Embed Size (px)
Citation preview
A multilingual advantage, or lack thereof?A comparative study of executive functions in bilinguals and multilinguals
Matilda
Greek Selin
Calum James
Psychology, bachelor's level
2021
Luleå University of Technology
Department of Health, Learning and Technology
Abstract
The present study aimed to examine how bilinguals and multilinguals performed in executive
functioning measures as well as potential differences in performance in terms of number of
languages spoken and language proficiency. A sample of 191 participants between the ages
50-75 who spoke 1-5 languages were administered six executive functioning tasks measuring
inhibition and switching performance. Three different language variables were examined,
namely self-reported number of languages spoken, language proficiency and recategorised
number of languages spoken based on proficiency. Analyses showed a positive correlation
(i.e., worse performance) between the reported number of languages spoken and the
switching task “colour-shape”. This correlation remained significant when analysing the
recategorised number of languages and the colour-shape task. The current results indicated no
significant performance benefits of multilingualism in executive functioning tasks and
showed that they may even have been disadvantaged in certain circumstances. Since
correlations were only found in one switching task, no wider generalisations as to the
advantages or disadvantages can be made based on the results in this study. However, no
multilingual advantage as reported in previous papers was found in the present study.
Key words: multilingualism, bilingualism, executive functioning, cognition, inhibition,
switching
Abstract
Denna studie syftade till att undersöka tvåspråkigas och flerspråkigas prestation i exekutiva
funktioner samt potentiella prestationsskillnader vad gäller antal språk och språkfärdighet. Ett
urval på 191 deltagare i åldrarna 50-75, som talades 1-5 språk, deltog i sex olika tester som
mätte prestation i de exekutiv funktionerna inhibering och växlande. Tre språkvariabler
undersöktes, nämligen självrapporterat antal språk, språkfärdighet och omkategoriserat antal
språk baserat på språkfärdighet. Analyser visade en positiv korrelation (d.v.s. sämre
prestation) mellan självrapporterat antal språk och växlandesuppgiften “colour-shape”. Denna
korrelation förblev signifikant vid analyser av omkategoriserat antal språk och
“colour-shape”-uppgiften. Resultaten indikerade inga signifikanta prestationsfördelar för
flerspråkiga personer vad gäller exekutiva funktioner, utan visade att detta skulle kunna vara
en nackdel under vissa omständigheter. Då korrelationerna endast hittades i en
växlingsuppgift kan inga större generaliseringar göras vad gäller fördelar eller nackdelar
baserat på vad som kommit fram i denna studie. Likväl kunde inga fördelar för flerspråkiga
som rapporterats i tidigare forskning finnas i denna studie.
A multilingual advantage, or lack thereof?: a comparative study of executive functions
in bilinguals and multilinguals
Language is a big part of our everyday lives. It affects the way we interpret the world around
us. Bilingualism versus monolingualism in terms of advantages and disadvantages have long
been disputed - in several countries resulting in children being discouraged to learn or use
their mother tongue, at least in school (Spernes, 2012). Although some research has been
found which indicates a slight disadvantage for bilinguals in terms of finding the right words
(due to the suppression of a non-target language, Kroll et al., 2008), bilingualism and
multilingualism have mostly been found to be cognitively advantageous (Bialystok et al.,
2004; Bialystok et al., 2014; Chertkow et al., 2010; Kavé et al., 2008). Despite the quantity of
research conducted on the topic, there exists a debate regarding the extent of any cognitive
advantages, how these advantages interact with age and the underlying neural mechanisms
that facilitate them (Bialystok, 2017), while other research findings call into questions the
existence of a “bilingual advantage” entirely (Lehtonen et al., 2018; Paap & Greenberg,
2013). Further research could help to crystalise what the benefits are and if these are
applicable to several groups of people or if it might be more advantageous for some.
Previous research has indicated a bilingual or even multilingual advantage in domains
ranging from a later onset of Alzheimer’s disease (Chertkow et al., 2010) to greater cognitive
reserve and state in old age to better working memory (Kavé et al., 2008). A considerable
amount of research has also been conducted on bilingual children. Bilingual infants, for
example, have been shown to be better at overriding habitual responses (in order to receive
rewards, Kovács & Mehler, 2009) and they have shown to be better at generalising memory
(Hayne, 2006). They are also better at noticing when someone switches to another language,
even if they do not know any of the languages spoken. Adults who have learned a second
language during their childhood have been found to have a higher density of grey matter in
the left inferior parietal cortex than do monolinguals (Mechelli et al., 2004). Another study
has found that bilinguals have more white matter in the frontal lobe, which is an indication
that they are better functioning (Olsen et al., 2015). Apart from these advantages, research
conducted within the last two decades has shown indications of a bilingual advantage in
relation to executive function performance (Bialystok et al., 2004; Bialystok et al., 2014;
Bialystok, 2017).
3
Definitions of executive functions
Executive functions (EF) are cognitive processes that can help us in terms of switching
between different tasks, planning ahead, encountering new things and situations and also aid
our self-control (Gilbert & Burgess, 2008). EF then could be categorised as non-automated,
or controlled, processing which are used in situations where “there is not a well-established
stimulus-response association, or where a behavioural impasse has occurred” (Gilbert &
Burgess, 2008, p. 110).
One example of an EF is inhibition and is sometimes also referred to as inhibitory control,
and refers to the deliberate suppression of an automatic or otherwise dominant response to
stimuli (Miyake et al., 2000). In a study examining the correlation between EF and
bilingualism across age groups, Kousaie et al. (2014) define inhibitory control as consisting
of two parts, interference suppression and response inhibition. The former refers to the ability
to inhibit task-irrelevant information while the latter refers to the inhibition of prepotent
responses - responses that tend to overpower others when conflicts arise between responses.
In addition, Kousaie et al. (2014) state that prior research indicated a bilingual advantage in
interference suppression, but not response inhibition.
Another EF is switching which refers to the switching of attention from one task or mental set
to another (Miyake et al., 2000). In their article, Rieker et al. (2020) examine two different
types of task switching, namely memory-based and cue-based. They characterise
memory-based task switching as predictable and heavily influenced by processes within the
working memory. In contrast, they characterise cue-based task switching as dependent on
shifting attention to new tasks based on an instructive cue and thus more unpredictable.
A third EF often presented in conjunction with the preceding is updating and is defined by
Miyake et al. (2000) as the monitoring and updating of representations within the working
memory. More specifically, it involves coding incoming information in terms of its relevance
to the current task and, where necessary, updating said information by replacing old and
outdated information with new and relevant information. However, updating is not part of the
focus of this thesis and therefore it will not be examined further here.
