Brain Signal Complexity and Creative Ability in Bilingual and Monolingual Children

by

Joshua William Villafuerte

A thesis submitted in conformity with the requirements for the degree of Master of Arts

Graduate Department of Psychology University of Toronto

© Copyright by Joshua William Villafuerte 2014

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Brain Signal Complexity and Creative Ability in Bilingual and

Monolingual Children

Joshua William Villafuerte

Master of Arts

Graduate Department of Psychology University of Toronto

2014

Abstract

Childhood bilingualism has long been associated with enhanced creative performance. The

neural mechanisms underlying this phenomenon, however, have yet to be characterized.

Research suggests bilingualism modifies neural networks for executive control. Such changes,

which can be assessed with estimation of neural signal complexity, are thought to increase

information processing capacity. We thus compared electroencephalographic signal complexity

during a Go/No-Go task in bilingual and monolingual children aged 4 to 6, and correlated these

complexity estimates with creative ability. Differences in EEG complexity were found in left

centro-parietal and right frontal scalp regions. Complexity, however, did not correlate with

children’s creative performance scores. Although our findings failed to explain the neural link

between bilingualism and enhanced creativity, we did find evidence to support the notion that

bilingualism during early childhood is associated with functional brain changes in areas related

to executive functioning.

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Acknowledgments

The success of this project would not have been possible without the support and guidance of

several individuals and institutions. First and foremost, to my advisor Sylvain Moreno, thank you

for your wisdom, guidance, and patience over the past year. It was an honour to begin this long

journey that is my postgraduate career with you. To Yunjo Lee, Patrick Bermudez, Lynn

Williams and Natasha Kovacevic, I thank you for all your teachings and supervision throughout

my data analysis. I also owe a debt of gratitude to Randy McIntosh and Cheryl Grady for being a

part of my thesis committee, as well as to the funding agencies that supported my research. I am

beyond grateful for having been given the opportunity to participate in this program and to work

alongside such a highly esteemed group of individuals.

And to my family, thank you for being the best support crew I could have ever asked for.

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Table of Contents

Acknowledgments .......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................. vi

List of Figures ............................................................................................................................... vii

List of Appendices ....................................................................................................................... viii

1 Introduction .............................................................................................................................. 1

1.1 Executive control and bilingualism ................................................................................... 1

1.2 Creativity ........................................................................................................................... 3

1.3 Brain signal complexity ..................................................................................................... 4

1.4 Purpose and hypotheses ..................................................................................................... 5

2 Methods .................................................................................................................................... 7

2.1 Participants ........................................................................................................................ 7

2.2 Background questionnaire ................................................................................................. 7

2.3 Test of creativity ................................................................................................................ 7

2.4 Stimuli and task procedure ................................................................................................ 8

2.5 EEG recording ................................................................................................................... 9

2.6 EEG preprocessing .......................................................................................................... 10

2.7 Multiscale entropy analysis ............................................................................................. 11

2.8 Statistical analysis ............................................................................................................ 12

Results ........................................................................................................................................... 14

2.9 Behavioural performance: Effects of bilingualism on executive and creativity

performance .............................................................................................................................. 14

2.10 Task PLS results: Effects of cognitive control on brain signal complexity .................. 14

2.11 Task PLS results: Effects of bilingualism on brain signal complexity .......................... 15

2.12 Behaviour PLS: Relationship between divergent thinking ability and brain signal

complexity ................................................................................................................................. 16

3 Discussion ............................................................................................................................... 21

3.1 Executive and creativity performance ............................................................................. 21

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3.2 Brain signal complexity, bilingualism and creativity ...................................................... 22

3.3 Conclusions and future directions ................................................................................... 24

References ..................................................................................................................................... 25

Appendix 1: Background questionnaire ........................................................................................ 32

vi

List of Tables

Table 1 ............................................................................................................................................. 7

Table 2 ........................................................................................................................................... 14

vii

List of Figures

Figure 1 ............................................................................................................................................ 9

Figure 2 .......................................................................................................................................... 16

Figure 3 .......................................................................................................................................... 17

Figure 4 .......................................................................................................................................... 17

Figure 5 .......................................................................................................................................... 18

Figure 6 .......................................................................................................................................... 19

Figure 7 .......................................................................................................................................... 20

viii

List of Appendices

Appendix 1 ..................................................................................................................................... 32

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Introduction 1The link between bilingualism and creativity has been the subject of much scientific inquiry over

the past 40 years. Behavioural studies suggest that bilinguals are in general more creative than

their monolingual counterparts (Ricciardelli, 1992b). What is perhaps most compelling is that

this bilingual advantage is apparent in childhood, as evidenced by performance on verbal and

nonverbal tests of divergent thinking (e.g. Garcia, 1996; Konaka, 1997; Stone, 1993). Studies

employing these divergent thinking tests, widely believed to measure several facets of creative

ability, have found that bilingual children display enhanced fluency (e.g. Carringer, 1974; Jacobs

& Pierce, 1966; Ricciardelli, 1992a), flexibility (e.g., Carringer, 1974; Konaka, 1997),

elaboration (e.g. Srivastava & Khatoon, 1980; Torrance, Gowan, Wu & Aliotti, 1970) and

originality (e.g. Cummins & Gulutsan, 1974; Konaka, 1997; Okoh, 1980), as compared to

monolingual children. Indeed, there is support that these childhood benefits persist throughout

the lifespan (Getzels & Jackson, 1962; Karapetsas & Andreou, 1999; Kharkhurin, 2008, 2009,

2010a), underscoring the potential long-term advantages of childhood bilingualism on creative

mentation. While there have been several behavioural findings to corroborate this bilingual

advantage in children, no studies to date have examined the brain mechanisms underlying this

phenomenon. Such an investigation would not only yield insights on the effects of bilingualism

on cognitive and creative development, but may also elucidate the processes and mechanisms

underlying creative cognition itself. Therefore, the impetus of the present investigation was to

identify and characterize the neurobiological basis of enhanced bilingual creativity.

