Bodily maps of emotions

Abstract

Significance
Emotions coordinate our behavior and physiological states during survival-salient events and pleasurable interactions. Even though we are often consciously aware of our current emotional state, such as anger or happiness, the mechanisms giving rise to these subjective sensations have remained unresolved. Here we used a topographical self-report tool to reveal that different emotional states are associated with topographically distinct and culturally universal bodily sensations; these sensations could underlie our conscious emotional experiences. Monitoring the topography of emotion-triggered bodily sensations brings forth a unique tool for emotion research and could even provide a biomarker for emotional disorders.

Full text

Bodily maps of emotions
Lauri Nummenmaa
a,b,c,1
, Enrico Glerean
a
, Riitta Hari
b,1
, and Jari K. Hietanen
d
a
Department of Biomedical Engineering and Computational Science and
b
Brain Research Unit, O. V. Lounasmaa Laboratory, School of Science, Aalto
University, FI-00076, Espoo, Finland;
c
Turku PET Centre, University of Turku, FI-20521, Turku, Finland; and
d
Human Information Processing Laboratory, School
of Social Sciences and Humanities, University of Tampere, FI-33014, Tampere, Finland
Contributed by Riitta Hari, November 27, 2013 (sent for review June 11, 2013)
Emotions are often felt in the body, and somatosensory feedback
has been proposed to trigger conscious emotional experiences.
Here we reveal maps of bodily sensations associated with different
emotions using a unique topographical self-report method. In five
experiments, participants (n=701) were shown two silhouettes of
bodies alongside emotional words, stories, movies, or facial expres-
sions. They were asked to color the bodily regions whose activity
they felt increasing or decreasing while viewing each stimulus.
Different emotions were consistently associated with statistically
separable bodily sensation maps across experiments. These maps
were concordant across West European and East Asian samples.
Statistical classifiers distinguished emotion-specific activation maps
accurately, confirming independence of topographies across emo-
tions. We propose that emotions are represented in the somatosen-
sory system as culturally universal categorical somatotopic maps.
Perception of these emotion-triggered bodily changes may play
a key role in generating consciously felt emotions.
embodiment
|
feelings
|
somatosensation
We often experience emotions directly in the body. When
strolling through the park to meet with our sweetheart we
walk lightly with our hearts pounding with excitement, whereas
anxiety might tighten our muscles and make our hands sweat and
tremble before an important job interview. Numerous studies
have established that emotion systems prepare us to meet chal-
lenges encountered in the environment by adjusting the activa-
tion of the cardiovascular, skeletomuscular, neuroendocrine, and
autonomic nervous system (ANS) (1). This link between emo-
tions and bodily states is also reflected in the way we speak of
emotions (2): a young bride getting married next week may
suddenly have “cold feet,”severely disappointed lovers may be
“heartbroken,”and our favorite song may send “a shiver down
our spine.”
Both classic (3) and more recent (4, 5) models of emotional
processing assume that subjective emotional feelings are trig-
gered by the perception of emotion-related bodily states that
reflect changes in the skeletomuscular, neuroendocrine, and auto-
nomic nervous systems (1). These conscious feelings help the
individuals to voluntarily fine-tune their behavior to better match
the challenges of the environment (6). Although emotions are
associated with a broad range of physiological changes (1, 7), it is
still hotly debated whether the bodily changes associated with
different emotions are specific enough to serve as the basis for
discrete emotional feelings, such as anger, fear, or happiness
(8, 9), and the topographical distribution of the emotion-related
bodily sensations has remained unknown.
Here we reveal maps of bodily sensations associated with dif-
ferent emotions using a unique computer-based, topographical
self-report method (emBODY, Fig. 1). Participants (n=701) were
shown two silhouettes of bodies alongside emotional words,
stories, movies, or facial expressions, and they were asked to
color the bodily regions whose activity they felt to be increased or
decreased during viewing of each stimulus. Different emotions
were associated with statistically clearly separable bodily sensa-
tion maps (BSMs) that were consistent across West European
(Finnish and Swedish) and East Asian (Taiwanese) samples, all
speaking their respective languages. Statistical classifiers dis-
criminated emotion-specific activation maps accurately, confirming
independence of bodily topographies across emotions. We pro-
pose that consciously felt emotions are associated with culturally
universal, topographically distinct bodily sensations that may
support the categorical experience of different emotions.
