This is the html version of the file https://www.taylorfrancis.com/chapters/edit/10.4324/9781003190912-20/neurocognition-social-learning-second-language-hyeonjeong-jeong-ping-li.
Google automatically generates html versions of documents as we crawl the web.
Page 1
217
DOI: 10.4324/9781003190912-20
16
NEUROCOGNITION OF SOCIAL
LEARNING OF SECOND
LANGUAGE
How Can Second Language be Learned as
First Language?
Hyeonjeong Jeong and Ping Li
Introduction
Both folk wisdom and scientific knowledge have pointed to the apparent differences between chil-
dren and adults in language learning, especially with regard to how native language (L1) acqui-
sition versus second language (L2) learning differ. As compared with child L1 learning, adult L2
learning not only tends to be less successful, but also displays highly variable learning outcomes
across individuals. According to the critical period hypothesis (Lenneberg, 1967), such differences
are due to biological constraints including the timing of maturation of brain functions (e.g., hemi-
spheric lateralization). In contrast to the original critical period hypothesis, Johnson and Newport
(1989) suggested the possibility of a cognitive account of how mechanisms of learning differ
in children versus adults, with particular reference to the way linguistic input is processed and
analyzed. More recent theories further suggest that the learning principles may not be fundamen-
tally different between L1 and L2, but the context, conditions, and environmental support to chil-
dren and adults are very different (i.e., different ecosystems; see Claussenius- Kalman et al., 2021),
along with different methods and manners of learning. For example, most adult learners do not have
the same opportunities for language learning as children (Caldwell- Harris & MacWhinney, 2023;
MacWhinney, 2012).
In this chapter, we attempt to provide a framework to address the issue of whether and how L2
learning by adults can occur like L1 learning by children. The framework called social L2 learning
(SL2) assumes that L2 learning, especially beyond the sensitive period, may benefit from social inter-
action and enriched exposure in real- life, as in L1 learning. SL2 also highlights the neurocognitive
correlates of perception, action, and multimodal processing of information relevant to the target L2
environment in real- world or simulated contexts (see Li & Jeong, 2020 for the details). In this chapter,
we first provide the key dimensions of the context and conditions under which children and adults
learn. Then, we will highlight in particular the social and affective dimensions of language learning,
along with the underlying cognitive and neural correlates that reflect learning differences.
Page 2
Hyeonjeong Jeong and Ping Li
218
Benefits of Social- Based Language Learning: Some Theoretical Considerations
The remarkable ability for an infant to acquire any human language has led some scholars, most not-
ably Chomsky (1981), to argue for an innate “language acquisition device” or “universal grammar.”
Because this theoretical approach focuses only on innate mechanisms as the core principles that pre-
pare humans to learn a language (the capacity or competence), they ignore the learning process itself.
In other words, the learning process could be impacted by a host of environmental and social factors,
but these factors are performance- related, and are external to the linguistic competence of the indi-
vidual. However, as cognitive science breaks the boundaries of language and cognition and abandons
the “modularity” hypothesis of Fodor (1983) that posits that language is an independent and separate
module from the rest of cognition (perception, memory, vision), it is important for us to look at how
children actually acquire language from the social environment and through social interactions (e.g.,
Kuhl, 2004).
When we start to look at social- based language learning process, we quickly see that adults learn
languages with very different methods and conditions that differ from children learning their L1 (for
information on child L2 neurocognition, see Ortiz Villalobos et al., this volume). An L1 is naturally
learned and acquired in a safe interpersonal space where the child integrates multidimensional linguistic
forms (e.g., spelling, grammar, pronunciation) with their meanings, integrates multimodal information
from auditory, visual, and tactile channels, and incorporates the actions and intentions of parents and
peers (Bloom, 2000; Tomasello, 2003). Such learning allows children to integrate the rich sensory and
perceptual experiences of the environment, interacting with objects and people, and performing actions.1
By contrast, perhaps most often, L2 acquisition in adults takes place in an instructional context, that is,
the classroom. For example, in Asian countries, learners of English as a foreign language are often asked
to perform mechanical memory- based grammar drills, word translations, comprehension checks, and
reading aloud, with limited input and practical language use in the social context and limited interpersonal
social interactions. These traditional learning experiences may weakly connect word forms, meanings,
and concepts, resulting in poor semantic representations that may be parasitic on L1 representations (see
Bowden & Faretta- Stutenberg, this volume, for more L2 neurocognition and L2 learning contexts).
Below we draw from conceptual frameworks in psycholinguistics, memory, and cognitive theories
to discuss first how L1 is learned and then what the benefits are when L2 is learned like L1.
Social Interaction
In the New Science of Learning framework, language learning is a social- based process, supported
by computational mechanisms and a neural circuit that supports and links cognition, perception,
and action (Meltzoff et al., 2009). Children rely on a multitude of social cues such as eye gazes,
facial expressions, and the intention of others, in order to understand what they need to learn and
when. Computation models based on data from mother– child interactions, which consider social
cues tend to perform better than models without those cues (Li & Zhao, 2017; Yu & Ballard, 2007).
In social interaction, joint attention (i.e., two social partners looking at the same object) is essen-
tial for early language learning and social skill development (Sanchez- Alonso & Aslin, 2022, for a
review). During joint attention, a child is susceptible to eye gazes from his/ her parents/ caregivers
and adjusts his/ her attention. For example, Yu and Smith (2016) found that parents’ gaze toward toys
positively facilitated infants’ attention to the toys and guided them to avoid distractions. For joint
attention to play a significant role, contingent, reciprocal interaction between infants and parents is
the key: Reciprocal interaction improves a range of skills such as sustained attention and social skills
(i.e., self- regulating and engaging conscious control of one’s attention).
Social interaction is imperative when infants and toddlers learn languages (Hakuno et al., 2017;
Kuhl et al., 2003). A baby’s ability to recognize the differences between the sounds of all languages
Page 3
Neurocognition of Social Learning of Second Language
219
declines between 6 and 12 months of age (Kuhl, 2004). Kuhl et al. investigated sufficient conditions
when such a decline in foreign- language phonetic perception may be delayed. Only infants exposed
to a live tutor, not the recorded video or audiotape conditions, showed similar discrimination ability
to native speakers. Although the presence of a live person is a clear advantage (compared to recorded
videos), children can also learn languages from video chat with a partner, as long as they can interact
with their partner, suggesting the importance of social contingency for learning (Myers et al., 2017).
