Neuroscience and learning through play: a review of the evidence

Abstract

Neuroscience helps explain how playful experiences can support learning. In this white paper, we find that each characteristic – joy, meaning, active engagement, iteration, and social interaction – is associated with brain processes involved in learning. These processes include reward, memory, cognitive flexibility, and stress regulation that are activated during learning and serves to prepare a child’s brain for further development.

Full text

November 2017
Claire Liu, S. Lynneth Solis, Hanne Jensen, Emily Hopkins, Dave Neale,
Jennifer Zosh, Kathy Hirsh-Pasek, & David Whitebread
Neuroscience and learning
through play: a review of
the evidence
White paper
ISBN: 978-87-999589-2-4

2
Table of contents
This white paper is published in 2017 and
licensed under a Creative Commons Attribution-
NonCommercial-ShareAlike 3.0 Unported License
(http://creativecommons.org/licenses/by-nc-sa/3.09)
ISBN: 978-87-999589-2-4
Suggested citation
Liu, C., Solis, S. L., Jensen, H., Hopkins, E. J., Neale, D.,
Zosh, J. M., Hirsh-Pasek, K., & Whitebread, D. (2017).
Neuroscience and learning through play: a review of
the evidence (research summary). The LEGO
Foundation, DK.
Table of contents
Introduction • 3
Joyful • 6
Meaningful • 10
Actively engaging • 14
Iterative • 16
Socially interactive • 18
Future directions • 20
Closing thoughts • 22

3
Introduction
In this white paper, our discussion of the neuroscience
and biological literature on learning focuses on
ve characteristics used to dene playful learning
experiences, joyful, meaningful, actively engaging,
iterative and socially interactive (see Zosh et al.,
2017). From a neurobiological perspective, these
characteristics can contribute to children’s ability to
attend to, interpret, and learn from experiences.
The neuroscience literature in brief
Our current understanding of how each of the
characteristics of playful experiences can support
learning processes is primarily informed by research
that concerns typically and atypically developing
adults and animal models. Animal models give us some
indication of possible mechanisms in the human brain,
but it is worth noting that human and animal models
are not perfect parallels. Additionally, adult studies
provide insights into human cortical networks, but
in brains that are less susceptible and vulnerable to
environmental forces than those of children. With this
in mind, we review the literature while in most cases
leaving open considerations for the ways in which each
characteristic may aect learning in children. It is also
worth noting that while this research shows how the
ve characteristics may contribute to learning, few
studies actually investigate the direct relationship
between play and learning. This too remains an area
open for future research.
In what follows, we rst describe the interconnected
nature of learning informing this review. From there,
we summarise each of the ve characteristics and
how they connect with learning, as seen through a
neuroscience lens.
Interconnected and holistic learning
As we dive into the ve characteristics of playful
experiences, it is important to view the various
experiences embodying these characteristics
in the larger context of brain development. Our
understanding is not that dierent parts of the brain
mature and dictate learning separately, but instead,
that each region relies on ongoing and specic external
input and connects robustly with other regions of
the brain. Overall, the ndings illustrate how the ve
characteristics of learning through play facilitate
the development and activation of interconnected
brain processes in growing children and support their
capacity to learn.
Our understanding of learning in the context of
experiences is holistic, meaning that it relates to
the development of multiple domains rather than
performance on a set of academic measures. Learning
in the brain refers to the neural capacity to process and
respond to dierent sensory, or multimodal, inputs, on
both basic and complex levels. Inputs across multiple
modalities are often helpful, if not essential, for the
proper development of learning behaviors for children.
Face-to-face interaction with a caregiver, for example,
provides an infant with visual, auditory, language,
and social-emotional inputs so that she may develop
visual acuity, phoneme recognition, facial recognition,
and secure emotional attachment (Fox, Levitt, &
Nelson, 2010). These outcomes in turn support the
development of language, cognitive control, and
emotion regulation skills as she continues to grow.
Introduction

