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Marketing actions can modulate neural
representations of experienced pleasantness
Hilke Plassmann*, John O’Doherty*, Baba Shiv
†
, and Antonio Rangel*
‡
*Division of the Humanities and Social Sciences, California Institute of Technology, MC 228-77, Pasadena, CA 91125; and
†
Stanford Graduate School
of Business, Stanford University, 518 Memorial Way, Littlefield L383, Stanford, CA94305
Edited by Leslie G. Ungerleider, National Institutes of Health, Bethesda, MD, and approved December 3, 2007 (received for review July 24, 2007)
Despite the importance and pervasiveness of marketing, almost
nothing is known about the neural mechanisms through which it
affects decisions made by individuals. We propose that marketing
actions, such as changes in the price of a product, can affect neural
representations of experienced pleasantness. We tested this hy-
pothesis by scanning human subjects using functional MRI while
they tasted wines that, contrary to reality, they believed to be
different and sold at different prices. Our results show that
increasing the price of a wine increases subjective reports of flavor
pleasantness as well as blood-oxygen-level-dependent activity in
medial orbitofrontal cortex, an area that is widely thought to
encode for experienced pleasantness during experiential tasks. The
paper provides evidence for the ability of marketing actions to
modulate neural correlates of experienced pleasantness and for
the mechanisms through which the effect operates.
orbitofrontal cortex 兩 modulation by marketing actions 兩
neuroeconomics 兩 taste
A
basic assumption in ec onomics is that the experienced
pleasantness (EP) from consuming a good depends only on
its intrinsic properties and on the state of the individual (1).
Thus, the pleasure derived f rom consuming a soda should
depend only on the molecular composition of the drink and the
level of thirst of the indiv idual. In opposition to this view, a
sizable number of marketing actions attempt to influence EP by
changing properties of commodities, such as prices, that are
unrelated to their intrinsic qualities or to the consumer’s state.
This type of influence is valuable for companies, because EP
serves as a learning signal that is used by the brain to guide future
choices. For example, when facing the choice between previously
ex perienced restaurants, one would tend to avoid locales where
previously meals were unsavory. Contrary to the basic assump-
tions of economics, several studies have provided behavioral
evidence that marketing actions can successfully af fect EP by
man ipulating nonintrinsic attributes of goods. For example,
k nowledge of a beer’s ingredients and brand can affect reported
t aste quality (2, 3), and the reported enjoyment of a film is
influenced by ex pectations about its quality (4). Even more
intriguingly, changing the price at which an energy drink is
purchased can influence the ability to solve puzzles (5).
Despite the importance and pervasiveness of various market-
ing actions, very little is known about the neural mechanisms
through which they affect decisions made by individuals. An
exception is a previous study demonstrating that k nowledge of
the brand of a culturally familiar drink, such as Coke, increases
activation in the hippocampus, parahipoccampus, midbrain,
dorsolateral prefrontral cortex, and thalamus (6). The authors of
the previous study interpreted such activity as evidence for
retrieval of brand infor mation during the consumption
ex perience.
Here, we propose a mechanism through which marketing actions
can affect decision making. We hypothesized that changes in the
price of a product can influence neural computations associated
with EP. This hypothesis is based on previous findings showing that
affective expectations influence appraisals made about hedonic
experiences and, through this, the actual quality of experiences
(2, 7, 8). Consider, for example, the experience of an individual
sampling a wine for which he or she has information about its retail
price. Because perceptions of quality are known to be positively
correlated with price (9), the individual is likely to believe that a
more expensive wine will probably taste better. Our hypothe sis goe s
beyond this by stipulating that higher taste expectations would lead
to higher activity in the medial orbitofrontal cortex (mOFC), an
area of the brain that is widely thought to encode for actual
experienced pleasantne ss (6, 10–16). The results described below
are consistent with this hypothesis. We found that the reported
price of wines markedly affected reported EP and, more impor-
tantly, also modulated the blood-oxygen-level-dependent (BOLD)
signal in mOFC.
