactive interference (PI) paradigm (Bialystok & Feng, 2009). However, at least one factor underlying these disadvantages is the smaller vocabulary in bilinguals: Fernandes et al. (2007) reported that the group difference in recall was eliminated after controlling for vocabulary difference. Bialystok and Feng (2009) also found that bilinguals recalled more words in a release from PI paradigm when their lower vocabulary performance was accounted for. In addition, bilinguals who achieved vocabulary scores equivalent to monolinguals also showed comparable levels of performance in category fluency (Bialystok, Craik, & Luk, 2008), a task that typically shows a bilingual disadvantage (Gollan et al., 2002). This evidence indicates a mediating role for vocabulary knowledge in bilingual performance on tasks of lexical access. As another example, using the process dissociation procedure (PDP) developed by Jacoby (1991) to separate the effect of controlled processing (called ‘‘recollection”) from that of automatic processing (called ‘‘familiarity”), Wodniecka, Craik, and Bialystok (2007) analyzed verbal memory performance in younger and older monolinguals and bilinguals and found that bilinguals outperformed monolinguals in recollection but groups performed comparably on familiarity, especially for the older participants. The difference between recollection and familiarity is the involvement of executive control in the former. These examples show that the commonly observed bilingual disadvantage in verbal tasks depends on both language proficiency, notably vocabulary size, and level of executive control involved in the task. In tasks that have little role for executive control and rely primarily on lexical access, such as category fluency, bilingual disadvantages may disappear if monolinguals and bilinguals are matched on a measure of language proficiency such as vocabulary knowledge. In contrast, bilingual advantages may emerge on verbal tasks that demand higher levels of executive control once relevant differences in language proficiency have been accounted for. Therefore, to understand the interaction between executive control advantages in nonverbal tasks and lexical retrieval disadvantages in verbal tasks, it is important to consider the contribution of language representations and executive control processes separately. 1.2. Verbal fluency performance in bilinguals The verbal fluency test is a widely used word retrieval task that contrasts the roles of executive control and language representation. Verbal fluency tasks typically have two conditions, phonemic (or letter) fluency and semantic (or category) fluency (e.g., the Controlled Oral Word Association Test, COWAT, Strauss, Sherman, & Spreen, 2006). The standard task requires participants to produce as many words as possible within 1 min that satisfy the stated criteria. In the letter fluency condition, participants are asked to produce words that start with a given letter, excluding numbers, proper names, places or words in different forms. In category fluency, participants are to produce words in a semantic category, such as animals or musical instruments, without the additional restrictions noted for letter

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fluency. In both conditions, repeated words are considered incorrect. The total score for each condition is the number of correct responses generated within the 1-min period. The two conditions place different cognitive demands on word retrieval. Although both conditions rely on vocabulary knowledge and executive control, they differ in the importance of each. Generating words in the category condition is similar to accessing a lexical item in interconnecting networks. This behavior resembles ordinary language use where target items are semantically related, for instance, coming up with a list of grocery items or a guest list for a dinner party. Psycholinguistic models generally agree that word representations are based on semantic associations. With regard to word production, a word is first selected on the basis of its semantic features; the semantic level is activated before the phonological level which leads to the oral production of the word (Levelt, 1999). Therefore, retrieving words based on their semantic categories is an overlearned process of language production. As a result, performance in category fluency is largely automatic and relies primarily on linguistic representation. The demands of the letter fluency are different: one needs to generate words from a phonemic category instead of from a semantic category. This is more effortful because phonemic generation is not a common strategy in word retrieval, nor is there an obvious congruency with the organization of words in some representational system (Strauss et al., 2006). Delis, Kaplan, and Kramer (2001) point out in the manual for the verbal fluency test that the difference between category and letter conditions is the increased demands for executive control in letter fluency. Although the exact cognitive processes underlying letter fluency performance are yet to be specified, neuropsychological and neuroimaging studies have shown converging evidence for the involvement of executive control in this task that is mediated by the frontal areas. For example, neuroimaging studies have shown that performance in letter fluency tasks is associated with frontal areas, specifically the posterior opercular area of Broca’s area (Paulesu et al., 1997), which is also recruited in cognitive tasks free of language production (Yeung, Nystrom, Aronson, & Cohen, 2006). A recent study demonstrated that high-proficiency bilinguals showed dissociating functional and structural correlates in letter and category fluency tasks (Grogan, Green, Ali, Crinion, & Price, 2009). Grey matter density in pre-supplementary motor area and left caudate, along with higher activation in these areas, was related to letter fluency relative to semantic fluency performance whereas greater activation and higher grey matter density in left inferior temporal cortex was related to semantic fluency relative to letter fluency performance. Clinical studies have also established that impaired performance in letter fluency is most apparent in patients with frontal lesions and executive dysfunctions, whereas impaired performance in category fluency is observed in patients with deficient semantic knowledge structures, such as patients with Alzheimer’s disease (AD; Martin, Wiggs, Lalonde, & Mack, 1994; Rascovsky, Salmon, Hansen, Thal, & Galasko, 2007). Perret (1974) suggested a possible explanation for the poor performance in letter fluency by patients with left frontal lesions: In addition to the failure in verbal produc-

