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The social and neural sciences share a common interest in understanding the mechanisms that underlie human behaviour. However, interactions between neuroscience and social science disciplines remain strikingly narrow and tenuous. We illustrate the scope and challenges for such interactions using the paradigmatic example of neuroeconomics. Using quantitative analyses of both its scientific literature and the social networks in its intellectual community, we show that neuroeconomics now reflects a true disciplinary integration, such that research topics and scientific communities with interdisciplinary span exert greater influence on the field. However, our analyses also reveal key structural and intellectual challenges in balancing the goals of neuroscience with those of the social sciences. To address these challenges, we offer a set of prescriptive recommendations for directing future research in neuroeconomics.
Neuroscience has been remarkably suc-
cessful in elucidating the mechanisms that
underlie human and animal behaviour. This
success has led to an explosion of interest in
translational research in which mechanisms
identified through basic science are brought
into direct clinical practice through connec-
tions between systems neurobiology1
and mental health2. Translations of neuro-
science research from the laboratory to the
wider society have been historically much
less frequent. However, in the past decade,
new scientific fields have arisen that apply
neuroscience to core questions in the social
sciences and humanities, including neuro-
marketing3, neuropolicy4, neuroethics5,
neuroesthetics6 and neuroeconomics7–12. In
this Perspective, we evaluate one of these
attempts, neuroeconomics, and its implica-
tions for guiding the integration of the
neural and social sciences.
Neuroeconomics comprises research on
the biological mechanisms of decision
making13,14. It combines concepts from
neuroscience, genetics, economics and
psychology, and seeks to identify general
mechanisms, from the response of single
neurons to the large-scale behaviour of
markets15. Early manifestos argued that the
goal of neuroeconomics was to draw a bio-
logically sound conception of rationality and
individual choice, two concepts at the core
of economic sciences16,17. Indeed, any shift
towards a more biological foundation would
reflect a radical turn for economics, as phys-
ics, rather than biology, has been the natural
science with the most influence on econom-
ics throughout most of the past century18,19.
Conversely, by introducing many of the core
methodological principles and models from
economics (potentially via the intermediary
of psychology) to neuroscience, neuroeco-
nomic research could lead to new interpreta-
tions for the mechanisms of decision making
studied by neuroscience14.
Here, we consider how neuroscience
data have influenced and should influence
economics, both through effects on research
communities and by effects on disciplinary
practices. First, we consider whether neuro-
economics has grown into an integrated
community. Social network analyses20
allow quantitative assessment of whether
its collaborations and publications span the
neural and social sciences or whether they
instead reflect juxtapositions without true
disciplinary coherence. Second, we examine,
on the basis of a textual analysis, whether
these collaborations stimulate the creation
of interdisciplinary topics and concepts and,
if so, how this research fits into the broader
relationships between economics and biol-
ogy. We conclude with suggested steps for
reducing discordance and increasing links
between the neural and social sciences.
An integrated community?
Neuroeconomics draws intellectual inspira-
tion from several subdisciplines of neuro-
science (such as systems neuroscience and
cognitive neuroscience) and from multiple
fields within the social sciences (including
behavioural economics, social psychology
and decision theory). Accordingly, its practi-
tioners exhibit a remarkable diversity in how
they form research groups and report results.
Such diversity could either catalyse progress
at the margins of disciplines or pose a
barrier for effective communication between
researchers depending on whether research-
ers tend to collaborate (and publish) across
traditional disciplines. To quantitatively
evaluate the intellectual connections within
the neuroeconomics field, we used sciento-
metric and online survey data to describe
how projects develop and scholars interact.
An elegant way to quantify and map
interactions between scientific disciplines is
to use recent graphical innovations in sci-
entometrics21–23. Following conventions in
the field (see REF.24 for example), scientific
disciplines can be organized on the basis of
the patterns of citations in scientific journals
(see Supplementary informationS1 (box) for
methods). Groupings of scientific journals
into disciplinary categories (as defined by
the scientific information provider Thomson
Reuters Web of Knowledge) can be repre-
sented as nodes in a network (FIG.1). For
instance, Nature Reviews Neuroscience, the
Journal of Neuroscience and 209 other jour-
nals are grouped in the ‘neurosciences’ subject
category. The strength of a link between two
nodes depends on their shared bibliographi-
cal references. As an example, the subject
Translating upwards: linking the
neural and social sciences via
Clement Levallois, John A.Clithero, Paul Wouters, Ale Smidts and Scott A.Huettel
Abstract | The social and neural sciences share a common interest in understanding
the mechanisms that underlie human behaviour. However, interactions between
neuroscience and social science disciplines remain strikingly narrow and tenuous.
We illustrate the scope and challenges for such interactions using the paradigmatic
example of neuroeconomics. Using quantitative analyses of both its scientific
literature and the social networks in its intellectual community, we show that
neuroeconomics now reflects a true disciplinary integration, such that research
topics and scientific communities with interdisciplinary span exert greater
influence on the field. However, our analyses also reveal key structural and
intellectual challenges in balancing the goals of neuroscience with those of the
social sciences. To address these challenges, we offer a set of prescriptive
recommendations for directing future research in neuroeconomics.
Nature Reviews Neuroscience
AOP, published online 4 October 2012; doi:10.1038/nrn3354
© 2012 Macmillan Publishers Limited. All rights reserved
Nature Reviews | Neuroscience
Health and
social issues
Environmental science
and technology
Economics, politics
and geography
Economics, politics
and geography
Biomedical sciences
Biomedical sciences
Business and
Business and
Neurosciences (211 journals, example: Nature Rev. Neurosci.)
Economics (191 journals, example: Q. J. Econ.)
Multidisciplinary sciences (50 journals, examples: Nature, Science, PNAS)
Behavioural sciences (45 journals, example: Behav. Brain Sci.)