Results
We ran five experiments, with 36–302 participants in each. In
experiment 1, participants reported bodily sensations associated
with six “basic”and seven nonbasic (“complex”) emotions, as
well as a neutral state, all described by the corresponding emo-
tion words. Fig. 2 shows the bodily sensation maps associated
with each emotion. One-out linear discriminant analysis (LDA)
classified each of the basic emotions and the neutral state against
all of the other emotions with a mean accuracy of 72% (chance
level 50%), whereas complete classification (discriminating all
emotions from each other) was accomplished with a mean ac-
curacy of 38% (chance level 14%) (Fig. 3 and Table S1). For
nonbasic emotions, the corresponding accuracies were 72% and
36%. When classifying all 13 emotions and a neutral emotional
state, the accuracies were 72% and 24% against 50% and 7%
chance levels, respectively. In cluster analysis (Fig. 4, Upper), the
positive emotions (happiness, love, and pride) formed one cluster,
whereas negative emotions diverged into four clusters (anger and
fear; anxiety and shame; sadness and depression; and disgust,
contempt, and envy). Surprise—neither a negative nor a positive
emotion—belonged to the last cluster, whereas the neutral
emotional state remained distinct from all other categories.
We controlled for linguistic confounds of figurative language
associated with emotions (e.g., “heartache”) in a control exper-
iment with native speakers of Swedish, which as a Germanic
language, belongs to a different family of languages than Finnish
(a Uralic language). BSMs associated with each basic emotion
word were similar across the Swedish- and Finnish-speaking
samples (mean r
s
=0.75), and correlations between mismatched
emotions across the two experiments (e.g., anger-Finnish vs.
Significance
Emotions coordinate our behavior and physiological states during
survival-salient events and pleasurable interactions. Even though
we are often consciously aware of our current emotional state,
such as anger or happiness, the mechanisms giving rise to these
subjective sensations have remained unresolved. Here we used
a topographical self-report tool to reveal that different emo-
tional states are associated with topographically distinct and
culturally universal bodily sensations; these sensations could
underlie our conscious emotional experiences. Monitoring the
topography of emotion-triggered bodily sensations brings forth
a unique tool for emotion research and could even provide a
biomarker for emotional disorders.
Author contributions: L.N., E.G., R.H., and J.K.H. designed research; L.N. and E.G. per-
formed research; L.N. and E.G. contributed new reagents/analytic tools; L.N. and E.G.
analyzed data; and L.N., E.G., R.H., and J.K.H. wrote the paper.
The authors declare no conflict of interest.
Freely available online through the PNAS open access option.
1
To whom correspondence may be addressed. E-mail: lauri.nummenmaa@aalto.fi or riitta.
hari@aalto.fi.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1321664111/-/DCSupplemental.
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happiness-Swedish) were significantly lower (mean r
s
=0.36)
than those for matching emotions.
To test whether the emotional bodily sensations reflect culturally
universal sensation patterns vs. specific conceptual associations
between emotions and corresponding bodily changes in West
European cultures, we conducted another control experiment with
Taiwanese individuals, who have a different cultural background
(Finnish: West European; Taiwanese: East Asian) and speak
Use the pictures below to indicate
the bodily sensations you
experience when you feel
For this body, please
color the regions whose
activity becomes
stronger or faster
SADNESS
For this body, please
color the regions whose
activity becomes weaker
or slower
CLICK HERE WHEN FINISHED
Initial screen with blank bodies
Subject-wise colored
activation and
deactivation maps
Subject-wise combined
activation-deactivation map
Random effects analysis
and statistical inference
Activations Deactivations
A
BC
Fig. 1. The emBODY tool. Participants colored the
initially blank body regions (A) whose activity they
felt increasing (left body) and decreasing (right
body) during emotions. Subjectwise activation–
deactivation data (B) were stored as integers, with
the whole body being represented by 50,364 data
points. Activation and deactivation maps were sub-
sequently combined (C) for statistical analysis.
Anger Fear Disgust Happiness Sadness Surprise Neutral
Anxiety Love Depression Contempt Pride Shame Envy 15
-15
0
10
5
-10
-5
Fig. 2. Bodily topography of basic (Upper)andnonbasic(Lower) emotions associated with words. The body maps show regions whose activation increased
(warm colors) or decreased (cool colors) when feeling each emotion. (P<0.05 FDR corrected; t>1.94). The colorbar indicates the t-statistic range.
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a language belonging to a family of languages distant from Finnish
(Taiwanese Hokkien: Chinese language). Supporting the cultural
universality hypothesis, BSMs associated with each basic emo-
tion were similar across the West European and East Asian
samples (mean r
s
=0.70), and correlations between mismatched
emotions across the two experiments (e.g., anger-Finnish vs.
happiness-Taiwanese) were significantly lower (mean r
s
=0.40)
than those for matching emotions.