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tion associated with left frontal areas, patients also failed to inhibit the relatively habitual semantic word generation strategy. Moreover, these cognitive deficits could be language-independent and share common ground with performance in the Stroop test. Other possible candidates for the increased executive demand include inhibiting inappropriate responses, self-monitoring, and avoiding perseveration. Some of these processes may also be involved in category fluency tasks, but their role is more decisive in letter fluency. Despite their use as benchmark clinical assessment tools for diagnosis of neurodegenerative diseases, little research has been conducted on the effects of different language experiences, such as bilingualism, on their performance. Bilinguals may demonstrate a disadvantage in verbal fluency for two reasons: (1) smaller vocabulary in bilinguals (Bialystok & Feng, in press; Portocarrero et al., 2007) may lead to fewer items that they are able to generate relative to monolinguals; (2) slower lexical retrieval (Gollan et al., 2005; Ivanova & Costa, 2008) may cause bilinguals to produce fewer words than monolinguals in the given time, possibly because of weaker links pointing to each target lexical item. Category fluency performance primarily relies on the linguistic representation and therefore bilinguals often perform more poorly in this task (Gollan et al., 2002; Rosselli et al., 2002). In contrast, executive control plays an important role in letter fluency performance. Therefore, the lack of a bilingual disadvantage in letter fluency in previous studies (Gollan et al., 2002; Rosselli et al., 2002) may be a consequence of the interplay between the linguistic disadvantages and the executive control advantages in bilinguals. Verbal fluency tasks have been widely used in neuropsychological assessments to dissociate the efficiency of executive control (letter fluency) and the integrity of semantic and lexical representation (category fluency). In the present study we used these tasks as means to disentangle the roles of executive control and linguistic representation in bilingualism, without attempting to specify the exact processes underlying either task. 1.3. The time-course of word retrieval The present study uses time-course analyses to assess the roles of retrieval speed, vocabulary knowledge, and executive control in verbal fluency performance. In verbal fluency tasks, the number of retrieved items declines as a function of time. Typically, participants produce more items at the beginning of the recall period than during a later period and eventually reach asymptote if given unlimited time. This pattern of declining retrieval can be captured by plotting the number of items generated against time (Fig. 1). The decline rate is reflected by the slope of the resulting function. According to the randomsearch model (Wixted & Rohrer, 1994), the declining curve is exponential. A central measure derived from this model is the mean retrieval latency, defined as the mean value of retrieval latencies of each recalled item relative to the onset of recall. Mean latency is not equivalent to retrieval speed. It is best described as the declining rate of recall: a short mean

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Fig. 1. Hypothetical bilingualism-related effects on the time course of verbal fluency performance. NM and NB are the number of recall totals for monolinguals and bilinguals, respectively. sM and sB are the mean latencies for monolinguals and bilinguals, respectively. The hypothesized mean latencies are marked on their respective time-course curves.

latency indicates a fast declining rate of recall because most items are produced at the beginning of the time period leaving few items by the end of the time. However, two different situations can lead to a long mean latency – a slow decline in performance or a slow retrieval speed for each item throughout the run. Each of these is associated with different recall totals, with more items being generated in the first situation. Therefore, combining recall totals, mean latencies and the decline rate of retrieval can differentiate between recall patterns. To isolate the preparatory period prior to the first response from subsequent responses, Rohrer, Wixted, Salmon, and Butters (1995) differentiated two measures of mean latency. First-response latency is the time interval from the beginning of a trial until the onset of the first response and subsequent-response latency is the mean value of time intervals between that first response and each subsequent response. Thus, subsequent latency provides a good estimate for mean retrieval latency and represents the time point at which half of the

total responses have been generated, and is referred to as the ‘‘fulcrum point” in Sandoval, Gollan, Ferreira, and Salmon (in press). Fig. 1 illustrates the hypothesized function for the timecourse retrieval curves showing the outcome of (A) slower lexical access, (B) smaller vocabulary, (C) and superior executive control. Slower retrieval speed (Fig. 1A) is characterized by fewer items produced in combination with longer mean retrieval latencies, generating a relatively flat curve for bilinguals because fewer items were produced during the initial ‘‘burst” period and each subsequent item is more effortful to retrieve. Smaller vocabulary size (Fig. 1B) is indicated by fewer responses with shorter mean latencies and faster declining rates for bilinguals as a result of early depletion of available words, but no difference in the retrieval speed for each word, hence no difference in slope. In contrast to the language factors, superior executive control would lead to slower decline in retrieval rate for bilinguals (Fig. 1C) in combination with a higher num-

