Post-print of the manuscipt published in Neuropsychologia https://doi.org/10.1016/j.neuropsychologia.2020.107528
Active bilingualism delays the onset of mild cognitive impairment
Marco Calabria 1,2, Mireia Hernández 3, Gabriele Cattaneo 4, 5, Anna Suades 6, 7, Mariona Serra 1,
Montserrat Juncadella 6, 7, Ramón Reñé 7, Isabel Sala 8, Alberto Lleó 8, Jordi Ortiz-Gil 9, 10,
Lidia Ugas 9, 11, Asunción Ávila 12, Isabel Gómez Ruiz 12, César Ávila 13, and Albert Costa 1, 14
1. Center for Brain and Cognition, Pompeu Fabra University, Barcelona, Spain
2. Faculty of Health Sciences, Universitat Oberta de Catalunya, Barcelona, Spain
3. Cognition and Brain Plasticity Group, Department of Cognition, Development, and
Educational Psychology, Institut de Neurociències, Universitat de Barcelona, Barcelona,
4. Institut Guttmann, Institut Universitari de Neurorehabilitació Adscrit a la UAB, Badalona,
5. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
6. ENTIA, Fundació de Neurorehabilitació i Recerca Cognitiva, Barcelona, Spain
7. Functional Dementia Unit, Bellvitge University Hospital, L'Hospitalet de Llobregat, Spain
8. Memory Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona,
9. Hospital General de Granollers, Barcelona, Spain
10. FIDMAG Sisters Hospitallers Research Foundation, Barcelona, Spain
11. Hospital Benito Menni, CASM, Sant Boi de Llobregat, Barcelona, Spain
12. Neurology Department, Hospital Moisès Broggi, Consorci Sanitari Integral, Sant Joan
Despí/L’Hospitalet, Barcelona, Spain
13. Neuropsychology and Functional Imaging Group, Department of Basic Psychology,
Clinical Psychology, and Psychobiology, Universitat Jaume I, Castellón, Spain
14. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
Faculty of Health Sciences
Universitat Oberta de Catalunya
Rambla del Poblenou, 156
Phone: (34) 934 505 418
Post-print of the manuscipt published in Neuropsychologia https://doi.org/10.1016/j.neuropsychologia.2020.107528
Lifelong bilingualism may contribute to cognitive reserve (CR) in neurodegenerative diseases as
shown by a delay of the age at symptom onset in bilinguals with Alzheimer’s disease (AD) and Mild
Cognitive Impairment (MCI). However, some studies have failed to show this bilingual advantage,
suggesting that it might depend on the type and degree of bilingualism. In the present study, we tested
the hypothesis that active bilingualism, defined as the continuous use of the two languages as opposed
to second language exposition only, may protect against cognitive decline. Moreover, we investigated
whether bilingualism as a CR factor may be explained by an advantage within the executive control
To do so, we collected clinical measures (age at onset of cognitive symptoms, age at the first
medical visit for cognitive impairments, and age at diagnosis) in patients with MCI and patients with
AD with different degrees of language experience and usage of Catalan and Spanish. Additionally, all
participants were tested on four EC tasks and one long-term memory recognition task.
First, results from multiple regression analyses showed that active bilingualism was a
significant predictor of delay in the age at onset for all the clinical measures in MCI, but not AD
patients. Second, the effect of active bilingualism was independent of occupation, educational level
and job attainment across the individuals’ lifespan. Finally, although we did not find an effect of
active bilingualism across all EC tasks, we did find an effect for conflict resolution.
These results are discussed in the context of CR hypotheses, suggesting that compensatory
mechanisms may play a role in protecting against cognitive decline.
Keywords: Alzheimer’s disease, bilingualism, cognitive reserve, cognitive resilience, mild
cognitive impairment, executive control
Post-print of the manuscipt published in Neuropsychologia https://doi.org/10.1016/j.neuropsychologia.2020.107528
The prevalence of dementia in countries with bilingual or multilingual speakers is half that of
countries where populations use only one language to communicate (Klein, Christie, & Parkvall,
2016). While other social and environmental factors might also contribute to such a stark difference in
prevalence, this worldwide study seems to support the idea that lifelong usage of two languages may
be an important factor in enhancing cognitive reserve. In fact, about 60% of studies on this
phenomenon have shown that bilingualism may delay the onset of dementia, or impart some
advantage in memory and/or executive functioning (Calvo, García, Manoiloff, & Ibáñez, 2016). In
spite of this, whether or not bilingualism confers any benefit in age-related disorders remains a hotly
debated topic. On the one hand, the linguistic profile of a bilingual, i.e language proficiency
(Bialystok & Feng, 2009; Xie, 2018) and usage (de Bruin, 2020), as well as other cognitive reserve
factors, i.e.- social activity (Evans et al., 2018; Scarmeas & Stern, 2003) and occupation (Darwish,
Farran, Assaad, & Chaaya, 2018), might act to highlight or, conversely, mask such a bilingual
advantage. On the other hand, the incomplete understanding of what drives the effect (if any) (Bak,
2016) makes it more difficult to define the outcome measures and thus increases the probability of
In the present study, we focused on these two aspects of bilingualism (i.e.- language proficiency
and usage) as cognitive reserve factors in Mild Cognitive Impairment (MCI) and Alzheimer disease
(AD). First, we approached the issue of defining bilingualism by using a continuum from passive to
active bilingualism, instead of simply classifying participants as monolinguals or bilinguals (for a
discussion see Bak, 2016). This methodological approach allows researchers to test participants who
have had diverse linguistic experiences––with variation in their second language (L2) usage, years of
L2 exposure, and age of L2 acquisition–– but who may have been living in the same geographic
location; this technique, thus, reduces the potential confounding effects of individual differences in
Second, to better understand the origin of the bilingual advantage, we tested participants on
several cognition tasks. We focused more on the executive control (EC) system as its efficiency in
bilinguals has been proposed to be the origin of said advantage (Bialystok, 2011). To this end, we
used a set of EC tasks that covered shifting, updating abilities and inhibitory control. We also
explored episodic memory due to its pertinence as one of the cognitive domains typically altered by
the onset of MCI and AD. Indeed, one might speculate that a greater efficacy of memory functions in
bilingual patients may be responsible for the delay in onset of symptoms, as proposed in some studies
(e.g. Bak, Nissan, Allerhand, & Deary, 2014; Perani et al., 2017; Rosselli et al., 2019).
1.1. What linguistic profile variables might modulate the bilingual advantage on
The existence of a bilingual advantage in non-linguistic processes, especially EC, has been
brought into question by many studies (de Bruin, Treccani, & Della Sala, 2015; Lehtonen et al., 2018;
Paap & Greenberg, 2013; Paap, Johnson, & Sawi, 2015, 2016) and some researchers have proposed
that it may depend on the type of bilingualism that is being considered (van den Noort et al., 2019).
One key aspect of experimental design that could lead to discrepancy in the literature is that
bilingualism cannot be considered as a categorical variable (Luk & Bialystok, 2013) and research has
suggested that adults’ language profiles may modulate the effects on non-linguistic control.
For instance, individuals with a higher rate of language switching in everyday life showed
reduced switch costs associated with reconfiguring a given task in a task-switching paradigm as
compared to speakers with lower rates of language switching and monolinguals (Prior and Gollan,
2011). Similarly, bilinguals who suffer less cross-language interference (less intrusions from the non-
target language) score better on EC tasks concerning inhibitory control and divided attention than
those bilinguals who make more cross-language intrusion errors and thus demonstrate poorer
language control (Festman & Münte, 2012; Festman, Rodriguez-Fornells, & Münte, 2010; Rodriguez-
Fornells, Krämer, Lorenzo-Seva, Festman, & Münte, 2012). This finding supports the idea that
linguistic and non-linguistic systems share similar control mechanisms; therefore, switching more
often between two languages could transfer its effects to non-linguistic switching abilities by reducing
the cost associated with conflict resolution (Verreyt, Woumans, Vandelanotte, Szmalec, & Duyck,
2016; but see also Jylkkä et al., 2017).
