The existence of bilingual advantage has been strongly debated. The inconsistent literature suggests that bilingual advantage only exists for specific groups. The adaptive learning hypothesis separates (ALH) bilinguals into 1) single-language context (SLC), 2) dual-language context (DLC), and 3) dense code-switching context (DCSC) (Green and Abutalebi, 2013). This study aims to investigate the association between bilingualism and increased inference control (IC) (a type of bilingual advantage) in young adult. Following ALH, this study excluded DCSC bilinguals from the analysis because they were expected to exhibit similar IC compared to monolinguals. Flanker effects measured using flanker tasks were used to operationalize IC. A series of flanker tasks was completed by 849 undergraduate students who then answered a questionnaire that separated them into monolinguals and bilinguals of different language context. A one-way ANOVA revealed no statistically significant differences in mean flanker effect between the groups; F(2, 543) = 2.96, p = .053, η2 = .01. A post hoc t-tests also found no significant differences in mean flanker effect between any pairs. The findings showed no strong correlation between bilingualism and increased IC. This null hypothesis does not support ALH that expected bilinguals to have better IC compare to monolinguals.
Increased Interference Control not Observed in Bilingual Young Adults
Australian education is promoting bilingualism (Kohler, 2017), yet the cognitive advantages of bilingualism are not fully understood. One of the motivating factors for this push could be the scientific literature that associated bilingualism with improved cognitive abilities in children. For example, Peal and Lambert (1962) found bilingual children to have higher nonverbal intelligence compared to monolingual children. The Bilingual Interactive Activation Model proposed by Dijkstra and Heuven (2002) models bilingual language production as a non-selective process where the lexicons of both languages are integrated and activated. The parallel activation of both language lexicons means bilinguals must inhibit the lexicons from the non-target language. Green and Abutalebi (2013) proposed that bilinguals have better interference control to compensate for the additional computational cost caused by the parallel activation of both language lexicons. This adaptive control hypothesis (ACH) is an extension of inhibitory control model (ICM) by Green (1998). ICM views control as a reactive process, whereas ACH views control as a proactive process. One of the control processes in ACH is interference control (IC).
Consequently, researches have been done to investigate weather bilingualism improves one's interference control ability. Neuroanatomically, structural MRI scans done by Abutalebi et al. (2011) revealed that bilinguals have increased grey matter volume in the left dorsolateral prefrontal cortex, an area vital for response selection. On studies that examines behaviours, Bialystok and Martin (2004) found bilingual children between the age of 4 and 5 to be better than monolingual children at dimensional change card sort task (Zelazo, Frye & Rapus, 1996) that required inhibitory control. In the adult literature. Bialystok, Craik, Klein, and Viswanathan (2004) gave Simon tasks (a task that requires inhibition of distractors) to adults and concluded that bilinguals were able to focus on the tasks better than monolinguals. A similar observation was discovered in a similar study where a variation of the Simon task was used to test both young and old adults (Bialystok, Craik & Luk, 2008).
In contrast, however, some literature has suggested that the bilingual advantage is either inconsequential or does not exist. Bialystok et al. (2004) demonstrated that the bilingual advantage in adult disappeared when participants performed prolonged practice on Simon tasks. This result suggests that bilingual advantage could be matched by practice. Peal and Lambert (1962) demonstrated bilingual advantage in children but they were criticised for failing to control social-economic factors, which Sameroff et al. (1987) shown to affect cognitive performance in children. Lehtonen et al. (2018) meta-analysis compared the performance of monolinguals and bilingual adults on cognitive tasks including tests for IC across 150 studies and found no statistically significant bilingual advantage.
The adaptive control hypothesis (ACH) by Green and Abutalebi (2013) proposes that bilingual advantage is more observable for specific bilinguals of specific language interactional contexts. The premise is that the additional cost associated with controlling the interference of two languages forces bilinguals to adopt a more efficient interference control (IC) system. The researchers theorised that there are three different types of language interactional context: 1) single-language context (SLC), 2) dual-language context (DLC), and 3) dense code-switching context (DCSC); all of which demand different levels of IC. DLC bilinguals have to switch languages with different people even in the same environment, therefore their interactions demand the most IC. SLC bilinguals speak different languages in different situations such as school and home. SLC bilinguals do not need to quickly engage and disengage languages for different speakers, therefore their interactions require more IC than monolinguals who does not experience the switching task. DCSC bilinguals utter a mixture of languages made up of lexicons that get activated without the need for inhibition, therefore the requirement for IC is comparable to that of monolinguals.
Although the bilingual advantage is the topic of many studies, little investigates was done to analysis how bilingual advantages might differ depending on bilingual’s language context. The current study aims to investigate the association between bilingualism and increased IC in young adult using the application of the adaptive control hypothesis to categorize participants based on language context. DCSC bilinguals are excluded from the experiment because they experience limited IC. IC is operationalized by the flanker effect, where a lower flanker effect indicates a higher IC (Fan, McCandliss, Sommer, Raz & Posner, 2002). It is hypothesized that 1) DLC bilinguals will obtain a significantly lower flanker effect than SLC bilinguals and monolinguals, and 2) SLC bilinguals will achieve a significantly lower flanker effect compared to monolinguals.