To investigate the impact of price on the neural computations
associated with EP, we scanned human subjects (n ⫽ 20) using
fMRI while they sampled different wines and an affectively neutral
control solution, which consisted of the main ionic components of
human saliva (17). We chose wine as a stimulus, because it is
relatively easy to administer inside the scanner using computerized
pumps, it induce s a pleasurable flavor sensation in most subjects,
and it varies widely in quality and retail price. Subjects were told
they were sampling five different Cabernet Sauvignons, that the
purpose of the experiment was to study the effect of degustation
time on perceived flavors, and that the different wines would be
identified by their retail prices (see Fig. 1A). Unbeknown to the
subjects, the critical manipulation was that there were only three
different wine s, and two of them (wines 1 and 2) were administered
twice, one identified at a high price and one at a low price. For
example, wine 2 was presented half of the time at $90, its retail price,
and half of the time at $10. Thus, the task consisted of six trial type s:
$5 wine (wine 1), $10 wine (wine 2), $35 wine (wine 3), $45 wine
(wine 1), $90 wine (wine 2), and neutral solution. The wines were
administered in random order, simultaneously with the appearance
of the price cue. Subjects were asked to focus on the flavor of the
wine during the degustation period and entered taste pleasantness
or taste intensity ratings in every other trial (Fig. 1B).
Results
Modulation of Reported Pleasantness and Taste Intensity by Price. We
measured the impact of price information on EP by comparing
the mean reported liking rating for wines 1 and 2 when admin-
istered at a high vs. a low price. We found significant differences
for both w ines (P ⬍ 0.001, Fig. 1C). In addition, reported
pleasantness was correlated with wine prices (r ⫽ 0.59, P ⬍
0.000). We could not find a similar behavioral ef fect for intensity
ratings (Fig. 1D). To explore further the role of prices on
Author contributions: H.P., J.O., B.S., and A.R. designed research; H.P. performed research;
H.P. analyzed data; and H.P., J.O., B.S., and A.R. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
‡
To whom correspondence should be addressed. E-mail: rangel@hss.caltech.edu.
This article contains supporting information online at www.pnas.org/cgi/content/full/
0706929105/DC1.
© 2008 by The National Academy of Sciences of the USA
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ex perienced pleasantness, we administered a follow-up behav-
ioral session 8 weeks after the main experiment, during which
wines were presented without price information. As expected, in
this case, there were no reported differences among the wines
(Fig. 1E). Interestingly, while the pleasantness ratings were
increasing in price in the main experiment al t ask (Fig. 1C), they
were not in the postscanning blind test (Fig. 1D). A potential
c oncern with these behavioral results is they might exhibit
‘‘ex perimenter demand’’ effects. In particular, some subjects
might deem it inappropriate to report to the experimenter that
a cheaper wine t astes better.
Modulation of Brain Correlates of Experienced Pleasantness by Price.
We analyzed brain imaging data using two different general linear
models [see Materials and Methods and supporting information (SI)
Text for details]. First, we looked for brain areas whose activity
increased with the price of wine. More concretely, we estimated the
BOLD response to each of the liquids at degustation and swallow-
ing and then analyzed the contrasts ‘‘high–low price’’ at degustation
separately for wines 1 and 2. Fig. 2 B and C describe the results of
this contrast for wine 1. We found increased activation in the left
mOFC and the left ventromedial prefrontal cortex (vmPFC).
Another cluster was found in a superior part of the vmPFC
adjoining the rostral anterior cingulate cortex (rACC). We also
found increased activation in the dorsolateral prefrontal cortex,
visual cortex, middle temporal gyrus, and cingulate gyrus (see SI
Table 2, upper). As shown in Fig. 2 E and F, the contrast generated
similar results for wine 2: increased activation was observed in the
bilateral mOFC, vmPFC, and rACC. In addition, for the wine 2, we
also found activation changes in the amygdala, lateral parts of the
OFC, dorsolateral prefrontal cortex, inferior and middle temporal
gyrus, and posterior cingulate cortex (see SI Table 3, upper).
A comparison of SI Tables 2, upper, and 3, lower,orofthe
relevant figures, suggests there might be small differences on the
areas of the medial prefrontal cortex activated by the two wine s. SI
Fig. 5 shows that, as the statistical threshold is lowered, these
differences disappear. To investigate this further, we performed a
conjunction analysis to identify areas in which brain activity was
higher on the high price condition for both wines. As shown in Fig.