4
Introduction
Joy
Emotions are
integral to
neural networks
responsible for
learning
Joy is associated
with increased
dopamine levels in
the brain’s reward
system linked
to enhanced
memory,
attention,
mental shifting,
creativity, and
motivation
Meaningful
Making connections
between familiar and
unfamiliar stimuli
guides the brain in
making eortful
learning easier
Meaningful
experiences
introduce novel
stimuli linking to
existing mental
frameworks;
processing these
stimuli recruits
networks in the brain
associated with
analogical thinking,
memory, transfer,
metacognition,
creating insight,
motivation and
reward
Active
Engagement
Active and engaged
involvement
increases brain
activation related
to agency, decision
making, and ow
Active engagement
enhances memory
encoding and
retrieval processes
that support
learning
Full engagement
in an activity
allows the brain to
exercise networks
responsible for
executive control
skills, such as
pushing out
distractions, that
benet short term
and lifelong learning
Iterative
Perseverance
associated with
iterative thinking is
linked to reward and
memory networks
that underpin
learning
With practice,
iteration increasingly
engages networks
related to taking
alternative
perspectives,
exible thinking, and
creativity
Socially
Interactive
Positive caregiver-
child interactions
help build the neural
foundations for
developing healthy
social emotional
regulation and
protecting from
learning barriers, such
as stress
Early social
interaction promotes
plasticity in the brain
to help cope with
challenges later in life
Social interaction
activates brain
networks related to
detecting the mental
states of others,
which can be critical
for teaching and
learning interactions
Key takeways
Playful learning experiences characterised by joy,
meaning, active engagement, iteration, and social
interaction can oer multimodal inputs that stimulate
interconnected networks involved in learning (see
highlighted areas in the illustration on page 5).
The quality of our experiences therefore aects
our development from an early age. With this
background in mind, our review explores how each
of the characteristics is related to these cognitive
processes. The table below summarizes key
takeaways from the neuroscience and biological
literature for each characteristic.

Hippocampus
Septum
Thalamus
Caudate nucleus
Posterior cingulate cortex (PCC)
Amygdala
Pre-frontal cortex (PFC)
Anterior cingulate cortex (ACC)
5
Introduction
Medial view of the brain and the areas related to the ve characteristics

Joyful
6
Joyful
Across cultures and animal species, play appears
to be a common experience innate to development
(Huizinga, 1950; Burghardt, 2010; Smith, 2010). Play
can rarely proceed without exhibiting positive aect
and joy, the feelings of enjoyment and fun (Huizinga,
1950; Rubin, Fein, & Vandenberg, 1983). Some may
argue that positive emotions such as joy have an
evolutionary role: they allow us to interact and respond
protectively and appropriately to our environment
(Burgdorf & Panksepp, 2006).
In the neuroscience literature, the connection between
joy and learning has been studied among adults and
animals (examples include Burgdorf & Panksepp,
2006; Söderqvist et al., 2011). Our ability as humans
to experience joy is regulated by subcortical limbic
networks (the light blue area in the illustration on page
5), which are associated with emotional functions and
found in animal models as well (Burgdorf & Panksepp,
2006). Networks that involve other brain regions
responsible for higher-order processing in learning
(cortical regions – the yellow area in the illustration on
page 5) respond adaptively to these experiences of
emotion (Burgdorf & Panksepp, 2006).
To adapt is to learn, and joy
exists to motivate us to continue
adapting to our environment and
to learn from it. Joy, it seems, has
an important relationship with our
propensity to learn.
Learning is emotional and associated with reward
Emotions were previously thought of as secondary
to cognition in learning, but developmental and
neuroscientic research is quickly revealing that the
two are interwoven (Immordino-Yang & Damasio,
2007). To consider emotion and cognition separately
would be incomplete. Emotions help to facilitate
rational thought by enabling us to apply emotional
feedback to our decision-making (Immordino-Yang &
Damasio, 2007). The role of emotion in our capacity to
take reasonable action in unpredictable circumstances
is what Immordino-Yang and Damasio (2007) coin the
“emotional rudder” (p. 3). Given the role of emotions
in priming us to learn, joy is perhaps one of the most
powerful forces.
Joy invokes a state of positive aect
that enables many higher cognitive
functions.
At a high level, the experience of joy is associated
with network changes in the brain, such as increases
in dopamine levels, that result in positive emotions.
Dopamine is a neurotransmitter that helps regulate
reward, pleasure, and emotion in the brain, as well as
our actions in response to reward. Eects of dopamine
are observed in brain regions identied as part of the
reward network, including the midbrain, striatum,
hippocampus, and prefrontal cortex (see illustration
to the right). Dopamine initiates interaction between
these various regions to alter our responses and
actions. Bromberg-Martin and Hikosaka (2009, as
cited in Cools, 2011) linked the presence of dopamine
neurotransmitters on neurons in the midbrain to the
process of expecting a reward and seeking information
in anticipation of this reward.