While interacting with others, children are sensitive to the speaker’s goal and communicative
intentions, and use these cues to infer word meanings (Frank & Goodman, 2014). This ability is
related to theory of mind and social reasoning skills. Although research with adults is still limited,
recent studies have shown that for adults as well, face- to- face interactions, social response contin-
gency, and social signals from others can lead to more effective learning by promoting higher levels
of attention, motivation, and emotional arousal (Caldwell- Harris et al., 2014; Verga & Kotz, 2017,
2019). This line of research indicates the importance of social interaction in language learning and
other types of learning, regardless of age.
Embodied Cognition
Action- based experiences, such as those that occur during L1 acquisition, are likely to help the
child build sensory and motor- based semantic representations in the brain. Based on embodied
cognition theory (Barsalou, 2008), our mental representation of concepts, objects, and behaviors
is embedded in our experiences of the body (e.g., mouth, hands, feet), as well as our experiences
in specific modalities (e.g., auditory, visual, tactile). Therefore, semantic/ conceptual knowledge
appears to be represented in the distributed networks associated with experiential information
such as perception, sensation, movement, hearing, and emotion in real- life (see Meteyard et al.,
2012 for a review). Behavioral and neurocognitive studies have so far mainly examined native
L1 speakers in providing supportive evidence for the embodied cognition hypothesis (e.g., Aziz-
Zadeh & Damasio, 2008; Gianelli & Dalla Volta, 2015). The limited number of neurocognitive L2
and bilingual studies have reported that, unlike in L1, the sensory- motor areas were not strongly
engaged in processing action- related words and sentences in the L2 (Xue et al., 2015; Zhang et al.,
2020). This finding suggests that the L2 representation in bilinguals (especially late learners) is
less embodied than their L1 representations. Different learning conditions, such as the age at
which learners begin to learn L2 (i.e., age of acquisition), limited real- life experiences (i.e., L2
exposure), and L2 proficiency levels, may all influence the degree of L2 embodiment (Hernandez
& Li, 2007; Zappa & Frenck- Mestre, this volume).
There is still little evidence that body- specific and modality- specific experiences during learning
would affect L2 representation, although a few recent studies have provided some initial evidence
for embodiment effects in the brain (Legault, Fang, et al., 2019; Mayer et al., 2015). For example,
Mayer et al. (2015) compared L2 vocabulary learning under three conditions: performing gestures,
viewing pictures, and no- enrichment control. When performing a translation task inside fMRI (func-
tional magnetic resonance imaging; see Kousaie & Klein, this volume for more on this neuroimaging
method) after learning, participants who learned words with pictures showed activity in the right
lateral occipital cortex, whereas those who learned words with gestures had more activity in the
posterior superior temporal sulcus and in the premotor area, regions that have been implicated in
multimodal and action- based information processing. Critically, brain activation in superior temporal
sulcus and premotor cortex was significantly correlated with behavioral performance. The L2 learners
showed significantly greater retention for words learned with gestures than those with pictures, even
after two to six months. These results indicate that body- specific activities are essential for adult L2
learning and are consistent with sensorimotor- based neural explanations of semantic representation.
Page 4
Hyeonjeong Jeong and Ping Li
220
Multimodal Learning and Elaborative Processing
The disparity between L1 and L2 during learning with respect to both qualitative and quantitative
information processing may lead to different degrees of the richness of semantic representation in the
acquisition of two languages, which profoundly impacts successful memory retrieval, as suggested
by previous memory research (Craik & Lockhart, 1972; Tulving & Thomson, 1973). According to
the “encoding specificity principle” (Tulving & Thomson,1973), semantic memories have the best
chance of being retrieved if recalled in the context in which they were initially encoded than other-
wise. A similar memory hypothesis, “level of processing theory” (Craik & Lockhart, 1972), also
suggests that more elaborate semantic processing during learning leads to more successful retrieval
than shallow or superficial processing of the same items.
Social learning involves elaborative semantic processing using various social cues and multimodal
information for language acquisition. The “dual coding theory” (Paivio, 1990) and the “multimedia
learning theory” (Mayer, 2014) both support the notion that the elaborate processing of multi-
modal information enhances the quality of semantic memory. Specifically, Mayer and colleagues
have postulated several principles that account for why people learn better and build better mental
representations with multimodal information (text, video, animation) than with information of only
one modality. For example, processing text with pictures and images is more effective than that of
text alone, indicating the multimodal advantage in both behavior and in the brain (e.g., Liu et al.,
2020). Furthermore, recent cognitive neuroscience research suggests that deep/ elaborative encoding
(involving active discovery, multiple sources of information, and social/ emotional processing) boosts
cortical activity during encoding, and this cortical activation plays a vital role in retaining information
in long- term memory (see Hebscher et al., 2019, for a review). This perspective is consistent with
emerging brain evidence that elaborative SL2 leads to the successful long- term effect of learning
(Jeong et al., 2021; Legault, Fang, et al., 2019; Mayer et al., 2015). This evidence will be reviewed
in more detail below.
Emergentist Perspectives of Language Learning
The competition model provides social- based and emergentist explanations of the distinctions between
L1 and L2 learning (Hernandez & Li, 2007; MacWhinney, 2012; see a recent volume in emergentist
approaches to language; MacWhinney et al., 2022). According to MacWhinney (2012), adult lan-
guage learning is susceptible to several major “risk factors” that may be particularly strong in late
adults. Such risk factors include (a) thinking in L1 only, which implies the need to translate from L2
to L1 rather than directly using L2; (b) social isolation, which means learning occurs in an individual
or within- group communities; and (c) the lack of perception– action embodied contexts due to lack
of real- life experiences in language learning. In particular, the lack of perception– action embodied
contexts may explain why adult learners’ parasitic L2- on- L1 representations are strengthened to
a high level (Zhao & Li, 2010). However, if adults are offered rich environmental support in the
learning context, similar to children, their L2 learning may be better positioned to fend off these risk
factors. This can result in the development of inner speech in L2, social integration, and independent
representations of L2, separate from L1 (Li, 2013; Li & Jeong, 2020).