Other factors that might shape the bilingual advantage are language usage, language proficiency
and age of L2 acquisition (Yow & Li, 2015). Despite bilinguals having been shown to hold an
advantage in conflict resolution and task switching, the individual differences in language profiles
within a given group of bilinguals could modulate the magnitude of the task-associated costs. For
instance, Bak, Vega-Mendoza, and Sorace (2014) found that early bilinguals had a greater reduction
in switch costs compared to late bilinguals, whereas Tao et al. (2011) showed that late bilinguals with
similar use of the two languages had the greatest advantage in conflict resolution, whereas early
bilinguals demonstrated enhanced monitoring processes (see also Singh & Mishra, 2013). Similarly,
language usage has been shown to have a modulatory effect on the aging-related EC decline in
bilinguals. For example, in bilinguals who were balanced in terms of language usage, the magnitude
of interference in a conflict task did not increase (Goral, Campanelli, & Spiro, 2015), suggesting a
protective effect of these variables associated to bilingualism.
However, the role of these separate linguistic variables in contributing to cognitive benefits for
bilingual patients with neurodegenerative diseases has not been explored yet and is arguably a better
way to investigate this issue, rather than subscribing to the monolingual-bilingual dichotomy.
Moreover, some of these variables are highly correlated; therefore, it is important to explore their
degree of contribution to the cognitive benefits associated with bilingualism. To do this, our study
considered a composite score of bilingualism (DiStefano, Zhu, & Mîndrilã, 2009). that was calculated
based on the weighted contribution of each variable, allowing us to obtain an individual score
indicating the degree of bilingualism.
1.2. Bilingualism and cognitive reserve: what are the underlying mechanisms?
Following the pioneering study by Bialystok and collaborators in Canada (Bialystok, Craik, &
Freedman, 2007), more studies have reported that bilingualism or multilingualism is a factor of
cognitive reserve, delaying the onset of cognitive symptoms associated with AD or MCI by up to 5
years (Alladi et al., 2013; Anderson, Saleemi, & Bialystok, 2017; Chertkow et al., 2010; Craik,
Bialystok, & Freedman, 2010; Ossher, Bialystok, Craik, Murphy, & Troyer, 2013; Woumans et al.,
2015; Woumans, Versijpt, Sieben, Santens, & Duyck, 2017; for a review Calvo et al., 2016).
However, other studies did not find such a bilingual advantage (Crane et al., 2009, 2010; Perquin et
al., 2013; Sanders, Hall, Katz, & Lipton, 2012; Zahodne, Schofield, Farrell, Stern, & Manly, 2014),
generating a debate over the reliability of bilingualism as a cognitive reserve mechanism and the role
of certain factors in determining its presence, such as immigration status, education, age at onset,
language proficiency, and frequency of language usage (Guzman-Velez, 2016).
Nevertheless, the most intriguing question about the relationship between cognitive reserve and
bilingualism is what drives this effect; that is, what are the underlying mechanisms that allow lifelong
usage of two languages to benefit cognition. At least two mechanisms have been described for
cognitive and neural reserve (Barulli & Stern, 2013; Stern, 2012; Stern et al., 2018). One would be
that individual differences in tolerating the disease-associated neuropathology are related to
differences in the efficiency of those areas affected by the disease. That is, cognitive reserve factors
act to enhance neural efficiency and this would subsequently protect the brain network affected by
AD neuropathology. A second mechanism would be alternative networks: those circuits not affected
by the disease could play a compensatory role in protecting against cognitive symptoms. In the
context of bilingualism, it seems that both mechanisms may contribute to cognitive reserve. For
instance, Schweizer, Ware, Fischer, Craik, & Bialystok (2012) showed that bilinguals had greater
amounts of brain atrophy in the areas associated with AD than monolingual peers, despite cognitive
decline of both groups being equal (for similar results see Duncan et al., 2018). This means that
bilinguals would have a higher threshold for reaching a diagnosis of dementia, likely because the
brain areas affected by the disease are more efficient and may cope better with the structural damage.
However, some other studies have shown that there could be a compensatory mechanism mediated by
the EC network, as suggested by an increased metabolic connectivity in the fronto-parietal network in
bilinguals (Perani et al., 2017). In line with this, bilingual older adults have shown to have better
functional efficiency (Gold, Kim, Johnson, Kryscio, & Smith, 2013), higher neural efficiency
(Abutalebi et al., 2014; Borsa et al., 2018; Del Maschio et al., 2018; Gold, Johnson, & Powell, 2013),
and better connectivity (Grady, Luk, Craik, & Bialystok, 2015; Luk, Bialystok, Craik, & Grady, 2011)
within the EC network while performing EC tasks when compared to their monolingual peers.
However, what has been shown to be less consistent are results indicating a more efficient EC
system at the behavioural level in bilinguals, at least in patients with AD and MCI. In Anderson,
Saleemi, and Bialystok’s (2017) study, only bilingual healthy adults (but not MCI or AD bilinguals)
showed better inhibitory control than monolinguals (for similar results in AD patients, see Clare et al.,
2016). Conversely, Bialystok, Craik, Binns, Ossher, and Freedman (2014) found that bilinguals with
AD or MCI exhibit a smaller Stroop effect than monolingual peers, but the trend of decline at one
year for both language groups was the same.
Beyond executive functions, some studies reported that long-term memory was more preserved
in bilinguals with AD than monolinguals (Perani et al., 2017), suggesting that bilingualism may also
act by increasing the efficiency of one of the cognitive domains most affected by AD, episodic
memory (see also Bak, Nissan, Allerhand, & Deary, 2014; Ljungberg, Hansson, Andrés, Josefsson, &
Nilsson, 2013). Similarly, it has been proposed that bilingual adults’ brains might work like those of
people with higher degree of cognitive reserve and less affected by the negative effect of aging
(Grant, Dennis, & Li, 2014; Grundy, Anderson, & Bialystok, 2017). Indeed, older adults with higher
degrees of cognitive reserve or resilience show patterns of functionality in posterior areas comparable
to those of young adults, whereas those with low levels of resilience have more frontally-distributed
activity and less efficient memory (e.g. Davis, Dennis, Daselaar, Fleck, & Cabeza, 2008).
Thus, in order to better characterise the origin of bilingualism’s capacity to delay the onset of
cognitive decline, we tested both EC and episodic memory with experimental tasks. Moreover, as EC
is made up of various processes, we tested its different sub-components according to what has been
proposed by some theoretical models as the differentiation between executive functions.
1.3. The present study
In our study, we introduced some methodological novelties in order to improve the assessment
of underlying factors that may promote bilingualism as a cognitive reserve mechanism. First, we
avoided the dichotomous classification of monolinguals versus bilinguals and instead used a number
of variables related to the experience of speaking two languages. This allows bilingualism to be
treated as a continuum and lets us explore which variables are crucial in making bilingualism a
contributing factor to cognitive reserve. This is feasible in part thanks to the unique linguistic
environment of Barcelona (Spain), where a large portion of the population is highly bilingual and is
constantly exposed to the two co-official languages, Catalan and Spanish. Given that these two
languages overlap substantially at both lexical and phonological levels, Spanish native speakers are
predisposed to have a good level of Catalan comprehension. That having been said, some Spanish
native speakers do not reach a high level of speaking in Catalan either because they began learning
Catalan later in life or because native Catalan speakers speak Spanish as well. Thus, within this
language context, Catalan native speakers typically have a language profile of early and high
proficient bilinguals, whereas Spanish natives are more variable in terms of Catalan proficiency, age
of acquisition and language usage. Thus, this linguistic variability produces several forms of
bilingualism as defined by the multiple intersections between variables composing each individual’s
Second, in order to minimize the impact of potential confounding factors such as socio-cultural
differences, we recruited all participants from the same city. Additionally, we assessed those factors
that are known to significantly increase cognitive reserve and brain resilience, such as leisure
activities, occupation, and education across an individual’s lifespan.
Finally, we investigated the origin of bilingualism as a cognitive reserve factor via experimental
measures of EC and long-term memory tasks. Indeed, some studies have highlighted the benefits of
bilingualism in elderly people demonstrated within various tasks involving components of the EC
system, such as attention (e.g., Bialystok, Craik, & Luk, 2008; Bialystok et al., 2004), working
memory (Luo, Craik, Moreno, & Bialystok, 2013) and switching task abilities (Gold et al., 2013); this
would suggest that the modulation of the EC abilities plays a potential role in delaying the onset of
cognitive decline in bilinguals.