1.1 Executive control and bilingualism

It has been well established that the regular use of two languages during childhood (and at all

ages, for that matter) bestows myriad advantages (Bialystok, 2001). Aside from the clear benefit

of having a second language at one’s disposal with which to communicate, the bilingual

experience appears to have a substantial impact on children’s cognitive functioning. This has

been exemplified by numerous studies, wherein bilingual children have been shown to

outperform their monolingual peers in various cognitive contexts, including the symbol-

reorganization task (Peal & Lambert, 1962), the number concept task requiring participant to

ignore misleading features (Bialystok & Codd, 1997), understanding object constancy (Feldman

& Shen, 1971), and spatial problems (Bialystok & Majumder, 1998). Much work has shown that

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in bilinguals the representational systems for both languages are simultaneously active in the

mind and accessible when either language is being spoken (Bialystok & Craik, 2010). In order to

resolve this conflict imposed by co-activation of languages, it is thought that executive functions

– a group of general-purpose control mechanisms used to manage one’s thoughts and actions –

are engaged during linguistic processing (Bialystok, 2011). Incidentally, this system is recruited

during nonverbal tasks demanding selection or conflict resolution as well (e.g. Miyake et al.,

2000). It is widely believed that continuous activation of this conflict management system during

speech production results in its functional enhancement, leading to better executive control not

only in language tasks but in non-linguistic tasks as well.

Of all the functions comprising the executive system, general consensus holds that bilingualism

preferentially enhances inhibitory function, the capacity to inhibit inappropriate thoughts and

behaviours, often in aid of selecting an appropriate alternative response (Bialystok, 2001; Green

& Bavelier, 2008). Support for enhanced inhibitory control in bilingual children was first

demonstrated by Bialystok (1999), who reported better bilingual performance on the dimensional

change card sort task. This original finding has since been substantiated by further research

employing varying methodologies (Bialystok & Martin, 2004; Bialystok & Viswanathan, 2009;

Martin-Rhee & Bialystok, 2008). Neuroimaging studies are also revealing how the brain may

bring about this cognitive plasticity in children. Clear language processing differences in the

brain between bilingual and monolingual individuals have already been well documented

(Abutalebi & Green, 2007). Indeed, mounting evidence suggests that bilingual language

processing has the capacity to induce both structural (e.g. Luk, Bialystok, Craik, & Grady, 2011;

Mechelli et al., 2004; Stein et al., 2012) and functional neuroplasticity (e.g. Bialystok et al.,

2005; Gold, Kim, Johnson, Kryscio, & Smith, 2013). In children, research has shown

bilingualism-related brain changes in linguistic processing (Rinker, Alku, Brosch, & Kiefer,

2010) and differences between monolinguals and bilinguals in white matter microstructure

(Mohades et al. 2012). Recently, Barac, Moreno, and Bialystok (submitted) provided further

support for bilingual brain plasticity, revealing evidence to corroborate enhanced cognitive

inhibition in children. Using electroencephalography to record neuroelectric responses during a

Go/No-Go task – used to assess children’s ability to inhibit a prepotent behavioural response –

Barac and colleagues found functional brain differences in EEG components related to inhibitory

control. Specifically, the researchers revealed shorter latencies for bilingual children on the N2

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component in the frontal region, in addition to a larger P3 mean amplitude. These results, though

preliminary, support the notion that better inhibition in bilingual children may be a consequence

of bilingualism-induced plasticity in networks for inhibitory control.

1.2 Creativity

It is thus apparent that early childhood bilingualism leads to precocious development of

cognitive abilities. This is underscored by the fact that bilingual children generally outperform

their monolingual counterparts on diverse cognitive tasks. Interestingly, among the many

cognitive advantages associated with bilingualism, none has puzzled researchers more than the

observation that bilingual children also tend to surpass monolingual children on tasks of

creativity.

Although several definitions of creativity have been put forward over the years, there is general

consensus that creativity involves the ability to produce novel, original ideas or works that are

both useful and appreciated by others (Sternberg & Lubart, 1996). Guilford (1967) proposed that

this ability required initiating periods of convergent and divergent thinking in a cyclical fashion.

Convergent thinking refers to a thought process used to come up with a single best answer to a

problem; conversely, divergent thinking is a thought process invoked to generate multiple

creative solutions. It was initially believed that both modes of thought differed in terms of

attentional demand, where convergent thinking requires a high degree of attentional resources,

while divergent thinking does not (e.g., Kasof, 1997), resulting in associative thought (e.g.

Koestler, 1964; Mednick & Mednick, 1967; Ward, Smith & Vaid, 1997). Although some of

these early views have since been refuted (see Dietrich, 2007), studies of divergent thinking have

nonetheless remained the gold standard in creativity research. Guilford (1967) characterized

divergent thinking according to four different traits: fluency, flexibility, elaboration and

originality. Fluency refers to the capacity to generate a multitude of ideas; flexibility is described

as the ability to evaluate several ideas at the same time; elaboration is viewed as the capacity to

produce increasingly complex ideas from simpler ones; and originality is regarded as the ability

to conceive ideas that deviate from the norm. Recent findings have shown that these traits can be

combined to yield two types of creative functioning, the generative and innovative capacities

(Kharkhurin & Samadpour Motalleebi, 2008; Kharkhurin, 2008, 2009). The generative capacity

refers to the ability to conjure up and elaborate upon a large number of remote ideas. The

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innovative capacity, on the other hand, is taken as the ability to generate both unique and

appropriate ideas. This distinction is noteworthy given that it draws many parallels to Sternberg

& Lubart's (1996) definition of creativity.

Although it has long been held that creative cognition relies upon the appropriate allocation of

attentional resources, empirical research supporting this proposition is still lacking. Indeed, only

a handful of studies to date have investigated the mechanisms of attentional control associated

with creative task performance. Based on the extant literature, however, there seems to be some

evidence that inhibitory processes may play a mediating role in creative ability (Dorfman,

Martindale, Gassimova, & Vartanian, 2008; Vartanian, Martindale, & Kwiatkowski, 2007).