Executive functions and bilingualism
In relation to EF, bilingualism has been associated with various performance advantages. An
example of this can be seen in a study on lifelong bilingualism by Bialystok et al. (2014)
4
focusing on EF in both monolinguals and bilinguals in different ages, which found that
bilinguals performed better and were more accurate than monolinguals. Reaction times (RT)
were shorter and more often correct for the younger participants. Nevertheless, the group that
seemed to be most advantaged by their bilingualism was the older group of bilinguals,
especially in the non-verbal tasks. Additionally, Rieker et al. (2020), in a task switching
study, reported better bilingual performance in cue-based tests compared to monolingual
participants during switch trials and generally lower switch costs for bilinguals during repeat
trials, meaning different kinds of bilingual advantages across both switch and repeat trials.
Yet another study by Hannaway et al. (2017) found results indicating an advantage in
inhibitory control as well as an executive processing advantage for bilinguals albeit a smaller
one for participants immersed in an environment where their second language (L2) was
prevalent. Further evidence of the advantages of bilingualism in relation to EF could be seen
in a study by Vega-Mendoza et al. (2015). They found an increased advantage in terms of
attentional switching when the participants had a greater fluency in their L2 than the groups
whose L2 was less proficient. This clearly suggests that the level of fluency in the L2 is
important in the study of the advantages of bilingualism. Research has also been conducted
which indicated that age of acquisition of L2 plays a role in the advantages of bilingualism on
EF, as well (Luk et al., 2011).
In a research review by Kroll et al. (2008), they suggested that both of the languages that are
spoken by bilinguals are active at the same time, although one is being suppressed, and that
these languages “compete for selection during spoken production” (Kroll et al., 2008, p. 426).
Even though a person is asked, and intends, to only speak one of their languages, it does not
hinder activation of the other language, which means that one language is always being
suppressed while the other is in use. In another study, Costa and Sebatián Gallés (2014) also
claimed that since bilinguals speak more than one language, it is necessary for them to have a
system that will make sure that they are only using words and other linguistics components
relevant to the situation in a way distinct from monolinguals who do not need to monitor their
language use in the same way. According to Green and Abutalebi (2013) a dilemma exists for
bilinguals in terms of switching between languages, which is that whilst one language is
being suppressed it takes much longer for that person to react to cues indicating that they
should switch to the suppressed language.
There are however studies that have also conducted research on bilingualism and EF which
have indicated that bilinguals are at a slight disadvantage (Lehtonen et al., 2018). A study by
5
Treccani et al. (2009) indicated that there were both advantages and disadvantages to
bilingualism on inhibition. In trials where task-relevant information was presented in a
section of a monitor that had not previously been primed as irrelevant, bilingual participants
were more efficient at inhibiting task-irrelevant information. In trials where target
information instead was presented in areas previously primed as distractors, this advantage
turned into a disadvantage instead.
Another aspect of bilingualism and its effect on certain attentional processes has been
researched by studying the linguistic distance between the bilinguals’ L1 and L2 (Wierzbicki,
2014). In that study, the groups compared were bilingual English-Cantonese and
English-German speakers and monolingual English speakers. The study showed a slight
advantage for bilinguals in the Simon task but found no clear evidence that the participants
who were proficient L2 speakers had a higher inhibitory control. Another study by Sörman et
al. (2019) which intended to clarify advantages of bilingualism and what effects linguistic
distance might have showed no results indicating a correlation between bilingualism and
cognitive control.
Previous testing of executive functions in relation to bilingualism
The following tasks presented regarding EF measures are commonly used in the literature of
language skills and cognitive functions to measure inhibitory control and switching abilities
(Bialystok et al., 2004; Paap & Greenberg, 2013; Prior & MacWhinney, 2010; Sörman et al.,
2019) . One test used to measure inhibition is the Stroop test (Stroop, 1935), where the Stroop
effect is measured (Lu & Proctor, 1995). This effect is the difference in RT that can be found
between congruent and incongruent trials. Another task measuring inhibition is the Simon
task (Simon & Wolf, 1963). The Simon effect shows a delay in RT when the color and the
press of the button is spatially incongruent (Bialystok et al., 2004; Lu & Proctor, 1995; Simon
& Wolf, 1963). A third test commonly used to measure inhibition is the flanker task. As in
the case of both the Simon and Stroop task, the flanker effect shows that the participants’
reactions are slower in incongruent trials as compared to congruent trials (Costa et al., 2009;
Pelham & Abrams, 2013). Costa et al. (2009) found (for the Flanker task) that the RT of
bilinguals was significantly shorter than that of monolinguals in both congruent and
incongruent tasks.
Onto task switching, where three different tests are commonly used, namely the colour-shape
task, the number-letter task and the global-local task. Conducting colour-shape tests, Miyake
6
et al. (2004) found that factors such as inner speech played a large role in facilitating more
effective task switching, particularly in cases where external cues were so weak that they
failed to trigger an automatic response. In the number-letter task, the task type switches every
two trials, which allows for comparisons of RT between switch trials and repeat trials (Rogers
& Monsell, 1995). They report a switching cost where participants incurred greater response
times and higher levels of inaccuracy during switch trials compared to repeat trials. Finally,
the global-local task shows a delay in RT in switching trials as compared to repeat trials
(Miyake et al., 2000).
Cognition and multilingualism
The research covered thus far in this study has primarily focused on younger groups of
participants and comparisons between monolinguals and bilinguals. However, when
examining older populations, the benefits of bi- and particularly multilingualism become
more prominent (Bialystok et al., 2014). For example, a longitudinal study by Kavé et al.
(2008) examined the cognitive state of a group of older Israelis (N=814) over a 12-year
period. Between 1989 and 1992 three waves of interviews were conducted. Participants were
administered a cognitive screening test designed by Katzman et al. (1983) in all three waves
and a Mini-Mental State Exam (MMSE) was administered during the second wave. In their
article, Kavé et al. (2008) reported that multilingual participants scored better on both tests
than their bilingual or trilingual counterparts. Additionally, they reported that the effects of
multilingualism on cognitive state were independent and distinct from other covariates such
as age, gender and education. The article concluded that multilingualism could act as a
determinant of cognitive state in old age and supply individuals with a boosted cognitive
reserve delaying the onset of decline.
Similarly, a study by Chertkow et al. (2010) showed even further evidence for the advantages
of multilingualism. They investigated how bilingualism and multilingualism affects the onset
of Alzheimer’s disease. It did not provide any clear evidence that bilingualism affects the
onset of Alzheimer’s disease, although there was a slight indication that it might have some
more subtle effects. The multilingual group, however, had a significantly later onset of
Alzheimer’s disease in comparison to the bilingual group.
Further findings regarding multilingualism were reported by Ihle et al. (2016). In a
cross-sectional study of a sample of older adults in Switzerland, they found significant
correlations between number of languages spoken and verbal abilities and processing speed.