Biology (general) (71 journals, example: PloS Biol.)
categories ‘neurosciences’ and ‘neuroimag-
ing’ are strongly connected, as the papers
published in journals in these disciplines fre-
quently cite the same bibliographical sources.
The resulting map — the spatial layout of
which minimizes the distance between related
nodes — reveals a clear structure: social sci-
ences are strongly connected to psychology,
which in turn is close to cognitive science
and the biomedical sciences. The physical and
material sciences are at the other end of the
spectrum and connect with the biomedical
sciences via chemistry24 (FIG.1a).
The positioning of neuroeconomics on
this map can be viewed by scaling the size
of each node according to the number of
neuroeconomics papers published in the
corresponding subject category (FIG.1b). The
resulting scientific landscape exhibits a range
of disparate peaks. Here, neuroeconomics
spans three main disciplinary domains —
cognitive sciences’, ‘economics’ and ‘bio-
medical sciences’ — and has a substantial
presence in two others (‘psychology’ and
‘business and management’). This diversity
of coverage is exceptional, as even interdisci-
plinary fields usually publish predominantly
in a restricted geographic region within
these scientific maps (see Supplementary
informationS2 (box) and S3 (figure) for a
comparison with two other interdisciplinary
fields: evolutionary economics, and social
and affective neuroscience)25. By itself, the
map provides evidence that neuroeconomics
research has permeated a range of scientific
disciplines, which provides favourable
conditions for the evolution of an inter-
disciplinary community.
But does interdisciplinary publication
necessitate an interdisciplinary community?
To address this second question, we used
network analyses based on a survey of 820
individuals to characterize the social con-
nections between neuroeconomists from
different academic backgrounds (BOX1).
Our survey indicated that many research-
ers in neuroeconomics have developed
connections to scholars from disciplines
other than their own. These collaborations
with a range of social scientists — not only
psychologists — are a relatively new feature
within neuroscience, as many of them are
only a few years old. Importantly, diversity
within research groups is greatest for groups
positioned near the centre of the network of
communications (FIG.2). This result can also
be appreciated on the visual display of the
network of communications (BOX1), in which
communities of researchers with a balanced
disciplinary composition occupy a central
position. This suggests that intellectually
diverse groups are engaged in more partner-
ships and reach a wider audience, possibly
leading to more influential research.
Thus, over the past decade, neuro-
economics has developed into an integrated
research community that spans a number of
traditional disciplines. However, as the size
of the field has grown, the relative usage of
the term ’neuroeconomics’ is decreasing in
favour of the term ‘decision neuroscience’
© 2012 Macmillan Publishers Limited. All rights reserved
(FIG.3 and Supplementary informationS4
(figure)). What could have caused this shift?
One possibility is that there has been asym-
metric influence across the disciplines; for
example, paradigms from economics may
inform neuroscience research, but data col-
lected in neuroscience may have little direct
relevance for research in economics and
the other social sciences26,27. Translating
behavioural findings downwards to shape
basic biological inquiry may be more practi-
cal than translating basic science findings
upwards to complex societal issues. Thus,
successful translations downwards (‘decision
neuroscience’) may simply be more frequent
than successful translations upwards into the
social sciences (‘neuroeconomics’). In the
following section, we consider what sorts of
inferences both the neural and social sciences
draw from neuroeconomics research.
An integrated literature?
The analyses in the previous section indicate
that neuroeconomics has come together as
an integrated research community, one that
draws scholars from the breadth of the social
and natural sciences. But does its research
output reflect a similar integration of these
various disciplines? Or do concepts from
particular disciplines dominate the existing
Mapping concepts in neuroeconomics. To
obtain a quantitative picture of the state of
the neuroeconomics literature, we applied
semantic network analytic methods to a com-
prehensive corpus of scientific articles in the
discipline. From an initial set of 27 review
articles surveying the neuroeconomics
literature (Supplementary informationS5
(box) and S6 (table)), we extracted every
article that was cited in at least two of those
reviews, leading to 259 unique references.
(Of note, the distribution of citations was
highly skewed, such that only 15 articles
were cited in more than one-third of those
reviews.) We used natural language processing
techniques to extract frequently mentioned
concept terms from the abstracts of those
259 references. The degree of connectivity
between two concepts was calculated on
the basis of the number of co-occurrences
of those terms within individual abstracts.
We created a semantic map that illustrates
the intrinsic structure of the neuroeconom-
ics literature based on the conventions that
more frequently occurring terms are
represented in larger size and connected
terms are depicted closely together (FIG.4).
Several features of this concept map
are apparent. First, there is considerable
intermixing of terms with origins that lie in
different disciplines. For example, the bot-
tom portion of the graph (green) contains
intercalated concepts from neuroscience
(for example, striatum or dopaminergic),
economics (for example, expected value or
incentive) and psychology (for example,
motivational). Thus, there is substantial local
heterogeneity within the neuroeconomics
literature, mirroring the heterogeneity of
research groups at central positions in the
network of neuroeconomists. Second, there
are several large clusters that correspond
to major topics of research. One cluster
(right middle and bottom) comprises terms
associated with reward evaluation and the
brain’s dopamine system, reflecting a clear
example of the integration of behavioural
modelling and neuroscience data28,29. Two
other clusters broadly contain terms associ-
ated with strategic decision processes (left
top, for example, cooperation and game)
and terms associated with emotional and
affective processes (left middle, for example,
arousal and insula), respectively. Third, there
is a notable method-based grouping, such
that terms associated with primate electro-
physiology tend to cluster with measures of
behaviour (for example, eye movement) and
neural targets for recording (for example,
dorsolateral prefrontal cortex). Last, there is
an intriguing large-scale structure that sug-
gests a progression from basic neurobiologi-
cal notions at the bottom right (for example,
caudate and ventral striatum) to more com-
plex economic and psychological concepts
at the upper left (for example, fairness and
economic decision). This ‘brain-to-function
axis’ indicates that despite the local het-
erogeneity mentioned previously, the neu-
roeconomics literature evinces stronger
connections between concepts originating
in the same discipline than between
concepts that cross disciplines.