Though preliminary, these findings may begin to elucidate the higher creativity observed in

bilingual children as compared to monolingual children. In fact, Kharkhurin (2011) recently

demonstrated for the first time the potential mediating role of inhibitory function in adult

bilingual creativity. By comparing moderately proficient bilinguals to highly proficiency

bilinguals, Kharkhurin revealed not only higher inhibitory skill in the highly proficient bilingual

group, but also a positive correlation between inhibitory function and creative performance

within this group. Despite the absence of a monolingual group in this study, Kharkhurin’s

findings nevertheless signal a potential relationship between enhanced inhibitory function and

higher creative potential in bilingualism.

1.3 Brain signal complexity

If inhibitory control does in fact play a role in creative functioning in monolingual and bilingual

speakers, it is a reasonable conjecture that the neurophysiological properties of the systems

governing inhibition function might also be related to creative cognition and behaviour.

Recently, there has been growing interest in studying moment-to-moment neural signal

complexity, i.e. transient temporal fluctuations in the signal, as a means of studying information

processing in the brain. It has become apparent that such fluctuations reveal considerable

information about network dynamics that average brain activity alone cannot provide (Garrett,

Kovacevic, McIntosh, & Grady, 2011; Vakorin, Lippé, & McIntosh, 2011). Computational work

has established via neural network modeling that information integration across widely

distributed neural networks is made possible by the spontaneous formation and dissolution of

correlated activity between network nodes over time and across multiple timescales (Deco, Jirsa,

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& McIntosh, 2011; Tononi, Edelman, & Sporns, 1998). It is these temporal dynamics of the

brain’s functional connectivity that result in the fluctuations observed in the acquired brain signal

(Breakspear, 2002; Freeman & Rogers, 2013; Friston, 2000; Honey, Kötter, Breakspear, &

Sporns, 2007). It has been proposed that functional networks with more configurations have a

greater capacity to dynamically explore and produce a more variable response (Deco et al., 2011;

Ghosh, Rho, McIntosh, Kötter, & Jirsa, 2008; Tsuda, 2001). As such, signal complexity may be

considered an index of the brain’s information processing capacity.

In a seminal empirical study by McIntosh, Kovacevic, & Itier (2008), complexity of single trial

event-related potentials was assessed and related to performance on a face memory task in

children and young adults. Counter-intuitively, complexity was found to increase rather than

decrease with age. Furthermore, this increase in complexity correlated negatively with

intrasubject reaction time variability and positively with performance accuracy. These results

lent support to the notion that maturation leads to greater signal variability, and thus greater

neural complexity. Indeed, during development, the brain becomes more differentiated and

specialized; however, with this comes a concurrent increase in widespread structural and

functional connectivity (Johnson, 2001). Therefore, as maturation progresses, the capacity of the

system to invoke more simultaneous processes at any instant increases, resulting in more

complexity in the brain’s activity (Friston, 1997). Critically, this complexity may be indicative of

a greater range of metastable brain states and easier transitions between them, resulting in more

stable behaviour. The authors further proposed that signal complexity might be used as a

measure of the brain’s cognitive capacity.

1.4 Purpose and hypotheses

Based on the findings described above, we sought to use brain signal complexity as a means to

investigate the neural mechanisms underlying creative performance in monolingual and bilingual

children. To this end, we first set out to examine how early childhood bilingualism affects brain

networks for inhibitory control. If bilingualism does indeed cause earlier development of

executive control functions (Bialystok, 2001; Carlson & Meltzoff, 2008; Kovács & Mehler,

2009), we reasoned that these enhanced cognitive abilities, particularly inhibitory control, would

be associated with greater complexity in corresponding brain networks. As such, we

hypothesized that bilingual children would exhibit greater complexity in inhibitory networks

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than monolingual children. Next, given preliminary research suggesting the role of cognitive

inhibition in creative performance (Dorfman et al., 2008; Stavridou & Furnham, 2006), we

looked at whether inhibitory network complexity was related to higher creativity in bilingual

relative to monolingual children. Our prediction was that greater inhibitory network complexity

would be associated with better creative performance.

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Methods 2The data in the present study were collected by Barac et al. (submitted).

2.1 Participants

A total of 38 children aged 4 to 6 were recruited for this study. The sample consisted of 19

bilinguals (8 females) and 19 monolinguals (12 females) matched on non-verbal IQ, age, and

socioeconomic status. Demographics are summarized in Table 1. Verbal consent was obtained

from children; parents provided written consent. Approval for the study was granted by the York

University Research Ethics Committee.

Table 1

Demographic characteristics in monolingual and bilingual participants

Monolinguals (n = 19) Bilinguals (n = 19)

Age (month) 65.3 (6.24) 65.95 (5.19)

Home language child speaks* 1.0 (0) 2.42 (1.07)

Home language child listens* 1.0 (0) 3.0 (1.41)

Socioeconomic status 3.63 (0.96) 3.53 (0.77)

Estimated full-scale IQ 105.5 (13.04) 107.37 (13.89) Values represent mean (SD)

Note: * indicates statistical significance (p < .05)

2.2 Background questionnaire

Parents filled out a background questionnaire (Appendix A). Information regarding their child’s

languages spoken, language proficiency and age of language acquisition, in addition to the

parent’s education, were gathered (see Table 1).

2.3 Test of creativity

Divergent thinking ability was assessed using the Thinking Creatively in Action and Movement

(TCAM, Torrance, 1981). Preschool-aged children (aged three to eight years) have limited

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capacities for expressing themselves through words or drawings. As such, rather than

communicate their thoughts through these modalities, they prefer to express themselves

kinaesthetically. The TCAM is thus appropriate as it relies on the kinaesthetic modality and does

not require verbal responses, although verbal responses are permissible. The TCAM assesses

aspects of divergent thinking – fluency, originality and imagination – through the following

movement and manipulation activities:

• Task 1: “How Many Ways?” asks the child to come up with different ways to move across

the floor

• Task 2: “Can You Move Like This?” has the child imitate the movements of an animal or a

tree

• Task 3: “What Other Ways?” entails having the child conjure up various ways of placing a

paper cup in a wastebasket

• Task 4: “What Might It Be?” requires the child to generate many uses for a paper cup

Scoring. Verbal and movements were recorded by the tester and rated according to the criteria

outlined in the scoring guide. Performance on tasks 1, 3 and 4 were used to measure fluency and

originality; task 2 was used to evaluate imagination. Fluency scores were calculated by tallying

the pertinent responses on each task. Responses were assigned 0 to 3 points for their originality;

this score was based on the statistical frequency of all responses. A 5-point Likert scale, which

ranged from “no movement” to “excellent; like the thing”, was used to obtain an imagination

score.