7
However, no such correlation was found between the number of languages spoken and
cognitive flexibility. Additionally, they found that the correlations between the number of
languages spoken and cognitive abilities were independent of covariates such as leisure
activities and physical demands of jobs throughout life, but not from covariates such as
educational attainment and cognitive demand of job in life (Ihle et al., 2016).
Notable here is that, while several studies have examined the interactions and effects of
multilingualism on cognitive reserve or mental processing or the onset of Ahlzheimer’s
disease, many fewer studies have focused on the interactions between multilingualism and EF
in samples of elderly individuals. This is a subject that the present study aims to address.
Purpose
The purpose of this essay is to study how older bilinguals and multilinguals (ages 50-75)
perform in the executive functions inhibition and switching and if there are any differences
between groups in terms of number of languages or languages proficiency. With this purpose
in mind, research questions (RQ) and hypotheses have been defined. The research questions
are listed below.
RQ 1: How does performance in executive functioning correlate with multilingualism in
terms of number of languages as self-reported?
RQ 2: How does performance in executive functioning correlate with multilingualism in
terms of number of languages as recategorised based on proficiency?
RQ 3: How does performance in executive functioning correlate with multilingualism in
terms of language proficiency through composite scores?
RQ 4: What difference in executive functioning can be seen between the above-mentioned
language variables?
The hypothesis of the present study is twofold. One hypothesis is that the number of
languages spoken will negatively correlate with performance in EF. Bilingual and
multilingual participants will perform better in EF tests relating to inhibition and task
switching than their monolingual counterparts within this dataset (due to the way that EF task
performance was measured in the dataset a negative correlation would denote better
performance). This would align with results from Kavé et al. (2008) where multilingual
participants obtained better scores in cognitive screening tests. It should however be noted
8
that their findings do not constitute a one-to-one comparison with the present paper as the
focus here is on executive functioning rather than a more general cognitive state.
The other hypothesis is that higher levels of language proficiency will also negatively
correlate with EF performance relating to inhibition and task switching, meaning that
participants who obtained higher scores in language proficiency tests will also perform better
in EF tests (here as well a negative correlation would mean better performance due to the way
that EF tasks were measured). Consistent with this hypothesis are findings from a study by
Vega-Mendoza et al. (2015) which indicated a bilingual advantage in performance on an
elevator task with switching as part of a test of everyday attention. In the study both year one
and year four university students partook and crucially the bilingual advantage was present in
the year four sample, where participants would have achieved a greater language proficiency.
Method
Participants
The data utilised in the present study was collected as part of a larger longitudinal project
called Successful Aging - A study of how bilingualism and choice of occupation contribute to
preserve attention and memory across the adult life span that started in Umeå, Sweden in
2015. Initially, the number of participants was 277. However, several participants had to be
excluded due to missing data relating to various variables. Some participants were excluded
due to missing data regarding age (n = 5). An additional 32 participants were also excluded
due to missing data regarding their primary language or L1 as well as 34 others lacking data
on one or more of their languages (not L1). In seven cases, the participants had classified
themselves as being bilinguals but were excluded from the study due to missing data
regarding L2 proficiency. Other participants (n = 5) were lacking information about one of
their languages or lacked proficiency ratings and were also excluded. Lastly, three more
participants were excluded due to the fact that they lacked test scores in any of the executive
functioning tasks analysed. Thus, the final sample size included in this study was 191.
Level of multilingualism
Participants were categorised, based on the number of languages they spoke (see Table 1),
into either monolinguals (n = 8), bilinguals (n = 95), trilinguals (n = 47), quadrilinguals (n =
23) and pentalinguals (n = 18).
9
Table 1.Number of languages (1-5) spoken by participants in the study sample, both self-reported number of languagesand number of languages according to the cut-off point of a composite score of 21 or more in the givenlanguages.
n
Number of languages 1 2 3 4 5
Self-reported: 8 95 47 23 18
With cut-off: 64 96 24 7 0*
* No data was collected in regard to L5 proficiency.
When analysing the data participants were recategorised regarding the number of languages
they spoke based on their reported proficiency scores for each language. A cut-off point was
set at 21 (out of a possible 40) for it to be considered a language possessed by the participant,
in a similar procedure to Marsh et al. (2019). After recategorising participants in accordance
with the cut-off, there were 64 monolinguals, 96 bilinguals, 24 trilinguals and seven
quadrilinguals in the sample. However, due to the design of the questionnaire, participants
could only report proficiency for a maximum of four languages. This meant that six of the
pentalinguals were reclassified as quadrilinguals even though they might have scored higher
than the cut-off for L5 had the opportunity to report L5 proficiency been available.
Materials
Background data
At the start of the study, the participants were asked to fill out a questionnaire regarding
gender, age, socioeconomic status, birthplace, spoken languages, age of acquisition, where
the language was learned, how proficient they judge themselves to be in each language (in the
four categories speech, comprehension, reading, and writing), and how much each language
is used. Apart from this questionnaire, the participants also partook in a short form of the
Raven’s advanced progressive matrices test, measuring intelligence, which consisted of only
twelve problems to solve (Arthur & Day, 1994). The measuring properties of this test was
similar to the original, longer test (r = .90, Arthur & Day, 1994). In this test the participants
were shown a picture (i.e., problem) with a puzzle piece missing and asked to pick the correct
one from a group of eight puzzle pieces and they were given 20 minutes to complete the test.
Executive functioning tasks
In this study, the participants were tested on the executive functions inhibition and switching.
The three tests that were used to measure inhibition were the Stroop, Simon, and the flanker
10
tasks, while global-local, colour-shape, and number-letter tasks were used to measure
switching. For each EF task participants conducted a number of practice trials preceding test
trials similar to a procedure from Sörman et al. (2019)
In the Stroop test (Stroop, 1935), which measures inhibitory control, the participants were
presented with a series of colour words written in different coloured ink (e.g., the word
“blue” was written in green ink). In this test the participants were presented with a colour
word and were asked to determine the colour that the colour word was written in. They did
this by indicating which of the two colour words presented to the left and right of the “target
colour word” was correct, by pressing the “X” key or the “M” key respectively. The response
times of participants during each task were then measured. The RT between congruent trials
and incongruent trials were then compared and the time difference shown is the Stroop effect.
In the second inhibition task, the Simon task, the participants were asked to press a button on
a keyboard with one hand when a certain type of stimulus appeared and press another button
with the other hand for the other type of stimulus, for example different coloured squares
(Bialystok et al., 2004; Lu & Proctor, 1995; Simon & Wolf, 1963). The stimulus could be
presented to either the right or the left side of the monitor. In the present data collection
participants were asked to determine the colour of the square as quickly as possible, by
pressing “M” for a green square and “X” for a red square. The Simon effect was then
measured by comparing RT for spatially congruent and incongruent trials.