Several models describe the architecture of the EC system and adopt different views on its
mechanisms and tasks to assess them (Botvinick, Braver, Barch, Carter, & Cohen, 2001; Botvinick,
Cohen, & Carter, 2004; Diamond, 2013; Jurado & Rosselli, 2007; Norman & Shallice, 1986; Stuss &
Alexander, 2000; Verhaeghen & Cerella, 2002). In our study, we adopted the EC model described by
Miyake et al. (2000) which clearly defines three sub-components (inhibitory control, updating, and
shifting) and suggests tasks associated with each of them. Updating mechanisms monitor the
information during the task, shifting refers to the process that underlies the ability to switch between
tasks, and inhibition suppresses prepotent non-target responses. Despite a recent revision of this
framework, its general architecture has not changed dramatically. In the most recent version of the
model (Miyake & Friedman, 2012), inhibition remains a crucial component of the EC system;
however, it is now described as a more general factor that shares importance with updating and
The main hypothesis of the study is that higher degrees of bilingualism, as defined by early L2
acquisition, balanced language usage, higher L2 proficiency, and higher frequency of language
switching would increase an individuals’ cognitive reserve, as indicated by a delay of dementia and
symptom onset as well as higher cognitive efficiency. Therefore, we expected a positive correlation
between MCI and AD individuals’ bilingualism composite scores and the clinical measures of age at
onset of cognitive symptoms, at first clinical visit and at diagnosis. Similarly, at the cognitive level,
we predicted that higher levels of bilingualism would positively correlate with the behavioural
measures of EC and memory tasks (higher accuracy, reduced conflict/switch/mixing costs, and faster
reaction times). Based on the previous studies in which a bilingual advantage was reliably found in
older adults, we expected to find that the inhibitory control (Anderson et al., 2017; Bialystok et al.,
2014) and updating (e.g., Gold et al., 2013; Grundy & Timmer, 2017) sub-components would be more
strongly related to bilingualism. Finally, we argue that a bilingual advantage on EC would indicate
that this cognitive system serves to compensate for the symptoms of cognitive decline, whereas an
advantage on memory would be more related to an increase in efficiency of processes that counteracts
the cognitive symptoms.
We recruited three groups of participants: 63 healthy individuals (M/F= 20/43), 135 patients with
MCI (M/F= 62/73) and 68 patients with AD (M/F= 22/46). The patients were recruited from four
different hospitals: Hospital de Bellvitge, Hospital de la Santa Creu i Sant Pau, Hospital General de
Granollers, and Hospital Moisès Broggi (Sant Joan Despí/L’Hospitalet) - Consorci Sanitari Integral.
Diagnoses were made by neurologists based on neurological and neuropsychological evaluations,
according to the published clinical criteria. For AD, the clinical criteria were those based on the
recommendations from the National Institute on Aging-Alzheimer's Association workgroups on
diagnostic guidelines for Alzheimer's disease (McKhann et al., 2011). According to ICD-10
classifications, this corresponds to ‘G30.1- Alzheimer disease with late onset,’ defined as a decline
with slow progression, with memory impairment as the principal feature and with an onset after the
age of 65 (usually in the late 70s or thereafter). According to the DSM-V, our AD patients met the
criteria for ‘Major neurocognitive disorder,’ with an insidious onset and gradual progression of
impairment in one or more cognitive domains that interfere with their independence.
For MCI, the diagnosis was based on the recommendations of Albert et al. (2011), comprised of
the following: lower performance in one or more cognitive domains including episodic memory,
independence of function in daily life, and no evidence of significant impairment in social and
occupational functioning. Single-domain and multiple-domain subtypes were classified as MCI only.
All AD patients received acetylcholinesterase inhibitors as pharmacological treatment, whereas the
MCI participants did not receive any medication for their diagnosis. According to ICD-10
classifications, this corresponds to ‘F06.7 - Mild cognitive disorder,’ which is characterized by the
following: impairment of memory, learning difficulties, reduced ability to concentrate on a task for
more than brief periods, marked feeling of mental fatigue, subjective feeling of difficulty in
performing a cognitive task, and by having excluded a diagnosis of either dementia, delirium, or other
mental and behavioural disorders. According to the DSM-V, our patients met the criteria for ‘Mild
neurocognitive disorder,’ which is based on evidence of a modest cognitive decline compared to
previous levels of performance in one or more cognitive domains without interfering with
independence in daily activities and having established that the cognitive deficits are not attributable
to another mental disorder.
Patients with potentially confounding neurological (other than MCI or AD) and psychiatric
disorders, clinically-known hearing or vision impairments, a past history of alcohol abuse, and/or
psychosis were excluded from the study. Healthy individuals had no previous neurological or
psychiatric diseases. Additionally, healthy individuals were excluded from the study if they showed
signs of cognitive deficits on a brief neuropsychological assessment (see below).
2.2. Materials and procedure
For the purpose of the study, we collected clinical measures including age at onset of cognitive
symptoms, age at the first medical visit for cognitive impairments, and age at diagnosis. Ages at the
time of diagnosis and first visits were collected from the hospital records of the patients. Age at onset
of cognitive symptoms was defined as the age of the patient when the first changes in cognition were
observed by a reliable family member or as reported in the clinical records, if available. Language
history and cognitive reserve measures were also collected for all participants. Finally, a brief
neuropsychological battery was administered along with experimental measures: four EC tasks and a
face memory recognition task detailed below.
Before starting the experimental procedure, the patients signed an informed consent approved by
the ‘Parc de Salut MAR’ Research Ethics Committee under the reference number 2014/6003/I. The
research was conducted in accordance with the Declaration of Helsinki (World Medical Association,
2013) and data protection procedures according to the General Data Protection Regulation 2016/679
(GDPR) of the European Union. The aims of the study were explained to participants at the beginning
of the study and there was a debriefing with them after every experimental session.
2.2.1. Language history measures
Language history was assessed using a questionnaire administered to the participants and an
interview with the patient and relatives (see Calabria et al., 2018). We excluded participants who
spoke a third language as we aimed to focus our research on bilingualism.
Four main measures were collected (see Table 1):
a) Age of acquisition of the two languages (Catalan and Spanish);
b) Self-rating of language proficiency consisting of their speaking, comprehension, writing and
reading abilities in each language on a four-point scale (1=poor, 2=regular, 3=good, 4=perfect);
c) Language usage represented by the frequency they spoke each of the two languages across
different periods of their lives (childhood, puberty and adulthood); this was expressed as a
percentage of Catalan usage, with 0% meaning only using Spanish throughout their lives, 100%
meaning only using Catalan, and ~50% signifying a balanced use of the two languages.
d) Frequency of language switching as measured by the overall score on the Bilingual Switching
Questionnaire (BSWQ) (Rodriguez-Fornells, Krämer, Lorenzo-Seva, Festman, & Münte, 2012).
The combination of all these measures was used to create a composite score of bilingualism
(based on the results of a factorial analysis) which functioned a continuous variable ranging from
passive bilingualism (being able to understand an L2, but with little or no usage of it) to active
bilingualism (high proficiency in L1 and L2 with a balanced usage of the two languages).
2.2.2. Cognitive reserve index
To have a proxy of cognitive reserve apart from bilingualism, we used the Cognitive Reserve
Index Questionnaire (CRIq) (Nucci, Mapelli, & Mondini, 2012). This includes 20 items that are
grouped into three dimensions including factors of education, working activity and occupation, and
leisure activity. This questionnaire yields a total score as well as three separate scores corresponding
to each dimension: CRI-Education, CRI-Working Activity, and CRI-Leisure Time (see Table 1).
Scores were calculated according the computation system provided by the authors of the CRIq,
available at http://www.cognitivereserveindex.org/.
2.2.3. Neuropsychological measures
Patients were assessed with hospital-specific neuropsychological batteries that each collaborating
hospital implemented for the diagnosis of cognitive decline. In order to establish a common set of test
scores for all the participants, we designed a brief neuropsychological assessment specifically for this
study that included: Mini Mental State Examination (MMSE) (Folstein, Folstein, & McHugh, 1975);
the CERAD Word List Memory (Morris et al., 1989), which measures long-term episodic verbal
memory; forward and backward Digit Spans (Pena-Casanova et al., 2009), which measure verbal
short-term memory; and the Trail Making Test part A (Peña-Casanova et al., 2009), which measures
visual attention and motor speed (see Table 2).