Has neuroscience influenced economics?
Economists and biologists have a history
of dialogue and exchange dating at least
from Darwin’s borrowing of the principle
of population growth from Malthus30. In
return, Darwinian evolutionary theory and
population genetics have had a pervasive
influence on the theorizing of the behaviour
of the firm and the dynamics of economic
systems19. The contacts between
economics and neuroscience are relatively
more recent and their influence on the
general economic literature less clear.
We examined the content of all abstracts
published over a recent 10-year period in
57 economics journals of general interest
(see Supplementary informationS7 (box),
S8 (box) and S9 (figure)). A total of 222
articles considered concepts from neurosci-
ence, genetics or other biological sciences.
Analysis of the content of their abstracts
revealed a striking disconnect with the
neuroeconomics literature; although the
222 articles considered topics such as ‘the
genetic basis’ of family- or health-related
phenomena (for example, genetic, twin, birth
weight, intergenerational and so on) or the
evolutionary character of strategic behaviour
(for example, evolutionarily stable, adaptive,
game and so on), few articles discussed neu-
roscience (see Supplementary informationS9
(figure)). Even in those economics articles
that included terms from neuroscience (for
example, dopamine, ventral, neural and so
on), these terms were tightly grouped in a
cluster, despite their demonstrated
relevance in a range of other topics (such as
Figure 1 | The disciplinary connectivity of
neuroeconomics research. Relationships
between scientific disciplines are depicted in a
network based on patterns of citations in scien-
tific journals (Supplementary informationS1
(box)). a | Each node represents one subject cat-
egory (categories as defined by the scientific
information provider Thomson Reuters Web of
Knowledge), and related subject categories are
grouped into broader scientific disciplines (each
shown in a different colour). For example, the dis-
cipline ‘cognitive sciences’ (shown in brown) con-
tains, among others, the subject categories
‘neurosciences’ and ‘neuroimaging’. Two subject
categories are linked if their journals make fre-
quent references to the same bibliographical
sources. For instance, the subject categories ‘neu-
rosciences’ and ‘neuroimaging’ are connected, as
the papers published in their journals frequently
cite the same bibliographical sources. The topol-
ogy of the map shows the overall organization of
scientific publication as a curved surface, such
that broad scientific fields (such as ‘infectious dis-
eases’ and ‘engineering’, which are shown in light
red and light blue, respectively) form an axis that
ranges from ‘economics, politics and geography’
(upper left) to ‘physics’ and ‘materials sciences’
(upper right). b | We rescaled each node (that is,
each subject category) shown in part a according to
the number of neuro-economics articles published
in the journals within that subject category (see
Supplementary informationS1 (box)). The smallest
circles reflect 0 publications in neuroeconomics.
Although most scientific disciplines comprise pub-
lications in a small number of neighbouring subject
categories25 (see Supplementary informationS2
(box) and S3 (figure) for a comparison of neuroe-
conomics with two other interdisciplinary fields),
this map shows that neuroeconomics articles are
published in a large number of journals with a
relatively heterogeneous set of subject categories
both from life and social sciences (data are from
© 2012 Macmillan Publishers Limited. All rights reserved
Nature Reviews | Neuroscience
game-theoretic approaches to behaviour31,32).
In short, there is little evidence
yet that neuroscience research — apart
from review or opinion pieces — has
penetrated the mainstream economics
Does this discordance between the
neuroeconomics and economics litera-
tures reflect a fundamental incompatibility
between the two fields? Some differences
between these literatures do reflect disparate
intellectual goals. The economics litera-
ture often evaluates the consequences of
decisions made in real-world contexts and
over long time horizons, such as in the case
of research on the effects of socioeconomic
status on decisions about health care or in
labour markets33. Experimental analysis of
such factors is beyond the current scope
of neuroeconomics, which typically explores
decisions in controlled laboratory settings
and about small-scale personal rewards34.
However, other topics are central to both
disciplines, such as risk preferences,
temporal discounting, social interactions and
strategic choice. Indeed, the economics
literature has considered these topics within
both experimental and theoretical research,
linking them to concepts drawn from evo-
lution and sociobiology35,36. Thus, the car-
dinal challenge for neuroeconomics is not
to convince economists (and other social
scientists) that biological findings can be
relevant for their work, but to demonstrate
that neuroscience can make unique and dis-
tinctive contributions to the understanding
of economic behaviour14,15. In the follow-
ing section, we recommend specific steps
towards thisgoal.
Box 1 | Social networks in neuroeconomics
Social network analysis provides a framework for the
identification and assessment of formal and informal
interpersonal networks. Although network analysis has been
used before to study social groups within science93–95, a direct
survey of the social network of an entire field has not been
attempted before. We contacted, via e‑mail, all scientists who
had co‑authored at least one publication mentioning
‘neuroeconomics’ in the title, abstract or keywords
(Supplementary informationS10 (box)). Within this survey, we
asked participants for the names of colleagues with whom
they discussed their research in neuroeconomics. This question
provided a total of 820 unique names; for each, current
organizations, research activities and training discipline were
identified via manual Internet searches. For the resulting
subset of 313 individuals who expressed neuroeconomics
among their interests, the interconnections were mapped and
quantitatively evaluated (see the figure, part a, which shows
the pairwise connections between scientists — each scientist
is shown as a single node whose size reflects the number of
connecting links). Of note, the resulting community is very
cohesive, with one connected component comprising 82% of
the neuroeconomists and 98% of the interconnections in the
network. This means that despite geographical distance and
differences in academic affiliations, neuroeconomists
interact through an inclusive and disciplinarily diverse
conversation network.
