REVIEW ARTICLE

Bilingualism: A Neurocognitive Exercise
in Managing Uncertainty

a n o p e n a c c e s s

j o u r n a l

Jason W. Gullifer

and Debra Titone

Department of Psychology, McGill University, Centre for Research on Brain, Language and Music, Montréal, Canada

Keywords: bilingualism, neurocognition, adaptation, uncertainty, entropy, individual differences

ABSTRACT

Bilinguals have distinct linguistic experiences relative to monolinguals, stemming from
interactions with the environment and the individuals therein. Theories of language control
hypothesize that these experiences play a role in adapting the neurocognitive systems
responsible for control. Here we posit a potential mechanism for these adaptations, namely that
bilinguals face additional language-related uncertainties on top of other ambiguities that regularly
occur in language, such as lexical and syntactic competition. When faced with uncertainty in the
environment, people adapt internal representations to lessen these uncertainties, which can aid
in executive control and decision-making. We overview a cognitive framework on uncertainty,
which we extend to language and bilingualism. We then review two “case studies,” assessing
language-related uncertainty for bilingual contexts using language entropy and network scientific
approaches. Overall, we find that there is substantial individual variability in the extent to which
people experience language-related uncertainties in their environments, but also regularity
across some contexts. This information, in turn, predicts cognitive adaptations associated with
language fluency and engagement in proactive cognitive control strategies. These findings
suggest that bilinguals adapt to the cumulative language-related uncertainties in the environment.
We conclude by suggesting avenues for future research and links with other research domains.
Ultimately, a focus on uncertainty will help bridge traditionally separate scientific domains, such
as language processing, bilingualism, and decision-making.

INTRODUCTION

Bilinguals, people who know and use more than one language, have different linguistic expe-
riences relative to monolinguals, who know only one language. These experiences stem from
different interactions with their environments and the individuals therein. Whether someone is
trying to decipher multilingual signs at high speeds on the highway, order coffee in a bilingual
city, or communicate academic research to multilingual peers, the people involved in these
interactions bring to the table their individual levels of language knowledge, language fluency,
language preferences, overt goals, and covert intentions. Bilingual environments thus have
fluctuating language demands (Anderson et al., 2018; Beatty-Martinez et al., 2020; Bice &
Kroll, 2019; Grosjean, 2001; Gullifer et al., 2021; Gullifer & Titone, 2020a; López, 2020;
López et al., 2020; Tiv et al., 2020b), which correspond to a set of cognitive, linguistic, and
social uncertainties. Individuals must resolve or adapt to these uncertainties by tuning the neu-
rocognitive systems responsible for language and cognitive control (Abutalebi & Green, 2016;
Green & Abutalebi, 2013; Green & Wei, 2014).

Citation: Gullifer, J. W., & Titone, D.
(2021). Bilingualism: A neurocognitive
exercise in managing uncertainty.
Neurobiology of Language, 2(4),
464–486. https://doi.org/10.1162/nol_a
_00044

DOI:
https://doi.org/10.1162/nol_a_00044

Supporting Information:
https://doi.org/10.1162/nol_a_00044

Received: 27 January 2021
Accepted: 10 June 2021

Competing Interests: The authors have
declared that no competing interests
exist.

Corresponding Author:
Jason W. Gullifer
jason.gullifer@mail.mcgill.ca

Handling Editor:
Anthony Steven Dick

Copyright: © 2021
Massachusetts Institute of Technology
Published under a Creative Commons
Attribution 4.0 International
(CC BY 4.0) license

The MIT Press

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

/

.

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

Fundamentally, bilinguals make choices about which languages to speak when and with
whom, and they must appropriately engage their language systems to realize these choices.
Even after an intended language has been chosen, bilinguals continue to experience lasting
cross-language activation and competition within their linguistic subsystems that can help or
hinder comprehension and production (Gullifer et al., 2013; Gullifer & Titone, 2019). To pro-
duce a word or utterance in the intended language, bilinguals must resolve this competition,
otherwise bilingual speech would exhibit rampant language errors. However, bilinguals rarely
commit this type of speech error (Poulisse, 2000); they have no apparent issue producing the
intended language. At the same time, there is evidence that some types of cross-language com-
petition may never be fully resolved, even in language production (Jacobs et al., 2016).

One thought is that bilinguals recruit a form of cognitive control to help manage cross-
language competition. Cognitive control is an umbrella term that refers to a set of latent
cognitive functions that may be differentially recruited by cognitive tasks (e.g., inhibition,
monitoring, updating, planning, switching; Miyake et al., 2000). Thus, the psychological
mechanisms implicated in bilingual cognitive control are many and are frequently under
debate (Costa et al., 2008; Declerck, 2020; Declerck et al., 2019; Gullifer & Titone, 2020b;
Kang et al., 2020; Ma et al., 2016). Neurally, there appears to be a broad network of brain
regions involved in language control, including cortical regions (notably, frontal cortex),
subcortical regions (notably, dorsal striatal regions: caudate and putamen), and cerebellar
regions. Regular recruitment of these systems over the lifespan leads to adaptive changes in
behavior and underlying brain architecture, including gray and white matter structures
(Abutalebi & Green, 2016; Bialystok, 2017; Pliatsikas, 2020).

However, there are several mutually nonexclusive points of debate surrounding these is-
sues (Baum & Titone, 2014; de Bruin & Della Sala, 2019; Hilchey & Klein, 2011; Leivada
et al., 2020). Stable patterns of adaptations are not consistently observed across studies and
geographic locations. This variation has led to questions about whether the observed cognitive
adaptations are due to low-powered investigations, questionable research practices, and hu-
man biases (de Bruin et al., 2015; Donnelly et al., 2019; Lehtonen et al., 2018; Paap et al.,
2015, 2019) or whether they are small effects that vary with respect to the population involved
(Bialystok et al., 2016; Gullifer & Titone, 2020b). While some critiques of methodological
practices are valid, in our view, they cannot simply explain away an entire body of evidence,
particularly when emerging studies with extremely high bars for methodological rigor largely
confirm prior results (Gullifer, Pivneva, et al., 2021; Gullifer & Titone, 2020b).

Of greater relevance, there are several substantive questions that warrant further investiga-
tion. Which neurocognitive mechanisms are involved in these adaptations, and are they specific
to language (Declerck, 2020; Gullifer & Titone, 2020b; Paap et al., 2019; Pivneva et al., 2014;
Takahesu Tabori et al., 2018)? How do these adaptations change over time, during language
learning/acquisition (Bogulski et al., 2019; Byers-Heinlein et al., 2017; Chai et al., 2016), and
as a function of learning, usage, and immersion (DeLuca et al., 2019; Pliatsikas, 2020)? Finally,
there are questions about which bilingual experiences are important, and how the context of
language usage, which might differ according to geographical locations, impacts these adapta-
tions (Adler et al., 2020; Beatty-Martínez & Dussias, 2017; Gullifer, Kousaie, et al., 2021; López
et al., 2020; Zirnstein et al., 2019).

The Current Study

In this review, we propose that centralizing bilingualism within a cognitive-linguistic frame-
work that emphasizes the more general idea of uncertainty provides a fruitful way to think

Neurobiology of Language

465

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

Entropy:
A concept from physics and
information theory that quantifies the
amount of uncertainty in a system, or
the potential of a system to convey
information.

about these issues. Uncertainty is a key principle in many domains of science, and it figures
centrally in neurobiology, attention, decision-making, and language processing. In the past,
the systems and principles underlying language were often studied separately from those un-
derlying cognition. However, the human neurocognitive system is best viewed as a set of in-
teractive and adaptive systems, and bilingualism has likely played a central role in elucidating
the linkages between language and other cognitive systems (Kroll et al., 2014). Namely, the
cognitive neuroscience of bilingualism is beginning to reveal the ways in which the cognitive
systems adapt to cope with the demands of the environment, which will differ according to
several factors and across geographical locations. Here, we first describe a cognitive-linguistic
perspective on uncertainty, in which uncertainty becomes a facet between these two fields.
We then highlight the advantages of this approach, that is, how each field can mutually benefit
the other, and describe some recent applications of uncertainty to the study of bilingualism.
Lastly, we pose some directions for future research.

A COGNITIVE-LINGUISTIC PERSPECTIVE ON UNCERTAINTY

As humans, we encounter various forms of uncertainty as we move through our daily lives.
These types of uncertainty occur with varying frequency (some occurring every day, others
once in a lifetime). They also carry consequences of varying magnitudes: What will I cook
for dinner; can I afford to cook dinner? Should I speak in English or French to this new person?
When will a vaccine be universally available to curb a global pandemic? Some uncertainties
may be unexpected, such as the onset of the COVID-19 global pandemic. Other uncertainties
may be expected, for example, in the case that money is routinely tight at the end of the
month, or the possibility of using either language that you know within a highly bilingual en-
vironment. Individuals must adapt their decision-making processes and underlying neurocog-
nitive mechanisms to cope with such uncertainties. Language provides an optimal domain in
which to study the impact of uncertainty because linguistic environments are rife with uncer-
tainties at multiple levels of representation. Crucially, people who are bilingual experience all
the typical uncertainties associated with language, as well as the added uncertainty of choos-
ing a particular language according to the demands of particular moments.

Uncertainty can be measured with a quantity known as entropy, a concept from physics
and information theory. Physically, entropy is a property of systems that is proportional to the
log-number of different configurations, or states, of those systems. Claude Shannon, a founder
of information theory, adapted entropy as a means to quantify the uncertainty of signals as
proportional to the number of potential signals that could have been received (Shannon,
1948; for a succinct history of entropy, see Hirsh et al., 2012). This uncertainty, in turn, relates
to the potential of a signal to carry information (surprisal). If a particular signal (or event) is
highly likely, it is not very surprising and carries little information. In contrast, an unlikely
event is more surprising and carries more information.

Uncertainty at the General Cognitive Level

Uncertainty and entropy have been used in psychological and neurocognitive theories such as
the psychological entropy framework (Hirsh et al., 2012) and the free energy principle
(Feldman & Friston, 2010; Friston, 2010; Peters et al., 2017), in the domains of decision-
making, stress, and anxiety. Fundamentally, these perspectives state that self-organizing
complex systems, like the brains or minds of humans, must maintain equilibrium within an
ever-shifting environment. They do so by limiting the possible set of internal states that can
be occupied by these systems (e.g., sensory states, brain states), which helps to minimize

Neurobiology of Language

466

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

surprisal for events that occur in the external environment. Failures to adapt to the environment
may lead to stress and anxiety, and, over the long term, other diseases (Peters et al., 2017).

People are sensitive to the statistical regularities that occur in their environments, and they
build expectations or heuristics that allow them to make inferences about upcoming informa-
tion or rewards. In contexts where a particular outcome is certain, heuristics can aid decision-
making. However, in novel contexts or when outcomes become otherwise uncertain or
ambiguous, such heuristics could fail, requiring reanalysis. To prevent this, in cases of uncer-
tainty, people become less sensitive to prior top-down heuristics: They suppress the use of
previously informative cues and expend cognitive effort to reduce uncertainty. In other words,
when people encounter uncertainty, they should lower the anticipation of an expected re-
ward. Task performance may become more variable as people try new strategies to learn more
about the context and seek out further information that could be used to make inferences (Hsu
et al., 2005; Yu & Dayan, 2005; see also, Kosciessa et al., 2021).