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ber of total responses, characterized by a difference in slope. In the first application of this analytical technique to verbal fluency performance, Rohrer et al. (1995) tested two competing hypotheses to account for impaired category fluency in Alzheimer’s disease (AD) patients: retrieval-slowing hypothesis (Fig. 1A) and structure-loss hypothesis (i.e., loss of vocabulary, Fig. 1B). Their results supported the structure-loss hypothesis by showing faster mean latency in AD patients than in normal controls, as shown in Fig. 1B. Sandoval et al. (in press) applied the same logic and found that the mean latencies in bilinguals were longer than those in monolinguals. They concluded that the bilingual linguistic disadvantage in verbal fluency was due to slower lexical access (Fig. 1A), rather than to smaller vocabulary size (Fig. 1B). Both Rohrer et al. (1995) and Sandoval et al. (in press) based their interpretations on the assumption that AD patients and bilinguals would recall fewer items than their controls as well as producing different patterns in the time-course analysis. Therefore, their hypotheses only included patterns illustrated in Fig. 1A and B and their analyses relied primarily on mean retrieval latencies. This assumption, however, may not apply to letter fluency in bilinguals. In the later period of the recall course, participants need to monitor a larger set of already produced responses to avoid perseveration and to suppress a stronger tendency to resample a previously retrieved response (Rosen & Engle, 1997). We predict that this additional executive control demand will favor the bilinguals and that this advantage will be more apparent at the end of the retrieval period than at the beginning. It will also be more apparent for letter fluency than category fluency because of the greater number of restrictions in letter fluency that needs to be monitored. Therefore, bilinguals will show a slower declining curve, with levels of performance exceeding their monolingual counterparts in the latter part of the recall period, producing more total responses and longer mean retrieval latencies. The analysis of mean retrieval latencies alone is not sufficient to differentiate between the hypotheses illustrated in Fig. 1A and C so our analysis is based on differences in intercept (or the starting point of a fitted function) and slope of the curves. Intercept is the point where the curve intersects the y-axis. Depending on the nature of the fitted function, a y-intercept may reach infinity when x is at zero. Although mathematically possible, this parameter does not entail any useful interpretation of human performance. Therefore, our analysis compares the starting point of the function when performance begins, what we call the ‘‘initiation parameter”. This initiation parameter does not necessarily involve the intersection of the function and the yaxis. It reflects, instead, the initial resource difference between the two curves at the beginning of the time-course. Our interpretation is that this initiation parameter reflects the breadth of lexical items available for the initial burst when the trial begins, determined largely by vocabulary size. Slope is the shape of the curve and reflects the dynamic process by which the initial resources are used and monitored over time, determined largely by executive control. As illustrated in Fig. 1, it represents the declining

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rate of retrieval output (the y-variable) as a function of change in time (the x-variable) during the recall period of 60 s. As a trial progresses, the initial resources deplete as a function of increased processing demand, such as monitoring, searching, and the need to resist the interference from a habitual semantic-based word searching strategy for letter fluency in particular (Perret, 1974). Given the previous results showing a bilingualism-related advantage in interference resolution (Bialystok, 2005, 2007), we expect that bilinguals will be better able to control such interference in letter fluency. Our critical prediction is that retrieval performance in letter fluency will decline more slowly in bilinguals than in monolinguals due to their superior executive control, creating a slope difference. Differences in initial linguistic representational resources, specifically vocabulary, will be reflected in lower performance at the beginning of the time course, creating a difference in the initiation parameter, which is the y-values representing the initiation of the function. Our analyses of the retrieval time-course functions generated by monolinguals and bilinguals were intended to provide quantitative comparison of the decline rate and the estimated initiation parameters of language representation in order to isolate the role of each of these on verbal fluency. It is important to note that the association between vocabulary and the initiation parameters and between executive control and slope indicate predominant roles for each aspect of proficiency in each function rather than pure measures. In particular, the slope will be affected by the initiation parameters, all else being equal, but the dissociation should nonetheless isolate the role of each in performance. Our hypothesis for superior control in bilingual verbal fluency is similar to the interference mechanism identified by Sandoval et al. (in press) but is conceptualized more broadly. Sandoval et al. argue that cross-language interference in word retrieval leads to slower retrieval and fewer words produced. In our view, such cross-language interference is one of the factors responsible for the enhancement of cognitive control in bilinguals (Bialystok, 2007; Schwartz & Kroll, 2006) but the mechanism resides in a domain-general set of processes responsible for attention, inhibition, and switching, namely, the executive function. Thus, unlike the language-specific mechanism described by Sandoval et al., our proposal is that a language experience in which attention and selection are routinely recruited enhances those control mechanisms in all domains. Evidence for this advantage in a verbal task, however, depends on adequate levels of language proficiency for the bilinguals to overcome the deficits that follow from the weaker representational base (e.g., Gollan, Montoya, Cera, & Sandoval, 2008). The curves representing bilingual performance under cross-language interference (Sandoval et al., in press, and Fig. 1A) and superior cognitive control (Fig. 1C) make different predictions in their initiation parameters but not slopes. Thus, the two accounts converge on the role of control factors but differ on the role of linguistic factors. In our view, the combined contribution of both factors explains why bilinguals sometimes show an advantage and sometimes a disadvantage on verbal tasks. Dissociating the con-

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tribution of control factors and linguistic factors by analyzing the slope and the initiation parameter of time-course functions in verbal fluency will provide support for this view. To distinguish between the contribution of vocabularyrelated factors and control-related factors to verbal fluency performance, the present study included two bilingual groups for contrast against a monolingual group. Both bilingual groups were fluent in both languages and perceived themselves to be equivalent to native speakers in English. However, their levels of performance in standardized English vocabulary tests (Peabody Picture Vocabulary Task, PPVT-III, Dunn & Dunn, 1997, and Expressive Vocabulary Task, EVT, Williams, 1997) differed such that the high-vocabulary bilingual group (HV) achieved equivalent vocabulary scores to their monolingual counterparts, whereas the low-vocabulary bilingual group (LV) had lower scores, but were still within the normal range of native English speakers. Controlling for vocabulary in the bilingual sample allows us to distinguish between language resources and executive control. We predict that the bilingualism-related control difference will be apparent only in letter fluency, which involves increased executive control demands. Therefore, the two bilingual groups should differ from the monolinguals in the slope of the retrieval curves but not differ from each other because slope is driven by the experience of bilingualism. In contrast, the difference between the initially available vocabulary levels of the two bilingual groups should be reflected in our proxy for the intercept. In this case, the function for the LV bilinguals should begin at a lower point on the y-axis (smaller initiation parameter) than either HV bilinguals or monolinguals in the letter fluency task.