When people recall bodily sensations associated with emotion
categories described by words, they could just report stereotypes
of bodily responses associated with emotions. To control for this
possibility, we directly induced emotions in participants using two
of the most powerful emotion induction techniques (10, 11)—
guided mental imagery based on reading short stories (experi-
ment 2) and viewing of movies (experiment 3)—and asked them
to report their bodily sensations online during the emotion in-
duction. We carefully controlled that emotion categories or
specific bodily sensations were not directly mentioned in the
stories or movies, and actual emotional content of the stories
(Fig. S1) was evaluated by another group of 72 subjects (see ref.
12 for corresponding data on movies). BSMs were similar to
those obtained in experiment 1 with emotion words (Figs. S2 and
S3). The LDA accuracy was high (for stories 79% and 48%
against 50% and 14% chance levels for one-out and complete
classification and for movies, 76% and 50% against 50% and
20% chance levels, respectively). The BSMs were also highly
concordant across emotion-induction conditions (stories vs.
movies; mean r
s
=0.79; Table S2).
Models of embodied emotion posit that we understand others’
emotions by simulating them in our own bodies (13, 14), meaning
that we should be able to construct bodily representations of
others’somatovisceral states when observing them expressing
specific emotions. We tested this hypothesis in experiment 4 by
presenting participants with pictures of six basic facial expres-
sions without telling them what emotions (if any) the faces
reflected and asking them to color BSMs for the persons shown
in the pictures, rather than the sensations that viewing the
expressions caused in themselves. Again, statistically separable
BSMs were observed for the emotions (Fig. S4), and the classi-
fier accuracy was high (70% and 31% against 50% and 14%
chance levels for one-out and complete classification schemes,
respectively; Fig. 3 and Table S1). Critically, the obtained BSMs
were highly consistent (Table S2) with those elicited by emo-
tional words (mean r
s
=0.82), stories (mean r
s
=0.71), and
movies (mean r
s
=0.78).
If discrete emotional states were associated with distinct pat-
terns of experienced bodily sensations, then one would expect
that observers could also recognize emotions from the BSMs of
others. In experiment 5, we presented 87 independent partic-
ipants the BSMs of each basic emotion from experiment 1 in
a paper-and pencil forced-choice recognition test. The partic-
ipants performed at a similar level to the LDA, with a 46% mean
accuracy (vs. 14% chance level). Anger (58%), disgust (43%),
happiness (22%), sadness (38%), surprise (54%), and the neutral
state (99%) were classified with high accuracy (P<0.05 against
chance level in χ
2
test), whereas the performance did not exceed
the chance level for fear (8%, NS).
Finally, we constructed a similarity matrix spanning the BSMs
of experiments 1–4 for the six basic emotions plus the neutral
emotional state (Fig. S5). BSMs were consistent across the
experiments (mean r
s
=0.83) for each basic emotion. Even
though there were significant correlations across mismatching
emotions across the experiments (e.g., anger in experiment 1 and
fear in experiment 2), these were significantly lower (mean r
s
=
0.52) than those for the matching emotions. Clustering of the
similarity matrix revealed a clear hierarchical structure in the data
(Fig. 4, Lower). Sadness, disgust, fear, and neutral emotional state
separated early on as their own clusters. Anger topographies in
the word and face experiments clustered together, whereas those
in the story experiments were initially combined with disgust.
Two categories of surprise maps were clustered together,
whereas the maps obtained in the word data were linked with
disgust. Only happiness did not result in clear clustering across
the experiments.
When LDA was applied to the dataset combined across
experiments, the mean accuracy for complete classification was
similar to that in the individual experiments (40% against 14%
chance level). LDA using all possible pairs of the experiments as
training and test datasets generally resulted in cross-experiment
classification rates (Table S3) exceeding 50% for all of the tested
experiment pairs, confirming the high concordance of the BSMs
across the experiments.
Discussion
Altogether our results reveal distinct BSMs associated with both
basic and complex emotions. These maps constitute the most
accurate description available to date of subjective emotion-
related bodily sensations. Our data highlight that consistent
patterns of bodily sensations are associated with each of the six
basic emotions, and that these sensations are represented in a
categorical manner in the body. The distinct BSMs are in line
with the evidence from brain imaging and behavioral studies,
highlighting categorical structure of emotion systems and neural
circuits supporting emotional processing (15, 16) and suggest
that information regarding different emotions is also represented
in embodied somatotopic format.