Neurally, decision-making in the face of uncertainty is thought to involve a fronto-striatal
network with differential involvement for unexpected and expected uncertainties (Elliott et al.,
2003; Hsu et al., 2005; T. Wu et al., 2020). This network interacts with broader networks in-
volved in cognitive control, including the frontal-parietal network and the cingulo-opercular
network (including anterior cingulate cortex, supplementary motor area, and insula; T. Wu
et al., 2020). Recent evidence suggests that the thalamaus may play a central role in cortical
shifts that occur during decision-making under uncertainty (Kosciessa et al., 2021). To give
one example, when comparing situations with unexpected uncertainties, where there is risk
that is unknown beforehand (e.g., a deck of cards where probabilities are unknown; also
called ambiguous choices), to situations with expected uncertainties, where risk is known be-
forehand (e.g., a familiar deck of cards where probabilities are known; also called risky
choices), there is differential activation of frontal (orbitofrontal cortex) vs. striatal (basal gan-
glia, caudate) areas. Expected uncertainties appear to activate striatal systems, whereas unex-
pected uncertainties down-regulate the striatal system and up-regulate orbitofrontal cortex
(Hsu et al., 2005). The two types of uncertainty also involve different neurotransmitters that
are thought to optimize learning and decision-making, with unexpected uncertainties regulated
by norepinephrine and expected uncertainties regulated by acetylcholine (Yu & Dayan, 2005).
Correspondingly, expected uncertainties are thought to rely on model-based, top-down mech-
anisms whereas unexpected uncertainties are thought to down-regulate model-based mecha-
nisms in favor of bottom-up mechanisms.

Uncertainty in Language

In the traditionally separate domain of language, the notion of uncertainty has also been a
central concept by way of ambiguity. Ambiguities can occur within a language at many levels
of linguistic representation. For example, we encounter ambiguous words with multiple
meanings, such as the word bank in English, which could refer to the edge of land near a body
of water or a financial institution. Ambiguities can occur at other levels of processing as well,
such as in phrasal attachment at the syntactic level. In the sentence The man threatened the
student with the knife, the prepositional phrase (with the knife) can either attach to the first
noun phrase (the man) or the second noun phrase (the student) leading to interpretations
where either the man or the student is carrying the knife.

For many readers, these types of ambiguities go unnoticed, because they tend to have a pre-
ferred or expected reading. Occasionally expected readings go awry, resulting in amusing in-
terpretations of sentences or news headlines. In the case of the headline woman pushes brown

Neurobiology of Language

467

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

bear as it climbs over fence to save her dogs, many readers may have been left wondering what
the woman did to her dog that prompted a bear to intervene. A key focus in psycholinguistics
has been to investigate how people resolve these types of ambiguities and misinterpretations in
the moment during comprehension and production. Do comprehenders simply rely on a strict
set of processing heuristics to reduce memory burden and interpret a sentence (Frazier, 1979;
Gibson, 1998), or do they use all available information in the context to make a flexible parse
(MacDonald & Seidenberg, 2006; Trueswell et al., 1994)? Generally, there is evidence for the
use of both heuristics and contextual integration, which can be captured by information
theoretic perspectives centered on the tracking and updating of uncertainty (Levy, 2008;
Levy et al., 2009). Here, bilingualism provides a unique perspective on this debate because
languages tend to differ in their attachment preferences, and thus readers experience
competition between their languages in terms of the best parse. There is evidence that
exposure and the behavioral context matter, with observations that the parsing heuristics in
an individual’s native language can shift toward their preferences in their second language
after a period of immersion (Dussias & Sagarra, 2007). While it may be tempting to consider
bilingualism as a special case of language processing, this would be unwise because it is esti-
mated that over half the world’s population knows more than one language. Thus, in order to
develop a more complete understanding of language and cognition, we should consider the full
diversity of individuals, from monolingual to bilingual. Uncertainty is one approach that could
capture this range of diversity in a general manner.

Uncertainty for Bilinguals

People who are bilingual must cope with all the uncertainties and ambiguities raised above that
occur within a language. Crucially, they experience an additional set of language-related uncer-
tainties as well, namely those that occur across languages. Again, these ambiguities occur at
various levels of linguistic representation, including the lexical (e.g., Duyck et al., 2007;
Gullifer et al., 2013; Libben & Titone, 2009; Van Hell & Dijkstra, 2002) and syntactic (e.g.,
Bernolet et al., 2007; Dussias & Sagarra, 2007; Loebell & Bock, 2003) levels, but are most
frequently studied at the lexical level. For bilinguals, nearly every concept can minimally be
ascribed to a word in each language, and word forms can be ambiguous across languages.
For example, in Spanish, un vaso is a drinking glass, but the word form looks strikingly like
the English word vase. While these concepts are distinct, even highly proficient bilinguals
experience momentary competition between conflicting meanings in the irrelevant language
during spoken comprehension (Titone et al., 2020; Van Hell & Dijkstra, 2002), written compre-
hension (Gullifer et al., 2013; Gullifer & Titone, 2019), and production (Dussias et al., 2016;
Gullifer et al., 2013). Managing this competition depends on individual differences in language
exposure and cognitive control abilities (Gullifer & Titone, 2019; Kroll et al., 2013, 2015, 2016;
Pivneva et al., 2014).

Competition between languages is similarly evident when bilinguals are tasked with switch-
ing between their languages (e.g., Meuter & Allport, 1999). A frequent observation from forced
language switching tasks is that trials requiring a switch in language are associated with a pro-
cessing cost relative to nonswitch trials. Often, but not always, these switches are asymmetric
in nature, where it is more difficult to switch to the, often dominant, native language and easier
to switch into the less dominant second language. This counterintuitive finding is taken as
evidence that bilinguals apply a form of control (e.g., inhibition) to the unintended language
which must be overcome when switching into that new language. Because suppression of the
dominant language requires stronger inhibition than that of the less dominant language, it is
harder to switch back to that language after it is suppressed.

Neurobiology of Language

468

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

At the same time, language switching costs can be linked to language-related uncertainty.
In fact, one of the earliest papers on language switching characterized costs as arising from
stimulus and response uncertainty (Macnamara et al., 1968). Importantly, language switching
tasks are not commonly reflective of how language is actually used. Instead, they typically
investigate lexical processing (production or comprehension) in a decontextualized manner,
where switching occurs between isolated words and where the probability of switching is
artificially controlled by the experimenter. Thus, the average language switching task could
be considered a highly uncertain situation for participants, albeit one where the probability
of switching becomes known over the course of the task. In contrast, naturalistic language
switching, as occurs in bilingual communities, tends to follow observable patterns established
by community language practices, which may function to reduce uncertainty.

In line with this view, psycholinguistic studies find that switching costs can be modulated by a
variety of situations (see Bobb & Wodniecka, 2013, for a review). For example, unbalanced
bilinguals are more likely than balanced bilinguals to exhibit asymmetric costs between languages
(Costa & Santesteban, 2004; Meuter & Allport, 1999). These bilinguals may, on average, partici-
pate in “low entropy” language environments, where the less dominant language is relatively
unlikely and benefits from strong suppression of the dominant language. In contrast, balanced
bilinguals may have adapted to higher entropy language situations in which both languages are
likely. Asymmetries or costs are also attenuated when more time is allotted to process the switch
(e.g., Verhoef et al., 2009), when bilinguals are allowed to switch at their own will (e.g., Gollan &
Ferreira, 2009), when switches are placed in sentence context (Gullifer et al., 2013; Ibáñez et al.,
2010), and when language switches follow linguistic patterns that conform to the patterns of
switching in a community (e.g., Beatty-Martínez & Dussias, 2017; Guzzardo Tamargo et al.,
2016). All of these situations might be characterized as reductions in language-related uncertainty,
and some may more closely approximate naturalistic language switching situations.

Still, in naturalistic environments, bilinguals are compelled to make decisions about which
language or languages will come next, and they constantly face a set of questions linked to
language-related uncertainty. Which of my languages do I speak with whom in the moment?
Should I choose a language I am less comfortable in to accommodate my conversational partner,
or would I express myself better with my most comfortable language at the risk of my partner failing
to understand? Will I be judged for my choice of language (politically, academically, intellectu-
ally)? In some cases, the answer to these questions is that both languages are acceptable, and
people will flexibly engage the entirety of their linguistic repertoires, as in the case of code-
switching (Lipski, 1977; Poplack, 1980) or translanguaging (García & Wei, 2012; Williams, 1994).

Language-related uncertainties start early and can be pervasive throughout the lifespan.
Even young children are aware of the social consequences of choosing a particular language
or dialect, as when Lambert (1967) recounts his multilingual daughter’s hesitancy to invite two
friends who speak different dialects for a ride to school. His daughter fears that inviting both
friends would force her to show a linguistic preference for one friend or the other. In some
cases, bilingual children as young as eight years of age may be called on to broker for their
parents in high-pressure situations, where they must translate complex information beyond
their years (e.g., legal or medical contexts). Brokering can have long-lasting cognitive and
emotional consequences (López, 2020; López et al., 2020). Thus, bilinguals routinely
encounter language-related uncertainties that depend on several factors, including the inter-
locutors, the communicative context, and individual preferences and proficiencies.

To begin to measure language-related uncertainties at a global level, we have developed a
methodological approach based on information theory (Gullifer et al., 2018; Gullifer, Kousaie,

Neurobiology of Language

469

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

/

.

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

Language entropy:
An extension of entropy; it provides
an estimate of language-related
uncertainty for an individual or
environment.

Behavioral context:
The set of statistical regularities
between objects and events within
an environment; often refers to the
properties of an experimental task.

Interactional context:
The typical patterns of language use
within a community of speakers;
could be thought of as extending the
idea of behavioral context to
language.

et al., 2021; Gullifer & Titone, 2020a). Specifically, we use language entropy as a means to
estimate language diversity and language-related uncertainty using questionnaire data. Similar
entropy measures have also been used to quantify language diversity among multilingual twit-
ter users (Eleta & Golbeck, 2014), within text-based code-switching corpora (Guzmán et al.,
2017), and for diversity in choice of programming language use among software developers
(Krein et al., 2009). We have shown that language entropy varies across communicative con-
texts within the same speakers and relates to differences in executive control engagement and
language proficiency (Gullifer, Kousaie, et al., 2021; Gullifer & Titone, 2020a, 2020b).