2. Method 2.1. Participants Sixty young adults consisting of 20 monolinguals and 40 bilinguals were included in the study. The monolinguals reported speaking only English and did not know another language. The bilinguals reported using English and another language on a daily basis. These languages included French (9), Cantonese (7), Hebrew (4), Hindi (3), Italian (3), Punjabi (2) and one participant each for Farsi, Gujarati, Japanese, Korean, Mandarin, Pakistan, Portuguese, Spanish, Tagalog, Tamil, Toisan and Urdu. All participants attended a large university in Toronto, Canada. All the participants were given the Peabody Picture Vocabulary Task third edition (Form A, PPVT-III; Dunn & Dunn, 1997) and the Expressive Vocabulary Task (EVT; Williams, 1997) which are standardized tests of English vocabulary a reported mean and standard deviation of 100 and 15, respectively. The bilinguals were categorized into two groups according to their performance. The 20 HV bilinguals had standard scores matching those of the monolinguals and reported English as their first language (L1). The 20 LV bilinguals achieved standard scores that were within one standard deviation below the reported

Table 1 Mean score in cm (out of 10) summarizing language background characteristics of HV and LV bilinguals. HV bilinguals

LV bilinguals

M

SD

M

Usage of English language At home Outside of home

7.9 9.2

2.1 1.4

5.9 8.5

2.8 2.3

Self-rated proficiency for English Non-English language Age of L2 formal acquisition Age of L2 informal acquisition Age of active bilingualism

9.5 6.7 7.0 2.9 8.6

1.6 2.6 3.6 5.1 6.1

9.1 6.7 8.0 4.3 10.2

0.9 2.9 3.4 4.2 4.5

SD

mean of 100. Half of the LV bilinguals reported English as L1 and the other half reported English as their second language (L2).1 The bilinguals were given a questionnaire to assess their daily language use patterns, self-rated proficiency, and age of L2 acquisition. The summarized characteristics from this questionnaire are presented separately for HV and LV bilinguals in Table 1. Balanced usage of languages was measured as the response on a 10 cm scale in which the right side indicated ‘‘100 – All English” and the left side was labeled as ‘‘0 – No English”. Participants were asked to draw a vertical line intersecting the scale between these two ends to indicate their relative use of the two languages for oral conversations at home and outside of home. The distance in cm to the vertical line was measured from the left end of the scale, so higher numbers reflect greater use of English. The questionnaire also elicited judgments of self-rated proficiency by asking participants to draw a vertical line on a similarly constructed scale indicating their self-perceived fluency in English and the other language, respectively. The right end of the scale was labeled as ‘‘100 – Native Like” and the left end as ‘‘0 – Non-native like”. Responses were measured as the distance in cm between the left end of the scale and the vertical line. A series of t-tests comparing the two bilingual groups indicated that the HV bilinguals reported higher usage of English at home than LV bilinguals, t(38) = 2.61, p < 0.02, d = 0.85. None of the other language background characteristics differed between the two groups, ts(38) < 1.2.

2.2. Tasks 2.2.1. Peabody Picture Vocabulary Task-III, Form A (PPVT-III, Dunn & Dunn, 1997) PPVT-III is a measure of English receptive vocabulary. The reported median Cronbach’s alpha of PPVT-III is 0.95 (Dunn & Dunn, 1997). For each item, a page of four black-and-white line drawings was shown along with a word read by the experimenter. Participants were asked 1 The effect of language acquisition order was examined in the LV bilingual group. There was no significant effect of acquisition order in either self-report measures or verbal fluency performance. The data were therefore collapsed for the LV bilingual group.

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to choose the picture that best described the word the experimenter said, either by saying the number of the picture or pointing to the picture. Items in PPVT-III are grouped in sets of 12 and arranged in increasing levels of difficulty, and basal and ceiling sets are established by the number of errors made in a set. Raw scores were transformed to standardized scores using an age-corrected norm table. Standardized scores were used in analyses. 2.2.2. Expressive Vocabulary Task (EVT, Williams, 1997) EVT was used to measure levels of expressive vocabulary in English. The reported median Cronbach’s alpha of EVT is 0.95 (Williams, 1997). EVT is co-normed with PPVT-III. Participants were asked to provide a one-word synonym for a presented picture and a word given by the experimenter. As in the PPVT-III, items are arranged in increasing levels of difficulty and basal and ceiling sets were established, but unlike PPVT-III, items are not grouped into sets. Calculation of raw and standardized scores was similar to that of PPVT-III. Age-corrected standardized scores were used in analyses. 2.2.3. Spatial span subtest from the Wechsler Memory Scale, third edition, WMS-III (cf., Corsi Block, Wechsler, 1997) Ten blue blocks were presented on a white platform. All the blocks were secured on the platform and could not be moved. The numbered sides of the blocks were facing the experimenter during the administration of the task. Participants were asked to repeat a sequence tapped by the experimenter both in the same order and reverse order. Test items started with two blocks and increased one block at a time. For each length of test string, there were two trials using different numbers. Testing terminated when participants responded incorrectly to two trials at the same length. Raw scores were the number of correct trials in the forward and backward conditions. The maximum possible scores for forward and backward conditions were 14 and 16, respectively. Raw scores were transformed to standardized scores controlling for age according to tabled norms. The reported median Cronbach’s alpha for this test is 0.84, and the standardized scores have a mean of 10 and a standard deviation of 3. 2.2.4. Cattell Culture Fair Test (Cattell, 1957) This test measures an individual’s nonverbal reasoning skills and includes four subtests. Participants were asked to choose one (or two, in the second test) answer(s) from a number of alternatives to complete a series of visual patterns. Raw scores were the total number of correct trials across the four tests. These were corrected for age and transformed to standardized scores on a normal distribution with a reported mean of 100 and a reported standard deviation of 15. 2.2.5. Verbal Fluency test from the Delis–Kaplan Executive Function (D-KEF) System (Delis et al., 2001) Participants were asked to produce as many English words as possible in 60 s. They were asked to produce words that start with letters F, A, and S and in two categories, clothing items and girls’ names. There were four restrictions for the letter conditions: (1) say different