2.4 Stimuli and task procedure

Executive functioning was assessed using a Go/No-Go task where participants were instructed to

signal (Go trials) the presence of white geometric shapes and to inhibit a response to purple

geometric shapes (No-Go trials) (see Figure 1). The complete set of stimuli included the

following: a white triangle, a purple triangle, a white square and a purple square. Two different

shapes were used to circumvent potential repetition effects caused by identical color-shape

pairing. Images were randomly presented to participants on a computer screen in the Go/No-Go

protocol. Test blocks began with a prompt that provided instructions. For each trial, a black

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background with a white cross at the center appeared on the screen for 500 to 1,000 ms. A shape

was subsequently displayed for a maximum of 500 ms, followed by a post-stimulus interval of

500 ms. Participants were required to press a key on Go trials, and to withhold a response on No-

Go trials. Feedback on performance was not provided. Accuracy rates were measured for both

stimulus events; reaction times were recorded for Go trials only. A total of 200 stimuli were

presented to the participants (80% Go trials and 20% No-Go trials), and the task lasted 15 min.

Presentation of the stimuli was carried out using the Presentation software package (Presentation

12.00, Neurobehavioral Systems, U.S.A.).

Figure 1. Schematic diagram of trial structure and stimuli

2.5 EEG recording

Electroencephalography (EEG) is a non-invasive technique for recording electrical brain activity

from the scalp. Brain wave patterns captured by EEG are the direct result of thousands to

millions of neurons generating ionic current flows within and across cell assemblies. The EEG

predominantly reflects voltage fluctuations induced by excitatory and inhibitory postsynaptic

potentials from apical dendrites of a large number of similarly oriented, synchronized cortical

neurons. Single-neuron electrical recordings from outside the head are unfeasible, for the

10

electrical output is far too faint to be detected. However, activity across myriad localized neurons

overcomes this impediment, as simultaneous post-synaptic currents within neural ensembles

amount to considerably larger currents. Such ionic currents have the capacity to traverse the

conductive medium of the brain, reaching the skull, where EEG recording devices can detect it.

Once picked up by electrodes along the scalp, these signals are amplified and subsequently

stored in digital format on a computer. EEG is highly regarded for its ability to examine brain

activity with high temporal resolution, on the order of milliseconds. This methodology,

unfortunately, provides low spatial resolution. As such, brain electrical responses cannot be

localized to specific anatomical structures and circuits. With a dense enough electrode array and

sophisticated software, however, voltage patterns across the scalp may be ascribed to their

electrical sources within the brain, although the reliableness of this technique is still highly

questionable.

For the present study, EEG was continuously recorded while participants completed the Go/No-

Go task. Recordings were obtained using a Biosemi amplifier system (BioSemi Active 2,

Amsterdam) from 64 scalp electrodes embedded in a child-sized cap, placed in accordance with

the international 10-20 system. A Common Mode electrode was used for on-line recording. To

register eye movements and blinks, an electrooculogram (EOG) was measured from four

additional electrodes, one located at each of the outer canthi and below each eye. The EEG signal

was digitized at 512 Hz, and impedances were maintained below 20 µV.

2.6 EEG preprocessing

EEG recordings were pre-processed using the EEGLAB software (Delorme & Makeig, 2004).

Data were digitally filtered with a 0.1 to 50 Hz bandpass filter. A subset of electrodes was

excluded from the data analysis because of excessive artefacts (Fp1, Fpz, Fp2, T7, TP7, T8, TP8,

Iz). Fifty-eight active electrodes remained for all participants. The continuous EEG was

segmented into Go and No-Go (-200 to 1400 ms) stimuli events. Trials containing excessive

amplitudes were removed, with amplitude threshold adjusted on a participant-by-participant

basis to include a minimum of 80% target stimuli. Thresholds ranged from 300 to 400 µV.

Further artefact removal was performed using independent component analysis (ICA). ICA

decomposition was performed on the remaining concatenated trials. Each component was

examined using its topography, power spectrum, and activity over time and trials. Components

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carrying ocular artefacts were removed from the data set. The number of rejected ICA

components was not statistically different between groups. Only correct trials were considered in

the analyses. The number of trials contributing to MSE estimation for the bilingual children was

129.94 (SD = 17.70) for the Go trials and 32.44 (SD = 4.52) for the No-Go trials. Likewise, the

number of trials contributing to MSE estimation for the monolingual children was 120 (SD =

15.72) for the Go trials and 29.56 (SD =5.99) for the No-Go trials. There were no significant

differences between the two language groups on either the number of Go or No-Go trials.

2.7 Multiscale entropy analysis

Multiscale entropy (MSE) was used to compute post-stimulus signal complexity at various

timescales. The algorithms needed to calculate these entropy measures are provided online at

www.physionet.org/physiotools/mse/. MSE analysis (Costa, Goldberger, & Peng, 2005) is a two-

step process. First, the EEG time series undergoes coarse-graining (down-sampling), which

generates several time series by averaging data points within non-overlapping windows of

increasing length, τ. This coarse-graining process is done across both trial and condition.

Individual elements comprising the coarse-grained time series, j, are computed using the

following equation (Equation 1):

(1)

τ is also referred to as the scale factor. With this value, the length of a coarse-grained time series,

N/τ, can be determined. The original time series constitutes the first timescale. In the second step

of the MSE analysis, sample entropy (SE) (Richman & Moorman, 2000) is quantified for each

scale. This measure provides an index for the appearance of repetitive patterns in the signal. The

equation for performing this step is provided below (Equation 2):

(2)

For a given time series, this formula computes the conditional probability that any two sequences

of m + 1 data points will match provided the first m points match. Sample entropy is the natural

12

logarithm of this value and provides an index of the variability of the time series. In our study,

the parameters m and r will be set to 2 and 0.5, respectively. That is, two consecutive data points

will be used for pattern matching and data points will be treated as equal if the absolute

difference between them is less than 50% of the time series standard deviation. A channel

specific MSE estimate was obtained for each participant as a mean across trial entropy measures

for scales 1 to 14. Sample entropy was not calculated for scales greater than 14, as the number of

time points within the corresponding downsampled time series were insufficient (<50 time

points) for reliable entropy estimation.