ADVANTAGES OF THE UNCERTAINTY APPROACH TO BILINGUALISM

In our view, a focus on uncertainty has the potential to mutually benefit and more closely
integrate multiple subdomains of cognitive science, including decision-making, language sci-
ence, and bilingualism. Attention and decision-making literature emphasize the role of uncer-
tainty in behavioral contexts, and bilingualism can provide researchers with new ways of
assessing contextual uncertainties through language. Behavioral context is defined as “a set
of stable statistical regularities that relate the myriad environmental entities, such as objects
and events, to each other and to our sensory and motor systems” (Yu & Dayan, 2005,
p. 681). Thus, the uncertainty within a context can be quantified as a function of these complex
features and interactions. Typically, contexts involve the entities and parameters within an
experimental task, such as probabilistic cueing tasks, attention shifting tasks, betting-style card
games, and generalizations of these tasks (e.g., Feldman & Friston, 2010; Hsu et al., 2005).
These tasks often contain cue-target relationships (or other probabilities) that are known or
learned over the course of the task and can be perturbed (or made ambiguous) in various ways,
allowing for the investigation of risk and ambiguity. Crucially, the concept of behavioral con-
text has been extended beyond isolated tasks into social psychological contexts (FeldmanHall
et al., 2015, 2018; FeldmanHall & Shenhav, 2019), and it may apply in a broader sense to the
social environments that people engage in during their daily lives in their communities. Thus,
out in the world, uncertainties exist, fluctuate, and interact across many levels, from personal
to ecological to societal (see the Systems Framework of Bilingualism, developed in Tiv et al.,
2021 and the topic of a keynote invited by Bilingualism: Language and Cognition [Titone &
Tiv, 2021]). Ultimately, one of the goals of cognitive science is to explain and make predic-
tions about these types of naturalistic phenomena.

The notion of behavioral context is central to many usage-based theories about language
and bilingualism, because people perceive and produce the various languages that they know
with interlocutors in their environments (such as at home or in the workplace). This rich con-
textualization of language has wide-ranging consequences for language fluency, processing,
representation, and control, and it may also carry consequences for domain general cognitive
control and underlying brain mechanisms (Adler et al., 2020; Anderson et al., 2018; Beatty-
Martinez et al., 2020; Green & Abutalebi, 2013; Grosjean, 2001, 2016; Gullifer & Titone,
2020a, 2020b; Hofweber et al., 2020; Tiv et al., 2020b). To give one example, the adaptive
control hypothesis (Green & Abutalebi, 2013) posits that language usage within particular
interactional contexts will have adaptive consequences for control and brain organization,
where interactional contexts consist of the “recurrent pattern of conversational exchanges
within a community of speakers” (Green & Abutalebi, 2013, p. 516). This notion is highly
compatible with that of behavioral context from the cognitive literature. Green and
Abutalebi delineate three specific types of contexts that are predicted to impact control pro-
cesses recruited by language: single language contexts (where primarily one language is used),
dual language contexts (where two languages are used and language switching occurs

Neurobiology of Language

470

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

/

.

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

primarily between individuals), and dense code-switching contexts (where two languages are
used and language switching occurs within individuals and within utterances).

Societies and communities may differ in aggregate along the lines of interactional context in
ways that impact language and cognitive control. For example, Beatty-Martinez and colleagues
have shown that otherwise comparable populations of highly proficient Spanish-English bilin-
guals differ in how they engage their languages. Participants living in Southern Spain tend to
engage in single language contexts, while participants in Puerto Rico and mainland USA tend
to exhibit behaviors associated with dual language or dense code-switching contexts (Beatty-
Martinez et al., 2020). They further showed that these contextual differences had consequences
for participants’ recruitment of cognitive resources for the purposes of language control.

We posit that contexts such as these differ with respect to language-related uncertainty,
with dual language and dense code-switching contexts having higher uncertainty relative to
single language contexts. The level of language-related uncertainty can be estimated, at a ba-
sic level, using entropy measures (Eleta & Golbeck, 2014; Gullifer & Titone, 2020a; Guzmán
et al., 2017), either at the aggregate level (for a sample of participants), or as an individual
difference measure (Gullifer, Kousaie, et al., 2021; Gullifer & Titone, 2020a; Guzmán et al.,
2017). An even richer characterization can be provided by network scientific approaches
(Eleta & Golbeck, 2014; Tiv et al., 2020b). Here, the entities in an environment and their in-
terrelationships are modeled as networks using graph theory, allowing for a set of measures,
including language entropy, to be extracted that provide information about the fundamental
structure of an interactional (or behavioral) context.

Thus, researchers interested in uncertainty from a cognitive, attention, or decision-making
perspective can exploit background language characteristics of participants as a sort of natural
experiment. For example, the long-term role of behavioral context in cognitive adaptation can
be investigated, between participants, by recruiting and contrasting participants who systemat-
ically vary in their language background in terms of interactional context (Beatty-Martinez et al.,
2020; Gullifer et al., 2018; Gullifer & Titone, 2020b; Hofweber et al., 2020), providing a sort of
naturalistic experiment. Within-participant comparisons can be made through longitudinal
studies, for example, by recruiting samples of participants beginning their studies in a new
(linguistic) environment and again several months later. Shorter term influences of behavioral
context can be investigated by manipulating the interactional context of the experimental
environment or interspersing cognitive tasks and language tasks that differ in language-related
uncertainty (Adler et al., 2020; Hofweber et al., 2020; Y. J. Wu & Thierry, 2013). In sum,
bilingual samples and their varied interactional contexts offer cognitive researchers a means
to investigate adaptations that occur due to uncertainty in different behavioral contexts through
observational and controlled experiments.

The neurocognitive study of uncertainty also has something to offer researchers interested
in language and bilingualism. Namely, this perspective allows for an integration with compu-
tational, neurobiologically plausible models of cognition and control (Bastos et al., 2012;
Friston, 2010; Yu & Dayan, 2005). For example, previously described entropy measures allow
for a mathematical quantification of a range of uncertainties from language-related uncertainty
with language entropy to uncertainty associated with task parameters. Uncertainty perspectives
are inherently complementary to, and often explicitly couched in, Bayesian computational
theories of cognition (Knill & Pouget, 2004). Such perspectives state that people maintain a
set of prior beliefs about their behavioral contexts which figure into the decision-making pro-
cesses. Priors are then adapted or optimized over time given exposure in the environment or
behavioral context. Bayesian statistical models can be hierarchical, allowing them to capture
the complexities of interactional contexts in a multilevel manner. Thus, with a Bayesian

Neurobiology of Language

471

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

approach, prior language demands and uncertainties could be modeled simultaneously at the
level of society, local communities, communicative contexts, and individuals. The tracking of
uncertainty also has the benefit of being a neurobiologically plausible process (Feldman &
Friston, 2010; Friston, 2010). For example, Friston (2010) applies an uncertainty perspective,
the free-energy principle, to several brain theories, including the Bayesian brain hypothesis,
efficient coding, and cell assembly theory.

In sum, by merging these perspectives within the general framework of uncertainty, we can
more tightly contrast uncertainty at two levels: local, in the moment uncertainty and global
uncertainty in the environment. Thus, the demands and processes involved in resolving local
uncertainty must take into account the properties of the global or historical context. This is an
essential link between general cognitive studies and linguistic approaches that examine how
the sociolinguistic demands impact local psycholinguistic processes. Next, we provide an
example of how uncertainty can be applied to the neurocognitive study of bilingualism by
reviewing two “case studies” in this domain.

Case Study #1: Language Entropy Captures Language-Related Uncertainty

We have used a measure of language entropy as a first approximation of language-related
uncertainty that individuals encounter in their day-to-day environments, as a way to approximate
interactional context. Language entropy is computed using Shannon entropy (Shannon, 1948),
P
H = −
n
i¼1 Pilog2(Pi). Here, entropy (H) is computed over the proportion of usage for a particular
language (Pi) in a set of languages (i = 1 to n, where n reflects the number of languages in the set).
The process can be repeated for any number of communicative contexts. Proportional usage is
derived from self-report questionnaire data commonly collected in the field, such as language
use in the home versus language use at work (Gullifer et al., 2018; Gullifer, Kousaie, et al.
2021; Gullifer & Titone, 2018, 2020a, 2020b). Importantly, the entropy measure is highly flexible
and can be adapted to a range of data sources with a range of different language sets and states
(including objective observations of language practices; e.g., Guzmán et al., 2017).

Language entropy can be thought of as providing a continuous index of language diversity
or language-related uncertainty for a particular communicative context (or individual), with a
range from 0 to some maximum value. Language entropy is at its minimum (H = 0) when one
language in a set is used all the time in that context (i.e., 100% of the time) and the other
languages never occur. A person with minimum language entropy in a context can be quite
certain that a particular language will be used, and they should experience low levels of
language-related uncertainty in this situation. The occurrence of the predictable language
would also carry little information, as it reflects business as usual. However, the spontaneous
use of another language would be highly unusual and convey information of some form.

Language entropy is at its maximum when the percentage of usage for two or more
languages is equal within a communicative context (i.e., H = 1 for a 50%−50% for a bilingual
individual; H = 1.585 for a 33%−33%−33% for a trilingual individual). A person with
maximum language entropy in a particular communicative context should experience high
levels of language-related uncertainty in this situation because either language is equipotent.
Figure 1 illustrates possible language entropy values for a bilingual individual or context.

Mathematically, language entropy carries some interesting properties. The maximum pos-
sible language entropy for a context or individual increases as a function of the number of
equally used languages (Hmax = log2(n)), illustrated in Figure 2. Thus, the largest increase in
maximum entropy occurs as the number of languages in a set increases from one to two (i.e.,

Neurobiology of Language

472

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

P

Figure 1. Relationship between L2 exposure (proportion) and language entropy for a hypothetical
bilingual individual / communicative context. Language entropy is computed using Shannon
n
entropy (Shannon, 1948), H = −
i¼1 Pilog2(Pi). In this plot, entropy (H ) is computed over a range
of proportions (0–1) for each of two languages (P1 and P2). Language entropy is at the minimum
(H = 0) when either language is used 100% of the time and the other is used 0% of the time (left
and right ends of the horizontal axis). Language entropy is at its maximum, equal to the logarithm
(base 2) of the number of languages (here, two languages; n = 2) when the percentage of usage for
two languages is equal within a communicative context (i.e., 50%−50% for a bilingual individual).
Language entropy extends flexibly to situations with more than two languages.

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

/

.

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Figure 2. Mathematical relationship between possible maximum language entropy and the
number of languages relevant for an individual or communicative context (top panel). Maximum
entropy occurs when the proportion of usage is split evenly between the number of languages.
Maximum entropy increases nonlinearly with the number of languages. The largest increase in
possible maximum language entropy occurs when the number of languages shifts from one to
two, observable in the top panel and illustrated in the bottom panel by the first derivative (rate
of change with respect to the number of languages) of the language entropy function.

Neurobiology of Language

473

Bilingualism and uncertainty

from monolingual to bilingual). This may reflect a boundary condition between monolingual
language experience and bilingual/multilingual language experience. In other words, a mono-
lingual individual who becomes bilingual has the possibility to experience a dramatic increase
in language-related uncertainty. An equivalent increase would not be possible for a bilingual
without the acquisition and usage of several additional languages. Moreover, while language
entropy increases indefinitely as new languages are added to a set, there may be practical
limits on language entropy that are imposed by a cap on the number of languages that highly
multilingual people tend to use in their environments.

