Neuromarketing: the hope and hype of neuroimaging in business
Dan Ariely and
Fuqua School of Business, Center for Cognitive Neuroscience, Department of Economics, and the
Department of Psychiatry and Behavioural Sciences, Duke University, Durham, North Carolina
2770, USA.
Gregory S. Berns
Department of Psychiatry and Behavioural Sciences, Economics Department, Center for
Neuropolicy, Emory University, Atlanta, Georgia 30322, USA.
Abstract
The application of neuroimaging methods to product marketing — neuromarketing — has recently
gained considerable popularity. We propose that there are two main reasons for this trend. First, the
possibility that neuroimaging will become cheaper and faster than other marketing methods; and
second, the hope that neuroimaging will provide marketers with information that is not obtainable
through conventional marketing methods. Although neuroimaging is unlikely to be cheaper than
other tools in the near future, there is growing evidence that it may provide hidden information about
the consumer experience. The most promising application of neuroimaging methods to marketing
may come before a product is even released — when it is just an idea being developed.
Despite many common beliefs about the inherently evil nature of marketing, the main objective
of marketing is to help match products with people. Marketing serves the dual goals of guiding
the design and presentation of products such that they are more compatible with consumer
preferences and facilitating the choice process for the consumer. Marketers achieve these goals
by providing product designers with information about what consumers value and want before
a product is created. After a product emerges on the marketplace, marketers attempt to
maximize sales by guiding the menu of offerings, choices, pricing, advertising and promotions.
In their attempts to provide these types of inputs, marketers use a range of market research
techniques, from focus groups and individual surveys to actual market tests — with many
approaches in between (see Supplementary information S1 (box)). In general, the simpler
approaches (focus groups and surveys) are easy and cheap to implement but they provide data
that can include biases, and are therefore seen as not very accurate1–4. The approaches that
are more complex and therefore harder to implement, such as market tests, provide more
accurate data but incur a higher cost, and the product, production and distribution systems have
to be in place for market tests to be conducted. There are some compromise approaches between
Correspondence to G.S.B. gberns@emory.edu.
Competing interests statement
The authors declare competing financial interests: see web version for details.
FURTHER INFORMATION
Dan Arieley’s homepage: http://www.predictablyirrational.com/
Gregory S. Berns’s homepage: http://www.neuropolicy.emory.edu/overview.html
Federal Election Commission: http://www.fec.gov/
SUPPLEMENTARY INFORMATION
See online article: S1 (box)
ALL LINKS ARE ACTIVE IN THE ONLINE PDF
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Published in final edited form as:
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these two extremes, which include simulated markets, conjoint analyses, markets for
information and incentive-compatible pricing studies (see Supplementary information S1
(box) ). As in all compromises, these approaches provide solutions with intermediate levels of
cost, simplicity, realism and quality of data (TABLE 1).
The incorporation of neuroimaging into the decision-making sciences — for example,
neuroeconomics — has spread to the realm of marketing. As a result, there are high hopes that
neuroimaging technology could solve some of the problems that marketers face. A prominent
hope is that neuroimaging will both streamline marketing processes and save money. Another
hope is that neuroimaging will reveal information about consumer preferences that is
unobtainable through conventional methods. Of course, with such high expectations, there is
the accompanying hype. Several popular books and articles have been published that push a
neuromarketing agenda, and there are now a handful of companies that market neuromarketing
itself5. In this Perspective, we aim to distinguish the legitimate hopes from the marketing hype.
As such, we hope that this article serves the dual purpose of recognizing the real potential of
neuro imaging in business and providing a guide for potential buyers and sellers of such
services.
Why use brain imaging for marketing?
Marketers are excited about brain imaging for two main reasons. First, marketers hope that
neuroimaging will provide a more efficient trade-off between costs and benefits. This hope is
based on the assumptions that people cannot fully articulate their preferences when asked to
express them explicitly, and that consumers’ brains contain hidden information about their true
preferences. Such hidden information could, in theory, be used to influence their buying
behaviour, so that the cost of performing neuroimaging studies would be outweighed by the
benefit of improved product design and increased sales. In theory, at least, brain imaging could
illuminate not only what people like, but also what they will buy.
Thus far, this approach to neuromarketing has focused on this post-design application, in
particular on measuring the effectiveness of advertising campaigns. The general approach has
been to show participants a product advertisement, either in the form of a print advertisement
or commercial, and measure the brain’s response in the form of a blood oxygenation leveldependent
(BOLD) measurement, which is taken as a proxy for neural activation.
The second reason why marketers are excited about brain imaging is that they hope it will
provide an accurate marketing research method that can be implemented even before a product
exists (FIG. 1). The assumption is that neuroimaging data would give a more accurate
indication of the underlying preferences than data from standard market research studies and
would remain insensitive to the types of biases that are often a hallmark of subjective
approaches to valuations. If this is indeed the case, product concepts could be tested rapidly,
and those that are not promising eliminated early in the process. This would allow more
efficient allocation of resources to develop only promising products.
Thus, the issue of whether neuroimaging can play a useful part in any aspect of marketing
depends on three fundamental questions, which we will address in this paper. First, can
neuromarketing reveal hidden information that is not apparent in other approaches? Second,
can neuromarketing provide a more efficient cost–benefit trade-off than other marketing
research approaches? Third, can neuromarketing provide early information about product
design?
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Revealing hidden information
Brain activity and preference measurement
Allowing for the assumption in neuromarketing that the brain contains hidden information
about preferences, it is reasonable to set aside, for the moment, the issue of ‘hidden’ and ask
what relationships are known to exist between brain activity and expressed (that is, not hidden)
preference.
As it turns out, different methods of eliciting a person’s preference often result in different
estimations of that preference3,4,6,7. This makes it difficult to know which method provides
the truest measure of ‘decision utility’ (that is, the expected utility, which would ultimately
drive choice in the marketplace). It is clear that market tests give the most accurate answer,
but having to run a market test on every product would defeat the purpose of market research
— namely, to provide early and cheap information. Similarly, we suspect (and economists are
certain) that methods that are incentive compatible are better than methods that are not.
Incentive-compatible elicitation methods are methods that encourage the participant to
truthfully reveal what is being asked of him because to do so would maximize the participant’s
satisfaction (for example, he would earn the most money or receive the product he likes the
best). In other words, it is in the participant’s interest to answer product-related questions
truthfully. However, using such methods is not always possible.
One important question for the potential of neuromarketing is whether the neural signal at the
time of, or slightly before, the decision (assumed to be a measure of decision utility) can be a
good predictor of the pleasure or reward at the time of consumption (the ‘experienced utility’)
8. A second question is whether the link between these two signals holds even when the
preference elicitation methods are not incentive compatible. If the answer to both of these
questions is positive, neuromarketing could become useful for measuring preferences.
Measurements such as willingness to pay (WTP) have only recently come under functional
MRI (fMRI) examination. In one experiment, subjects bid on the right to eat snacks during the
experiment. The amount they were willing to pay (a measure of decision utility) correlated
with activity levels in the medial orbitofrontal cortex (OFC) and prefrontal cortex (PFC)9,10.