2.8 Statistical analysis

The behavioural data from the Go/No-Go task were examined with regards to reaction time (RT)

on Go trials, accuracy (percent correct responses) on both conditions and sensitivity (d`). The

measure of accuracy for Go and No-Go performance was defined, respectively, as the number of

hits divided by the total number of Go stimuli and the number of correct rejections divided by

the total number of No-Go stimuli expressed as a percentage. D-prime values were calculated as

the difference between the z-transformation of the hit rate (correct “Go” response for “Go”

items) and the z-transformation of the false alarm rate (incorrect “Go” response for “No-Go”

items). Two-way mixed analyses of variance (ANOVAs) were performed separately for each

behavioural measure, with language group as a between-participant factor and condition (Go and

No-Go) as a repeated within-participant factor. A two-way mixed ANOVA was conducted on the

TCAM scores, with language group as a between-participant factor and divergent thinking trait

(fluency, imagination and originality) as a within-participant factor. Where sphericity

assumptions were violated, probability estimates are based on Greenhouse-Geisser corrections.

Statistical assessment of language group and condition on MSE scores was conducted by partial

least squares (PLS) (Krishnan, Williams, McIntosh, & Abdi, 2011; Lobaugh, West, & McIntosh,

2001; McIntosh, Bookstein, Haxby, & Grady, 1996). PLS is a multivariate statistical technique

that identifies a set of latent variables (LVs) that account for maximum covariance between brain

activity and experimental design (task PLS) or behavioural measurements (behaviour PLS). Task

PLS was carried out on data matrices consisting of participant and channel specific measures.

Participants by language group were designated to rows, and post-stimulus measures for MSE

estimation by channel were assigned to columns. Matrices were then mean-centered within a

13

group. That is, in mean-centered task PLS, the task means are represented as the deviation

around the grand mean (computed within each group) for each electrode and time point. The data

are averaged within a condition and then these task means are averaged to obtain a grand mean.

This grand mean is subtracted from the task means to obtain mean-centered values for each

electrode and time point. The mean-centered matrices were decomposed with singular value

decomposition (SVD) to identify the strongest condition differences and the corresponding scalp

topography. This produced a set of orthogonal latent variables (LVs). Each LV comprises a

“brain LV” (the brain portion of the LV) and a “design LV” (design portion of the LV). The

brain LV denotes the weighted linear combination of electrode sites and time points that co-vary

with the design LV pattern. Projecting the brain LV onto each participant’s EEG data by

condition yields scalp scores, which can be positive or negative, depending on the relation

between electrode/time and design LV. For behaviour PLS analyses, correlations were computed

between the TCAM scores (fluency, originality, imagination) and MSE estimates across the

entire sample.

Effect significance for the association between task conditions and brain response across

language groups (task PLS), and between brain and behaviour performance (behaviour PLS),

were determined using permutation tests (500). Using 500 bootstrap samples, bootstrap

estimation of confidence intervals was used to assess the accuracy of the scalp topographies.

Bootstrap ratios were subsequently obtained by dividing singular vector weights for each channel

by bootstrap estimated standard error.

Univariate analyses were used to further probe group differences in MSE. To that end, we

selected and clustered electrodes at which the contrast across conditions was most stable, as

determined by bootstrapping, following task PLS. Mixed ANOVAs were conducted at these sites

to evaluate localized MSE differences between groups for each task condition. Group (2 levels;

bilingual, monolingual) was the between-subjects factor while temporal scale (3 levels; 1, 7, 14)

served as the within-subjects factor. Greenhouse-Geisser corrections were used where sphericity

assumptions were violated. Multiple pairwise comparisons were adjusted with Bonferroni

corrections to control Type 1 error inflation.

14

Results 3

3.1 Behavioural performance: Effects of bilingualism on executive and creativity performance

Behavioural findings are summarized in Table 2. A one-way ANOVA revealed no significant

group difference for reaction time on Go trials (F<1); however, it did find a near significant

effect for d’ (F1,37 = 3.72, p = 0.06) suggesting bilingual children’s better performance (Fig. 2).

To investigate accuracy rates on the task, a two-way mixed measures ANOVA with group as a

between-subjects factor and condition (Go, No-Go) as a within-subjects factor was carried out.

There was a near significant main effect of group (F1,36 = 3.72, p = 0.06), but no main effect of

condition (F<1). There was no significant interaction (F < 1) (Fig. 3). Bilingual children tend to

score more accurately than monolingual children in both conditions.

Analysis of the TCAM scores revealed no group effect (F1,36 = 2.06, p = 0.16), nor group x trait

interaction (F2,72 = 0.34, p = 0.72) (Fig. 4).

Table 2

Behavioural performance on Go/No-Go task by language group

Monolinguals (n = 19) Bilinguals (n = 19)

Percentage correct Go trials 75.66 (9.51) 80.76 (12.01)

RT mean go trials 658.04 (86.76) 636.73 (57.13)

Percentage correct No-Go trials 77.11 (14.05) 82.11 (12.62)

Discriminability index (d’) 1.58 (0.57) 2.05 (0.89)

Values represent mean (SD)

Note: * indicates statistical significance (p < .05)

3.2 Task PLS results: Effects of cognitive control on brain signal complexity

The task PLS analysis examined signal complexity across the whole scalp between the two task

conditions across all participants. A single LV was significant with p = 0.004 (see Fig. 5). The

topographic plot depicts the spatiotemporal distribution of this contrast. The spatial distribution

15

of the contrast was particularly stable at electrodes over right frontal and occipital regions for Go

trials, and stable at electrodes over the left central parietal region for No-Go trials.