Added to the risk factors facing adults is a consolidated L1, often precluding the L2 of adults, espe-
cially late adult learners, from reaching a level of native competency. Adults usually begin learning
L2 after establishing their L1, which facilitates the association of L2 to L1 translation, but greater
experience in learning and use of L2 in real- life situations may enhance the direct connection between
L2 words and objects/ concepts (see Kroll & Stewart, 1994). Research findings that study- abroad
experiences of late learners tend to weaken the interference of L1 on L2 may support the importance
of social experiences in language learning (Linck et al., 2009).
Page 5
Neurocognition of Social Learning of Second Language
221
Such series of empirical and theoretical studies imply that language learning does not occur solely
as an individual cognitive activity; by contrast, language experience in real life is deeply involved in
learning and processing L2 through interaction with others. This notion is consistent with historical
and recent trends in various fields of language studies: sociocultural theory (Lantolf, 2006), usage-
based learning (Ellis, 2019), interaction hypotheses (Mackey et al., 2012), and neuroemergentism
(Claussenius- Kalman et al., 2021). All of these perspectives emphasize the characteristics and
conditions of social interaction and learning environments.
Neural Representations of Social L2 Learning
Different Brain Networks for L1 vs. L2
Although it is clear that L2 researchers are now paying more attention to the role of social learning,
relatively few studies in the past have tapped into the neural substrates of SL2. Many neuroimaging
studies have been performed in the domain of L2 learning, but so far, most of them have focused
on brain changes as a result of L2 learning experiences (see Abutalebi et al., 2005; Li et al., 2014,
for reviews). In addition, most published neuroimaging studies have relied on traditional learning
tasks, such as rote memorization and translation training, either in the classroom or lab- based inten-
sive training settings (e.g., Breitenstein et al., 2005; Grant et al., 2015; Qi et al., 2015; Yang et al.,
2015). Generally, findings from these studies suggest that classic language- related brain networks
(e.g., frontoparietal area) and memory- related brain regions (e.g., hippocampus) in the left hemi-
sphere are involved in learning and consolidating linguistic information. Such findings, however,
may be insufficient to reveal the potential differences between L1 and L2 brain networks when
the two languages are learned differently. Despite the argument that the same neural substrates
may be recruited for L2 as for L1 processing (Abutalebi et al., 2005; Abutalebi & Green, 2007),
there is now growing evidence that L1 vs. L2 processing and representation may involve the
same regions but different brain network configurations or computations (Li, 2013; Xu et al.,
2017). Specifically, new brain data suggest that the connectivity patterns in semantic representa-
tion may significantly differ between L1 and L2. For example, Zhang et al. (2020) showed that the
processing of nouns and verbs in L1 engages a more integrated network that connects language
areas with sensorimotor processing and semantic integration regions (e.g., caudate nucleus and
supramarginal gyrus), whereas such connections are weak or absent in L2 processing even for
highly proficient L2 speakers.
In contrast to the results of previous studies of traditional learning, recent studies on social- based
L2 learning are beginning to provide new evidence that such L1– L2 differences may be attenuated,
as SL2 positively influences successful learning of the L2 and enhances the semantic representation
of the L2 with embodied, multimodal, and richer contextual information. Furthermore, the cognitive
demands during social learning similar to L1 may influence the development of the brain systems
underlying L2 knowledge (see Li & Jeong, 2020, for a review), making L1 and L2 representations
more on a par with each other. In what follows, we provide an overview of some neural evidence on
how adult foreign language learning can be optimally facilitated by incorporating some features of
social learning.
Role of the Right Hemisphere: Temporal- Parietal Junction and Adjacent Regions
Previous studies on SL2 have consistently reported that the activation of the right- hemisphere brain
regions, including social cognition and action perception areas as well as both cortical and subcortical
areas, play an essential role in SL2 (e.g., Jeong et al., 2021; Jeong et al., 2010; Legault, Fang, et al.,
2019; Verga & Kotz, 2019; see Li & Jeong, 2020, for a review) (see Figure 16.1).
Page 6
Hyeonjeong Jeong and Ping Li
222
Most of these previous studies on SL2 showed involvement of the right temporal- parietal junction
(TPJ) and adjacent areas such as the right inferior parietal lobule, including supramarginal gyrus
(SMG) and angular gyrus. The right TPJ has been implicated as a multimodal association area that
integrates multisensory information (Carter & Huettel, 2013). This region has long been recognized
as one of the social cognition areas associated with the perception of various social stimuli, attention
to social cues, and higher cognitive processing of social reasoning such as theory of mind (i.e.,
thinking about the beliefs, emotions, and intentions of others) (e.g., Deen et al., 2015). For example,
Jeong et al. (2010), one of the initial studies on SL2, trained Japanese native speakers to learn Korean
words under the following two conditions: (a) L1 translation and (b) simulated video. The stimulated
videos included joint activities using target words in real- life situations (e.g., a video showing an
actor trying to move a heavy bag and asking another actor for help, using the L2 target Korean word
dowajo which means help me in English). After participants had remembered all the target words,
they performed a retrieval task (i.e., testing) inside the MRI scanner. Results showed that the right
SMG became more activated when retrieving words learned through simulated videos than words
learned through translation. Also, brain activity in the right SMG for processing L2 words encoded
via stimulated videos was similar to processing the participants’ L1 words (learned through daily life
as a child). Jeong et al. interpreted these results as suggesting that L2 words learned through real-
life situations might be processed similarly to L1 words in the brain, even when the learning was
conducted through simulated videos in relatively short sessions.
In a follow- up study, Jeong et al. (2021) used the same learning conditions (simulated video vs.
L1 translation) to determine the extent to which the qualitative and quantitative involvement of brain
systems during actual form- meaning mapping (i.e., encoding) affects the acquisition of semantic
representations of L2 words. The left inferior frontal gyrus (one of the core language- related areas)
was activated during learning in both social learning and L1 translation conditions. In contrast, the
Figure 16.1 The Neural Network Underlying Social L2 Learning.