Interestingly, similar activation in the OFC has been observed when subjects anticipate a
pleasant taste11, look at pretty faces12, hear pleasant music13, receive money14,15 and
experience a social reward16,17. Such generally close correspondence in regional brain activity
between the anticipation of rewarding events, the consumption of enjoyable goods and the
willingness to pay for them suggests that the representation of expected utility may rely, in
part, on the systems that evaluate the quality of the consumption experience. The theme of
common systems for expectation and experience also applies to things that are unpleasant or
even painful (although this involves a different network including the insula)18–21. Such
similarities suggest that neuroimaging can become a useful tool in measuring preferences,
particularly when incentive compatibility is important but there is no easy way to achieve it
(for example, when the products have not been created). However, such similarities do not
necessarily mean that brain activation is the same across different elicitation methods, and there
are differences between the neural activation representing decision utility and that representing
experienced utility14,22,23. This caveat aside, the generally close correspondence does
suggest that neural activity might be used as a proxy for WTP in situations in which WTP
cannot easily be determined — although this has yet to be demonstrated.
Reverse inference and reward
The practice of measuring an increase in BOLD activity in a region such as the ventral striatum
or OFC and then concluding that a ‘rewardrelated’ process was active has become increasingly
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common. This form of deductive reasoning is known as ‘reverse inference’24,25. Given the
readiness of many to interpret brain activation as evidence of a specific mental process, it is
worth examining this type of inference. Using a Bayesian analysis, it is possible to estimate
the specificity of activation in a particular region of the brain for a specific cognitive process.
For example, Poldrack used the BrainMap database to analyse the frequency of activation of
Broca’s area in language studies24. He found that activation of Broca’s area implied a Bayes
factor of 2.3 for language involvement, which means that taking brain activity into account
can make a small but significant improvement to one’s prior estimate of whether a language
process was involved.
Many studies have shown that striatal activity correlates with hedonic rating scales26.
Neuromarketers have been quick to invert this finding and use ventral striatal activity as an
indication that an individual likes something; but what is the evidence for this? Using
Poldrack’s method to analyse the BrainMap database, we estimated the posterior probability
for a reward process given the observation of nucleus accumbens (NAc) activation27. The prior
probability of engaging a reward-related process was assumed to be 0.5 (1:1 odds). According
to this estimation, based on the number of fMRI papers reported in the BrainMap database with
and without ‘reward’, and with and without NAc activation, NAc activation increases the
probability of a reward-related process taking place to 0.90 (odds 9:1). This yields a Bayes
factor of 9, which is considered moderate to strong evidence for a causal relationship (BOX
1). Although meaningful in a statistical sense, the assumptions behind such a calculation are
rather liberal and may suffer from a publication bias for positive results as well as differing
definitions of reward. In real-world settings, the ability to infer whether an individual likes
something based on NAc activation alone may be substantially less.
In the context of a product likeability experiment, Knutson et al. found significant correlations
between NAc activity and product preferences in college students28. However, in logistic
regression (R2) calculations aimed at predicting consumer choice, self-reported preferences
outperformed brain activation alone. Adding brain activation to a logistic model improved
predictions, but only slightly (increasing R2 from 0.528 to 0.533). Re-analysis with more
sophisticated machine-learning algorithms further improved the predictive value of brain
activation29.
Although some have argued for the existence of a “buy button” in the brain5, current evidence
suggests that the cognitive processes associated with purchase decisions are multi factorial and
cannot be reduced to a single area of activation. Conversely, a given brain region may be
involved in multiple cognitive processes. A recent review of value-based decision making
divided the process of making a choice into five categories: representation of the decision;
assignment of value to different actions; action selection; outcome evaluation; and
learning30. Even within this simplified framework, current data suggest that responses to
marketing efforts and consumer choices depend on an array of neurobiological processes, and
that no single brain region is responsible for a consumer choice. But is it possible that some
brain regions are more involved than others? Because the field of neuroeconomics grew out
of early brain-imaging studies of the neurobiology of reward31,32, most of the neuroeconomic
data are about valuation mechanisms and the associated responses of dopamine-rich brain
regions. The OFC and striatum have been consistently implicated in goal-directed action9,22,
33–35. It is also generally accepted that the insula has a key role in physiological arousal, which
is typically, although not exclusively, aversive in nature21. But because of the reverse inference
problem, using striatal and OFC activity as a read-out of ‘liking’ and the insula as a ‘disgustmeter’
is probably too simplistic to be of use in a real-life setting. In the context of
neuromarketing, the statistical power of these single-region correlations may be too low for
the correlations to be of use as predictors of consumption unless, perhaps, the neuroimaging
data is combined with other measures of preference.
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fMRI as a brain decoder
Given the limited power of reverse inference from single-region brain activations, more datadriven
methods for interpreting brain imaging data have been at the forefront of analysis
techniques. These techniques treat sites of brain activity agnostically — that is, without
reference to prior hypotheses. The primary assumption is that, regardless of how an individual’s
brain represents information, it does so consistently. The representations may be spatially
dispersed, and they may be distributed differently in different individuals, but they can still be
reliably detected through multi-voxel pattern analysis (MVPA). Because MVPA methods are
not reliant on the activation of a small subset of brain regions, they have substantially increased
sensitivity to detect activation36. A crucial advantage of MVPA techniques over approaches
in which activation in a particular brain region of interest is measured is that MVPA has the
statistical power to predict the individual choices of a subject. Because MVPA involves
statistical associations of complex activation patterns that occur when an individual choice is
being made, it does not depend on the vagaries of an experimenter interpreting the meaning of
an activation map. Some of the most impressive demonstrations of MVPA have been in
decoding visual responses to simple stimuli37–39 and subsequently, to watching films40, the
meanings of nouns41, event boundaries of written narratives42 and city navigation43,44.
Box 1 NAc activation in studies of tasks with and without reward
The BrainMap database was searched for functional MRI studies with and without a reward
task and with and without nucleus accumbens (NAc) activation. The NAc was defined as
a bilateral region of interest with vertices from MNI (Montreal Neurological Institute)
coordinates (−12, 0, −12) to (12, 12, 0). The frequencies that were obtained are shown in
the table below.
Assuming that the prior probability of engaging in a reward-related process is 0.5,
calculations showed that NAc activation increases the probability of a reward-related
process taking place to 0.90, yielding a Bayes factor of 9:
Probability of NAc activation given a reward task = 27/68 = 0.397
Probability of NAc activation given no reward task = 59/1283 = 0.046
Assuming the prior probability of reward = 0.5, then
Reward task No reward task
NAc activated 27 59
NAc not activated 41 1,224
It is possible, even likely, that such methods will soon be able to handle almost any
circumstance that can be created in an MRI environment. With increasing stimulus complexity,
simple interpretations of brain activation will become more difficult. However, for real-world
marketing applications, it may be more important to predict future behaviour than to understand
the ‘why’ of behaviour. Such a data-driven application of imaging (perhaps even lacking an
underlying theory) is analogous to identifying a genetic polymorphism associated with a
particular cancer without understanding what that gene does — which is likely to yield specific
but not general insights.