3.3 Task PLS results: Effects of bilingualism on brain signal complexity

The task PLS examined MSE between the two task conditions with two language groups. No LV

was significant. One limitation of PLS is that it uses mean-centered values of each group (i.e.,

deviations from a grand mean within a group) and therefore may not be an optimal method to

compare groups. Group differences were thus further explored by univariate analyses of MSE

values in each condition, within left central parietal and right frontal regions where the contrast

across conditions was most stable. To this end, we averaged MSE between electrodes C3 and

CP3 (i.e., CP3/C3 cluster), and between F4 and F6 (i.e., F4/F6 cluster). MSE values are depicted

across all temporal scales in Figures 6 and 7. A group x temporal scale ANOVA on the CP3/C3

cluster revealed that Go MSE was significantly higher in monolinguals (F1,36 = 5.08, p = 0.03). A

significant group x timescale interaction was also found (F2,72 = 6.25, p = 0.003): the two groups

showed similar MSE values in temporal scale 1 but differed in later scales. Indeed, post hoc tests

revealed higher MSE at temporal scale 14 in monolinguals than in bilinguals (p = 0.007).

Analysis of No-Go MSE found no group effect (F1,36 = 2.78, p = 0.1), but did reveal a significant

interaction between group and temporal scale (F2,72 = 3.49, p = 0.04) suggesting that the groups

showed different patterns of MSE across temporal scales. Post hoc analysis did not reveal any

significance group differences.

A group x temporal scale ANOVA run on Go MSE data in the F4/F6 cluster showed no group

effect (F1,36 = 0.47, p = 0.5); however, it did reveal a significant group x temporal scale

interaction (F2,72 = 12.03, p < 0.001). Post hoc tests determined that MSE was higher for the

bilingual group at temporal scale 1 (p = 0.01), but higher for the monolingual group at temporal

scale 14 (p = 0.008). An ANOVA with No-Go MSE values revealed a similar pattern of results:

a significant interaction (F2,72 = 11.23, p < 0.001) but no group effect (F1,36 = 0.14, p = 0.71).

Post hoc analysis showed higher MSE at scale 1 for bilinguals (p = 0.01) and higher MSE at

scale 14 for monolinguals (p = 0.01).

16

3.4 Behaviour PLS: Relationship between divergent thinking ability and brain signal complexity

The behavioural PLS analysis was first used to assess the relationship between divergent

thinking ability and brain signal complexity across all participants. We correlated participants’

TCAM scores with their MSE values for both task conditions. No significant LVs were

identified. Then, the behaviour PLS was run within each group separately; we failed to observe

any significant relationships within either group.

Figure 2. Group mean d-prime scores with corresponding standard errors.

0.00#

0.50#

1.00#

1.50#

2.00#

2.50#

d"prim

e( Bilinguals#

Monolinguals#

17

Figure 3. Group mean Go/No-Go task accuracy with corresponding standard errors.

Figure 4. Group mean TCAM trait scores with corresponding standard errors.

60.00$

65.00$

70.00$

75.00$

80.00$

85.00$

90.00$

Go$ No,Go$

Accuracy'(%

)'

Condi0on'

Bilinguals$

Monolinguals$

60.00$

70.00$

80.00$

90.00$

100.00$

110.00$

120.00$

Fluency$ Originality$ Imagina9on$

Score&

TCAM&Trait&

Bilinguals$

Monolinguals$

18

Figure 5. Contrasting the MSE response to Go vs. No-Go trials across all subjects. The bar

graph depicts the contrast between the conditions, which was significantly expressed across the

entire data set as determined by permutation tests and bootstrapping. The image plot highlights

the electrodes at which the contrast across conditions was most stable as determined by

bootstrapping. The head plot illustrates the spatial distribution for a representative timescale.

Values represent ~z scores and negative values denote significance for the inverse condition

effect. Red: Go trials; Blue: No-Go trials.

19

Figure 6. Group-related differences in multiscale entropy across temporal scale and task

condition for channel cluster C3-CP3. Group means for MSE estimates are shown, along with

corresponding standard errors.

0.00#

0.10#

0.20#

0.30#

0.40#

0.50#

0.60#

0.70#

0.80#

0.90#

1.00#

1# 2# 3# 4# 5# 6# 7# 8# 9# 10# 11# 12# 13# 14#

Sample'En

trop

y'

Temporal'Scale'

Go'Condi4on'

Bilingual#

Monolingual#

0.00#

0.10#

0.20#

0.30#

0.40#

0.50#

0.60#

0.70#

0.80#

0.90#

1.00#

1# 2# 3# 4# 5# 6# 7# 8# 9# 10# 11# 12# 13# 14#

Sample'En

trop

y'

Temporal'Scale'

No1Go'Condi6on'

Bilingual#

Monolingual#

20

Figure 7. Group-related differences in MSE across temporal scale and task condition for channel

cluster F4-F6. Group means for MSE estimates are shown, along with corresponding standard

errors.

0.00#

0.10#

0.20#

0.30#

0.40#

0.50#

0.60#

0.70#

1# 2# 3# 4# 5# 6# 7# 8# 9# 10# 11# 12# 13# 14#

Sample'En

trop

y'

Temporal'Scale'

Go'Condi4on'

Bilingual#

Monolingual#

21

Discussion 4Bilingual and monolingual children completed a series of tasks measuring executive control,

namely inhibitory function, and creative ability. Electrical brain activity recorded during the

executive task was contrasted between both groups of children to test for differences in

inhibitory network complexity. These complexity estimates were subsequently correlated with

children’s creativity scores to assess the association between inhibitory network efficiency and

creative output. The behavioural results (d’ and accuracy) from the go/no-go task found a

marginal advantage of bilingualism in inhibitory control. Our results of signal complexity

revealed differences between bilingual and monolingual children over localized regions of the

scalp. These brain differences, however, were not found to be associated with the children’s

performance on the creativity task. Each of these findings will be discussed in turn.

