Discussion Provide at least two references for your initial po

Discussion
Provide at least two references for your initial post.
Recent developments within cognitive psychology have
contributed to the development of the interdisciplinary field of
cognitive science. Describe how the contributions of both
neurophysiology and computer science have helped us to
understand more about how people think.
You will create a PowerPoint presentation with a realistic case
study and include appropriate and pertinent clinical information
that will be covering the following:
1. Subjective data: Chief Complaint; History of the Present
Illness (HPI)/ Demographics; History of the Present Illness
(HPI) that includes the presenting problem and the 8 dimensions
of the problem; Review of Systems (ROS)
2. Objective data: Medications; Allergies; Past medical history;
Family history; Past surgical history; Social history; Labs; Vital
signs; Physical exam.
3. Assessment: Primary Diagnosis; Differential diagnosis
4. Plan: Diagnostic testing; Pharmacologic treatment plan; Non-
pharmacologic treatment plan; Anticipatory guidance (primary
prevention strategies); Follow up plan.
5. Other: Incorporation of current clinical guidelines;
Integration of research articles; Role of the Nurse practitioner
· Cited in the current APA style, including citation of
references.
· The presentation should consist of 10-15 slides and less than 5
minutes in length.
· Incorporate a minimum of 4 current (published within the last
five years) scholarly journal articles or primary legal sources

(statutes, court opinions).
Language experienced in utero affects vowel perception after
birth: a two-country study
Christine Moon1, Hugo Lagercrantz2, and Patricia K Kuhl3
1Pacific Lutheran University, Tacoma, Washington, USA
2Neonatology Unit, Karolinska Institute, Stockholm, Sweden
3Institute for Learning & Brain Sciences, University of
Washington, Seattle, Washington USA
Abstract
Aims—To test the hypothesis that exposure to ambient language
in the womb alters phonetic
perception shortly after birth. This two-country study aimed to
see if neonates demonstrated
prenatal learning by how they responded to vowels in a category
from their native language and
another nonnative language, regardless of how much postnatal
experience the infants had.
Method—A counterbalanced experiment was conducted in
Sweden (n=40) and the USA (n=40)
using Swedish and English vowel sounds. The neonates (mean
postnatal age = 33 hrs) controlled
audio presentation of either native or nonnative vowels by
sucking on a pacifier, with the number
of times they sucked their pacifier being used to demonstrate
what vowel sounds attracted their
attention. The vowels were either the English /i/ or Swedish /y/
in the form of a prototype plus 16
variants of the prototype.

Results—The infants in the native and nonnative groups
responded differently. As predicted, the
infants responded to the unfamiliar nonnative language with
higher mean sucks. They also sucked
more to the nonnative prototype. Time since birth (range: 7–75
hours) did not affect the outcome.
Conclusion—The ambient language to which foetuses are
exposed in the womb starts to affect
their perception of their native language at a phonetic level.
This can be measured shortly after
birth by differences in responding to familiar vs. unfamil iar
vowels.
Keywords
fetal; language; learning; neonatal; vowels
Introduction
It is now well established that listening to a specific language
early in life alters young
infants’ perception of speech sounds even before they begin to
produce their first words.
Within the first months of postnatal life, infants discern
differences between phonemes 1–4
and they group consonants into perceptual categories regardless
of whether the sounds are
from their native or a nonnative language5,6. Between six and
12 months, their ability to
discriminate between different native language speech sounds
increases, but it declines
sharply for nonnative sounds7,8.
A question often raised, both for theoretical and practical
reasons, is: how early in life does
experience with language affect infants? A laboratory study

showed that the effect of
Corresponding author: Christine M. Moon, Dept. of Psychology,
Pacific Lutheran University, Tacoma, WA 98447, Tel (253)
535-7471, Fax (253) 535-8305, [email protected]
NIH Public Access
Author Manuscript
Acta Paediatr. Author manuscript; available in PMC 2014
February 01.
Published in final edited form as:
Acta Paediatr. 2013 February ; 102(2): 156–160.
doi:10.1111/apa.12098.
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experience appears earlier for vowels than for consonants and
can be measured by six
months of age. Testing in Stockholm and Seattle revealed that
infants in both Sweden and
the USA were able to group native vowel sounds into
categories, but were unable to do so

for nonnative vowels9. It is not surprising that experience with
vowels would affect
perception earlier than consonants, because vowels are louder,
longer in duration and carry
salient prosodic information (melody, rhythm and stress).
This paper reports the results of an investigation into an area
that has not been studied
before: does experience in the womb affect infants’ perception
of vowel sounds? We already
know that hearing begins at the onset of the third trimester of
pregnancy and that sound is
transmitted primarily through bone conduction, from the
amniotic fluid through the foetal
skull and into the inner ear10. Reliable foetal responses to pure
tones presented via a
loudspeaker have been recorded as early as 12–13 weeks before
birth 11 and late-term
foetuses not only detect vowels12,13 but show heart rate
changes when the vowels /a/ as in
pot and /i/ as in fee shift position in [ba]-[bi] to [bi]-[ba]14.
Recordings made in utero show
sufficient preservation of formant frequencies to make vowel
discrimination possible and
intelligibility tests show that adults can identify about 30–40%
of phonetic content of speech
recorded in the womb 15,16. What is unknown, however, is
whether an ability to detect and
to discriminate vowel sounds in the womb is accompanied by an
ability to learn vowels from
prenatal experience17.
Learning from natural exposure to sound has been shown in
neonatal experiments using
entire sentences or phrases, and this is thought to reflect
learning about prosodic aspects of

language 2,18–22. However, newborns have been considered to
be phonetically naïve—
influenced only by their innate, universal capacities—and ready
for learning through
postnatal experience. The aim of the current study was to
investigate whether neonates are
capable of learning phonetically in utero by examining the
effects of language experience on
phonetic perception at the youngest feasible age.
Study participants were from Stockholm and Tacoma,
Washington, and the stimuli for the
neonate study were the same as those used in the Sweden/USA
study of six-month-olds by
Kuhl et al., 19929. The native and nonnative vowels were the
English vowel /i/ as in fee and
the Swedish vowel /y/ as in fy (like the German /ü/). The 19929
experiment used a prototype
of each of the two native vowels and 32 variants of each
category to test vowel category
perception. Speech prototypes are defined as the best example
of a category as judged by
adult speakers of the language23. In the present study, our goal
was to ascertain whether
neonates in the two countries showed any tendency to group
native vowels into a category,
pre-dating the abilities of six-month-olds. If neonates showed
an effect of learning, and if
time since birth did not affect how they responded, it would
provide evidence of learning in
utero. We used a contingent procedure in which each sucking
response by the infant would
produce a particular vowel sound and the number of sucking
responses provided a measure
of the infant’s interest in each sound. We hypothesised that
infants would show less interest

in the familiar sound stream (native language vowels) due to the
vowels’ equivalency with
each other as members of a category and their repetitive nature.
In contrast, the nonnative
vowels would seem to be in a constantly changing sound stream
and therefore more likely to
attract the infant’s attention24,25. We used the prototype vowel
of the English and Swedish
vowel category as well as variants of the prototype of each of
these two categories used by
Kuhl et al. (1992)9.
We specifically predicted that infants in the native language
group would show a lower level
of attention to the familiar category of native vowels and, and a
result, suck their pacifiers
less often when they heard these than infants in the nonnati ve
group. We also predicted that
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the distinction could be particularly evident for the prototype
vowels, given that prototypes
are considered representative of categories as a whole26.
Method
Participants
Neonates (M = 32.8 hrs postnatal age, range = 7–75 hrs) were
tested in hospitals inTacoma,
Washington, USA (N = 40) and Stockholm, Sweden (N = 40).
Infants were eligible if: a)
pregnancy, birth and neonatal hearing tests were typical, b) the
maternal native language
was English (in Tacoma) or Swedish (in Stockholm) and c) the
infant’s mother did not speak
a second language more than rarely during the final trimester of
pregnancy, as assessed
informally in the USA and by a questionnaire in Sweden. In
addition to the 80 total infants
in the study, 15 additional infants were excluded from the
analysis for: cessation of sucking
during the experiment (5), fussiness (1), drowsiness (5),
parental interference (1) and
equipment or experimenter error (3). This represents a low (16
%) attrition rate for neonatal
behavioural tests of perception. Half of the infants in each
country were assigned to hear the
Swedish vowel stimuli and half the English. There were no
statistically significant
differences between the two groups in terms of gender, test age,
gestational age, birth weight
or number of sucks in five minutes.

