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Foundations of Neuroeconomics: From Philosophy to Practice

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Abstract

How can we use neuroscience to better understand economic behavior? By quelling concerns about the nascent field of neuroeconomics, the authors defend future integrations of the biological and social sciences.
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Essay
Evidence that neuroscience
improves our understanding of
economic phenomena [1–4]
comes from a broad array of novel
experimental findings, including
demonstrations of brain regions
that guide responses to fair [5,6]
and unfair [7] social interactions,
that resolve uncertainty during
decision making [8], that track loss
aversion [9] and subjective value
[10], and that encode willingness
to pay [11,12] and reward error
signals [13,14]. Yet, neuroeconomics
has been characterized as a faddish
juxtaposition, not an integration,
of disparate domains [15]. More
damningly, critics have charged that
neuroscience and economics are
fundamentally incompatible [16],
an argument that resonates with
many social scientists. Economics
thrived for centuries in the absence
of neuroscience and some economists
argue that existing neuroeconomics
research is not useful to mainstream
economics [17,18].
We reject the fundamental charge
that neuroscience cannot influence
economic modeling, even in principle,
and focus on two criticisms of
integrating these fields, which we label
the Behavioral Sufficiency and Emergent
Phenomenon arguments. We show here
that these arguments contain hidden
assumptions that render them unsound
within the practical constraints of
science.
We go on to explore two interrelated
questions: is there a unique niche for
a field of neuroeconomics, and, if
so, what are its proper foundational
principles? We do not rely on the
recent demonstrations of brain systems
that support economic behavior nor
recount the valid concerns about
potential technological constraints of
neuroscience. Rather, we attempt to
clarify the necessary foundations for
neuroeconomics research [19–21], for
which we identify two core principles,
Mechanistic Convergence and Biological
Plausibility.
We then ask how information about
neural mechanisms improves the
predictive and explanatory power of
economic models. Importantly, the
points we raise here recapitulate both
the cognitive revolution [22] and the
subsequent intertwining of cognitive
psychology and cognitive neuroscience
[23,24]. We believe that the seemingly
disparate neural and social sciences
have much to gain from each other.
Neuroeconomics: Promise
Unfulfilled?
We define neuroeconomics as
the convergence of the neural
and social sciences, applied to the
understanding and prediction of
decisions about rewards [25], such as
money, food, information acquisition,
physical pleasure or pain, and social
interactions. Neuroscience brings a
wealth of technological approaches,
including brain imaging [e.g.,
functional magnetic resonance
imaging (fMRI)], lesion studies,
molecular biology, pharmacology, and
electrophysiology. Economics adds
conceptual principles (e.g., rationality
and utility), statistical techniques,
and rigorous modeling. Psychology
provides evidence for decision biases
such as heuristics, framing effects, and
emotional influences. Finally, genetics
[26,27], computer science [28], and
philosophy [21,29,30] contribute to
neuroeconomic research. Numerous
reviews summarize research at the
intersection of these fields [1,3,31–35].
Among neuroscientists, the
incorporation of economic concepts
has generated much excitement.
Economic models make assumptions
about “covert preferences” [36], or
value judgments, because measuring
actual preferences with only behavioral
methods is difficult [37]. But
neuroscience may provide a means
to measure those covert preferences
[18], potentially eliminating the need
for those assumptions. More broadly,
neuroscience often incorporates
economic models to explain brain
function, both when investigating
decision making under risky conditions
[38–40] and examining information
acquisition during learning [41,42].
Based on the breadth of research so
far, introducing economic concepts has
led to clear advances within social and
decision neuroscience.
Nonetheless, detractors dispute
the value of neuroeconomics, often
citing poor statistical practice [17],
the answering of irrelevant questions
[16], skepticism about the relevance
of nonhuman animal studies [16],
and the interpretational difficulties
associated with neuroimaging data
[43]. Whereas neuroscience data is
compelling because it seems to reveal
previously inaccessible truths [44,45],
the lack of functional specificity of
Foundations of Neuroeconomics:
From Philosophy to Practice
John A. Clithero, Dharol Tankersley, Scott A. Huettel*
Academic Editor: James Ashe, University of
Minnesota, United States of America
Citation: Clithero JA, Tankersley D, Huettel SA (2008)
Foundations of neuroeconomics: From philosophy to
practice. PLoS Biol 6(11): e298. doi:10.1371/journal.
pbio.0060298
Copyright: © 2008 Clithero et al. This is an
open-access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution,
and reproduction in any medium, provided the
original author and source are credited.