4.1 Executive and creativity performance

We assessed aspects of executive control in young bilingual and monolingual children with a

Go/No-Go paradigm, in which children were required to withhold a habitual response to

infrequent stimuli. In this way, we were able to investigate the putative effects of early

bilingualism on executive functioning. The marginal main effect of language group on d’ showed

a bilingual advantage on task performance, in that bilingual children had less difficulty in

accurately identifying the task stimuli than monolingual children. Moreover, overall accuracy on

the Go/No-Go task was marginally significantly higher in the bilingual children. Reaction time

was no different between groups, however. Failure to reach statistical significance for the d’ and

overall accuracy measures in particular may be accounted for by a few possibilities. First, lack of

power may have precluded us from observing any significant differences in our sample.

Secondly, the bilingual children’s language proficiency may not be high enough to have

conferred any cognitive benefits (Cummins, 1976). Unfortunately, we did not have a proficiency

measure to reconcile this issue. Alternatively, there is some literature showing that both

monolingual and bilingual children are equally accurate at inhibiting a prepotent response (e.g.

Bialystok & Martin, 2004; Martin-Rhee & Bialystok, 2008). Instead, it has been argued that

superior bilingual cognitive performance is linked to enhanced interference suppression, another

component of inhibitory control, which refers to the ability to ignore irrelevant information in

favour of selecting a target. Subsequent research should make efforts to address these concerns.

22

Analysis of the children’s creative performance revealed that bilingual and monolingual children

performed equivalently. This observation challenges our initial hypothesis, which predicted that

the former group would exhibit higher creativity. One reason we may have failed to observe any

significant differences in creative performance may again be related to levels of language

proficiency in our bilingual sample. This interpretation is in line with results obtained by

Ricciardelli (1992a), who found evidence to support a relationship between bilingual language

proficiency and enhanced creativity in children. In comparing high- and low-proficient bilingual

children with monolingual children, Ricciardelli noted higher creativity only in the high

proficient group, compared to the monolingual group. This phenomenon has recently been

observed in adult samples as well (Kharkhurin, 2010a, 2010b). Alternatively, we may not have

observed a group difference because of the creative task we employed. It has been argued that

many of the tasks currently in use mix both divergent and convergent processes, which may rely

on different cognitive mechanisms (Hommel, Colzato, Fischer, & Christoffels, 2011). It is

possible that the task we opted for, the TCAM, does not exclusively tap divergent thinking

processes, as previous literature claims, thus obscuring a potential significant relationship

between enhanced inhibitory function and better divergent thinking ability in bilingualism. Our

inability to reproduce previous findings is not entirely unexpected, as Ricciardelli (1992a) found

a number of studies that did not find divergent-thinking advantages in bilingual individuals.

Presumably, varying results within the field are related to a lack of conceptual clarity as seen

here, in addition to prodigious methodological and sample heterogeneity. Future research will no

doubt need to refine current conceptionalizations of creativity and its measurement before the

link between bilingualism and creativity can be reliably assessed, at least from a behavioural and

cognitive perspective.

4.2 Brain signal complexity, bilingualism and creativity

Using multiscale entropy, we found a robust dissociable scalp topography between task

conditions. Go trials were associated with greater MSE in right frontal and occipital leads; No-

Go trials, with greater MSE in left centro-parietal leads. Furthermore, these contrast patterns

were consistent across all timescales. Vakorin et al., (2011) recently showed a relationship

between MSE timescale and local versus distributed information processing. Specifically, the

researchers found that finer and coarser temporal scales were linked to local and distributed

23

information processing, respectively. Our findings thus suggest that Go and No-Go task demands

increase information processing across both local and distributed regions.

An obvious drawback to our MSE data, however, lies in its low spatial resolution. Without

running a source analysis, it is difficult to comment on the exact brain structures responsible for

generating these scalp topographies. Drawing on studies of response inhibition using high-spatial

resolution neuroimaging techniques, and work using source analyses with high-density

electrophysiological data (e.g. Bunge, Dudukovic, Thomason, Vaidya, & Gabrieli, 2002;

Ciesielski, Harris, & Cofer, 2004), we hold that the neural signals obtained at these electrodes

originate from nearby structures. In doing so, the topographies obtained from our analyses

strongly suggest the involvement of executive control networks implicated in Go and No-Go

performance. Higher MSE over right frontal and occipital regions may signify increased top-

down attentional control of visual processing during Go trials. Interestingly, greater MSE in the

No-Go condition was confined to an area over the left centro-parietal cortex, possibly indicative

of functions related to attention shifting (Behrmann et al., 2004). This lends support to the

proposition that No-Go related activity recruits more posterior regions during childhood, but

with development, engages more frontal areas (Bunge et al., 2002). Source analyses of our own,

however, will be required to fully substantiate these claims.

Our local analyses within left centro-parietal and right frontal scalp regions revealed significant

group differences, although the patterns of results were not as we expected. In the centro-parietal

region, Go-related complexity was higher in monolinguals than in bilinguals. We propose that

this observation may still be related to precocious development of executive networks in

bilingual children. As described above, development may bring with it more recruitment of

frontal regions (Bunge et al., 2002); if bilingualism does indeed lead to faster brain development,

lower complexity in the centro-parietal region in bilingual children may be related to the

spreading of brain activity over a more distributed fronto-parietal network. Our complexity

findings from the right frontal scalp region may also lend support to the notion of faster brain

development as a result of early bilingualism. Although complexity was higher at the last

temporal scale in monolinguals compared to bilinguals, the reverse was observed at the first

temporal scale, whereby bilinguals were characterized by higher complexity. As described by

Vakorin et al. (2011), greater complexity at finer temporal scales may be indicative of greater

neural specialization.

24

Lastly, we did not manage to find any significant relationship between signal complexity in

inhibitory networks and creative performance. As we noted previously, this may be due to the

fact that bilinguals were not proficient enough in both their languages to have gained any

cognitive advantage over their monolingual counterparts. Alternatively, if response inhibition is

in fact not the mediating mechanisms underlying enhanced bilingual creativity, it follows that its

corresponding brain activity would not be linked to creative performance either.