TABLE 1 ABOUT HERE
2.2.4. Experimental tasks
All participants were tested on the five experimental tasks detailed below. Said tasks were
administered over two sessions in no specific order. The four EC tasks included a flanker task
(Calabria, Grunden, Serra, García-Sánchez, & Costa, 2019; Costa, Hernandez, & Sebastian-Galles,
2008), a Spatial Stroop task (Funes, Lupiáñez, & Milliken, 2007), a task switching task (Cattaneo,
Calabria, Marne, Gironell, Abutalebi, & Costa, 2015) and an n-back task (Braver et al., 1997); the
final task was a facial recognition memory task. All these tasks were administered to participants on a
laptop computer and responses were collected with DMDX software (Forster & Forster, 2003). All
the instructions for the experimental tasks were written on the screen and read by the participants
before the task started. Additionally, the experimenter checked whether the participant correctly
understood the instructions before running the task. After each session, there was a debriefing session
with the participant.
Flanker task. Target stimuli consisted of a row of five horizontal black lines with arrowheads
pointing left or right, with the central arrow acting as the true target. Participants were instructed to
indicate the direction (left or right) of the central arrow by pressing one of two keys (‘V’ or ‘M’) on
the keyboard. The target (central arrow) was presented in two main conditions: with congruent
flankers (same direction as the target) or incongruent flankers (opposite direction). The experiment
consisted of two blocks of 48 trials each, for a total of 96 trials. The proportion of congruent trials was
75% (n= 72) to 25% incongruent trials (n= 24). The event presentation started with a fixation point
(+) shown at the centre of the screen for 500 ms. Then, the target arrow was presented simultaneously
with the flankers until the participants responded or for up to 2000 ms (for similar versions see
Calabria, Grunden, Serra, García-Sánchez, & Costa, 2019; Costa, Hernandez, & Sebastian-Galles,
Spatial Stroop task. The stimuli consisted of an arrow that appeared on the left or right half of
the screen. In the congruent condition, the direction of the arrow and its position on the screen was the
same, while in the incongruent condition the direction of the arrow and its location was not the same
(for instance, arrow pointing to left presented in the right-side part of the screen). The proportion of
congruent/incongruent trials was 50%. The task consisted of 192 trials that were divided into four
blocks of 48. Participants responded according to the left/right direction via pressing one of two keys
(‘V’ if the arrow pointed to the right, ‘M’ if the arrow pointed to the right) on the keyboard (for a
similar version of this task see Funes, Lupiáñez, & Milliken, 2007). The event presentation started
with a fixation point (+) shown at the centre of the screen for 500 ms, which was followed by an
arrow presented until the participants responded or for up to 2500 ms. This is an adapted version of
the task used in the study by Funes, Lupiáñez, & Milliken, 2007).
Task switching. Three shapes (square, circle and triangle) and three colours (red, green and blue)
were used in the task. Shapes and colour were combined in various ways, resulting in a total of nine
possible coloured shapes. Participants were presented with an array containing three coloured shapes,
two at the top and one at the bottom of the screen. They were instructed to match one of the coloured
shapes at the top with the coloured shape at the bottom, according to the criteria of “colour” or
There were two types of blocks: single blocks and mixed blocks with a sandwich design such that
participants completed two single blocks and 3 mixed blocks, followed by two more single blocks. In
each single block (24 trials), the sorting criteria (shape or colour) was held constant; over a total
number of 96 trials, 48 single trials required sorting by shape and 48 single trials by colour. In the
mixed blocks, however, participants had to sort the stimuli either by colour or shape according with
the cue word appearing on the screen with the pictures. There were two types of trials: repeated trials
in which participants had to use the same sorting criteria as in the previous trial and switch trials in
which participants were required to match the coloured shapes in the opposite sorting criteria with
respect to the previous trial.
Participants gave their response by pressing one of two keys (‘M’ or ‘V’) according to the
position of the matching picture at the top of the array. Specifically, they had to press the ‘M’ key
when the correct answer was in the top-right part of the array and the ‘V’ key when it was in the top-
left part of the array. The criterion they had to use was indicated by a cue word appearing in the centre
of the array for each trial (‘COLOR’ for colour, ‘FORMA’ for shape). The event presentation started
with a fixation point (+) shown at the centre of the screen for 500 ms. Then, fthe cue word and the
coloured shapes were presented simultaneously, remaining on the screen until the participants
responded or for up to 2500 ms. This is an adapted version of the task that was used in the study by
Cattaneo et al. (2015).
N-back task. Letters (consonants) were serially presented, and participants were required to
match said letters in three different memory load conditions across separate blocks. In the 0-back
condition, the target was any letter that matched a pre-specified letter (e.g., “X”); in the n-1 and n-2
back conditions, the target indicated when the letter on the screen matched the letter presented one or
two trials before, respectively. There were three blocks of 25 trials containing 7 target trials (28%)
and 18 no target trials (72%) in each. Target trials were never presented consecutively. Participants
responded to every trial by pressing one key if the present stimuli matched the target or another if the
stimuli did not match (‘M’ or ‘V’, counterbalanced across participants).
The event presentation started with a fixation point (+) shown at the center of the screen for 500
ms. Then, the stimuli were presented for a duration of 1500 ms or until the participants responded.
The task was adapted for the version used in the study by Braver et al. (1997).
Face recognition memory task. The stimuli consisted of grey-scale pictures of faces that were
downloaded from electronic datasets along with other resources on the web and then processed with
Adobe Photoshop 5.0. A set of 60 pictures of unfamiliar faces (half men and half women) was
selected; they were scaled to 210 x 263 pixels and presented with a grey background.
During the encoding phase, a set of 30 pictures were presented twice and participants required to
judge the attractiveness of the faces. The event presentation started with a fixation point shown at the
center of the screen for 500 ms. Then, a face was presented during 3000 ms. Finally, the question
“Attractive or not attractive?” was presented which remained visible up to 5000 ms. Participants
responded to this question by pressing one of two keys on the keyboard (‘V’ or ‘M’). This procedure
was adopted in order to reinforce the creation of a memory trace during encoding. Moreover,
participants were instructed to remember the encoded faces, since they were tested in a recognition
memory task afterwards.
A recognition test was administered after a ten-minute delay. In this test all faces shown during
encoding (old items) were presented along with an additional set of 30 that were not presented
previously (new items). The event presentation started with a fixation point (+) shown at the center of
the screen for 500 ms. Then, a face was presented until they responded or for up to 10000 ms.
Participants had to indicate whether the face was presented previously during the encoding phase or
not by pressing one of two keys on the keyboard (‘V’ or ‘M’).
3. Data Analyses
We performed separate stepwise multiple regression analyses on clinical measures (age at onset
of cognitive decline, symptoms, and first visit) for the two patient groups and for the performance on
each experimental task for both patients and healthy controls (Yaremko, Harari, Harrison, & Lynn,
1986). The regressors included in the analyses were the bilingualism composite factor, the cognitive
decline composite factor and the CRIq score. Years of education were not included in the analysis
since we had a similar measure (CRI-Education score) in the calculation of the total CRIq score.
The bilingualism composite factor was calculated by performing a principal component analysis
(Jolliffe & Cadima, 2016; Sellbom & Tellegen, 2019). that included the following variables: years of
language exposure to Spanish and Catalan (current participant’s age – age of language acquisition);
self-rating of language proficiency in both languages for speaking, comprehension, writing and
reading; percentage of language usage; and frequency of language switching (for a similar approach
see Anderson, Hawrylewicz, & Bialystok, 2018).
Similarly, the composite score for the level of cognitive decline was calculated by performing a
principal component analysis that included the scores obtained on the neuropsychological tests. We
used all the neuropsychological measures together since using only the MMSE score might not have
detected mild cognitive impairment (Carnero-Pardo, 2014; Diniz, Yassuda, Nunes, Radanovic, &
When the regressors showed a significant effect, partial correlation coefficients were calculated
to determine the contribution of each predicting variable on the dependent variable (Freund, Wilson,
& Mohr, 2010). Therefore, the square of each of these coefficients indicates the individual
contribution of each predicting variable to the square of total R.
4.1. Bilingualism composite factor
We ran a principal component analysis that included the following variables: years of Catalan
exposure, years of Spanish exposure, percentage of language usage, frequency of language switching,
fluency in Catalan, fluency in Spanish, comprehension in Catalan, and comprehension in Spanish.