We have found that bilinguals and multilinguals living in Montréal exhibit individual dif-
ferences in language entropy as a function of the communicative context (Gullifer, Kousaie,
et al., 2021; Gullifer & Titone, 2020a), and that these contextual differences are captured by
latent variable analyses. For example, Gullifer and colleagues (Gullifer, Kousaie, et al., 2021)
probed language usage and language entropy across 16 different communicative contexts or
domains (see Table 1 for descriptive statistics from that study and Figure 3 for an illustration of
the distribution of data). Using factor analysis, they identified three latent domains of language
entropy: entropy for internal aspects of language, entropy for external or professional aspects
of language, and entropy for the consumption of media (see Figure 4, adapted from Gullifer,
Kousaie, et al., 2021). Gullifer and Titone (2020a) observed a similar distinctiveness for lan-
guage entropy in professional settings. More work is needed (with expanded language history
questionnaires) to determine the ideal set of contexts within which to measure language

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Table 1. Descriptive statistics for language entropy by language context from Gullifer, Kousaie, et al. (2021)

Measure
Dreaming

Talking to oneself

Doing arithmetic

Remembering numbers

Thinking

Expressing emotion / anger

Speaking with family

Speaking with friends

Speaking with classmates

Speaking with colleagues

Writing e-mails

Writing papers

Reading for fun

Reading online

Listening to radio / watching TV

Reading for work

Neurobiology of Language

M

0.60

0.71

0.52

0.57

0.80

0.76

0.41

0.61

0.31

0.54

0.55

0.21

0.39

0.45

0.40

0.36

SD

0.42

0.38

0.45

0.43

0.30

0.35

0.42

0.35

0.36

0.41

0.39

0.32

0.40

0.39

0.38

0.42

Min
0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Max
1.15

1.39

1.35

1.00

1.39

1.53

1.00

1.13

1.00

1.00

1.00

1.00

1.00

1.03

1.00

1.00

474

Bilingualism and uncertainty

Figure 3.
adapted from Gullifer, Kousaie, et al., 2021.)

Illustration of the distribution of language entropy by communicative context. (Data

entropy and to assess the consequences of moving between contexts. However, language en-
tropy appears to provide a first approximation of the extent to which people jointly engage
their two languages, on average, within their various communicative contexts. From an uncer-
tainty standpoint, people with high language entropy, who report using two or more languages
to an equal degree in their communicative contexts, likely experience higher degrees of
language-related uncertainty in their daily lives that they learn to adapt to.

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

/

.

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Figure 4.
Illustration of the latent structure for language entropy. The vertical axis depicts each communicative context for which language
entropy was computed. The horizontal axis depicts the factor loading. Each latent factor is displayed as a separate panel, encompassing lan-
guage entropy for internal purposes, language entropy for external or professional purposes, and language entropy for media consumption.
(Figure reproduced from Gullifer, Kousaie, et al., 2021.)

Neurobiology of Language

475

Bilingualism and uncertainty

Accordingly, we have found that individual differences in language entropy are related to
neurocognitive aspects of executive control and language proficiency, suggesting that
language-related uncertainty adapts the neurocognitive systems responsible for language
and cognitive control. For example, individual differences in language entropy predict the
functional organization of brain networks implicated in language and executive control
(Gullifer et al., 2018) and aspects of language proficiency (Gullifer, Kousaie, et al., 2021;
Gullifer & Titone, 2020a), as predicted by theories of neurocognitive adaptation and control
(Abutalebi & Green, 2016; Green & Abutalebi, 2013). People with high language entropy (av-
eraged over communicative contexts) exhibit greater resting-state functional connectivity
among a network of areas associated with language and executive control (see Figure 5,
adapted from Gullifer & Titone, 2018), and greater attention to goal-relevant cues that must
be maintained to predict upcoming information in proactive control tasks like the AX-
continuous performance task (AX-CPT; Gullifer et al., 2018; see Figure 6, adapted from
Gullifer & Titone, 2020b). Comparable brain connectivity results have also been observed
in another laboratory with a qualitatively different sample of bilinguals (Sulpizio et al.,
2019), bolstering this method’s theoretical importance. Language entropy has been shown
to relate to self-report and objective language proficiency (Gullifer, Kousaie, et al., 2021;
Gullifer & Titone, 2020a), the ability to mentalize (or engage in social-cognitive processing)
in the native and second languages (Tiv, O’Regan, & Titone, 2021), and other patterns of dual-
language use such as engagement in language mixing (Kałamała et al., 2020).

On the one hand, the findings of proactive engagement of contextual information (and un-
derlying brain networks) for high entropy bilinguals might go against the predictions of
decision-making theories based on uncertainty, namely, that highly uncertain or ambiguous
situations should down-regulate predictive mechanisms. However, these results can be ex-
plained under an adaptive mechanism in which participants who routinely experience high
entropy environments may be better able to reduce internal uncertainty. We have speculated
that bilinguals might adapt to contexts with language-related uncertainty by attending to other
cues that are present in the environment. For example, phonetic or lexical cues encoded in the
linguistic signal can preempt code switches; or particular interlocutors may have a tendency to

Figure 5. Depiction of the relationship between language entropy and resting-state functional con-
nectivity. Language entropy (averaged across communicative contexts) is associated with greater
resting-state functional connectivity between regions involved in language and control, particularly
between ACC and putamen (Panel 1); and between left caudate and STG (Panel 2). ACC-putamen
connectivity was, in turn, associated with greater reliance on proactive control in a behavioral task
conducted outside the scanner. (Figure reproduced from Gullifer et al., 2018.)

Neurobiology of Language

476

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

Figure 6. Depiction of the relationship between general language entropy and performance on the
AX-CPT (reaction times). High general language entropy is associated with larger proactive cost
scores (AY [red] vs. BX [blue]), signifying greater attention to goal-relevant information that is used
in a proactive manner. (Figure reproduced from Gullifer & Titone, 2020b.)

use a particular language. These cues may be important for high entropy bilinguals who need
to identify rapidly what language will come next to resolve language-related uncertainty in the
environments at multiple levels.

There is also a possibility given our reading of the uncertainty literature, that high entropy
bilinguals adapt to linguistically uncertain environments by creating a set of internal bilingual
attractor states. For example, perhaps a new set of states is created that is related to a dual
language (Green & Abutalebi, 2013) or a bilingual mode (Grosjean, 2001). Perhaps code-
switching is a cognitive adaptation: an additional state that allows for the reduction of internal
uncertainties for bilinguals in highly diverse language environments. These internal attractor
states may provide bilinguals with an avenue for resolving language-related uncertainties dur-
ing language processing in terms of generating predictions about what type of information will
come next. If these possibilities are true, then language entropy (as a measure) may underes-
timate the diversity of language states, particularly for high entropy bilinguals. Other finer-
grained methods may be able to more accurately estimate the diversity of language states.
For instance, network science provides a means to measure entities and their interrelationships
within an interactional or behavioral context.

Case Study #2: Network Science Characterizes Behavioral/Interactional Context

While network models of multilingual language usage have been constructed from online
sources, such as Twitter (e.g., Eleta & Golbeck, 2014), they have not, to our knowledge, been
used to assess in-person, bilingual language usage. In a recent paper, we provide an example
of how network science can be leveraged to uncover information about naturalistic language
usage (Tiv et al., 2020b). We surveyed individuals about the languages that they use to discuss
several topics of conversation (e.g., politics, sports, moral issues, religious issues) throughout
different communicative contexts (e.g., at home, at work). We modeled these data as network
graphs, in which topics of conversations were treated as nodes in a graph that were connected
either by virtue of being discussed within the same context (and weighted based on the num-
ber of languages used to discuss these topics) or in the same language (and weighted based on

Neurobiology of Language

477

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

Figure 7. Depiction of the topic network for each of five communicative contexts. Nodes represent topics of conversation and edges indicate
whether topics co-occurred in each domain. Edges are weighted by the total number of languages used to discuss two topics in a given
domain: Green and blue hues indicate more languages, and pink and yellow hues indicate fewer languages. Topics that co-occur in work
contexts tend to be discussed with fewer languages. Topics that co-occur in social contexts tend to be discussed with more languages. (Figure
reproduced from Tiv et al., 2020b.)

the number of contexts they were discussed in). This allowed us to assess how topics of con-
versation co-occur within contexts and within languages.

In the context networks, we found that the various communicative contexts evidenced dis-
tinct configurations in terms of the topics that were discussed within those contexts (see
Figure 7). In particular, few languages were used to discuss topics in the work environment,
representative of highly compartmentalized language usage and low language-related uncer-
tainty for these topics. In contrast, many languages were used to discuss the topics that oc-
curred in individuals’ social contexts, representative of highly integrated language usage
and high language-related uncertainty for these topics. In the language networks, we also
found that there was greater specificity for the topics discussed in individuals’ less dominant
language relative to the dominant language. Like the results for language entropy, the results
here again confirm that language-related uncertainty can vary in a consistent manner ac-
cording to the behavioral or communicative context.

We are now expanding the level of analysis to individuals’ language-tagged social networks
(Tiv et al., 2020a) with the goal of assessing language usage for individuals (i.e., egos), between
egos and their associates (i.e., ego-alter connections), and among their associates (alter-alter
connections). Thus it will be possible to compute language entropy measures at these different
levels (Eleta & Golbeck, 2014) and assess the extent to which they covary. Ultimately, we
believe that the combination of language entropy and network science will be ideal for repre-
senting complex patterns of language practices and language-related uncertainty, as well as how
these practices align with the language practices in their broader communities.

SUMMARY AND NEW QUESTIONS

To sum up, we have brought together recent work showing how language-related uncertainty
can be measured or estimated using language entropy and network science, and we have
shown some of the interactions with other aspects of neurocognition, including language pro-
ficiency, brain organization, and proactive executive control abilities (Gullifer et al., 2018;
Gullifer, Kousaie, et al., 2021; Gullifer & Titone, 2020a, 2020b; Kałamała et al., 2020;
Sulpizio et al., 2019; Tiv et al., 2020b; Tiv, O’Regan, & Titone, 2021). This work is the
beginning of a new paradigm in the domain of language science and bilingualism, and there

Neurobiology of Language

478

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

/

.

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

are several aspects to be addressed going forward, related not only to measurement validity
and generalization but also to linking theoretical domains and findings.

Measures like entropy and those computed from network analysis provide estimates of
language-related uncertainty that are derived from self-report questionnaires. Future work
should attempt to more closely approximate naturalistic language-related uncertainties
through the use of objective measures such as corpus/dialogue analysis or the observation
of naturalistic productions among bilinguals and multilinguals. Doing so will allow for further
measurement validation and expansion of language-related uncertainty. For example, partic-
ipants could complete language history or language-tagged social network questionnaires and
then consent to having portions of their daily conversations recorded through a smartphone
app. Or, they might respond to intermittent text message probes that inquire about language
usage in the moment. Language entropy and usage patterns could be computed from the data
elicited by these instruments. An advantage of a smartphone app or text message probes is that
research could reach a broader and more diverse portion of the population than is typically
sampled in experimental psychology.

