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www.sciencemag.org/cgi/content/full/323/5918/1222/DC1
Supporting Online Material for
In Bad Taste: Evidence for the Oral Origins of Moral Disgust
H. A. Chapman,* D. A. Kim, J. M. Susskind, A. K. Anderson*
*To whom correspondence should be addressed. E-mail:
[email protected] (H.A.C.); [email protected] (A.K.A.)
Published 27 February 2009, Science 323, 1222 (2009) DOI:
10.1126/science.1165565
This PDF file includes: Materials and Methods
SOM Text
Figs. S1 to S3
Table S1
References
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Supporting Online Material
Materials and Methods
Experiment 1
Participants: Twenty-seven healthy adults (19 female; mean age
20.6 yrs) participated in
the study for course credit or $20. Participants with past or
current history of psychiatric
disorder were excluded, as were participants with a known
disorder of taste or smell. All
procedures in this and subsequent experiments were approved by
the Research Ethics
Board at the University of Toronto, and participants gave
informed consent prior to
beginning the studies.
EMG apparatus: EMG data were acquired using a BIOPAC MP-150
system (S1). At
acquisition, data were amplified ( 1000) and filtered with a
bandpass of 100-500 Hz. Electrodes were placed over the levator
labii muscle region on the left side of the face,
using the placements suggested by (S2).
Chemosensory stimuli: Solutions of quinine sulfate (ranging from
1.0 x 10-3 to 1.0 x 10-
5M), sodium chloride (5.6 x 10-1 1.0 x 10-1M), citric acid (1.0
x 10-1 1.8 x 10-3M) and
sucrose (1.0 0.18M) were used as bitter, salty, sour and sweet
stimuli respectively.
Tastant concentrations were selected individually for each
participant by having
participants sample and rate the intensity and valence of 5
concentrations of the bitter and
sour solutions (which were most aversive, as indicated by pilot
testing) and 4
concentrations of the salty and sweet solutions. Tastants were
presented in small cups,
with a sample size of 2ml. Valence and intensity were rated on
11-point scales, relative to
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tap water: participants were instructed to assume that water has
zero intensity and neutral
valence. After all the solutions were rated, the experimenter
reviewed the participants
responses and selected bitter, salty and sour concentrations
with ratings close to very
unpleasant. Sucrose concentrations were selected so as to be
approximately equivalent
in perceived intensity to the other solutions (strong
intensity).
Procedure: Following the rating procedure, the experimenter
applied the EMG
electrodes, set up the sample cups for the first block of the
experiment, and left the testing
booth. The experiment was divided into five blocks, one each for
water and the bitter,
sweet, salty, and sour solutions. Because the taste of quinine
lingers in the mouth for an
extended period, the bitter block was always presented last,
while the other tastants were
presented in counterbalanced order. Blocks consisted of five
taste trials in which
participants sampled 2ml of the appropriate liquid. To control
the timing of each trial,
written instructions were presented on a computer monitor using
E-Prime experimental
software (S3). Trials began with a 20s baseline period during
which participants rested
quietly. Next came an 8s period when participants grasped the
first sample cup and
brought it to their lips. This was followed by 8s during which
participants sipped the
contents of the sample cup, swished the liquid twice in their
mouth and held the liquid in
their mouth without swallowing. Participants next swallowed the
liquid, and after a
further 8s, rinsed their mouths with ~10ml of water, swallowing
when finished. Finally,
participants rated the valence and intensity of the preceding
sample using the scales
described above. After participants completed all five trials of
a block, the experimenter
entered the booth to arrange the sample cups for the next
block.
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EMG analysis: EMG data were analyzed using custom software
written in MATLAB
(Version 7; S4). A 30 Hz high-pass filter was applied to the EMG
data to reduce low-
frequency noise. The signal was then rectified with an absolute
value function and
smoothed with a 10ms window. The period of interest in each
trial was the 8s of the
swallow phase, which is least likely to be contaminated by
extraneous muscle
activation due to sipping. Signal from the final 8s of the
preceding baseline period was
used to calculate signal change during the swallow phase. A
contour following integrator
was then applied to compute a running sum of sample values. The
final value of the sum
at the end of each swallow period was used for statistical
analysis.