Reading and writing scores were not included in the analysis since most of the participants were only
educated in Spanish, thereby generating possible language differences that could not be explained in
terms of proficiency.
To improve internal consistency, we normalized the variable’s scores by transforming them into
z scores. After this transformation, the Cronbach’s alpha reached .72 (Taber, 2018), indicating that
internal consistency was acceptable (Tavakol & Dennick, 2011) and thus stipulating that the principal
component analysis was appropriate for those variables. To correct for the common covariance of the
variables, a rotation with the direct oblimin method was applied to the factor matrix.
After rotation, the result of the principal component analysis suggested that all the variables
saturated into three factors, with a cutoff value of 1 for eigenvalues. The first factor included fluency
in Catalan (.93), language usage (.86), frequency of language switching (.83), comprehension in
Catalan (.80), and years of Catalan exposition (.77). The second factor included fluency in Spanish
(.73) and comprehension in Spanish (.73). The third factor only included the years of Spanish
exposition (-.95). The results indicated that these three factors explained only 44% of the total
One explanation of this low amount of explained variance is the fact that all the variables related
to Spanish proficiency and exposure have low variability. This is reasonable since all Catalan-Spanish
bilinguals have also acquired Spanish early in life (more or less at the same time as Catalan) and have
a high level of proficiency in Spanish. Therefore, we ran a second principal component analysis that
included the language variables for Catalan only (fluency, comprehension, and years of exposition),
language usage, and frequency of language switching.
The internal consistency with this set of variables was excellent as suggested by a very high
Cronbach’s alpha of .90. The solution of this Catalan-based principal component explained 70% of
the total variance and the variables saturated in one just factor. The component matrix showed the
following loadings: fluency in Catalan (.93), language usage (.86), frequency of language switching
(.86), comprehension of Catalan (.76), and years of Catalan exposition (.79).
Accordingly to this solution, the individual scores for bilingualism (hereafter, composite
bilingualism scores) were calculated using a regression method in which positive coefficients were
indicative of higher degrees of bilingualism and negative ones of low proficiency and usage of
4.2. Cognitive composite factor
The test scores were transformed into z scores and subsequently underwent a reliability analysis.
The analysis that included all variables showed a poor internal consistency (Cronbach’s alpha= .55).
A higher internal consistency (Cronbach’s alpha= .76) was found when the following measures were
included: MMSE score and CERAD scores for recognition and free recall.
The solution of the principal component analysis suggested that all the variables saturated into
one just factor that explained 66.6% of the variance. To correct for the common covariance of the
variables, a rotation with the direct oblimin method was applied to the factor matrix. After rotation,
the loadings of the factor were the followings: CERAD recognition (.56), CERAD free recall (.27),
and MMSE score (.22).
Accordingly to this solution, the individual composite scores for cognition were calculated using
a regression method in which positive coefficients indicated that cognition was within the normal
range and negative ones below the normal range.
4.3. The effect of bilingualism on delaying cognitive symptoms
The results were consistent for three regression models for age at symptoms onset (F (3, 189) =
10.15, p< .001, R2= .14), first visit (F (3, 189) = 12.56, p< .001, R2= .17), and diagnosis (F (3, 189) =
11.91, p< .001, R2= .16) (Table 2). That is, CRI score was not a significant predictor of the age at any
time point (symptoms onset: β= -.09, p= .24; first visit: β= -.12, p= 60; diagnosis: β= -.11, p= .16),
whereas both bilingualism and cognition as composite scores were. Cognition was a significant
predictor of age at onset of symptoms (β= -.29, p<.001, r=-.29), of first visit (β= -.29, p<.001, r= -.29),
and at diagnosis (β= -.27, p<.001, r= -.28). The degree of bilingualism was a positive predictor of the
three age measures (symptoms onset: β= .29, p<.001, r= .26; first visit: β= .33, p<.001, r= .31;
diagnosis: β= .39, p<.001, r= .32).
However, when the analyses were performed separately for the two groups of patients, the
regression models were significant in MCI patients for the three dependent variables [age at symptom
onset: F (3, 124) = 11.49, p< .001, R2= .22; age at first visit: F (3, 124) = 11.54, p< .001, R2= .22; age
at diagnosis: F (3, 124) = 12.18, p< .001, R2= .23], but not in AD patients [age at symptom onset: F
(3, 61) =.59, p= .63, R2= .03; age at first visit: F (3, 61) = 1.14, p= .34, R2= .05; age at diagnosis: F (3,
61) = 1.03, p= .38, R2= .05].
In MCI patients, both bilingualism and the degree of cognitive decline were significant
predictors of the three age variables (see Figure 1 and Table 2 for partial correlations). Moreover, CRI
significantly predicted, but in a negative direction, the age at onset of symptoms (β= -.26, p= .01) and
age at diagnosis (β= -.22, p= .03).
As some research has shown that gender might be an important factor that modulates the
prevalence of AD (Malpetti, Ballarini, Presotto, Garibotto, Tettamanti, & Perani, 2017; Rocca, 2017)
and second language acquisition (van der Slik, van Hout, Schepens, 2015), we reran the regression
analyses with gender included as predictor. The results for the three regression models were still
significant: age at symptoms onset (F (4, 188) = 7.93, p< .001, R2= .14), first visit (F (4, 188) = 9.69,
p< .001, R2= .17), and diagnosis (F (3, 188) = 9.05, p< .001, R2= .16). However, gender was not a
significant predictor in any regression model: age at symptoms onset (β= .07, p= .27, r= .07), first
visit (β= .07, p= .30, r= .07), and diagnosis (β= .05, p= .46, r= .05), suggesting that gender was not a
modulating factor in delaying the symptoms of cognitive decline and unrelated to the effects of
TABLE 2 ABOUT HERE
FIGURE 1 ABOUT HERE
4.4. The effect of bilingualism on EC and memory
4.4.1. Flanker task
RTs and conflict cost. Regression models on RTs for the two conditions (congruent and
incongruent) revealed that bilingualism was not a significant predictor (congruent: β= .02, p= .29, r=
.06; incongruent: β= .06, p= .41, r= .05), but CRI (congruent: β= -.18, p= .01, r= -.16; incongruent: β=
-.17, p= .01, r= -.16), cognition (congruent: β= -.24, p< .001, r= -.22; incongruent: β= -.32, p< .001, r=
-.30), and age (congruent: β= .20, p= .002, r= .18; incongruent: β= .16, p= .01, r= .1) were.
Furthermore, an analysis was performed on the conflict cost as measured by the difference
between RTs of the incongruent and congruent trials. To control for group differences in speed of
processing, proportional costs were calculated, defined as the conflict cost divided by RTs of
congruent trials. The regression model showed that cognition was a significant predictor (β= -.21, p=
.002, r= -.20), but CRI (β= -.01, p= .94, r= -.01) and bilingualism (β= .07, p= .37, r= .06), and age (β=
-.01, p= .87, r= -.01) were not (see Figure 2).
When gender was included in the analyses, the results showed that it was not a significant
predictor in any regression model for RTs (congruent: β= .02, p= .72, r= .01; incongruent: β= -.01, p=
.80, r= -.01) or conflict cost (β= -.005, p= .94, r= -.005).
Accuracy. The regression model for accuracy was significant (F (4, 228) = 15.87, p< .001, R2=
.20). Cognition (β= .37, p< .001, r= .34) and age (β= -.17, p= .01, r= -.15) were significant predictors,
but bilingualism (β= -.07, p= .33, r= -.07) and CRI (β= .12, p= .08, r= .10) were not. When gender
was included in the analyses, the results showed that it was not a significant predictor (β= .07, p= .27,
4.4.2. Spatial Stroop task
RTs and conflict cost. Regression models on RTs for the two conditions (congruent and
incongruent) showed that bilingualism was not a significant predictor (congruent: β= .10, p= .15, r=
.08; incongruent: β= .05, p= .04, r= .08), but CRI (congruent: β= -.18, p= .01, r= -.15; incongruent: β=
-.13, p= .05, r= -.11), cognition (congruent: β= -.24, p< .001, r= -.22; incongruent: β= -.29, p< .001, r=
-.27), and age (congruent: β= .25, p< .001, r= .23; incongruent: β= .25, p< .001, r= .23) were.
Additionally, an analysis was performed on the conflict cost measured by the difference between
the RTs of the incongruent and congruent trials. To control for group differences in speed of
processing, proportional costs were calculated as the conflict cost divided by RTs of congruent trials.