7
Joyful
The resulting positive aect is linked to a series of
cognitive benets, such as enhanced attention,
working memory, mental shifting, and improved
stress regulation, that are useful to learning (e.g.,
Cools, 2011; Dang, Donde, Madison, O’Neil, Jagust,
2012; McNamara, Tejero-Cantero, Trouche, Campo-
Urriza, & Dupret, 2014). There are multiple proposed
mechanisms for how dopamine precisely acts on brain
structures (Cools, 2011), however, it is well supported
that the presence of dopamine associated with
joyful experiences can result in an enhanced ability to
process and retain information. Thus, understanding
the reward system can help us explore its role in
memory, mental shifting, motivation, and creativity, as
they contribute to learning.
Medial view of the brain showing the dopamine pathways

8
Memory
Examples of dopamine’s eect on memory and
learning are found in animal models. Among mice,
dopaminergic stimulation in the midbrain while the
mice engaged in new spatial environments were
associated with greater activity in the hippocampal
region, which seemed to improve their recall of
the task (McNamara et al., 2014). Furthermore,
dopaminergic stimulation initiated while learning
the location of a new goal was associated with
better activation of hippocampal neurons during
resting state. These ndings suggest a benecial
role of dopamine while encoding and recalling
new information, at least in the case of spatial
representation and memory (McNamara et al., 2014).
Attention to goals and mental shifting
Guided by the presence of dopamine during joyful
experiences, the regions associated with reward and
planning often work in tandem to allow individuals to
focus on information relevant to their goals (Vincent,
Kahn, Snyder, Raichle, & Buckner, 2008, as cited in
Dang et al., 2012). This allows individuals to decide
not only which information to attend to, but also plan
corresponding goal-directed behaviors. That is, in
learning situations, dopamine can help with the mental
shifting required as we consider what information to
select in order to plan for appropriate goals.
Motivation and curiosity
Intrinsic motivation and curiosity are two traits that
readily come to mind in our discussion of the ve
characteristics of learning through play, but especially
in joy, perhaps owing to its spontaneous nature. The
literature points to the inuence of curiosity and
intrinsic motivation in enhanced neural activity as well
(Kang et al. 2009). fMRI results show that the more
we anticipate a positive outcome, as is often the
case when we are intrinsically motivated, the more
the activity in these brain structures enhances our
ability to retain the information that follows (Gruber,
Gelman, & Ranganath, 2014). Small changes in our
environmental settings can inspire us to anticipate
the learning to come and prime the brain to retain
information more eectively (Weisberg, Hirsh-Pasek,
Golinko, & McCandliss, 2014).
Creativity
Dopamine can enhance processes that have been
shown to correlate with creative thinking, such as
working memory, but the relation could be more
direct. While the exact mechanism is unclear, individual
creativity has been found to relate to activation in
brain structures associated with the dopamine reward
system (Takeuchi et al., 2010), suggesting perhaps that
joyful experiences are related to creative thinking.
Plasticity
There is also evidence to suggest that the chemical
responses in the brain associated with joyful
experiences can inuence plasticity, meaning the
brain can continue to adapt to new information and
environmental inputs (Nelson, 2017; Söderqvist et
al., 2011). In this way, joyful experiences that raise
dopamine levels in the brain may result in an increased
ability to adapt to and learn from new learning
situations.
Joyful

