Abstract

Relatively little is known about differences in English-administered, clinical neuropsychological test performance between native versus non-native English speakers, with prior literature yielding mixed findings. The purpose of this study was to examine the performance of native and non-native English speakers with similar age and educational backgrounds on a variety of cognitive tests. Participants were 153 university students (115 native and 38 non-native English speakers) who completed a neuropsychological battery during two testing sessions. Multiple regression analyses examined relations of native language to cognitive performance after adjustment for age, education, sex, and depressive symptomatology. Results showed that native English speakers outperformed non-native English speakers on several language-mediated tasks—Letter and Category Fluency and the Cognitive Estimation Test—as well as Trails A (p's < .05). The two groups performed similarly on tests of executive functions, perceptuo-motor speed, verbal memory, and visuospatial abilities. These results suggest that non-native English language may have a negative influence predominantly on language-dependent tasks.

Introduction

In the USA, the population of non-native English speakers is growing rapidly. Nonetheless, relatively little is known about differences in English-administered neuropsychological test performance between native versus non-native English speakers. Results of studies examining the relations of non-native language acquisition and/or bilingualism to cognitive function are decidedly mixed. Several studies have reported an absence of significant performance differences on neuropsychological tests between native and non-native English-speakers when tested in English. Low and Siegel (2005) found that native and non-native English-speaking children performed equally well on nearly all reading and verbal ability tests. In contrast, other studies have shown that non-native English speakers perform significantly worse on select neuropsychological measures. For example, Walker, Batchelor, Shores, and Jones (2010) found that brain-injured individuals with an educational background in a non-English language performed more poorly than English-educated individuals on tests of memory, verbal ability, and visual ability. Other research found that non-native English speakers performed significantly worse on tests of language, attention, and constructional ability, but not processing speed, visual memory, verbal memory, or executive function (Boone, Victor, Wen, Razani, & Ponton, 2007). This literature indicates a heterogeneity of findings, but many of these studies used clinical samples (Walker et al., 2010), included participants from a variety of educational backgrounds (Boone et al., 2007; Walker et al., 2010), or did not consider acculturation (Low & Siegel, 2005; Walker et al., 2010), all of which could confound the results obtained.

A related body of literature examines bilingualism, defined as active fluency in two languages (and thereby more narrowly operationalized than basic native language status), and yields similarly mixed findings. In that regard, most researchers have noted discrepancies between native and non-native neuropsychological test performance among bilinguals (Byrd et al. 2008; Loewenstein, Arguelles, Barker, & Duara, 1993). Others have observed that monolinguals and bilinguals perform equally well on neuropsychological tests. For instance, bilingual Spanish-English individuals performed as well as monolinguistic English speakers on verbal fluency and repetition tests administered in English (Rosselli et al., 2000). However, others have observed that bilinguals perform significantly better than monolinguals on tests of executive function, inhibitory control, and cognitive flexibility (Bialystok & Viswanathan, 2009; Bialystok, 2010; Carlson & Meltzoff, 2008). Further, bilinguals have been found to develop language skills and build vocabulary in new languages more easily than monolinguals (Kaushanskaya & Marian, 2009). Lastly, several researchers have observed that monolingual individuals outperform bilingual individuals on the standard word generation tasks, including letter and semantic fluency (Gollan, Montoya, & Werner, 2002). In a recent review of this literature, Bialystok, Craik and Luk (2012) explain that for bilinguals, there is joint functional activation of both languages, which may cause attentional difficulties in a bilingual that do not occur in a monolingual individual. This idea is consistent with the language inhibition model, which again shows that both languages are activated by the bilingual individual, necessitating inhibition of one language to communicate in the other (Kroll, Bobb, Misra, & Guo, 2008). These reviews describe a trend in decreased verbal performance in bilinguals compared with monolinguals, but native language status has not been investigated to a similar extent.