The regression model showed that bilingualism (β= -.20, p= .01, r= -.16), cognition (β= -.14, p= .03,
r= -.13), and CRI (β= .19, p= .01, r= .16) were significant predictors but age was not (β= -.04, p= .55,
r= -.03) (see Figure 2).
When gender was included in the analyses, the results showed that it was not a significant
predictor in any regression model for RTs (congruent: β= .03, p= .57, r= .04; incongruent: β= .04, p=
.69, r= .05) or conflict cost (β= .05, p= .77, r= .05).
Accuracy. The regression model for accuracy was significant (F (4, 233) = 14.29, p< .001, R2=
.20). Age (β= -.18, p= .004, r= -.19) and cognition (β= .34, p< .001, r= .32) were significant
predictors, but bilingualism (β= -.11, p= .14, r= -.09) and CRI (β= .01, p= .96, r= .03) were not. When
gender was included in the analyses, the results showed that it was not a significant predictor (β= .07,
p= .24, r= .08).
4.4.3. Task switching
RTs, switch cost, and mixing cost. Regression models on RTs for the three conditions (single,
repeat, switch) showed that bilingualism was not a significant predictor (single: β= .03, p= .71, r= .02;
repeat: β= .05, p= .44, r= .04; switch: β= .07, p= .33, r= .06). CRI (single: β= -.23, p= .001, r= -.18;
repeat: β= -.23, p= .001, r= -.19; switch: β= -.24, p= .001, r= -.20), age (single: β= .21, p= .001, r=
.19; repeat: β= .19, p= .002, r= .18; switch: β= .23, p< .33, r= .22), and cognition (single: β= -.31, p<
.001, r= -.28; repeat: β= -.29, p< .001, r= -.27; switch: β= -.30, p< .001; r= -.28) were significant
Additional analyses were performed on switch and mixing costs as two measures of cognitive
control: reactive and proactive control, respectively (see Braver, 2012; Cattaneo et al., 2015). To
control for group differences, proportional costs were calculated. Proportional switch costs were
calculated as the difference between RTs in switch trials and repeat trials (mixed blocks) divided by
RTs in repeated trials. Proportional mixing costs were calculated as the difference between RTs in
repeat and single trials divided by RTs in single trials.
The regression models for both proportional costs were not significant (switch cost: F (4, 234) =
1.97, p= .10, R2= .03; mixing cost: F (4, 234) = .73, p= .57, R2= .01), suggesting that no regressor
predicted the magnitude of these costs (see Figure 2).
When gender was included in the analyses, the results showed that it was not a significant
predictor in any regression model for RTs (single: β= .03, p= .47, r= .03; repeat: β= .04, p= .68, r=
.04; switch: β= .06, p= .30, r= .07), switch costs (β= .04, p= .56, r= .04) or mixing cost (β= .08, p=
.21, r= .08).
Accuracy. The regression model for accuracy was significant (F (4, 234) = 13.47, p< .001, R2=
.15). CRI (β= .21, p= .004, r= .17), cognition (β= .25, p< .001, r= .24), and age (β= -.22, p= .001, r= -
.21) were significant predictors, but bilingualism was not (β= -.05, p= .53, r= -.04). When gender was
included in the analysis, the results showed that it was not a significant predictor (β= .02, p= .74, r=
4.4.4. N-back task
The dependent variable of the regression analyses was composed of d’ scores. These were
calculated as the difference between the Z transformations of the percentages of hits and false alarms
(d’= ZHit – ZFA) for n-0, n-1, and n-2 conditions separately. The percentage of hits was calculated as
the proportion of correct responses over the total number of targets, and the percentage of false alarms
as the proportion of false alarms over the total number of non-target trials.
The regression models were significant for the three memory load conditions (n-0: F (4, 219) =
10.78, p< .001, R2= .16; n-1: F (4, 212) = 13.75, p< .001, R2= .21; n-2: F (4, 199) = 14.93, p< .001,
R2= .23). Bilingualism (n-0: β= -.02, p= .79, r= -.01; n-1: β= -.07, p= .31, r= -.06; n-2: β< .01, p= .99,
r<.01) and age (n-0: β= -.06, p= .38, r= -.05; n-1: β= -.01, p= .81, r= -.01; n-2: β= -.01, p= .06, r=-.10)
were not significant predictors of the n-back task performance. CRI (n-0: β= .20, p= .009, r= .17; n-1:
β= .22, p= .003, r= .18; n-2: β= .16, p= .05, r= .12) and cognition (n-0: β= .29, p< .001, r= .27; n-1: β=
.36, p< .001, r= .33; n-2: β= .36, p< .001, r= .33) were significant predictors (see Figure 2).
When gender was included in the analyses, the results showed that it was not a significant
predictor in any regression model for the d’ values (n-0: β= .07, p= .25, r= .08; n-1: β= -.01, p= .083,
r= -.01; n-2: β= -.02, p= .73, r= -.3).
4.4.5. Face recognition memory task
The dependent variable of the regression analyses was defined as d’ scores (d’= ZHit – ZFA).
The percentage of hits was calculated as the proportion of correct responses (old items responded as
‘old’) over the total number of old items in the recognition memory task, and the percentage of false
alarms (new items responded as ‘old’) as the proportion of false alarms over the total number of new
items in the recognition memory task.
The regression model was significant: F (4, 241) = 19.61, p< .001, R2= .25). Bilingualism was
not a significant predictor of the recognition memory performance (β= .11, p= .10, r= .09, (see Figure
2), but CRI (β= .13, p= .05, r= .11), cognition (β= .29, p< .001, r= .27), and age (β= -.25, p< .001, r= -
When gender was included in the analyses, the results showed that it was not a significant
predictor (β= -.05, p= .34, r= -.06).
FIGURE 2 ABOUT HERE
In this study, we explored the relationship between bilingualism and CR by introducing some
methodological novelties that challenge the traditional classification of monolingualism versus
bilingualism and by employing experimental tasks with the intention of better characterizing the
origin of the so-called “bilingual advantage” (Duñabeitia & Carreiras, 2015). Additionally, we
controlled for other CR factors such as education, occupation and leisure activities, as they could
potentially be related to bilingualism (Foubert-Samier et al., 2012; Fratiglioni, Paillard-Borg, &
Winblad, 2004; Opdebeeck, Martyr, & Clare, 2016; Scarmeas & Stern, 2003; Stern, 2012). The main
result of the study is a demonstrable and reliable effect of bilingualism in delaying the onset of
cognitive symptoms and age of diagnosis in MCI patients. Such a bilingual advantage was only
partially explained by a more efficient EC system and is not due to superior long-term memory in
One of the long-standing limitations of the research that has investigated the benefits of
bilingualism on cognition has been the debate over how to establish a clear definition of bilingualism
(Bak, 2016; Duñabeitia & Carreiras, 2015; Valian, 2015). To overcome this limitation, we decided to
use a composite score of bilingualism, including several variables associated with the degree to which
a given individual masters two languages and then calculating the specific weight of each component
on a continuum, from passive to active bilingualism (for a similar approach, see Anderson et al.,
2018). In our study, we showed that in the calculation of the bilingualism score almost all the
variables contributed in a similar way to the continuum from passive to active bilingualism, with
Catalan proficiency in speaking as the most important variable. Consequently, this result would
support the idea that language proficiency may have more of an impact on determining a high degree
of bilingualism than, for instance, age of L2 acquisition. This is not a new result, since some previous
studies had already found that language proficiency is one of the main components, together with
language usage, in defining the degree of bilingualism as a continuous variable (Luk & Bialystok,
2013). However, it should be acknowledged that other variables can enhance an individual’s degree of
language proficiency and that this is not the only dimension that defines a person as a bilingual. For
instance, individuals with early bilingual experience tend to also have higher L2 proficiency (Luk, De
Sa, & Bialystok, 2011), despite the fact that in some cases these two variables can differently
modulate the pattern of brain activation during lexical retrieval of the two languages (Perani et al.,
2003). Also, language usage, more than language proficiency, modulates white matter in the brain
areas related to language control, such as the anterior cingulate cortex (Del Maschio et al., 2019); in
other cases, both language proficiency and age of L2 acquisition strongly predict the density of grey
matter in the left inferior parietal cortex (Mechelli et al., 2004). Interestingly, beyond the realm of
language, age of L2 acquisition and language proficiency have been shown to be the two main factors
that enhance EC efficiency, as shown in tasks of conflict monitoring (Luk, De Sa, & Bialystok, 2011).