Statistics: The correlation between levator labii region EMG and
valence was computed
on intertrial variability, in part because of variability in the
overall strength of the EMG
response across individuals. Each trial yielded both an EMG
response and a paired self-
report of valence. For each participant, the EMG/self-report
pairs for all 25 trials were
rank-ordered by decreasing valence. The EMG responses at each
rank were then
averaged across participants, and the average values were
correlated with valence rank.
Experiment 1b
Appearance model: An additional group of 20 participants (13
female, mean age 23.2)
were filmed as they underwent the taste sampling procedure
described above in order to
train a computerized facial appearance model to uncover the
underlying action tendencies
associated with the distaste response. Since not all
participants showed visible facial
actions in response to ingestion of the unpleasant tastants, the
upper quartile with the
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strongest overt facial movements were included for training the
appearance model. Still
images capturing these participants peak responses to the
bitter, sweet and neutral
tastants were pulled from the video clips; responses to salty
and sour tastants were
excluded from the analysis so as not to bias the model toward
unpleasant tastes. For those
individuals who were wearing eyeglasses, the spot healing brush
tool in Photoshop was
used to remove the glasses from the still images, to reduce
artifacting in subsequent
analysis steps.
A facial appearance model was created from these images using
the Cootes et al.
methodology (S5). First, each of the face exemplars was manually
annotated with the
two-dimensional Cartesian coordinates (relative to the image
frame) of 68 distinct facial
landmarks. The set of landmarks conforms to an annotation scheme
designed to provide
feature information for the facial contour, eyebrows, eyes,
nose, and mouth, based on a
subset of features specified by the MPEG-4 coding standard for
facial expression
animation (S6). All faces were cut out from the background image
using a polygonal
region defined by the outer contour delimited by the labeling
scheme. Pixels inside the
face region were histogram equalized to remove variability due
to lighting differences
across faces. All of the faces were then commonly aligned to
remove global differences
in size, rotation, and translation, using a generalized
Procrustes transformation (S7).
Principal components analysis (PCA) was applied to the
covariance matrix of the shape
data using eigenvalue decomposition, resulting in
eigenvalue/eigenvector pairs used to
parameterize uncorrelated dimensions of shape variation. The
pixel textures for each face
were piecewise affine warped to a common shape-normalized
reference frame defined as
the mean shape computed from the entire set of annotated facial
landmarks. Textures
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extracted in this fashion are known in the literature as
shape-free, having the property
that internal features in the texture map for each face are
relatively well-aligned (5). PCA
was then applied via eigenvalue decomposition on the covariance
matrix of the shape-
free pixel vectors, resulting in parameters describing
uncorrelated dimensions of texture
variation. In order to collapse important covariations between
face shape and texture, a
third, combined PCA was performed on concatenated shape and
texture parameter
vectors (S5). First, the shape PCA projections were re-weighted
to the same variance
metric as the texture projections by multiplying the shape
coefficients by the sum of the
texture eigenvalues over the sum of the shape eigenvalues. Then
the shape and texture
vectors were concatenated into single vectors for each face
exemplar, and eigenvalue
decomposition was performed on the covariance matrix of the
combined shape and
texture vectors. The resulting combined shape and texture basis
retained the full rank of
the covariance matrix of the face data (there was no data
compression). Each face in the
dataset was thus parameterized by a vector of appearance
loadings.
Photorealistic face images were created using the appearance
model by reversing the
process that converted images to appearance loadings: given a
vector of combined PCA
loadings, the vector was transformed back into a concatenated
shape and texture vector,
and the shape coefficients were re-weighted to their original
metric. Then the shape and
texture PCA coefficients were transformed back into Euclidean
shape coordinates and
shape-free pixel values. Finally, the shape-free pixels were
warped to the specified shape
coordinates from the shape coordinates of the average face.
Expression prototypes for
bitter, neutral, and sweet tastants were created by averaging
the vector representations of
all exemplars of a category and synthesizing face images via the
above process. The
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resulting prototype taste images were exaggerated in intensity
to accentuate their
characteristic action tendencies by scaling each prototype
vector by 2.