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Costs and benefits
As noted above, it is not yet clear whether neuroimaging provides better data than other
marketing methods (TABLE 1), but through the use of MVPA methods it might be possible
to reveal the ‘holy grail’ of hidden information. Assuming that this is the case, will using
expensive neuroimaging ultimately be more efficient than using cheaper methods? Typical
charges for scanning in a university research setting average about US$500 per hour. In a
commercial setting, these will be higher. However, actual scan charges account for a small
portion of the total cost, with personnel and overhead expenses accounting for at least 75% of
the costs of an imaging project. If neuromarketing is to compete with conventional marketing
approaches on the basis of efficiency, then the costs of labour and overheads will have to be
reduced.
One area in which the cost of neuroimaging can be compared with conventional marketing
approaches is in the post-design phase, the goal of which is to increase sales of an existing
product — for example, through advertisements and other types of framing effects. Early
neuromarketing studies therefore used imaging approaches to evaluate consumer responses to
advertisements. At this point, it is important to distinguish between neural responses to the
consumption of a product (that is, experienced utility) and neural responses to representations
of the product that may lead to future consumption. Only certain types of products can be
consumed in an MRI scanner. Therefore, much of the post-design neuromarketing literature
has focused on brain responses to visual representations of products, such as pictures28,45 or
advertisements for the product46–48; however, these advertisement studies, which used
magneto encephalography and electroencephal ography (BOX 2), did not link imaging data to
actual purchase decisions or other ratings, so it is not yet possible to determine the value of
this approach.
The role of expectations
It has long been known that the manner in which choices are presented can have a dramatic
effect on decisions49. This is where advertisements and product placement come into play. To
date, experiments have examined fairly simple choices and responses to things that can be
presented in an MRI scanner. Before neuroimaging can be used to predict consumer choice, a
greater understanding of the interplay between the decision maker, the elicitation method and
the decision context is needed.
BOLD responses are influenced by so-called ‘expectation’ effects, which include pricing
effects, biases in the way the choice is presented50 and placebo responses. This suggests that
neuromarketing could be helpful in identifying individual differences in consumer reactions
to different types of inputs. In a study of neural responses to sips of wine, medial OFC responses
were higher when subjects were told that the wine was expensive ($90 per bottle) versus
inexpensive ($5 per bottle)23. Activity in this region also correlated with self-report ratings of
how much participants liked the wine, even though all wines were actually the same. These
results suggest that the instantaneous experience of pleasure from a product — that is,
experienced utility — is influenced by pricing, and that this effect may be mediated by the
medial OFC9. This result parallels a similar, behavioural finding that the strength of the placebo
effect for analgesia is greater for more expensive ‘medications’51. Subjects’ expectations also
play an important part in how the experimenter should interpret striatal responses. Many studies
have shown that the reward-related signals in the ventral striatum and NAc can be more
accurately linked to prediction errors for reward than to reward itself22,52,53.
Placebo responses are an interesting aspect of neuromarketing. The mechanism of the placebo
response has been debated for decades54, but ultimately it can be considered an effect of
marketing (that is, the actions of a doctor, pharmaceutical company or experimenter). The
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neural correlates of the analgesic placebo effect are widespread but generally point to a
modulation of the cortical pain matrix in the brain55,56. Because consumers cannot consciously
report placebo effects, the demonstration of neural correlates of these effects suggests that
having access to hidden brain information could enable a marketer to measure the effectiveness
of a placebo marketing strategy in a particular individual. How well this type of information
generalizes to a larger population will determine the cost–benefit ratio of doing neuroimaging.
The aforementioned manipulations of expectations are simple and direct. For example, the
experimenter can manipulate a single dimension of expectation, such as price or descriptive
words (for example, “ultra” and “new and improved”), and measure the effect on the consumer
behaviourally and neurally. More cognitively complex forms of expectations can be created
through advertisements and commercials. Post-design applications of neuroimaging have, for
the most part, confirmed what was known about the behavioural effects of product placement,
which bypass the counter-arguments in which people naturally engage when facing
advertisements. The imaging studies confirm that there are neural correlates of exposure to
advertisements but do not directly suggest that maximizing activity in a particular brain region
results in more sales.
Box 2 Neuromarketing technologies
Functional MRI (fMRI)
The technique uses an MRI scanner to measure the blood oxygenation level-dependent
(BOLD) signal. The BOLD changes are generally correlated with the underlying synaptic
activity. Spatial resolution is 1–10 mm, and temporal resolution is 1–10 s. In general, the
higher the spatial resolution, the lower the temporal resolution. Of the three imaging
technologies described in this Box, fMRI has a substantial advantage in resolving small
structures and those that are deep in the brain. However, some important brain regions,
especially the orbitofrontal cortex, are affected by signal artefacts that may reduce the ability
to obtain useful information. State of the art MRI scanners cost approximately US$1 million
per Tesla and have annual operating costs of $100,000–$300,000.
Electroencephalography (EEG)
EEG uses electrodes applied to the scalp and measures changes in the electrical field in the
brain region underneath. EEG has very high temporal resolution (milliseconds) and can
therefore detect brief neuronal events. Because the skull disperses the electrical field, EEG
has low spatial resolution (~1 cm) that depends on how many electrodes are used. The
number of electrodes can be as few as two or range up to hundreds in high-density arrays.
The greater the number of electrodes, the better the spatial resolution. Apart from the low
spatial resolution, EEG has poor sensitivity for deep brain structures. Equipment costs can
be low (<$10,000) but increase with high-density arrays and the concomitant resources
needed to process the data. A common technique is to measure the left–right asymmetry of
the frontal EEG78. This is typically measured by the power in the alpha band (8–13 Hz).
This research has suggested that relatively greater activity in the left frontal region is
associated with either positive emotional states or the motivational drive to approach an
object79. Although there are strong correlations between frontal EEG asymmetry and
personality traits, the degree to which the asymmetry changes from moment to moment is
still debated. Some have suggested a minimum of 60 s to reliably estimate power
asymmetry80, in which case the temporal advantage of EEG over fMRI is lost. Although
some have used this approach to measure momentary fluctuations in emotion in response
to advertisements81, without accounting for autocorrelations in time or multiple statistical
comparisons, the validity of such approaches is dubious.
Magnetoencephalography (MEG)
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An expensive cousin of EEG, MEG measures changes in the magnetic fields induced by
neuronal activity. Thus, MEG has the same advantage of high temporal resolution and,
because the magnetic field is less distorted by the skull than is the electrical field, it has
better spatial resolution than EEG. Like EEG, MEG is most sensitive to superficial cortical
signals (primarily in the sulci). MEG requires a magnetically shielded room and
superconducting quantum interference detectors to measure the weak magnetic signals in
the brain. An MEG set-up costs approximately $2 million.