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words, (2) no names of people, (3) no names of places, and (4) no numbers. The only restriction on category fluency conditions was to say different words. Responses were recorded on a digital recorder. Raw scores were obtained by subtracting incorrect responses (words that did not start with the specified letter or not in the designated categories) and repeated words from the total number of responses. Verbal responses were scored and their temporal characteristics coded after the testing session. 3. Data coding and analysis 3.1. Data coding Two trained research assistants who were blind to the participants’ group affiliations processed the digital recordings of verbal fluency responses with AudacityÒ on Windows platform. The research assistants first listened to the recording to identify the correct responses and then recorded their associated time-stamp. The time-stamp of a response is obtained from AudacityÒ as the time elapsed from the beginning of the recording until the onset of the response to the accuracy of 1 ms. The beginning of each trial was also time-stamped for calculation purposes. Based on the time-stamps, correct responses were grouped into 5-s bins over each 1 min trial, producing twelve bins. The following codes were used for each correct response in subsequent analyses: (1) serial number, indicating the serial position of a response in the trial; (2) latency, measured by the time between the beginning of trial till the onset of the response; and (3) bin number, indicating the 5-s bin into which the response falls. First-response latencies and subsequent-response latencies were calculated following Rohrer et al. (1995)’s procedure. Three mean scores were obtained for each participant in each task: (1) total number of correct responses, (2) first-response latency, and (3) subsequent-response latency. These scores were averaged across F, A, S trials in the letter task, and across clothing and girls’ name trials in the category task to produce the overall mean scores for each task. 3.2. Time course The group means for response are plotted by the midpoint of every 5-s bins in Fig. 2 for each task. The scatterplots were fitted with exponential and logarithmic functions for each group separately. Table 2 presents the fitted functions for the 12 mean values per group. In the functions reported in Table 2, y stands for the estimated value from the function at different levels of time (t). The intercept-equivalent parameters of the logarithmic function signifies the value of y when ln(t) equals zero: When ln(t) = 0, then t = 1. Therefore, the parameter estimates for the intercept term in the logarithmic functions are calculated from the point at which t = 1. However, since t – 0, this parameter cannot be labeled as the intercept. Instead, this parameter is labeled as the initiation parameters to signify the starting point of the logarithmic functions. Therefore, the parameters extracted from the logarithmic

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Fig. 2. Number of items produced as a function of time in (A) category task and (B) letter task. Best fit lines are logarithmic functions.

Table 2 Best fitting functions for the time-course of verbal fluency output. Task

Exponentiala Estimated function

Letter Monolingual HV bilingual LV bilingual Category Monolingual HV bilingual LV bilingual

y = 1.89e y = 2.10e y = 1.62e y = 3.18e y = 2.98e y = 2.82e

0.023t 0.015t 0.018t

0.021t 0.019t 0.020t

Logarithmic 2

Multilevel model 2

R

Estimated function

R

0.84 0.75 0.72

y = 2.96 y = 3.06 y = 2.39

0.61 ln(t) 0.52 ln(t) 0.44 ln(t)

0.96 0.93 0.90

y = 2.93 y = 2.91 y = 2.39

0.58 ln(t) 0.49 ln(t) 0.43 ln(t)

0.90 0.78 0.88

y = 4.54 y = 4.59 y = 4.02

0.87 ln(t) 0.89 ln(t) 0.75 ln(t)

0.98 0.94 0.96

y = 4.47 y = 4.28 y = 3.99

0.83 ln(t) 0.79 ln(t) 0.73 ln(t)

a Exponential: exponential functions fitted with 12 observation means per group; logarithmic: logarithmic functions fitted with 12 observation means per group; multilevel model: logarithmic function estimates obtained from multilevel modeling with all observations.

models were the initiation parameters and the slopes of the functions. Exponential functions have been most commonly used to describe free recall performance as in the random-

search model (McGill, 1963) and were used in the study by Rohrer et al. (1995) described above. However, it is evident from Table 2 that logarithmic functions account for a larger proportion of variance in our data. Therefore, our