Moreover, data from other geographic locations will be crucial in assessing the generaliz-
ability of these measures, methods, and theoretical perspectives. At the moment, only a few
studies have assessed language entropy, most in the highly multilingual Montréal context
(Gullifer et al., 2018; Gullifer, Kousaie, et al., 2021; Gullifer & Titone, 2020a, 2020b; Tiv,
O’Regan, & Titone, 2021). However, there is emerging work from Italian (Sulpizio et al.,
2019) and Polish (Kałamała et al., 2020) contexts as well. Thus, more research is needed
before an initial sketch can be drawn across geographical locations and before we can deter-
mine the optimal level at which to measure uncertainty.

In terms of linking linguistic and cognitive perspectives (Feldman & Friston, 2010; Hirsh
et al., 2012; Hsu et al., 2005; Peters et al., 2017; T. Wu et al., 2020; Yu & Dayan, 2005), going
forward, we need to develop a greater understanding of how cumulative exposure to longer
term environmental uncertainties interacts with shorter term local uncertainties in the moment,
and how bilinguals represent and adjust to these uncertainties internally. This can be achieved
by hierarchically integrating data at various levels from various sources, including macro
social contextual information, such as language usage data present in population censuses;
micro social contextual information, such as language usage data at the participant level;
and local task-based information, such as language demands required by an experimental task
in the moment. There are also links to be built with other domains that we only touched on
briefly above, such as code-switching, learning, memory, and even mental health.

Links to Code-Switching and Translanguaging

A crucial question is how bilingual practices such as code-switching or translanguaging fit with
ideas of interactional context and language entropy. Code-switching is the practice of flexibly
mixing languages (Lipski, 1977; Poplack, 1980). Sometimes languages are mixed between
utterances, sentences, or interlocutors. Sometimes they are mixed within the same sentence
(dense code-switching). The adaptive control hypothesis posits that dense code-switching
contexts are theoretically distinct from dual language contexts, requiring the engagement of
different control modes or cognitive mechanisms. However, in many ways dual language
contexts could be viewed as a precondition for dense code-switching to occur. Code-switching
tends to occur between bilinguals (who prefer to code switch) when the use of two languages is
jointly viewed as acceptable, conditions that can be satisfied by a dual language context. While
we have not assessed how language entropy relates to code-switching practices in Montréal,

Neurobiology of Language

479

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

others have shown that rates of language mixing are higher for high entropy bilinguals (Kałamała
et al., 2020), suggesting that the two are correlated. At the same time, not all bilinguals code-
switch, even if they are continually exposed to highly integrated or uncertain (high entropy)
linguistic environments. People who routinely engage in high entropy situations should develop
internal attractor states that allow them to reduce internal entropy and predict upcoming infor-
mation. For example, people could attract to a particular language state (e.g., either English or
French) and default to a particular language; they could attract to a bilingual (French + English)
state that results in frequent language switching between individuals or contexts; or they could
attract to a code-switching state that involves frequent, dense code-switching. Here, there are
likely be individual tendencies, but people may also be influenced by aspects of the social con-
text, including their interlocutors (Kootstra et al., 2010).

Translanguaging is a perspective on bilingual language practices that is ostensibly similar to
language switching (García & Wei, 2012; Williams, 1994). However, it characterizes lan-
guage in a way that is distinct from typical conceptualizations in psycholinguistics, linguistics,
and applied linguistics. These traditional perspectives tend to view languages as discrete en-
tities in the environment. For example, although psycholinguistics shows evidence for cross-
language activation during production and comprehension, and it often models the bilingual
mind as massively integrated (e.g., Bernolet et al., 2007; Dijkstra & van Heuven, 1998, 2002;
Hartsuiker & Pickering, 2008; Li & Farkas, 2002; Shook & Marian, 2012), there is a dominant
focus on aspects like “native language” and “second language” and other individual traits, like
proficiency, age of acquisition, and language dominance. These aspects are largely antithet-
ical to translanguaging, which refers broadly to the language practices that bilinguals and mul-
tilinguals engage in. Translanguaging views languages as social constructs (largely imposed by
monolingual majorities) as opposed to “ontologically real” entities (Makoni & Pennycook,
2007). Thus, in this perspective, language usage among bilinguals and multilinguals tran-
scends the usage of individual languages, independently or jointly. In some ways, we view
language entropy and (to some extent) network approaches as compatible with translangua-
ging. For example, entropy provides a measure that abstracts away from individual languages,
and instead measures the diversity of or uncertainty associated with language usage. At the
same time, in order to compute language entropy, information about usage of particular lan-
guages is elicited from participants, meaning that it is not completely abstracted away.

Links to Learning and Memory

Mastering a second language is notoriously difficult, and recently the process of language ac-
quisition has been characterized as a desirable difficulty (Bjork & Kroll, 2015; Kornell et al.,
2009). A desirable difficulty is one in which there are initial costs to learning or performance that
facilitate or enhance later learning. Desirable difficulties specifically engage the core processes
involved in learning, comprehension, and memory. They include variable learning conditions
(as opposed to predictable learning conditions), spaced study sessions (as opposed to mass study
sessions), and interleaved practice (as opposed to blocked practice). Desirable difficulties have
been applied to language learning through the observation that bilingualism often results in
observable costs during language processing (thought to be the result of cross-language
activation or competition) but other benefits in certain aspects of novel language learning
(Kaushanskaya & Marian, 2009a, 2009b) and executive control abilities (Bialystok et al., 2012).

We note that several aspects of a desirable difficulty approach can be linked to notions of
uncertainty. For example, inducing variable learning conditions and interleaving practice all
function to increase uncertainty with respect to the nature of the task or learning environment.

Neurobiology of Language

480

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

Moreover, in the uncertainty literature on decision-making there are suggestions that unexpected
uncertainties in a new behavioral context encourage the exploration of new options, as
participants try to identify the operative states that are conducive to task performance (Hirsh
et al., 2012; Yu & Dayan, 2003, 2005). Thus, when faced with uncertainty, task performance
becomes more variable and may encourage learning in the short term. Over the long term, learners
may adapt their neurocognitive systems to expect or otherwise manage the types of persistent,
ambient uncertainties that regularly occur in the environment (e.g., Beatty-Martinez et al.,
2020). These adaptations could take many forms, including shifting expectations about altering
linguistic material, altering cognitive control strategies, or incorporating code-switching or trans-
languaging practices. Ultimately these adaptations could allow for better control over language
(Gullifer & Titone, 2020b) and changes in subjective and objective language proficiency (Gullifer,
Kousaie, et al., 2021; Gullifer & Titone, 2020a).

However, there are issues to be resolved between an uncertainty perspective and a desir-
able difficulties perspective. For example, a key notion in desirable difficulties in language
learning is that suppression of the native language plays a key role in the process of learning
another (e.g., third) language (Bjork & Kroll, 2015; Bogulski et al., 2019). Thus, it may not
solely be increases in general uncertainty that encourage language learning, but uncertainty
that specifically involves the native language.

Links to Language-Related Stress and Anxiety

Bilingual environments have been associated with language-related stress and anxiety for in-
dividuals who do not adapt to an immersion environment. This is shown primarily through
social network analysis. The structural properties of individuals’ networks have implications
for language proficiency, educational outcomes, and overall well-being. For example, when
considering people who move to a new linguistic environment (e.g., students during study
abroad or immigrants in a new country), social network structure (network size, density, inter-
connectedness) is positively associated with proficiency gains during language learning and
educational outcomes (Baker-Smemoe et al., 2014; Doucerain et al., 2015; Gollan et al.,
2015; Wiklund, 2002) as well as individuals’ overall sense of well-being. Notably, people with
larger social networks during language immersion (i.e., networks from the host country) have
fewer instances of language-related stress and depression (Church, 1982; Hendrickson et al.,
2011). Inclusiveness and density of second language networks have been associated with the
degree of communication-related stress in an immersion environment (Doucerain et al., 2015).
In turn, a learner’s ability to cope with stressors is related to willingness to communicate and
confidence in using that language: Students who are less burdened by stressors are more will-
ing to communicate in a second language (Gallagher, 2013; MacIntyre et al., 2001). These
results together suggest that a tight relationship between the properties of a learner’s social
network, well-being, and willingness to use a language, and the proficiency gains made in
that language. Thus, developing one’s social network expands opportunities for language
use, and may force speakers to confront and adapt to various language-related uncertainties.
Failure to adapt one’s internal representations to minimize uncertainty has been linked with
stress, anxiety, and the occurrence of other diseases (FeldmanHall et al., 2015; Hirsh et al.,
2012; Peters et al., 2017).

Conclusion

Casting bilingualism as an exercise in managing language-related uncertainty has several
benefits that can drive future research in various subdomains. As reviewed above, a focus

Neurobiology of Language

481

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

on uncertainty allows for tighter integration between linguistic and computational cognitive
theories that are neurally plausible. Such computational perspectives provide various metrics
and measures that can be leveraged, including entropy. This integration will help in achieving
common goals, such as investigating the impacts of behavioral context (global and local) on
behaviors and brain organization. Ultimately, developing proficiency in a second language
may be an exercise in reducing or adapting to uncertainty, allowing for efficient comprehen-
sion and production according to the behavioral or interactional context.

FUNDING INFORMATION

Debra Titone, Natural Sciences and Engineering Research Council of Canada, individual
Discovery Grant, Award ID: 264146. Jason W. Gullifer and Debra Titone, the Social Sciences
and Humanities Research Council of Canada, Insight Development Grant, Award ID: 00935.

AUTHOR CONTRIBUTIONS

Jason W. Gullifer: Conceptualization: Lead; Funding acquisition: Equal; Investigation: Lead;
Project administration: Supporting; Writing – original draft: Lead; Writing – review & editing:
Equal. Debra Titone: Conceptualization: Supporting; Funding acquisition: Equal; Project
administration: Lead; Resources: Lead; Supervision: Lead; Writing – review & editing: Equal.

