The role of embodied cognition for transforming learning

Abstract

Cognitive psychology has undergone a paradigm shift in the ways we understand how knowledge is acquired and represented within the brain, yet the implications for how this impacts students’ learning of material across disciplines has yet to be fully applied. In this article, we present an integrative review of embodied cognition, and demonstrate how it differs from previously held theories of knowledge that still influence the ways in which many subjects are taught in the classroom. In doing so, we review the literature of embodied learning in the areas of reading instruction, writing, physics, and math. In addition, we discuss how these studies can lead to the development of new learning strategies that utilized the principles of embodied cognition.

Full text

The role of embodied cognition for transforming learning
Jennifer M. B. Fugate
a
, Sheila L. Macrine
b
, and Christina Cipriano
a
a
Department of Psychology, University of Massachusetts Dartmouth, North Dartmouth, Massachusetts, USA;
b
Department of STEM Education
and Teacher Development, University of Massachusetts Dartmouth, North Dartmouth, Massachusetts, USA
ABSTRACT
Cognitive psychology has undergone a paradigm shift in the ways we understand how knowl-
edge is acquired and represented within the brain, yet the implications for how this impacts
students’learning of material across disciplines has yet to be fully applied. In this article, we
present an integrative review of embodied cognition, and demonstrate how it differs from
previously held theories of knowledge that still influence the ways in which many subjects are
taught in the classroom. In doing so, we review the literature of embodied learning in the areas of
reading instruction, writing, physics, and math. In addition, we discuss how these studies can lead
to the development of new learning strategies that utilized the principles of embodied cognition.
KEYWORDS
embodied cognition;
embodied learning;
classroom body-based
learning
Traditional theories of cognition emphasize the body as a
“passive”observer to the brain, and necessary only in the
execution of motor actions. Moreover, such mental repre-
sentations within the brain are usual (if not always)
abstractions of the original information (i.e., mental repre-
sentations). Said another way, the body is seen as “serving
the mind”(cf. Leitan & Chaffey, 2014, p. 3). Theories of
embodied cognition, on the other hand, suggest that
informationisgroundedinbothperceptionandaction,
and that cognition is deeply dependent upon features of
the physical body of an agent (e.g., Barsalou, 1999, 2008;
see also Clark, 2008; Golonka & Wilson, 2012; Lakoff &
Johnson, 1999; Pfeifer & Bongard, 2007; Shapiro, 2011;
Willems & Francken, 2012; Winn, 2003; for varying the-
ories of embodiment). Embodied cognition is being
researched internationally in different fields. Outlined in
the fields of robotics and computer science (e.g., Arbib,
2006; Ziemke, 2002), linguistics (Lakoff, 2012), and phi-
losophy (e.g., Chemero, 2009; Hutto & Myin, 2013; Noë,
2004; Shapiro, 2011; Ziemke, 2002), Krois and colleagues
(2007) also mention the fields of art history, literature,
history of science, religious studies, biology, and neuro-
anthropology. Embodied cognition, argue Krois and col-
leagues (2007), has transformed the scientific study of
intelligence and has the potential to reorient cultural
studies. This embodied cognition perspective demon-
strates that cognition is grounded in bodily interactions
with the environment and culture, and that abstract con-
cepts are tied to the body’s sensory and motor system
(Leung, Qiu, Ong, & Tam, 2011).
The antecedents of embodied cognition in psychology
reach back to the work of William James (1884) and John
Dewey (1925/1958). It was not until the pioneering per-
ceptual work of James Gibson (1979), however, that psy-
chology understood that the brain has direct access to
action th rough distributed networks. According to
Gibson’s“ecological theory,”the environment provides
numerous options to action, called “affordances”(Gibson,
1979). The notion of affordance integrates perceptual, cog-
nitive, and motor functions, so that perceiving an object,
conducting cognitive operations on it, and executing motor
actions with it cannot be considered as independent func-
tions (Pellicano, Borghi, & Binkofski, 2017). Accordingly,
action and perception are not seen as two separate entities.
For example, in traditional views of cognition, think-
ing about writing is fundamentally different from the
action of writing itself, where thinking about writing
would activate knowledge from semantic memory (e.g.,
the symbolic storage of words, including feature lists)
but not that involved in the actual motor movements
associated with writing. As a result, such amodal the-
ories provide the knowledge used in cognitive pro-
cesses, but do not reflect the original sensorimotor
states themselves (see Barsalou, 1999, 2003, 2008). In
terms of the brain, amodal descriptions are created
when the original content is translated into a new,
symbolic format and stored in areas of the association
cortex, clearly separate from the sensory and motor
cortices within the brain. Theories of embodied cogni-
tion, on the other hand, propose that knowledge is
reenacted (i.e., simulated) through the perceptual and
CONTACT Jennifer M. B. Fugate jfugate@umassd.edu
INTERNATIONAL JOURNAL OF SCHOOL & EDUCATIONAL PSYCHOLOGY
https://doi.org/10.1080/21683603.2018.1443856
© 2018 International School Psychology Association

sensory systems (e.g., auditory, visual, motor, and
somatosensory) such that thinking about an action
will evoke the same visual stimuli, motor movement,
and tactile sensations that occur during the act itself
(Barsalou, 2003, 2008). The experience is captured by
the sensory and perceptual systems and can be later
used to recreate (through simulation) the experience
without the actual stimulus (i.e., when just thinking
about the knowledge).
1
Such simulations can be partial,
biased, and even occur without awareness (Barsalou,
2003). Accordingly, any knowledge associated with a
concept is often represented by numerous simulations,
specific to individual instances or encounters with the
stimulus. As a result, no sole simulation gives a com-
plete account of the entire concept, and multiple simu-
lations underlie any concept (Barsalou, 2003, 2008).
While there are a number of theories of embodied
cognition, they all share an emphasis on the body func-
tioning as a “constituent of the mind”rather than second-
ary to it (cf. Leitan & Chaffey, 2014, p. 3; see also Shapiro,
2007). Today, embodied cognition encompasses a loose-
knit family of cognitive science research programs that
often share a commitment to replacing traditional
approaches to cognition and cognitive processing (R.