In light of this variable literature, it remains unclear whether native language status has a beneficial, inhibitory, or neutral effect on cognitive performance during English-based testing of different neuropsychological domains. Culture alone can impact cognitive development of skills such as learning, memory, literacy, spatial abilities, and problem solving (Mishra, 2001). Understanding the nature of these differences becomes critical when using norms from native English-speaking samples to interpret the quality of test performances of non-native English speaking individuals in both clinical and research settings. To the neuropsychologist, it is not only important to know whether the examinee is English-proficient, but also whether that person will be tested in their native language. The sparse available literature examining this matter has largely examined bilingualism, used small samples, and examined circumscribed cognitive abilities, and few studies have used traditional clinical neuropsychological tests. Insufficiently aided by the limited and inconsistent research in this area, clinicians and researchers are, by default, left to rely on common sense assumptions about the cognitive effects of testing in an individual's non-native language, or simply assume that proficiency in English is sufficient for fair neuropsychological assessment.

In an effort to begin to address several of the limitations of the current literature, we studied variability in performance on a battery of neuropsychological tests between native and non-native English-speaking undergraduate students. Specifically, we sought to examine the association between native language status and cognitive performance in a participant sample with a high representation of non-native English speakers, in which English proficiency is guaranteed and educational attainment is roughly equivalent. We hypothesized that non-native English speakers would perform more poorly on language-mediated tasks than native English speakers. We also hypothesized that non-native English speakers would demonstrate commensurate performance on tests without significant language demands.

Methods

Participants

Participants were 153 undergraduate students (mean age = 21.35 years, SD = 3.78, range = 18–44) enrolled at a mid-sized, mid-Atlantic university. Students were recruited from psychology courses using the incentive of course credit. The students were queried regarding native language status. Those individuals who indicated that English was not their native language were labeled “non-native English speakers,” and those who indicated that English was their native language were placed into the “native English speaker” category. Non-native English speakers were further queried regarding native language and the age at which he or she began to speak English on a daily basis. In order to enroll in the university, all non-native English speakers were required to pass the Test of English as a Foreign Language, the nationwide standard for English fluency. One-hundred fifteen participants identified their native language as English, and 38 participants identified their native language as a language other than English. All participants provided written informed consent according to the guidelines of the Institutional Review Board of University of Maryland, Baltimore County.

Procedure

Students participated in two study sessions, each at the same time of day, 1 week apart. After responding to a series of demographic questions regarding age, self-reported ethnicity, and highest level of education completed, participants were administered a battery of neuropsychological tests spanning multiple domains of cognitive function. Participants completed several tests with significant language demands, including (a) California Verbal Learning Test, Version 2 (CVLT-2; sum of Trials 1–5; Delis, Kramer, Kaplan, & Ober, 2000), (b) Letter Fluency (FAS; Benton & des Hamsher, 1989) and Category Fluency (animals and supermarket items; Randolph, Braun, Goldberg, & Chase, 1993), and (c) Cognitive Estimation Test (CET; Axelrod & Millis, 1994). More visually oriented tests, with fewer language demands, included (a) Trails A and B (Reitan, 1992), (b) Symbol Digit Modalities Test (SDMT; Smith, 1982), (c) Rey-Osterrieth Complex Figure Test (RCFT) copy and short delay recall (Osterrieth, 1944), (d) Tower of London (Culbertson & Zillmer, 2001), and (e) Benton Judgment of Line Orientation test (JLO; Benton, Hannay, & Varney, 1975). All neuropsychological tests and instructions were given in English. All tests were administered in the first study session except for Benton JLO. The Center for Epidemiological Studies-Depression (CESD) scale (Radloff, 1977) was administered during both study sessions to evaluate depressive symptoms.

SPSS Statistical software version 17.0 was used for all analyses. Multiple regression analyses were performed using the dichotomous group of native (coded as 0) versus non-native (coded as 1) English speaker as the primary term of interest. Independent samples t-tests were used to compare native and non-native English speakers on age, sex, education, and depressive symptoms. Separate multiple regressions were performed for each neuropsychological outcome measure. Level of education (in years), age, sex, and CESD score from the respective testing session served as covariates in each analysis. We hypothesized that for tasks with significant language requirements, non-native English speakers would perform significantly worse than native English speakers, whereas on less language-mediated tasks, non-native English speakers would perform equivalent to native English speakers.

Two post hoc analyses were performed to probe multiple regression results further. First, the primary analyses were repeated, statistically adjusting for ethnicity. Two dummy-coded variables were used to represent the three ethnicities present in the sample (Caucasian, n = 70; African American, n = 18; and Asian, n = 59). Self-reported Hispanic individuals (n = 4) were excluded from these analyses due to insufficient sample size. Ethnicity was not recorded for two individuals due to administrator error; these participants were excluded from this analysis as well.