Transcranial magnetic stimulation (TMS)
TMS uses an iron core, often in the shape of a toroid wrapped in electrical wire, to create
a magnetic field strong enough to induce electrical currents in underlying neurons when
placed on the head82. TMS can be used as a single pulse, paired pulse or repetitive
stimulation, and the neuronal effects range from facilitation to inhibition of synaptic
transmission. As a research tool, TMS has been used to study the causal role of specific
brain regions in particular tasks by temporarily taking them ‘offline’.
Culture and advertising
Neuroimaging is often hyped as an exciting new tool for advertisers. Despite its enormous cost,
advertising effectiveness is a poorly understood area of marketing. Although advertising has
been investigated in a few neuroimaging studies57,58, it is still unknown whether
neuroimaging can prospectively reveal whether an advertisement will be effective. In a famous
Coke–Pepsi study, participants who described themselves as Coke drinkers showed significant
activation in the hippocampus and right dorsolateral PFC when they were cued about the
upcoming drink of Coke45. Self-described Pepsi drinkers did not have this response. In the
absence of brand information, there was no significant difference in preference during a taste
test. The study suggested that any differences in the response (behavioural and neural) to the
two brands must be culturally derived. One possibility is that brands achieve a life of their own
by becoming animate objects, sometimes with human attributes, in the minds of consumers.
However, one fMRI study that compared brain responses to persons and brands found that
activation patterns for brands differed from those for people — even for brands with which
subjects are identified — suggesting that brands are not perceived in the same way as
people59. Another possibility is that specific emotions can be elicited in response to
advertisements, although whether neuroimaging will help to reveal these emotions may
ultimately be limited by reverse inference constraints, especially if tied to specific regions.
The issue of how culturally derived identities become embedded in the brain is of great interest,
not only from a marketing perspective. Although neoclassical economic theory describes a
framework in which individuals assess costs and benefits during their decision-making
processes, it is clear that people base many decisions on sociocultural rules and identities. Some
are in a commercial context (for example, “I am a PC” or “I am a Mac”) but many are not (for
example, “I am a Democrat” or “I am a Republican”). These issues extend beyond the mundane
questions of advertisement effectiveness and raise the more profound question of how the
marketing of ideas affects decision making. But whether neuroimaging provides an efficient
tool to answer this question has yet to be shown.
Early product design
As the ability of neuroimaging to predict or influence post-design purchase decisions seems
to be limited (see above), neuroimaging may be better suited to gauging responses before
products are marketed. The primary reason is that neuroimaging may yield insights into the
product experience itself.
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Food products
Various food products and beverages have been administered in the MRI scanner, from simple
sugar solutions to chocolate, wine, sports drinks and colas. Beverages are particularly easy to
administer, with the usual route through a computercontrolled pump attached to a tube that
delivers controlled amounts of fluid into the participant’s mouth. The perception of flavour is
a multisensory integration process and thus provides several opportunities for neuroimaging
to disentangle a complex perception that subjects might not be able to articulate; taste, odour,
texture, appearance and even sound all contribute to the gustatory experience. These different
dimensions have been mapped onto distinct brain regions but with substantial overlap60,61.
The OFC is consistently linked to perceived pleasantness, whereas viscosity and fat content
seem to be represented in the insula62. The use of neuroimaging by commercial manufacturers
to design a more appealing food product is both feasible and likely. For this to work, however,
one would need to identify which dimension of gustation is to be studied (for example, taste,
odour or texture) and maximize a brain response to variations in that dimension.
Box 3 The ethics of neuromarketing
The introduction of neuroimaging into an environment in which the ultimate goal is to sell
more product to the consumer may raise ethical issues.
• Businesses will be able to read the minds of consumers. This concern is about
the privacy of thoughts. Can neuroimaging be used to gauge a person’s preferences
outside of the specific task being performed? Possibly. This concern may be
mitigated through transparency of purpose: subjects must know what kind of
endeavour they are helping, and their data should only be used for that purpose.
• Private versus public information about preferences. Individuals need to be able
to exercise control over what they choose to reveal about their personal
preferences. A privacy breach occurs if neuroimaging reveals a private preference
that is outside the scope of the neuromarketer’s research question.
• Information will be used to discriminate against individuals or exploit particular
neurological traits found in a subgroup of individuals. Neuroimaging data could
potentially target marketing to specific people or groups. Many people would find
this tactic repugnant because it exploits a biological ‘weakness’ that only exists in
some people. Similarly, this information could be used to time pricing moves to
capitalize on individual weaknesses that are known to coincide with particular
biological states (for example, raising beverage prices when someone is known to
be thirsty).
• Central versus peripheral routes of influence. A central route aims to influence
consumers’ preferences about the functional aspects of the product (for example,
fewer calories in a beer). A peripheral route attempts to manipulate preferences
through things that are peripherally related to the product (for example, sex appeal
of people in advertisements). Neuroimaging could potentially be used to enhance
both types of influence, but some consider the attempts to optimize the peripheral
route more ethically dubious.
• Brain responses obtained from a small group of subjects will be used to
generalize to a large population. Of course, this is done all the time in the scientific
literature. If neuromarketing data are used in product design and the product injures
someone, neuroimaging will be partly to blame.
• Abnormal findings. Approximately 1% of the population will have an abnormality
on their MRI83. In a population without clinical symptoms, the clinical significance
of an MRI abnormality is unknown. Many will be false positives; others will be
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real and require referral. Currently, there is no standard for how to handle these
situations. However, it is standard practice to have a written policy in place for
abnormal findings. Failure to do so opens both the neuromarketing firm and their
clients to medical liability.
• A lack of regulation. Traditional marketing methods, because they are not
typically viewed as experimentation, have not been subject to institutional review
board (IRB) oversight. MRI scans are approved by the US Food and Drug
Administration (FDA) for clinical use but, because no diagnosis is being made in
a marketing setting, there is the potential to circumvent both FDA and IRB
requirements. The burgeoning neuromarketing industry would be well advised to
adopt an industry standard of independent review. Clients should demand it.
• Management of perceptions. How will the public react when they discover that
neuroimaging has been used to design or market a product? The public’s response
to genetically modified food could provide an indication.
• Companies might not be primarily concerned with the best interests of the
consumer. Companies and consumers maintain complex relationships in which
some of their goals are compatible while others are in conflict. On the one hand,
companies seek to design, manufacture and sell products that consumers seek to
buy, resulting in compatible goals that benefit both parties. On the other hand,
companies also aim to maximize their short- or long-term profits, sometimes to
the detriment of their consumers. Much like marketing itself, understanding
consumer preferences can be used for goals that are in the best interests of both
the company and their consumers or for objectives that are in the interests of the
company and to the detriment of their consumers. Which approaches
neuromarketers choose is an open question.