Stimuli
The stimuli from Kuhl et al. (1992)9 were computer-generated
sound files of 17 different
examples of the English front unrounded vowel /i/ as in “fee”
and 17 instances of the
Swedish front rounded vowel /y/ as in “fy.” The vowel
inventories in both Washington State
and Sweden do not include the nonnative vowels used in the
study. The prototype vowel for
each language had been experimentally determined by native
speakers in Seattle and
Stockholm, who rated the vowels as the best representatives of
the category9,27 The 16
vowel variants were created by altering the first and second
formant frequencies of the
prototype in a step-wise fashion, to create two concentric rings
of eight stimuli around each
prototype (see Figure 1). The vowel stimuli were each 500 ms in
duration and were
delivered at 72 dB (Bruel & Kjaer Model 2235, Scale A)
through headphones (Grado 225).
Design, equipment and procedure
A between-subjects design was chosen because exposure to one
vowel category could affect
performance on the second vowel category. This also reduced
the session time and attrition
rate. Sessions were conducted in a quiet room with infants lying
supine in their bassinets
with headphones placed next to their ears (see Figure 2). Each
infant was offered a pacifier
and the data collection began if the infant accepted it and
started sucking in a typical,
rhythmic burst/pause pattern. The protocol stipulated a five-
minute test period to allow

presentation of each of the 17 vowels in the set.
Pacifiers were fitted with a sensor connected to a computer that
delivered auditory stimuli
when the infants sucked on the pacifier. When they completed
the second suck in a burst,
one of the 17 stimuli was presented in random order. Each
vowel was repeated until there
was a pause in sucking of one second or longer. Resumption of
sucking resulted in a new
vowel stimulus from the set. Equipment consisted of an air
pressure sensor for the pacifier,
A/D converter, sound amplifier (Klevang Electronics, Portland,
Oregon) and a Dell laptop
computer with custom software (Klevang Electronics). An air
pressure threshold was set so
that virtually all the sucks after the first one in a sucking burst
resulted in sound
presentation. Stimuli were delivered in random order without
replacement. The equipment
used in both countries was identical and the same experimenter
(CM) conducted the tests in
both countries using the same protocol. Examination of the
randomisation showed that the
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prototype appeared in all 17 possible serial positions, was
roughly equally distributed among
positions (χ2 = 15.6, p = .48) and the test groups did not differ
when it came to the serial
position of prototype (Kruskal Wallace χ2= 1.61, p = .66).
Average time to complete the
series of 17 stimuli was 135.1 seconds (SD 50.2).
Results
Data from the first presentation of each of the 17 stimuli were
analysed and the dependent
measure was number of sucks per vowel stimulus. A mixed
ANOVA was conducted to
examine the effects of Language Experience: (native vs.
nonnative), Prototypicality
(prototype vs. nonprototype) and Country (USA vs. Sweden),
with hours since birth as a
covariate. We examined hours since birth to determine whether
learning effects could be
attributed to pre- vs. postnatal experience. There was no
significant main effect (F1,75 = .04,
p =.85) of hours since birth, nor any interaction effects with
hours since birth. As a result,
this factor was not included in subsequent analyses.
The effect of Language Experience was significant (F1,75 =

4.95, p = .029), with a greater
number of sucks overall during the nonnative (MNonnative=
7.1, SD = 2.9) than during the
native language (MNative= 6.5, SD = 3.3). The results show
that the native prototype and its
variants received fewer sucking responses than the nonnative
prototype and its variants.
Results also showed an interaction effect of Language
Experience and Prototypicality (F1,75
= 4.6, p =.035) (see Figure 3). To further examine the
interaction, planned t-tests were
conducted. For the 40 infants who heard the native vowel, there
was no difference in mean
sucks during the prototype vs. the nonprototype (t39 = −1.08, p
= .29). However, for the 40
infants who heard the nonnative vowel, the difference in
response to the prototype was
significantly greater than the response to the nonprototype (t39
= 2.03, p = .049).
In the overall ANOVA, the effect of Country was significant
(F1,75 = 18.4, p < .001), with
USA mean sucks per stimulus (MUSA=8.1, SD = 3.0) greater
than Sweden sucks (MSweden=
5.4, SD = 2.6). No other main or interaction effects were
significant.
Although there were no significant interaction effects involving
Country, a further analysis
was undertaken to measure the infants’ responses to the native
and nonnative prototypes in
each of the two language communities. The difference between
native and nonnative
prototypes was significant in the Sweden infants (t38 =3.58,
p=.001), but not in the USA
infants (t38 = .68, p>.05). A nonparametric analysis was

conducted in which the 17 stimuli
were rank ordered for individual infants according to how many
sucks they received from
the infant with Rank 1 being the stimulus with the highest
number of sucks and Rank 17
being the stimulus with the lowest number of sucks from that
infant. For the USA nonnative
group, 16 of 20 infants had the prototype among ranks 1–8,
roughly the top half of the 17
ranks (binomial test, p<.05, two-tailed). For the USA native
language group, 9 of 20 infants
had the prototype in the top eight ranks (binomial test, p>.05).
The difference between the
USA native and nonnative groups was significant (Pearson χ2=
5.2, p=.024, two-tailed.)
Discussion
The results of our study support the hypothesis that language
experienced in utero affects
vowel perception. Neonates in the two different language
communities responded to vowels
in their familiar native language category as though they were
equivalent to each other. In
contrast, they did not treat vowels in an unfamiliar, nonnative
category as equivalent. The
infants did this in two ways: 1) they sucked less overall when
the sound stream was from
their native language, presumably because successive sounds
were from the same category
and repetitive and 2) they behaved as though the native
prototype was equivalent to the other
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vowels in the category. The absence of significant interaction
effects for Country indicates
that responding to native/nonnative vowels and
prototype/nonprototype did not differ
between the groups from Sweden and the USA. We argue that
the difference in response to
the native and nonnative vowels can be attributed to prenatal
perceptual experience, even
though infants had between seven and 75 hours of postnatal
exposure to their native
language. Our results show that there was no effect of the
number of hours of postnatal
exposure to ambient language on infant responding to familiar
and unfamiliar vowels.
We found that neonates, having heard native vowels many times
before birth, sucked less
when they were exposed to them after birth and they sucked as
much to the native prototype

as they did to the native variants. Our interpretation, following
the prototype literature, is
that experience with native vowels renders the native prototype
very similar to its variants
(see Bremner, 201128, for a discussion of face prototypes, and
Kuhl, 199129, for a discussion
on prototypes and speech categories). Because the infants have
never experienced nonnative
vowels in the womb, they perceive them as more distinct from
one another. One interesting
result is the large difference between sucking responses to the
nonnative prototype and its
variants. Sucking responses were largest to the nonnative
prototype. There are data to
suggest that, regardless of experience, vowel prototypes are
more salient than
nonprototypes30. Our results only show enhanced prototype
salience for unfamiliar vowels.
Further studies with equivalent methods are needed to examine
this difference in results.
Additional studies will be necessary to examine whether the
results reported here can be
generalised to other vowels and languages. It will be especially
interesting to compare
newborn perception of vowels that are more extreme with
regard to their location in the
vowel space (such as the vowels used in the present study), with
ones that are less extreme.
Data suggest that more extreme vowels are generally more
salient for infants31.
We found that overall pacifier sucking rates were higher in the
USA than in Sweden. This
could be due to differences in pre- and perinatal care practices
in the two countries, such as