However, large‑scale heterogeneity could mask small‑scale
homogeneity if individual neuroeconomic research groups
are biased towards one disciplinary background or another.
Using an algorithm for non‑supervised community detection
in networks96, we found 47 distinct communities in the
network of neuroeconomists (groups are shown by arbitrary
colours in the figure, part a). Not surprisingly, these
communities tend to correspond to key laboratories or
centres at major institutions. For each community, we
assessed whether its disciplinary composition is balanced, in
the sense that no one discipline provides a majority of its
members. Some communities primarily consist of individuals
from the neural sciences (including non‑neuroscience
biologists), from economics or from psychology (see the
figure, part b, in which these are shown in green, red and
yellow, respectively), but a large proportion (62%) of the 47
communities have a balanced composition between the
three disciplines (shown in turquoise in the figure, part b).
Moreover, balanced communities tend to occupy more
central positions in the network, as can be observed visually
(see the figure, part b) and evaluated statistically with
centrality measures (FIG.2 and Supplementary
informationS11 (box)).
© 2012 Macmillan Publishers Limited. All rights reserved
Nature Reviews | Neuroscience
0.5 1.0
Size examples
Connecting the neural and social sciences
Neuroeconomics has overcome some of
the challenges that are typical to interdisci-
plinary research — such as how to develop
interdisciplinary courses, how to withstand
waves of scepticism and how to create a
social group of scientists with varied
disciplinary backgrounds — to function as a
burgeoning interface between neuroscience
and economics. Interdisciplinary neuro-
economics communities can now serve as
fertile ground for training students at the
boundaries of disciplines. Indeed, although
extensive training programmes are still in
development, some researchers now work
as neuroscientists in business schools or as
economists within cognitive neuroscience
groups. The hope is that these individu-
als operate as channels for communication
between disciplines, both within their
institution and in the largerfields.
However, some obstacles continue to
hamper connections between neuroscience
and economics. For example, the research
cultures of neuroscience and economics can
seem foreign and inscrutable from the other’s
perspective. Neuroscientists are incentivized
to work in large collaborative teams, to
continually seek extramural funding and to
prioritize research directions that rapidly lead
to high-impact work, whereas economists
operate within a different model for research,
funding and mentorship. Economics journals
operate on a different timescale (for exam-
ple, articles and review cycles are longer),
collaborations are usually on a smaller scale
(for example, fewer co-authors on articles)
and research directions may be disconnected
from the goals of funding agencies. Given the
size and history of both disciplines, neuro-
economics is unlikely to completely
overcome these differences.
How then should future research in neu-
roeconomics go about building connections
to economics and other social sciences? On
the basis of the results from our analyses
in the previous sections — including the
evidence that neuroeconomics has been
effective at growing local interdisciplinary
communities — we argue that neuroeco-
nomics can be a dedicated communication
channel between disciplines. By allowing
researchers to trade concepts across disci-
plines and develop new hypotheses within
disciplines, neuroeconomics can improve
the match between the demand for bio-
logical concepts in economics and what
economic models can offer to neuroscience.
Below, we indicate four approaches by which
neuroeconomics can improve communica-
tion between neuroscience and economics.
Targeting general neural mechanisms.
Neuroscience investigations of how the brain
computes preferences and makes choices
ultimately aim to identify the fundamental
neural mechanisms underlying these pro-
cesses. From the perspective of the social
sciences, economics stands in a similar posi-
tion. From small sets of axioms, it creates
models of multiple facets of decision mak-
ing — from analyses of investment decisions
to studies in consumer preferences — that
share fundamental economic principles.
Unfortunately, the fundamental elements
that are identified separately by neuroscience
and economics are not easily comparable27,
making it difficult for the two disciplines to
adopt common hypotheses. Here, neuroeco-
nomics can develop the conceptual interface
needed to help neuroscience and economics
identify general principles of behaviour.
Substantial progress has been made in
this direction with the detailed description
of the neural circuits involved in value-based
choice15,37,38. With a fully fledged correspond-
ence between economic and neurobiological
descriptions of decision making, each disci-
pline could in principle appeal to the results
obtained in the other. Economists would be
able to prune their models based on the neu-
roscientific plausibility of their underlying
hypotheses. (For example, should valuation
models systematically include a reference
point? Neuroeconomics points in this direc-
tion15.) New hypotheses could be generated,
such as testing whether two previously
independently studied types of economic
behaviour (for example, moral behaviour
and consumer spending) are related, con-
sidering that the same well-specified neural
mechanisms (including cognitive control)
are involved in both. (Note that similar pro-
posals have been made for understanding
clinical conditions39.)
Neuroscience would also benefit if its
models of decision making could be related
to economic models of decision making.
Shifting contexts (such as making decisions
for oneself versus for a friend) can change
the extent to which decision makers rely
on particular sorts of neural computations
(for example, working memory versus emo-
tion). Accounting for the possibility of such
a cognitive shift — via some parameter in a
model — can improve the ability to isolate
the contributions of particular neural pro-
cesses to an observed behaviour. In addition
to choice context, neuroscience routinely
studies conditions that alter basic informa-
tion processing in the brain (for example,
state manipulations). Differences in infor-
mation processing have been found with
pharmacological studies40, manipulations
such as sleep deprivation41, or comparisons
of different age groups42,43. Identification of
a general neural mechanism of decision
making requires an understanding of the
limits of such adaptive flexibility and a
clear specification of the decision process,
both of which can be provided by studies in
Increasing the focus on individual differ-
ences. How individuals make decisions has
long been a central question within econom-
ics. However, few economic models provide
insight into the way in which choices are
affected by individual differences in person-
ality, cognitive abilities or other factors, let
alone into how such individual differences
arise or are distributed throughout a popula-
tion. Neuroscience, by contrast, provides
a wealth of tools for assessing differences
among individuals. Differences in brain struc-
ture (for example, local grey matter density
and white matter tractography) can predict
individual differences in cognitive function44,
Figure 2 | Interdisciplinary research groups
occupy more central positions in the commu-
nity of neuroeconomists. For each cluster in
the social network of neuroeconomists (BOX1),
we calculated a diversity index (x-axis of graph),
with values of 0 representing a cluster with only
social scientists, 1 representing a cluster with
only biological scientists, and 0.5 representing a
completely balanced cluster. We next calculated
the betweenness centrality for every cluster
in the network; essentially, this provides a meas-
ure of the frequency with which indirect connec-
tions between any two clusters pass through that
cluster (so that clusters lying between many
other clusters score highly on this measure). We
found that clusters with balanced disciplinary
composition (x-axis) tend to occupy more central
places (y-axis) in the discipline.