Wilson & Foglia, 2017). In sum, these theories recognize
a full range of perceptual, cognitive, and motor capacities
that are dependent upon features of the physical body.
That said, no single theory of embodied cognition
captures all the nuances of this idea, and there remain
no shortage of individual theories. In fact, some
researchers speculate up to six types of embodiment
(see M. Wilson, 2002). Although there are many indivi-
dual views of embodied cognition, nearly all ascribe to
two shared features: (a) Cognition involves the body and
its interactions with the world, and (b) such interactions
of the body with the world are represented in the brain
in a nonabstracted sense (e.g., Barsalou, 1999, 2008;
Lakoff & Johnson, 1999; for reviews see also Borghi &
Caruana, 2015; Shapiro, 2011; L. B. Smith, 2005). While
the ideas of embodied cognition (sometimes also called
“grounded cognition”) are becoming more accepted in
the fields of cognitive psychology and neuroscience, the
implications of what this means for how individuals best
learn in formal settings such as the classroom (and also
for how teachers teach) are less explored.
The purpose of this paper is twofold. First, we review
the significant behavioral and neuroscientific findings
of embodied cognition from the laboratory. Second, we
detail how embodied learning strategies make use of
embodied cognitive principles to improve student
learning in a variety of classroom content areas. In
doing this, we review demonstrations of embodied
learning strategies in the domains of reading, writing,
physics, and math within the classroom. Ultimately, we
show how and why embodied approaches can lead to
improved student learning and how they can be incor-
porated into existing curriculum.
Methods
This paper utilizes an integrative review method that
allows for the combination of diverse methodologies
(i.e., experimental and nonexperimental research), and
has the potential to play a greater role in evidence-
based practice (Whittemore & Knalfl, 2005). Data col-
lection involved keyword searches of electronic data-
bases, including PsycINFO, NCBI, PubMED,
MEDLINE, EBM Reviews, and Google Scholar in
October–November of 2016 and follow-up searches in
May–June of 2017. We used search terms that included
“embodied cognition,”“embodiment,”“embodied lan-
guage,”“affordance,”“embodied mind,”and “embo-
died learning.”Interestingly, a keyword search on
Google Scholar using “embodied cognition”alone
revealed over twenty thousand books and articles pub-
lished since the year 2000. Part 1 of this review focuses
on empirical behavioral and neuroscientific evidence
for embodied cognition, mainly from psychology (out-
side the classroom). Part 2 focuses on demonstrations
of embodied learning in the classroom in the content
areas of reading, writing, physics, and math. Table 1
includes the empirical experiments referenced in Part 2.
Although the review focuses mainly on empirical stu-
dies, we also included theoretical pieces and systematic
reviews describing processes and models for assessing
educational research related to embodied cognition.
Part 1: Theories of embodied cognition
Embodied cognition has gained much traction over the
past 20 years and is supported by numerous empirical
research at the behavioral and neurological levels. Here
we highlight in brief some of the key demonstrations of
embodied cognition in concept understanding and
reading, but refer the reader to extensive reviews for
more in-depth understanding in each of these areas
(e.g., Barsalou, 2008; Glenberg & Kaschak, 2002). The
goal is not to provide a comprehensive review of
demonstrations of embodiment but rather to provide
readers, who may be unfamiliar with embodied
1
In some theories of embodied cognition, simulation refers only to the motor system, whereas simulation of the other systems
amounts to “mental imagery”(e.g., Jeannerod, 2006; Decety & Grèzes, 2006).
2J. M. B. FUGATE ET AL.

Table 1. Empirical studies reviewed in Part II of paper.
Authors Year Area Sample Procedure Significant effects/Statistic value
Glenberg, et al.,
(Study 1 & 3)
2004 Reading 25 first and second
graders (Study 1); 25 first
and second graders
(Study 3)
Participants simulated the meaning of a sentence by either
manipulated toys (Study 1) or imagining manipulating them (Study 3)
to simulate meaning of the sentence or reread the sentences (control).
Participants who manipulated toys had better free recall, p= .004 and
cued recall, p= .001 (Study 1) and better recall, p= .005
free recall only (Study 3) vs. control participants.
Glenberg,
Goldberg, et al.
2011 Reading 53 first and second
graders
Participants manipulated toys physically (PM condition) or electronically
toys (on a computer screen; CM condition) to simulate the meaning of
the sentence, or reread sentences (control); multiple session training;
Moved by Reading Technique.
Participants in both simulation conditions had higher comprehension
scores of familiar sentences vs. control participants, p= .01 (CM),
p= .05 (PM).
Glenberg,
Willford al.
2011 Reading 97 third and fourth
graders
Participants physically manipulated sentences, then imagined
manipulating sentences or reread sentences (control); Moved by
Reading Technique.
Participants in the physical/imagined condition solved more problems
correctly, had greater proportion of correct solution procedures, and
included less irrelevant information vs. controlparticipants, all ps < .05.
Marley et al. 2007 Listening
Comprehension
45 third through seventh
graders with learning
difficulties
Participants listened to narratives in which they manipulated the action,
observed the action (visual), or thought about the action (control).
Participants in the manipulate and visual conditions had better cued
recall, p< .05, and better free recall, p< .05, vs. control participants, all
ps < .05.
James &
Engelhardt
2012 Handwriting 15 children, four-yr and
five-yr-old children
Participants trained in typing, tracing, or writing letters; fMRI when
shown those letters.
Participants; trained to handwrite letters showed a greater activation
of their left posterior and their left anterior fusiform gyrus when
viewing letters that they trained on vs. those who traced or typed
those letters, p< .001.
Kiefer et al. 2015 Handwriting 23 five-yr-old children Participants engaged in handwritten or typed letter training. Participants in the handwriting condition showed improved letter
recognition (p< .0003), and improved letter naming (p< .001) vs. the
typing condition.
Longcamp et al. 2005 Handwriting 13 adults Participants shown single letters, single pseudoletters, or a control
stimulus while being analyzed by fMRI.
Participants showed more activation in motor areas of the brain when
viewing letters and pseudoletters vs. when viewing control stimuli, p<
.001.