rates of neonatal pacifier use, epidural anesthesia and breast-
feeding.
These results suggest that birth is not a benchmark that reflects
a complete separation
between the effects of nature versus those of nurture on infants’
perception of the phonetic
units of speech. Our results indicate that neonates’ perception
of speech sounds already
reflects some degree of learning. Although technically daunting,
research during the foetal
period is warranted to provide a complete developmental picture
of phonetic perception. The
finding also raises questions regarding what sounds are
available to the developing fetus,
how they are processed by the developing brain and how further
experience during
development continues to shape perception, both in typically
developing children and also
clinical populations.
Acknowledgments
The opinions or assertions contained in this paper are the
private views of the authors and should not to be
construed as official or as reflecting the views of the United
States Department of Defense. This research was
supported by a National Institutes of Health Grant HD 37954 to
Patricia K. Kuhl and by the S. Erving Severtson
Forest Foundation Undergraduate Research Program. We would
like to thank the families and staff at Madigan
Army Medical Center in Tacoma and The Astrid Lindgren
Children’s Hospital in Stockholm. Student research
assistants were: K. Fashay, A. Gaboury, J. Jette, K. Kerr, S.
Rebar, M. Spadaro, G. Stock, and C. Zhao.
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Key Notes

• Being exposed to ambient language in the womb affects foetal
phonetic
perception.
• Eighty neonates (mean: 33 hrs since birth) in Sweden and the
USA responded
differently to vowels sounds, depending on whether they were
from their
familiar native or an unfamiliar nonnative language.
• The neonates sucked their pacifiers more frequently to
activate recordings of
unfamiliar nonnative vowel sounds, and the hours that had
elapsed since birth
had no effect on these rates.
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Figure 1.
The 17 stimuli for the English vowel /i/ and Swedish /y/ mapped
according to their formant
frequencies converted to mels which are psychophysical units.
The figure is adapted from
Kuhl et al, 19929.
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Figure 2.
An infant, 20 hours after birth, takes part in the procedure in
which hearing speech sounds
through headphones is contingent on sucks on a pacifier.
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Figure 3.
Infant sucking response to contingent auditory presentations of
vowels from the familiar
native language or the unfamiliar nonnative language. Means
represent the mean of sucks
that produce the prototype vowel and the means of 16
nonprototype vowels.
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Perspectives on Psychological Science
8(5) 573 –585
© The Author(s) 2013
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1745691613498098
pps.sagepub.com
In 1916, Margaret Floy Washburn, the first woman to
receive a doctorate in psychology, championed the need
to connect the facts of mental life with those of bodily
movement. In contrast, the behaviorists that followed,
led by John B. Watson, ousted the study of mental
life from scientific psychology—for a time—while retain-
ing the study of motor responses. In the mid-1960s,
a backlash to behaviorism, the cognitive revolution,
occurred. Mental life was back, not only for people but
even for computers, with their gargantuan size, kludgy
switches, fans, and paper tapes. By 1988, the cognitive
revolution was complete: Behaviorism was vanquished,
but in the ensuing enthusiasm for studying the mind, the
relation that Washburn had seen as so worthy of study—

that human consciousness is grounded in the human
body and movements—was nearly forgotten. Thought,
and with it consciousness, was seen, by the standard
view, as disembodied, with the contribution of action
being relegated, along with behaviorism, to the third sub-
basement. Today, though, the view of disembodied cog-
nition is being challenged by approaches that emphasize
the importance of embodiment.
Here we present an idiosyncratic account of what the
field of cognition was and how it has evolved since 1988.
We then describe our approach to embodied cognition.
In preview, the fundamental tenet of embodied cognition
research is that thinking is not something that is divorced
from the body; instead, thinking is an activity strongly
influenced by the body and the brain interacting with the
environment. To say it differently, how we think depends
on the sorts of bodies we have. Furthermore, the reason
why cognition depends on the body is becoming clear:
Cognition exists to guide action. We perceive in order to
act (and what we perceive depends on how we intend to
act); we have emotions to guide action; and understand-
ing even the most abstract cognitive processes (e.g., the
self, language) is benefited by considering how they are
grounded in action. This concern for action contrasts
with standard cognitive psychology that, for the most
part, considers action (and the body) as secondary to
cognition.
We believe that this approach is pushing the evolution
of the field with a surprising (for some of us) but happy
498098PPSXXX10.1177/1745691613498098Glenberg et
al.From the Revolution to Embodiment
research-article2013

Corresponding Author:
Arthur M. Glenberg, Department of Psychology, Arizona State
University, Mail Code 1104, Tempe, AZ 85287-1104
E-mail: [email protected] asu.edu
From the Revolution to Embodiment:
25 Years of Cognitive Psychology
Arthur M. Glenberg1,2, Jessica K. Witt3, and Janet Metcalfe4
1Arizona State University, 2University of Wisconsin-Madison,
3Colorado State University,
and 4Columbia University
Abstract
In 1988, the cognitive revolution had become institutionalized:
Cognition was the manipulation of abstract symbols
by rules. But, much like institutionalized political parties, some
of the ideas were becoming stale. Where was action?
Where was the self? How could cognition be smoothly
integrated with emotions, with social psychology, with
development, with clinical analyses? Around that time, thinkers
in linguistics, philosophy, artificial intelligence, biology,
and psychology were formulating the idea that just as overt
behavior depends on the specifics of the body in action, so
might cognition depend on the body. Here we characterize
(some would say caricature) the strengths and weaknesses
of cognitive psychology of that era, and then we describe what
has come to be called embodied cognition: how
cognition arises through the dynamic interplay of brain
controlling bodily action controlling perception, which changes
the brain. We focus on the importance of action and how action
shapes perception, the self, and language. Having
the body in action as a central consideration for theories of
cognition promises, we believe, to help unify psychology.

Keywords
action, performance, cognition, language, communication,
memory
574 Glenberg et al.
conclusion: Simple principles of embodiment may pro-
vide a unifying perspective for psychology (Glenberg,
2010; Schubert & Semin, 2009).
Cognition in 1988: Thinking Is Symbol
Manipulation
The standard approach to the study of cognition in 1988
was formalized a decade earlier by two of the giants in the
field, Alan Newell and Herbert Simon, under the guise of
the physical symbol system hypothesis (PSSH; Newell &
Simon, 1976). Of course, not every cognitive theory was a
perfect exemplar of the PSSH, but it provided the back-
ground and a set of default assumptions. It was also bril -
liant. Start with the following conundrum: Computers
appear to think. People appear to think. But computers
are lifeless silicon and humans are living flesh. What could
they have in common that could produce thinking? The
answer, according to the PSSH, was that both are symbol
processors with three features in common.
Three features of PSSH thinking
“machines”
First, the symbols have a physical instantiation (e.g.,
bistable memory cells that store patterns of zeros and
ones in computer memory or, in its strong form, physical
objects such as physical tokens). These physical symbols