The discernible sensation patterns associated with each emo-
tion correspond well with the major changes in physiological
functions associated with different emotions (17). Most basic
emotions were associated with sensations of elevated activity in
the upper chest area, likely corresponding to changes in breathing
and heart rate (1). Similarly, sensations in the head area were
shared across all emotions, reflecting probably both physiological
changes in the facial area (i.e., facial musculature activation, skin
temperature, lacrimation) as well as the felt changes in the
contents of mind triggered by the emotional events. Sensations in
the upper limbs were most prominent in approach-oriented emo-
tions, anger and happiness, whereas sensations of decreased limb
activity were a defining feature of sadness. Sensations in the
digestive system and around the throat region were mainly found
in disgust. In contrast with all of the other emotions, happiness
was associated with enhanced sensations all over the body. The
nonbasic emotions showed a much smaller degree of bodily
Experiment 1 - Words Experiment 2 - Stories
Experiment 3 - Movies Experiment 4 - Faces
Anger
Disgust
Fear
Happiness
Neutral
Sadness
Surprise
Anger
Disgust
Fear
Happiness
Neutral
Sadness
Surprise
Anger
Disgust
Fear
Happiness
Neutral
Sadness
Surprise
Disgust
Fear
Happiness
Neutral
Sadness
Anger
Disgust
Fear
Happiness
Neutral
Sadness
Surprise
Anger
Disgust
Fear
Happiness
Neutral
Sadness
Surprise
Anger
Disgust
Fear
Happiness
Neutral
Sadness
Surprise
Disgust
Fear
Happiness
Neutral
Sadness
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Fig. 3. Confusion matrices for the complete classification scheme across
experiments.
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sensations and spatial independence, with the exception of a high
degree of similarity across the emotional states of fear and sad-
ness, and their respective prolonged, clinical variants of anxiety
and depression.
All cultures have body-related expressions for describing emo-
tional states. Many of these (e.g.,having“butterflies in the stom-
ach”) are metaphorical and do not describe actual physiological
changes associated with the emotional response (18). It is thus
possible that our findings reflect a purely conceptual association
between semantic knowledge of language-based stereotypes as-
sociating emotions with bodily sensations (19). When activated,
such a conceptual link—rather than actual underlying physio-
logical changes—could thus guide the individual in constructing
a mental representation of the associated bodily sensations (9).
However, we do not subscribe to this argument. First, all four
types of verbal and nonverbal stimuli brought about concordant
BSMs, suggesting that the emotion semantics and stereotypes
played a minor role. Second, consistent BSMs were obtained
when participants were asked to report their actual online bodily
sensations during actual emotions induced by viewing movies or
reading stories (the emotional categories of which were not in-
dicated), thus ruling out high-level cognitive inferences and
stereotypes. Third, a validation study with participants speaking
Swedish—a language distant from Finnish—replicated the orig-
inal findings, suggesting that linguistic confounds such as figu-
rative language associated with the emotions cannot explain the
findings. Fourth, bodily sensation maps were also concordant
across West European (Finland) and East Asian (Taiwan) cul-
tures (mean r
s
=0.70), thus exceeding clearly the canonical limit
for “strong”concordance. Thus, BSMs likely reflect universal
sensation patterns triggered by activation of the emotion systems,
rather than culturally specific conceptual predictions and associ-
ations between emotional semantics and bodily sensation pat-
terns. Despite these considerations, the present study cannot
completely rule out the possibility that the BSMs could never-
theless reflect conceptual associations between emotions and
bodily sensations, which are independent of the culture. How-
ever, where then do these conceptual associations originate and
why are they so similar across people with very different cultural
and linguistic backgrounds? A plausible answer would again
point in the direction of a biological basis for these associations.