Notes: The left hemisphere regions (blue) control lexical- semantic processing, whereas the right hemisphere cortical
plus the subcortical regions (green) engage in social learning. IFG = inferior frontal gyrus; SMG = supramarginal gyrus;
AG = angular gyrus; LG = lingual gyrus; CN = caudate nucleus; MTG/ ITG = middle temporal gyrus/ inferior temporal
gyrus. (From Li & Jeong, 2020; reproduced with permission from Springer Nature.)
Page 7
Neurocognition of Social Learning of Second Language
223
social learning condition uniquely induced neural activation in the right inferior parietal lobule, the
posterior superior temporal sulcus, and the posterior middle temporal gyrus. These areas may have
been engaged due to processing of multiple perceptual, action- related, social, and emotional cues in
the simulated real- life video condition. Consistent with the encoding theories mentioned earlier, such
elaborative cognitive processes during learning may have led to the more enriched semantic represen-
tation of L2 words.
Notably, successful learners in the social learning condition recruited the right TPJ, motor areas
(post and precentral areas), and right hippocampus more strongly than did less successful learners.
In Jeong et al. (2021), those who had higher activation in the right TPJ, motor areas, and right hippo-
campus during the initial stage of learning performed significantly better on a delayed vocabulary
test where they applied target words to novel social situations. In contrast, those who encoded L2
words through L1 translation did not perform as well in novel contexts when recalling the L2 words.
They may have relied on rote associative memory processes for L1– L2 word pairs in the translation
condition, resulting in surface and weaker encoding of words. It is interesting to note that the L1-
translation learners recruited only limited brain areas (e.g., left inferior frontal gyrus) as compared
with the SL2 learners during encoding. Thus, SL2 may be a successful learning process that can lead
to an integrated brain network to support multimodal integration, social reasoning, motor simulation,
and long- term memory.
The crucial role of the right inferior parietal lobule, including both SMG and angular gyrus, in
social learning has also been supported by experiments with virtual reality (VR)- based interactive
learning of L2 words. Legault, Fang et al. (2019) investigated the differential effects of different
learning contexts on structural brain changes. Two groups of English L1 speakers participated in
Chinese vocabulary learning with a paired picture– word association or VR environment training
for 20 days. The VR group engaged in an interactive 3D environment in which they dynamically
interacted with target words such as objects and animals. It was found that intensive VR vocabu-
lary learning enhanced the cortical thickness of the right SMG compared to L2- picture associative
learning (within the same amount of time and learning the same material). Furthermore, its cortical
thickness showed a positive correlation with better scores at a delayed retention test.
Verga and Kotz (2019) reported that the right SMG was more activated in simulated partner- based
word learning than individual- based learning when their participants explored the meanings of target
words with contextual information. Also, during partner- based learning, activity in the right lin-
gual gyrus and right caudate nucleus, known as the visuospatial attentional network, correlated with
better temporal coordination between a learner and a partner. Furthermore, learners with greater right
inferior frontal gyrus activity showed better learning outcomes during the partner- based learning
condition, but there was no such correlation in the individual- based learning condition. Unlike L2
classroom learning contexts in which social cues are generally not present, these findings suggest that
awareness of partners during social interaction facilitates L2 learning success by directing learners’
attention to the correct L2 referent from alternative mappings, in a similar way as social cues can
enhance L1 acquisition (e.g., Kuhl, 2004; Yu & Smith, 2016). This effect is further identified in an
fMRI study (Jeong et al., 2011) that showed that L2 learners are more responsive to a live person
than a recorded person when communicating in L2 (cf. Kuhl et al., 2003). The live person condition
activated more brain regions associated with L1 communication than the recorded person condition.
There is also supportive evidence that adaptive and social enriched exposure can change the sub-
stantial neural plasticity of L2 phonetic perception in adulthood (Zhang et al., 2009). Zhang et al.
stimulated multimodal and enriched exposure of English sound categories (/ r/ and / l/ ) to Japanese
adults who had received limited English exposure. They used a computer- adaptive training program
with visible facial articulation cues, acoustic exaggeration, and high multiple- talker variability. They
measured brain changes by magnetoencephalography with an oddball task before and after intensive
Page 8
Hyeonjeong Jeong and Ping Li
224
training for two weeks. Enriched exposure induced significant improvement of speech discrimination
scores, and enhanced neural sensitivity to phonetic distinction and neural efficiency during passive
listening. Furthermore, behavioral improvement was positively correlated with increased neuro-
physiological response. This finding suggests that enriched exposure develops new memory traces of
L2 phonetic representations in the adult brain.
In summary, the previous findings suggest that SL2 may result in stronger activation of brain regions
or networks linked to multimodal, visual, and spatial processing, social, affective, and perception-
action- related processing, enhancing the rich semantic representation of L2 (Jeong, et al., 2021; Jeong,
et al., 2010; Legault, Fang, et al., 2019; Verga & Kotz, 2019). Contrary to the notion that a child’s
brain, not an adult’s, is sensitive to social cues in learning, such studies show that the adult brain also
exhibits significant neuroplasticity and changes in response to social learning even during short- term
training. When L2 words are initially learned in a socially interactive condition (even in a simulated
context, Jeong et al., 2010), those words could be stored and processed in the same brain area as
L1 words.
Practical Applications in Technology- Enhanced Social L2 Learning
The theoretical and empirical evidence reviewed so far suggests that social learning not only posi-
tively impacts L2 learning success but also leads to the neural representation of L2 more similar to
that of L1 due to its enriched, embodied, and multimodal information. However, it is often difficult
to provide adults directly with a rich social learning environment similar to what children receive
for L1 acquisition. One way is for adults to study abroad. Although it is undoubtedly effective for
L2 learning, studying abroad is not practical or feasible for everyone due to its costs, time, and
family separation. Li and Lan (2021a) suggest that digital language learning (DLL) can be one of the
best solutions for providing an environment conducive to social learning (e.g, VR, mobile- assisted
language learning, game- based language learning, and even robot- assisted language learning). For
example, Legault, Fang et al. (2019) is a VR study that found that simulated physical interaction with
objects allowed participants to acquire L2 words like in L1 contexts, leading to a positive impact on
the learner’s brain functionally and anatomically.