have representational properties, that is, the symbol
stands in for real things like colors, emotions, images, and
activities. But in contrast to William James’s insight that
mentally we never descend twice into the same stream,
these physical symbols were thought to be context invari-
ant, static, and disembodied. They were characterized by
what came to be known as the “trans-situational identity
assumption” (Tulving, 1983)—the assumption that a par-
ticular symbol was static and immutable, remaining the
same regardless of when or how it was used.
Second, the symbols are manipulated by rules, namely
the if–then operations of programs in a computer, which
were equated to learned rules, associations, and produc-
tions in humans. Thought was taken to be the manipula-
tion of symbols using rules, in both computers and
people.
Third, and importantly, both the computer symbols
and human symbols were seen as arbitrarily (i.e., by con-
vention alone) related to the referent. Thus, just as a
sequence of zeros and ones in a computer representing
the concept, say, bird, does not look or sound or behave
as a real bird, neither does the human mental symbol
resemble a bird. This arbitrariness ensured that the sym-
bols could be manipulated solely by the properties rele-
vant to the rules; it ensured efficiency because there is
nothing in the symbol that is extraneous; it ensured the
possibility of limitless symbols even if the human ability
to discriminate along some dimension was limited; and
finally, it ensured that computer symbols and human
symbols were the same in kind.
A good analogy for understanding the PSSH is lan-
guage. In many analyses, languages have words that act

like symbols and syntax that acts like rules for combining
the symbols into new thoughts. It is easy to see that many
words are arbitrarily related to their referents. The word
bird no more looks like, sounds like, or acts like a bird
than does the equally arbitrary French oiseau or German
Vogel. This sort of analogy led Fodor (1975) to explicitly
propose that thinking is a language-like activity: the lan-
guage of thought. Just as language has symbols and syn-
tax, thought also has symbols and syntax.
The PSSH provided the background for many cogni-
tive theories of the day, even though the particular
assumptions were not often explicitly stated or tested.
For example, Anderson’s ACT theory (Anderson, 1990),
Collins and Quillian’s (1969) spreading activation model,
and Kintsch’s construction-integration theory (Kintsch,
1988) were built on the notion of propositions, or units
of meaning consisting of symbols and the relations
among them. Those symbols were the arbitrary symbols
of the PSSH, and the relations and processes were the
rules.
Four problems with PSSH
Symbols. Given the success of PSSH approaches to cog-
nition, what is the problem? Even before the advent of
embodied cognition, cognitive theories were evolving
away from the PSSH. First, if symbols are part of human
cognition, they are not arbitrary but grounded, that is,
traceable to referents in the world. Mental operations
depend on the brain’s neural layers interacting in particu-
lar ways that could be thought of as transformations and
that have particular functions. These operations take
information that originates in the world and subject it to
transformations (such as lateral inhibition in the retina,
autocorrelation in the memory systems, and Fourier

transforms in the auditory system) that are useful to the
person or animal. What is important, however, is that
even with the transformations, the internal representa-
tions at deeper and deeper brain levels are ultimately
traceable to the outside world, that is, grounded.
An example of an early model attempting to ground
symbols is Metcalfe’s CHARM (Metcalfe, 1990; Metcalfe
Eich, 1982, 1985) model. Whereas it is true, even in this
model, that the numbers used to represent a bird do not
in any way look like a bird, the representations were not
completely arbitrary—namely, the model used complex
operations of convolution and correlation for memory
encoding and retrieval, and these operations preserved
From the Revolution to Embodiment 575
similarity among symbols across transformations. Also,
this model and other related non-PHHS models (e.g.,
McClelland & Elman, 1986; Murdock, 1982) were begin-
ning to strive for representations that could potentially be
instantiated in neurons and that bore a more realistic
relation to the structure of semantic memory.
Furthermore, the notion of transsituational identity of
symbols, although fine for computers, did not work well
for humans. The experimental data demonstrated that it
was simply untrue that the symbols used by humans
were immutable. The “train” that a person imagined in
the context of freight was a different “train” than was
encoded in the context of black, and memory was con-
text specific—dependent on the specificity of encoding
(Tulving & Thomson, 1973).

Representations separate from processes. The PSSH
makes a strong distinction between representations and
the processes that act on them. Whereas this distinction
holds for some computer languages, it is not at all clear
that the distinction can be made for humans. For exam-
ple, from a connectionist–constraint satisfaction perspec-
tive, representations arise from patterns of activation
produced by processing, and they are not separable from
the processing. From the dynamic systems perspective,
there is no representation without process. From the per-
ception–action perspective, what we perceive is neces-
sarily related to how we act (Gibson, 1979).
The Cartesian distinction. The PSSH makes a Carte-
sian distinction between thought and action, treating
mind as disembodied. That is, according to PSSH, the
exact same thoughts occur when a computer is manipu-
lating symbols by using rules and when a person is
manipulating the same symbols by using the same rules.
The particulars of the body housing the symbol manipu-
lation were thought to be irrelevant. Later, we review
work within the tradition of embodied cognition that
shows that the Cartesian distinction is false.
The role of the self in cognitive processing. Although
PSSH models accounted for much of the data on human
memory and cognition, there was no hint of a “self” in
these models. Theorists such as Tulving (1993) had
insisted that episodic memory involves a special kind of
self-knowing or autonoetic consciousness, and evidence
was mounting for this view (Wheeler, Stuss, & Tulving,
1997). Autobiographical memory for events that were
specific to the individual was extensively studied. But
conceptualizing and formally implementing something
like a “self” and characterizing its function were then
(and largely remain) beyond the scope of models of

memory and cognition. One reason may have been that
although the operations used in these models
were neurologically plausible, they relied only on the
perceptual system, not the motor system or any feedback
from the latter.
In contrast to the PSSH, memory is radically enhanced
when the self is involved. Thus, for example, literally
enacting a task, such as breaking a toothpick, produces
much better memory for the event than watching another
perform the task (e.g., Engelcamp, 1995). Similarly, mem-
ory is also enhanced when the encoder relates the items
to his- or herself (Cloutier & Macrae, 2008; Craik et al.,
1999; Macrae, Moran, Heatherton, Banfield, & Kelley,
2004). What is this self-knowledge, self-involvement, and
self-reflective consciousness, what are its effects, and
how did it come about? We propose that some notion of
embodiment is the needed but missing ingredient.
Cognition in 2013: Embodiment
Just as there are many “standard” approaches to cognition,
not just the PSSH, there are also many embodied
approaches. And these approaches can differ in some fun-
damental ways. For example, Lakoff and Johnson’s (1980)
work on metaphor and Barsalou’s (1999) work on con-
cepts rely strongly on involvement of representations,
whereas Beer’s (1996) work on understanding “minimally”
cognitive tasks and Gibson’s (1979) work on direct per-
ception—which has inspired many embodied cognition
theorists—are explicitly representation-less. Nonetheless,
there are also some general themes that resonate in the
embodiment community.
Cartesian dualism is wrong

As noted above, thinking is not something that is divorced
from the body; instead, thinking is influenced by the
body and the brain interacting with the environment.
This claim can be fleshed out in a number of ways. For
example, Proffitt’s (2006) work on visual perception
demonstrates that perceived distance is affected by the
energetic demands of the body needed to traverse the
distance. Thus, the same distance looks longer when you
are tired, when wearing a heavy backpack, or when of
low physical fitness. Casasanto’s (2011) studies reveal
surprising differences between left-handers and right-
handers. Left-handers and right-handers use different
parts of the brain when thinking about action verbs; they
think about abstract concepts such as “goodness” differ-
ently; and, perhaps most amazingly, a few minutes of
experimentally induced changes in use of the hands pro-
duces differences in how people think about good and
bad. Work on brain imaging (e.g., Hauk, Johnsrude, &
Pulvermüller, 2004) has shown that when people hear a
verb such as “pick,” areas of the motor cortex used to
control the hands are quickly activated, whereas on
576 Glenberg et al.
hearing a verb such as “kick,” areas of motor cortex used
to control the legs are activated.
Furthermore, changes in the body produce changes in
cognition. Blocking the use of the corrugator (frowning)
muscle by cosmetic injection of Botox selectively slows
the processing of sentences describing angry and sad
events but not happy events (Havas, Glenberg, Gutowski,
Lucarelli, & Davidson, 2010). Patients with severe spinal