REFERENCES

Abutalebi, J., & Green, D. W. (2016). Neuroimaging of language
control in bilinguals: Neural adaptation and reserve. Bilingualism:
Language and Cognition, 19(4), 689–698. https://doi.org/10.1017
/s1366728916000225

Adler, R. M., Valdés Kroff, J. R., & Novick, J. M. (2020). Does
integrating a code-switch during comprehension engage cogni-
tive control? Journal of Experimental Psychology: Learning,
Memory, and Cognition, 46(4), 741–759. https://doi.org/10
.1037/xlm0000755, PubMed: 31448933

Anderson, J. A. E., Hawrylewicz, K., & Bialystok, E. (2018). Who is
bilingual? Snapshots across the lifespan. Bilingualism: Language
and Cognition, 23(5), 929–937. https://doi.org/10.1017
/s1366728918000950

Baker-Smemoe, W., Dewey, D. P., Bown, J., & Martinsen, R. A.
(2014). Variables affecting L2 gains during study abroad.
Foreign Language Annals, 47(3), 464–486. https://doi.org/10
.1111/flan.12093

Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P.,
& Friston, K. J. (2012). Canonical microcircuits for predictive
coding. Neuron, 76(4), 695–711. https://doi.org/10.1016/j
.neuron.2012.10.038, PubMed: 23177956

Baum, S., & Titone, D. (2014). Moving toward a neuroplasticity
view of bilingualism, executive control, and aging. Applied
Psycholinguistics, 35(5), 857–894. https://doi.org/10.1017
/s0142716414000174

Beatty-Martínez, A. L., & Dussias, P. E. (2017). Bilingual experience
shapes language processing: Evidence from codeswitching.
Journal of Memory and Language, 95, 173–189. https://doi.org
/10.1016/j.jml.2017.04.002

Beatty-Martinez, A. L., Navarro-Torres, C. A., Dussias, P. E., Bajo,
M. T., Guzzardo Tamargo, R. E., & Kroll, J. F. (2020).
Interactional context mediates the consequences of bilingualism
for language and cognition. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 46(6), 1022–1047. https://doi
.org/10.1037/xlm0000770, PubMed: 31580119

Bernolet, S., Hartsuiker, R. J., & Pickering, M. J. (2007). Shared syn-
tactic representations in bilinguals: Evidence for the role of word-
order repetition. Journal of Experimental Psychology: Learning,
Memory, and Cognition, 33(5), 931–949. https://doi.org/10
.1037/0278-7393.33.5.931, PubMed: 17723070

Bialystok, E. (2017). The bilingual adaptation: How minds accom-
modate experience. Psychological Bulletin, 143(3), 233–262.
https://doi.org/10.1037/bul0000099, PubMed: 28230411

Bialystok, E., Craik, F. I., & Luk, G. (2012). Bilingualism:
Consequences for mind and brain. Trends in Cognitive
Sciences, 16(4), 240–250. https://doi.org/10.1016/j.tics.2012.03
.001, PubMed: 22464592

Bialystok, E., Martin, M. M., & Viswanathan, M. (2016).
Bilingualism across the lifespan: The rise and fall of inhibitory
control. International Journal of Bilingualism, 9(1), 103–119.
https://doi.org/10.1177/13670069050090010701

Bice, K., & Kroll, J. F. (2019). English only? Monolinguals in linguis-
tically diverse contexts have an edge in language learning. Brain
and Language, 196, 104644. https://doi.org/10.1016/j.bandl
.2019.104644, PubMed: 31279148

Bjork, R. A., & Kroll, J. F. (2015). Desirable difficulties in vocabu-
lary learning. The American Journal of Psychology, 128(2), 241.
https://doi.org/10.5406/amerjpsyc.128.2.0241, PubMed:
26255443

Bobb, S. C., & Wodniecka, Z. (2013). Language switching in picture
naming: What asymmetric switch costs (do not) tell us about inhibi-
tion in bilingual speech planning. Journal of Cognitive Psychology,
25(5), 568–585. https://doi.org/10.1080/20445911.2013.792822
Bogulski, C. A., Bice, K., & Kroll, J. F. (2019). Bilingualism as a desir-
able difficulty: Advantages in word learning depend on regulation
of the dominant language. Bilingualism: Language and Cognition,
22(5), 1052–1067. https://doi.org/10.1017/S1366728918000858,
PubMed: 31762685

Byers-Heinlein, K., Morin-Lessard, E., & Lew-Williams, C. (2017).
Bilingual infants control their languages as they listen. Proceedings

Neurobiology of Language

482

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

of the National Academy of Sciences, 114(34), 9032–9037. https://
doi.org/10.1073/pnas.1703220114, PubMed: 28784802

Chai, X. J., Berken, J. A., Barbeau, E. B., Soles, J., Callahan, M.,
Chen, J.-K., & Klein, D. (2016). Intrinsic functional connectivity
in the adult brain and success in second-language learning. The
Journal of Neuroscience, 36(3), 755–761. https://doi.org/10.1523
/JNEUROSCI.2234-15.2016, PubMed: 26791206

Church, A. T.

(1982). Sojourner adjustment. Psychological
Bulletin, 91(3), 540–572. https://doi.org/10.1037/0033-2909.91
.3.540

Costa, A., Hernández, M., & Sebastián-Gallés, N. (2008).
Bilingualism aids conflict resolution: Evidence from the ANT task.
Cognition, 106(1), 59–86. https://doi.org/10.1016/j.cognition
.2006.12.013, PubMed: 17275801

Costa, A., & Santesteban, M. (2004). Lexical access in bilingual
speech production: Evidence from language switching in highly
proficient bilinguals and L2 learners. Journal of Memory and
Language, 50, 491–511. https://doi.org/10.1016/j.jml.2004.02.002
de Bruin, A., & Della Sala, S. (2019). The bilingual advantage
debate. In J. W. Schwieter (Ed.), The handbook of the neurosci-
ence of multilingualism (pp. 736–753). John Wiley & Sons.
https://doi.org/10.1002/9781119387725.ch35

de Bruin, A., Treccani, B., & Della Sala, S. (2015). Cognitive
advantage in bilingualism: An example of publication bias?
Psychological Science, 26(1), 99–107. https://doi.org/10.1177
/0956797614557866, PubMed: 25475825

Declerck, M. (2020). What about proactive language control?
Psychonomic Bulletin & Review, 27(1), 24–35. https://doi.org
/10.3758/s13423-019-01654-1, PubMed: 31410740

Declerck, M., Eben, C., & Grainger, J. (2019). A different perspec-
tive on domain-general language control using the flanker task.
Acta Psychologica, 198, 102884. https://doi.org/10.1016/j.actpsy
.2019.102884, PubMed: 31306968

DeLuca, V., Rothman, J., Bialystok, E., & Pliatsikas, C. (2019).
Redefining bilingualism as a spectrum of experiences that differ-
entially affects brain structure and function. Proceedings of the
National Academy of Sciences of the United States of America,
116(15), 7565–7574. https://doi.org/10.1073/pnas.1811513116,
PubMed: 30914463

Dijkstra, T., & van Heuven, W. J. B. (1998). The BIA model and
bilingual word recognition. In J. Grainger & A. M. Jacobs
(Eds.), Localist connectionist approaches to human cognition
(pp. 189–225). Erlbaum.

Dijkstra, T., & van Heuven, W. J. B. (2002). The architecture of the
bilingual word recognition system: From identification to deci-
sion. Bilingualism: Language and Cognition, 5, 175–197. https://
doi.org/10.1017/S1366728902003012

Donnelly, S., Brooks, P. J., & Homer, B. D. (2019). Is there a
bilingual advantage on interference-control tasks? A multiverse
meta-analysis of global reaction time and interference cost.
Psychonomic Bulletin & Review, 26(4), 1122–1147. https://doi
.org/10.3758/s13423-019-01567-z, PubMed: 30815795

Doucerain, M. M., Varnaamkhaasti, R. S., Segalowitz, N., & Ryder,
A . G .
( 2 0 1 5 ) . S e c o n d l a n g u a g e s o ci a l n e t w o rk s a n d
communication-related acculturative stress: The role of intercon-
nectedness. Frontiers in Psychology, 6, Article 1111. https://doi
.org/10.3389/fpsyg.2015.01111, PubMed: 26300809

Dussias, P. E., Gullifer, J., & Poepsel, T. (2016). How psycholinguis-
tics can inform contact linguistics: Converging evidence against
a decreolization view of Palenquero. In A. Schwegler, J.
McWhorter, & L. Ströbel (Eds.), Spanish and Portuguese in con-
tact with other languages (pp. 181–204). Iberoamericana
Vervuert. https://doi.org/10.31819/9783954878949-010

Dussias, P. E., & Sagarra, N. (2007). The effect of exposure on
syntactic parsing in Spanish-English bilinguals. Bilingualism:
Language and Cognition, 10, 101–116. https://doi.org/10.1017
/S1366728906002847

Duyck, W., Van Assche, E., Drieghe, D., & Hartsuiker, R. J. (2007).
Visual word recognition by bilinguals in a sentence context:
J o u r n a l o f
E v i d e n c e f o r n o n s e l ec t i v e l e xi c a l ac c e s s .
Experimental Psychology: Learning, Memory, and Cognition,
33, 663–679. https://doi.org/10.1037/0278-7393.33.4.663,
PubMed: 17576146

Eleta, I., & Golbeck, J. (2014). Multilingual use of Twitter: Social
networks at the language frontier. Computers in Human
Behavior, 41, 424–432. https://doi.org/10.1016/j.chb.2014.05.005
Elliott, R., Newman, J. L., Longe, O. A., & Deakin, J. F. W. (2003).
Differential response patterns in the striatum and orbitofrontal
cortex to financial reward in humans: A parametric functional
magnetic resonance imaging study. Journal of Neuroscience,
23(1), 303–307. https://doi.org/10.1523/ JNEUROSCI.23-01
-00303.2003, PubMed: 12514228

Feldman, H., & Friston, K. (2010). Attention, uncertainty, and free-
energy. Frontiers in Human Neuroscience, 4, Article 215. https://
doi.org/10.3389/fnhum.2010.00215, PubMed: 21160551

FeldmanHall, O., Otto, A. R., & Phelps, E. A. (2018). Learning
moral values: Another’s desire to punish enhances one’s own
punitive behavior. Journal of Experimental Psychology: General,
147(8), 1211–1224. https://doi.org/10.1037/xge0000405,
PubMed: 29878807

FeldmanHall, O., Raio, C. M., Kubota, J. T., Seiler, M. G., & Phelps,
E. A. (2015). The effects of social context and acute stress on
decision making under uncertainty. Psychological Science,
26(12), 1918–1926. https://doi.org/10.1177/0956797615605807,
PubMed: 26546080

FeldmanHall, O., & Shenhav, A. (2019). Resolving uncertainty in a
social world. Nature Human Behaviour, 3(5), 426–435. https://
doi.org/10.1038/s41562-019-0590-x, PubMed: 31011164

Frazier, L. (1979). On comprehending sentences: Syntactic parsing
strategies [Doctoral thesis]. University of Connecticut. https://
opencommons.uconn.edu/dissertations/AAI7914150

Friston, K. (2010). The free-energy principle: A unified brain theory?
Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org
/10.1038/nrn2787, PubMed: 20068583

Gallagher, H. C. (2013). Willingness to communicate and cross-
cultural adaptation: L2 communication and acculturative stress
as transaction. Applied Linguistics, 34, 53–73. https://doi.org/10
.1093/applin/ams023

García, O., & Wei, L. (2012). Translanguaging. In C. A. Chapelle
(Ed.), The encyclopedia of applied linguistics (pp. 1–7). John
Wiley & Sons. https://doi.org/10.1002/9781405198431
.wbeal1488

Gibson, E. (1998). Linguistic complexity: Locality of syntactic de-
pendencies. Cognition, 68(1), 1–76. https://doi.org/10.1016
/S0010-0277(98)00034-1, PubMed: 9775516

Gollan, T. H., & Ferreira, V. S. (2009). Should I stay or should I
switch? A cost-benefit analysis of voluntary language switching
in young and aging bilinguals. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 35, 640–665.
https://doi.org/10.1037/a0014981, PubMed: 19379041

Gollan, T. H., Starr, J., & Ferreira, V. S. (2015). More than use it or
lose it: The number-of-speakers effect on heritage language profi-
ciency. Psychonomic Bulletin & Review, 22(1), 147–155. https://
doi.org/10.3758/s13423-014-0649-7, PubMed: 24942146

Green, D. W., & Abutalebi, J. (2013). Language control in bilinguals:
The adaptive control hypothesis. Journal of Cognitive Neuroscience,

Neurobiology of Language

483

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

/

.