4.3 Conclusions and future directions

In the present study, we found brain complexity differences between bilingual and monolingual

children in regions associated with executive control. These brain differences, however, were not

related to enhanced creative performance. As an extension of this work, the same data set could

be further analyzed using more traditional mean amplitude and spectral power approaches and

subsequently correlated with our creativity scores. This may be instructive on account that these

measures reflect properties of brain function different from those revealed by MSE. Future

research aiming to characterize the link between bilingualism and creativity should consider

language proficiency as well as the specific inhibitory control components (i.e. response

inhibition vs. interference suppression) that may be affected by bilingualism. Further, it may be

informative to record brain activity while bilingual and monolingual children engage in a

creativity task, thus providing a more direct window into the brain dynamics linked to creative

cognition. Doing so will provide a more comprehensive understanding of the effect of early

bilingualism on the specific cognitive and neural mechanisms underlying creative ability.

25

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Appendix 1: Background questionnaire

Version 2 (10/1/2012) Page 1 of 6

Smarter Kids

Language and Social Background Questionnaire To Be Completed by Parents

Today’s date: _________________ Completed by: Mother Father Other ___________

PART A:

The following information refers to the CHILD:

Participant ID: _____________

Date of birth (Day/Month/Year):________________________________ Gender: _____________

Country of birth: __________________________ Handedness: _____________________

If not born in Canada, when did your child come to Canada? (Month/Year) ________________

The following information refers to PARENTS:

Country of birth of MOTHER: ___________________

If not born in Canada, when did the mother come to Canada? (Month/Year) ________________

What language(s) does the mother speak? (please list) _________________________________

_____________________________________________________________________________

_____________________________________________________________________________

Country of birth of FATHER: ___________________

If not born in Canada, when did the father come to Canada? (Month/Year) _________________

What language(s) does the father speak? (please list) __________________________________

_____________________________________________________________________________

_____________________________________________________________________________

33

Version 2 (10/1/2012) Page 2 of 6

Please indicate the highest level of education and occupation for each parent:

Mother Father

_____No high school diploma _____No high school diploma

_____High school graduate _____High school graduate

_____Some college or college diploma _____Some college or college diploma

_____Bachelor’s Degree _____Bachelor’s Degree

_____Graduate or professional degree _____Graduate or professional degree

Occupation: ___________________ Occupation: ___________________

PART B:Please answer the following questions about your child’s language abilities:

1. a) What language did your child first speak?

English Other language (Please specify: _________________ ) Both

2. a) Where did your child learn English?

Home School Community Other: __________________________________

b) Where does your child speak English now (circle all that apply)?

Home School Community Other: ____________________________________

3. a) Does your child understand any language other than English? Yes No

If Yes, what language(s)? ________________________________________________________

How would you rate your child’s understanding of the other language? Excellent Fair Poor

How would you rate your child's understanding of English? Excellent Fair Poor

b) Does your child speak any language other than English? Yes No

If Yes, what language(s)? ________________________________________________________

34

Version 2 (10/1/2012) Page 3 of 6

How would you rate your child’s speaking ability of the other Excellent Fair Poor language?

How would you rate your child’s speaking ability in English? Excellent Fair Poor

4. a) If your child speaks any other language, other than English, where did your child learn the other

language?

Home School Community Other: __________________________ N/A

b) At what age did your child begin learning the other language(s)? ____________________________

c) How often does your child speak the other language(s) now?

Daily Weekly Monthly Occasionally Other: _____________ N/A

d) Where does your child speak this other language(s) (circle all that apply)?

Home School Family Other: _________________________ N/A

5. a) Does your child attend any language or school program other than regular school? Yes No

If Yes, what program? __________________________________________________________

How often does your child attend this program?

Everyday Once a week Other:__________________________

When did your child begin this program? ____________________________________________

35

Version 2 (10/1/2012) Page 4 of 6

PART C:

We would like to know more about the languages used in your home. Please think about a typical week in your child’s life. Please fill out the percentage of time that he/she uses English and any other language(s) when engaged in the following activities: Note: this information may pertain to child OR parents. Please read carefully. English Second Language

(please specify): ______________

Third Language (please specify): _______________

Total

Language spoken by your child to parents

= 100%

Language spoken by child to siblings

= 100%

Language spoken by child to other family members

= 100%

Language spoken by child to friends

= 100%

Language spoken among adults at home

= 100%

Language spoken by parents to your child

= 100%

Language spoken by other family members to your child

= 100%

Language in which your child watches TV/videos

= 100%

Language in which parents watch TV/videos

= 100%

Language in which parents and child read stories together

= 100%

Language in which parents read Newspaper/Books

= 100%

Language child uses during extra-curricular activities (e.g., sports, music lessons etc.)

=100%

Overall estimate of your child’s language environment

= 100%

36

Version 2 (10/1/2012) Page 5 of 6

PART D:

We would like to know more about your child’s musical experience. Please answer the following questions.

1. Has your child ever taken music lessons? Yes No

If YES please answer the following:

Private Lessons _______ Group Lessons ______ School/day care Instruction ______ Voice ______ Instrument (Please specify: ) Date lessons began: Number of lessons per week: Length of each lesson: Does your child practice at home between lessons? Yes No

How many minutes does he/she practice each day? ________ minutes Total amount of musical training (months)

* Please count one full year of lessons as 10 months (September to June) 2. Does your child listen to music at home? Yes No

If YES, what kind of music does your child prefer to listen to?

3. Is either parent a musician (i.e., professional or hobby) Yes No

If YES, please describe (i.e which instrument, how many times per week are you playing?).

37

Version 2 (10/1/2012) Page 6 of 6

PART E:

We would like to know more about your child’s visual art experience. Please answer the following questions.

1. Has your child ever taken art lessons? Yes No

If YES please answer the following:

Private Lessons _______ Group Lessons ______ School/day care Instruction ______ Medium (e.g. drawing, painting, pottery, etc.) Date lessons began: Number of lessons per week: Length of each lesson: Does your child practice at home between lessons? Yes No

How many minutes does he/she practice each day? ________ minutes Total amount of visual art training (months)

* Please count one full year of lessons as 10 months (September to June) 2. Is either parent an artist? (i.e., professional or hobby) Yes No

If YES, please describe.