cord injury have a reduction in ability to perceive differ -
ences in human gaits, although not in perception of grat-
ings (Arrighi, Cartocci, & Burr, 2011). These results
suggest the possibility of what philosophers call “consti -
tution”: Activity in the body and sensorimotor cortices of
the brain not only contribute to cognition—that activity is
cognition.
These data help to explicate the notion that thinking is
grounded in the sensorimotor system. Thus, the psycho-
logical meaning of distance is grounded in the body’s
energy consumption; the meanings of words like “kick”
and “pick” are grounded in how we interact with the
world by using our legs and hands; and the meaning of
anger is at least in part its expression using the facial
muscles. Ideas are not platonic abstractions that can be
as easily implemented in a computer as in a person.
Instead, ideas and the very act of thinking are simulations
using sensory, motor, and emotional systems.
The importance of action
A second theme of embodiment is an emphasis on action.
It seems certain that one evolutionary pressure on cogni-
tion is the need to control action: Without action there is
no survival. Furthermore, according to the noted biolo-
gist Rudolfo Llinas (2001, p. 15), “A nervous system is
only necessary for multicellular creatures . . . that can
orchestrate and express active movement.” And ever
since the seminal work of James Gibson (1979) on direct
perception and affordances, psychologists are increas-
ingly convinced that action changes perception and ani-
mals have perceptual systems because of the need to
control action. Thus, the perception–action cycle is at the
heart of cognition.

The amazing Held and Hein (1963) kitten carousel
experiment illustrates the role of action on perception in
development. Pairs of kittens were exposed to a visual
environment in which one kitten controlled its own loco-
motion while the other was in a yoked gondola—receiving
the same visual input but without self-controlled locomo-
tion. Whereas all kittens had a normal response to light
and visually pursued moving objects, the passive kittens
exhibited impaired blink responses and impaired visually
guided paw placement. They also failed to discriminate
depth, frequently walking over a visual cliff (which in
the real world could, of course, be fatal). Campos et al.
(2000) documented a similar need to link perception and
self-locomotion in human development. Whereas tradi-
tional approaches consider perception to operate inde-
pendently of action, these results demonstrate the
necessity of action even for learning how to perceive.
Overview
Below we expand on the action theme in three ways.
First, we describe the role of action in perception.
Traditional models consider perception to be indepen-
dent and prior to action; here we describe evidence that
action should be considered as an intricate part of per-
ception. Second, we speculate on how an understanding
of the underlying mechanisms of action can reveal insights
into what appears to be the most abstract of concepts: the
self. Consequently, the inclusion of action in processes
such as perception and memory results in the inclusion of
the self in these processes. Third, we demonstrate the
practical side of embodiment by describing an embodied
reading comprehension intervention based on action.
These three areas of research are not typically associated
with action; thus, demonstrating the connection helps to

justify our claim that cognition is thoroughly embodied
because it is for action.
Action’s effect on perception
During the past 25 years, much of the research on per-
ception has resonated with themes found in other areas
of cognitive research. Starting with Marr’s (1982) seminal
book, researchers used the analogy of computers to
study perception. Marr distinguished three levels at which
a process could be assessed: a computational level (What
does a process do?), an algorithmic level (How does the
process work?), and an implementation level (How is the
process realized?). In this framework, it is irrelevant
whether the implementation occurs on neural hardware
or computer hardware. The focus was instead on the
computations needed to perceive.
The prevalence of this approach—treating human
perception as analogous to the processes of computer
symbol manipulators—is revealed by the methods
used to study perception. A typical setup is shown in
Figure 1. The observer’s head is stabilized on a chin rest
so as to eliminate head motion, thus allowing the experi-
menter to have complete control over what the observer
views. The observer’s task is nearly always a judgment
of some sort but not in the context action. Indeed, the
setup intentionally removes any potential for action, forc-
ing on the human observer the constraints and limita-
tions of a computer. Furthermore, the conjectured goals
of vision also align with those that could be equally
shared by a computer program, namely, of formulating a

representation of shape (Marr, 1982), object identification
(Yantis, 2000), or spatial layout (Palmer, 1999), based on
the impoverished information given in the display unin-
formed by human action and its consequents.
These PSSH-oriented approaches were challenged by
Gibson (1979) in favor of an approach that captures sev-
eral of the key ideas of embodied cognition, including a
role for the self. According to Gibson’s theory of direct
perception, the information for perception comes from
invariants and variants within the optic array as the per -
ceiver moves. These invariants and variants specify both
the external environment and the perceiver. For example,
the close correlation between self-movement and change
in the optic array indicates that the self is moving (e.g.,
Blakemore, Wolpert, & Frith, 1998); changes in the optic
array without self-action indicate a changing world; and
the two together specify the dimensionality of space
(Philipona, O’Regan, & Nadal, 2003). Consequently, the
self is necessarily perceived when perceiving the envi -
ronment, and perception of the environment could not
be achieved without also perceiving the self.
Gibson’s theory of affordances emphasized the role of
action in perception. Affordances are possibilities for
action or for a consequence on the perceiver in a given
environment. According to Gibson, affordances are the
main object of perception. In making this claim, Gibson
stressed action as a key concept in perception, rather
than behaviorally independent representations of spatial
layout and object identity. But although this concept was
accepted for animal vision, mainstream researchers, such
as Marr (1982), held to the idea that perception’s goal is
to recover platonic geometric properties rather than to
uncover affordances:

The usefulness of a representation depends upon
how well suited it is to the purpose for which it is
used. A pigeon uses vision to help it navigate, fly,
and seek out food. . . . Human vision, on the other
hand, seems to be very much more general. (p. 32)
Perhaps it is a testament to the importance of action in
perception that its role continues to reemerge. Today,
there are many different findings of effects of action on
perception. For instance, a slight modification to the typi -
cal setup for perception experiments —putting one’s
hands next to the display—modifies visual processes
related to attention, such as detection, search, and inhibi -
tion (see Brockmole, Davoli, Abrams, & Witt, 2013). Thus,
perception is influenced by the mere proximity of one’s
hands and their potential to act (e.g., Reed, Betz, Garza,
& Roberts, 2010). Another example of action’s influence
is apparent in action observation—the perception of oth-
ers performing actions (Knoblich, Thornton, Grosjean, &
Shiffrar, 2006). For example, apparent motion is the illu-
sion of motion from static images of the endpoints of the
motion. What is important is that when the endpoints
depict humans, people perceive biologically plausible
paths rather than paths along the shortest distance, as
would be expected when observing apparent motion
with nonbiological objects (Shiffrar & Freyd, 1990).
Perception of object features, such as position and direc-
tion, is also influenced by the perceiver’s actions and
intentions to act (Lindemann & Bekkering, 2009; Müsseler
& Hommel, 1997).
Action-specific account of perception. The main
claim of the action-specific account of perception (Prof-
fitt, 2006; Witt, 2011a) is that perception is influenced by
a person’s ability to act. For example, softball players