9

Meaningful
Learning usually involves moving from the unknown to
the known, or from eortful to automatic processing.
Meaningful experiences can provide a space for these
progressions. Opportunities for contextual learning,
analogical reasoning, metacognition, transfer, and
motivation can support the development of deeper
understanding in such experiences.
Guiding learning from eortful to automatic
The neuroscience literature illustrates how
meaningful experiences recruit multiple networks
in the brain to help us make sense of what we learn.
Learning new material is assumed to involve two
networks (Luu, Tucker, Stripling, 2007): the fast
learning system and the later stage of learning
(Bussey et al., 1996; Keng and Gabriel, 1998, as cited
in Luu, Tucker, Stripling, 2007). The rst network
assists with rapid and focused acquisition, scanning
for inconsistencies or perceived threats. The
second network is then recruited to help us put new
information in context of our already-constructed
mental models (Luu, Tucker, Stripling, 2007).
Analogical reasoning
Meaningful experiences can serve as opportunities
for children to bridge the unknown to models
already familiar to them through higher cognitive
processes. One form of cognitive process studied
in the literature, analogical reasoning, is employed
in making connections between the known and the
unknown. Analogical reasoning is a type of thinking
that helps us see beyond surface-level dierences to
understand underlying similarities in objects, concepts,
or relationships (e.g., understanding that honey from
a bee is like milk from a cow, or that we can observe
triangles in everyday life). Evidence shows that this
type of thinking recruits domain-general regions
of the brain operant in abstract thinking, as well as
domain-specic regions related to the analogical task
at hand (Hobeika, Diard-Detoeuf, Garcin, Levy, and
Volle, 2016). The authors suggest that the increased
functional connectivity between these two regions
helps connect external stimuli with existing
cognitive models.
Knowledge transfer
When we nd learning meaningful, gained knowledge in
one domain may be transferred to new and real-world
settings. There is evidence to support neurological
changes when meaningful experiences provide
opportunities for transfer of knowledge, even in
the absence of a cognitive task (Gerraty, Davidow,
Wimmer, Kahn, and Sohomy, 2014). Moreover,
Gerraty et al. (2014) observed that active transfer
may be related to activity connecting to regions
responsible for memory-related learning and exible
representation. Lastly, decreases in eortful activity
in regions associated with encoding new memories
have been observed as new knowledge becomes more
integrated with prior knowledge.
Metacognition and condence
Metacognition is often identied as an important
component to self-regulated learning. The ability
to recognise and understand our own abilities
and thought processes is useful in navigating new
contexts, reasoning with new information, and building
meaningful experiences. Monitoring ourselves in our
environments and making judgments in response rely
on the ability to access working memory and predict
future performance (Müller, Tsalas, Schie, Meinhardt,
Proust, Sodian, & Paulus, 2016). When metacognitive
skills allow us to make meaning out of our surroundings
and accurate predictions about our performance, our
condence levels increase. It has been suggested that
condence, invoked through metacognitive skills, can
prompt reward seeking and improved memory retrieval
in meaningful situations (Molenberghs, Trautwein,
Böckler, Singer, & Kanske, 2016). Gains in condence
have been associated with increased activity as well as
levels of dopamine in regions that are linked to reward,
memory, and motor control (Molenberghs et al., 2016).
10
Meaningful

Deeper learning
allows us to connect
factual knowledge
with real-world
experiences and
really grasp their
implications
Surface learning
means we
memorise key
facts and principles A hexagon has
six straight sides
and six angles
If you make a triangle out of
three sticks with hinges in the
corners, it stays rigid. That’s why
triangles are used in bridges,
cranes, houses and so on.
Notice how snowakes are
symmetrical hexagons?
This shape reects
how the crystal’s water
molecules are connected.
Hexagons are useful shapes,
for example in beehives.
They use the least amount
of wax to hold to most
weight of honey.
A triangle has three
straight sides and
three angles – the sum
of its angles is 180º

Taken together, discovering meaningful relationships
in learning through play may build on various cognitive
processes, activate the brain’s reward system, and
strengthen the encoding of these memories. These
can be forceful drivers of learning. It makes sense,
then, to provide children with material that is both
novel and familiar. We can use this approach to
facilitate meaningful experiences for children, guiding
them in new explorations through play, for example,
that deepen their own relationship with the world.
Memory
Meaningful experiences present a blend of familiar
and novel stimuli, initiating neural networks involved
with novelty processing, memory, and reward seeking
exploration that are useful in learning (Bunzeck,
Doeller, Dolan, & Duzel, 2012). Studies demonstrate
that familiar inputs combined with a novel reward
can result in stronger hippocampal activity than
familiar stimuli with familiar reward predicted
(Bunzeck et al., 2011).
Insight
One can recall the “aha” moment that often
accompanies solving a problem. Researchers
hypothesise that forming novel insights requires
breaking mental representations to accommodate
new information and make meaningful connections
(Qiu, Li, Yang, Luo, Li, Wu & Zhang, 2008). Studies
show that participants who make a sudden connection
exhibit strong activation in cortical regions that are
often involved in cognitive control, such as conict
monitoring, inhibitory control and task switching
(Carlson, Zelazo, & Faja, 2013; Kizilirmak, Thuerich,
Folta-Schoofs, Schott, & Richardson-Klavehn, 2016).
Reward and motivation
Lastly, some evidence tells us that the formation
of new insights are encoded in our memories by
activating the brain’s internal reward network
(Kizilirmak et al., 2016). Involving the intri nsic reward
system activates the hippocampus, which is useful
for encoding the meaningful relationship we have
just acquired, as well as its later retrieval (Kizilirmak
et al., 2016).
12
Meaningful