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analyses were based on logarithmic functions for the following reasons: (1) the characteristics of our data are best described with logarithmic functions so choosing logarithmic functions will minimize residual variance in the linear mixed effect (LME) models described below; (2) Rohrer reported that their analyses of observed latencies led to the same conclusion as that of estimated latencies, so they concluded that the choice of observed or estimated values did not affect the interpretation; and (3) we performed the analyses on both exponential and logarithmic models and the pattern of group differences did not differ across the two models, so the choice of function did not affect our conclusion or interpretation with regards to group effects. Note that the functions in the first two entries of Table 2 and in Fig. 2 were derived from only 12 means for each group (one value per 5-s bin over 1 min). To take into account all observations as well as both within- and between-subjects variance, group effects were assessed by multilevel modeling with the LME (linear mixed effect) package in GNU-R. That is, 12 observations corresponding to the twelve 5-s bins were taken from each individual, resulting in 720 total observations (12 per individual  20 per group  3 groups) in the whole model. The level-1 component of the multilevel model represents the change within each individual over the timecourse; the level-2 component represents the relationship between inter-individual differences in the change trajectories and time-invariant characteristics of the individual. In our data, the level-1 component characterizes the within-individual change in the number of items generated over time (in 5-s bins); the level-2 component tests the effects of group on the change-over-time trajectories. The multilevel model was fitted by maximum likelihood methods, and the logarithmic transformation of 5-s time bins [ln(x), in seconds] was entered to represent logarithmic functions of change. The obtained estimates of parameters were similar to those obtained from 12 observation means per group, as shown in Table 2. Our analysis focused on mean numbers of correct responses, mean subsequent-response latencies, and parameters (initiation parameter and slope of logarithmic functions) of estimated time-course functions to test the hypothetical effects in Fig. 1. The following orthogonal contrasts were performed to evaluate the role of bilingualism and language proficiency: (1) All bilingual participants (HV and LV combined) were contrasted against monolingual participants to examine the overall effect of bilingual-

ism; and (2) HV bilinguals and LV bilinguals were contrasted against each other to examine the effect of vocabulary. 4. Results 4.1. Background measures The mean background and language measures for all participants are presented in Table 3. The three groups were equivalent in age, F(2, 57) = 1.76, n.s., and spatial memory performance measured by forward and backward corsi block, Fs < 1. As expected, the three groups differed in English receptive vocabulary performance measured by PPVT-III, F(2, 57) = 68.5, MSE = 16.3, p < 0.0001, g2 = 0.7, and English expressive vocabulary performance measured by EVT, F(2, 57) = 8.8, MSE = 115.2, p < 0.0006, g2 = 0.24. Post hoc pairwise comparisons using Tukey test revealed that the group difference in both tests was driven by the lower performance of the LV bilinguals. There was also a significant group difference in Cattell Culture Fair test, F(2, 55) = 4.4, MSE = 173.8, p < 0.02, g2 = 0.13, with the monolinguals achieving higher standardized scores than the LV bilinguals, and the HV bilinguals not different from either group. 4.2. Recall total and latencies The mean numbers of correct responses reported in Table 4 were analyzed in a language group (monolingual, HV bilingual, LV bilingual) by task (letter vs. category fluency) mixed ANOVA. There was a significant interaction of group and task, F(2, 57) = 5.08, MSE = 11.17, p < 0.001, g2 = 0.15. Performance in the category task did not differ across groups, F < 1, but there were group differences in the letter task, F(2, 57) = 7.71, MSE = 12.98, p < 0.01, g2 = 0.21. Linear contrasts showed that HV bilinguals generated more items in the letter task than either the monolinguals, t(57) = 3.12, p < 0.01, or LV bilinguals t(57) = 3.63, p < 0.001, who did not differ from each other. For the category task, there were no group effects in either first-response latency, F < 1, or subsequent-response latency, F (2, 57) = 1.62, p = 0.21 (see Table 4). For the letter task, significant group differences were found in subsequent-response latency, F(2, 57) = 3.36, MSE = 10.45, p < 0.05, g2 = 0.11, but not in first-response latency, F < 1. Linear contrasts revealed a different pattern of group ef-

Table 3 Means and standard deviations of cognitive and verbal background variables. HV bilinguala

Monolingual

a

LV bilingual

M

SD

M

SD

M

SD

Age in years Cattell standard score

20.6 119.9

1.3 12.6

21.1 113.1

1.4 11.2

20.3 107.5

1.6 15.3

Corsi block Forward Backward PPVT-III standard score EVT standard score

9.3 11.1 106.4 98.9

2.9 2.0 4.1 8.4

9.4 10.4 107.5 97.3

2.4 2.2 3.6 13.0

9.7 10.1 94.1 85.8

3.3 3.0 4.4 10.2

Two participants from the HV bilingual group failed to complete the Cattell Culture Fair Test.

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Table 4 Mean scores and standard deviations for responses and latencies (in seconds). Task