The third test used to measure inhibition was the flanker task in which the participants were
presented with five arrows and their assignment was to indicate what direction the central
arrow was pointing by pressing “X” with their left index finger when the arrow pointed left
and pressing “M” with their right index finger for an arrow pointing right (Costa et al., 2009;
Pelham & Abrams, 2014). Just as in the Stroop and Simon tasks, the flanker effect was
shown by comparing the RT between congruent and incongruent trials.
The global-local task, which was used to measure switching, consisted of several trials where
participants were presented with a “Navon figure” or a figure consisting of several smaller
figures (Navon, 1977). For instance, these figures could be smaller geometric shapes forming
a larger geometric shape and could be either congruent (e.g., a triangle made out of several
triangles) or incongruent (e.g., a square made out of several circles). In this study, participants
had to either identify the large (global) geometric figure if the figure presented was blue or
identify the smaller (local) figures if the figure presented was black. Each of the four possible
11
figures (circle, cross, triangle and square) were assigned to the keys 1, 2, 3 and 4 and the
participants used these to identify the target figure. Switching cost in this task was calculated
by comparing the RT between switching trials and repeat trials.
Another test used to measure switching in this study was the color-shape test (Rogers &
Monsell, 1995) in which the participants were presented with a stimulus and were asked to
indicate either the color or the shape of the given stimulus. This test consisted of three parts:
distinguishing the colour of the symbols; distinguishing the shape of the symbols;
distinguishing either colour or shape for different symbols. If the colours were blue or red
they were asked to press “Z” or “X” respectively. If the symbols were either a triangle or a
circle, they were asked to press “M” or “N” respectively. To indicate whether they were to
distinguish between colour or shape, a rainbow was shown on the monitor for colour and a
triangle with a circle inside it was shown for shapes. Yet again, the switching cost could be
seen in this task through comparisons of RT for switching and repeat trials.
The last switching task was the number-letter task (Rogers & Monsell, 1995). Here,
participants were presented with a character pair in one of four quadrants of a computer
screen. They were then tasked with either determining whether the letter in the pair was a
vowel or a consonant (letter task) or determining whether the number in the pair was odd or
even (digit task). The character pairs consisted of either a letter and a number (e.g., “R6”), a
letter and a neutral character (e.g., “#U”) or a number and a neutral character (e.g., “4*”). The
position of the characters to the left or the right of the pair was randomised (Rogers &
Monsell, 1995). If the pair was displayed in either of the top corners the participants were to
determine whether the digit was odd or even and press “X” for odd and “M” for even. If the
pair was shown in either of the bottom corners the participants were to indicate whether the
letter was uppercase or lowercase by pressing “M” for uppercase and “X” for lowercase.
Which task participants engaged in was determined by which quadrant the character pair was
displayed in and after each trial the position in which the character pair was displayed moved
clockwise. This ensured that participants completed two consecutive trials of the same task
before switching. Participants were additionally instructed to ensure that their RT were as low
as possible while also minimising errors. The switching cost for this task was also calculated
by comparing RT when tasks were consecutive versus when the active task was switched.
12
Procedure
The participants in the Successful Aging project were tested on a number of tasks as a part of
a large battery, but only the tests measuring inhibition and switching are being considered in
this study. The tests performed to measure inhibition were the Stroop test, the Simon task,
and the Flanker task. Tasks used to measure the participants’ switching abilities were a
global-local, color-shape, and number-letter task. Important to note is that scores relating to
all the above-mentioned tests were not pure RT but rather calculated scores of switching costs
in the case of switching tests and RT from congruent vs. incongruent trials in the case of
inhibition tests. This meant that in the case of the inhibition tasks what was analysed was the
“effect” scores (i.e., Stroop effect, Simon effect, and flanker effect).
Data analysis
IBM SPSS Version 25 (IBM Corp, 2017) was used to process and analyse the data in order to
find out if there was any statistical significance in the result between the different groups and
scoring. Both parametric and non-parametric tests were utilised. Three different analyses
were conducted. In the first analysis, EF test results were compared with the number of
languages spoken using a Spearman’s correlation analysis. In the second analysis the
participants’ number of languages were decided in accordance with their proficiency in every
given language, where they needed to score at least 21 out of 40 to be viewed as to be in
possession of the given language. This recategorised number of languages was then
compared to executive functioning scores using a Spearman’s correlation analysis.
Spearman’s correlation coefficient is a nonparametric measure and was employed here due to
the fact that the above-mentioned variables were ordinal, since it cannot be ascertained that
the distance between languages are equidistant. In the third analysis, all the scores the
participants had ranked in every language were added up to give them a total composite score
for proficiency. The results were then analysed in relation to this composite score with a
Pearson’s correlation analysis. The reason behind using Pearson’s correlation analysis was
because it is used for parametric measure, which fitted well with the total composite score
variable. In cases where statistically significant correlations were found between language
variables and other outcome variables, linear regression analyses would be conducted in
order to further investigate the relationships between variables and determine to what extent
the language variables could account for said correlations. For all tests conducted the p-value
needed to be equal to or below 0.05 for the result to be considered statistically significant.
13
Research ethics
All participants included in the Successful Aging project gave written consent in accordance
with the Declaration of Helsinki. The declaration, written by the World Medical Association,
is an international declaration of ethics relating to medical and other kinds of research. It
stipulates ethical principles regarding informed consent, participant privacy and
confidentiality amongst others (World Medical Association, 2018) Additionally, the project
was approved by the Regional Ethics Committee at Umeå University.
Results
Basic descriptive statistics of the whole sample examined in the present report, including
number of languages spoken, age, years of education, Raven’s test scores and composite
score for language proficiency can be seen in Table 2 below. The mean age of the sample was
65.5 years old and the mean number of languages spoken by participants was 2.73.
According to Finney and DiStefano (2006) suggested thresholds for skewness and kurtosis
are 2 and 7 respectively which means that skewness and kurtosis scores should be below
these limits. As presented in Table 2, both skewness and kurtosis for all variables examined
were within these bounds suggesting that the data were fairly normally distributed.
Table 2.Descriptive statistics of entire sample (n = 191)
n Mean SD Minimum Maximum Skewness Kurtosis
Number oflanguages 191 2,73 1,046 1 5 0,844 -0,180
Number oflanguages(cut-off) 191 1,84 0,769 1 4 0,729 0,385
Totalcomposite* 191 71,36 25,56 20 158 0,737 0,653
L1 composite 191 34,94 5,227 14 40 -1,216 1,574
L2 composite 183 26,49 10,203 1 40 -0,357 -0,958
L3 composite 88 17,37 10,782 2 40 0,167 -0,867
L4 composite 39 12,46 9,893 2 40 1,271 1,013
Age 191 65,5 5,917 50 75 -0,742 0,141
Years ofeducation 185 13,58 4,543 2 28 -0,179 0,252
Raven's scores 184 4,99 2,722 0 11 0,181 -0,766
* No data was collected in regard to L5 proficiency.