© 2012 Macmillan Publishers Limited. All rights reserved
Nature Reviews | Neuroscience
PubMed hits for ‘neuroeconomic*’
PubMed hits for ‘decision neuroscience’
5 10 15 20 25 30 35 40
which in turn may predict real-world behav-
iour. Moreover, functional responses of the
brain have been linked to a host of personal-
ity factors45–47 and to specific choice biases48.
In addition, genetic differences may explain
some of the inter-individual variability in eco-
nomic preferences49, as several gene variants
that associate with differences in brain struc-
ture and function have been identified44,49.
As shown by these examples, neuroscience
research could enable the characterization of
lower-level individual differences in neural
computations that may be linked to inter-
individual variability in economic behaviour.
An increased focus on individual
differences in neuroeconomics would also
pair well with modelling work in cognitive
psychology50–52. Consider self-control issues,
which have been modelled extensively in
both economics53,54 and neuroscience55–58. A
demographic factor such as socioeconomic
status may influence self-control through a
number of pathways. For example, low soci-
oeconomic status has been associated with
poor nutrition and increased stress during
early life59, and these conditions are known
to impair brain development in childhood60,
which in turn may affect prefrontal cortex
function in adulthood61. As different parts
of the prefrontal cortex contribute to various
computations involved in self-control55,62,63,
socioeconomic status may have latent effects
on how people express preferences and make
decisions. Thus, expansion of decision mak-
ing models to include factors associated with
individual differences in various decision
parameters would increase the mutual rel-
evance of neuroscience and economics26,64,65.
Moving beyond the laboratory. It could be
argued that the ultimate test for the useful-
ness of a research finding is whether it has
external validity. Laboratory experiments are
now widely accepted in the economics com-
munity 66, in part because of links between
laboratory results and field measures in
economics67,68. Could neuroscientific labora-
tory studies also predict everyday economic
behaviours? Neuroeconomics is ideally suited
to develop this new form of translational
research, if neuroscientists obtain neuro-
physiological measurements in a controlled
environment and economists relate these
measures to economic behaviour observed
at the population level. Consider the findings
in economics that hypothetical time–money
discounting survey responses predict sub-
sequent actual choices for a savings com-
mitment programme and that behaviour in
a laboratory trust game predicts successful
loan repayment69,70. Neuroeconomists could
extend this result by identifying neural mark-
ers of self-control — obtained either from
social71 or economic55 domains — that can
predict spending habits or debt management.
It has been claimed that such mechanistic
information about brain function is irrelevant
because the neural data are not part of eco-
nomic models27. However, if the out‑of‑sample
predictive power of a model were enhanced by
inclusion of neurobiological data, there would
be clear empirical benefits. In other words,
although there is already evidence that labo-
ratory behaviour can be linked to everyday
behaviour, adding a mechanistic layer — at
the neurobiological level — would enhance
our understanding of some traditional eco-
nomic variables (for example, prices at which
exchanges are made)26.
Neuroscience may not only contribute to
studies in economics but also to studies in
other areas of the social sciences. Core field
measures of interest in these areas include
a range of societal outcomes: happiness,
productivity, political preferences, health
and social stability. These outcomes are
usually assessed through accumulation of
macroscopic techniques outside the labora-
tory (for example, surveys and economic
indicators), even though the interventions
that are used to shape those outcomes often
have varied effects on specific individuals.
Just as laboratory studies in experimental
economics have inspired interventions in
financial markets72,73, studies in neuroeco-
nomics may be useful for directing interven-
tions in the applied social sciences. One way
in which neurobiological concepts could be
integrated with large-scale survey data may
be through assessing genetic influences on
brain function, and indeed the collection
of genetic data in social science research is
increasing in popularity49,74.
Research on brain function may also aid
the development of customized interven-
tions for different subpopulations. Cues that
nudge behaviour — such as encouraging
reflection on one’s likely activities at a future
date — can alter economic preferences (for
example, through temporal discounting75)
and subsequent real-world choices. How a
given person responds to these cues may be
shaped by demographic differences, such
as gender, by dysfunction or disease state
or even by computational style39. There are
both philosophical and methodological
hurdles to overcome before neural measures
can be related to such outcome measures:
for example, can the subjective experience
of happiness be compared between subjects
and, if so, how can it be measured neurologi-
cally in practice26? However, a core goal of
neuroeconomics should be to design studies
that would assist both neuroscientists
and economists in assessing the benefits
(and limitations) of expanding the predictive
range of either discipline beyond individual
choices to aggregate-level outcomes76.
Balancing the demands of the parent dis-
ciplines. Of course, achieving the potential
gains from these three pursuits (targeting
general neural mechanisms, increasing the
focus on individual differences and moving
beyond the laboratory) requires more than
comprehensively assessing neuroeconomics
in isolation. The future of neuroeconomics
will also be shaped in relation to the evolu-
tion of its parent disciplines — neuroscience
and economics.