Longcamp et al. 2005 Handwriting 76 children, three-
andfive-yr olds
Participants either learned letters by typing or writing. Participants who learned the letters by writing had more correct
responses in letter recognition tests vs. those who learned by typing in
the older children, p< .02.
Longcamp, et al. 2006 Handwriting 12 adults, mean age 25 Participants learned 10 unknown characters in a period of 3 weeks,
either by typing the characters or by physically writing them.
Participants in group who wrote the characters had a better ability to
discriminate between correct and incorrectly oriented characters after
training vs. those who typed, p< .001.
Mueller &
Oppenheimer *
2014 Handwriting 67 undergraduate
students
Participants given TED Talks to watch and instructed to take notes on
them using their normal note-taking strategy (either with a laptop or
with a notebook).
Participants who took notes with a laptop performed significantly
worse on conceptual questions vs. those who took handwritten notes,
p= .03.
Peverly et al. * 2013 Handwriting 70 undergraduate
students
Participants’notes analyzed after watching a videotaped lecture. The quality of the participants’handwritten notes was correlated with
sustained attention, p< .01, and written recall, p= .01.
Chao et al. * 2013 Gesture 32 adults Participants assigned into either an action-based (performance) or a
computer-based condition (repeated learning) to memorize phrases.
Participants in the action-based condition had better free recall of
learned phrases vs. repeated learning condition, p= .003.
Hwang et al. * 2014 Gesture 39 tenth graders Participants taught vocabulary words either in a body interactive
mechanism teaching condition or through a computer program
(control).
Participants in the body interactive mechanism condition had better
free recall of phrases vs. control group, p< .05.
Participants in the experimental condition had better retention for the
words vs. the control group on follow, p< .05.
Macedonia &
Klimesch *
2014 Gesture 29 German
undergraduate students
Participants learned words either by A-V (read, heard, and spoke) or
gesture (with an accompanying gesture).
Participants in the gesture condition improved vocabulary learning
over time vs. A-V condition, p < .001.
Rauscher et al. * 1996 Gesture 41 undergraduate
students
Participants described spatial information (or non-spatial) with gesture
allowed or gesture prevented.
Participants who were prevented from gesturing had less fluent
speech for spatial information only vs. those allowed to gesture,
p< .001.
Johnson-
Glenberg &
Megowan-
Romanowicz *
2017 Physics 166 undergraduate
Psychology students
Participants either assigned to text or game-like multimedia instruction
(high or low embodiment) of physics; Kinect Sensor; 1 hr learning; pre–
post.
Participants had greater learning in “high embodied”conditions
vs.“low/text”conditions, p < .05, and higher engagement for “high
embodiment”conditions vs. “low/text”conditions, p< .001.
(Continued )
INTERNATIONAL JOURNAL OF SCHOOL & EDUCATIONAL PSYCHOLOGY 3

Table 1. (Continued).
Authors Year Area Sample Procedure Significant effects/Statistic value
Johnson-
Glenberg et al.
2016 Physics 109 undergraduate
Psychology students
Participants learned about centripetal force either through high or low
embodied condition on one of three learning platforms (SMALLab,
Whiteboard, desktop); pre–post & follow-up.
Participants in all conditions improved in declarative knowledge pre–
post, ps < .001. High embodiment conditions vs. low embodiment had
higher generative knowledge on follow-up, p= .03 (interaction term)
Kontra, et al.
(Study 1 & 2)
2015 Physics 44 (Study 1); 36 (Study 2)
undergraduate students
Participants assigned in pairs (one active and one observed) to learn
about angular momentum; pre–post.
Participants only in active group improved pre–post, p= .006 (Study
1); p= .031 (Study 2).
Kontra, et al.
(Study 3)
2015 Physics 35 college-age students Participants assigned to either active or observed condition of angular
momentum while undergoing fMRI; pre–post.
Participants in active group improved more than observed group pre–
post, p= .049. Activation in L M1/S1 predicted performance gain for
either group, p= .009.
Badets and
Pesenti
2010 Math 160 undergraduate
students
Participants shown large or small numbers with congruent or
incongruent hand grip.
Participants took longer to respond to small numbers with an
incongruent grip, p< .001. Participants also took longer to respond to
large numbers with an incongruent grip, p< .02.
Berteletti and
Booth
2015 Math 40 children 8–13 yrs old Participants solved small and large math tasks; behavioral and fMRI. Participants performed more slowly and less accurately on larger tasks
vs. smaller, p< .001. More complex tasks were correlated with greater
activation of motor regions in the brain, p< .05.
Broaders, et al.,
(Study 1)
2007 Math 106 third and fourth
graders
Participants divided into 3 groups and asked to solve and explain math
problems on a chalkboard, either with or without gesturing while
explaining their results.
Participants told to gesture while giving their own explainations solved
more math problems correctly post vs. those who did not gesture, p<
.04. Participants who added gesture had better post-test performance,
p< .03.
Broaders, et al.,
(Study 2) *
2007 Math 70 third and fourth
graders
Participants divided into groups and asked to solve math problems on
chalkboard (as in Study 1), but allowed to supplement gestures to see
whether increased problem knowledge.
Participants told to gesture while giving their own explanations solved
more math problems correctly on their posttest vs. students who did
not gesture during their explanation, p< .04.
Di Luca et al. 2006 Math 122 undergraduate
students
Participants trained finger-digit mapping based on Arabic numbers 0–9.
Either assigned training comparable with global SNARC orientation
(small numbers on left and larger numbers on right hand) or opposite
orientation.
Participants who were trained in finger-digit mappings that were
SNARC-congruent mappings were faster with their responses vs. those
trained in SNARC-incongruent mappings, p< . 001.
Domahs, et al. 2007 Math 137 children
(approximately 7 yrs old)
Participants tested individually on simple and complex math tasks for
number of split-five errors common for finger counting; longitudinal
study.
Participants showed a greater amount of split-five errors compared to
other errors for complex math tasks, ps < .01.
Martin &
Schwartz *
2005 Math 32 children, between 9
and 10-yrs old
Participants filmed solving problems with physical pie wedges or
pictorially.