What is the difference
between a novice and expert?
It’s not how much they know
but their ability to recognise
meaningful patterns in that
knowledge, see relationships
and grasp the bigger picture.
(DeHaan, 2009)
“

Active engagement in an experience demands both
attention and response. Activities and events that
are able to elicit active engagement are uniquely
pertinent in our discussion of learning through play; it
is dicult to imagine that an experience can aect our
awareness and thought without being able to captivate
us rst. Feeling actively engaged is an experience that
can be viscerally familiar, as those who are actively
engaged in activities often express that they are “in
the driver’s seat”, “immersed”, and “losing a sense
of time”. The characteristics of this experience are
sometimes described as involving agency or inducing
ow (Csikszentmihalyi, 1975).
Neurally, active engagement is associated with
networks involved in attention control, goal-directed
behavior, reward, temporal awareness, long term
memory retrieval, and stress regulation. Studies
examining the neural correlates of experiences
characterised by active engagement implicate the left
inferior frontal gyrus (IFG) and left putamen (Ulrich,
Keller, Hoenig, Waller, & Grön, 2013). The left IFG has
been associated with a sense of control, especially
in sophisticated and challenging tasks (Ulrich et al.,
2013). As it turns out, the left putamen is often linked
to goal-directed behavior (Ulrich et al., 2013). Together,
the identication of these neural substrates supports
the hypothesis that active engagement recruits higher
cognitive processes that are benecial to learning.
Agency
Our discussion of active engagement would not be
complete without highlighting the role of agency.
Perhaps acting as a catalyst to learning, agency helps in
guiding our voluntary behaviors, motivating us to seek
information and take action. Agency itself can set o
a cycle of positive reinforcement, invoking feelings of
condence, progress, and positive aect, leading to
Actively engaging
more agency (Kuhn, Brass, & Haggard, 2012). For more
research highlighting the link between agency and
processes such as memory, see Kaiser, Simon, Kalis,
Schweizer, Tobler, and Mojzisch (2013), Holroyd and
Yeung (2012), and Jorge, Starkstein, & Robinson, 2010.
Flow
An important element of experiences that are actively
engaging is that they present stimuli at just the
right levels; appropriate experiences and inputs can
help to immerse us in activities and activate reward
networks as long as they are not overwhelming.
This is represented in the literature in studies that
show decreased neural activity in the amygdala in
participants actively engaged in tasks (Ulrich et al.,
2013). The amygdala helps with coding perceived
threats and plays a central role in stress regulation
via the HPA axis (Shonko & Garner, 2012). In self-
reported ow experiences, those who described
feeling more immersed were seen to have greater
decreases in their amygdala (Ulrich et al., 2013).
These results uphold the relationship between lower
amygdala activity and positive emotions that, as
discussed previously, can enhance our motivation to
learn, and more broadly, between active engagement
and our ability to learn.
Memory
Engaging children at appropriate levels might also
play a role in the strengthening memory, particularly
short term memory. Some research suggests a
correlation between active engagement and memory
development and information retrieval (Johnson, Miller
Singley, Peckham, Johnson, & Bunge, 2014).
14
Active Engagement

Executive functions
From behavioral studies with children, we learn that
active engagement in an activity is related to executive
function skills, such as inhibitory control. Sustained
engagement in an activity demands the ability to
stay selectively focused on the situation at present,
tune out distractions, and hold the information in our
heads (Diamond, 2013). We can observe the eects of
active engagement on executive function skills (EFs)
in a study comparing children assigned to Montessori
and non-Montessori schools, which discovered that
the Montessori children, who had fewer interruptions
during their learning activities, performed better at EF
tasks than the other group (Lillard & Else-Quest, 2006,
as cited in Carlson, Zelazo, & Faja, 2013). Thus, this
evidence poses interesting implications to further study
the relationship between active engagement, executive
function skills, and learning in young children.