The drawback to such an approach is the possibility of creating food products that are so highly
tuned to neural responses that individuals may over-eat and become obese (see BOX 3 for a
discussion of some ethical issues related to neuromarketing). Is it possible that such a
neuroimaging approach could create a ‘super-heroin of food’ — a product so delicious that all
but the most ascetic individuals would find it irresistible? It is an extreme but real possibility.
However, that does not mean that neuroimaging is necessarily problematic for food product
development. Indeed, the same techniques could be applied to making nutritious foods more
appealing.
Entertainment
As a typical big-budget Hollywood film costs over $100 million, with almost as much spent
on marketing, it would be surprising if film producers were not interested in using
neuroimaging to improve their product. After static images, films are probably the easiest
product to present in the scanner. Moreover, an fMRI measurement is time locked to the film
timeline. A film presents the same basic visual and auditory stimuli to everyone viewing it and
thus should serve as a cognitive synchronizer. Indeed, an fMRI study of subjects viewing a
segment of the classic Western The Good, the Bad and the Ugly40 showed that large extents
of the cortex responded similarly in time across subjects, suggesting that much of the cortical
response is essentially stereotypical. In another study, the ability to recall narrative content of
the TV sitcom Curb Your Enthusiasm three weeks later was correlated with the strength of
hippocampal and temporal lobe responses during viewing63.
Such stereotypical responses suggest that fMRI could be used during the editing process. For
example, different cuts of a movie could be measured against these cortical responses, which
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could then be used to select the final cut for release. Although it seems hopelessly complex to
interpret such brain responses, it may not be necessary if the only goal is to release the most
profitable movie. Provided there were a metric of quality (for example, box office returns or
test audience reports), brain activation patterns could be chosen to optimize outcomes without
any knowledge of what the patterns meant. Several neuromarketing companies have targeted
their efforts towards the entertainment industry but, as most of this work is unpublished, it is
difficult to evaluate the quality of the product. However, guidelines for general quality of
scientific work can be formulated based on two decades of neuroscience research. Thus,
without passing judgment on whether neuromarketing works, we can at least identify the items
to look for in a quality operation (BOX 4).
Box 4 What to look for when hiring a neuromarketing firm
We provide a list, which is by no means exhaustive, of what could be considered standard
practice in the application of neuroimaging methods in cognitive neuroscience and related
fields. It is based on standard criteria for reviewing research proposals and adapted to a
business setting.
• What is to be gained from neuroimaging? Good neuromarketers will begin by
discussing the pros and cons of the proposal in detail. For example: what will
neuroimaging yield over traditional methods? Ask for data about the predictive
value of neuroimaging findings in a real-world setting.
• What are the dependent and independent measurements? Assessing brain
activation is not generally useful without correlating it with some other
measurement. It is necessary to have another behavioural measurement to anchor
the interpretation of the brain activation. Be wary if someone claims to know what
a person thinks based solely on brain activation.
• How many subjects are needed? Apart from the simplest of tasks, any task invoking
a response that is expected to vary across individuals demands a sample size of at
least 30 (REF. 84). If groups of individuals are being compared under different
treatments or conditions, the sample size will need to be much greater to detect
differences between groups and between different treatments.
• What is the nature of the stimuli? Simple stimuli are the easiest to analyse. Realworld
images, as might appear in an advertisement, become difficult to
characterize unless one element at a time is varied. For statistical power, a
minimum of 10 repetitions within a stimulus category are required, although 20–
30 would be more likely to achieve meaningful results.
• What type of software will be used to analyse the neuroimaging data? Several
software packages exist, and although these programmes make neuroimaging
seem simple, it takes a minimum of 1 year of training to be able to use them and
3 years to become fully competent.
• How will motion correction be performed?
• Are conditions balanced in time? If not, how will subjects’ drifting attention be
compensated for?
• Is this a whole-brain analysis or is a specific part of the brain being examined?
These necessitate different thresholds of identifying activation. The chance of an
activation appearing somewhere in the brain is high due to random noise.
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• Will regions of interest be defined a priori? If so, what is the justification for this?
Conclusions based on activation of a single region will have relatively little
predictive power over conventional behavioural methods.
• If multi-voxel pattern analysis (MVPA) methods will be used, will they be
completely data-driven (principal component analysis or independent component
analyses) or will they be based on classifier training of subject responses (support
vector machine, relevance vector regression or Gaussian process regression)? How
will the resulting activity maps be interpreted?
• How robust are the results? Ask for a ‘bootstrap’ — for example, testing on a
‘fresh’ subsample of data.
• What type of scanner will be used? Either 1.5 or 3 Tesla scanners can yield images
of acceptable quality. Open MRIs do not have the field homogeneity or the gradient
technology necessary for fMRI. What quality control checks are performed to
make sure the scanner is operating optimally and consistently from day to day?
What steps will be taken to minimize signal artefacts in areas with poor signal?
Architecture
A growing number of neuro-scientists and architects have begun to consider the relationships
of the brain to the architectural experience64. The neuroscience of architecture could be
considered from two perspectives: first, the neural activity associated with seeing specific
aspects of a building; and second, the use of neural responses to guide the architectural design
process. Clearly, one would need to identify these neural responses before attempting to use
them in architectural design, but it is precisely the application in design that places
neuroimaging within the neuromarketing framework.
Virtual reality can provide a surprisingly accurate simulation of an architectural experience
and can be used in an MRI scanner. It has already been used to understand neural activation
during automobile driving65,66. In spatial navigation tasks such as driving, and presumably
navigating a building, the hippo campus has a key role. These early virtual reality experiments
suggested that the hippocampus is active when the subject makes navigation decisions but not
when they are externally cued65. Perhaps taking into account ‘hippocampal load’ may be a
useful tool in architectural design — for example, to make buildings easier to navigate.
Extending this idea by considering the neurobiological changes associated with ageing, it might
be possible to design buildings and retirement communities that mitigate the memory loss
associated with Alzheimer’s disease.
Political candidates
Finally, neuromarketing might be applied to perhaps the greatest marketing campaign of all:
politics. According to the Federal Election Commission (see Further information), the cost of
the 2008 US Presidential race was approximately $1.6 billion. It was also around that time that
neu-roimaging made its way into politics, perhaps most prominently in the form of a New York
Times op-ed piece67. Peer-reviewed studies have shown a complex pattern of activation in
response to statements about candidates; these patterns have been interpreted as evidence that
motivated reasoning involves activation in the ventromedial PFC, the anterior cingulate cortex,
the posterior cingulate cortex and the insula68. Subsequent studies have suggested that
activation of the medial PFC might be associated with maintaining a subject’s preference for
a candidate in response to advertisements, whereas activity in the lateral PFC might be
associated with changing candidates69.
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In marketing terms, the political candidates are the products that must be sold to the electorate.
Therefore, like other products, candidates and their campaigns have pre-and post-design
phases. Political marketing is aimed at selling an existing candidate but, with more foresight,
can also be used to ‘design’ a better candidate. The aforementioned neuroimaging studies have
focused on the post-design responses to advertisements for political candidates68,69.