14
As mentioned in the method section, Spearman’s and Pearson’s correlation analyses were
used in the present study. Spearman’s correlations were employed in analyses involving the
number of languages spoken by participants, both self-reported and recategorised based on
the cut-off. As these variables were ordinal as opposed to continuous variables, Spearman’s
would fit better than Pearson’s which requires interval or ratio variables. The remaining
analyses did not involve the two above-mentioned variables, meaning that all variables
involved were continuous (i.e., total composite score, years of education, Raven’s scores, age,
and all EF tasks). Therefore, Pearson’s correlations were used on these (see Table 3). The
correlations conducted concerning self-reported number of languages only found a significant
positive correlation with colour-shape task (95% BCa CI [.015 , .3] p = .042), indicating that
the more languages spoken by the participant the longer their RT for the colour-shape task,
which could be an indication of poorer switching ability. The remaining EF tasks showed no
significant correlations (all ps > .05).
When conducting histograms, two outliers were found in the Stroop test and their scores were
removed from further analysis. However, removing these outliers did not affect the statistical
significance of the result of the analysis.
The correlational analysis conducted in regard to the recategorised number of languages also
showed a positive correlation with the colour-shape task (95% BCa CI [.016, .332] p = .025).
This result would indicate that even after they were recategorised in terms of number of
languages according to their abilities, participants with a greater number of languages would
incur greater RT during the colour-shape task which could indicate a poorer switching ability.
The remainder of the EF tasks showed no significant correlations with the recategorised
number of languages spoken (all ps > .05). Moreover, in regard to the total composite scores,
there were no significant correlations with any EF tasks (all ps > .05).
Due to the lack of statistically significant correlations between language variables and
outcome variables no regression analyses were performed. While the analysis did find a
significant correlation between the “number of languages'' variables and the colour-shape
task, it showed no relationship to any of the analysed covariates. A regression analysis would
therefore likely not have added any new information regarding the relationship between the
language variables and the colour-shape task.
15
Table 3.Correlations between number of languages, recategorised number of languages, total composite score, and EF tasks as well as correlation between covariates (age, years of education, andRaven’s scores), using Spearman’s and Pearson’s correlation coefficient.
1. Age2. Years ofeducation
3. Raven’sscores
4. Flankertask
5. Strooptask
6. Simontask
7. Number-letter
8. Colour-shape
9. Local-global
10. Nr. oflanguages
11. Nr. oflanguages,
recategorised
12. Totalcomposite
score
1. Age -
2. Years of education -,025b -
3. Raven’s scores - ,453**c ,231**e -
4. Flanker task ,156*l ,036m -,173*m -
5. Stroop task ,227**u -,045w -,186*w ,058z -
6. Simon task ,172*h -,104i -,203**l ,065s ,063y -
7. Number-letter ,097d -,009f -,106g -,012p -,103v -,007j -
8. Colour-shape ,035l ,016o -,134n ,043t ,058y ,141s ,204**o -
9. Local-Global ,075h ,014j -,112k ,062r ,122x -,033q ,121k ,051p -
10. Nr. of languages - ,098a ,300**b ,188*c -,081l -,131u -,057h ,006d ,157*l ,117h -
11. Nr. of languages,recategorised -,133a ,173b ,116c -,018 l ,091u -,063h -,049d ,174*l ,065h ,504**a -
12. Total composite score -,180*a ,264**b ,178*c ,051l -,010u -,102h -,039d ,133l ,081h ,746**a ,847**a -
*p < ,05. **p < ,01. an = 191. bn = 185. cn = 184. dn = 182. en = 179. fn = 177. gn = 176. hn = 174. in = 171. jn = 169. kn = 168. ln = 167. mn = 164. nn = 163. on = 162. pn = 160. qn = 158.rn = 155. sn = 154. tn = 151. un = 149. vn = 147. wn = 144. xn = 139. yn = 138. zn = 134.Bootstrap results are based on 1000 bootstrap samples.
16
Further, Table 3 shows that Raven’s scores negatively correlate with all EF tasks relating to
inhibition (95% BCa CI [-.308, -.025] p = .027 for Flanker; 95% BCa CI [-.332, -.024] p =
.026 for Stroop; 95% BCa CI [-.352, -.045] p = .009 for Simon), indicating that better
Raven’s scores lead to shorter RT for inhibition tasks. As mentioned in the introduction, due
to the way that EF tasks were measured, such a correlation would indicate better
performance. The self-reported number of languages also had a positive correlation with
Raven’s scores, which is an indication that those who reported to speak more languages also
score higher on the Raven’s test (95% BCa CI [.034, .327] p = .011).
Age had a positive correlation with all inhibition tasks (95% BCa CI [.010, .303] p = .043 for
Flanker; 95% BCa CI [.077, 378] p = .005 for Stroop; 95% BCa CI [.039, .306] p = .024 for
Simon), indicating that older participants have a longer RT and therefore poorer inhibitory
control. Age also correlated negatively with total composite score (95% BCa CI [-.325, -.029]
p = .013), meaning that older participants obtained lower proficiency scores. Lastly, there was
no significant correlation between the covariate years of education and EF tasks (all ps > .05).
Discussion
The hypotheses presented in this report were (1) that the number of languages spoken would
correlate with better EF task performance and (2) that language proficiency would also
correlate with improved performance in EF tasks. Analyses of the data, as presented in the
results, found no support for either hypothesis. Instead, a positive correlation between the
number of languages spoken and the colour-shape task was identified. This positive
correlation was also found when the recategorised number of languages and the colour-shape
task were analysed which would indicate that participants with a greater number of languages
performed poorer in the colour-shape task, a bilingual and multilingual disadvantage. Our
results here differ from ones found in Reiker et al. (2020), which used the colour-shape task
to measure switching abilities and reported generally lower switching costs and greater
performance for bilingual participants. Our own results showed an opposite relationship
between the variables.
Moreover, no significant correlations between any of the language variables and the EF tasks
were detected, indicating that they were unrelated. These results are inconsistent with those
of Bialystok et al. (2014) and Bialystok et al. (2004). In those studies, also examining older
individuals amongst others, results indicated that bilingual participants experienced lower
17
levels of interference while conducting a Stroop task in the former study and lower Simon
effect costs in the latter study. In both studies, they also highlight that the greatest benefit was
enjoyed by older participants, which stands in stark contrast to our own results regarding
bilinguals and also multilinguals. Furthermore, our findings contrast, at least in part, with
those from Ihle et al. (2016), another study with a sample of older adults. Their results
indicated an advantage for multilinguals in verbal abilities and processing speed. It should be
noted, however, that while focusing on cognitive abilities, they did not examine the EF tasks
present in this study. But the overall conclusion of a bilingual advantage in cognitive abilities
was not present in our own findings. Interesting to note is that the results also showed no
significant correlations (save for one between colour-shape and number-letter) between any
of the inhibition tasks or any of the switching tasks despite them being measures of the same
underlying executive functions.