Another potential use of DLLs is to facilitate the affective and emotional processing of L2, which
is often lacking in the traditional language classroom. Recently developed DLL tools and platforms
may provide L2 learners with reciprocal feedback and social reward during learning. For example,
automatic feedback may be embedded in mobile- assisted language learning apps, avatars with
emotional expressions can be built in VR platforms, and performance- contingent rewards can be
a feature of game- based language learning (Park et al., 2019). Social interaction is one of the most
crucial contributors to L2 learning motivation in applied linguistics (MacIntyre et al., 2011). This is
supported by a brain imaging study (Ripoll�s et al., 2014) that shows that feedback during learning
increased activation of the reward system in the brain, which in turn fostered motivation to learn.
Adult L2 learning is affected by individual cognitive abilities and learner characteristics (e.g., L1
background, age, proficiency, motivation, aptitude, working memory; for more on L2 neurocognition
and individual differences, see Luque & Covey, this volume). DLL learning may be able to provide
methods for both revealing and reducing these individual differences due to the design features and
its connection to big data analytics. For example, Legault et al. (2019) found that the outcome of
learning for successful learners was high in both VR and non- VR conditions, but for less successful
learners, the VR condition significantly facilitated their learning. In other words, VR learning assisted
and facilitated L2 learning for learners who are typically “struggling” in the language classroom.
Thus, it appears that VR may benefit some individual learners more than others.
This last example points to a future research direction for understanding the interaction between
technology and the learner (see Li & Lan, 2021b), which will enable us to develop learning platforms
Page 9
Neurocognition of Social Learning of Second Language
225
to accommodate the characteristics and needs of individual learners with the specific design features
of VR and DLL more generally. With DLL tools, students do not have to limit their language learning
experience in the classroom, and can learn L2 anywhere and anytime. For researchers, this provides a
unique opportunity to study and track individual learners in terms of their cognitive and linguistic abil-
ities and profiles over time, and develop curriculum that is tailor- made to individual students’ learning
to promote learning success. This will ride on the tide of the so- called “personalized learning,” for not
only language learning but education in general. Consequently, big data analytics based on machine
learning and artificial intelligence will also have a prominent place in L2 learning and education (see
a recent call for integration in Luan et al., 2020).
Future Directions
In this chapter, we have provided an overview of the framework of SL2 and the theoretical models
and hypotheses that support social learning for language acquisition in general, and we reviewed the
neural evidence that supports the SL2 framework, showing significantly different brain networks
may be implicated in social- based learning as compared with those in traditional classroom- based
learning. Further, we suggested that it is possible to leverage the rapidly developing digital technolo-
gies to simulate the conditions of social learning, which may produce the relevant neural and cogni-
tive changes in the brain.
There are a number of new exciting avenues along which we can pursue future studies in this
domain. The first avenue is to examine the neural basis of social interaction at the interpersonal level. As
discussed in Li and Jeong (2020; see their Figure 1), whereas previous research has focused primarily
on the structure and function of individual brains (i.e., single brain), the hyperscanning approach (i.e.,
inter- brain) allows the investigation of real- time dynamics between two or more interacting brains
during social interaction (e.g., Redcay & Schilbach, 2019). Recent advances in both imaging tech-
nologies and the data analytics have enabled us to pursue this exciting research direction (e.g., Noah
et al., 2020; Piazza et al., 2020). The second possibility is to study the role of motivation and emotion
as important ingredients of learning to accelerate SL2. It is essential to understand how SL2 influences
affective processing (e.g., emotion and motivation) and how it interacts with the limbic and subcor-
tical reward systems of the brain during SL2. The third avenue is to use machine learning approach
to analyze large- scale real- time interaction data to identify individual learner profiles, with the aim
of providing personalized learning (e.g., through feedback and reciprocal interpersonal interactions)
as in real social learning (see also Li & Lan, 2021a). Adult L2 learning can be expected to develop
to greater levels of success than in traditional learning contexts if we can capitalize on technology-
enhanced language learning and optimized social learning that incorporates key dimensions of indi-
vidual differences. Finally, to understand different aspects of L2 learning from multi- level language
systems, future studies should extend their focus from the lexico- semantic level to phonological, mor-
phological, syntactic, and discourse levels with the SL2 approach (Hagoort, 2019).
Note
1 This is what distinguishes a human child from a Large Language Model (LLM) such as ChatGPT. LLMs
can approximate human language performance by analyzing and aggregating large-scale text data but do not
interact with objects and people in a social context as humans do, and do not display a developmental trajec-
tory in language or knowledge acquisition.
Further Readings
This article provides a first attempt at integrating the theoretical, empirical, and neural bases of SL2 framework.
Li, P., & Jeong, H. (2020). The social brain of language: Grounding second language learning in social inter-
action. npj Science of Learning, 5, Article 8. https:// doi.org/ 10.1038/ s41 539- 020- 0068- 7
Page 10
Hyeonjeong Jeong and Ping Li
226
This article provides an overview of the current trends and future promises of DLL for L2 from a neurocognitive
approach. (see also the accompanying article that discusses the interaction between technology and the learner: Li
& Lan 2021b in the References below)
Li, P., & Lan, Y.J. (2021a). Digital language learning (DLL): Insights from behavior, cognition, and the brain.
Bilingualism: Language and Cognition, 25(3), 361– 378. https:// doi.org/ 10.1017/ S13667 2892 1000 353
This article highlights the significant role of social interaction in facilitating sustained attention and language
acquisition in young children.
Yu, C., & Smith, L.B. (2016). The social origins of sustained attention in one- year- old human infants. Current
Biology, 26(9), 1235– 1240. https:// doi.org/ 10.1016/ j.cub.2016.03.026
References
Abutalebi, J., Cappa, S.F., & Perani, D. (2005). Functional neuroimaging of the bilingual brain. In J.F. Kroll &
A. de Groot (Eds.), Handbook of bilingualism: Psycholinguistic approaches (pp. 497– 515). Oxford, Oxford
University Press.
Abutalebi, J., & Green, D. (2007). Bilingual language production: The neurocognition of language representation
and control. Journal of Neurolinguistics, 20(3), 242– 275. https:// doi.org/ 10.1016/ j.jne urol ing.2006.10.003
Aziz- Zadeh L., & Damasio, A. (2008). Embodied semantics for actions: Findings from functional brain imaging.