Prior work suggests that voluntary reproduction of physiological
states associated with emotions, such as breathing patterns (20)
or facial expressions (21), induces subjective feelings of the cor-
responding emotion. Similarly, voluntary production of facial
expressions of emotions produces differential changes in physi-
ological parameters such as heart rate, skin conductance, finger
temperature, and muscle tension, depending on the generated
expression (22). However, individuals are poor at detecting spe-
cific physiological states beyond maybe heart beating and palm
sweating. Moreover, emotional feelings are only modestly associated
with specific changes in heart rate or skin conductance (23) and
physiological data have not revealed consistent emotion-specific
patterns of bodily activation, with some recent reviews pointing
to high unspecificity (9) and others to high specificity (8). Our data
reconcile these opposing views by revealing that even though
changes in specific physiological systems would be difficult to
access consciously, net sensations arising from multiple physio-
logical systems during different emotions are topographically dis-
tinct. The obtained BSM results thus likely reflect a compound
measure of skeletomuscular and visceral sensations, as well as
the effects of autonomic nervous system, which the individuals
0
0.5
1
1.5
Happiness
Anger
Fear
Disgust
Surprise
Sadness
Neutral
Sadness
Sadness
S
Sadness
Disgust
Disgust
Disgust
Happiness
Surprise
Surprise
Happiness
Anger
Anger
Fear
Happiness
Fear
Fear
Neutral
Neutral
Neutral
S
S
S
S
S
S
F
F
F
F
F
F
F
M
M
M
M
M
W
W
W
W
W
W
W
0.8
0.9
1
1.1
Happiness
Love
Pride
Anger
Fear
Anxiety
Shame
Disgust
Contempt
Surprise
Envy
Sadness
Depression
Neutral
Fig. 4. Hierarchical structure of the similarity between bodily topographies associated with emotion words in experiment 1 (Upper) and basic emotions across
experiments with word (W), story (S), movie (M), and Face (F) stimuli (Lower).
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cannot separate. As several subareas of the human cortical so-
matosensory network contain somatotopic representations of the
body (24), specific combinations of somatosensory and visceral
afferent inputs could play a central role in building up emotional
feelings. It must nevertheless be emphasized that we do not
argue that the BSMs highlighted in this series of experiments
would be the only components underlying emotional experience.
Rather, they could reflect the most reliable and systematic
consciously accessible bodily states during emotional processing,
even though they may not relate directly to specific physiological
changes.
These topographically distinct bodily sensations of emotions
may also support recognizing others’emotional states: the BSMs
associated with others’facial expressions were significantly cor-
related with corresponding BSMs elicited by emotional words,
text passages, and movies in independent participants. Partic-
ipants also recognized emotions related to mean BSMs of other
subjects. Functional brain imaging has established that the pri-
mary somatosensory cortices are engaged during emotional per-
ception and emotional contagion (25, 26), and their damage (27)
or inactivation by transcranial magnetic stimulation (28) impairs
recognition of others’emotions. Consequently, emotional per-
ception could involve automatic activation of the sensorimotor
representations of the observed emotions, which would sub-
sequently be used for affective evaluation of the actual sensory
input (13, 29). The present study cannot nevertheless establish
a direct link between the BSMs and an underlying physiological
activation pattern. Even though whole-body physiological responses
cannot be mapped with conventional psychophysiological tech-
niques, in the future, whole-body perfusion during induced emo-
tions could be measured with whole-body
15
O-H
2
O PET imaging.
These maps could then be correlated with the BSMs to investigate
the relationship between experienced regional bodily sensations
and physiological activity during emotional episodes.
Conclusions
We conclude that emotional feelings are associated with dis-
crete, yet partially overlapping maps of bodily sensations, which
could be at the core of the emotional experience. These results
thus support models assuming that somatosensation (25, 27) and
embodiment (13, 14) play critical roles in emotional processing.
Unraveling the subjective bodily sensations associated with hu-
man emotions may help us to better understand mood disorders
such as depression and anxiety, which are accompanied by altered
emotional processing (30), ANS activity (31, 32), and somato-
sensation (33). Topographical changes in emotion-triggered sen-
sations in the body could thus provide a novel biomarker for
emotional disorders.
Materials and Methods
Participants. A total of 773 individuals took part in the study (experiment 1a:
n=302, M
age
=27 y, 261 females; experiment 1b: n=52, M
age
=27 y, 44
females; experiment 1c: n=36, M
age
=27 y, 21 females; experiment 2: n=
108, M
age
=25 y, 97 females; experiment 3: n=94, M
age
=25 y, 80 females;
experiment 4: n=109, M
age
=28 y, 92 females; and experiment 5: n=72,
M
age
=39 y, 53 females). All participants were Finnish speaking except those
participating in experiment 1b who spoke Swedish and those participating
in experiment 1c who spoke Taiwanese Hokkien as their native languages.