Second, we were interested in the impact of symbolic versus non-symbolic non-native language on obtained results. The group of non-native English speakers was therefore classified further by primary language. Those individuals whose native language was symbolic (Bengali, Cantonese, Japanese, Korean, and Vietnamese) were placed into one group, and those individuals whose native language was non-symbolic and non-English (Amharic, Farsi, Filipino, French, German, Guajarati, Hindi, Malayalam, Romanian, Russian, Twi, Urdu, Wolof, and Yoruba) were placed into a second group. Multiple regressions were then repeated among only non-native English speakers, with symbolic versus non-symbolic language as the primary term of interest. Each neuropsychological measure again served as the outcome variable in separate multiple regressions.

Results

Table 1 presents demographic information stratified by native language (English and non-English). In general, t-tests demonstrated that native and non-native English speakers did not differ significantly on level of education, age, sex, or symptoms of depression during the first testing session. The two samples were statistically different on depressive symptoms during the second session, such that non-native English speakers scored higher (M = 12.87) on the CESD than native English speakers—M = 9.84; t(147) = 2.22, p = .028. However, the clinical significance of this difference is questionable, given both mean scores fall well below the traditional cutoff for “mild” depressive symptoms (i.e., 16).

Table 1.

Demographic characteristics

Demographic variable Native English speakers (n = 115) Non-native English speakers (n = 38) 
Age (M [SD]) 21.43 (4.16) 21.11 (2.29) 
Sex (% Men) 18.3 26.3 
Education (M [SD]; years) 14.23 (1.46) 13.68 (3.54) 
Ethnicity (% Caucasian, % African American, % Asian) 59.5, 10.8, 29.7 11.1, 16.7, 72.2 
Center for Epidemiological Studies: Depression scale from Day 1 (M [SD]) 11.84 (7.98) 13.92 (8.32) 
Center for Epidemiological Studies: Depression scale from day 2 (M, [SD]) 9.84 (7.03) 12.86 (7.69) 
Demographic variable Native English speakers (n = 115) Non-native English speakers (n = 38) 
Age (M [SD]) 21.43 (4.16) 21.11 (2.29) 
Sex (% Men) 18.3 26.3 
Education (M [SD]; years) 14.23 (1.46) 13.68 (3.54) 
Ethnicity (% Caucasian, % African American, % Asian) 59.5, 10.8, 29.7 11.1, 16.7, 72.2 
Center for Epidemiological Studies: Depression scale from Day 1 (M [SD]) 11.84 (7.98) 13.92 (8.32) 
Center for Epidemiological Studies: Depression scale from day 2 (M, [SD]) 9.84 (7.03) 12.86 (7.69) 

As hypothesized, non-native English speakers performed significantly more poorly on several language- and culture-mediated tasks, such that non-native English speakers had fewer valid responses on Category Fluency (β = 0.28, p = .001) and greater total deviation scores on CET (β = −0.25, p = .002). There was also a marginally significant finding, suggesting that non-native English speakers had fewer valid responses on Letter Fluency (β = 0.16 p = .050). As also hypothesized, performances on non-language dependent tasks including JLO (β = 0.09, p = .263), RCFT copy time (β = 0.05, p = .506), RCFT copy score (β = −0.05, p = .698), RCFT 3-minute recall score (β = 0.12, p = .370), SDMT score (β = 0.13, p = .121), and Trails B time (β = −0.14, p = .089) were not significantly different between native and non-native English speakers. However, contrary to our hypotheses, there were no significant group differences in performance on CVLT-2 (β = 0.02, p = .857). In addition, a significant group difference arose for Trails A, a non-language-dependent task (β = −0.20, p = .017), such that non-Native English speakers required more time to complete the task than native English speakers. Table 2 presents descriptive statistics for the neuropsychological tests, stratified by group. Table 3 presents a summary of multiple regression results.

Table 2.