Iterative experiences are characterised by repetition
of activity or thought, to potentially discover new
insights with each round. Engaging in and building
upon this cognitive skill is a critical step to early
and lifelong learning. In today’s environment of
constant change, problem-solving is more salient
than ever. Whether the situation calls for building a
speedy racecar, troubleshooting a broken household
appliance, or working through a challenging project
with unrealised answers, iterative thinking pushes
us to novel solutions. Iterative thinking is involved in
experimentation, imagination, and problem-solving.
Through continued trial and error, we also build
resilience, an asset to lifelong learning. Our analysis of
iterative experiences at the neural level examines many
relevant and studied cognitive processes, including
perseverance, counterfactual reasoning, cognitive
exibility, and creativity or divergent thinking.
Perseverance
Any iterative thinking experience involves an element
of perseverance. Cortically, perseverance implicates
the nucleus accumbens (NA), which plays a central
role in the processing of reward (O’Doherty et al.,
2004; Nemmi, Nymberg, Helander, & Klingberg,
2016). Connectivity between two regions (the NA and
the ventral striatum) has also been associated with
perseverance (Myers et al., 2016). Not surprisingly,
the ventral striatum is also involved in inhibitory
control and cognitive exibility (Voorn, Vanderschuren,
Groenewegen, Robbins, & Pennartz, 2004), both of
which are skills that assist us in pursuing our goals.
Some research also suggests a correlation between
perseverance and positive outcomes in cognitive
training for children involving working memory (Nemmi
et al., 2016).
Iterative
Counterfactual thought and perspective-taking
Beyond perseverance, iterative experiences involve
mentally weighing alternative options in one’s mind
in contrast to reality, or counterfactual reasoning.
Counterfactual reasoning helps us rationalise the past,
make cognitive and emotional judgments, and adapt
behaviour accordingly (Van Hoeck, Watson, & Barbey,
2015), employing a trio of cognitive processes that
shapes how we learn. The implicated brain regions
are involved in mental representations of multiple
scenarios, autobiographical memory, and perspective-
taking abilities (Boorman, Behrens, & Rushworth,
2011; Van Hoeck, Watson, & Barbey, 2015). These
processes prepare us to make predictions about our
decisions and adapt to new information. To do this, the
brain interprets external feedback from our decisions
and exercises judgements in the future to reinforce
favorable outcomes (Van Hoeck, Watson, & Barbey,
2015). Iterative experiences thus nudge us to engage
in counterfactual thinking prior to taking action.
Cognitive exibility and creative thinking
Weisberg and Gopnik (2013) have also suggested that
through similar cognitive computations involved in
counterfactual reasoning, employing their imagination
allows children to consider past results and possible
alternative outcomes to plan future interventions
accordingly. Doing so calls for aspects of cognitive
exibility, such as letting go of existing views to change
one’s perspective based on updated requirements
(Diamond, 2013). In contrast to mental rigidity,
cognitive exibility paves the way for creative thinking.
Divergent thinking, a facet of creativity, can act as both
a driver and a product of iterative thinking. Research
from fMRI studies measuring divergent thinking
16
Iteration

in verbal and visuospatial domains consistently
implicates the lateral prefrontal cortex (PFC) in
creative outcomes (Arden, Chavez, Grazioplene, &
Jung, 2010; Dietrich & Kanso, 2010; Kleibeuker, De
Dreu, & Crone, 2016). This nding is interesting on two
fronts. The lateral PFC is involved with higher cognitive
functions, such as cognitive exibility and problem-
solving (Johnson & deHaan, 2015; Kleibeuker, De Dreu,
& Crone, 2016), and experiences a surge in growth
in adolescence (Johnson & deHaan, 2015). In light
of heightened development in the prefrontal cortex
during adolescence, this may be a unique period for
individuals to engage in creative problem-solving and
iterative thinking.
Plasticity
Evidence may suggest that the more iterative thinking
we engage in, the better prepared we become to
iterate further. This is perhaps obvious and highly
visible in jazz musicians who must improvise at length.
In fact, fMRI studies among adult musicians have
observed patterns in functional connectivity related
to increases in improvisational training (Pinho, De
Manzano, Fransson, Eriksson, Ullén, 2014; Gibson,
Folley, Park, 2008), suggesting that creative outputs
are not necessarily the eortless endeavors they
often seem, but can be strengthened by training even
through adulthood.
Iteration