Mean number correct responses

First-response latency

Subsequent-response latency

M

SD

M

SD

M

SD

Letter Monolingual HV bilingual LV bilingual

11.8 15.4 11.2

3.5 4.0 3.3

1.8 1.8 2.1

1.0 0.6 1.1

24.1 26.7 25.9

3.6 2.9 3.2

Category Monolingual HV bilingual LV bilingual

21.1 20.0 19.1

4.2 6.1 3.3

1.8 1.5 1.6

1.4 0.7 0.6

23.9 23.9 24.5

3.3 4.1 2.2

fects for mean latencies than for total responses. Both HV and LV bilingual participants showed longer mean latencies than monolinguals, t(57) = 2.47, p < 0.05, but the two bilingual groups did not differ from each other, |t(57)| < 1. The group differences in total responses and mean latencies support Rohrer’s position that these two measures are dissociable and demonstrate that bilingualism and language proficiency have differential effects on each. Specifically, bilingualism primarily affects mean latencies, with both bilingual groups showing longer latencies than the monolingual group, but the number of correct responses is modulated by the combination of bilingualism and vocabulary level. The next section presents the timecourse analysis to extract the parameters modulating the time dynamics of the recall performance by monolinguals and bilinguals. 4.3. Time-course analysis Group differences in verbal output over time were analyzed by fitting multilevel models to letter and category fluency data separately. Estimated functions from the multilevel models are presented in Table 2. For the category task, no group effects were found in either initiation parameter (when t = 1), F(2, 57) = 1.52, p = 0.23, for main effect, or slope, F < 1, or interaction. For the letter task, there was a significant main effect of group, F(2, 57) = 5.94, p < 0.01, g2 = 0.17, and group by time interaction, F(2,632) = 4.82, p < 0.01, g2 = 0.015. Group contrasts were conducted to clarify these effects. When the two bilingual groups were contrasted against the monolingual group, there was a difference in slope, t(632) = 2.82, p < 0.01, and difference in the initiation parameter (when t = 1) that approached significance, t(57) = 1.78, p = 0.08. There was no difference between HV and LV bilingual groups for slope, t(632) = 1.33, n.s., but the initiation parameter (when t = 1) were different, t(57) = 2.88, p < 0.01. The difference in the initiation parameters between bilinguals and monolinguals was solely driven by the LV bilingual group, t(57) = 2.98, p < 0.01, with no difference in the same parameter between HV bilinguals and monolinguals, |t| < 1. These results show that bilingualism affected slope but not the initiation parameters of the functions, whereas vocabulary size had a selective effect on the initiation parameter of the logarithmic functions rather than slope. The time course curves for both bilingual groups are flatter than those of the monolingual group

(Fig. 2) and are parallel to each other, with the low-vocabulary bilinguals shifted down from the high-vocabulary bilinguals, and no difference in the initiation parameter of the logarithmic functions for HV bilinguals and monolinguals who are matched on proficiency, at least in terms of vocabulary knowledge.2

5. Discussion We assessed three aspects of verbal fluency performance in monolinguals, HV bilinguals, and LV bilinguals: number of correct responses, mean retrieval latency, and the time-course function of retrieval. The groups were matched on nonverbal background measures, yet the results revealed a double dissociation between two factors involved in verbal fluency performance: linguistic representation as indicated by vocabulary level and executive control. This dissociation designates representation as language-specific, relevant for distinguishing between performance of bilinguals with different levels of proficiency, and executive control as domain-general, relevant for distinguishing performance of bilinguals from that of monolinguals. This approach incorporates both the advantages and disadvantages previously reported for bilinguals on verbal fluency tasks into a single framework and reconciles previously contradictory results. No group difference was found in category fluency, but performance in letter fluency differed in the following ways: first, differences in total responses replicated Bialystok et al.’s (2008) finding that bilinguals outperformed monolinguals when vocabulary level was controlled. Lower vocabulary in the task language is a likely candidate to account for a substantial part of bilingualism-related differences in complex verbal behavior. These results illustrate the importance of including objective measures of language knowledge when assessing verbal behavior in bilinguals. When language vocabulary is equivalent, the

2 An alternative way to test the hypothesis of initial performance is to analyze the observed performance in the initial period of the time course (i.e. items generated in the first 10 s). The results were in line with our analysis of the initiation parameters estimated in the logarithmic functions: the LV bilingual group differed significantly from the HV bilingual group, t(117) = 2.25, p < 0.05, and the difference between LV bilingual and monolingual groups approached significance, t(117) = 1.82, p = 0.07. The HV bilinguals and monolinguals did not differ from each other, |t| < 1.

L. Luo et al. / Cognition 114 (2010) 29–41

bilingual advantage in the letter fluency task can be explained by its high demand for executive control. Second, consistent with Sandoval et al.’s (in press) finding, our analysis showed both bilingual groups had longer mean retrieval latencies than the monolingual group in the letter fluency task. The retrieval slowing account adopted by Rohrer and Gollan cannot explain the longer mean retrieval latencies in the HV bilingual group. If HV bilinguals accessed lexical items more slowly than monolinguals, then they would generate fewer words than monolinguals in the same amount of time. Our results indicate otherwise: HV bilinguals produced more correct responses in the letter fluency task in spite of longer mean latencies. HV bilinguals and monolinguals achieved equivalent scores in both receptive and expressive vocabulary tests, so the possibility that HV bilinguals outperformed monolinguals due to their larger vocabulary size can also be excluded. Our interpretation is that the high demand for controlled processing in the letter fluency task enabled bilinguals to outperform monolinguals once language proficiency had been controlled. Third, the most novel result comes from the analysis of the time-course functions for the letter fluency task. The best-fitting curves representing the retrieval decline accounted for 93% of the total variance and distinguished between the effects of resources, interpreted as vocabulary, and processing, interpreted as control. The comparison between HV and LV bilinguals reflected differences in the extent of their knowledge of English that affected the initiation parameters; the comparison between bilinguals and monolinguals reflected differences in their processing, monitoring, and updating that affected slope. As shown in Fig. 2B, the contrast between HV bilinguals and monolinguals is consistent with the prediction illustrated in Fig. 1C that the retrieval rate in HV bilinguals will decline less rapidly than in monolinguals because of superior executive control. The interference from habitual semantic search strategies increases as retrieval becomes more effortful at the end of the recall period (Perret, 1974), and HV bilinguals showed greater resistance to this interference and a slower decline in retrieval. Using this approach, descriptions of language proficiency must be designated by both components of performance: linguistic representations indicating available resources (vocabulary) interact with automatic linguistic retrieval (language-specific access) and controlled processing (domain-general control such as maintaining a novel retrieval strategy, monitoring and switching) to produce responses. All these elements are affected by bilingualism. However, the demand for controlled processing increases as a trial progresses and retrieval speed becomes slower, but the demand for automatic lexical access remains invariant throughout the trial. Therefore, HV bilinguals showed the greatest advantage relative to monolinguals at the end of the retrieval period in letter fluency due to their superior control. The curves for HV and LV bilinguals in Fig. 2B were parallel but differed in the initiation parameters of the logarithmic functions. As predicted, the difference in the initiation parameters reflected the contribution of vocabulary. This interpretation is supported by a significant correlation be-