Descriptive statistics of neuropsychological performance stratified by native English-speaker status (native English speakers n = 115 and non-native English speakers n = 38)

Cognitive test Measure Native English speakers
 
Non-native English speakers
 
M SD Range M SD Range 
VF FAS total score 40.13 9.52 21–67 36.82 11.07 15–62 
Category total score 46.77 9.91 23–73 40.47 9.44 24–61 
CVLT Sum of five learning trials 55.10 8.11 37–76 56.41 7.76 40–69 
CET Total deviation score 5.33 2.22 1–13 6.78 2.78 1–15 
SDMT Total correct 60.20 10.88 32–92 56.78 9.45 34–72 
TMT Trails A time to complete 25.38 10.65 12–82 29.95 12.39 12–65 
Trails B time to complete 55.20 22.94 29–215 62.87 23.80 32–142 
RCF Copy time 140.69 82.79 53–581 129.39 64.15 52–390 
Copy total score 33.67 2.52 26.36 34.33 2.06 30–36 
3 min recall rime 116.98 63.05 28–394 110.46 58.55 31–360 
3 min recall total score 21.42 7.19 3.5–34 20.05 7.73 7.5–30 
TOL Total of excess moves 32.89 18.61 2–10 37.86 14.45 0–62 
Total time to complete 231.45 97.07 95–610 229.84 84.15 116–425 
JLO Total from 30 stimuli 23.97 5.01 5–30 23.00 3.79 12–30 
Cognitive test Measure Native English speakers
 
Non-native English speakers
 
M SD Range M SD Range 
VF FAS total score 40.13 9.52 21–67 36.82 11.07 15–62 
Category total score 46.77 9.91 23–73 40.47 9.44 24–61 
CVLT Sum of five learning trials 55.10 8.11 37–76 56.41 7.76 40–69 
CET Total deviation score 5.33 2.22 1–13 6.78 2.78 1–15 
SDMT Total correct 60.20 10.88 32–92 56.78 9.45 34–72 
TMT Trails A time to complete 25.38 10.65 12–82 29.95 12.39 12–65 
Trails B time to complete 55.20 22.94 29–215 62.87 23.80 32–142 
RCF Copy time 140.69 82.79 53–581 129.39 64.15 52–390 
Copy total score 33.67 2.52 26.36 34.33 2.06 30–36 
3 min recall rime 116.98 63.05 28–394 110.46 58.55 31–360 
3 min recall total score 21.42 7.19 3.5–34 20.05 7.73 7.5–30 
TOL Total of excess moves 32.89 18.61 2–10 37.86 14.45 0–62 
Total time to complete 231.45 97.07 95–610 229.84 84.15 116–425 
JLO Total from 30 stimuli 23.97 5.01 5–30 23.00 3.79 12–30 

Notes: VF = Verbal Fluency; CVLT = California Verbal Learning Test; CET = Cognitive Estimation Test; SDMT = Symbol Digit Modalities Test; TMT = Trail Making Test; RCF = Rey-Osterrieth Complex Figure; TOL = Tower of London; JLO = Judgment of Line Orientation.

Table 3.

Results from multiple regression analyses of native English-speaker status (native English speakers n = 115 and non-native English speakers n = 38) and neuropsychological performance

Cognitive test Measure p-value β-value Standard error Cohen's d 
Verbal Fluency FAS total score .050 0.16 1.90 0.32 
Category total score .001 0.28 1.87 0.65 
California Verbal Learning Test Sum of five learning trials .857 0.02 1.51 0.17 
Cognitive Estimation Test Total deviation score .002 −0.25 0.44 0.58 
Symbol Digit Modalities Test Total correct .121 0.13 1.98 0.34 
Trail Making Test Trails A Time to complete .017 −0.20 2.08 0.40 
Trails B Time to complete .089 −0.14 4.25 0.33 
Rey-Osterrieth Complex Figure Copy time .506 0.05 13.04 0.15 
Copy total score .698 −0.05 0.81 0.29 
3 min recall time .539 0.05 11.25 0.11 
3 min recall total score .370 0.12 2.54 0.18 
Tower of London Test Total of excess moves .067 −0.15 3.28 0.30 
Total time to complete .775 −0.02 17.61 0.02 
Judgment of Line Orientation Total score .263 0.09 0.89 0.22 
Cognitive test Measure p-value β-value Standard error Cohen's d 
Verbal Fluency FAS total score .050 0.16 1.90 0.32 
Category total score .001 0.28 1.87 0.65 
California Verbal Learning Test Sum of five learning trials .857 0.02 1.51 0.17 
Cognitive Estimation Test Total deviation score .002 −0.25 0.44 0.58 
Symbol Digit Modalities Test Total correct .121 0.13 1.98 0.34 
Trail Making Test Trails A Time to complete .017 −0.20 2.08 0.40 
Trails B Time to complete .089 −0.14 4.25 0.33 
Rey-Osterrieth Complex Figure Copy time .506 0.05 13.04 0.15 
Copy total score .698 −0.05 0.81 0.29 
3 min recall time .539 0.05 11.25 0.11 
3 min recall total score .370 0.12 2.54 0.18 
Tower of London Test Total of excess moves .067 −0.15 3.28 0.30 
Total time to complete .775 −0.02 17.61 0.02 
Judgment of Line Orientation Total score .263 0.09 0.89 0.22 