Two of the more daunting challenges
for neuroeconomics are methods-based
and thus, perhaps unsurprisingly, shared by
neuroscience. First, the rapid acceleration
of research has necessitated new methods
for effectively accumulating and synthesiz-
ing research results77,78. The development of
such methods will aid neuroeconomics in
Figure 3 | Literature usage of ‘neuroeconomic*’
and ‘decision neuroscience’. We collected the
absolute frequency of the terms ‘neuroeco-
nomic*’ and ‘decision neuroscience’ in PubMed
(retrieved on 20 January 2012), with the goal of
assessing its relative usage within the larger sci-
entific literature. We found the first appearance
of ‘neuroeconomic*’ in 2003 (two articles). A
year-to-year breakdown demonstrates a constant
increase since 2003 in articles referring to ‘deci-
sion’ + ‘neuroscience’ (arguably reflecting an
overall increase in the size of the field), but a
recent downward trend of articles using the term
‘neuroeconomic*’, with a peak in 2008 (37 arti-
cles). Note that the combined term ‘decision neu-
roscience’ entered the literature in 2009, the first
year following the point of inflection on the
graph. An analogous search on Thomson Reuters
Web of Knowledge yielded a similar trend (see
Supplementary informationS4 (figure)).
© 2012 Macmillan Publishers Limited. All rights reserved
Nature Reviews | Neuroscience
Mental states
Social behavior
Affective Ambiguity
Human social Skin conductance USA
Autonomic Neuroimaging
Ventral striatum
Human brain
Strategic Sham
Free Opponent
Animal choice
Choice behavior
Eye movement
Juice reward
Dorsolateral prefrontal cortex
Frontal lobe
Economic decision
Ventromedial prefrontal cortex
Somatic marker
Blood oxygen
Medial prefrontal cortex
Reward value
Reward and punishment
Expected value
Basal ganglia
Reward expectation
Dopamine neuron
Expected reward
Reinforcement learning
routinely conveying findings to economics.
Second, the most common experimental
technique in neuroeconomics is functional
MRI (fMRI), and the sustained popular-
ity of fMRI is a mixed blessing: it is non-
invasive and provides a view of the spatial
distribution of the cognitive processes in
humans, but it cannot provide insight into
the neuronal physiology of these processes.
All research agendas under the umbrella of
neuroscience — including neuroeconomics
— must find ways by which fMRI results can
be generalized to other neural measures79,80.
Neuroeconomics has already made signifi-
cant progress in this direction with substan-
tive contributions from single-unit recording
and other techniques.
A third challenge lies in overcoming
mutual misperceptions about the nature of
research in the neural and social sciences. A
common misconception in neuroscience is
that economic models fare poorly at predict-
ing behaviour (‘economic models only work
with rational behaviour’), and economists
sometimes misconceive neuroscience meth-
ods as poorly executed or overly simplistic
(‘brain scans are eye candy’). Recent promi-
nent papers in cognitive neuroscience and
economic theory should clear up the misper-
ceptions of limited scope or lack of sophis-
tication. Indeed, recent empirical studies in
neurophysiology81,82 and fMRI83,84, as well
as their associated statistical analyses, dem-
onstrate rich potential for further research
and education15,85. Nevertheless, greater
effort should be made to exploit the direct
links between some components of the data
analyses in economics and neuroscience (for
example, time series analysis, which is cen-
tral both to financial econometrics and fMRI
data processing86,87). Similarly, economic
theory has many models that incorporate
and explain various behavioural phenomena
observed in both the laboratory and the
field88–90. Neuroeconomists should pay atten-
tion to these models to overcome mutual
Last, the incentives of an aspiring student
or faculty member in neuroeconomics must
be considered. Although neuroeconomics
may be stimulating as an interdisciplinary
research venture, the career of individual
researchers still hinges on publishing only
in journals that are deemed relevant by
their primary discipline. A mechanism for
encouraging cross-fertilization of research
would require universities to appreci-
ate publication records that include both
neuroscience and social science journals.
For example, universities are already
Figure 4 | Knowledge domains in the neuroeconomics literature. From
a set of 27 review articles in neuroeconomics, we identified all unique refer-
ences to other articles in the primary literature (N = 259). The abstracts of those
references were analysed using semantic network analyses, which created a
map of conceptual terms within that literature. The relative size of terms is pro-
portional to their frequency of occurrence; for example,neuron’ and fMRI’
occur relatively frequently, whereas caudateandstrategic ‘do not. Terms that
frequently co-occur within an abstract are positioned closely together (distance
calculated by a positioning algorithm); for example,dopamine’ and expected
reward’ often co-occur. Colours indicate groups that were formed by an algo-
rithm that identifies densely connected clusters; these groups reflect the topi-
cal themes around which the neuroeconomics literature is organized.
© 2012 Macmillan Publishers Limited. All rights reserved
constructing interdisciplinary cognitive
neuroscience centres, and we are hopeful
that these centres will embrace research that
includes economics and other social
sciences, in the best tradition of interdisci-
plinary research91,92. We further anticipate
connections in the opposite direction,
with business and economics departments
recognizing the relevance of neuroeco-
nomics research. In addition, new funding
mechanisms, new courses, seminars and
collaborative laboratory spaces are exam-
ples of ways in which interdisciplinarity
can be promoted. Efforts, on any level, to
facilitate interdisciplinary work will cer-
tainly incentivize future students to choose
neuroeconomics as their field of interest and
improve the quality of future research in
Neuroeconomics as a locus for translation
Neuroscience has the potential to connect
to the social sciences in several ways. Many
of the topics that are most central to social
science research — such as the nature of
individual choice, the factors that shape
social interactions and the ways societies
respond to unexpected events — would
benefit from an improved understand-
ing of the neurocognitive mechanisms
underlying them. The depth of disciplinary
specialization that characterizes modern
academic institutions has presented bar-
riers to connections between the neural
and social sciences. However, our analyses
show that there are reasons for optimism.