Experiment 2
Participants: Nineteen healthy adults participated in this
experiment. Participants with
current or past history of psychiatric disorders were excluded.
EMG data from one
participant were eliminated for technical reasons, for a final
sample size of 18 (8 female,
mean age 20.4 years).
Stimuli: Twenty disgusting and twenty sad photographs were
chosen from the
International Affective Picture System (IAPS; S8) in a two-step
selection process. In the
first step, published emotional category ratings (S9) were used
to identify disgusting
photographs that were rated as high in disgust but low in
sadness, as well as sad
photographs that were high in sadness but low in disgust. A
group of 20 neutral
photographs low in both sadness and disgust was also
selected.
In the second step, ratings from the IAPS norms were used to
match the sad and
disgusting photographs on mean valence. The final set of 20
disgusting photographs was
largely composed of images of contamination and uncleanliness,
such as body products,
body envelope violations, and insects. The 20 sad photographs
included (among others)
homeless persons, traffic accidents, and distraught or ill
individuals (illness photographs
did not involve overt injury or mutilation, contagious disease
or body products). Neutral
photographs included household objects, outdoor scenes and
abstract images.
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Procedure: The experimenter first applied the EMG electrodes and
left the room. The 60
photographs were then presented once in random order. Each trial
began with a 6s
baseline period during which participants viewed a fixation
cross, followed by 6s of
image presentation. Participants then rated the preceding image
on sadness and disgust,
using 9-point scales, before continuing to the next
photograph.
EMG analysis: EMG data were processed largely as in the first
experiment. The period of
interest in each trial was the 6s image presentation period.
Signal from the 6s of fixation
preceding each image was used as a baseline to calculate signal
change scores.
Statistics: The correlations between levator labii region EMG
and disgust/sadness ratings
were computed similarly to Experiment 1. Disgust and sadness
ratings for all 60
photographs were rank-ordered for each participant, in order of
increasing disgust or
sadness. The paired levator labii region responses at each rank
were then averaged across
participants, and these values were correlated with rank.
Experiment 3
Participants: Twenty-one healthy volunteers with no current or
past history of
psychiatric disorder participated in this study. Five
participants were disqualified because
debriefing revealed that they had suspected the deception
involved in the experiment. The
final sample size was 16 (11 female; mean age 22.7 yrs).
EMG acquisition: EMG data were acquired as in the previous
experiments.
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Procedure: Participants were tested in groups of 2-3, with each
participant seated in a
private booth. The experimenter first explained the nature and
rules of the UG, and
informed participants that they would always play the role of
responder. Participants
were next introduced to a group of 10 proposers (confederates)
seated at computer
terminals in two adjoining rooms. All players were told that
they would be paid
according to their choices in the game. Participants returned to
the testing room and the
EMG electrodes were applied.
In the UG, each participant played 20 rounds of the game, one
with each of the 10
confederates and 10 with an avowed computer partner. In reality,
all offers were
generated by a computer algorithm so as to control the size and
number of offers made.
Rounds were presented in random order. The 10 offers from the
computer partner were
identical to those from the human partners, and mimicked the
range and distribution of
offers made in uncontrolled versions of the game, in which
actual human proposers make
offers (e.g., S10): 50% fair ($5:$5) offers, 20% $9:$1 offers,
20% $8:$2 offers and
10% $7:$3 offers. No $6:$4 offers were presented, as we were
primarily interested in
exploring the response to offer that were more strongly unfair.
As offer acceptance, self
reported emotions, and EMG activity did not differ significantly
between partner types
(see supplementary results) data were collapsed across partner
type for all analyses.
Each UG round began with a 6s waiting period followed by 6s of
fixation. The
participant then saw the photograph and name of their partner in
that round for 6s. Next,
participants saw the offer proposed by their partner for 6s,
after which came a 6s window
in which they indicated whether they accepted or rejected the
offer by pressing one of
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two keys on the keyboard. To reinforce the social nature of the
interaction, names and
photographs of proposers remained visible throughout the offer
and decision phases.