Abbreviations: fMRI : functional magnetic resonance
imaging
John A. Clithero, Dharol Tankersley, and Scott A.
Huettel are at the Center for Cognitive Neuroscience,
Duke University, Durham, North Carolina, United
States of America. John A. Clithero is also in the
Department of Economics and Scott A. Huettel is also
in the Department of Psychology and Neuroscience,
Duke University, Durham, North Carolina, United
States of America. Dharol Tankersley’s current
address is the Department of Psychiatry at the
University of California San Diego, La Jolla, California,
United States of America.
* To whom correspondence should be addressed.
E-mail: scott.huettel@duke.edu
[ These authors contributed equally to this work.
Essays articulate a specific perspective on a topic of
broad interest to scientists.
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many brain regions (at least at the level
accessible to common neuroimaging
techniques) often precludes strong
conclusions about links between brain
regions and behavior [46]. Even where
neuroscience can ask well-formed
questions, the economic literature
may have different disciplinary
conventions (e.g., statistical analyses
and decision models) that preclude
ready translation between the fields
[17,18]. Without a common language
or principles to bridge the disciplines
[19,20], neuroeconomics may become
increasingly brain-centric.
Moreover, neuroscientists and
social scientists work with different
methods and datasets. Neuroscientists
frequently require expensive hardware,
use invasive techniques, and draw data
from a small sample of humans or
animals. Social scientists, in contrast,
generally measure information
about choice preferences (or other
forms of behavior) through relatively
inexpensive laboratory testing, and
often use data from observations in
natural environments (e.g., housing
prices), across large and diverse
samples of subjects. These many
disciplinary contrasts have led critics
to make two arguments, which we here
label Behavioral Sufficiency and Emergent
Phenomenon, that neuroscience data
cannot influence economic modeling,
even in principle.
Arguments Against
Neuroeconomics
Behavioral Sufficiency. Some theorists
argue that economic hypotheses cannot
be falsified using neuroscience data
[16,47]. Since economic models make
no assumptions about the mechanisms
underlying behavior, the argument
goes, no data about those mechanisms
could confirm or refute any economic
model [16]. To falsify an economic
model, researchers must manipulate
some environmental factor and observe
a change in behavior contrary to the
model’s predictions. In this Behavioral
Sufficiency argument, behavioral data
are both necessary and sufficient to
evaluate the validity of economic
models, leaving only brain function or
clinical disorders for neuroeconomics
to address.
This argument builds from the
concept of revealed preference in
economics [48,49]. Economic models
emphasize observable choice data to
construct sets of preferences sufficient
to model and predict choice [21]. For
example, consider the hypothesis that
affective feelings (e.g., sadness) exert
an effect on decisions [50]. Although
this hypothesis is a natural candidate
for models inspired by neuroscience,
in that it posits specific intervening
states that influence choice, at present
identifying those states requires self-
reported behavioral data (e.g., feelings
of sadness), not neuroscience proxies
(e.g., amygdala activation). Even if
nothing were known about the neural
mechanisms of emotion, choice, or
their interaction, behavioral research
could reveal that sadness biases
decisions [51]. Models can incorporate
other affective states (e.g., anger or
fear) by inducing feelings and then
testing the consequences on decisions.
In all cases, purely behavioral data
(the subjects’ responses to induced
states) would be sufficient, in principle,
to identify relationships between
independent variables associated with
an environmental manipulation and
a dependent variable associated with
a real-world decision measure. Thus,
critics argue, behavioral models, not
mechanism-based models, can facilitate
prediction.
Researchers can collect behavioral
data from hundreds of individuals at
relatively little cost. For many economic
questions, data from laboratory
experiments merge with observations
of real-world behavior, providing
important checks on the validity of
research phenomena. In contrast,
neuroscience experiments require
large-scale capital investments and
specialized skills for data collection
and analyses, and necessarily constrain
participants’ behavior dramatically:
body movements, face-to-face
interactions, and verbal expressions
of decisions are all restricted. The
small sample size of neuroscience
experiments complicates analyses
of individual differences, and even
well-conducted, adequately powered
experiments may lead to equivocal
conclusions, because of inherent
limitations in the experimental
methods [43,52] and incomplete
knowledge about underlying brain
function [46]. In short, given
the challenges of neuroscience
experimentation compared to
traditional social science methods,
the Behavioral Sufficiency argument
seems to sound the death knell for
neuroeconomics.