Journal of Physiology- Paris, 102(1– 3), 35– 39. https:// doi.org/ 10.1016/ j.jph yspa ris.2008.03.012
Barsalou, L.W. (2008). Grounding symbolic operations in the brain’s modal systems. In G.R. Semin & E.R.
Smith (Eds.), Embodied grounding: Social, cognitive, affective, and neuroscientific approaches (pp. 9– 42).
Cambridge University Press.
Bloom, P. (2000). How children learn the meanings of words. Cambridge, MA: MIT Press. https:// doi.org/
10.7551/ mitpr ess/ 3577.001.0001
Bowden, H., & Faretta- Stutenberg, M. (this volume). Context of learning in second language neurocognition.
In K. Morgan- Short & J.G. van Hell (Eds.), The Routledge handbook of second language acquisition and
neurolinguistics. Routledge.
Breitenstein, C., Jansen, A., Deppe, M., Foerster, A.- F., Sommer, J., Wolbers, T., & Knecht, S. (2005).
Hippocampus activity differentiates good from poor learners of a novel lexicon. NeuroImage, 25(3), 958–
968. https:// doi.org/ 10.1016/ j.neu roim age.2004.12.019
Caldwell- Harris, C.L. (2014). Emotionality differences between a native and foreign language: Theoretical
implications. Frontiers in Psychology, 5, Article 1055. https:// doi.org/ 10.3389/ fpsyg.2014.01055
Caldwell- Harris, C.L., & MacWhinney, B. (2023). Age effects in second language acquisition: Expanding the
emergentist account. Brain and Language, 241, 105269. https:// doi.org/ 10.1016/ j.bandl.2023.105 269
Carter, M.R., & Huettel, S.A. (2013). A nexus model of the temporal- parietal junction. Trends in cognitive
sciences, 17, 328– 36. https:// doi.org/ 10.1016/ j.tics.2013.05.007
Chomsky, N. (1981). Lectures on Government and Binding. Foris.
Claussenius- Kalman, H., Hernandez, A., & Li, P. (2021). Expertise, ecosystem, and emergentism: Dynamic devel-
opmental bilingualism, Brain and Language, 222,Article 105013. https:// doi.org/ 10.1016/ j.bandl.2021.105 013
Craik, F.I., & Lockhart, R.S. (1972). Levels of processing: A framework for memory research. Journal of Verbal
Learning & Verbal Behavior, 11(6), 671– 684. https:// doi.org/ 10.1016/ S0022- 5371(72)80001- X
Deen, B., Koldewyn, K., Kanwisher, N., & Saxe, R. (2015). Functional organization of social perception and
cognition in the superior temporal sulcus. Cerebral Cortex, 25(11), 4596– 4609. https:// doi.org/ 10.1093/ cer
cor/ bhv 111
Ellis, N.C. (2019). Essentials of a theory of language cognition. The Modern Language Journal, 103, 39– 60.
https:// doi.org/ 10.1111/ modl.12532
Fodor, J.A. (1983). The modularity of mind. MIT Press. https:// doi.org/ 10.7551/ mitpr ess/ 4737.001.0001
Frank, M.C., & Goodman, N.D. (2014). Inferring word meanings by assuming that speakers are informative.
Cognitive Psychology, 75, 80– 96. https:// doi.org/ 10.1016/ j.cogps ych.2014.08.002
Gianelli, C., & Dalla Volta, R. (2015). Does listening to action- related sentences modulate the activity of the
motor system? Replication of a combined TMS and behavioral study. Frontiers in Psychology, 5, Article
1511. https:// doi.org/ 10.3389/ fpsyg.2014.01511
Grant, A.M., Fang, S.- Y.Y., & Li, P. (2015). Second language lexical development and cognitive control: A lon-
gitudinal fMRI study. Brain and Language, 144, 35– 47. https:// doi.org/ 10.1016/ j.bandl.2015.03.010
Hagoort, P. (2019). The neurobiology of language beyond single- word processing. Science, 366(6461), 55– 58.
https:// doi.org/ 10.1126/ scie nce.aax0 289
Page 11
Neurocognition of Social Learning of Second Language
227
Hakuno, Y., Omori, T., Yamamoto, J., & Minagawa, Y. (2017). Social interaction facilitates word learning in pre-
verbal infants: Word– object mapping and word segmentation. Infant Behavior and Development, 48, 65– 77.
https:// doi.org/ 10.1016/ j.inf beh.2017.05.012
Hebscher, M., Wing, E., Ryan, J., & Gilboa, A. (2019). Rapid cortical plasticity supports long- term memory for-
mation. Trends in Cognitive Sciences, 23(12), 989– 1002. https:// doi.org/ 10.1016/ j.tics.2019.09.009
Hernandez, A., & Li, P. (2007). Age of acquisition: Its neural and computational mechanisms. Psychological
Bulletin, 133(4), 638– 650. https:// doi.org/ 10.1037/ 0033- 2909.133.4.638
Jeong, H., Hashizume, H., Sugiura, M., Sassa, Y., Yokoyama, S., Shiozaki, S. & Kawashima, R. (2011). Testing
second language oral proficiency in direct and semidirect settings: A social- cognitive neuroscience perspec-
tive. Language Learning, 61(3), 675– 699. https:// doi.org/ 10.1111/ j.1467- 9922.2011.00635.x
Jeong, H., Sugiura, M., Sassa, Y., Wakusawa, K., Horie, K., Sato, S., & Kawashima, R. (2010). Learning second
language vocabulary: Neural dissociation of situation- based learning and text- based learning. NeuroImage,
50(2), 802– 809. https:// doi.org/ 10.1016/ j.neu roim age.2009.12.038
Jeong, H., Li, P., Suzuki, W., Kawashima, R., & Sugiura, M. (2021). Neural mechanisms of language learning
from social contexts. Brain and Language, 212, Article 104874. https:// doi.org/ 10.1016/ j.bandl.2020.104 874
Johnson, J.S., & Newport, E.L. (1989). Critical period effects in second language learning: The influence of mat-
urational state on the acquisition of English as a second language. Cognitive Psychology, 21, 60– 99. https://
doi.org/ 10.1016/ 0010- 0285(89)90003- 0
Kousaie, S., & Klein, D. (this volume). Using functional neuroimaging to investigate second language organiza-
tion. In K. Morgan- Short & J.G. van Hell (Eds.), The Routledge handbook of second language acquisition
and neurolinguistics. Routledge.