After seeing the trials outcome displayed for 6s, participants
rated, on a 1-7 scale,
how well their feelings about the preceding offer were
represented by a reliably
recognized facial expression of a universal emotion (S11). The
seven emotion rating
slides (happiness, sadness, anger, fear, disgust, surprise,
contempt) that followed each
offer were presented in random order. Finally, participants were
debriefed and paid for
their participation according to their choices in the games.
EMG analysis: Using the same custom software, EMG data were
first filtered with a 55-
65Hz notch filter and then with a 30Hz high-pass filter, before
rectifying and smoothing
as above. We analyzed the 6s period during which the proposers
fair or unfair offer was
revealed; recordings from 6s before the onset of the offer
display (i.e., during the partner
display) were used as a baseline from which to calculate signal
change scores. Lastly, a
contour following integrator was applied.
Statistics: Preliminary analyses revealed only a few small
differences between responses
to human vs. computer partners. Results presented in the main
text thus give combined
data from all 20 trials.
The correlations between disgust, anger, sadness and contempt
endorsement and
levator labii region EMG were computed similarly to the previous
experiments. Emotion
ratings for all 20 trials were rank-ordered for each participant
by increasing endorsement,
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and the corresponding EMG values were averaged across
participants at each rank.
Finally, the average EMG values were correlated with emotion
rank.
The correlations between levator labii region activity and
behavioral responses to
offers were computed by rank-ordering the levator labii region
EMG response for all 10
unfair trials (offers of $7:$3 or less) for each participant, in
order of increasing activation.
The corresponding accept/reject decisions (1 or 2) at each rank
were then averaged across
participants, and the average values were correlated with
emotion rank. The correlations
between emotion endorsement and behavioral response were
computed similarly, except
that decisions were rank-ordered by increasing self-reported
disgust, sadness or anger.
Labeling of expressions used in self-report task: To examine
whether the non-verbal self-
report method that we employed separates anger from disgust, a
separate group of 12
participants (7 female, mean age 27.1 years) matched canonical
emotional facial
expressions to written emotion descriptors. On each trial,
participants viewed an array of
emotional expressions (angry, disgusted, contemptuous, sad,
fearful, surprised and happy;
S11) as well as a single written emotion descriptor. The task
was to select the expression
that was the best match for the descriptor. Descriptors were
synonyms for the classical
emotion terms. Four disgust-themed descriptors were presented
(tastes something bad,
smells something bad, touches something dirty, grossed out), as
were four anger-
themed labels (frustrated, annoyed, pissed off, irritated).
Descriptors for
contempt (disapproving), happiness (cheery), surprise (amazed),
fear (scared) and
sadness (glum) were also presented. For analysis, responses to
the four disgust-themed
descriptors were collapsed together, as were response to the
four anger-themed
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descriptors. The frequency with which the anger and disgust
expressions were selected to
match the anger- and disgust-themed descriptors was analyzed
using chi-square tests.
Appearance model: The pattern of negative emotions endorsed for
$9:$1 offers was
depicted using modified versions of the faces used in the
self-report task. The larger
facial appearance model was derived from a standard
cross-cultural dataset of posed
facial expressions that included the self-report faces (S11),
using the same methodology
described for Experiment 1 above. The intensity of disgust,
anger and sadness
expressions used in the self-report task was scaled by the
degree of emotion endorsement
for each expression. More specifically, the vector
representations for each face were
multiplied by the ratio of the mean emotion endorsement value
for that emotion to the
maximum possible endorsement. This resulted in new faces whose
expression intensity
visually reflects the strength of emotion endorsement for that
emotion.
Indexing similarities between expressions: Similarity between
the disgust expression
prototype and the bitter expression from Experiment 1 as well as
seven canonical facial
expressions of emotion (S11) was assessed using the appearance
model. The bitter
expression prototype was fit to the appearance model after
training the model on images
of the seven basic emotions, allowing a test of the
generalization of the model to a novel
expression. Pearson correlations were measured between the
vector coding the disgust
expression prototype and the vectors coding bitter, anger,
contempt, sadness, surprise,
fear and happiness.