3A, bilateral mOFC and adjoining rACC exhibited this pattern.
To investigate whether the increase in price had a differential
ef fect on the two wines, we performed an interaction analysis
(see Materials and Methods and SI Tex t for details). We found
that the effect of price on mOFC activity was higher for the
cheap $5 wine than for the expensive $90 wine. This suggests that
the effect of a price increase on mOFC activity might be larger
at low than at high prices.
Fig. 1. Experimental design and behavioral results. (A) Time course for a
typical trial. (B) Reported pleasantness and intensity rating scales. (C) Reported
pleasantness for the wines during the cued price trials. (D) Taste intensity
ratings for the wines during the cued price trials. (E) Reported pleasantness for
the wines obtained during a postexperimental session without price cues.
Fig. 2. The effect of price on each wine. (A) Wine 1: averaged time courses
in the medial OFC voxels shown in B (error bars denote standard errors). (B)
Wine 1: activity in the mOFC was higher for the high- ($45) than the low-price
condition ($5). Activation maps are shown at a threshold of P ⬍ 0.001 uncor-
rected and with an extend threshold of five voxels. (C) Wine 1: activity in the
vmPFC was also selected by the same contrast. (D) Wine 2: averaged time
courses in the medial OFC voxels shown in E.(E) Wine 2: activity in the mOFC
was higher for the high- ($90) than for the low-price condition ($10). (F) Wine
2: activity in the vmPFC was higher for the same contrast.
Fig. 3. The effect of price on both wines. (A) Conjunction analysis. Activity
in the mOFC/rACC was higher in the high- than in the low-price condition for
both wines 1 and 2. (B) Correlation of behavioral and BOLD responses (r ⫽ 0.49,
P ⬍ 0.001). Each point denotes an individual wine pair. The horizontal axis
measures the change in reported pleasantness between the high- and low-
price conditions. The vertical axis computes an analogous measure using the
betas from the general linear model in a 5-mm spherical volume surrounding
the area depicted in A.
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Discussion
The main hypothesis of this study was that an increase in the
perceived price of a wine should, through an increase in taste
ex pectations, increase activity in the mOFC. The results de-
scribed above provide evidence consistent with the hypothesis.
The hypothesis was motivated by several previous studies, which
have shown that activit y in the mOFC is correlated with behav-
ioral pleasantness ratings for odors (10–13), tastes (6, 14, and
15), and even music (16). This, together with our behavioral
results and the additional imaging results described below,
support the interpretation that, by modulating the activit y in the
mOFC, changes in the price of a wine might lead to a change in
the actual EP derived from its consumption.
We performed two additional analyses to provide further support
for this interpretation. First, for each individual and wine, we
computed the change in reported EP between the high and low
price conditions. We also computed the analogous difference in
parameter e stimate s for the BOLD response from the general
linear model in an area surrounding the mOFC. Fig. 3B shows that
the neural and behavioral estimates were positively and highly
correlated (r ⫽ 0.49, P ⬍ 0.001). Second, we verified that the results
of the previous literature also held in our study by estimating a
different general linear model and looking for brain regions whose
activity was correlated with reported EP from sampling the differ-
ent stimuli (see SI Text for details). The results replicated the
findings of previous studie s: activity in the mOFC was correlated
with absolute reports of pleasantne ss (Fig. 4).
Import antly, we did not find evidence for an effect of prices
on areas of the primary taste areas such as the insula cortex, the
ventroposterior medial nucleus of the thalamus, or the prabra-
chial nuclei of the pons. A natural interpretation is that the
top-down cognitive processes that encode the flavor ex pectan-
cies are integrated with the bottom-up sensory components of
the wine in the mOFC, thus modulating the hedon ic experience
of flavor, but that the flavor expectancies generated by the
change in prices do not impact more basic sensory representa-
tions. Interestingly, an analogous mechanism has been proposed
for pain placebo effects (7).