Stimuli. Experiment 1 a–c: emotion words. Participants evaluated their bodily
sensations (BSMs) associated with six basic (anger, fear, disgust, happiness,
sadness, and surprise) and seven nonbasic emotions (anxiety, love, depres-
sion, contempt, pride, shame, and envy) as well as a neutral state. Each word
was presented once in random order. The participants’task was to evaluate
which bodily regions they typically felt becoming activated or deactivated
when feeling each emotion; thus the task did not involve inducing actual
emotions in the participants. Experiment 1a was conducted using Finnish
words and Finnish-speaking participants, experiment 1b with corresponding
Swedish words and Swedish-speaking participants, and experiment 1c with
Taiwanese words and Taiwanese-speaking participants. For the Swedish and
Taiwanese variants, the Finnish emotion words and instructions were first
translated to Swedish/Taiwanese by a native speaker and then backtranslated
to Finnish to ensure semantic correspondence.
Experiment 2: guided emotional imagery. Participants rated bodily sensations
triggered by reading short stories (vignettes) describing short emotional and
nonemotional episodes. Each vignette elicited primarily one basic emotion
(or a neutral emotional state), and five vignettes per emotion category were
presented in random order. Such text-driven emotion induction triggers
heightened responses in somatosensory and autonomic nervous system (34)
as well as brain activation (35), consistent with affective engagement. The
vignettes were generated in a separate pilot experiment. Following the
approach of Matsumoto et al. (36), each emotional vignette described an
antecedent event triggering prominently one emotional state. Importantly,
none of the vignettes described the actual emotional feelings, behavior, or
bodily actions of the protagonist, thus providing no direct clues about the
emotion or bodily sensations being associated with the story [e.g., It’sa
beautiful summer day. You drive to the beach with your friends in a con-
vertible and the music is blasting from the stereo”(happy). “You sit by the
kitchen table. The dishwasher is turned on”(neutral). “While visiting the
hospital, you see a dying child who can barely keep her eyes open.”(sad)].
Normative data were acquired from 72 individuals. In the vignette evalua-
tion experiment, the vignettes were presented one at a time in random
order on a computer screen. Participants were asked to read each vignette
carefully and report on a scale ranging from 1 to 5 the experience of each
basic emotion (and neutral emotional state) triggered by the vignette. Data
revealed that the vignettes were successful in eliciting the targeted, discrete
emotional states. For each vignette, rating of the target emotion category
was higher than that of any other emotion category (P<0.001; Fig. S1).
K-means clustering also classified each vignette reliably to the a priori target
category, Fs (6, 28) >36.54, P<0.001.
Experiment 3: emotional movies. The stimuli were short 10-s movies eliciting
discrete emotional states. They were derived from an fMRI study assessing the
brain basis of discrete emotions, where they were shown to trigger a reliable
pattern of discrete emotional responses (12). Given the inherent difficulties
associated with eliciting anger and surprise with movie stimuli (37), these
emotions were excluded from the study. Five stimuli were chosen for each
emotion category (fear, disgust, happiness, sadness, and neutral). Each film
depicted humans involved in either emotional or nonemotional activities.
The films were shown one at a time in random order without sound. Par-
ticipants were able to replay each movie and they were encouraged to view
each one as many times as was sufficient for them to decide what kind of
responses it elicited in them.
Experiment 4: embodying emotions from facial expressions. The stimuli were
pictures of basic facial expressions (anger, fear, disgust, happiness, sadness,
and surprise) and a neutral emotional state, each posed by two male and two
female actors chosen from the Karolinska facial expressions set (38).
Experiment 5: recognizing emotions from emBODY BSMs. The stimuli were
unthresholded emBODY BSMs for each basic emotion averaged over the 302
participants in experiment 1a.
Data Acquisition. Data were acquired online with the emBODY instrument
(Fig. 1) developed for the purposes of this study. In this computerized tool,
participants were shown two silhouettes of a human body and an emotional
stimulus between them. The bodies were abstract and 2D to lower the
cognitive load of the task and to encourage evaluating only the spatial
pattern of sensations. The bodies did not contain pointers to internal organs
to avoid triggering purely conceptual associations between emotions and
specific body parts to (e.g., love–heart). Participants were asked to inspect
the stimulus and use a mouse to paint the bodily regions they typically felt
becoming activated (on the left body) or deactivated (on the right body)
when viewing it. Painting was dynamic, thus successive strokes on a region
increased the opacity of the paint, and the diameter of the painting tool was
12 pixels. Finished images were stored in matrices where the paint intensity
ranged from 0 to 100. Both bodies were represented by 50,364 pixels. When
multiple stimuli from one category were used (experiments 2–4), subjectwise
data were averaged across the stimuli eliciting each emotional state before
random effects analysis. In experiment 4, instead of evaluating emotions
that the faces would trigger in themselves, the participants were asked to
rate what the persons shown in the pictures would feel in their bodies.