Materials and Measures
Participants first completed a standard arrow Flanker task adapted from Fan, McCandliss, Sommer, Raz, and Posner (2002). On each trial, a fixation cross appeared for 500 ms, which was then replaced by a line of five arrows. Participants were instructed to make a left or right button press response according to the direction in which the central arrow was pointing, and to ignore the arrows on both sides of the central arrow, which were pointing in either the same or the opposite direction to the target. The arrows remained on-screen until a response was made, and the time from stimulus presentation until response was recorded. In the event of an incorrect response, the word “WRONG” was presented for 800 ms before the next trial began; no feedback was given for correct responses. The intertrial interval was 1000 ms. There were 120 trials in total (60 congruent trials and 60 incongruent trials), divided across four blocks of 30 trials each, with accuracy displayed at the end of each block. The trial order was randomized for each participant.
Participants then completed a questionnaire about their language background and usage. They were asked to list the languages they spoke, and to rate their proficiency in and frequency of use of each language from one to ten. Then, they were given a list of seven common situations and were asked to rate their agreement from one to seven with three items for each situation: “I tend to speak to some people in one language, and other people in a different language”, “I tend to only speak in one language”, and “I tend to use more than one language within one sentence”. These items reflected the dual-language, single-language, and dense-code switching interactional context, respectively. The scores for each situation were weighted according to the percentage of time participants estimated themselves to spend in each situation in a given week, and then scores for each item were summed across all situations to produce a total score for each of the three items.
Data Processing
Participants who achieved below 80% accuracy on the Flanker task were excluded from analyses, resulting in the loss of 0.12% participants. Of those remaining, RTs faster than 200 ms or slower than 1000 ms were excluded from the analyses, which resulted in 0.58% of trials lost. A Flanker Effect (FE) was calculated for each participant by computing the average RT for correct trial responses in each of the congruent and incongruent conditions and then subtracting the average for the congruent condition from the average for the incongruent condition.
For the language questionnaire, participants were classified as monolingual if they reported speaking only one language or if they reported speaking more than one language but rated their proficiency or frequency of use in their additional languages as two or less. The remaining participants were classified into the dual-language, single-language, or dense code-switching context based on the corresponding item on which they scored the highest. Participants who fell into the dense code-switching group were then excluded from the analyses. To ensure a balanced design, 182 participants were then randomly selected from the two largest groups in order to match the size of the smallest group, giving 182 participants in each group.
Discussion
The present study investigated whether bilingual young adults have a better IC compared to monolingual young adults. We hypothesized that 1) DLC bilinguals will have a significantly lower flanker effect than SLC bilinguals and monolinguals, and 2) SLC bilinguals will have a significantly lower flanker effect compared to monolinguals. Both one-way ANOVA and the post hoc t-test found no statistically significant differences between group’s performance on flanker tasks. This finding did not support neither hypothesis.
The lack of a statistically significant differences between SLC bilingual, DLC bilingual, and monolinguals is consistent with previous studies that rejected the bilingual advantage. A meta-analysis by Lehtonen et al. (2018) showed no statistically significant bilingual advantage across 150 studies. Bialystok et al. (2004) were only able to find a statistically significant bilingual advantage for Simon tasks for older participants. The researchers conjectured that the high inhibitory control exhibited by young adults overshadowed the small bilingual advantage. Their conjecture is supported by De Luca and Leventer (2010) experiment that found people achieving maximum inhibitory control at age 20 to 29.
Findings for hypothesis one and two were inconsistent with past studies that observed the bilingual advantage. The post hoc t-tests found no statistically significant differences between any groups. A similar experiment conducted by Costa et al. (2009) on 244 undergraduate students found bilingual students to exhibit lower flanker effect compared to monolinguals. The null results could be the result of a confounding factor.
A confounding factor of the experiment is language similarity and it could explain the null result for both hypotheses. According to Parallel Activation (Gareth Gaskell & Marslen-Wilson, 2002) and Bilingual Interactive Activation Model (Dijkstra & Heuven 2002), only the words that are that have similar phonology or orthography characteristics are activated. Languages that do not share many characteristics such as English and Chinese might not cause parallel activation and reduce the frequency of inhibitory control. If a certain sound only exists in a certain language, then there is no need for cross language inhibitory control. Costa et al. (2009) were able to observe bilingual advantage on undergraduate SLC bilingual students who speak Spanish and English; two languages that share similar characteristics such as the usage of alphabets.
The current study has two limitations that need to be addressed. First, the ambiguity of the questionnaire might have caused experimental error. The questionnaire asked students to report the language they use at home and other social environments. For international students, it is unclear whether the questions were referring to their home country or Australia. Second, current study had an indirect control the age of language acquisition. The questionnaire categorized students based their proficiency. Although early language acquisition can result in high language proficiency, high language proficiency does not imply early language acquisition. Students who grew up in bilingual family and developed low proficiency are characterized as monolinguals. Kapa and Colombo (2013) demonstrated that children who acquired both languages before the age of 3 had the fastest reaction time to flanker tasks. Maybe learning languages before the age of 3 is a decisive factor in CI development.
In summary, the current study does not support the association between bilingualism in young adults and increased interference control. In concert with previous studies, and in light of the limitations, the results suggest that bilingual advantage are observable in a specific group of bilinguals. Not only should bilinguals be categorized by international language context but also the age of language acquisition and the language similarities. The question as to whether bilingual advantage exists is still debatable; however further studies on other types of cognitive benefits of bilingualism would help us to better understand the nature of bilingualism.
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