In the post hoc analyses that included ethnicity as a covariate, there were four participants who identified as Caucasian non-Native English speakers, six African-American/African non-native English speakers, and 26 Asian non-native English speakers. In this analysis, non-native English speakers continued to perform significantly more poorly on Category Fluency (β = 0.25, p = .004) and CET (β = −0.18, p = 0.026) compared with native English speakers.

In the post hoc analysis of symbolic language, there were 13 participants who listed their native language as symbolic and 25 participants who listed their native language as a non-symbolic, non-English language. When comparing these groups though, multiple regressions showed no significant differences in performance (all p's > .05). However, post hoc power analysis, undertaken due to concerns regarding sample size reduction, suggested insufficient power to detect significant small effects. Specifically, a sample size of 38 was powered to detect a medium f2 estimate of 0.22 at conventional levels of power (0.80) and α (0.05). Nonetheless, results are presented in order to encourage further research pertaining to this topic.

Discussion

In a sample of undergraduate students, we found that non-native English speakers performed significantly more poorly on neuropsychological measures that required language processing, including Letter fluency, Category Fluency, CET, as well as a non-language-mediated test, Trails A. Non-native English speakers scored roughly one-half of a standard deviation worse on these tests compared with native English speakers, which is consistent with findings cited in the Wechsler Intelligence Scale for Children-Spanish version literature (Harris, Muñoz, & Llorente, 2008). Though one half of a standard deviation admittedly will not influence clinical interpretation in most cases, the magnitude is non-negligible and could impact diagnostic decisions in certain subgroups, particularly those within the borderline range, as well as individuals undergoing repeat testing. Our study hypotheses were largely supported, as we identified significant group differences on three of four language-mediated tasks, such that non-native English speakers performed more poorly than native English speakers. Further, as hypothesized, there were no significant group differences on the vast majority of non-language-mediated tasks, with the exception of Trails A.

This study explicitly examined the impact of native language status on neuropsychological test performance. The results both corroborate and contradict findings from the literature examining native language status. In that regard, our results are consistent with those of Boone and colleagues (2007), who found that non-native English speakers performed worse on tests of language and attention, but not tests of memory and executive functioning. In contrast, Low and Siegel (2005) noted no difference in verbal and reading abilities among native and non-native English speakers, whereas the current study noted that several verbally mediated measures were significantly different between the two groups. Low and Siegel conducted their study on children in the sixth-grade, so it is possible the gap between native and non-native speakers is not present until later adulthood. Additionally Low and Siegel did not use tests that were focused on semantic knowledge, as this study did, but instead focused on language exposure. Also, Walker and colleagues (2010) indicated that non-native English speakers performed worse on measures of memory, verbal, and visual abilities. Of these domains, only the relations of native language to verbal ability were corroborated. In the present study, there were no differences between native and non-native English speakers on the CVLT, a test of verbal memory, and the RCFT and JLO, tests of visual abilities. Importantly, Walker and colleagues examined a clinical population, which may indicate that their pattern of findings is attributable to factors other than native language alone.

The results from the current study similarly corroborated and contradicted findings from the closely related literature examining bilingualism. Our results are consistent with the findings of Gollan and colleagues (2002), who noted that Letter and Category Fluency performance were inhibited by bilingualism. Mindt and colleagues (2008) have suggested that bilinguals experience “interference” in the brain with regard to language, but advantages in cognitive control tasks. That notion was supported, given that native and non-native English speakers performed worse on many tests that required language processing. Thus, the degree to which test performance is predicated on language-based cognitive processing may determine how well a non-native English speaker will perform.

