Neuroeconomicscross-currents.pdf

Neuroeconomics: cross-currents
in research on decision-making
Alan G. Sanfey

1
, George Loewenstein

2
, Samuel M. McClure

3
and Jonathan D. Cohen

3

1
Department of Psychology, University of Arizona, Tucson, AZ 85721, USA

2
Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA

3
Department of Psychology, and Center for the Study of Brain, Mind and Behavior, Princeton University, Princeton, NJ 08544, USA
Despite substantial advances, the question of how we

make decisions and judgments continues to pose

important challenges for scientific research. Historically,

different disciplines have approached this problem using

different techniques and assumptions, with few unifying

efforts made. However, the field of neuroeconomics has

recently emerged as an inter-disciplinary effort to bridge

this gap. Research in neuroscience and psychology has

begun to investigate neural bases of decision predict-

ability and value, central parameters in the economic

theory of expected utility. Economics, in turn, is being

increasingly influenced by a multiple-systems approach

to decision-making, a perspective strongly rooted in

psychology and neuroscience. The integration of these

disparate theoretical approaches and methodologies

offers exciting potential for the construction of more

accurate models of decision-making.
Introduction

The question of how we make, and how we should make,
judgments and decisions has occupied thinkers for many
centuries, with different disciplines approaching the
problem with characteristically different techniques. A
very recent approach, popularly known as neuroeco-
nomics, has sought to integrate ideas from the fields of
psychology, neuroscience and economics in an effort to
specify more accurate models of choice and decision (for
reviews from the perspective of economics, see [1,2]).

How profitable the neuroeconomic approach will be is
still unclear. Predictably, there are strong opinions on both
sides. On the one hand, its strongest advocates (aided by
some exaggerated media reporting) have presented
neuroeconomics as a new paradigm that will eventually
replace the classical approaches. On the other, skeptics in
both communities have argued that economic models and
neuroscientific techniques reflect disparate levels of
analysis that have little to offer one another. Economists
have, historically, been skeptical of the ability of ‘process
measures’ to contribute to our understanding of economic
and social behavior [3]; and neuroscientists commonly
view economics as too abstract and removed from the
mechanisms of interest in the brain.
Corresponding author: Sanfey, A.G. ([email protected]).
Available online 8 February 2006

www.sciencedirect.com 1364-6613/$ – see front matter Q 2006 Elsevier Ltd. All rights reserved
Although we are perhaps not as optimistic as the most
ardent believers in neuroeconomics when it comes to the
time-line of progress, we do believe that the field has real
potential for making important contributions to our
understanding of decision-making, above and beyond
what has and will continue to be learned from work
within each discipline independently. This is because
neuroeconomics is able to draw upon the complementary
strengths of its contributing disciplines. In fact, the
benefits of increasing contact between neuroscience,
psychology and economics are already apparent.

The central argument of this article is that economics,
psychology and neuroscience can each benefit from taking
account of the insights that the other disciplines have to
offer in understanding human decision-making. In the
following, we first address how neuroscience can, and
already has, benefited from economics’ unitary perspec-
tive. We then discuss how economics can, and has begun
to, be enriched by taking account of cooperation and
competition between multiple specialized neural systems,
before closing with some thoughts on potentially fruitful
future research directions.
The view from economics: one unified theory

Economics contributes to the joint endeavor of neuroeco-
nomics by bringing its insights into the diverse outcomes
that can arise from the strategic and market interactions
of multiple agents, and through a set of precise, formal,
mathematical models to describe these interations and
outcomes. However, the aspect of economics that may
prove most useful to neuroscientists (and, indeed, that has
already begun to bear fruit) is its embracing of a unified
theoretical framework for understanding human behavior
– namely the idea that behavior can be interpreted as
choosing alternatives with the goal of maximizing utility.

The unitary perspective of economics can be seen in the
assumptions that it makes about the two essential
dimensions of decision-making: choice (the evaluation of
options and selection of actions), where economics
assumes a consistent, stable set of preferences; and
judgment (information processing and probability esti-
mation), with the assumption of a general reasoning
system applicable to a wide range of problems. These
assumptions have been criticized, as will be discussed
later, but the concept of decisions being made by
comparing the utility signals for each of the decision
Review TRENDS in Cognitive Sciences Vol.10 No.3 March 2006
. doi:10.1016/j.tics.2006.01.009