who are hitting better than others see the ball as bigger
(Witt & Proffitt, 2005). In addition to effects of sports per -
formance on apparent target size (e.g., Gray, in press;
Lee, Lee, Carello, & Turvey, 2012; Witt & Dorsch, 2009;
Witt & Sugovic, 2010), the energetic costs associated with
performing an action also influence perception. Hills
look steeper and distances look farther to perceivers who
are fatigued, out of shape, encumbered by a heavy load,
or elderly (Bhalla & Proffitt, 1999; Sugovic & Witt, in
press). Furthermore, changes to a perceiver’s ability to
perform an action, such as via tool use, also influences
perception. Targets that would otherwise be beyond
reach look closer when the perceiver intends to reach
with a tool (Witt, 2011b; Witt & Proffitt, 2008; Witt, Prof-
fitt, & Epstein, 2005; see also Bloesch, Davoli, Roth,
Brockmole, & Abrams, 2012; Brockmole et al., 2013;
Davoli, Brockmole, & Witt, 2012; Kirsch, Herbort, Butz, &
Kunde, 2012; Osiurak, Morgado, & Palluel-Germain,
Fig. 1. A typical setup for studying perception. Chin rests are
used to
minimize head movements.
578 Glenberg et al.
2012). In addition, changing the apparent size of one’s
body in virtual reality (Linkenauger, Mohler, & Bülthoff,
2011; Mohler, Creem-Regehr, Thompson, & Bülthoff,
2010; Van der Hoort, Guterstam, & Ehrsson, 2011) or
through optic magnification (Linkenauger, Ramenzoni, &
Proffitt, 2010; Linkenauger, Witt, & Proffitt, 2011) influ-
ences perceived distance to and size of objects. This
research demonstrates that the same object, which gives
rise to the same optical information, looks different

depending on the perceiver’s abilities.
That the perceptual system is tightly connected to the
motor system seems necessary from an evolutionary per-
spective. Sensory processes are costly in terms of ener-
getics, and only sensory processes that are useful will
confer an adaptive advantage that will likely be promoted
through reproduction. Both nonhuman animals and
humans have perceptual processes relevant to their spe-
cific bodies and capabilities. The time has come to con-
sider the perceptual system as part of an integrated
perception–action system.
Necessity of embodiment. The studies mentioned above
make a case for a role of embodiment in perception, but
they do not speak to whether embodiment is necessary for
perception. Indeed, people can perceive objects for which
action is not very likely (such as the moon, although of
course perception of the moon is not very accurate). There
are some reasons to believe that action might be necessary
for perception. First, developing the ability to perceive in a
way that is useful for acting requires experience with the
pairing of perceptual information while acting (Held &
Hein, 1963; see also Campos et al., 2000).
In addition, a new proposal for the mechanism of
action-specific effects called the perceptual ruler hypoth-
esis suggests that embodiment may be necessary for per-
ception. The perceptual ruler hypothesis (Proffitt &
Linkenauger, 2013) solves a problem that is largely
ignored: All optical information takes the form of angles,
such as angles of retinal projection or angles of disparity,
or changes in these angles. To perceive dimensions such
as distance and size, these angles must be transformed
accordingly. Yet little is known about these transforma-
tion processes or “rulers.” According to the perceptual

ruler hypothesis, these rulers are based on the body. For
example, eye height is used to perceive distance and
object height (Sedgwick, 1986), the hand is used for scal-
ing the size of graspable objects (Linkenauger, Witt, et al.,
2011), and the arm is used for scaling the size of reach-
able objects (Witt et al., 2005). Similarly, the body’s abili -
ties in terms of physiological and behavioral potential
scale other aspects of the environment, such as hills, far
distances, and the size of targets.
In summary, the perceptual ruler account solves the
important problem of scaling visual information (i.e.,
turning visual angles into meaningful information), and
the ruler used to perform the scaling is the body. In fact,
no one has proposed a ruler that is not body-based.
Thus, this solution emphasizes that the body not only
influences perception, it is necessary for perception, too.
Forward models: A mechanism for
action simulation, prediction, and
the self
Barsalou (1999) made a strong case that tracing the trans-
formations from perception at the level of the retina or
the ear, or action at the level of external bodily move-
ment, through to coherent high-level representations is
necessary and a central goal of embodied cognition
research. In fact, only by grounding representational cog-
nition in the world can symbols take on meaning (Searle,
1980). Although a general well-specified model integrat-
ing cognition and action does not yet exist, some inroads
have been made. As we shall see, to model actions, both
internal representations (simulations) and a computation
that provides the basis for a “self”—a missing component
in cognitive models—are required.

One of the most striking of these action models comes
from endeavors undertaken by Wolpert (e.g., Wolpert,
Ghahramani, & Jordan, 1995). The argument is that action,
from the simplest finger movement to the most complex
social interactions (e.g., Wolpert, Doya, & Kawato, 2003),
requires two types of models. A controller model gener-
ates the efferents or commands to move the muscles. But
how does the system determine that the movement is, or
is likely to be, successful? Of course, sensory feedback is
important, but for many situations in which action timing
is important (e.g., playing the piano, walking down stairs,
control of the speech articulators, or holding a conversa-
tion), sensory feedback is too slow. The solution is a sec-
ond type of model variously called a predictor model or
forward model. The forward model uses an exact copy of
the efferent signal to simulate the action and predict the
effects of the action. A comparator can then make several
types of computations. First, it can be used to determine
whether the action is unfolding correctly: Do the predic-
tions from the forward model match the desired outcomes?
Second, it allows knowledge of whether the action was
effective: Does the sensory feedback match the prediction
from the forward model?
Critically, the forward model can also be used imagi-
natively, in mental practice, to evaluate outcomes without
performing them (e.g., Grush, 2004). That is, imagination
is generating motor commands but inhibiting the efferent
signal before the muscles are moved. Nonetheless, gen-
erating the efferent commands also generates the signal
used by the forward model, and the predictions gener-
ated by the forward model are tantamount to imagery.

Combining the notions of imagination, prediction, and
action prompted several independent applications of for -
ward models to language. Pickering and Garrod (2013)
developed the idea that forward models are used to pre-
dict how and what conversational partners are going to
say, and thus forward models provide a mechanism for
coordinating discourse through alignment in speaking
rate, word choice, and syntax. The action-based language
theory of Glenberg and Gallese (2012) suggests that for-
ward models play an essential role in predicting (simulat-
ing), and thus understanding, the content of language. In
fact, Lesage, Morgan, Olson, Meyer, and Miall (2012)
demonstrated that disrupting forward models in the
motor system (using repetitive transcranial magnetic
stimulation of the cerebellum) disrupted prediction in
language processing.
Forward models and the self. Although the compara-
tor model rests firmly in the domain of the motor system,
an extension of this model to the role of the self in cogni-
tion has been explicitly proposed. Many investigators
(e.g., Blakemore et al., 1998) realized that a close match
of actual and predicted outcomes justifies the inference
that the person himself or herself was in control. A mis-
match implies the work of external forces and a lack of
personal control. This simple mechanism, then, provides
a basis for people’s knowledge of their own agency. Dis-
crepancies between one’s own intention and the out-
come could be used in making judgments of self- or
other attribution. Indeed, Blakemore, Wolpert, and Frith
(2002; and see Blakemore & Frith, 2003; Decety & Lamm,
2007) soon proposed brain-based frameworks using the
comparator model to make testable predictions concern-
ing people’s feelings of agency—their feelings of the

involvement of self.
Action, the self, and memory. The “self” in memory
tasks has always been mysterious. William James referred
to it in his seminal writings. Tulving, too, makes use of
this construct, in distinguishi ng between semantic mem-
ory and episodic memory and in discussing different pur-
ported kinds of consciousness. The “highest” of these, he
claimed, is self-knowing—autonoetic—consciousness.
Nevertheless, the construct of self has always been
slightly disreputable for cognitive psychologists, perhaps
because it is difficult to define and elusive to model.
Even so, the many cases where the involvement of the
“self” has a large impact on memory involve either physi -
cal or mental action. The connection to the motor system
may not be accidental. Enactment effects, wherein mem-
ory is enhanced for those things one does oneself, as
compared with those that someone else does (e.g.,
Engelcamp, 1995), is pervasive for all but some people
with autism. The benefits of active versus passive
learning are probably due to involvement of the self.
Nairne and Pandeirada’s (2008) findings that survival -
relevant scenarios are easily remembered may also be
due to “self” involvement or some implicit threat to the
self (and see Humphrey, 2006). The generation effect,
whereby memory is greatly improved when the answers
are self-generated rather than given externally, also impli-
cates an active self, which alters memory processing. It
seems, then, that despite its elusiveness, the self has
importance for human memory, and the motor system—
which appears to be the basis of this construct—may be
implicated at deep levels.
Action and language