Yet, the simplicity of this argument
belies a hidden premise that undercuts
its practical validity. We do not
disagree that researchers could falsify
any possible economic model using
behavioral data and may predict
behavioral phenomena without
an understanding of mechanism.
However, Behavioral Sufficiency rests on
the premise that the data necessary
to falsify or support a model can, in
practice, be identified and collected.
Many important decision phenomena
require a wide range of tests for their
experimentation. For the hypothesis
described earlier [50], researchers
could test effects of a whole range
of affective states, such as anger,
depression, elation, and sleep
deprivation. With comprehensive
data about how preferences change
across these states, theorists could
validate, reject, or refine relevant
economic models, but accumulating
those data would be time-consuming
and expensive, especially when testing
interacting factors (e.g., the effects of
depression in adolescents upon delay
discounting). Thus, the argument
succeeds in principle but fails in
practice: no researcher—indeed, no
collection of researchers—can obtain
all possible data about all possible
behaviors.
These practical limitations leave
an opening for neuroscience data
to influence economic modeling
by directing the course of research.
Continuing the above example,
long-standing neuroscience work
distinguishes emotional states as
resulting from different mechanisms,
with disgust, pain, and fear, for
example, all reflecting different
neural substrates [53,54], despite
some superficial similarities. So,
neuroscience data that map particular
affective systems (e.g., posterior
rather than anterior insula) to specific
forms of choice (e.g., purchasing
decisions) may suggest new directions
for subsequent behavioral research
[12]. We believe that neuroscience
could make important contributions
to economics by improving the efficacy
(both in falsification and explanation)
of behavioral research.
Emergent Phenomenon. A second
criticism focuses on the methods of
neuroeconomics, which frequently
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place human (or monkey or rodent)
subjects in a mock economic setting
to elicit a desired behavior—such
as differential framing of gains and
losses [9,55], rejection of unfair
offers [7], or incentivized memory
retention [56]—and identify its
neural correlates. Neuroscientists
make similar extrapolations from
animal studies when monkeys make
decisions regarding juice [10] or rats
make decisions involving drops of
sucrose [57]. In effect, these studies
create a simplified “toy model” of a
real-world phenomenon in order to
test hypotheses about an underlying
mechanism. Toy models have long
been used in both the natural sciences
(e.g., placing small-scale structures
into wind tunnels to understand
fluid mechanics) and the biological
sciences (e.g., in vitro studies to
understand cellular properties),
but economists have only relatively
recently started to use them, creating
simple markets (or other economic
institutions) within the laboratory
[58,59] and inducing subjects to
behave in a self-interested manner
reflective of real-world behavior.
These markets obeyed basic research
principles: participants received full
and accurate information, decisions
had meaningful (usually monetary)
consequences, and deception
was prohibited. Neuroeconomic
experiments typically follow these
principles. In recent studies of
purchasing decisions, for example,
subjects had the opportunity to spend
real money and receive real goods
(e.g., iPods and candy) in return
[11,12]. Researchers extrapolate that
the neural mechanisms recruited in
such laboratory studies also underlie
real-world purchasing behavior.
The validity of a toy model rests on
the assumption that the principles
of interest are maintained from the
laboratory setting to the natural
environment. When the physical
principles change—as when moving
from small-scale models of buildings
to their real-world counterparts—an
extrapolation from toy models may
have disastrous consequences [60].
Neuroeconomic experiments can
suffer from similar problems: principles
that shape behavior in the laboratory
(e.g., experimenter demand effects)
do not necessarily influence real-
world phenomena (e.g., amount
of charitable giving). Suppose that
an economist and neuroscientist
create a mock retirement-planning
fMRI experiment to identify ways
to encourage participation in a
retirement savings plan and discover
patterns of neural activation that
predict decisions to save money. Critics
charge that because information about
neural mechanisms was collected
from a few dozen subjects, it cannot
generate a better understanding of
retirement planning behavior for
millions of adults. In this argument,
the underlying mechanisms have no
bearing on economic theories that
describe aggregate data. Similar and
notable claims have been made within
economics about market phenomena
[61]. We refer to this as the Emergent
Phenomenon argument: the denial
that an understanding of mechanism
has relevance for understanding
phenomena at the aggregate societal
level. Similar reasoning pervades
criticisms of behavioral economics.
Consistent with substantial evidence
that emergence is common within
complex economic systems [62,63],
this argument limits the scope of
neuroscience data from generalizing to
higher-level phenomena.