In experiment 5, participants were asked to recognize the average
heatmaps of basic emotions and the neutral emotional state based on 302
respondents in experiment 1. The heatmaps were color printed on a ques-
tionnaire sheet alongside instructions and six emotion words and the word
“neutral.”The participants were asked to associate each heatmap with the
word that described it best. Two different randomized orders of the heat-
maps and words were used to avoid order effects.
650
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www.pnas.org/cgi/doi/10.1073/pnas.1321664111 Nummenmaa et al.

Statistical Analysis. Data were screened manually for anomalous painting
behavior (e.g., drawing symbols on bodies or scribbling randomly). Moreover,
participants leaving more than mean +2.5 SDs of bodies untouched were
removed from the sample. Next, subjectwise activation and deactivation
maps for each emotion were combined into single BSMs representing both
activations and deactivations and responses outside the body area were
masked. In random effects analyses, mass univariate ttests were then used
on the subjectwise BSMs to compare pixelwise activations and deactivations
of the BSMs for each emotional state against zero. This resulted in statistical
t-maps where pixel intensities reflect statistically significant experienced
bodily changes associated with each emotional state. Finally, false discovery
rate (FDR) correction with an alpha level of 0.05 was applied to the statistical
maps to control for false positives due to multiple comparisons.
To test whether different emotions are associated with statistically dif-
ferent bodily patterns, we used statistical pattern recognition with LDA after
first reducing the dimensionality of the dataset to 30 principal components
with principal component analysis. To estimate generalization accuracy, we
used stratified 50-fold cross-validation where we trained the classifier sep-
arately to recognize one emotion against all of the others (one-out classi-
fication), or all emotions against all of the other emotions (complete
classification). To estimate SDs of classifieraccuracy,the cross-validation scheme
was run iteratively 100 times.
To assess the similarity of the BSMs associated with different emotion
categories, we performed hierarchical clustering. First, for each subject we
created a similarity matrix: for each pair of emotion categories we computed
the Spearman correlation between the corresponding heatmaps. To avoid
inflated correlations, zero values in the heatmaps (i.e., regions without paint)
were filled with Gaussian noise. The Spearman correlation was chosen as the
optimal similarity metric due to the high dimensionality of the data within
each map: with high dimensionality, Euclidean metrics usually fail to assess
similarity, as they are mainly based on the magnitude of the data. Fur-
thermore, as a rank-based metric, independent of the actual data values, it is
also less sensitive to outliers compared with Pearson’s correlation. We also
evaluated cosine-based distance as a possible metric, but the normalization
involved in the computation lowered the sensitivity of our final results, as
cosine distance uses only the angle between the two vectors and not their
magnitude. We averaged individual similarity matrices to produce a group
similarity matrix that was then used as distance matrix between each pair of
emotion categories for the hierarchical clustering with complete linkage.
The similarity data were also used for assessing reliability of bodily top-
ographies across languages and experiments.
ACKNOWLEDGMENTS. We thank Drs. Kevin Wen-KaiTsaiandWei-TangChang
and Professor Fa-Hsuan Lin for their help with acquiring the Taiwanese
dataset. This research was supported by the Academy of Finland grants
265917 (MIND program grant to L.N.), 131483 (to R.H.), and 131786 (to
J.K.H.); European Research Council Starting Grant 313000 (to L.N.); Ad-
vanced Grant 232946 (to R.H.); and an aivoAALTO grant from Aalto
University. All data are stored on Aalto University’s server and are available
upon request.
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Supporting Information
Nummenmaa et al. 10.1073/pnas.1321664111
Fig. S1. Mean levels of experience (ranging from 1 to 5) of basic emotions while reading the stories used in experiment 2. Target emotions for each story are
represented on the perimeter, different lines show experience of each emotion. AN, anger; FE, fear; DI, disgust; HA, happiness; SA, sadness; SU, surprise; NE, neutral.
Anger Fear Disgust Happiness Sadness Surprise Neutral
0
10
5
-10
-5
Fig. S2. Bodily topography of basic emotions triggered by emotional imagery guided by narratives. The body maps show regions whose activation increased
(warm colors) or decreased (cool colors) when feeling each emotion (P<0.05 FDR corrected; t>2.11). The colorbar indicates the t-statistic range.