To answer RQ 1, based on the present findings there are no indications of a multilingual
advantage in EF performance for either inhibition or switching. Overall, multilingual
participants performed no different from their monolingual counterparts with the exception of
the colour-shape task where a disadvantage was reported. However, as this only constitutes
one of three measures of switching, that disadvantage should be interpreted cautiously. RQ 2
can be answered similarly. Even after recategorisation based on proficiency, no multilingual
advantage was found. Based on these results, it seems that the number of languages does not
affect EF performance. This contrasts with findings by e.g., Kavé et al. (2008) where results
indicated that multilingualism was a boon to cognitive functions and in particular cognitive
reserve. It should be repeated, however, that the focus of that study was cognitive reserve as
opposed to EF. The contrast between their results and ours are not a direct comparison. Our
findings also stand in opposition to results found by Vega-Mendoza et al. (2015) indicating
that greater language proficiency leads to greater cognitive functioning, something that was
not supported by any means through the analyses conducted in the present study. However,
this study was conducted on a sample of university students, and thus a difference in age
might help explain the contrasting findings. It should additionally be noted that their article
primarily examined tasks relating to the auditory modality whereas the tasks included in the
present study have been more visually oriented. Regarding RQ 3, our findings indicated that
language proficiency did not significantly correlate with better EF task performance within
our sample. Language proficiency has no effect on EF performance, at least within the
present dataset. These findings go hand in hand with previous research on multilingualism
18
and EF, which indicate that the effect multilingualism has on EF is very limited (Lehtonen et
al., 2018; Paap & Greenberg, 2013). In relation to RQ 4, the present findings also indicate no
difference in EF performance overall. It remains unrelated and does not change irrespective
of whether examination focuses on the number of languages spoken by a participant or their
total proficiency scores.
Regarding covariates, the analyses found that Raven’s scores were consistently and
negatively correlated with all three EF tasks relating to inhibition. Participants with higher
Raven’s scores performed better in the Flanker, Stroop and Simon tasks. However, as
mentioned above, no language variables were correlated with these tasks so they would not
have affected the present findings. Raven’s scores were also positively correlated with the
variables number of languages and total composite scores. This would mean that participants
with a greater number of languages and higher proficiency scores also obtained higher
Raven’s scores. It should also be noted here that the correlations, while significant, would
only be classified as weak. The same could be said for age as a covariate. Positive
correlations were found between it and EF tasks for inhibition (meaning poorer performance
for older participants) as well as a negative correlation between age and total composite
scores indicating that older participants obtained lower proficiency scores. Any further
investigation into potential interactions between these variables would fall beyond the scope
of this report.
Discussion of methodology
A clear strength that can be seen in this study is the multitude of variables and testing that has
been included. Three measures of each of the EF studied were used as well as other predictor
variables. Moreover, the present study wanted to examine if multilingualism affected EF
performance in several ways by examining three different aspects of language (i.e.,
self-reported number of languages, recategorised number of languages based on proficiency,
and a total composite score of proficiency). The hope in doing this was to see if any of the
three variables had a greater impact on EF.
Another strength of the present report is its sample. The sample examined consisted of older
adults, which in research on interactions between multilingualism and executive functions is
relatively uncommon. As the study deals with older participants, the results found in the
current study might give some insight on cognitive decline and what cognitive components
may or may not interact or affect it. The present sample is also a larger one (N = 191) which
19
in comparison to many other studies in this area is also fairly uncommon. The present sample
has, therefore, a greater chance of being more representative of the larger population. Finally,
a measure of intelligence was also utilised as a covariate in the present study which is also
quite unique for the larger field of study that it exists within. As such, the study was able to
control for intelligence and examine how it may have affected the outcomes.
One possible weakness with the current study was that no position was taken as to what
constitutes a language. Some participants had written certain Swedish dialects as a language
they learned to speak that were separate from Swedish. When looking at the data, these
dialects were allowed to be counted just as any other language, even though these are in fact
variations of a language and not a separate language (which they were categorised as). This
might have had an impact on the results found, since these participants were, possibly
incorrectly, given both a higher proficiency score and a higher total number of languages
spoken. However, it should also be stated that dialects do differ from the standard language to
some extent, which could mean that they have the same effect on EF as speaking any other
language would have, in which case this would not have had any misleading effect on the
result. Noteworthy is also the fact that only about a handful of participants had a dialect listed
as a language, meaning that it should not have affected the result to a great extent within the
present sample. However, the lack of a standpoint either way could have led to some
inconsistencies within the current report. Had a standpoint been taken, it may allow future
replications of this study to be more consistent throughout their data processing and analysis.
Another possible weakness that became evident was that no composite scores had been
collected for L5. This meant that a part of the sample was categorised as only speaking four
languages, in the recategorised variable, even though they might have been proficient enough
in their fifth language to be seen as pentalinguals in accordance to the cut-off point. This also
affected the total composite score of proficiency, where the maximum score was 160 (40
points for each language) but should have been 200, seeing as there were pentalinguals in the
study. Had they been able to score their fifth language as well, the analyses conducted in
regard to this variable might have yielded different results. Additionally, it would have been
more consistent in terms of design to collect proficiency scores in all languages. Although the
result might have differed somewhat, there were only six participants who could have made
the cut-off but were counted as quadrilinguals instead, meaning that their impact on the study
would have been quite limited.
20
Concerning proficiency levels and how to decide where the cut-off should be for someone to
be seen as proficient enough in a language, inspiration was taken from Bialystok (2017) who
claimed that even just a little knowledge in another language could give a “bilingual
advantage”. Although this was stated in relation to brain structure rather than executive
functions, the decision was made to not be as strict as in the cut-off as in a study by Marsh et
al. (2019). In said study, cut-off points had required participants to be proficient to a certain
level (more than 50%) in all four linguistic categories as opposed to having a total composite
score of proficiency in the given language. This more extreme way of categorising possession
of a language seemed a bit too drastic considering the indications of a bilingual advantage
that can be seen in Bialystok’s work. This meant that the participants could have scored
themselves nines in reading and listening and only twos in speaking and writing and still pass
the cut-off.
Another possible weakness with this study could have been how some tests were
administered, in particular the Stroop test. This test was administered in Swedish to all
participants no matter what their dominant language was. This could have meant that there
was less of an interference, when the participants were asked to name the colour of the word
as opposed to the word, for those participants who did not have Swedish as their dominant
language. On the other hand, participants who were highly proficient in Swedish, irrespective
of whether or not it was their L1, might still have experienced interference. Additionally, this
problem only occurred in the Stroop test, which was the only test with a verbal component,
meaning that it should not have had a great effect on the study of inhibition performance as a
whole.