Neuroeconomics has become an interdis-
ciplinary research community in which
influential research groups have a balanced
composition of members from neural and
social sciences. Moreover, neuroeconomics
research includes publications in journals
that cover diverse and otherwise unrelated
areas of science (from theoretical finance to
clinical medicine), demonstrating an ability
to reach throughout the scientific milieu and
connect different networks.
At the small-scale level of laboratories
and research projects, neuroeconomics
has provided proof-of-concept evidence
that neuroscientists and economists can
interact with mutual benefit. How might
local connections — such as conversations
between members in an interdisciplinary
laboratory — scale up to facilitate stronger
integration of the neural and social sci-
ences? In our view, neuroeconomics holds
particular promise for providing a lingua
franca, or trade language, that facilitates
communication between disparate cultures.
Trade languages arise when two or more
cultures come into unexpected contact, at a
site where individuals move back-and-forth
between that site and their home culture,
and when transactions are mutually benefi-
cial. Neuroeconomics acts as such a locus
— it enables individuals from different sci-
entific disciplines to collaborate towards the
solution of a common problem. Its ‘language’
does not provide the full range of expression
of either discipline in isolation, but it does
enable core concepts from one discipline to
move into theother.
Our suggestions for the three pursuits on
which neuroeconomics should focus fit with
this model for translation of knowledge. If
the understanding of the neurobiological
underpinning of behaviour continues to
improve, there must also be an interdiscipli-
nary mechanism in place by which this bio-
logical knowledge can be transferred across
the boundaries of behavioural disciplines.
With success, neuroeconomics will identify
foundational concepts — ones that make
joint predictions about biology and behav-
iour — that can be readily translated to other
social sciences. In this way, neuroeconomics
can serve as a directive for dialogue between
neuroscience and other disciplines.
Clement Levallois and Ale Smidts are at the Rotterdam
School of Management, Erasmus University, 3062 PA,
The Netherlands.
Community detection
(Also known as cluster analysis). The identification of
groups of relatively tightly connected nodes in a network
on the basis of an algorithmic analysis of the graph formed
by the nodes and edges.
Connected component
In a network, a group of nodes that are all connected
either directly or through other nodes.
Expected value
The weighted, probabilistic average of all possible values
for an uncertain reward.
Natural language processing
A set of methods from computational linguistics to extract
meaningful features (such as the language or the topic) of a
Out‑of‑sample predictive power
When fitting a model to data, the predictive power or
generalizability of that model can be tested on data not
used to estimate the model (that is, out‑of‑sample data).
Semantic network analytic
Application of the methods and tools of network analysis
to textual data; it creates networks based on semantic
relationships or co‑occurrences of terms in a text corpus.
Temporal discounting
(Also known as delay discounting).The tendency to reduce
the subjective value associated with rewards as the delay
until their receipt increases.
Clement Levallois is also at the Erasmus Virtual
Knowledge Studio, Erasmus University, 3062 PA,
The Netherlands.
John A.Clithero is at the Division of the Humanities
and Social Sciences, California Institute of Technology,
Pasadena, California 91125, USA.
Paul Wouters is at the Centre for Science and
Technology Studies, Leiden University, Leiden,
2300 AX, The Netherlands.
Scott A.Huettel is at the Department of Psychology &
Neuroscience, and the Duke Center for Interdisciplinary
Decision Sciences, Duke University, Durham,
North Carolina 27708, USA.
Clement Levallois and John A.Clithero contributed
equally to this work.
Correspondence to S.A.H.and A.S.
Published online 4 October 2012
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We thank M. Fan and H. Gold for assistance in data collection.
For comments and discussions, we thank A. Beaulieu,
M. Carter, L. Harris, L. Leydesdorff, P. Mehta, I. Rafols, C.
Reeck, M. van Overveld, participants in the annual meetings
of the Society for Neuroeconomics and anonymous reviewers.
Funding for this research comes from an Incubator Award
from the Duke Institute for Brain Sciences (S.A.H), a US
National Institutes of Mental Health National Research
Service Award F31‑086255 (J.A.C.), the Erasmus Research
Institute of Management (C.L. and A.S.), and the Open
Research Area programme from the European Science
Foundation (NESSHI 464‑10‑029; C.L., A.S. and P.W.).
Competing interests statement
The authors declare no competing financial interests.
John A. Clithero’s homepage: http://www.hss.caltech.
Duke Center for Interdisciplinary Decision Sciences: http://
Erasmus Center for Neuroeconomics: http://www.erim.eur.
Society for Neuroeconomics:
See online article: S1 (box) | S2 (box) | S3 (figure) | S4 (figure) |
S5 (box) | S6 (table) | S7 (box) | S8 (box) | S9 (figure) |
S10 (box) | S11 (box)
© 2012 Macmillan Publishers Limited. All rights reserved
... Concomitantly, we have witnessed a wealth of new technologies, wearable devices, and software applications. Hence, neuroscience has not only moved from classic laboratory-based approaches to more real-world domains (21); but it has also extended its application into a variety of different fields-such as marketing (22), economics (23), and educational (24) sciences. ...
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Interdisciplinarity in behavioral economics (BE) has often been described as limited or decreasing since the 1980s. In this article, we investigate the interdisciplinary influences of behavioral economists using quantitative techniques. We find that following an intense period of interdisciplinary exchange among a handful of individuals, interdisciplinarity between economics and psychology has decreased in BE since the 1980s. However, this decreasing interdisciplinarity in BE has been compensated for by the rise of BE in the wider field of economics. While individual BE articles have become less intensely related to psychology, the growing number of BE articles in economics as a whole has intensified the overall interdisciplinarity between economics and psychology. Moreover, the decreasing interdisciplinarity between economics and psychology in BE has not resulted in a return to a self-sufficient economic approach. Instead, we observe a rise in the importance of management studies, as well as a variety of other disciplines in the social and natural sciences, as behavioral economists have diversified their interdisciplinary relationships since the 2000s. Finally, the level of interdisciplinarity between economics and psychology in behavioral economics remains higher than the average economics' article, making the specialty distinctively interdisciplinary.