Kroll, J.F., & Stewart, E. (1994). Category interference in translation and picture naming: Evidence for asym-
metric connections between bilingual memory representations. Journal of Memory and Language, 33, 149–
174. https:// doi.org/ 10.1006/ jmla.1994.1008
Kuhl, P.K. (2004). Early language acquisition: Cracking the speech code. Nature Reviews Neuroscience, 5(11),
831– 843. https:// doi.org/ 10.1038/ nrn1 533
Kuhl, P., Tsao, F.M., & Liu, H.M. (2003). Foreign- language experience in infancy: Effects of short- term exposure
and social interaction on phonetic learning. Proceedings of the National Academy of Science, 100(15), 9096–
9101. https:// doi.org/ 10.1073/ pnas.153 2872 100
Lantolf, J. (2006). Sociocultural theory and L2: State of the art. Studies in Second Language Acquisition, 28,
67– 109. https:// doi.org/ 10.1017/ S02722 6310 6060 037
Legault, J., Fang, S., Lan, Y., & Li, P. (2019). Structural brain changes as a function of second language vocabu-
lary training: Effects of learning context. Brain and Cognition, 134, 90– 102. https:// doi.org/ 10.1016/
j.bandc.2018.09.004
Legault, J., Zhao, J., Chi, Y- A., Chen, W., Klippel, A., & Li, P. (2019). Immersive virtual reality as an effective
tool for second language vocabulary learning. Languages, 4(1), Article 13. https:// doi.org/ 10.3390/ langu ages
4010 013
Lenneberg, E.H. (1967). Biological foundations of language. Oxford, UK: Wiley. https:// doi.org/ 10.1080/ 21548
331.1967.11707 799
Li, P. (2013). Computational modeling of bilingualism. A special issue of Bilingualism: Language and Cognition,
16(2), 241– 366. https:// doi.org/ 10.1017/ S13667 2891 3000 059
Li, P., & Jeong, H. (2020). The social brain of language: Grounding second language learning in social inter-
action. npj Science of Learning, 5, Article 8. https:// doi.org/ 10.1038/ s41 539- 020- 0068- 7
Li, P., & Lan, Y.J. (2021a). Digital language learning (DLL): Insights from behavior, cognition, and the brain.
Bilingualism: Language and Cognition, 25(3), 361– 378. https:// doi.org/ 10.1017/ S13667 2892 1000 353
Li, P., & Lan, Y.J. (2021b). Understanding the interaction between technology and the learner: The case of DLL.
Bilingualism: Language and Cognition, 23(3), 402– 405.
Li, P., Legault, J., & Litcofsky, K.A. (2014). Neuroplasticity as a function of second language learning: Anatomical
changes in the human brain. Cortex, 58, 301– 24. https:// doi.org/ 10.1016/ j.cor tex.2014.05.001
Li, P., & Zhao, X. (2017). Computational modeling. In A. de Groot & P. Hagoort (Eds.), Research methods in
psycholinguistics and the neurobiology of language: A practical guide (pp. 208– 229). John Wiley & Sons.
Linck, J., Kroll, J., & Sunderman, G. (2009). Losing access to the native language while immersed in a second
language: Evidence for the role of inhibition in second- language learning. Psychological Sciences, 20(12),
1507– 1515. https:// doi.org/ 10.1111/ j.1467- 9280.2009.02480.x
Liu, C., Wang, R., Li, L., Ding, G., Yang, J., & Li, P. (2020). Effects of encoding modes on memory of naturalistic
events. Journal of Neurolinguistics, 53, Article 100863. https:// doi.org/ 10.1016/ j.jne urol ing.2019.100 863
Luan, H., Geczy, Z., Gobert, J., C., Yang., S., Ogata, H., Baltes, J., Guerra, R., Li, P., & Tsai, C.C. (2020).
Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology,
11, Article 580820. https:// doi.org/ 10.3389/ fpsyg.2020.580 820
Page 12
Hyeonjeong Jeong and Ping Li
228
Luque, A. & Covey, L. (this volume). Factors accounting for individual differences in second language
neurocognition. In K. Morgan- Short & J.G. van Hell (Eds.), The Routledge handbook of second language
acquisition and neurolinguistics. Routledge.
MacIntyre, P.D., Burns C., & Jessome A. (2011). Ambivalence about communicating in a second language: A
qualitative study of French immersion students’ willingness to communicate. Modern Language Journal,
95(1), 81– 96. https:// doi.org/ 10.1111/ j.1540- 4781.2010.01141.x
Mackey, A., Abbuhl, R., & Gass, S. (2012). Interactionist approach. In S. Gass & A. Mackey (Eds.), The Routledge
handbook of second language acquisition (pp. 7– 24). Routledge. https:// doi.org/ 10.4324/ 978141 0612 564
MacWhinney, B. (2012). The logic of the Unified Model. In S. Gass & A. Mackey (Eds.), The Routledge hand-
book of second language acquisition (pp. 211– 227). Routledge.
MacWhinney, B., Kempe, V., Brooks, P., & Li, P. (2022). Emergentist approaches to language. Frontiers in
Psychology, 12, Article 6580. https:// doi.org/ 10.3389/ 978- 2- 88974- 483- 1
Mayer, K.M., Yildiz, I.B., Macedonia, M., & von Kriegstein, K. (2015). Visual and motor cortices differentially
support the translation of foreign language words. Current Biology, 25(4), 530– 535. https:// doi.org/ 10.1016/
j.cub.2014.11.068
Mayer, R.E. (2014). Principles based on social cues in multimedia learning: Personalization, voice, image, and
embodiment principles. In Mayer, R.E. (Ed.), The Cambridge handbook of multimedia learning (pp. 345–
370). Cambridge University Press. https:// doi.org/ 10.1017/ CBO97 8113 9547 369.017
Meltzoff, A., Kuhl, P., Movellan, J., & Sejnowski, T. (2009). Foundations for a new science of learning. Science,
325, 284– 288. https:// doi.org/ 10.1126/ scie nce.1175 626
Meteyard, L., Cuadrado, S.R., Bahrami, B., & Vigliocco, G. (2012). Coming of age: A review of embodiment and
the neuroscience of semantics. Cortex, 48, 788– 804. https:// doi.org/ 10.1016/ j.cor tex.2010.11.002
Myers, L., LeWitt, R., Gallo, R., & Maselli, N. (2017). Baby FaceTime: Can toddlers learn from online video
chat? Developmental Science, 20, e12430. https:// doi.org/ 10.1111/ desc.12430
Noah, J.A., Zhang, X., Dravida, S., Ono, Y., Naples, A., McPartland, J.C., & Hirsch, J. (2020). Real- time eye- to-
eye contact is associated with cross- brain neural coupling in angular gyrus. Frontiers in Human Neuroscience,
14, Article 19. https:// doi.org/ 10.3389/ fnhum.2020.00019
Ortiz Villalobos, V., Kovelman, I., & Satterfield, T. (this volume). The neurocognition of child second language
development. In K. Morgan- Short & J.G. van Hell (Eds.), The Routledge handbook of second language
acquisition and neurolinguistics. Routledge.