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tween vocabulary score and initiation parameters, r(60) = 0.32, p < 0.01. LV bilinguals generated fewer words than HV bilinguals because they had fewer initial linguistic resources. Interestingly, the HV and LV bilingual samples were equivalent in all self-reported language background characteristics except for the proportion of daily English usage. It appears, therefore, that subjective self-report measures lack the sensitivity to detect the differences revealed by objective, standardized measures of proficiency such as PPVT and EVT. Objective proficiency measures are essential to the interpretation of bilingual difference in verbal abilities. Finally, the contrast between LV bilinguals and monolinguals in letter fluency curves replicated Sandoval et al.’s (in press) results. Sandoval et al. argued that between-language interference slows retrieval speed for bilinguals, resulting in longer mean latencies and a retrieval curve resembling Fig. 1A. However, our results indicate that language proficiency as indicated by vocabulary size plays a critical role in whether bilingual differences in letter fluency are predominately driven by language interference (LV bilinguals) or by superior executive control (HV bilinguals). Therefore, single-factor models are not sufficient to account for bilingual performance in verbal fluency, whereas considering both factors can explain previous inconsistencies in the literature. Our category fluency data failed to replicate the bilingual disadvantage previously reported in the literature (Gollan et al., 2002; Rosselli et al., 2002). There are two possible reasons for this: First, the two semantic categories used in our study were both large categories (clothing and girls’ names), so all participants were able to generate a large number of responses (M = 20.0) compared to those reported by Gollan et al. (2002; M = 14.6) and Rosselli et al. (2002; M = 16.8). Therefore, these categories may not have been sufficiently sensitive to detect performance difference in highly fluent bilinguals. Second, Bialystok et al. (2008) found a bilingual disadvantage in category fluency in the context of lower levels of English proficiency in bilinguals, but neither of the two previous studies reported objective measures of language proficiency. Given the strong relationship between verbal fluency performance and measures of vocabulary (Ruff, Light, Parker, & Levin, 1997), it is difficult to know whether the reported difference in semantic fluency was secondary to lower levels of word knowledge. All our bilingual participants achieved vocabulary scores within the normal range of native speakers and therefore may have had little initial difficulty with semantic retrieval. Our analysis of letter fluency and Gollan’s analysis of category fluency performance jointly demonstrate the recurring pattern of control advantages in combination with verbal disadvantages associated with bilingualism. Our results further illustrate a bilingual advantage in a verbal task when level of language proficiency is matched and the task places a high demand on EF. Is it possible that a common mechanism underlies the opposite effects found for letter and category fluency performance? Here we attempt to give an affirmative answer by proposing a unified interference account to accommodate both effects. Both letter and category fluency tasks are essentially retrieval tasks from declarative memory. According to

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L. Luo et al. / Cognition 114 (2010) 29–41

Moscovitch (1994), interference effects on retrieval processes depend on the nature of both the retrieval processing and the concurrent task. Specifically, retrieval is only disrupted when either (1) the retrieval processing relies on frontally mediated strategic processes, or (2) both the retrieval processing and the concurrent task rely on the same representational system. Using a motor-based and a conceptual-based concurrent task, Martin et al. (1994) demonstrated dissociable effects in retrieval processes to letter and semantic cues, such that letter fluency performance was disproportionately interrupted by the motor task, whereas category fluency was only affected by the conceptual object identification task. Following the same logic, bilinguals may experience two different types of interference in the letter and category fluency tasks. Cross-language interference may selectively affect category fluency but not letter fluency. In contrast, letter fluency may be disrupted more by interference from well-learnt semantic searching processes which affect monolinguals and bilinguals to the same degree. According to Perret (1974), a unique aspect of letter fluency is the need to suppress interference from habitual semantic-based word-finding strategies. Therefore, interference in letter fluency task is at the ‘‘control” or ‘‘processing” level rather than the ‘‘representation” or ‘‘lexical” level. For the former type of interference, both monolinguals and bilinguals are affected to the same degree, and bilinguals with matched level of proficiency showed an advantage due to their superior ability to resolve interference. For the latter type of interference, only bilinguals are affected, accounting for their disadvantages in category fluency documented in the literature, although not in this study. Verbal fluency tasks are a standard measure of neuropsychological functioning, and the growing bilingual population makes it essential to ensure the diagnostic validity of the task for populations with different language experiences. According to the results of the present study, bilinguals showed different temporal characteristics in producing verbal responses. These characteristics were also modulated by the executive demands of the tasks when language proficiency was properly controlled between groups. However, it is not certain if bilingualism affects the standardization of the verbal fluency tasks in the general population. The magnitude of the language and the control factors investigated in the present study was not quantified. It is possible for bilinguals with high vocabulary in English (or the language in which the tests are administered) to have a higher mean performance than monolinguals because of their equivalent language knowledge and enhanced executive control. Therefore, it is important for future research to determine whether the current benchmark for verbal tasks to diagnose neurodegenerative diseases is applicable to monolinguals and bilinguals alike. Acknowledgement This research was funded by Grant A2559 from National Sciences and Engineering Research Council of Canada (NSERC) and Grant MOP57842 from the Canadian Institutes of Health Research (CIHR) to the last author. We

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