Nummenmaa et al. www.pnas.org/cgi/content/short/1321664111 1of4

0
10
8
-8
-4
6
4
2
-2
-6
-10
Fear Disgust Happiness Sadness Neutral
Fig. S3. Bodily topography of basic emotions triggered by watching emotional movies. The body maps show regions whose activation increased (warm colors)
or decreased (cool colors) when feeling each emotion (P<0.05 FDR corrected; t>2.11). The colorbar indicates the t-statistic range.
0
10
8
-8
-4
6
4
2
-2
-6
-10
Anger Fear Disgust Happiness Sadness Surprise Neutral
Fig. S4. Bodily topography of basic emotions inferred from others’emotional expressions. The body maps show regions whose activation participants
evaluated as increased (warm colors) or decreased (cool colors) in the person displaying each facial expression. (P<0.05 FDR corrected; t>2.09). The colorbar
indicates the t-statistic range.
Nummenmaa et al. www.pnas.org/cgi/content/short/1321664111 2of4

Anger
Fear
Disgust
Joy
Sadness
Surprise
Neutral
Anxiety
Love
Depression
Contempt
Pride
Shame
Envy
Anger
Fear
Disgust
Joy
Sadness
Surprise
Neutral
Fear
Disgust
Joy
Sadness
Neutral
Anger
Fear
Disgust
Joy
Sadness
Surprise
Neutral
Anger
Fear
Disgust
Joy
Sadness
Surprise
Neutral
Anxiety
Love
Depression
Contempt
Pride
Shame
Envy
Anger
Fear
Disgust
Joy
Sadness
Surprise
Neutral
Fear
Disgust
Joy
Sadness
Neutral
Anger
Fear
Disgust
Joy
Sadness
Surprise
Neutral
2
1.5
1
0.5
0
-0.5
-1
-1.5
-2
Words Stories Movies Faces
Words
Stories
Movies
Faces
Z score
Fig. S5. Similarity matrix of bodily sensations associated with emotions across experiments 1–4. Colorbar shows the z-transformed Spearman correlation
across emotion pairs. Note that nonmatching emotion pairs across experiments have been toned down slightly to improve readability.
Table S1. Means and SDs of classification accuracy (in %) with
one-out and complete classification schemes
Words Stories Movies Faces
Emotion M SD M SD M SD M SD
One-out classification
Anger 78 0.18 78 0.36 76 0.33
Fear 70 0.25 81 0.28 72 0.50 63 0.60
Disgust 70 0.23 79 0.33 86 0.25 64 0.55
Happiness 76 0.17 84 0.26 77 0.37 76 0.41
Neutral 76 0.22 81 0.31 70 0.42 68 0.48
Sadness 64 0.31 72 0.42 70 0.54 64 0.55
Surprise 70 0.16 74 0.33 74 0.39
Mean 72 78 75 69
Complete classification
Anger 42 0.61 43 0.83 32 1.32
Fear 32 0.71 45 1.06 57 0.93 20 1.33
Disgust 22 0.68 40 0.96 35 1.28 14 1.50
Happiness 40 0.57 50 1.14 52 1.02 38 1.02
Neutral 74 0.41 76 0.93 72 1.03 71 1.15
Sadness 30 0.63 42 1.23 34 1.42 24 1.19
Surprise 24 0.49 47 1.49 15 1.09
Mean 38 49 50 30
The experiments used word (Exp 1a), story (Exp 2), movie (Exp 3), and face
(Exp 4) stimuli.
Nummenmaa et al. www.pnas.org/cgi/content/short/1321664111 3of4

Table S2. Mean similarities for emotion topographies across experiments
Mean similarity (r
s
) Anger Fear Disgust Happiness Sadness Surprise Neutral
With same emotion across
experiments
0.90 0.82 0.87 0.59 0.93 0.85 0.44
With different emotions across
experiments
0.49 0.50 0.60 0.57 0.55 0.41 0.23
All differences between correlations in both rows except for happiness are significant at P<0.001 in Meng’stest.
Table S3. Classification accuracy (in %) when using
independent experiments as training (rows) and test (columns)
sets
Experiment Words Stories Movies Faces
Words 56 66 72 69
Stories 62 41 69 69
Movies 63 62 38 61
Faces 70 72 73 57
Diagonal shows classification accuracies for the within-experiment 50-fold
cross-validation in the full dataset. Note that these values differ slightly from
those using data from single experiments, as the dimensionality reduction
was performed for the whole dataset with a larger number of principal
components (50 vs. 30) to account for differences between experiments.
Nummenmaa et al. www.pnas.org/cgi/content/short/1321664111 4of4