Paivio, A. (1990). Mental representations: A dual coding approach. Oxford University Press. https:// doi.org/
10.1093/ acp rof:oso/ 978019 5066 661.001.0001
Park, J., Kim, S., Kim, A., & Yi, M.Y. (2019). Learning to be better at the game: Performance vs. completion
contingent reward for game- based learning. Computers & Education, 139, 1– 15. https:// doi.org/ 10.1016/
j.comp edu.2019.04.016
Piazza, E.A., Hasenfratz, L., Hasson, U., & Lew- Williams, C. (2020). Infant and adult brains are coupled to the
dynamics of natural communication. Psychological Science, 31(1), 6– 17. https:// doi.org/ 10.1177/ 09567 9761
9878 698
Qi, Z., Han, M., Garel, K., Chen, E., & Gabrieli, J. (2015). White- matter structure in the right hemisphere
predicts Mandarin Chinese learning success. Journal of Neurolinguistics, 33, 14– 28. https:// doi.org/ 10.1016/
j.jne urol ing.2014.08.004
Redcay, E., & Schilbach, L. (2019). Using second- person neuroscience to elucidate the mechanisms of social
interaction. Nature Reviews Neuroscience, 20, 495– 505. https:// doi.org/ 10.1038/ s41 583- 019- 0179- 4
Ripoll�s, P., Marco- Pallar�s, J., Hielscher, U., Mestres- Miss�, A., Tempelmann, C., Heinze, H.- J.J., Rodr�guez-
Fornells, A., & Noesselt T. (2014). The role of reward in word learning and its implications for language
acquisition. Current biology, 24(21), 2606– 11. https:// doi.org/ 10.1016/ j.cub.2014.09.044
Sanchez- Alonso, S., & Aslin, R. (2022). Towards a model of language neurobiology in early development. Brain
and Language, 224, Article 105047. https:// doi.org/ 10.1016/ j.bandl.2021.105 047
Tomasello, M. (2003). Constructing a language: A usage- based theory of language acquisition. Harvard
University Press.
Tulving, E., & Thomson, D.M. (1973). Encoding specificity and retrieval processes in episodic memory.
Psychological Review, 80(5), 352– 373. https:// doi.org/ 10.1037/ h0020 071
Verga, L., & Kotz, S.A. (2017). Help me if I can’t: Social interaction effects in adult contextual word learning.
Cognition, 168, 76– 90. https:// doi.org/ 10.1016/ j.cognit ion.2017.06.018
Verga, L., & Kotz, S.A. (2019). Spatial attention underpins social word learning in the right fronto- parietal net-
work. NeuroImage, 195, 165– 173. https:// doi.org/ 10.1016/ j.neu roim age.2019.03.071
Page 13
Neurocognition of Social Learning of Second Language
229
Xu, M., Baldauf, D., Chang, C.Q., Desimone, R., & Tan, L.H. (2017). Distinct distributed patterns of neural
activity are associated with two languages in the bilingual brain. Science Advances, 3(7), Article e1603309.
https:// doi.org/ 10.1126/ sci adv.1603 309
Xue, J., Marmolejo- Ramos, F., & Pei, X. (2015). The linguistic context effects on the processing of body– object
interaction words: An ERP study on second language learners. Brain Research, 1613, 37– 48. https:// doi.org/
10.1016/ j.brain res.2015.03.050
Yang, J., Gates, K., Molenaar, P., & Li, P. (2015). Neural changes underlying successful second language
word learning: An fMRI study. Journal of Neurolinguistics, 33, 29– 49. https:// doi.org/ 10.1016/ j.jne urol
ing.2014.09.004
Yu, C., & Ballard, D. (2007). A unified model of early word learning: Integrating statistical and social cues.
Neurocomputing, 70(13– 15), 2149– 2165. https:// doi.org/ 10.1016/ j.neu com.2006.01.034
Yu, C., & Smith, L.B. (2016). The social origins of sustained attention in one- year- old human infants. Current
Biology, 26(9), 1235– 1240. https:// doi.org/ 10.1016/ j.cub.2016.03.026
Zappa, A., & Frenck- Mestre, C. (this volume). Embodied second language processing and learning from a
neurocognitive perspective. In K. Morgan- Short & J.G. van Hell (Eds.), The Routledge handbook of second
language acquisition and neurolinguistics. Routledge.
Zhang, Y., Kuhl, P.K., Imada, T., Iverson, P., Pruitt, J., Stevens, E.B., Kawakatsu, M., Tohkura, Y., & Nemoto, Y.
(2009). Neural signatures of phonetic learning in adulthood: A magnetoencephalography study. Neuroimage,
46, 226– 240. https:// doi.org/ 10.1016/ j.neu roim age.2009.01.028
Zhang, X., Yang, J., Wang, R., & Li, P. (2020). A neuroimaging study of semantic representation in first and
second languages. Language, Cognition and Neuroscience, 35(10), 1223– 1238. https:// doi.org/ 10.1080/
23273 798.2020.1738 509
Zhao, X., & Li, P. (2010). Bilingual lexical interactions in an unsupervised neural network model.
International Journal of Bilingual Education and Bilingualism, 13, 505– 524. https:// doi.org/ 10.1080/ 13670
050.2010.488 284