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

25(5), 515–530. https://doi.org/10.1080/20445911.2013.796377,
PubMed: 25077013

Green, D. W., & Wei, L. (2014). A control process model of code-
switching. Language Cognition & Neuroscience, 29, 499–511.
https://doi.org/10.1080/23273798.2014.882515

Grosjean, F. (2001). The bilingual’s language modes. In J. Nicol
(Ed.), One mind, two languages: Bilingual language processing
(pp. 1–25). Wiley-Blackwell.

Grosjean, F. (2016). The complementarity principle and its impact
on processing, acquisition, and dominance. In C. Silva-Corvalan
& J. Treffers-Daller (Eds.), Language dominance in bilinguals
(pp. 66–84). Cambridge University Press. https://doi.org/10.1017
/CBO9781107375345.004

Gullifer, J. W., Chai, X. J., Whitford, V., Pivneva, I., Baum, S., Klein,
D., & Titone, D. (2018). Bilingual experience and resting-state
brain connectivity: Impacts of L2 age of acquisition and social
diversity of language use on control networks. Neuropsychologia,
117, 123–134. https://doi.org/10.1016/j.neuropsychologia.2018
.04.037, PubMed: 29727624

Gullifer, J. W., Kousaie, S., Gilbert, A. C., Grant, A., Giroud, N.,
Coulter, K., Klein, D., Baum, S., Phillips, N., & Titone, D.
(2021). Bilingual language experience as a multidimensional
spectrum: Associations with objective and subjective language
proficiency. Applied Psycholinguistics, 42(2), 1–34. https://doi
.org/10.1017/S0142716420000521

Gullifer, J. W., Kroll, J. F., & Dussias, P. E. (2013). When language
switching has no apparent cost: Lexical access in sentence
context. Frontiers in Psychology, 4, Article 278. https://doi.org
/10.3389/fpsyg.2013.00278, PubMed: 23750141

Gullifer, J. W., Pivneva, I., Whitford, V., Sheikh, N. A., & Titone, D.
(2021). Bilingual language experience and conflict adaptation:
Investigating reactive inhibitory control among bilingual young
adults in Montréal [Manuscript submitted for publication].
Department of Psychology, McGill University.

Gullifer, J. W., & Titone, D. (2018). Compute language entropy
with {languageEntropy}, Github. https://github.com/jasongullifer
/languageEntropy

Gullifer, J. W., & Titone, D. (2019). The impact of a momentary
language switch on bilingual reading: Intense at the switch but
merciful downstream for L2 but not L1 readers. Journal of
Experimental Psychology: Learning, Memory, and Cognition, 45(11),
2036–2050. https://doi.org/10.1037/xlm0000695, PubMed:
30883173

Gullifer, J. W., & Titone, D. (2020a). Characterizing the social diversity
of bilingualism using language entropy. Bilingualism: Language
and Cognition, 23(2), 283–294. https://doi.org/10.1017
/s1366728919000026

Gullifer, J. W., & Titone, D. (2020b). Engaging proactive control:
Influences of diverse language experiences using insights from
machine learning. Journal of Experimental Psychology:
General, 150(3), 414–430. https://doi.org/10.1037/xge0000933,
PubMed: 33001688

Guzmán, G., Ricard, J., Serigos, J., Bullock, B. E., & Toribio, A. J.
(2017). Metrics for modeling code-switching across corpora. In
18th Annual Conference of the International Speech Communication
Association: Interspeech 2017 (Stockholm, Sweden, August 2017,
pp. 67–71). Curran Associates. https://doi.org/10.21437
/Interspeech.2017-1429

Guzzardo Tamargo, R. E., Valdés Kroff, J. R., & Dussias, P. E.
(2016). Examining the relationship between comprehension
and production processes in code-switched language. Journal
of Memory and Language, 89, 138–161. https://doi.org/10.1016
/j.jml.2015.12.002, PubMed: 28670049

Hartsuiker, R. J., & Pickering, M. J. (2008). Language integration
in bilingual sentence production. Acta Psychologica, 128(3),
479–489. https://doi.org/10.1016/j.actpsy.2007.08.005,
PubMed: 17870040

Hendrickson, B., Rosen, D., & Aune, R. K. (2011). An analysis of
friendship networks, social connectedness, homesickness, and
satisfaction levels of international students. International Journal
of Intercultural Relations, 35, 281–295. https://doi.org/10.1016/j
.ijintrel.2010.08.001

Hilchey, M. D., & Klein, R. M. (2011). Are there bilingual advan-
tages on nonlinguistic interference tasks? Implications for the
plasticity of executive control processes. Psychonomic Bulletin
& Review, 18, 625–658. https://doi.org/10.3758/s13423-011
-0116-7, PubMed: 21674283

Hirsh, J. B., Mar, R. A., & Peterson, J. B. (2012). Psychological
entropy: A framework for understanding uncertainty-related
anxiety. Psychological Review, 119(2), 304–320. https://doi.org
/10.1037/a0026767, PubMed: 22250757

Hofweber, J., Marinis, T., & Treffers-Daller, J. (2020). Experimentally
induced language modes and regular code-switching habits boost
bilinguals’ executive performance: Evidence from a within-
subject paradigm. Frontiers in Psychology, 11, Article 542326.
https://doi.org/10.3389/fpsyg.2020.542326, PubMed: 33250805
Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. F.
(2005). Neural systems responding to degrees of uncertainty in
human decision-making. Science, 310(5754), 1680–1683.
https://doi.org/10.1126/science.1115327, PubMed: 16339445
Ibáñez, A. J., Macizo, P., & Bajo, M. T. (2010). Language access and
language selection in professional translators. Acta Psychologica,
135, 257–266. https://doi.org/10.1016/j.actpsy.2010.07.009,
PubMed: 20705277

Jacobs, A., Fricke, M., & Kroll, J. F. (2016). Cross-language activa-
tion begins during speech planning and extends into second
language speech. Language Learning, 66(2), 324–353. https://
doi.org/10.1111/lang.12148, PubMed: 27773945

Kałamała, P., Szewczyk, J., Chuderski, A., Senderecka, M., &
Wodniecka, Z. (2020). Patterns of bilingual language use and
response inhibition: A test of the adaptive control hypothesis.
Cognition, 204, Article 104373. https://doi.org/10.1016/j
.cognition.2020.104373, PubMed: 32585471

Kang, C., Ma, F., Li, S., Kroll, J. F., & Guo, T. (2020). Domain-
general inhibition ability predicts the intensity of inhibition on
non-target language in bilingual word production: An ERP study.
Bilingualism: Language and Cognition, 23(5), 1056–1069. https://
doi.org/10.1017/S1366728920000085

Kaushanskaya, M., & Marian, V. (2009a). Bilingualism reduces
native-language interference during novel-word learning.
Journal of Experimental Psychology: Learning, Memory, and
Cognition, 35(3), 829–835. https://doi.org/10.1037/a0015275,
PubMed: 19379054

Kaushanskaya, M., & Marian, V. (2009b). The bilingual advantage
in novel word learning. Psychonomic Bulletin & Review, 16(4),
705–710. https://doi.org/10.3758/ PBR.16.4.705, PubMed:
19648456

Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of
uncertainty in neural coding and computation. Trends in
Neurosciences, 27(12), 712–719. https://doi.org/10.1016/j.tins
.2004.10.007, PubMed: 15541511

Kootstra, G. J., van Hell, J. G., & Dijkstra, T. (2010). Syntactic align-
ment and shared word order in code-switched sentence produc-
tion: Evidence from bilingual monologue and dialogue. Journal
of Memory and Language, 63, 210–231. https://doi.org/10.1016
/j.jml.2010.03.006

Neurobiology of Language

484

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

/

.

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

Kornell, N., Hays, M. J., & Bjork, R. A. (2009). Unsuccessful retrieval
attempts enhance subsequent learning. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 35(4), 989–998.
https://doi.org/10.1037/a0015729, PubMed: 19586265

Kosciessa, J. Q., Lindenberger, U., & Garrett, D. D. (2021).
Thalamocortical excitability modulation guides human percep-
tion under uncertainty. Nature Communications, 12(1), 1–15.
https://doi.org/10.1038/s41467-021-22511-7, PubMed:
33893294

Krein, J. L., MacLean, A. C., Delorey, D. P., Knutson, C. D., &
Eggett, D. L. (2009, June 6). Language entropy: A metric for
characterization of author programming language distribution
[Paper presentation]. WoPDaSD 2009: 4th Workshop on
Public Data about Software Development, Skovde, Sweden.
Kroll, J. F., Bobb, S. C., & Hoshino, N. (2014). Two languages in
mind: Bilingualism as a tool to investigate language, cognition,
and the brain. Current Directions in Psychological Science, 23(3),
159–163. https://doi.org/10.1177/0963721414528511, PubMed:
25309055

Kroll, J. F., Gullifer, J. W., McClain, R., Rossi, E., & Martin, M. C.
(2015). Selection and control in bilingual comprehension and
production. In J. W. Schwieter (Ed.), The Cambridge handbook
of bilingual processing (pp. 485–507). Cambridge University
Press. https://doi.org/10.1017/CBO9781107447257.021

Kroll, J. F., Gullifer, J. W., & Rossi, E. (2013). The multilingual
lexicon: The cognitive and neural basis of lexical comprehension
and production in two or more languages. Annual Review of
Applied Linguistics, 33, 102–127. https://doi.org/10.1017
/S0267190513000111

Kroll, J. F., Gullifer, J. W., & Zirnstein, M. (2016). Literacy in adult-
hood: Reading in two languages. In E. Nicoladis & S. Montanari
(Eds.), Bilingualism across the lifespan: Factors moderating lan-
guage proficiency. American Psychological Association. https://
doi.org/10.1037/14939-013

Lambert, W. E. (1967). A social psychology of bilingualism. Journal
of Social Issues, 23(2), 91–109. https://doi.org/10.1111/j.1540
-4560.1967.tb00578.x

Lehtonen, M., Soveri, A., Laine, A., Jarvenpaa, J., de Bruin, A., &
Antfolk, J. (2018). Is bilingualism associated with enhanced
executive functioning in adults? A meta-analytic review.
Psychological Bulletin, 144(4), 394–425. https://doi.org/10.1037
/bul0000142, PubMed: 29494195

Leivada, E., Duñabeitia, J. A., Westergaard, M., & Rothman, J.
(2020). On the phantom-like appearance of bilingualism effects
on cognition: (How) should we proceed? Bilingualism: Language
and Cognition, 24(1), 197–210. https://doi.org/10.1017
/s1366728920000358

Levy, R. (2008). Expectation-based syntactic comprehension.
Cognition, 106, 1126–1177. https://doi.org/10.1016/j.cognition
.2007.05.006, PubMed: 17662975