Participants in physical manipulate-condition were more likely to try
several strategies and were more accurate vs. drawing-condition,
ps < 001.
Srinivasan et al. * 2016 Special
Populations–
ASD
36 ASD children,
between 5 and12-yrs old
ASD children assigned to either whole-body rhythm (e.g., imitation, and
movement based on rhythm, melody, and phrasing), robotic (e.g.,
samebut with robots), or standard (tabletop) therapies; pre–post.
Participants in the whole-body rhythm showed higher social bids (total
word count) after intervention vs. other conditions, p<.03. Robotic
group vs. other conditions showed more greater self-directed
vocalization vs. other conditions, ps = .001. No difference between
conditions post on the joint attention task (JTAT).
“Empirical evidence not referenced in text, but in which readers might be interested.”
4J. M. B. FUGATE ET AL.

cognition, with empirical support from specific areas in
psychology that have significance for embodied learn-
ing in the classroom.
Evidence for embodied concepts
Embodied theories of cognition often suggest that
concepts are understood via sensorimotor simula-
tions (Borghi & Pecher, 2011). Feature verification
paradigms are often used to test one’s understanding
of a concept. For example, a participant is asked
whether a certain physical property is characteristic
or diagnostic of a group (i.e., Do birds have wings?).
In one classic study of image scaling, participants
were slower to verify that a cat has eyes when the
cat was imagined next to an elephant, but faster to do
so when it was imagined next to a flea (Kosslyn,
1975). This classic finding suggests that a judgment
about size of an imagined object relies on the actual
size of the object as experienced (at least as ima-
gined) by the visual system. If real-world size was
not a part of the concept itself (as predicted by a
traditional view), then manipulating the relative size
of the cat in one’s mind would have no bearing on
the speed that participants can use that information.
Likewise, when participants read text that mentioned
birds in flight, they were faster at recognizing a
picture of a bird with its wings outstretched than a
picture of the same bird with its wings folded
(Zwaan, Stanfield, & Yaxley, 2002). The results
demonstrated that new information can be verified
by simulating previous knowledge that bears some
resemblance.
Other evidence of embodied concepts comes from
neuroscientific inquiries. For instance, when people are
asked about objects, they often imagine the use or
function of that object (i.e., “action features”). To sup-
port this supposition, participants who viewed pictures
of tools while undergoing neuroimaging showed activa-
tion in the parts of the brain that are involved in
movement (e.g., motor cortex; Martin, 2007).
Therefore, when participants thought about tools, they
thought about physically manipulating them as if they
were actually using them (Grèzes, Tucker, Armony,
Ellis, & Passingham, 2003; Tucker & Ellis, 1998).
Patients with naturally occurring lesions to the motor
cortex were found to be selectively impaired for con-
ceptual processing of action-related verbs, but not
nouns (which typically do not activate action features;
see Martin, 2007).
Evidence for embodied language
A large number of empirical studies suggest that part of
a person’s ability to comprehend language involves his
or her ability to simulate the action involved in the
meaning. In one study, participants were faster to
advance sentences presented as a narrative on a com-
puter screen when the action in the sentence matched
the action needed to move the text forward (Zwaan &
Taylor, 2006). For example, participants who turned a
knob counterclockwise to advance the sentence, “When
he walked into the room, John turned down the radio,”
did so faster than those who were asked to turn the
knob clockwise (Zwaan & Taylor, 2006). Therefore,
movements of the body congruent to the written con-
tent facilitated reading. According to the Indexical
Hypothesis (Glenberg, 1999), these experiential compo-
nents are crucial for language comprehension.
Therefore, understanding language consists of indexing
words to perceptual symbols, deriving affordances (or
structural relations) from those symbols, and meshing
those affordances to create a simulation of the
described situation (Glenberg & Robertson, 1999; see
Kaschak & Jones, 2014, for a review).
Neuroimaging studies are also consistent with embo-
died language comprehension. For example, partici-
pants who read or listened to words or phrases of
words about specific bodily actions showed activation
within the brain consistent with moving that part of the
body. To illustrate, participants who simply read an
action word (e.g., kick) showed strikingly similar acti-
vation of the region of the motor cortex dedicated to
moving one’s foot as those who actually kicked their leg
while in the scanner (Hauk, Johnsrude, & Pulvermüller,
2004; for additional examples, see Aziz-Zadeh &
Damasio, 2008; Tettamanti et al., 2005).
Even the rules of syntax can be embodied. For exam-
ple, Glenberg and Gallese (2012) propose that syntax
emerges from action control of the body. They believe
that the motor system is functionally organized in terms
of goal-directed actions (e.g., Rochat et al., 2010), not just
motor actions, such that the brain uses contextually
appropriate action to solve syntactical meaning. In early
language acquisition, a child’s syntactical knowledge is
limited by the syntactic constructions he or she has
experienced, and therefore is not likely to be the same as
an adult’s. Said another way, the ability to generalize and
integrate individual tokens into types is limited by what
the child has experienced or witnessed so far in life. As a
child experiences more action outcomes, the outcomes
are incorporated into the system and eventually become
more heavily weighted in further simulations.
INTERNATIONAL JOURNAL OF SCHOOL & EDUCATIONAL PSYCHOLOGY 5

We believe that the more the initial information
engages the sensory and motor cortices, the richer the
simulation, and ultimately the better the recall and use
of the material. For example, imagine that a child first
learns about an airplane when someone points to one
in the sky and labels it. The child encodes the richness
of the visual experience, the movement associated with
looking upward, the sounds the airplane makes, as well
as the sound the person makes to label it. These
“experiential traces”are later reactivated when acces-
sing the category “airplane.”Fundamentally, these
traces bear a resemblance to the perceptual and action
processes that generated them (Barsalou, 1999). As a
result, the more initial input into the experience, the
richer the later simulation.