Our results have implications for several disciplines. First, the
EP signal plays a central role in neuroeconomics, because it
serves as a teaching signal that guides future behavior. Unfor-
tunately, very little is known about the factors that affect the
neural computation of this signal. A natural st arting hypothesis
is the economic view, which states that EP depends only on the
sensory properties of the item being c onsumed (i.e., its molec-
ular properties) and the state of the consumer. Our results
suggest that the brain might compute EP in a much more
sophisticated manner that involves integrating the actual sensory
properties of the substance being consumed with the expect a-
tions about how good it should be. It is important to emphasize
that it might be adaptive for the brain to do this. To make good
decisions in the future, the brain needs to carry out good
measurements of the quality of current experiences. In a world
of noisy measurements, the use of prior knowledge about the
qualit y of an experience provides additional valuable informa-
tion. A related study (13) provides additional supporting evi-
dence for this point by show ing that giving a c ognitive label to
an ambiguous odor (‘‘cheddar cheese’’ or ‘‘body odor’’) can
af fect both subjective pleasantness reports and neural activity
related to EP. Unlike the current paper, however, de Araujo et
al. (13) do not provide evidence that marketing actions, such as
pricing, can affect neural correlates of EP.
Sec ond, our findings also have implications for marketing.
Whereas there is ample behavioral evidence that various mar-
keting actions are successful in influencing the EP of individuals,
that they can modulate neural represent ations of this signal had
not been reported before. Furthermore, the neural findings also
provide some clues about the mechanisms involved. In particu-
lar, it seems that price changes modulate the representations of
ex perienced utility but not the encoding of the sensory proper-
ties of taste in the primary gustatory c ortex.
Third, our results have implications for economics. EP is an
import ant component of experienced utility, which is the econ-
omist’s term for subjective well being. We show that, contrary to
the standard economic view, EP depends on nonintrinsic prop-
erties of products, such as the price at which they are sold. It then
follows that marketing manipulations might affect subjective
perceptions of well being. This raises several dif ficult questions
for the field. Should the effect of prices on experienced utility be
c ounted as real ec onomic well being or as a mistake made by
individuals? To what extent are measurable differences in pref-
erences based on intrinsic dif ferences between products and
price ef fects we have identified? What happens to the efficiency
of competitive markets when firms can influence experienced
utilit y by changing the price of items?
A n important task for future research is to develop a more
c omplete characterization of the range of marketing actions that
can influence the neural computation of EP. We conjecture that
any action affecting expectations of product quality, such as
ex pert qualit y ratings; peer reviews; information about country
of origin, store, and brand names (especially those associated
with luxury products); and repeated exposure to advertisements
might lead to effects similar to those identified here.
Materials and Methods
Subjects. Twenty normal-weight subjects participated in the experiment (11
males, ages 21–30; mean age, 24.5 yr). One additional subject participated in
the experiment but was excluded from the analysis, because he reported
being confused about the task during a debriefing at the end of the experi-
ment. All subjects were right-handed and healthy; had normal or corrected-
to-normal vision and no history of alcohol abuse, psychiatric diagnoses, or
neurological or metabolic illnesses; and were not taking any medications that
interfere with the performance of fMRI. All subjects were screened for liking,
and at least occasionally drinking, red wine. At the beginning of each exper-
iment, subjects were required to show an official form of identification to
Fig. 4. Neural correlates of liking ratings. (A) Activity in the mOFC and the
midbrain correlated with the reported pleasantness of the six liquids at degus-
tation time. For illustration purposes, the contrast is shown both at P ⬍ 0.001 and
P ⬍ 0.005 uncorrected and with an extend threshold of five voxels. (B) Correlation
of pleasantness ratings and BOLD responses (r ⫽ 0.593, P ⬍ 0.000). Each point
denotes a subject-price pair. The horizontal axis measures the reported pleasant-
ness. The vertical axis computes the betas from the general linear model in a
5-mm spherical volume surrounding the area depicted in A.
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provide evidence they were ⬎21 yr old. Subjects were informed about the
experiment and gave written consent before participating. California Insti-
tute of Technology’s institutional review board approved the study.