At first blush, the notion that action systems play a role in
language seems close to absurd: Language appears to be
a mental activity, not a bodily one (e.g., Hauser, Chomsky,
& Fitch, 2002). But in fact, the data are clear that action
systems play an important role in language production,
perception, and comprehension (e.g., Glenberg &
Gallese, 2012). In this section, we review the data sup-
porting this claim and then demonstrate its importance
for both theory and practice by describing an embodied
reading comprehension intervention called Moved by
Reading (MbR).
Language comprehension as a simulation pro-
cess. Work in cognitive neuroscience demonstrates
many links between language and action. As noted
before, Hauk et al. (2004) used functional MRI to demon-
strate that just hearing words such as “pick” activated
motor areas of the brain controlling the hand. Further-
more, language–action links are bidirectional (e.g., Arav-
ena et al., 2010): Preparing to move the hand affects
understanding language about hand movements, and lan-
guage understanding affects motor preparation. Even the
specifics of motor experience matter. For instance, hockey
experience modifies motor areas of the brain (including
those used in prediction), and those areas are then used
in the understanding of language about hockey (Beilock,
Lyons, Mattarella-Micke, Nusbaum, & Small, 2008).
Behavioral data tell a similar story. For example, turn-
ing a knob clockwise or counterclockwise to reveal the
next portion of a story affects the reading of sentences
implying clockwise (e.g., “He turned the key to start the
car”) or counterclockwise actions as shown by Zwaan,
Taylor, and De Boer (2010).

These results support the claim that language compre-
hension is a simulation process (Aziz-Zadeh, Wilson,
Rizzolatti, & Iacoboni, 2006; Barsalou, 1999; Glenberg
& Gallese, 2012). In brief, words and phrases are
transformed into a simulation of the situation described.
Furthermore, the simulation takes place in neural
580 Glenberg et al.
systems normally used for action, sensation, and emo-
tion. Thus, language about human action is simulated
using motor cortices; comprehending descriptions of
visual scenes activates areas of the brain used in visual
perception (e.g., Rueschemeyer, Glenberg, Kaschak,
Mueller, & Friederici, 2010); and understanding language
about emotional situations calls on neural systems
engaged by emotion (e.g., Havas et al., 2010). In all of
these cases, forward models based in the motor system
are used to guide the simulations, but the forward mod-
els make use of sensory and emotional systems.
Action and language: An implication for educa-
tion. Given that language comprehension is a process
of simulation, it follows that one component in teaching
children how to read for comprehension should be
teaching them to simulate. That is the goal of the MbR
intervention.
The MbR computer program consists of two parts. In
the first part, physical manipulation (PM), children read
multiple texts from a scenario (e.g., stories that take place
on a farm). After reading an action-oriented sentence
(indicated to the child by a green traffic light), the child
manipulates images on the computer screen to simulate

the content of the sentence (See Fig. 2). For example,
if the child reads, “The farmer drives the tractor to the
barn,” the child moves the image of the farmer to the trac-
tor and then moves the conjoined image of the tractor and
farmer to the image of the barn. In the second stage,
called imagine manipulation (IM), the child is taught to
imagine manipulating the scene in the same way that he
or she physically manipulated the scene. IM, in contrast to
simple imagery instructions, is likely to engender a signifi -
cant motor component in addition to visual imagery.
The PM and IM procedures enhance simulation and
comprehension by encouraging (a) vocabulary develop-
ment by grounding word meaning in the neural represen-
tation of the pictures and objects, (b) appreciation of the
function of syntax by grounding the syntax (i.e., the who
does what to whom) in the child’s own actions, and
(c) the integration of words in a sentence and the integra-
tion of sentences across the text as the visually depicted
scene is updated by the child. The advantages of MbR
have been demonstrated for texts that have been trained
with the technique (Glenberg, Gutierrez, Levin, Japuntich,
& Kaschak, 2004), generalize to texts read after training,
can be implemented in small reading groups (Glenberg,
Brown, & Levin, 2007), extend to solving math problems
(Glenberg, Willford, Gibson, Goldberg, & Zhu, 2012), and
benefit nonnative speakers (Marley, Levin, & Glenberg,
2007, 2010).
If a child understands oral language, and thus is simu-
lating language by using appropriate neural systems,
why does the child need to be taught how to simulate
when reading? In learning an oral language, the linking
of symbols (spoken words and phrases) to sensorimotor
activity is frequent and immediate. For example, a

mother will say, “Here is your bottle” and give the
baby the bottle; or, a father will say, “Wave bye-bye”
and gesture waving. From these interactions, the process
of moving from the auditory linguistic symbol to the
neural representations of objects and actions is highly
practiced and becomes fast and automatic. The key to
MbR is to make reading more like oral language: Teach
the child how to ground written words in sensorimotor
activity.
Conclusion
Approaching solutions to four
problems
Earlier, we noted four problems with the PSSH. Does the
embodied approach help to solve those problems?
Consider first arbitrary, ungrounded symbols. Although
there are debates among embodied cognition theorists as
to necessity of representations, they all agree that cogni -
tion is grounded in the body’s actions in the world and is
not just the manipulation of arbitrary symbols. Instead,
what we perceive is related to how we can act in the
world; our sense of self is determined by the relations
among our actions, their expected effects, and the
observed effects; our understanding of language depends
on simulations using neural and bodily systems of action,
perception, and emotion.
Second, are there static symbols that form the core of
our knowledge? Perhaps there are some, but the data
strongly imply that our activity influences and coordi-
nates that knowledge. Our skill in acting affects how we
perceive the world, and changing that skill changes
what we perceive (Lee et al., 2012; Witt, Linkenauger,
Bakdash, & Proffitt, 2008; Witt & Proffitt, 2005).

Third, is the mind disembodied? Can there be a brain
or mind in a vat? Not if that vat is unconnected to a sens-
ing and acting body. But is it right to consider the bodily
activity as part of cognition rather than just the mecha-
nism of input and output? Shapiro (2011) makes an inter-
esting analogy. A car engine’s exhaust seems to be just an
external waste product of the generation of energy. But
consider when the exhaust powers a turbocharger that
forces more air into the cylinders, thereby boosting
energy output. The exhaust now becomes an integral
component of energy production. Similarly, the predic-
tions of sensory feedback generated by forward models,
bodily activity, and the feedback from activity become
integral to cognition.
Fig. 2. The screenshots illustrate physical manipulation (a)
before reading the sentence “He drives the tractor to
the barn” (b) midway through manipulating for the sentence,
and (c) after successful manipulation. The green
traffic light is the signal for the child to manipulate the toys to
correspond to the sentence.
582 Glenberg et al.
Finally, as a result of the interaction between action
and feedback, the very perception of the self is grounded
in activity—it is embodied.
The notion that embodiment can help to unify psy-