One common criticism of many embodied theories of
language is that they are ill-equipped to deal with abstract
information (Zwaan & Madden, 2005; see also Borghi &
Caruana, 2015). Several criticisms of EC have been noted,
including that the theories offer nothing new, or are
unfalsifiable (Mahon, 2015). In that context, some
researchers have tried to suggest that embodied and tradi-
tional theories are no longer dichotomous and that there
is room for both. Specifically, Mahon believes that there is
a middle ground that combines the two perspectives, such
that sensory and motor information may instantiate
online abstract and symbolic processing (Mahon &
Caramazza, 2008). However, several approaches to this
problem have been introduced. One such solution is that
abstract representations are created from concrete repre-
sentations by way of metaphorical extension (Gallese &
Lakoff, 2005; Lakoff, 1987, 2012; Lakoff & Johnson, 1980).
Lakoff extensively documented the use of metaphoric
language to ground spatial and body-centric metaphors
in concrete representations (e.g., “life is a journey,”“in
over one’shead”; see Lakoff & Johnson, 1980). Therefore,
the function for such extensive use of metaphors in
English, as well as other languages, is not only to com-
municate such abstract concepts but also to provide a
tangible “grounding”to the body and to the physical
world. It is likely that some sensory and motor involve-
ments led to better metaphoric extension than others.
Once new action outcomes are acquired, they are
unified into a category by application of the same
label or word. Such a label or word can serve as an
anchor to later simulate the initial action. As a result,
as multiple tokens and experiences with the word
build up within the brain, the word alone can come
to serve as the catalyst of the simulation. This view is
similar to that proposed by Borghi and colleagues, in
which words serve as “social tools”(Borghi &
Binkofski, 2014; Borghi, Scorolli, Caligiore,
Baldassarre, & Tummolini, 2013). It is also consistent
with developmental psychological research on the
acquisition of language. Many studies show that lan-
guage (e.g., words) can serve as a placeholder to
teach category members (Xu, Cote, & Baker, 2005),
and that words facilitate learning new categories
(Lupyan, Rakison, & McClelland, 2007). Therefore,
a word, through its phonetic form, can bind together
individualized action outcomes into a meaningful
category representation. Said another way, individual
tokens are thereby linked into cohesive types (con-
cepts) through words. For a similar view, see the
language-as-context hypothesis,whichsuggeststhat
words provide an internal context that helps con-
strain the flow of information (see Barrett, 2009).
Similarly, other theories suggest that words are an
effective means of propagating neural activity because
they can activate a distributed representation of
related content that can be assigned to multiple cate-
gories depending on context and goal-relevancy (see
Lupyan & Clark, 2015).
Both of these views represent a modern-day
Whorfian hypothesis for how language affects
thought. To this end, words within a language set
thestageforwhatwillbecomemeaningfulconcepts,
which in turn enable the simulations underlying cog-
nitive thought. Said another way, the structural
aspects of any language produce a tangible grounding
of embodied experiences to produce unified cate-
gories in the brain, where the contents of these cate-
gories can then, in turn, be accessed by words. The
greater the number and precision of words that are
linked to the category, the more likely words can be
used as analogical mapping tools to further ground
abstract categories. In this sense, words are not only
human inventions; they are also inventors of new
connections. Therefore, in a language that has no
word or few words to label an experience, informa-
tion will be represented and stored differently com-
pared to a language that has many words to describe
and make meaningful the same experience.
While we believe that language (including indivi-
dual words) is often embodied, we are not suggesting
that language is always so. Likewise, we do not
believe that all embodied instances are anchored by
words: those that afford direct action may be stored
in absence of semantic networks. Thus, even in the
absence of linguistic mapping, some action outcomes
can still be simulated, but only when the context of
that initial action is replicated with near-perfect fide-
lity. Similar ideas have been put forth by “hybrid”
approaches to conceptual processing (Barsalou,
Santos, Simmons, & Wilson, 2008; Connell &
Lynnot, 2013; Louwerse, 2011; see reviews by
6J. M. B. FUGATE ET AL.

Andrews, Frank, Vigliocco, 2014).
2
According to
some of these hybrid views, meaning can occur
through embodied simulations, but also through
more “shallow”processing, which does not require
embodiment, but rather draws upon distributed lin-
guistic shortcuts.
Summary
In part one of this paper, we identified how psychology
has undergone a paradigm shift in understanding the
workings of the brain. Rather than knowledge being
recoded and removed from the initial sensory and
motor experience, embodied cognition posits that the
brain simulates these details when recalling and using
the knowledge garnered through that experience.
Therefore, the richer and more nuanced the encoding,
the richer and more nuanced the simulation of that
information will be (i.e., in the use or recall of that
information). Individual words within a language are
often mapped to embodied instances and set the stage
for the category learning. As a result, words come to
serve as shortcuts in the later simulation of those
instances. Language can also help ground abstract
information through linguistic metaphor.
Part II: The embodied cognition classroom
Embodied learning as an extension of embodied cogni-
tion is at odds with traditional views of cognition that are
described in Part 1. Many educators have noted the effec-
tiveness of body-based learning in the classroom, yet
among teachers there is often confusion as to why these
strategies are effective and how they relate to embodied
cognition. In addition, there is often confusion between
embodied learning and technology-based learning. While
there are many embodied learning strategies that make
use of technology (some which we review below), simply
having students use technology or move their bodies does
not constitute embodied learning.
Theories of experiential and hands-on learning have
been around for more than a century, describing pro-
cesses that drive learning (Dewey, 1925/1958; Kolb,
2014). For example, the Montessori (1966) learning
approach emphasizes independence, freedom within
limits, hands-on learning, and respect for a child’s
natural psychological, physical, and social development.
Yet, the specific mechanism through which these
processes occur has not been well defined. Embodied
cognition is relevant to these pedagogical ideas and
offers potentially useful tools for educators. Some edu-
cators, however, argue that perceptually rich practices
are not optimal and may even be detrimental (e.g.,
Finkelstein et al., 2005; Pouw, Van Gog, & Paas,
2014). While embodied cognition is one theory for
understanding learning, we acknowledge that some
information might be better acquired through other
approaches. The purpose of this paper, however, is to
highlight embodied cognitive strategies in classroom
learning.
Reading and instruction
The Indexical Hypothesis, introduced in Part 1, sug-
gests that language is learned and understood by evok-
ing the sensorimotor systems to simulate the situation
or the intention of the action described by the language
(Glenberg & Robertson, 1999; Glenberg & Gallese,
2012; Kaschak & Glenberg, 2000; see Kaschak &
Jones, 2014). Therefore, according to an embodied
learning view, physically moving or engaging the body
and senses in ways that are congruent with the actions
of the situation and what the situation affords should
enhance beginning reading instruction.