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Los eventos de esports (deportes electrónicos) son considerados como una herramienta de marketing parte de las empresas que prestan patrocinio con el objetivo de atraer a nuevos consumidores. Es por ello que la eficacia publicitaria se torna de vital importancia teniendo como objetivo llegar a todos los potenciales consumidores, pero la creciente saturación de los espacios destinados a la publicidad en los medios audiovisuales hace cada vez más difícil el encontrar un lugar o una manera donde la exhibición de la marca tenga el alcance deseado por un anunciante. Esta situación obliga a los profesionales de la publicidad y el marketing a mantener un continuo esfuerzo para descubrir nuevas formas de alcanzar sus objetivos. El análisis del proceso cognitivo y emocional en los consumidores mediante técnicas de neuromarketing, permite describir la percepción de los impactos publicitarios en el propio contexto de consumo. Objetivo: El estudio pretende analizar y describir la eficacia de la publicidad en eventos de esports transmitidos por streaming, en función de las características de la marca, con la ayuda de herramientas pertenecientes al neuromarketing. Además de generar una base de conocimiento científico acerca del comportamiento no consciente de los espectadores de esports, en relación a la publicidad en esports. Hipótesis: Las variables extrínsecas de marca influyen en la eficacia publicitaria durante los eventos de esports transmitidos por streaming. Muestra: La muestra estuvo compuesta por un total de 48 sujetos, todos de género masculino, con una edad media de 23,4 ± 17 años, quienes observaban streaming una media de 9,42 ± 45 horas semanales y practican algún esports 16,4 ± 37 horas semanales. Criterio de inclusión: 1) tener entre 18 y 35 años y 2) ser consumidores de esports de manera asidua, ya sea jugando o visionando streams Variables independientes: Tamaño, localización, color, complejidad y tiempo de exposición de las marcas publicitarias. Variables dependientes: Comportamiento visual, impacto emocional, valencia y recuerdo. Material e instrumental: Se utilizó un electroencefalograma, un medidor de respuesta galvánica de la piel, un eye tracker y un test de recuerdo de marcas (Top of Mind). Además se utilizó un video de 10 minutos con 32 segundos de la final de la SuperLiga Orange (League of Legends) 2018 en España, en la fase de picks and bans. Conclusión: la hipótesis general no se confirma cien porciento, ya que el color no afecta el comportamiento visual y al recuerdo de los espectadores. Por otro lado, las variables como el tamaño, la complejidad, el color y el tiempo de exposición no afectan a la valencia experimentada por los sujetos de estudio.
The present article offers the first quantitative history of behavioural economics (BE) from the 1970s to the 2010s. We document the foundation of the field by Kahneman and Tversky in the 1980s and 1990s; the separation of experimental market economics and BE in the 1990s; the decreasing importance of psychology in the 1990s onward; and the rise of European authors after the 2000s. Overall, we show that after the 1990s, BE transformed from a unified American research program with a clearly identifiable core, to a multipolar and international research program with relatively independent subspecialties. Despite claims that BE is mostly an empirical venture, we show that the field is heavily structured by theoretical contributions. A handful of seminal models capture most of the citations in the field and explain how the subspecialties in BE emerged, stabilised, and became more autonomous from the historical core.
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Game theory, the formalized study of strategy, began in the 1940s by asking how emotionless geniuses should play games, but ignored until recently how average people with emotions and limited foresight actually play games. This book marks the first substantial and authoritative effort to close this gap. Colin Camerer, one of the field's leading figures, uses psychological principles and hundreds of experiments to develop mathematical theories of reciprocity, limited strategizing, and learning, which help predict what real people and companies do in strategic situations. Unifying a wealth of information from ongoing studies in strategic behavior, he takes the experimental science of behavioral economics a major step forward. He does so in lucid, friendly prose. Behavioral game theory has three ingredients that come clearly into focus in this book: mathematical theories of how moral obligation and vengeance affect the way people bargain and trust each other; a theory of how limits in the brain constrain the number of steps of "I think he thinks . . ." reasoning people naturally do; and a theory of how people learn from experience to make better strategic decisions. Strategic interactions that can be explained by behavioral game theory include bargaining, games of bluffing as in sports and poker, strikes, how conventions help coordinate a joint activity, price competition and patent races, and building up reputations for trustworthiness or ruthlessness in business or life.
Neuroeconomics is the study of the neurobiological and computational basis of value-based decision making. Its goal is to provide a biologically based account of human behaviour that can be applied in both the natural and the social sciences. This Review proposes a framework to investigate different aspects of the neurobiology of decision making. The framework allows us to bring together recent findings in the field, highlight some of the most important outstanding problems, define a common lexicon that bridges the different disciplines that inform neuroeconomics, and point the way to future applications.
Recent advances in the cognitive neuroscience of motivation and learning have demonstrated a critical role for midbrain dopamine and its targets in reward prediction. Converging evidence suggests that midbrain dopamine neurons signal a reward prediction error, allowing an organism to predict, and to act to increase, the probability of reward in the future. This view has been highly successful in accounting for a wide range of reinforcement learning phenomena in animals and humans. However, while current theories of midbrain dopamine provide a good account of behavior known as habitual or stimulus-response learning, we review evidence suggesting that other neural and cognitive processes are involved in motivated, goal-directed behavior. We discuss how this distinction resembles the classic distinction in the cognitive neuroscience of memory between nondeclarative and declarative memory systems, and discuss common themes between mnemonic and motivational functions. Finally, we present data demonstrating links between mnemonic processes and reinforcement learning.