Could neuroimaging also be used to design a candidate? Although potential nominees already
go through a ‘grooming’ process, it is worth examining this prospect. A candidate’s appearance,
trustworthiness and message content might determine a voter’s decision. Considerable
neuroimaging work has been done on the perception of human faces70 and features such as
facial symmetry, skin colour and attractiveness. Key brain structures in visual processing
include the fusiform face area for basic face processing71, the superior temporal sulcus for gaze
direction and intention and the NAc for attractiveness12. A recent study on the effect of political
candidates’ appearance found that insula activation in response to seeing a picture of a
candidate was associated with a greater likelihood of that candidate losing the election72. In
addition, dorsolateral PFC and anterior cingulate cortex activation occurred when subjects
viewed images of a candidate of a political party different from their own73. The neurobiology
of trust has also become quite popular to study with both fMRI and, more recently,
pharmacological manipulations74–76. These studies have found that different dimensions of
trust, such as reputation, fairness and uncertainty, correlate with activity in different brain
regions. Moreover, the hormone oxytocin affects human behaviour in various economic
exchanges that depend on social interactions77. Finally, a candidate’s message content could
be viewed as an experiential product. One could theoretically attempt to maximize striatal and
OFC responses to platform statements although, for the reasons stated above, this is not
necessarily predictive of success.
Conclusions and future directions
Neuromarketing has received considerable attention in both the scientific community and the
media. Although few scientific neuro marketing studies have been conducted, the existing
evidence suggests that neuroimaging could be used advantageously in several domains of
marketing. For a marketer, neuroimaging could be attractive because it might be cheaper and
faster than current marketing tools, and because it could provide hidden information about
products that would otherwise be unobtainable. We think it unlikely that neuroimaging will be
more cost-effective than traditional marketing tools, and so the first point is mostly hype.
However, continuing developments in analytical tools for neuroimaging data — for example,
MVPA — suggest that neuro imaging will soon be able to reveal hidden information about
consumer preferences. Although this information could boost post-design sales efforts, we
think that the real pay-off will come during the design process. Using fMRI data during design
could affect a wide range of products, including food, entertainment, buildings and political
candidates.
There are two sides to the use of such information. Product manufacturers could use neural
information to coerce the public into consuming products that they neither need nor want.
However, we hope that future uses of neuromarketing will help companies to identify new and
exciting products that people want and find useful. One example is a new trend in ‘user design’
in which companies allow consumers to participate, through the internet, in the design of new
products and by doing so create products that are more useful for the companies and for their
customers. Perhaps a next phase in user design is one that incorporates not only what consumers
express, but also what they think.
Finally, we return to the opening question: hope or hype? It is too early to tell but, optimists
as we are, we think that there is much that neuromarketing can contribute to the interface
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between people and businesses and in doing so foster a more human-compatible design of the
products around us. At the same time, neuromarketing as an enterprise runs the risk of quickly
becoming yesterday’s fad. Seasoned marketers still remember the hype around subliminal
advertising, which quickly faded and died despite the research interest that surrounded it (and
research on subliminal priming remains a large part of academic research in social psychology).
How can we make sure that neuromarketing will not suffer a similar fate? For one, the academic
community should take this topic seriously and not leave it to the neuromarketers and the oped
page of the New York Times. We should also ask deeper questions on how marketing works
— and not simply examine whether type X of advertising works better or worse than type Y.
If we take neuromarketing as the examination of the neural activities that underlie the daily
activities related to people, products and marketing, this could become a useful and interesting
path for academic research and at the same time provide useful inputs to marketers.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
This work was supported by grants to G.S.B. from the National Institute on Drug Abuse (R01DA016434 and
R01DA025045), the Office of Naval Research and Air Force Office of Scientific Research, and the National Science
Foundation (BCS0827313).
References
1. Beckwith NE, Lehmann DR. The importance of halo effects in multi-attribute attitude models. J. Mark.
Res 1975;12:265–275.
2. Day GS. The threats to marketing research. J. Mark. Res 1975;12:462–467.
3. Griffin A, Hauser JR. The voice of the customer. Mark. Sci 1993;12:1–27.
4. Green PE, Srinivasan V. Conjoint analysis in marketing: new developments with implications for
research and practice. J. Mark 1990;54:3–19.
5. Lindstrom, M. Buyology. Truth and Lies About Why We Buy. New York: Doubleday; 2008.
6. Hauser JR, Shugan SM. Intensity measures of consumer preference. Oper. Res 1980;28:278–320.
7. Buchanan B, Henderson PW. Assessing the bias of preference, detection, and identification measures
of discrimination ability in product design. Mark. Sci 1992;11:64–75.
8. Kahneman D, Wakker PP, Sarin R. Back to Bentham? Explorations of experienced utility. Q. J. Econ
1997;112:375–405.
9. Plassmann H. O’Doherty, J. & Rangel, A. Orbitofrontal cortex encodes willingness to pay in everyday
economic transactions. J. Neurosci 2007;27:9984–9988. [PubMed: 17855612]
10. Hare TA, Camerer CF, Rangel A. Self-control in decision-making involves modulation of the vmPFC
valuation system. Science 2009;324:646–648. [PubMed: 19407204]
11. O’Doherty JP, Deichmann R. Critchley, H. D. & Dolan, R. J. Neural responses during anticipation
of a primary taste reward. Neuron 2002;33:815–826. [PubMed: 11879657]
12. Aharon I, et al. Beautiful faces have variable reward value: fMRI and behavioral evidence. Neuron
2001;32:537–551. [PubMed: 11709163]
13. Zatorre RJ, Chen JL, Penhume VB. When the brain plays music: auditory-motor interactions in music
perception and production. Nature Rev. Neurosci 2007;8:547–558. [PubMed: 17585307]
14. Knutson B, Adams CM, Fong GW, Hommer D. Anticipation of increasing monetary reward
selectively recruits nucleus accumbens. J. Neurosci 2001;21:RC159. [PubMed: 11459880]
15. O’Doherty J, Kringelbach ML, Rolls ET, Hornak J, Andrews C. Abstract reward and punishment
representations in the human orbitofrontal cortex. Nature Neurosci 2001;4:95–102. [PubMed:
11135651]
Ariely and Berns Page 14
Nat Rev Neurosci. Author manuscript; available in PMC 2011 April 1.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
16. Izuma K, Saito DN, Sadato N. Processing of social and monetary rewards in the human striatum.
Neuron 2008;58:284–294. [PubMed: 18439412]
17. Rilling JK, et al. A neural basis for social cooperation. Neuron 2002;35:1–20. [PubMed: 12123599]
18. Ploghaus A, Becerra L, Borras C, Borsook D. Neural circuitry underlying pain modulation:
expectation, hypnosis, placebo. Trends Cogn. Sci 2003;7:197–200. [PubMed: 12757820]
19. Ploghaus A, et al. Dissociating pain from its anticipation in the human brain. Science 1999;284:1979–
1981. [PubMed: 10373114]
20. Koyama T, McHaffie JG, Laurienti PJ, Coghill RC. The subjective experience of pain: where
expectations become reality. Proc. Natl Acad. Sci. USA 2005;102:12950–12955. [PubMed:
16150703]