This is to say that the combination of experience-based factors in using and knowing the two
languages may shape the impact of related benefits (DeLuca, Rothman, Bialystok, & Pliatsikas,
2019), potentially ranging from an absence of effects in passive bilinguals to positive effects in active
bilinguals with strong immersive language experience.
However, although we cannot definitively conclude which is the most relevant variable in
determining a high degree of bilingualism, we found, as expected, a significant impact of active
bilingualism in delaying the average age at symptom onset, first visit and diagnosis. The effect of
bilingualism was found in MCI but not in AD patients (for reviews, see Calvo et al., 2016; Guzman-
Velez, 2016). This result contributes to literature concerning the effect of bilingualism in MCI that
was not consistently found in previous studies (Rosselli et al., Duncan et al., Ramakrishnan et l.,
Kowoll et el.).
The fact that bilingualism acts as a CR factor only within MCI patients could be explained by
two main reasons, among others. First, it could be that the AD group lacked a sufficient level of
power. Specifically, we had only 63 patients while there were more than double this amount in the
MCI group; therefore, it might be the case that a larger amount of noise cancelled out any small effect
of bilingualism in the AD group while, in contrast, there was enough power in the MCI group to
capture such an effect. Second, it could be that the protective effect of bilingualism in delaying the
impact of disease acts only until a certain stage of pathology and then it reverses. Even if it is true
that, according the neural reserve hypothesis, people with a higher level of CR may cope better with
the symptomatology of cognitive decline despite having a larger amount of neuropathology, this
might depend on the stage of the disease itself. According to this hypothesis (Stern, 2012), it is
expected that bilingual AD patients would tolerate disease-related pathology more than monolinguals
(or passive bilinguals), but their rate of decline over time would be faster than that of monolingual
individuals. That is, once AD emerges, there is a point at which the underlying pathology is so severe
that any possible cognitive advantage would no longer be maintained and individuals with both high
and low levels of CR would show the same cognitive decline (Stern, Albert, Tang, & Tsai, 1999;
Stern, Tang, Denaro, & Mayeux, 1995). Therefore, it might be the case that, in our sample, AD
patients with a higher degree of bilingualism had already reached the point at which the
neuropathology was severe enough to counteract the protective effect of speaking two languages (for
a difference in brain atrophy patterns between MCI and AD in bilinguals and monolinguals, see also
Duncan et al., 2018). Additionally, the null effect found in AD patients might be explained by the fact
that the benefit associated with active bilingualism in our study was smaller than that reported by
other studies, where monolinguals and bilinguals were compared. Previous studies with AD patients
found a protective effect of bilingualism of 4-5 years (Alladi et al., 2013; Anderson, Saleemi, &
Bialystok, 2017; Chertkow et al., 2010; Craik, Bialystok, & Freedman, 2010; Ossher, Bialystok,
Craik, Murphy, & Troyer, 2013; Woumans et al., 2015; Woumans, Versijpt, Sieben, Santens, &
Duyck, 2017; for a review Calvo et al., 2016); however, in our study it was reduced at about 2 years
in MCI likely because we compared active and passive bilinguals. Therefore, the smaller effect found
in MCI could have been undetected in the AD group, where the neuropathology had increased to such
an extent that the positive effects of CR associated with bilingualism was not able to compensate for
the presence of cognitive deficits.
Interestingly, the effect of bilingualism on delaying cognitive symptoms and the onset of
diagnosis in MCI patients was independent from other CR factors such as occupation, leisure
activities and education. To measure the contribution of these factors on CR, we used a questionnaire
(Nucci et al., 2012) that included items for all three dimensions. The results of the regression analyses
did not show a positive effect on delaying the age of disease onset or cognitive symptoms for any of
these CR contributors. Additionally, after controlling for these factors, our results showed that the
effect of actively speaking two languages predicted age of onset (~.35) as well as cognitive decline
(~.30). This adds a new piece of evidence into the debate surrounding the effect of bilingualism on
dementia and it suggests a need to control for these other CR factors which were not controlled for in
some prior studies. Moreover, we can exclude the possibility of immigration acting as a confounding
variable in our sample, an issue faced in many studies concerning the effect of bilingualism on CR. In
fact, our more active bilinguals were those that were native Catalan speakers and passive ones were
immigrant individuals; in other words, the opposite situation existed in our study sample compared to
the usual trend of bilinguals also being immigrants (e.g., Bialystok et al., 2007; Chertkow et al., 2010;
Kowoll et al., 2016).
In replicating the effect of bilingualism on delaying cognitive symptoms and the age of diagnosis
for MCI, the challenge still remains in explaining the origin of such an advantage in
neurodegenerative diseases. In our study, we explored the underlying cognitive mechanisms by
investigating individual performance on several EC and long-term memory tasks. The main reason
behind focusing our research on EC came from extensive previous research in healthy individuals that
found evidence of a bilingual advantage in this cognitive domain (e.g. in older adults Bialystok et al.,
2004, 2008; for reviews see Lehtonen et al., 2018; Paap et al., 2015; Valian, 2015). The standing
hypothesis is that bilinguals continuously use language control mechanisms to avoid cross-language
interference and this lifelong training in control would transfer its benefits to domain-general
cognitive processes, resulting in increased efficiency within the EC system (Prior & Gollan, 2013;
Timmer, Calabria, & Costa, 2019). Therefore, one could expect that lifelong training of actively
speaking two languages would be beneficial in counteracting the negative effects of cognitive decline
in bilinguals. Indeed, some studies have demonstrated that EC networks in older bilinguals have
higher neural efficiency than those of monolinguals (Gold et al., 2013; 2015; Luk et al., 2011) or are
less susceptible to disruption in bilingual individuals with dementia (Borsa et al., 2018; Perani et al.,
The novel portion of our study was to include a set of EC tasks aimed at assessing three sub-
processes defined by Miyake et al. (2000) as the main components of the system, namely switching,
updating, and inhibitory control. According to the existing evidence of the bilingual advantage, we
expected to see an effect of active bilingualism on the inhibitory control system, observed in older
adults’ performance on the flanker task, the Simon task and the ANT (e.g., Anderson et al., 2017;
Bialystok et al., 2004; Bialystok & Craik, 2010; Goral et al., 2015). The evidence of a bilingual
advantage in working memory is scarce, mostly because of the limited number of published studies
involving older bilinguals (Grundy & Timmer, 2017). Similarly, the bilingual advantage in switching
abilities remains controversial as it has been shown in few studies (Prior & Gollan, 2011; Prior &
Macwhinney, 2010) and not always replicated (Hernández, Martin, Barceló, & Costa, 2013).
In the present study, although we did not find an effect of active bilingualism across all EC tasks,
we did find a consistent effect in tasks classified as inhibitory control tasks according to Miyake and
colleagues. The result of this “limited” bilingual advantage effect might be related to the fact that
people who had a higher degree of bilingualism also showed a delay in the onset of cognitive
symptoms. Therefore, one might expect no group difference in cognition since these individuals
already have an advantage in terms of cognitive decline for its delayed onset. In most of the studies
where bilingualism has been shown to be a CR factor, monolinguals and bilinguals were matched for
the degree of cognitive impairment as a way of excluding potential differences between groups due to
disease impact (Calvo et al., 2016). Thus, in order to find evidence of an advantage in EC, one should
compare bilinguals and monolinguals who were diagnosed at the same age and see whether bilinguals
outperform monolinguals in cognitive control tasks.
Nevertheless, the use of experimental tasks in our study aimed at providing more fine-grained
measures (for instance, RTs) of cognitive system functioning and thereby better capture individual
differences. The results showed that participants with higher degrees of bilingualism exhibited a lesser
conflict cost than those with passive bilingualism and, similar to other findings, the bilingual
advantage on the spatial Stroop task was independent from other CR factors measured with the CRI.