chology is hinted at in this essay where we have tried to
illustrate links between perception, action, memory, lan-
guage, the self, and applications of psychology. Given
that the body is present when we are sensing, thinking,
acting, emoting, socializing, and obeying cultural impera-
tives, it is a good bet that considering the effects of the
body in these endeavors will lead to a more unified,
coherent, comprehensive, and useful psychology.
Cognition in 2038
Even the best forward models are hard-pressed to predict
more than a second or two into the future, let alone
25 years. Whether or not embodiment survives as a via-
ble theoretical framework for decades to come, it has set
a salutary course that we hope will continue—namely, it
provides new perspectives, new theories, and new meth-
ods that may help to unify psychology. If this unification
is to emerge by 2038, much work needs to be done. In
particular, embodiment researchers must move from
demonstrations of embodiment to theoretical approaches
that have broad reach. These theoretical approaches
must make strong predictions about the directions of the
effects, and must be able to describe the underlying
mechanisms, including how information from forward
models is incorporated into other processes, such as
memory and perception. In addition, the concept of the
self needs development. Here, we described the self as if
it were a singular concept, but it is likely there are mul -
tiple aspects of the self that play different roles in cogni -
tion (e.g., Damasio, 2010).
Theoretical approaches also need to continue to make
the distinction between the idea that the body can influ-
ence cognition and the idea that the body is necessary to
understand cognition. Given the omnipresence of the

body in human activity, we think that developing such an
approach is not only possible but essential for develop-
ment and unification of our field.
Acknowledgments
The authors contributed equally to this article. We thank Sian
Beilock, Douglas Hintzman, Dennis Proffitt, and tDavid Sbarra
for helping to improve this article.
Declaration of Conflicting Interests
Arthur M. Glenberg is co-owner of Moved by Reading, LLC.
Funding
In preparing this article, Arthur M. Glenberg was partially sup-
ported by the National Science Foundation (Grant Number
1020367), Jessica K. Witt was supported by the National
Science
Foundation (Grant Number BCS-0957051), and Janet Metcalfe
was supported by the James S. McDonnell Foundation (Grant
Number 220020166). Any opinions, findings, and conclusions
or recommendations expressed in this material are those of the
authors and do not necessarily reflect the views of the funding
agencies.
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Foundations for a New Science
of Learning
Andrew N. Meltzoff,1,2,3* Patricia K. Kuhl,1,3,4 Javier
Movellan,5,6 Terrence J. Sejnowski5,6,7,8
Human learning is distinguished by the range and complexity of
skills that can be learned and the
degree of abstraction that can be achieved compared with those
of other species. Homo sapiens is
also the only species that has developed formal ways to enhance
learning: teachers, schools, and
curricula. Human infants have an intense interest in people and
their behavior and possess powerful
implicit learning mechanisms that are affected by social
interaction. Neuroscientists are beginning

to understand the brain mechanisms underlying learning and
how shared brain systems for
perception and action support social learning. Machine learning
algorithms are being developed that
allow robots and computers to learn autonomously. New
insights from many different fields are
converging to create a new science of learning that may
transform educational practices.
C
ultural evolution, which is rare among
species and reaches a pinnacle in Homo
sapiens, became possible when new forms
of learning evolved under selective pressure in
our ancestors. Culture underpins achievements in
language, arts, and science that are unprecedented
in nature. The origin of human intelligence is still
a deep mystery. However, the study of child de-
velopment, the plasticity of the human brain, and
computational approaches to learning are laying
the foundation for a new science of learning
that provides insights into the origins of human
intelligence.
Human learning and cultural evolution are sup-
ported by a paradoxical biological adaptation: We
are born immature. Young infants cannot speak,
walk, use tools, or take the perspective of others.
Immaturity comes at a tremendous cost, both to
the newborn, whose brain consumes 60% of its
entire energy budget (1), and to the parents. Dur-
ing the first year of life, the brain of an infant is
teeming with structural activity as neurons grow
in size and complexity and trillions of new con-
nections are formed between them. The brain

continues to grow during childhood and reaches
the adult size around puberty. The development of
the cerebral cortex has “sensitive periods” during
which connections between neurons are more
plastic and susceptible to environmental influence:
The sensitive periods for sensory processing areas
occur early in development, higher cortical areas
mature later, and the prefrontal cortex continues
to develop into early adulthood (2).
Yet immaturity has value. Delaying the matu-
ration and growth of brain circuits allows initial
learning to influence the developing neural archi-
tecture in ways that support later, more complex
learning. In computer simulations, starting the learn-
ing process with a low-resolution sensory system
allows more efficient learning than starting with a
fully developed sensory system (3).
What characterizes the exuberant learning that
occurs during childhood? Three principles are
emerging from cross-disciplinary work in psychol-
ogy,neuroscience,machinelearning,andeducation,
contributing to a new science of learning (Fig. 1).
These principles support learning across a range of
areas and ages and are particularly useful in ex-
plaining children’s rapid learning in two unique
domains of human intelligence: language and so-
cial understanding.
Learning is computational. Discoveries in de-
velopmental psychology and in machine learning
are converging on new computational accounts
of learning. Recent findings show that infants and

young children possess powerful computational
skills that allow them automatically to infer struc-
tured models of their environment from the statis-
tical patterns they experience. Infants use statistical
patterns gleaned from experience to learn about
both language and causation. Before they are three,
children use frequency distributions to learn which
phonetic units distinguish words in their native
language (4, 5), use the transitional probabilities
between syllables to segment words (6), and use
covariation to infer cause-effect relationships in
the physical world (7).
Machine learning has the goal of developing
computer algorithms and robots that improve
automatically from experience (8, 9). For exam-
ple, BabyBot, a baby doll instrumented with a
video camera, a microphone, and a loudspeaker
(10), learned to detect human faces using the
temporal contingency between BabyBots’ pro-
grammed vocalizations and humans that tended to
1Institute for Learning and Brain Sciences, University of Wash-
ington, Seattle, WA 98195, USA. 2Department of Psychology,
University of Washington, Seattle, WA, 98195, USA. 3Learning
in Informal and Formal Environments (LIFE) Center, University
of
Washington, Seattle, WA 98195, USA. 4Department of Speech
and Hearing Sciences, University of Washington, Seattle, WA,
98195, USA. 5Institute for Neural Computation, University of
California at San Diego, La Jolla, CA 92093, USA. 6Temporal
Dynamics of Learning Center (TDLC), University of California
at
San Diego, La Jolla, CA 92093, USA. 7Howard Hughes Medical
Institute, Salk Institute for Biological Studies, La Jolla, CA
92037, USA. 8Division of Biological Sciences, University of

California at San Diego, La Jolla, CA 92093, USA.
*To whom correspondence should be addressed. E-mail:
[email protected]
Fig. 1. The new science of learning has arisen from several
disciplines. Researchers in developmental
psychology have identified social factors that are essential for
learning. Powerful learning algorithms from
machine learning have demonstrated that contingencies in the
environment are a rich source of infor-
mation about social cues. Neuroscientists have found brain
systems involved in social interactions and
mechanisms for synaptic plasticity that contribute to learning.
Classrooms are laboratories for discovering
effective teaching practices. [Photo credits: R. Goebel
(neuroscience), iStockphoto.com/J. Bryson (education),
Y. Tsuno/AFP/Getty Images (machine learning)]
17 JULY 2009 VOL 325 SCIENCE www.sciencemag.org284
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http://www.sciencemag.org
respond to these babylike vocalizations. After 6
min of learning, BabyBot detected novel faces
and generalized to the schematic faces used in
studies of infant face recognition.
Statistical regularities and covariations in the
world thus provide a richer source of information
than previously thought. Infants’ pickup of this
information is implicit; it occurs without parental
training and begins before infants can manipulate
the physical world or speak their first words. New
machine learning programs also succeed with-
out direct reinforcement or supervision. Learning
from probabilistic input provides an alternative to
Skinnerian reinforcement learning and Chomskian
nativist accounts (11, 12).
Learning is social. Children do not compute
statistics indiscriminately. Social cues highlight
what and when to learn. Even young infants are
predisposed to attend to people and are motivated
to copy the actions they see others do (13). They
more readily learn and reenact an event when it is
produced by a person than by an inanimate device
(14, 15).
Machine learning studies show that system-
atically increasing a robot’s social-like behaviors
and contingent responsivity elevates young chil-
dren’s willingness to connect with and learn from
it (16). Animal models may help explain how

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