Glenberg and colleagues created the Moved by
Reading approach that incorporates embodied learning
in children’s reading comprehension and teaches simu-
lation or “acting-out”reading in two stages (Glenberg,
Goldberg, & Zhu, 2011). In the first stage, called physical
manipulation, children manipulate toys to simulate the
story they are reading. The approach is meant to
increase comprehension by indexing the major content
words to images or objects, on a word-by-word basis
that does not require understanding the full sentence. It
also does so by constraining the objects the words index.
After a child succeeds in this stage, they can transition
relatively easily to the imagined manipulation stage.
Now children can imagine or mentally simulate doing
these actions themselves while they read. Glenberg and
colleagues showed that first and second graders who
underwent this approach recalled 33% more information
(compared to those who had toys or objects present but
were not allowed to manipulate them; Glenberg,
Gutierrez, Levin, Japuntich, & Kaschak, 2004). In a
Web-based follow-up study, children manipulated the
objects or images on a computer screen rather than
2
More radical views of embodied cognition completely reject the idea of representations of any kind within the brain, such that
cognition is considered a dynamical system in which continuously changing variables are interdependent on one another for
meaning (see Spivey, 2007; Borghi & Caruana, 2015 for a review). In these views, since there are no mental representations,
reenactment of them becomes impossible.
INTERNATIONAL JOURNAL OF SCHOOL & EDUCATIONAL PSYCHOLOGY 7

directly hands-on (Glenberg et al., 2009; Glenberg,
Willford, Gibson, Goldberg, & Zhu, 2011). They
reported a similar-sized effect to the original study.
Importantly, the effect transferred to other genres, as
well (e.g., mathematical problem stories), demonstrating
that this approach can be applied across domains and
tasks. More intriguingly, this approach seems to be
effective with students with learning differences. One
study, utilizing this approach, found that children with
learning disabilities had better free and cued recall for
propositions, objects, and actions than those in the con-
trol condition (where children simply listened to the
experimenter and were instructed to think about each
sentence; Marley, Levin, & Glenberg, 2007).
Glenberg’s (2011) findings also support the decades-
old multisensory–multimodal approaches to reading
remediation particularly suggested for students with
learning disabilities. In 1943, Dr. Grace Fernald devel-
oped a multisensory intervention called the Fernald
Method of VAKT—Visual, Auditory, Kinesthetic and
Tactile. Today’s VAKT continues as a successful and
prescribed reading intervention for students with learn-
ing disabilities and cognitive challenges. This approach
uses a combination of verbal and auditory input, while at
the same time tactically tracing the letters on the back of
the student or on sandpaper to make a “kinesthetic
imprint on the brain”(Fernald, 1943). In Fernald’s
time, it was unclear why this approach worked well
and more so than other methods. Today, however, we
can attribute the method’s success to the principles
underlying embodied cognition. Specifically, this
includes the idea that perceptual simulations in modal-
ity-specific systems underlie conceptual processing.
Writing
Kiefer and colleagues (2015) examined whether handwrit-
ing and reading comprehension differed in children who
engaged mainly in modes of digital writing (e.g., compu-
ters, tablet PCs, or mobile phones) compared to physical
writing (Kiefer et al., 2015). They found that physically
writing improved the processes of letter recognition, nam-
ing, and composition, and increased reading comprehen-
sion. They argued that physically writing linked the form to
the concept, which promoted the mental representation
needed to write and comprehend language at a higher,
more symbolic level (see also Kiefer & Trumpp, 2012).
Specifically, we suggest that the benefit comes from the
embodied nature of the information acquisition.
Handwriting, compared to typing, requires increased
motor movements. These increased movements provide
a richer encoding of the information, which allows a better
representation from which they can later draw. We
suggest that future empirical studies test this notion
specifically.
Other studies support this idea as well. Physically writ-
ing letters and words prompt students to be more
thoughtful and engaged, improving their written commu-
nication and improving later reading comprehension
(James & Engelhardt, 2012). In addition, the National
Early Literacy Panel (2008) identified handwriting as a
predictor of later reading ability and general learning
abilities, even after controlling for IQ and socioeconomic
status (see Graham & Santangelo, 2012, for a meta-ana-
lysis). Further, both preschool children and adults show
better letter recognition when learning to write letters by
hand rather than by typing them (Longcamp, Anton,
Roth, & Velay, 2003; Longcamp, Zerbato-Poudou, &
Velay, 2005). The same stored motor programs in the
brain used for handwriting are activated when simply
reading letters (Longcamp et al., 2003). These findings
provide a close functional relationship between reading
and handwriting movements (see James & Engelhardt,
2012). In another study, participants who learned new
characters by copying them by hand (compared to typing
them on a keyboard) made fewer mistakes about the
orientation of letters later on. Specifically, they were less
likely to confuse mirror images of the characters for the
correct ones (Longcamp, Boucard, Gilhodes, & Velay,
2006). Therefore, the ability to remember correctly was
facilitated by the specificity of the movements associated
with learning them.
Taken together, these studies demonstrate that hand-
writing is critical to setting the foundations for learning
to read and to understand information at a higher level.
These findings come on the heels of a rigorous effort of
many school districts to remove writing (namely, cur-
sive) from the curriculum. Many schools view cursive as
a long-lost art, replaceable by typing electronically. We
argue that nothing is further from the truth.
Handwriting (i.e., the physical and tactile act of moving
one’s pen) provides more stimulation and precision for
the brain to capture—and therefore recall—than any
keystroke associated with typing. Some state administra-
tors, who originally dropped handwriting, have now
reinstated handwriting and cursive instruction into
their curriculum (Hochman & MacDermott-Duffy,
2015) Writing, whether print or cursive, provides a
range of individualized movements associated with
each letter. This specificity has a fuller, more nuanced
representation in the brain for this information.
Math and physics
Embodied learning has been shown to be effective in
advancing students’STEM achievement, particularly
8J. M. B. FUGATE ET AL.