An additional aspect of the method that could be questioned is the use of Pearson’s
correlation coefficient for the total composite score of proficiency variable considering the
fact that the proficiency scores were self-reported. As the composite scores were based on
self-reports it could be seen as an ordinal variable - one participant’s understanding of e.g.,
the score 5 on the scale may be different than another participant’s. Therefore, the use of a
Pearson’s correlation may have been a poor fit. However, the same scale was used for all
participants giving a consistent baseline and meaning that you could speak in terms of
“participant A who scored a 6 on comprehension rated themself as twice as good as
participant B who scored 3”. This problem could have been sidestepped if the participants’
proficiency had been measured. While trying to measure proficiency in all the languages
included in the study would have taken a considerable amount of time and resources and
21
might have been implausible, it would have made for a more objective measure which could
have improved the study’s validity.
A final potential limitation with the current study was the tasks used to measure EF. Seeing as
there were three tasks measuring each function, these should correlate with each other. Based
on our findings, however, they did not, which has also been seen, and criticised, in a previous
study by Paap and Greenberg (2013). In their article, they stated that several tasks claiming to
measure the same executive function should correlate as they all should be tapping into the
same underlying mechanism. They subsequently examined previous research on inhibition
tasks and found that correlations between them were insignificant. The current findings for
the most part align with these claims. The fact that these tasks do not correlate or show
similar, significant, results does not make sense as they are all supposed to measure the same
underlying executive function. A possible reason behind this might be that there is a possible
confounder which has caused this and which has not become evident throughout this study.
The question then is how these tasks can be seen as reliable when they do not even correlate
with one another and how these findings will affect the validity of studies using these tasks.
Concluding remarks
In summary, the present report sought to examine how bilingual and multilingual participants
in a sample of older adults performed in EF tasks relating to inhibition and switching. It
found that neither the number of languages spoken nor the level of language proficiency
correlated with EF tasks, with the notable exception of a single weak correlation indicating a
multilingual disadvantage in switching compared to monolinguals. However, this correlation
was found in only one out of six EF tasks, meaning that it should be interpreted sparingly and
not as an indication of a wider performance disadvantage. No investigation into potential
differences between predictor variables was conducted due to a lack of significant results.
However, the bilingual and multilingual advantages reported in previous studies have not
been found in the present dataset.
Future studies
Suggestions for future studies would be to firstly collect proficiency scores in all languages
spoken by each participant, and given the resources gather these using tests to measure their
proficiency as opposed to self-reports. That could help to add further nuance and give more
complete data as well as removing some of the subjectivity and unreliability that comes with
22
self-reports. Additionally, investigating how dialects may affect EF and whether they have
similar effects to speaking an additional language, would add further understanding to
interactions between EF and multilingualism.
As mentioned previously, the findings in this study as well as a study conducted by Paap and
Greenberg (2013) call into question the validity and reliability of the measures currently used
to measure EF. Future studies should therefore investigate whether or not these measures are
in fact reliable or valid, or alternatively examine which of these measures are reliable and
which are not. If they are found not to be reliable or valid, efforts should be put towards
redesigning them for higher reliability and validity or finding other, more dependable,
measures.
In order to clarify and contextualise the correlations found in the present study between
Raven’s scores and the EF tasks on inhibition, future research could examine the interactions
between multilingualism and intelligence. Such studies could work toward determining
whether there exists an indirect correlation between multilingualism and EF via measures of
intelligence by trying to find the same correlations in a larger sample. Alternatively, they
could look at other explanations for the correlations found in this report. The present results
also suggest that Raven’s scores correlated with the number of languages spoken by
participants. Finding possible explanations for this correlation could also be of interest.
23
References
Arthur, W., and Day, D. (1994). Development of a short form for the raven advanced
progressive matrices test. Educational and Psychological Measurement, 54(2),
394–403. doi: 10.1177/0013164494054002013.
Bialystok, E. (2017). The bilingual adaptation: How minds accommodate experience.
Psychological Bulletin, 143(3), 233-262. doi: 10.1037/bul0000099.
Bialystok, E., Craik, F. I. M., Klein, R., and Viswanathan, M. (2004). Bilingualism, aging,
and cognitive control: Evidence from the Simon task. Psychology and Aging 19(2),
290–303. doi: 10.1037/0882-7974.19.2.290.
Bialystok, E., Poarch, G., Luo, L., and Craik, F. I. M. (2014). Effects of bilingualism and
aging on executive function and working memory. Psychology and Aging 29(3),
696–705. doi: 10.1037/a0037254.
Chertkow, H., Whitehead, V., Phillips, N., Wolfson, C., Atherton, J., & Bergman, H. (2010).
Multilingualism (but not always bilingualism) delays the onset of Alzheimer disease:
Evidence from a bilingual community. Alzheimer Disease & Associated Disorders,
24(2), 118-125. doi: 10.1097/WAD.0b013e3181ca1221.
Costa, A., Hernández, M., Costa-Faidella, J., and Sebastián-Gallés, N. (2009). On the
bilingual advantage in conflict processing: Now you see it, now you don’t. Cognition
113, 135–149. doi: 10.1016/j.cognition.2009.08.001.
Costa, A., & Sebastián-Gallés, N. (2014). How does the bilingual experience sculpt the
brain?. Nature Reviews Neuroscience, 15(5), 336-345. doi:10.1038/nrn3709.
Finney, S. J., and DiStefano, C. (2006). Non normal and categorical data in structural
equation modeling. In G. R. Hancock and R. O. Mueller (Eds.) Structural Equation
Modeling: A Second Course (pp. 269–314). Greenwich, Connecticut: Information Age
Publishing.
Gilbert, S. J., & Burgess, P. W. (2008). Executive function. Current Biology, 18(3),
R110-R114. doi: 10.1016/j.cub.2007.12.014.
Green, D. W., and Abutalebi, J. (2013). Language control in bilinguals: the adaptive control
hypothesis. Journal of Cognitive Psychology, 25(5), 515–530. doi:
10.1080/20445911.2013.796377.
Hannaway, N., Opitz, B. & Sauseng, P. (2017). Exploring the bilingual advantage:
manipulations of similarity and second language immersion in a Stroop task. Cognitive
Neuroscience, 10(1), 1-12. doi: 10.1080/17588928.2017.1389874.
24
Hayne, H. (2006). Age-related changes in infant memory retrieval: Implications for
knowledge transfer. In Y. Munakata & J. H. Johnson (Eds.), Processes of change in
brain and cognitive development. Attention and Performance XXI (pp. 209 –231).
Oxford: Oxford University Press.