The Emergent Phenomenon argument,
however, rests on the assumption
that emergence not only exists but
subsumes any influences from lower
levels. Yet, as has been argued with
respect to macroeconomic modeling
[64], emergence may not be ubiquitous
among economic phenomena.
Microeconomic theory invokes the
concept of a general equilibrium [65]
to explain aggregate market outcomes
based on individual behavior and
preferences, a foundational concept
that explains higher-level outcomes
from lower-level data. Consider
drug addiction, a social problem of
increasing interest to economists
[66,67]. No scientist claims that
understanding the neural mechanisms
of addiction provides a complete
explanation of drug abuse, but that
understanding undeniably clarifies
both the etiology of addiction, as
evident in genetic influences [68]
and the success of interventions like
nicotine patches, leading to clear
changes in public policy. Other
economic phenomena may have only
limited emergence. For example,
retirement planning in older adults is
likely influenced by general cognitive
decline with aging [69], and the
financial decisions of teenagers reveal
a broad pattern of impulsivity, which
reflects delayed neural development
of the prefrontal cortex compared to
other brain systems [70]. That some
economic phenomena have some
emergent properties restricts the
explanatory power of hierarchical,
mechanistic models, but does not
render those models logically invalid.
Social science models are now
increasingly likely to incorporate
some mechanistic explanations that
account for effects across levels. As an
example, economic experiments aimed
at implementing general equilibrium
theory in the laboratory use individual
portfolio choices to explain financial
market behavior [71]. To the extent
that researchers can more accurately
specify the mechanisms underlying
the behavior of an individual, some
phenomena of interest to economists
will be better modeled. A core goal of
neuroeconomics will be identifying
those economic phenomena to which
neuroscience can be most profitably
applied.
Foundations of Neuroeconomics
Mechanistic Convergence in experiments.
Behavioral economic research [72,73]
could proceed without neuroscience,
but we believe that neuroscience data
will increase the efficiency of this
research. Specifically, via Mechanistic
Convergence, neuroscience experiments
can guide the generation and direction
of future behavioral studies with a
multi-stage “behavior-to-brain-to-
behavior” approach.
By identifying interesting choice
behavior and creating models for
the associated cognitive processes,
neuroeconomics research can
generate better paradigms for human
neuroimaging studies and target
behavior to replicate in animal
and clinical studies (behavior). By
grounding conclusions about brain
function in behavioral effects such
as choice parameters or individual
decisions, neuroeconomics can
unify cognitive and neural theories
of behavior [18]. Knowledge about
the underlying mechanisms (brain)
can generate novel hypotheses about
external modulating factors, which
in turn can be generalized to new
samples, tasks, and experimental
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environments (behavior). Well-designed
neuroscience experiments can speed
the course of behavioral research,
effectively using mechanistic knowledge
to target observable behaviors for
subsequent experimentation. This
use of convergent evidence from
neuroscience refines and reduces the
number of experiments necessary for
understanding individual behavior.
Neural and behavioral studies
should interact to identify interesting
phenomena, to suggest mechanisms
that underlie those phenomena, and
to map out the biological substrates
that support those mechanisms
[18]. This iterative approach is also
important when multiple decision or
psychological processes could lead to
the same choice behavior [20]. For
example, temptation (e.g., purchasing
a sale item because it is a good deal)
and regret (e.g., purchasing a sale item
to avoid future regret) are different
subjective phenomena but can lead
to similar purchasing decisions [74],
despite likely having distinct neural
substrates [75]. Neuroscience data
that distinguish these affective states
can guide the construction of new
behavioral experiments and more-
targeted hypotheses. Such data could
differentiate properties of regret
and temptation, along with related
phenomena that may share some of
their neural mechanisms, increasing
the efficiency of behavioral research.
Biological Plausibility in models.
Aside from producing new hypotheses,
neurobiological knowledge can also
introduce constraints. Models of
neural function have guided theories
of executive control and decision
making [76–82]. Likewise, integrating
psychological concepts into models
is not new to economics [83,84]. We
argue that neuroscience can inspire
models of behavior that conform to
our current scientific knowledge,
i.e., behavioral models that have
Biological Plausibility. The advantages
of mechanistic knowledge are well
documented in the psychological,
philosophical, and economic literatures
[19,85]. For example, a combination
of rodent [57,86], nonhuman primate
[87,88], and human studies [41,42,89]
have led to theorizing about the role
of dopamine in reward processing
and prediction error. To the extent
that neuroeconomics provides insight
into the mechanisms guiding different
forms of utility, such knowledge
constrains candidate models of
individuals’ choice processing.