mathematics (e.g., Clements, 2000; Martin & Schwartz,
2005). Historically, finger-counting was disapproved of
within formal education and shamed by the public
(Moeller, Martignon, Wessolowski, Engel, & Nuerk,
2011). Current evidence, however, suggests that both
hand and finger representations positively influence
children’s and adults’numerical processing (Badets &
Pesenti, 2010; Di Luca & Pesenti, 2008; Domahs,
Krinzinger, & Willmes, 2008). For example, when
8–12-year-old students are given complex subtraction
problems to solve without using their fingers, there is
still increased activation in the somatosensory area of
the brain that is normally activated by tactile sensations
(e.g., using the fingers to count) (Berteletti & Booth,
2015). Interestingly, the more complex the math pro-
blem (i.e., subtraction), the more activation of the
somatosensory area of the brain. In a math meta-ana-
lysis of children ages 7–11 years, instruction involving
concrete manipulatives provided children with the
most benefit. Older children benefited less than
younger children, however, a finding that can be partly
explained by their increased ability to reason abstractly
(Carbonneau, Marley, & Selig, 2013).
Other demonstrations show that the better knowl-
edge of one’s fingers is in the first grade, the better the
number comparison and estimation in the second
grade (Boaler & Chen, 2016). Such knowledge even
predicts students’calculus scores in college (Berteletti
& Booth, 2015; see also Penner-Wilger & Anderson,
2013). Finally, when students are told to use gestures
when solving math problems (including finger count-
ing), they produce new and novel insights into problem
solving, as well as benefiting more from formal instruc-
tion compared to those students who do not gesture
(Broaders, Cook, Mitchell, & Goldin-Meadow, 2007).
This suggests that finger-based numerical representa-
tions are beneficial for later numerical development,
and that children might build upon concrete structured
representations to learn mental representations
(Moeller et al., 2011). Furthermore, embodied mathe-
matical cognition is thought to broaden the range of
activities and emerging technologies that count as
mathematical, and helps students to envision alterna-
tive forms of engagement with mathematical ideas (e.g.,
De Freitas & Sinclair, 2014). Here cultural influences on
the representation of numbers come into play: Finger-
based counting and other body-based counting is per-
formed differently in different cultures (e.g., Liutsko,
Veraksa, & Yakupova, 2017; Selin, 2001), resulting in
different embodied representations of numbers within
the brain.
The Seeing Change Project brings these ideas to life
in the classroom (Abrahamson, 2012). Here, students
learn about compound probability problems through
embodied games. The project uses both traditional
media (marbles, cards, crayons) and computer-based
modules (NetLogo simulations), which allow students
to work off their basic intuitions to establish mathema-
tical models. As part of the project, students often learn
how their preanalytic judgments are incorrect. The idea
is that students will modify their erroneous theory in
the face of empirical evidence that contradicts their
inferences (Abrahamson, 2012). With this hands-on
approach of bridging informal and formal visualiza-
tions of probability experiments, students in Grades
4–6 show better abilities to predict probabilities
(Abrahamson, 2012).
In another applied-math learning project called the
Kinemathics project, students (Grades 4–6) move their
arms in proportional distances to measurements of
similar magnitude displayed on a screen
(Abrahamson, Trninic, Gutiérrez, Huth, & Lee, 2011).
Correct answers make a screen turn green, and incor-
rect make the screen turn red. Using this embodied
learning strategy, students mainly engaged in trial and
error to learn the rules underlying the relationship.
Qualitative data suggest that students who learned
through this strategy were more productive in their
problem solving (Abrahamson et al., 2011).
Outside of math, there are emerging applications for
effective embodied learning strategies in the STEM
fields. One successful example with college-aged stu-
dents comes from physics (Kontra, Lyons, Fischer, &
Beilock, 2015). Students were tested on their knowledge
about angular momentum after actually feeling forces
(by spinning a wheel) or watching someone else per-
form the same action. Brief exposure to actually feeling
the force (the embodied manipulation) improved quiz
scores by approximately 10% (Kontra et al., 2015,
Experiment 1). Moreover, when these students under-
went neuroimaging, the activation in the sensorimotor
cortices predicted the improvement and understanding
of the properties associated with angular momentum.
In one specialized application, Abrahamson and
Lindgren (2014) developed MEteor, an interactive MR
simulation that uses a laser and floor-projected imagery
to help middle-schoolers develop ideas about how
objects move through space. In this application, a stu-
dent becomes an asteroid by attaching himself to a digital
asteroid that is launched into a simulated outer space
where other objects affect the asteroid’s movement. The
student must move his or her body to move the digital
asteroid around objects that are coming toward him or
her. This requires learning about formal concepts such
as gravitational acceleration and mass. In one evaluation
of the technology, students improved their performance
INTERNATIONAL JOURNAL OF SCHOOL & EDUCATIONAL PSYCHOLOGY 9

by 76% on the second trial compared with 51% for those
who used the simulation without bodily cues (as
reported in Abrahamson & Lindgren, 2014).
In another study, college students engaged in one of
three different simulated conditions to learn about centri-
petal force (Johnson-Glenberg, Megowan-Romanowicz,
Birchfield, & Savio-Ramos, 2016). Each had a low and
high embodied condition, in which the “high embodied”
condition had students physically move their bodies to
examine the construct. The “low embodied”condition
replaced the individual activities with button pushes depict-
ing the same information. Students’learning of the lesson
was significantly better across all “high embodiment”con-
ditions compared to the “low embodiment”condition.
Moreover, only students in the “high embodiment”condi-
tion maintained their knowledge after one week (Johnson-
Glenberg et al., 2016). These demonstrations show the
value of physical experience in science learning, and lead
the way for classroom practices where movement with the
physical world is an integral part of learning.
We recommend that students in the STEM fields
engage in various learning modalities that utilize multi-
ple sensory and motor domains. These could include
project-based learning and haptic technology (e.g.,
touch-screen tablet displays with feedback in visual and
auditory domains). Other potentially beneficial haptic
technologies might include new motion-tracking tech-
nologies, augmented reality, and gesture recognition.