Finally, we dive into the role of socially interactive
experiences in learning. At its core, learning might
be considered a social enterprise. We learn in our
interactions with others and within the context
of our environment and culture (Vygotsky, 1978).
Popular frameworks of child development such as
Bronfenbrenner’s ecological systems theory remind
us that the well-being of an individual is sensitive to
the dynamic interactions between social networks,
such as caregivers, schools, and society at large
(Bronbenbrenner, 1977).
Neuroscience research has shown that from early in
development, social interaction plays a remarkable
role in shaping our brain and behavioral development
(Nelson, 2017). Beginning immediately after birth,
children’s interactions with supportive and responsive
adults (also called serve-and-return interactions) help
to build a strong neural foundation in brain regions
responsible for the development of basic functional
systems such as vision and language (Fox, Levitt, &
Nelson, 2010).
Beyond basic sensory functions, social interaction is
also critical for the development of higher processes
(Hensch, 2016). The same serve-and-return functions
that underpin our visual and auditory capacities fuel
our cognitive, social, and emotional regulation later in
life. Research shows that babies can process an adult’s
emotional reaction and respond adaptively, such as
returning a joyous smile or avoiding an object to which
the caregiver shows fear (Happé & Frith, 2014). This
sensitivity to social cues sets the neural stage for
regulatory processes that have been demonstrated
through research and practice to be essential for
learning in complex environments.
Caring relationships are essential
The quality of caregiver interactions matters, as
they present a major source of social emotional
development. Positive caregiver relationships can
Socially interactive
buer children from the deleterious eects of adverse
experiences by helping to regulate stress responses,
allowing other brain networks to help us navigate
through everyday life (Center on the Developing Child
at Harvard University (CDC), 2012; CDC, 2016). Under
adversity, the brain expends more energy detecting
and protecting the individual from possible threat,
taking resources away from more long term planning,
cognitive development, and self-regulation needed
to overcome dicult circumstances (Lupien, Gunnar,
McEwen & Heim, 2009).
Another way to underscore the importance of
positive and nurturing caregiver relationships in early
childhood is to examine what happens to a child in the
absence of social interaction. Without supportive,
stimulating, and caring relationships, brain function
at its full potential becomes a challenge. Children who
experience extreme neglect and social deprivation
in institutionalised settings exhibit decreased brain
electrical activity associated with learning diculties,
as measured by EEG (Nelson, Fox & Zeanah, 2013).
Nevertheless, timely and appropriate interventions
in which children are placed in environments where
they are surrounded by positive and supportive
social interactions can help ameliorate or even
reverse the negative cognitive, social, and physical
effects of neglect.
Peer interaction and self-regulation
Whereas social and emotional systems of children
depend primarily on interactions with their caregivers
or other caring adults early in life, they are more
attuned to their peers later in childhood and through
adolescence (Nelson, 2017). Interactions with peers
can help children develop skills such as language
acquisition, cooperation, and social learning. Social
interactions can also provide the context for a child
to practice self-regulatory skills, such as control
of inhibitions and take appropriate actions, that
develop executive function skills (Diamond, 2013).
18
Socially Interactive