Yet, only a handful of microeconomic
models strive for biological plausibility.
The dual-systems framework postulates
that choice reflects the interaction of
two distinct neurocognitive systems
with complementary strengths
and weakness [83]. One common
dichotomy separates automatic or “hot”
affective processes from controlled or
“cold” cognitive processes, and similar
divisions are used in several economic
models [67,90–93]. Critically, the
dual-systems models are compelling
because they are intuitively plausible
and supported by both human [55,94]
and animal [95,96] neuroscience
data. These models generate testable
hypotheses about the nature and
timing of interactions between
competing brain systems, which may,
of course, lead to the rejection of these
models. For example, recent studies
suggest that a more unified set of
neural processes support the evaluation
of options and decisions [9,97,98].
Constraining theories in accordance
with our best neurobiological
knowledge is critical for moving
beyond behavioral conflations of
several distinct affective states. Full
understanding of the mechanisms
of choice will require more precise
characterization of precipitating,
modulating, and inhibiting factors
[85], potentially through neuroscience
data.
We note that most current
neuroscientific methods provide only
coarse information about mechanism.
The dominant technique, fMRI,
provides temporal information about
the relative metabolic demand of
populations of hundreds of thousands
of neurons [43]. Models based on
these methods must make simplifying
assumptions: e.g., region A activates
as if it is modulated by region B.
Note that this simplification is similar
to the aforementioned “revealed
preference” foundation of economic
choice models: the actual mechanistic
relation between regions A and B is
not being modeled, just as the actual
computations underlying preferences
are generally ignored. New methods in
fMRI, notably those that characterize
how information shapes connections
between regions [99], promise to
create integrated models that include
both a description of information flow
among brain regions and the effects
of behavior upon the connections
between those regions. Integrating
techniques within single studies, such
as fMRI and genetics [27,100], will be
critical for producing mechanistically
complete and biologically plausible
explanations of behaviors.
Conclusion
Neuroeconomics is at a crossroads,
poised to demonstrate that
neuroscience can provide the same
types of benefits it has long received
from the social sciences. Ideas from
game theory and expected utility theory
can explain the responses of individual
neurons to incoming information
[2]. Similarly, aspects of utility theory
can be used to describe the activity
of populations of neurons within the
brain’s reward system [101]. There is
also an opportunity for the axiomatic
approach of decision theory to explain
decision-making mechanisms [20],
such as building from the response
properties of dopaminergic neurons
[102]. Without comparable examples
of neuroscience data contributing
to economic models, critics could
argue that neuroeconomics research
is a brain-centric enterprise that
incorporates ideas from the social
sciences without reciprocation [16,17].
We agree that neuroeconomic
research has indeed been brain-centric,
but stress that it need not remain so.
The core criticisms of neuroeconomics
constrain the practice of this field,
but do not render it meaningless.
Clear foundational principles remain.
Neuroeconomics via Mechanistic
Convergence can more efficiently direct
the course of future behavioral studies.
As an historical parallel, economists
have explored psychological concepts
(e.g., emotional influences on decision
making) for many years [84,103–105],
sparking a broad array of new
behavioral experiments and theories.
Furthermore, neuroeconomics can
facilitate the creation and testing
of models that adhere to Biological
Plausibility. Social scientists (and
neuroscientists) should not treat
decision-making phenomena
as irreducible and mechanism-
independent. Instead, the joint
investigation of brain and behavior
will lead to greater success than either
discipline could achieve in isolation. ◼
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Acknowledgments
We thank the members of the Duke
Center for Neuroeconomic Studies for
discussions that motivated this essay. We also
thank McKell Carter, Ben Hayden, Sarah
Heilbronner, and Michael Platt for their
comments.
Funding. This work was supported by the
National Institute of Mental Health grant
70685 and by an Incubator Award from the
Duke Institute for Brain Sciences.
Competing interests. The authors have
declared that no competing interests exist.
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... Research tackling aspects of this challenge have been conducted over the past decade, but recently more findings and experiments have started to be used outside of the laboratory. One way to make applicable research is through generating biologically plausible models and through the technique of mechanistic convergence (Clithero et al., 2008). This method studies behavior that is hard to understand through simple observations, correlates it with brain activity, and uses these findings to inform a deeper understanding of the behavior observed (Clithero et al., 2008). ...