Levy, R., Bicknell, K., Slattery, T., & Rayner, K. (2009). Eye move-
ment evidence that readers maintain and act on uncertainty
about past linguistic input. Proceedings of the National
Academy of Sciences, 106(50), 21086–21090. https://doi.org
/10.1073/pnas.0907664106, PubMed: 19965371

Li, P., & Farkas, I. (2002). A self-organizing connectionist model of
bilingual processing. Advances in Psychology, 134, 59–85.
https://doi.org/10.1016/S0166-4115(02)80006-1

Libben, M. R., & Titone, D. A. (2009). Bilingual lexical access in
context: Evidence from eye movements during reading. Journal
of Experimental Psychology: Learning, Memory, and Cognition,
35, 381–390. https://doi.org/10.1037/a0014875, PubMed:
19271853

Lipski, J. M. (1977). Bilingual code-switching and linguistic compe-
tence. In Fourth LACUS forum (pp. 263–277). Hornbeam Press.
Loebell, H., & Bock, K. (2003). Structural priming across languages.
Linguistics, 41, 791–824. https://doi.org/10.1515/ling.2003.026
López, B. G. (2020). Incorporating language brokering experiences
into bilingualism research: An examination of informal transla-
tion practices. Language and Linguistics Compass, 14(1), Article
e12361. https://doi.org/10.1111/lnc3.12361

López, B. G., Zhang, M., Arredondo, M. M., & Kim, S. Y. (2020).
The Simon effect in bilingual language brokers: A role for emo-
tion and proficiency. International Journal of Bilingualism, 25(1),
100–119. https://doi.org/10.1177/1367006920939659

Ma, F., Li, S., & Guo, T. (2016). Reactive and proactive control in
bilingual word production: An investigation of influential factors.
Journal of Memory and Language, 86, 35–59. https://doi.org/10
.1016/j.jml.2015.08.004

MacDonald, M. C., & Seidenberg, M. S. (2006). Constraint satisfac-
tion accounts of lexical and sentence comprehension. In M. J.
Traxler & M. A. Gernsbacher (Eds.), Handbook of psycholinguis-
tics (2nd ed., pp. 581–611). Academic Press. https://doi.org/10
.1016/B978-012369374-7/50016-X

MacIntyre, P. D., Baker, S. C., Clément, R., & Conrod, S. (2001).
Willingness to communicate, social support, and language-
learning orientations of immersion students. Studies in Second
Language Acquisition, 23, 369–388. https://doi.org/10.1017
/S0272263101003035

Macnamara, J., Krauthammer, M., & Bolgar, M. (1968). Language
switching in bilinguals as a function of stimulus and response
uncertainty. Journal of Experimental Psychology, 78, 208–215.
https://doi.org/10.1037/h0026390, PubMed: 5722432

Makoni, S., & Pennycook, A. (Eds.). (2007). Disinventing and
reconstituting languages. Multilingual Matters. https://doi.org/10
.21832/9781853599255

Meuter, R. F. I., & Allport, A. (1999). Bilingual language switching
in naming: Asymmetrical costs of language selection. Journal of
Memory and Language, 40, 25–40. https://doi.org/10.1006/jmla
.1998.2602

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H.,
Howerter, A., & Wager, T. D. (2000). The unity and diversity
of executive functions and their contributions to complex “frontal
lobe” tasks: A latent variable analysis. Cognitive Psychology, 41,
49–100. https://doi.org/10.1006/cogp.1999.0734, PubMed:
10945922

Paap, K. R., Anders-Jefferson, R., Mikulinsky, R., Masuda, S., &
Mason, L. (2019). On the encapsulation of bilingual language
control. Journal of Memory and Language, 105, 76–92. https://
doi.org/10.1016/j.jml.2018.12.001

Paap, K. R., Johnson, H. A., & Sawi, O. (2015). Bilingual advan-
tages in executive functioning either do not exist or are restricted
to very specific and undetermined circumstances. Cortex, 69,
265–278. https://doi.org/10.1016/j.cortex.2015.04.014,
PubMed: 26048659

Peters, A., McEwen, B. S., & Friston, K. (2017). Uncertainty and
stress: Why it causes diseases and how it is mastered by the
brain. Progress in Neurobiology, 156, 164–188. https://doi.org
/10.1016/j.pneurobio.2017.05.004, PubMed: 28576664

Pivneva, I., Mercier, J., & Titone, D. (2014). Executive control mod-
ulates cross-language lexical activation during L2 reading:
Evidence from eye movements. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 40(3), 787–796.
https://doi.org/10.1037/a0035583, PubMed: 24446754

Pliatsikas, C. (2020). Understanding structural plasticity in the
bilingual brain: The dynamic restructuring model. Bilingualism:

Neurobiology of Language

485

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Bilingualism and uncertainty

Language and Cognition, 23(2), 459–471. https://doi.org/10.1017
/S1366728919000130

Poplack, S. (1980). Sometimes I’ll start a sentence in Spanish y
termino en español: Toward a typology of code-switching.
Linguistics, 18, 581–618. https://doi.org/10.1515/ling.1980.18.7
-8.581

Poulisse, N. (2000). Slips of the tongue in first and second language
production. Studia Linguistica, 54(2), 136–149. https://doi.org/10
.1111/1467-9582.00055

Shannon, C. E. (1948). The mathematical theory of communication.
The Bell System Technical Journal, 27(3), 379–423. https://doi
.org/10.1002/j.1538-7305.1948.tb01338.x

Shook, A., & Marian, V. (2012). The bilingual language interaction
network for comprehension of speech. Bilingualism: Language
and Cognition, 16(2), 304–324. https://doi.org/10.1017
/S1366728912000466, PubMed: 24363602

Sulpizio, S., Del Maschio, N., Del Mauro, G., Fedeli, D., &
Abutalebi, J. (2019). Bilingualism as a gradient measure modu-
lates functional connectivity of language and control networks.
NeuroImage, 205, Article 116306. https://doi.org/10.1016/j
.neuroimage.2019.116306, PubMed: 31654763

Takahesu Tabori, A. A., Mech, E. N., & Atagi, N. (2018). Exploiting
language variation to better understand the cognitive conse-
quences of bilingualism. Frontiers in Psychology, 9, Article 1686.
https://doi.org/10.3389/fpsyg.2018.01686, PubMed: 30245660
Titone, D., Mercier, J., Sudarshan, A., Pivneva, I., Gullifer, J. W., &
Baum, S. (2020). Spoken word processing in bilingual older
adults: Assessing within- and cross-language competition using
the visual world task. Linguistic Approaches to Bilingualism, 11(4),
578–610. https://doi.org/10.1075/lab.18028.tit

Titone, D., & Tiv, M. (2021). Rethinking multilingual experience
through a systems framework of bilingualism [Manuscript sub-
mitted for publication]. Department of Psychology, McGill
University.

Tiv, M., Gullifer, J., Feng, R., & Titone, D. (2020a, November 19–22).
The talk of the town: A network approach to characterizing
bilingual conversational topics in Montreal [Paper presentation].
The 61st Annual Meeting of the Psychonomic Society, Virtual
Conference.

Tiv, M., Gullifer, J. W., Feng, R. Y., & Titone, D. (2020b). Using
network science to map what Montréal bilinguals talk about
across languages and communicative contexts. Journal of
Neurolinguistics, 56, Article 100913. https://doi.org/10.1016/j
.jneuroling.2020.100913, PubMed: 32905520

Tiv, M., Kutlu, E., Gullifer, J. W., Feng, R. Y., Doucerain, M., &
Titone, D. (2021). Bridging personal and ecological language
dynamics: A systems framework of bilingualism [Manuscript
submitted for publication]. Department of Psychology, McGill
University.

Tiv, M., O’Regan, E., & Titone, D. (2021). In a bilingual state of
mind: Investigating the continuous relationship between bilin-
gual language experience and mentalizing. Bilingualism:
Language and Cognition, 1–14. https://doi.org/10.1017
/S1366728921000225

Trueswell, J. C., Tanenhaus, M. K., & Garnsey, S. M. (1994). Semantic
influences on parsing: Use of thematic role information in syntactic
ambiguity resolution. Journal of Memory and Language, 33(3),
285–318. https://doi.org/10.1006/jmla.1994.1014

Van Hell, J. G., & Dijkstra, T. (2002). Foreign language knowledge
can influence native language performance in exclusively native
contexts. Psychonomic Bulletin & Review, 9, 780–789. https://
doi.org/10.3758/BF03196335, PubMed: 12613683

Verhoef, K., Roelofs, A., & Chwilla, D. J. (2009). Role of inhibition
in language switching: Evidence from event-related brain poten-
tials in overt picture naming. Cognition, 110, 84–99. https://doi
.org/10.1016/j.cognition.2008.10.013, PubMed: 19084830

Wiklund, I. (2002). Social networks from a sociolinguistic perspec-
tive: The relationship between characteristics of the social net-
works of bilingual adolescents and their language proficiency.
International Journal of the Sociology of Language, 2002(153),
53–92. https://doi.org/10.1515/ijsl.2002.005

Williams, C. (1994). Arfarniad o ddulliau dysgu ac addysgu yng
nghyd-destun addysg uwchradd ddwyieithog [An evaluation of
teaching and learning methods in the context of bilingual
secondary education; Unpublished doctoral thesis]. University
of Wales, Bangor.

Wu, T., Chen, C., Spagna, A., Wu, X., Mackie, M.-A., Russell-
Giller, S., Xu, P., Luo, Y., Liu, X., Hof, P. R., & Fan, J. (2020).
The functional anatomy of cognitive control: A domain-general
brain network for uncertainty processing. Journal of Comparative
Neurology, 528(8), 1265–1292. https://doi.org/10.1002/cne
.24804, PubMed: 31674015

Wu, Y. J., & Thierry, G. (2013). Fast modulation of executive func-
tion by language context in bilinguals. Journal of Neuroscience,
33(33), 13533–13537. https://doi.org/10.1523/JNEUROSCI.4760
-12.2013, PubMed: 23946411

Yu, A. J., & Dayan, P. (2003). Expected and unexpected uncertainty:
ACh and NE in the neocortex. Advances in Neural Information
Processing Systems, 15, 157–164.

Yu, A. J., & Dayan, P. (2005). Uncertainty, neuromodulation, and
attention. Neuron, 46(4), 681–692. https://doi.org/10.1016/j
.neuron.2005.04.026, PubMed: 15944135

Zirnstein, M., Bice, K., & Kroll, J. (2019). Variation in language
experience shapes the consequences of bilingualism. In Sekerina,
I. A., Spradlin, L., & Valian, V. (Eds.), Bilingualism, executive
function, and beyond: Questions and insights (Studies in
Bilingualism, 57, pp. 35–47). John Benjamins Publishing. https://
doi.org/10.1075/sibil.57.03zir

Neurobiology of Language

486

l

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

p

:
/
/

d
i
r
e
c
t
.

m

i
t
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

2
4
4
6
4
1
9
7
1
9
6
3
n
o
_
a
_
0
0
0
4
4
p
d

.

/

l

f

b
y
g
u
e
s
t

t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

Download pdf