Ransdell and Fischler (1987) found that bilinguals performed equally well on lexical decision and verbal memory tasks, but overall their task completion times were generally slower. Based on these findings, Ransdell and Fischler hypothesized that bilinguals have a slower processing speed of language alone. This reduction in processing speed may be a consequence of (a) the process of translation of the task into their native language and then back into English, or (b) the extra higher-order step of identifying the response language. An important note is that reductions in processing speed for language-mediated tasks may reflect that non-native English speakers must process more information prior to responding, which slows down their completion time, rather than indicating actual reductions in processing speed per se. Applied to the present study, these hypotheses would suggest that non-native English speakers may have generated fewer words for both Letter and Category Fluency due to additional language-related processing speed demands. This idea could also help explain why non-native English speakers performed equally well on the CVLT-2 as native English speakers in our study, given that CVLT-2 performance is less dependent on response time and long-term semantic storage and retrieval, and more dependent on basic semantic knowledge and working memory.

Though the Trails A result is contradictory to our hypothesis, it is important to note that this finding may be spurious. Consistent with this postulation, the effect size is comparable to the non-significant effect size of Trails B. Further investigation of these effects is needed to determine if differences truly exist between these two groups on Trails A and B.

Post hoc analyses including ethnicity as a covariate did not produce significant change in the results for either Category Fluency or CET. However, the associations between native language status and both Letter Fluency and Trails A became non-significant with the inclusion of ethnicity. It is possible that CET performance may have been influenced by another confounding variable, such as birth in the USA versus birth in another country. Country of birth could easily impact whether an individual is familiar with culturally sensitive information found in the CET, which requires the participant to give unusual estimations, such as the temperature in Alaska or how fast a racehorse gallops. Previous research has found that the CET is strongly tied to fund of culturally relevant information (Spencer & Johnson-Greene, 2009). We also explored post hoc if symbolic versus non-symbolic native language would be associated with performance on certain neuropsychological tests. For instance, Trails A performance is highly dependent on processing of the Arabic numeral system, which symbolic languages do not use. Though our results showed no difference in performance between students whose native language was symbolic versus non-symbolic, post hoc power analysis suggested insufficient power to detect significant effects. Additional research is thus needed to further explore differences in symbolic and non-symbolic native language.

Several limitations of this study warrant mention. First, we did not gather information on birthplace of the participants, which would have permitted more in-depth analysis of cultural impact on CET and other measures in the battery. There was also a large discrepancy in the sample sizes of the native and non-native English speakers that may reflect group differences (e.g., high-achieving non-native speakers). However, both samples had respectable numbers of participants based on precedent in the literature. Another limitation involves our usage of an undergraduate student sample, which may not be generalizable to clinical samples. However, this sample provides information about a group that is both diverse in native language status yet similar in level of educational attainment, which sets it apart from other studies in the literature. Also, few studies in this area have examined such a diverse number of cognitive measures. The breadth of our battery therefore represents a particular strength. Lastly, we had limited statistical power to test our post hoc hypotheses regarding symbolic versus non-symbolic language.

Additional research is needed to investigate whether the present findings extend to active bilingualism and non-English languages. The important roles of culture of origin and level of acculturation also deserve particular attention. Future research would benefit from improved consideration of these factors in assessing impact of language acquisition on neuropsychological performance. Proficiency in the English language should also be explored as a moderator of effects between native English status and neuropsychological outcomes. In addition, more emphasis should also be placed on studying individuals with various language backgrounds rather than limiting studies to particular bilingual subtypes. Though this approach introduces additional confounds (e.g., increased variability with respect to culture of origin or language-specific processing demands), it allows results to be more easily generalized to the entire bilingual population, thereby facilitating corresponding norm development.

The present study supports the idea that non-native English speakers do perform differently on select neuropsychological measures when compared with native English speakers. Our findings show that the relation is largely dependent on the degree of language dependency of the test, such that native language may significantly interfere with language-dependent tasks. The association between native language status and neuropsychological test performance deserves continued attention in the literature. At a minimum, our findings suggest that allowances should be made for fluency tasks and highly culturally sensitive tests such as the CET. Normative data are sorely needed for the growing U.S. population of non-native English speakers.

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