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In the past decade, decision neuroscience and its subfield of neuroeconomics have developed many new insights in the study of decision making. This review provides a comprehensive update on how the field has advanced in this time. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across psychology, neuroscience, and economics. We will first review progress in basic value processes involved in reward learning, explore-exploit decisions, risk and uncertainty, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choice, and exciting developments in prediction and neuroforecasting of future decisions. Finally, we will consider overall progress in the field of decision neuroscience in reconciling past challenges, identifying new challenges, and recent exciting applications of this research.
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Chapter
Financial decision-making often involves the assessment of risk and reward across a given time frame, along with any number of contextual factors that could impact decision strategies and outcomes. These complex decisions have a significant long- and short-term impact on people’s lives – from investment and financial planning to healthcare and medical decisions. A central goal of decision neuroscience research has been to improve our understanding of decision preferences and strategies, taking into account biological constraints and limitations. Scientists have incorporated neuroimaging and neuroscientific techniques in both human and animal models to better understand the biological basis of decision processes and subsequent individual differences in decision-making. More recently, researchers have begun to take network and computational approaches to understand the neurobiological mechanisms that influence decision-making across a variety of domains. In this chapter, we provide a brief overview of the field of decision neuroscience and the criticisms and progress made over the past two decades. Next, we will outline key regions and systems of the brain that map onto distinct decision variables and inform our understanding of financial decision-making processes and mechanisms. Lastly, we will identify and review critical findings surrounding the neuroscience of how people employ different decision-making strategies. As the field of decision neuroscience matures, we also identify future areas of research and its impact on financial decision-making.
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Neurobiological factors contributing to violence in humans remain poorly understood. One approach to this question is examining allelic variation in the X-linked monoamine oxidase A (MAOA) gene, previously associated with impulsive aggression in animals and humans. Here, we have studied the impact of a common functional polymorphism in MAOA on brain structure and function assessed with MRI in a large sample of healthy human volunteers. We show that the low expression variant, associated with increased risk of violent behavior, predicted pronounced limbic volume reductions and hyperresponsive amygdala during emotional arousal, with diminished reactivity of regulatory prefrontal regions, compared with the high expression allele. In men, the low expression allele is also associated with changes in orbitofrontal volume, amygdala and hippocampus hyperreactivity during aversive recall, and impaired cingulate activation during cognitive inhibition. Our data identify differences in limbic circuitry for emotion regulation and cognitive control that may be involved in the association of MAOA with impulsive aggression, suggest neural systems-level effects of X-inactivation in human brain, and point toward potential targets for a biological approach toward violence. Neurobiological factors contributing to violence in humans remain poorly understood. One approach to this question is examining allelic variation in the X-linked monoamine oxidase A (MAOA) gene, previously associated with impulsive aggression in animals and humans. Here, we have studied the impact of a common functional polymorphism in MAOA on brain structure and function assessed with MRI in a large sample of healthy human volunteers. We show that the low expression variant, associated with increased risk of violent behavior, predicted pronounced limbic volume reductions and hyperresponsive amygdala during emotional arousal, with diminished reactivity of regulatory prefrontal regions, compared with the high expression allele. In men, the low expression allele is also associated with changes in orbitofrontal volume, amygdala and hippocampus hyperreactivity during aversive recall, and impaired cingulate activation during cognitive inhibition. Our data identify differences in limbic circuitry for emotion regulation and cognitive control that may be involved in the association of MAOA with impulsive aggression, suggest neural systems-level effects of X-inactivation in human brain, and point toward potential targets for a biological approach toward violence.
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Neuroimaging studies of decision-making have generally related neural activity to objective measures (such as reward magnitude, probability or delay), despite choice preferences being subjective. However, economic theories posit that decision-makers behave as though different options have different subjective values. Here we use functional magnetic resonance imaging to show that neural activity in several brain regions—particularly the ventral striatum, medial prefrontal cortex and posterior cingulate cortex—tracks the revealed subjective value of delayed monetary rewards. This similarity provides unambiguous evidence that the subjective value of potential rewards is explicitly represented in the human brain.
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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.
Chapter
As models are often subjected to empirical testing to evaluate their ability to predict economically sound choices, these models are also solved by means of using optimization measures that are grounded on understanding both the quantitative and qualitative links of the actual choice data to the models. What sets economics apart from the other social sciences is how the decision-theoretic methodology brings about influential positive externalities. Although this aspect of economics seems to be well-organized, others would notice how classical decision theory does not integrate psychological factors. This chapter attempts to illustrate the developments that have furthered the use of nonstandard economic models while also accounting for the new forms of "psychological data" which involves eye movements, neurological responses, and other such indicators in expanding economic theory.