IBM Corp. (2017). IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.
Ihle, A., Oris, M., Fagot, D. & Kliegel M. (2016). The relation of the number of languages
spoken to different cognitive abilities in old age. Journal of Clinical and Experimental
Neuropsychology, 38(10), 1103-1114. doi: 10.1080/13803395.2016.1197184.
Katzman, R., Brown, T., Fuld, P., Peck, A., Schechter, R. & Schimmel, H. (1983). Validation
of a short orientation-memory-concentration test of cognitive impairment. American
Journal of Psychiatry, 140(6), 734–739. doi: 10.1176/ajp.140.6.734.
Kavé, G., Eyal, N., Shorek, A., & Cohen-Mansfield, J. (2008). Multilingualism and cognitive
state in the oldest old. Psychology and Aging, 23(1), 70-78. doi:
10.1037/0882-7974.23.1.70.
Kovács, A. M., & Mehler, J. (2009b). Flexible learning of multiple speech structures in
bilingual infants. Science, 325(5940), 611– 612. doi: 10.1126/science.1173947.
Kousaie, S., Sheppard, C., Lemieux, M., Monetta, L. & Taler, V. (2014). Executive function
and bilingualism in younger and older adults. Frontiers in Behavioural Neuroscience,
5, 1-12. doi: 10.3389/fnbeh.2014.00250.
Kroll, J. F., Bobb, S. C., Misra, M., and Guo, T. (2008). Language selection in bilingual
speech: evidence for inhibitory processes. Acta Psychologica. 128(3), 416–430. doi:
10.1016/j.actpsy.2008.02.001.
Lehtonen, M., Soveri, A., Laine, A., Järvenpää, J., de Bruin, A., and Antfolk, J. (2018). Is
bilingualism associated with enhanced executive functioning in adults? A meta-analytic
review. Psychological Bulletin 144(4), 394–425. doi: 10.1037/bul0000142.
Lu, C. H., & Proctor, R. W. (1995). The influence of irrelevant location information on
performance: A review of the Simon and spatial Stroop effects. Psychonomic Bulletin
& Review, 2(2), 174-207. doi: 10.3758/BF03210959.
Luk, G., De Sa, E., and Bialystok, E. (2011). Is there a relation between onset age of
bilingualism and enhancement of cognitive control? Bilingualism 14(4), 588–595. doi:
10.1017/S1366728911000010.
Marsh, J. E., Hansson, P., Sörman, D. E., & Ljungberg, J. K. (2019). Executive processes
underpin the bilingual advantage on phonemic fluency: Evidence from analyses of
25
switching and clustering. Frontiers in Psychology, 10, 1355. doi:
10.3389/fpsyg.2019.01355.
Mechelli, A., Crinion, J. T., Noppeney, U., O’Doherty, J., Ashburner, J., Frackowiak, R. S., &
Price, C. J. (2004). Neurolinguistics: Structural plasticity in the bilingual brain. Nature,
431, 757. doi: 10.1038/431757a.
Miyake, A., Emerson, M. J., Padilla, F., & Ahn, J. C. (2004). Inner speech as a retrieval aid
for task goals: The effects of cue type and articulatory suppression in the random task
cuing paradigm. Acta Psychologica, 115(2-3), 123-142. oi:
10.1016/j.actpsy.2003.12.004.
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A. & Wager, T. D.
(2000). The unity and diversity of executive functions and their contributions to
complex “frontal lobe” tasks: a latent variable analysis. Cognitive Psychology, 41(1),
49-100. doi:10.1006/cogp.1999.0734.
Navon, D. (1977). Forest before trees: the precedence of global features in visual perception.
Cognitive Psychology 9(3), 353–383. doi: 10.1016/0010-0285(77)90012-3.
Olsen, R. K., Pangelinan, M. M., Bogulski, C., Chakravarty, M. M., Luk, G., Grady, C. L., &
Bialystok, E. (2015). The effect of lifelong bilingualism on regional grey and white
matter volume. Brain Research, 1612, 128 –139. doi: 10.1016/j.brainres.2015.02.034.
Paap, K. R., and Greenberg, Z. I. (2013). There is no coherent evidence for a bilingual
advantage in executive processing. Cognitive Psychology, 66(2), 232–258. doi:
10.1016/j.cogpsych.2012.12.002.
Pelham, S. D., & Abrams, L. (2013). Cognitive advantages and disadvantages in early and
late bilinguals. Journal of Experimental Psychology: Learning, Memory, and
Cognition. Advance online publication. doi: 10.1037/a0035224.
Prior, A., and MacWhinney, B. (2010). A bilingual advantage in task switching. Bilingualism,
13(2), 253–262. doi: 10.1017/S1366728909990526.
Rieker, J., Reales, J. & Ballesteros, S. (2020) The effects of bilingualism on cue-based vs.
memory-based task switching in older adults. Frontiers in Human Neuroscience, 14.
1-11. doi:10.3389/fnhum.2020.610548.
Rogers, R. D., & Monsell, S. (1995). Costs of a predictable switch between simple cognitive
tasks. Journal of Experimental Psychology: General, 124(2), 207-231. doi:
10.1037/0096-3445.124.2.207.
26
Simon, J. R., & Wolf, J. D. (1963). Choice reaction time as a function of angular
stimulus-response correspondence and age. Ergonomics, 6(1), 99-105. doi:
10.1080/00140136308930679.
Spernes, K. (2012). ‘I use my mother tongue at home and with friends–not in
school!’Multilingualism and identity in rural Kenya. Language, Culture and
Curriculum, 25(2), 189-203. doi: 10.1080/07908318.2012.683531.
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of
Experimental Psychology, 18(6), 643–662. doi: 10.1037/h0054651.
Sörman, D. E., Hansson, P., & Ljungberg, J. K. (2019). Different features of bilingualism in
relation to executive functioning. Frontiers in Psychology, 10, 1-13. doi:
10.3389/fpsyg.2019.00269.
Treccani, B., Argyri, E., Sorace, A., & Della Sala, S. (2009). Spatial negative priming in
bilingualism. Psychonomic Bulletin & Review, 16(2), 320-327.
doi:10.3758/PBR.16.2.320.
Vega-Mendoza, M., West, H., Sorace, A., & Bak, T. H. (2015). The impact of late,
non-balanced bilingualism on cognitive performance. Cognition, 137, 40-46. doi:
10.1016/j.cognition.2014.12.008.
Wierzbicki, S. (2014). An investigation into the relationship between bilingualism and
attentional networks with a focus on linguistic distance and language proficiency.
Master’s thesis, Witwatersrand: University of the Witwatersrand.
World Medical Association (2018). WMA declaration of Helsinki - Ethical principles for
medical research involving human subjects. Retrieved 8 June 2021 from
https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-
medical-research-involving-human-subjects/.
27