Stimuli. During the course of the fMRI experiment, subjects sampled three
different Cabernet Sauvignon wines and an affectively neutral tasteless con-
trol solution that consisted of the main ionic components of human saliva (25
mM KCl and 2.5 mM NaHCO
3
). The wines were administered in random order
and simultaneously with the appearance of a price identifier. Two of the three
wines were administered twice, once identified by their actual retail price and
once by a 900% markup (wine 1: $5 real retail price, $45 fictitious price) or a
900% reduction (wine 2: $90 real retail price, $10 fictitious price). The third
wine was used as a distracter and was identified by its retail price (wine 3: $35).
We also carried out a follow-up behavioral tasting session 8 weeks after the
main experiment. In this postexperimental session, the wines were presented
without price information (see SI Table 1 for a complete description).
In each trial, 1 ml of each stimulus was delivered by a system of electronic
syringe pumps (one for each stimulus) positioned in the scanning control
room. These pumps transferred the stimuli to the subjects via ⬇10-m polyeth-
ylene plastic tubes (6.4-mm diameter) and a perfusion manifold. The perfusion
manifold allowed six incoming tubes to be connected to one output tube with
a minimum of dead space to avoid the mixing of the wines. The subjects were
instructed to hold the output tube between their lips like a straw while they
lay in a supine position in the scanner. We made an effort to keep the tubes
free of air bubbles to avoid wine oxygenation. Between experiments, the
wines were preserved with a special corking system, which is typically used by
wineries and wine shops to prevent oxidation.
The stimulus presentation and response recording was controlled by Co-
gent 2000 (Wellcome Department of Imaging Neuroscience) installed on a
computer positioned in the control room that also received trigger pulses
from the scanner to control timing. The visual stimuli were presented by using
video goggles (Resonance Technologies).
Task. The task consisted of six trial types: $5, $10, $35, $45, and $90 wines and
a neutral liquid. Each trial type occurred 16 times during the course of the
experiment, resulting on a total of 96 trials. Subjects were instructed to sample
the liquid on each trial while it was on their mouth (for a period of 10 s), to
evaluate its pleasantness during this time, and to swallow only when in-
structed. Between every wine administration, there was a rinse period in
which the neutral solution was delivered. The rinse period was implemented
to avoid taste spillovers across trials. Trials were separated by a random
intertrial interval drawn from a Poisson distribution with a mean of 10 s (see
Fig. 1A for a detailed description of the timing of each trial).
In every other trial, subjects were instructed to enter a rating of either
flavor pleasantness or taste intensity. Thus, a total of four pleasantness ratings
and four taste intensity ratings were sampled for each liquid. We used a
six-point rating scale (1 ⫽ do not like it at all/not intense at all; 6 ⫽ like it very
much/very intense) (see Fig. 1B). The timing of rating trials was identical to
nonrating trials, except that, after swallowing the liquid and before rinsing,
subjects were given6stoenter their ratings.
After an initial instruction period, subjects were trained on the use of the
response boxes to enter the ratings. Given the large number of potential
ratings, subjects entered ratings with both hands (three ratings for each
hand). The assignment of ratings to buttons was counterbalanced across
subjects to avoid motor artifacts.
Unbeknown to the subjects, the critical manipulation was that the $5 and $45
wines and the $10 and $90 wines were identical. This manipulation was not
revealed to the subjects. Instead, the subjects were told they would be sampling
five different Cabernet Sauvignons, that the purpose of the experiment was to
study the effect of sampling time on perceived flavors, and that the different
wines will be identified by their retail prices. Evidence for the success of our cover
story was that all subjects reported at the end of the experiment being able to
taste five different wines. Although the experiment contains an element of
deception, subjects were not debriefed after the experiment to avoid contami-
nating California Institute of Technology’s small subject pool.
Data Acquisition and Preprocessing. The brain imaging was conducted in a 3-T
Siemens Trio MRI scanner (Siemens). We acquired gradient echo T⫻2
weighted echoplanar images (EPI) with BOLD contrast and used a special
sequence designed to optimize functional sensitivity in orbitofrontal cortex
(18). This consisted of a tiled acquisition in an oblique orientation at 30° to the
AC-PC line. In addition, we used an eight-channel phased array coil that yields
a 40% signal increase in OFC over the standard head coil. The sequence
enabled 32 axial slices of 3-mm thickness and 3 mm in-plane resolution that
could be acquired with a TR of 2 s. A T1-weighted structural image was also
acquired for each subject. Functional imaging data were acquired in four
separate sessions of ⬇13 min each.