Neuroscientic and biological studies of play in animals
appear to indicate that social interactions during play
can help support the development of brain regions and
neural networks essential for learning these skills.
Social interactions in rodents characterised as rough-
and-tumble play appear to shape the PFC and have
an impact on self regulation and planning (Bell, Pellis,
& Kolb 2010; for reviews also see Pellis & Pellis, 2007;
Pellis, Pellis, & Himmler, 2014). It appears that through
playful experiences involving social interactions,
juvenile rats and perhaps children develop essential
neural networks that are foundational for learning.
Adaptability
Furthermore, social interactions involved in juvenile
playful experiences appear to enhance animal
adaptability to unexpected circumstances. Indeed, it
appears that “the juvenile experience of play renes
the brain to be more adaptable later in life” (Pellis,
Pellis, & Himmler, 2014, p. 73) by enhancing neural
plasticity (Maier & Watkins, 2010; Himmler, Pellis, &
Kolb, 2013). Conversely, animals deprived of social
play in early in development and adolescence display
rigidity and abnormally low levels of social behavior
later in their development, even when resocialised
(Baarendse, Counotte, O’Donnell & Vanderschuren,
2013; Nelson, 2017). Studies of rats that are socially
isolated early in life show that they are more prone
to impulsivity, irregular behaviors, and poor decision-
making in novel and eortful situations (Baarendse et
al., 2013).
Detecting mental states
Social interaction may also help us exercise the mental
processes involved with understanding perspectives
other than our own even in the absence of prompting
(German, Niehaus, Roarty, Giesbrecht, & Miller, 2004).
Contexts that provide opportunities to engage
in interpreting the mental state of others might
suciently initiate processes such as theory of mind
(German et al., 2004), which can be integral for formal
and informal teaching and learning interactions.
Whether from caregivers or peers, these ndings
highlight the importance of positive, social interactions
in establishing healthy development. For this reason,
we emphasise the role that socially interactive playful
learning experiences can hold in productively shaping a
child’s developmental trajectory. While more rigorous
investigations into play among children are needed
to better understand these eects, the coupling of
social interactions that are enriching with the neural
foundations for learning is one worthy of attention.
Socially Interactive

There is a rich body of literature establishing the neural
correlates of the dierent facets of learning that align
with the ve characteristics of playful experiences. This
allows us to further explicate how playful experiences
can support learning. We nd that mechanisms
described in the literature show a generally positive
cycle. In other words, each characteristic is associated
with neural networks involved in brain processes,
including reward, memory, cognitive exibility, and
stress regulation that are activated during learning. In
turn, the activation of these neural networks serves
to prepare a child’s brain for further development
(Puschmann, Brechmann, & Thiel, 2013). Thus, having
experiences that are joyful, help children nd meaning
in what they are doing or learning, and involve active
engagement, iterative thinking and social interaction
can provide children with the foundations for lifelong
learning.
Our understanding of the neural underpinnings of
learning thus far is supported primarily through adult
and animal studies. Most of what we know of learning
in children comes from behavioural studies in the
developmental and cognitive sciences. However,
recent advances in non-invasive neuroimaging
techniques allow us to observe changes in brain
activity among adolescents and younger children in
informal settings that are relevant to our exploration.
Using these techniques, future research can seek
to validate some of the ndings observed among
adult and animal studies. In particular, research may
be able to experiment in novel settings that are
less restricted by static machinery, such as playful
experiences themselves. Designing studies using
Closing thoughts
movement-friendly tools such as fNIRS, and EEG can
help us pinpoint specic patterns of brain activity in
playful learning experiences in situ. This will allow us
to identify contexts that are conducive to learning,
the specic ways in which they encourage learning,
and for whom the contexts are most productive.
Having this more nuanced understanding may help us
design neuroscience research that further elucidates
the underlying neural mechanisms that can facilitate
learning through play.
The direction of the relationship between play and
learning, as well as the relationship among the dierent
characteristics of playful experiences, also warrants
further exploration. For example, we may nd that
joyful experiences involve reward systems that incline
individuals to engage in creative and exible thinking;
alternatively, it is possible that the opportunity to
engage in creative and exible thinking is what makes
playful experiences joyful.
From the perspective of geographical contexts, we
also nd that research in this area is weighted toward
western (e.g., North American and European) settings.
While diverse and positive experiences matter for
brain development and neural network formation
across all cultures, we might seek to better understand
how these experiences manifest depending on their
cultural contexts. Certain dimensions of culture (e.g.,
language experience such as bilingualism, (Barac,
Bialystok, Castro, & Sanchez, 2014)) might inuence
children’s neural development in ways that shape
their learning and cognition. To support a more
complete understanding, further research in diverse
geographical and cultural contexts is needed.
20
Closing thoughts

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Image credits
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26
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27
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