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Results
Comparison of responses to human vs. computer partners in
Experiment 3: Previous
research has found that behavioral and neural responses to
unfair offers differ depending
on whether the offer was made by a human or a computer partner
(S12). In view of these
findings, we included a manipulation of partner identity in
Experiment 3, with the
expectation that unfair offers from a computer partner might
evoke weaker emotional
responses. However, contrary to this hypothesis, we did not find
strong differences
between responses to humans and computers. For example, our
participants accepted
only marginally fewer unequal offers from computers than from
humans (paired samples
t-test: t[15] = 1.88, p = 0.08). In particular, participants
rejected the most unfair offers
($9:$1) as often from computers as from humans (t < 1).
Self-reported emotion in
response to the offers did not differ significantly between
humans and computers
(repeated measures ANOVA: F[1,15] = 1.30, p = 0.28). Finally,
levator labii region
EMG did not differ between offers from humans and computers
(repeated measures
ANOVA: F[1,15] = 2.0, p = 0.18).
It is not clear why we failed to replicate the previously
observed differences due to
partner identity, but we note that there are other experimental
paradigms in which
participants experience strong emotional reactions to computers.
For example, exclusion
from a virtual ball-toss game results in anger and hurt feelings
as well as decreases in
self-esteem and feelings of belonging (S13). The strength of
these reactions does not
depend on whether participants believe they are playing with a
computer or with other
humans (S13). Thus, individuals may not always distinguish
strongly between human and
computer partners in social interactions, as our participants
evidently did not.
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Labeling of expressions used in self-report task: Our aim in
this experiment was to test
whether anger and disgust facial expressions were matched to
distinct (and appropriate)
emotion descriptors. Results supported a separation between
labeling of anger and disgust
expressions: anger expressions were reliably matched to anger
descriptors and not disgust
descriptors, while disgust expressions were matched to disgust
descriptors but not anger
descriptors (Table S1). Averaging across the four anger-themed
descriptors, 60.4% of
participants chose the anger expression as the best match, while
disgust was selected by
only 12.4%. Across the four disgust-themed descriptors, the
opposite pattern obtained:
85.4% of participants chose the disgust expression as the best
match, while 4.2% chose
the anger expression. Chi-square tests on the frequency of
selection of disgust and anger
expressions showed that these differences were significant
(anger themed descriptors,
2(1) = 17.8, p < 0.001; disgust-themed descriptors, 2(1) =
35.4, p < 0.001).
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Captions
Fig. S1. Mean proportion of offers accepted in Experiment 3, by
offer type. Error
bars show +1 SEM, calculated within-subjects (S14).
Fig. S2. Mean self-reported emotion in response to different
offers in the
Ultimatum Game (N = 16), showing all emotions. Higher numbers
indicate
greater endorsement. Emotions that did not vary significantly
with offer size are
shown in dashed lines.
Fig. S3. Analysis of similarity between the computer model of
the disgust
expression prototype and other expression prototypes developed
for Experiment
3, plus the average distaste expression observed in Experiment
1B. Average
expressions of disgust and the other basic emotions were
generated from
photographs from a standard set (S11). Points on the plot show
Pearson
correlations comparing the visual similarity of the average
disgust expression
vector representation to other average expressions and the
average distaste
expression from Experiment 1B. Confidence envelope shows 95%
CI.
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Tables
Table S1. Match between emotion descriptors and facial
expressions used for
the self-report task in Experiment 3. The proportion of times
each expression was
selected as the best match for a particular descriptor is given
(averages across
the four items are shown for disgust and anger). Modal response
is highlighted.
Emotion Descriptor
Facial
Expression
Disgust-
themed
Anger-
themed
Disapproving Glum Amazed Scared Cheery
Disgust 0.85 .12 .17 0 0 0 0
Anger .042 .60 .083 0 0 0 0
Contempt 0 .23 .67 0 0 0 0
Sadness .083 .042 .083 1.00 0 .083 0
Surprise 0 0 0 0 1.00 .083 0
Fear .021 0 0 0 0 .83 0
Happiness 0 0 0 0 0 0 1.00
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References and Notes
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University Press,
Cambridge, England, 2000) pp. 163-198.
S3. Psychcholgy Software Tools. (Pittsburg, PA).
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Affective Picture System
(IAPS). Technical Report A-6. (University of Florida,
Gainesville, FL, 2005).
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Expressions of
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