The question as to why active bilinguals outperformed passive ones only in this task may have an
explanation within the bilingual language control system. According to Green and Abutalebi (2013),
eight different processes are involved in bilingual language control, but three of them are highly
engaged in individuals who actively speak their two languages in the same context: goal maintenance,
conflict monitoring and interference suppression. Goal maintenance is required to maintain the
activation of one language while the other two processes are necessary for avoiding cross-language
interference from the non-intended language. Additionally, for accurate selection of a new language
when required by the context, interference suppression would modulate the activation/inhibition of the
other language. According to Green and Abutalebi (2013), all these processes are strictly related to the
inhibitory control needed for the selection of the output, as much in one language as in the other.
Interestingly, this cognitive perspective complements the findings that domain-general areas of
conflict monitoring are also involved in response selection in bilinguals (Branzi, Della Rosa, Canini,
Costa, & Abutalebi, 2016). Neural models of bilingualism (Abutalebi & Green, 2008; Calabria, Costa,
Green, & Abutalebi, 2018) have also proposed that more anterior areas of the brain, including the
anterior cingulate cortex and prefrontal cortex, are typically active during language switching and
language selection tasks, especially when bilinguals are placed in dual-language circumstances
(Abutalebi & Green, 2016; Borsa et al., 2018). Interestingly, the anterior cingulate cortex is also
involved in conflict and error monitoring tasks within EC (Botvinick, Nystrom, Fissell, Carter, &
Cohen, 1999) and bilinguals use this structure more efficiently than monolinguals to monitor non-
linguistic conflicts (Abutalebi et al., 2012).
Therefore, according to these findings, we might speculate that more efficient non-linguistic
processes of conflict monitoring in active bilinguals, via inhibitory control, may contribute to
compensation of cognitive decline symptoms and, in turn, delay their onset. The fact that the bilingual
advantage was not found in the flanker task most likely means that the benefits rely more on the
response selection rather than at the stimulus interference level. Indeed, to solve the conflict in the
spatial Stroop task, individuals needed to be more efficient at solving the incompatibility between the
location of the stimulus and the response. This incompatibility is not present in the flanker task, but it
is for language selection and, consequently, inhibitory control is enhanced in bilinguals who switch
more and actively use both languages. Finally, active bilinguals did not outperform passive ones in the
long-term memory tasks, as found in some studies that contrasted bilinguals and monolinguals with
neurodegenerative diseases (Perani et al., 2017; Rosselli et al., 2019). This means that the delay of
cognitive symptoms of MCI patients is not completely explained by a protective effect originating
from a more efficient memory system. One of the hypotheses of the underlying mechanisms of CR is
that individuals with higher levels of CR would be protected from the negative effects of the
neuropathology because they have developed a more efficient memory system (Barulli & Stern, 2013;
Scarmeas & Stern, 2003; Stern, 2012; Stern et al., 2018). A better memory would give them a higher
threshold for receiving a diagnosis of dementia and thus high-CR individuals would be diagnosed
later compared to individuals who have lower levels of CR. This is one of the possible hypotheses that
we proposed to explain the bilingual advantage, since recent findings have shown that memory-
related areas could be less impacted by the effects of aging in people who speak two languages (Heim
et al., 2019). Moreover, Grant et al. (2014) proposed that the posterior-to-anterior shift of brain
activity observed in monolingual older adults while they performed memory tasks (Davis et al., 2008)
is not expected in bilinguals. Instead, bilinguals would show a preservation of the brain activity in the
posterior regions, thus allowing them to have a more efficient memory and to be protected against
cognitive decline. However, our results do not support the view of a more efficient memory system in
active bilinguals versus passive bilinguals, at least concerning the capacities we tested, such as non-
However, our study has some limitations. First, we were not able to find a positive effect of
bilingualism in AD, as has been previously reported in other studies (for a review Calvo et al., 2016).
While we were able to provide some plausible explanations of this null effect, we have to
acknowledge that the lack of statistical power for a smaller sample size might explain why active and
passive AD bilinguals had a similar age at the onset of symptoms, first visit, and diagnosis.
Accordingly, the results from MCI patients are more reliable given that the sample was larger.
Second, we failed to find an effect of gender on CR benefits associated with bilingualism. Some
studies have highlighted that the gender is a relevant variable in the prevalence of AD (Malpetti et
al., 2017) and L2 acquisition (van der Slik et al., 2015). In our study, the number of female and
male individuals was balanced within the two patient groups and this might have hidden an
effect of gender in delaying the cognitive decline and modulating task performance. Indeed, it
has been shown that women are disproportionally more affected with AD than men (Mielke,
Vemuri, & Rocca, 2014) and this factor should be into account since it might modulate the
magnitude of CR benefits (Rocca, 2017). Therefore, the fact that we used a balanced number
of men and women in patient groups might have limited the possibility of finding a
modulatory effect of gender on CR and bilingualism.
Finally, some studies have highlighted that the self-rating of language proficiency is not
always a reliable measure of bilinguals’ true language proficiency (MacIntyre, Noels, &
Clément, 1997; Marian, Blumenfeld, & Kaushanskaya, 2007). In our study, we only used
self-ratings and we acknowledge that this method might have captured some subjective
distortions of their perceived language proficiency.
Nevertheless, these limitations can help to shape the future directions of research on
bilingualism and CR. First, future studies should consider bilingualism as a continuous
measure instead of comparing monolinguals and bilinguals as categorical variables. This
method allows us to define the relative influence of each linguistic dimension of bilingualism
on relative cognitive advantage. In the same vein, objective measures of language proficiency
should be adopted, allowing studies to overcome the problem of reliability in subjective
measures (Tomoschuk, Ferreira, & Gollan, 2019). Second, future research should explore the
effect of bilingualism on CR with longitudinal studies (Costumero et al., 2020). Studying how
bilingualism benefits individuals with neurodegenerative diseases over time is crucial for testing the
CR hypothesis (Stern, 2012). According to this hypothesis, the benefits of CR should last until a
certain degree of neuropathogoly is reached and then, beyond such a threshold, a slopper decline in
cognition should be observed in patients with higher CR compared to those with lower CR. The
research on the long-term benefits of speaking two languages could also add evidence to this
hypothesis. Third, future research should investigate the effect of bilingualism in other pathologies,
such as Parkinson’s or Huntington’s disease (Calabria, Cattaneo, & Costa, 2017). These two
pathologies are good candidates since they primarly affect EC processes and we know that EC is
intimately related to bilingual language control. Because this relationship is the supposed origin of the
bilingual advantage (Abutalebi & Green, 2016), we should expect a more consistent advantage on EC
measures for bilingual patients with Parkinson’s or Huntington’s disease compared to other bilingual
patients with less pathological affectation of EC functions.
In conclusion, our results replicate previous findings that speaking two languages may delay the
cognitive symptoms of MCI. Moreover, we were able to show that the effect of bilingualism was
independent from other CR factors such as education, leisure activity, and occupation. Additionally,
we suggest the use of a bilingual composite score as a better way to reduce the impact of potential
socio-cultural group differences when comparing bilinguals to monolinguals.
This work was supported by the Fundació La Marató de TV3 (373/C/2014), the European
Union’s Seventh Framework Program for Research (No. 613465), the Agencia Estatal de
Investigación (AEI, National Research Agency) and Fondo Europeo de Desarrollo Regional (FEDER,
European Regional Development Fund) under the projects PSI2017-87784-R and RED2018-102615-
T. The Ramón y Cajal research program of the Spanish Ministry of Science, Innovation, and
Universities supported MC (RYC-2013-14013) and MH (RYC-2016-19477).
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Descriptive statistics for socio-demographic variables, language profile, and CRIs for AD patients, MCI patients and healthy controls
Age at onset of symptoms
Age at first visit
Age of diagnosis
Age at testing
Years of Catalan
Years of Spanish
Language usage (%)
Language switching score
Results of the multiple regression analyses and partial correlation coefficients between socio-demographic variables and CRI scores,
bilingualism and neuropsychological assessment for MCI and AD patients.
Age at symptoms
Bilingualism factor score
R2 = .20
Cognitive factor score
Bilingualism factor score
Cognitive factor score
Age at first visit
Bilingualism factor score
R2 = .19
Cognitive factor score
Bilingualism factor score
Cognitive factor score
Age at diagnosis
Bilingualism factor score
R2 = .20
Cognitive factor score
Bilingualism factor score
Cognitive factor score
Figure 1. Partial regression plots for the effect of bilingualism on age at symptom onset, first visit, and diagnosis
Figure 2. Partial regression plots for the effect of bilingualism on the performance on EC and memory tasks.