To detect transient head movements due to swallowing, we attached a
1.5-cm-long copper coil with a radius of 0.5 cm to the neck of each subject. The
setup was similar to those used by previous studies in which liquid food had been
administered (14). Small movements of the coil induced a current in the magnetic
field that could be detected after amplification using an EEG system positioned
in the scanner room (Biopac Systems). This produced a time series of events
reflecting transient larynx movement that was used in the general linear model
(GLM) in two ways. First, the time series of the signal detected by the coil was
added as an additional motion regressor in the GLM as regressor of no interest;
this was done to take out the variance due to swallowing induced head move-
ment. Second, swallowing events were either assigned to experimental condi-
tions or classified as noninstructed swallowing. The former were used to correct
the timing of swallowing onsets for each stimulus type that were entered in the
GLM as a regressor of interest (see SI Text for details about the models). The latter
were entered into the data analysis as a regressor of no interest.
fMRI data analysis was performed by using the Statistical Parametric Map-
ping software (SPM05; Wellcome Department of Imaging Neuroscience). We
applied the following preprocessing steps to the imagining data: (i) slice-
timing correction (centered at TR/2) (ii) realignment to the last volume, (iii)
spatial normalization to a standard T2* template with a resampled voxel size
of3mm
3
,(iv) spatial smoothing using a Gaussian kernel with full width at half
maximum of 8 mm, and (5) intensity normalization and high-pass temporal
filtering (filter width 128 s). The structural T1 images were coregistered to the
mean functional EPI images for each subject and normalized using parameters
derived from the EPI images.
fMRI Data Analysis 1: Influence of Price on Wine Sampling. We estimated a
general linear model in which the delivery of each of the six different liquids
was entered as a regressor of interest. In addition, the swallows of each liquid
type were also entered as separate regressors. Each of these regressors plus
additional regressors of no interests were convolved with a canonical hemo-
dynamic response function. We then calculated first-level single-subject con-
trasts to compare the administration of an identical wine at a high minus a low
price. Finally, for each of these first-level contrasts, we calculated a second-
level group contrast using a one-sample t test (see SI Text for details).
We also performed an interaction analysis to compare the effect of prices
on the two wines. We calculated a first-level single-subject contrast to com-
pare the administration of the low-quality wine at a high minus a low price
minus the administration of the high quality wine at a high minus a low price.
We also calculated a first-level single-subject contrast to compare the admin-
istration of the high-quality wine at a high minus a low price minus the
administration of the low-quality wine at a high minus a low price. For each
of these first-level contrasts, we calculated a second-level group contrast using
a one-sample t test (see SI Text for details).
fMRI Data Analysis 2: Neural Representation of Experienced Pleasantness. We
estimated a second general linear model in which the delivery and swallow of
all liquids (independently of type) were entered as regressors. In addition, we
entered the reported pleasantness and intensity ratings as parametric mod-
ulators for both regressors. We then computed first-level single-subject con-
trasts for the parametric modulators at both liquid sampling and swallowing
by reported EP and taste-intensity ratings. Finally, for each of these first-level
contrasts, we calculated a second-level group contrast using a one-sample t
test (see SI Text for details).
ACKNOWLEDGMENTS. We thank Vivian Valentin, Jan Glaescher, and Axel
Linder for their help with this project and Hackjin Kim, Sam Huang, and Shawn
Wagner for developing the coil we used to detect swallowing movement and for
providing support to process the swallowing signal. Financial support from the
National Science Foundation (Grant SES-0134618) is gratefully acknowledged.
This work was also supported by a grant from the Gordon and Betty Moore
Foundation to the California Institute of Technology Brain Imaging Center.
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1054
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www.pnas.org兾cgi兾doi兾10.1073兾pnas.0706929105 Plassmann et al.