21. Craig AD. How do you feel? Interoception: the sense of the physiological condition of the body.
Nature Rev. Neurosci 2002;3:655–666. [PubMed: 12154366]
22. Hare TA, O’Doherty J, Camerer CF, Schultz W, Rangel A. Dissociating the role of the orbitofrontal
cortex and the striatum in the computation of goal values and prediction errors. J. Neurosci
2008;28:5623–5630. [PubMed: 18509023]
23. Plassmann H, O’Doherty J, Shiv B, Rangel A. Marketing actions can modulate neural representations
of experienced pleasantness. Proc. Natl Acad. Sci. USA 2008;105:1050–1054. [PubMed: 18195362]
24. Poldrack RA. Can. cognitive processes be inferred from neuroimaging data? Trends Cogn. Sci
2006;10:59–63. [PubMed: 16406760]
25. Poldrack RA. The role of fMRI in cognitive neuroscience: where do we stand? Curr. Opin. Neurobiol
2008;18:223–227. [PubMed: 18678252]
26. Delgado MR. Reward-related responses in the human striatum. Ann. NY Acad. Sci 2007;1104:70–
88. [PubMed: 17344522]
27. Fox PT, Lancaster JL. Mapping context and content: the BrainMap model. Nature Rev. Neurosci
2002;3:319–321. [PubMed: 11967563]
28. Knutson B, Rick S, Wimmer GE, Prelec D, Loewenstein G. Neural predictors of purchases. Neuron
2007;53:147–156. [PubMed: 17196537]
29. Grosenick L, Greer S, Knutson B. Interpretable classifiers for FMRI improve prediction of purchases.
IEEE Trans. Neural Syst. Rehabil. Eng 2008;16:539–548.
30. Rangel A, Camerer C, Montague PR. A framework for studying the neurobiology of value-based
decision making. Nature Rev. Neurosci 2008;9:545–556. [PubMed: 18545266]
31. Camerer C, Loewenstein G, Prelec D. Neuroeconomics: how neuroscience can inform economics. J.
Econ. Lit 2005;43:9–64.
32. Glimcher PW. Decisions, decisions, decisions: choosing a biological science of choice. Neuron
2002;36:323–332. [PubMed: 12383785]
33. Yin HH, Ostlund SB, Balleine BW. Rewardguided learning beyond dopamine in the nucleus
accumbens: the integrative functions of cortico-basal ganglia networks. Eur. J. Neurosci
2008;28:1437–1448. [PubMed: 18793321]
34. Padoa-Schioppa C, Assad JA. Neurons in the orbitofrontal cortex encode economic value. Nature
2006;441:223–226. [PubMed: 16633341]
35. Schoenbaum G, Roesch M. Orbitofrontal cortex, associative learning, and expectancies. Neuron
2005;47:633–636. [PubMed: 16129393]
36. Norman KA, Polyn SM, Detre GJ, Haxby JV. Beyond mind-reading: multi-voxel pattern analysis of
fMRI data. Trends Cogn. Sci 2006;10:424–430. [PubMed: 16899397]
37. Haynes JD, Rees G. Decoding mental states from activity in humans. Nature Rev. Neurosci
2006;7:523–534. [PubMed: 16791142]
38. Kamitani Y, Tong F. Decoding the visual and subjective contents of the human brain. Nature Neurosci
2005;8:679–685. [PubMed: 15852014]
39. Kay KN, Naselaris T, Prenger RJ, Gallant JL. Identifying natural images from human brain activity.
Nature 2008;452:352–356. [PubMed: 18322462]
40. Hasson U, Nir Y, Levy I, Fuhrmann G, Malach R. Intersubject synchronization of cortical activity
during natural vision. Science 2004;303:1634–1640. [PubMed: 15016991]
Ariely and Berns Page 15
Nat Rev Neurosci. Author manuscript; available in PMC 2011 April 1.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
41. Mitchell TM, et al. Predicting human brain activity associated with the meanings of nouns. Science
2008;320:1191–1195. [PubMed: 18511683]
42. Speer NK, Zacks JM, Reynolds JR. Human brain activity time-locked to narrative even boundaries.
Psychol. Sci 2007;18:449–455. [PubMed: 17576286]
43. Spiers HJ, Maguire EA. Spontaneous mentalizing during an interactive real world task: an fMRI
study. Neuropsychologia 2006;44:1674–1682. [PubMed: 16687157]
44. Spiers HJ, Maguire EA. Decoding human brain activity during real-world experiences. Trends Cogn.
Sci 2007;11:356–365. [PubMed: 17618161]
45. McClure SM, et al. Neural correlates of behavioral preference for culturally familiar drinks. Neuron
2004;44:379–387. [PubMed: 15473974]
46. Ambler T, Ioannides A, Rose S. Brands on the brain: neuro-images of advertising. Bus. Strategy Rev
2000;11:17–30.
47. Rossiter JR, Silberstein RB, Harris PG, Nield G. Brain-imaging detection of visual scene encoding
in long-term memory for TV commercials. J. Advert. Res 2001;41:13–22.
48. Astolfi L, et al. Neural basis for brain responses to TV commercials: a high-resolution EEG study.
IEEE Trans. Neural Syst. Rehabil. Eng 2008;16:522–531.
49. Tverksy A, Kahneman D. The framing of decisions and the psychology of choice. Science
1981;211:453–458. [PubMed: 7455683]
50. De Martino B, Kumaran D, Seymour B, Dolan RJ. Frames, biases, and rational decision-making in
the human brain. Science 2006;313:684–687. [PubMed: 16888142]
51. Waber RL, Shiv B, Carmon Z, Ariely D. Commercial features of placebo and therapeutic efficacy.
JAMA 2008;299:1016–1017. [PubMed: 18319411]
52. Montague PR, Berns GS. Neural economics and the biological substrates of valuation. Neuron
2002;36:265–284. [PubMed: 12383781]
53. Schultz W, Dayan P, Montague PR. A neural substrate of prediction and reward. Science
1997;275:1593–1599. [PubMed: 9054347]
54. Colloca L, Benedetti F. Placebos and painkillers: is mind as real as matter? Nature Rev. Neurosci
2005;6:545–552. [PubMed: 15995725]
55. Wager TD, et al. Placebo-induced changes in fMRI in the anticipation and experience of pain. Science
2004;303:1162–1167. [PubMed: 14976306]
56. Benedetti F, Mayberg HS, Wager TD, Stohler CS, Zubieta JK. Neurobiological mechanisms of the
placebo effect. J. Neurosci 2005;25:10390–10402. [PubMed: 16280578]
57. Kenning PH, Plassmann H. How neuroscience can inform consumer research. IEEE Trans. Neural
Syst. Rehabil. Eng 2008;16:532–538.