These instructional strategies can be adapted and gen-
eralized to support young children’s and older students’
science and mathematics learning in the classroom.
Importance of cross-cultural considerations in
embodied cognition and learning
An embodied cognition approach can help educators to
rethink their pedagogy and consider ways of learning that
are inclusive of both individual and cultural perspectives
(Cohen & Leung, 2009; Cohen, Leung, & Ijzerman, 2009;
Leung et al., 2011). To the extent that a person’sinteraction
with the world is individualized (acquired through their
own motor and perceptual systems), and that those
instances are made meaningful by previous interactions,
they will be influenced by culture (see Leung et al., 2011).
Therefore, particular instances will be situated differently
in various cultures, as well as the degree to which particular
instances are utilized (see also Gibson, 1979; Schubert &
Semin, 2009; Varela, Thompson, & Rosch, 1991). Simply
put, the cognitive structure of an individual, as defined by
his or her own experiences and those supported by cultural
norms and language, informs how information is first
experienced, as well as later simulated. This implies two
things: First, similar actions will be integrated and mapped
differently within the brains of different individuals since
their perceptual and motor systems will have a different set
of experiences that inform the current. Second, the repre-
sentation of this information will be different for different
cultures, which have different priorities, rules, words, and
linguistic metaphors to explain the world around them. To
illustrate: Consider that Westerners tend to adopt a first-
person perspective in which social interactions are often
referenced from an egocentric point of view, whereas
Easterners tend to adopt a third-person point of view (cf.
Leung et al., 2011). European Americans tend to describe
actions as going toward others, whereas Asian Americans
aremorelikelytodescribeactionascoming toward them
(Leung & Cohen, 2007). The body will not only represent
the action differently in each case, but also such metaphors
will further affect the representation and understanding of
this information.
Wilson (2010) calls the effects of culture on cogni-
tive thought cognitive retooling, in which an individual’s
cultural knowledge and experiences not only shape (in
development) but also reshape his or her cognitive
system over their lifetime. Kövecses (2002) describes
this idea eloquently when he writes: “Social construc-
tions are given bodily basis and bodily motivation is
given social-cultural substance”(p. 14).
Summary and significance for designing
embodied curriculum
In this paper, we reviewed how embodied cognition
differs from traditional theories of cognitive function-
ing, while summarizing some of the key empirical
laboratory-based demonstrations in concept learning
and reading. We also showed how these principles
can be applied in the classroom to facilitate learning
in the fields of reading, writing, math, and physics.
Specifically, we proposed that the more nuanced the
encoding (including the more the senses and the body
are involved, as well as the more instances of encoding),
the better the recall and use of that information.
Although we have reviewed numerous applications of
embodied learning in the classroom, there is still much
room for systematic empirical studies that compare
embodied versus traditional theories back-to-back. In
addition, we need more research to help researchers
and others to further implement embodied cognition
into students’curriculum (including mandatory curricu-
lum), to assess the gains in knowledge as a result, to
develop teacher pedagogy, and finally to leverage this
knowledge for curriculum and policy makers in the
future. One key thing to consider is that assessments
should be developed in tandem with the curriculum,
such that assessments that emphasize the format in
10 J. M. B. FUGATE ET AL.

which the material was learned may show better out-
comes, especially for early learners who are more driven
by concrete manipulatives.
Increased understanding of embodied cognition
among educators will likely show improved learning in
the classroom. For example, providing teachers with
instruction in neuroscience and cognitive functioning
has the potential to directly transform teacher prepara-
tion and professional development, and ultimately to
affect how students think about their own learning (e.g.,
Dubinsky, Roehrig, & Varma, 2013). Then, when tea-
chers shared that knowledge with their students, the
students’own metacognitive awareness for their perfor-
mance is increased (e.g., Dubinsky et al., 2013).
To conclude, it is important for contemporary cognitive
science to continue to investigate the implications of
embodied cognition, including testing the success of
newly developed body-based learning strategies in the
classroom. It should also be understood and highlighted
that different individuals—from different cultures with a
different set of cultural norms and habits and speaking
different languages—might have vastly different represen-
tations within the brain because any new experience is
grounded within previous experiences. As a result, more
cross-cultural research is needed to address individual
differences within and across cultures in how particular
cognitive tasks are embodied while being cognizant of
local cultural variations. In sum, embodied cognition
shows promise for learning effectiveness and this under-
standing can further the deployment of embodied teaching
and learning in the classroom and in teacher education.
Acknowledgments
A special thank you to B. Mcloughlin, who helped with the
table.
About the authors
Jennifer Fugate, PhD, is an Assistant Professor at the
University of Massachusetts Dartmouth. Her research focuses
on how language shapes emotion percepts, and the role that
language plays in grounding abstract categories. She is the
author of several book chapters and articles, and her work on
facial depictions of emotion has received recognition in sev-
eral popular press books and in the Court of Law. She is a
certified FACS-coder.
Sheila Macrine, PhD, is a Professor at the University of
Massachusetts Dartmouth. Her research interests focus on
two areas: 1) school psychology including alternative assess-
ment and embodied cognition; and 2) connecting the cul-
tural, political, and institutional contexts of critical pedagogy
as they relate to the public sphere, democratic education and
social imagination. She is a critical feminist and has
published numerous articles, grants and books including:
Critical Pedagogy in Uncertain Times: Hope and Possibilities.
Christina Cipriano, PhD, is an Assistant Professor at the
University of Massachusetts Dartmouth. Her research focuses
on serving vulnerable youth through systematic examination
of the interactions within their homes, schools, and commu-
nities to promote pathways to optimal developmental out-
comes. She is a Service Learning Fellow, Community
Engaged Research Scholar, and Principle Investigator of the
Recognizing Excellence in Learning and Teaching (RELATE)
Project. She directs several research initiatives and regularly
disseminates her science in both academic journals and pro-
fessional development workshops for pre-service and inser-
vice educators and school personnel.
ORCID
Jennifer M. B. Fugate http://orcid.org/0000-0003-0831-
4234
Sheila L. Macrine http://orcid.org/0000-0002-8600-0938
Christina Cipriano http://orcid.org/0000-0002-7414-1821
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