58. Lee N, Broderick AJ, Chamberlain L. What is neuromarketing? A discussion and agenda for future
research. Int. J. Psychophysiol 2007;63:199–204. [PubMed: 16769143]
59. Yoon C, Gutchess AH, Feinberg F, Polk TA. A functional magnetic resonance imaging study of
neural dissociations between brand and person judgments. J. Consum. Res 2006;33:31–40.
60. Small DM, Prescott J. Odor/taste integration and the perception of flavor. Exp. Brain Res
2005;166:345–357. [PubMed: 16028032]
61. Rolls ET. Brain mechanisms underlying flavour and appetite. Philos. Trans. R. Soc. Lond. B Biol.
Sci 2006;361:1123–1136. [PubMed: 16815796]
62. De Araujo IET, Rolls ET. The representation in the human brain of food texture and oral fat. J.
Neurosci 2004;24:3086–3093. [PubMed: 15044548]
63. Hasson U, Furman O, Clark D, Dudai Y, Davachi L. Enhanced intersubject correlations during movie
viewing correlate with successful episodic encoding. Neuron 2008;57:452–462. [PubMed:
18255037]
64. Eberhard JP. Applying neuroscience to architecture. Neuron 2009;62:753–756. [PubMed: 19555644]
65. Spiers HJ, Maguire EA. Neural substrates of driving behaviour. NeuroImage 2007;36:245–255.
[PubMed: 17412611]
66. Calhoun VD, et al. Different activation dynamics in multiple neural systems during simulated driving.
Hum. Brain Map 2002;16:158–167.
67. Freedman J. This is your brain on politics. New York Times. 2005 Jan 18;
Ariely and Berns Page 16
Nat Rev Neurosci. Author manuscript; available in PMC 2011 April 1.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
68. Westen D, Blagov PS, Harenski K, Kilts C, Hamann S. Neural bases of motivated reasoning: an FMRI
study of emotional constraints on partisan political judgment in the 2004 US Presidential election. J.
Cogn. Neurosci 2006;18:1947–1958. [PubMed: 17069484]
69. Kato J, et al. Neural correlates of attitude change following positive and negative advertisements.
Front. Behav. Neurosci 2009;3:6. [PubMed: 19503749]
70. Tsao DY, Livingstone MS. Mechanisms of face perception. Ann. Rev. Neurosci 2008;31:411–437.
[PubMed: 18558862]
71. Kanwisher N, Yovel G. The fusiform face area: a cortical region specialized for the perception of
faces. Philos. Transact. Roy. Soc. B Biol. Sci 2006;361:2109–2128.
72. Spezio ML, et al. A neural basis for the effect of candidate appearance on election outcomes. Soc.
Cogn. Affect. Neurosci 2008;3:344–352. [PubMed: 19015087]
73. Kaplan JT, Freedman J, Iacoboni M. Us versus them: political attitudes and party affiliation influence
neural response to faces of presidential candidates. Neuropsychologia 2007;45:55–64. [PubMed:
16764897]
74. Fehr E, Camerer C. Social neuroeconomics: the neural circuitry of social preferences. Trends Cogn.
Sci 2007;11:419–427. [PubMed: 17913566]
75. Kosfeld M, Heinrichs M, Zak PJ, Fischbacher U, Fehr E. Oxytocin increases trust in humans. Nature
2005;435:673–676. [PubMed: 15931222]
76. King-Casas B, et al. Getting to know you: reputation and trust in a two-person economic exchange.
Science 2005;308:78–83. [PubMed: 15802598]
77. Baumgartner T, Heinrichs M, Vonlanthen A, Fischbacher U, Fehr E. Oxytocin shapes the neural
circuitry of trust and trust adaptation in humans. Neuron 2008;58:639–650. [PubMed: 18498743]
78. Davidson RJ, Ekman P, Saron CD, Senulis JA, Friesen WV. Approach-withdrawal and cerebral
asymmetry: emotional expression and brain physiology I. J. Pers. Soc. Psychol 1990;58:330–341.
[PubMed: 2319445]
79. Harmon-Jones E. Clarifying the emotive functions of asymmetrical frontal cortical activity.
Psychophysiology 2003;40:838–848. [PubMed: 14986837]
80. Huster RJ, Stevens S, Gerlach AL, Rist F. A spectroanalytic approach to emotional responses evoked
through picture presentation. Psychophysiol 2008;72:212–216.
81. Ohme R, Reykowska D, Wiener D, Choromanska A. Analysis of neurophysiological reactions to
advertising stimuli by means of EEG and galvanic skin response measures. J. Neurosci. Psychol.
Econ 2009;2:21–31.
82. Kobayashi M, Pascual-Leone A. Transcranial magnetic stimulation in neurology. Lancet Neurol
2003;2:145–156. [PubMed: 12849236]
83. Illes J, et al. Incidental findings in brain imaging research. Science 2006;311:783–784. [PubMed:
16469905]
84. Thirion B, et al. Analysis of a large fMRI cohort: statistical and methodological issues for group
analyses. NeuroImage 2007;35:105–120. [PubMed: 17239619]
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Figure 1. Product development cycle
Neuromarketing applications of functional MRI (fMRI) can potentially enter into the product
development cycle in two places. In the first, fMRI can be used as part of the design process
itself. Here, neural responses could be used to refine the product before it is released. In the
second, fMRI can be used after the product is fully designed, typically to measure neural
responses as part of an advertising campaign to increase sales.
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NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
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Table 1
Comparison of selected marketing research approaches
Focus groups Preference
questionnaires
Simulated choice
methods
Market tests
What is measured Open-ended answers,
body language and
behaviour; not suitable for
statistical analysis
Importance weighting for
various product attributes
Choices among products Decision to buy and
choice among products
Type of response process Speculative, except when
used to assess prototypes
The respondent must try
to determine his decision
weightings through
introspection, then map
those weightings into the
response scale
A hypothetical choice,
so the same process as
the actual purchase —
but without monetary
consequences
An actual choice, with
customers’ own
money, and therefore fully
consequential
Typical use in new-
product
development processes
Early on to aid general
product design; at user
interface design for
usability studies
Design phase, when
determining customer
trade-offs is important
Design phase, when
determining customer
trade-offs is important;
may also be used as a
forecasting tool
End of process, to forecast
sales and measure
the response to other
elements of marketing,
such as price
Cost and competitive risk Low cost; risk comes only
from misuse of data by the
seller
Moderate cost and
some risk of alerting
competitors
Moderate cost (higher
if using prototypes
instead of descriptions)
and some risk of alerting
competitors
High cost and high risk of
alerting competitors, plus
the risk of the product
being reverse engineered
before launch
Technical skill required Moderation skills for
inside the group and
ethnographic skills for
observers and analysts
Questionnaire design and
statistical analysis
Experiment design
and statistical analysis
(including choice
modelling)
Running an instrumented
market and forecasting
(highly specialized)
Nat Rev Neurosci. Author manuscript; available in PMC 2011 April 1.