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Review TRENDS in Cognitive Sciences Vol.10 No.3 March 2006 109
alternatives has led to some real developments and has
played an increasingly important role in guiding research
investigating the underlying brain mechanisms. Addition-
ally, it is possible that well-established ideas from
economics will shed light on one of the least developed,
but most important, riddles for neuroscience: how the
multiple, diverse and specialized neural systems of the
brain coordinate their activities to solve complex and often
novel problems and give rise to coherent, goal-directed
behavior (see Box 1).
How economics can inform neuroscience: the benefits of

a unitary perspective

In recent years, a growing number of neuroscientists have
turned to economic models as a framework both for
interpreting results and guiding new experimental work
(e.g. [4,5]). This follows a small, but growing, tradition in
neuroscience in which optimal performance is defined for
a given behavioral domain, and is then used for
Box 1. Brain systems and economics

One potential area where economics can contribute is in under-

standing the dynamic processes by which the brain coordinates its

diverse systems to perform new, complex tasks. This problem has

received remarkably little attention in neuroscience research,

although it is well-explored terrain in economics [76,77].

There are striking parallels between the brain and a modern

corporation. Both can be viewed as complex systems transform-

ing inputs into outputs. Both involve the interaction of multiple,

highly similar, agents (neurons are similar to one another, just as

are people), which, however, are specialized to perform particular

functions. Thus, in corporations, units often take the form of

departments that perform functions such as research, marketing,

and so on. Similarly, the brain has systems specialized for

different functions. As in a corporation, these functions may be

more or less spatially segregated in the brain, depending upon

the processing requirements of the specific functions and

their interactions.

Furthermore, there is hierarchical structure in both brains and

corporations. Both rely on ‘executive’ systems that make judgments

about the relative importance of tasks and decide how to mobilize

specialized capabilities to perform those tasks. Several neuroima-

ging studies have demonstrated that brain areas involved

in executive function are actively engaged during the performance

of a novel and demanding task, and then show progressively less

activity as the task becomes more automatic [78,79]. These changes

closely parallel the increase in activity in more specialized brain

areas, as well as increases in performance speed. Similar improve-

ments in speed and efficiency are observed in industry. Studies of

shipbuilding during World War II found that labor productivity rose

at an annual rate of 40 percent, and that speed of production

increased even more rapidly. Although it often took as much as

300 days for a yard to deliver its first ship, by 1943 delivery times

were often less than one month and even as short as five days [80].

Presumably such changes were accompanied by diminished

reliance on administrative and executive involvement, as is

observed in the brain.

It remains to be determined whether these similarities go deeper

than mere analogy, and reflect fundamental principles of aggregate

behavior that apply across different levels of analysis. However,

whether or not the principles of interaction are the same,

the theoretical methods that have been developed within

economics to analyze and construct models of interactions among

economic units, such as principal-agent theory, general equilibrium

theory, and the analysis of the firm, are likely to be useful in studying

the interactions among brain systems that determine

individual behavior.

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constructing theories about underlying neural function.
Even as it is recognized that the brain (and consequent
behavior) does not operate perfectly optimally, there are
several reasons why these assumptions can nevertheless
be valuable. First, although complex forms of behavior
might not be optimal, simpler evolutionarily conserved
mechanisms might prove to be closer to optimal, or at least
to have been so in the environment in which they evolved.
Second, an assumption of optimality can be a crucial step
in the development of formal theory, as it is often easiest to
define and precisely characterize the optimal behavior of a
system. Formal theory, in turn, enables the generation of
precise, testable predictions about the system’s behavior.
Finally, even when behavior (or neural function) turns out
to be suboptimal, defining optimal performance can
provide a useful benchmark against which to compare
actual behavior. Identifying ways in which behavior
systematically deviates from optimality can then generate
new insights into underlying mechanisms. The use of the
Expected Utility (EU) model [6] is one example of this
approach, and has been productively applied to research
on the neural bases of reward and decision-making.

According to the classical EU model, utility is computed
as the product of the value and the probability of each
potential outcome (see Figure 1). Using this as a starting
point, research has sought to distinguish and identify the
neural substrates for each of these components and to
study their interaction. Although this is still a relatively
new direction in neuroscience research, some useful
progress has been made.

Neural basis of value and reward

Several decades of research have focused on the neural
bases of reward and punishment (the value function of the
EU model), identifying several systems that are consist-
ently responsive to reinforcement. However, we still have
a relatively weak understanding of the underlying neural
computations and their influence on decision-making.
Early studies of brain reward systems were concerned
primarily with establishing the generality of their
function and their neurochemical bases. These exper-
iments demonstrated that animals will withstand electric
shock, exert significant physical effort, and even reduce
food intake to obtain electrical stimulation in appropriate
brain areas [7]. Furthermore, the reward value of
stimulation was shown to be reduced when dopamine
receptor binding was inhibited, particularly in the ventral
striatum [8].

Inspired at least in part by an awareness of economic
theory, more recent experiments have recognized the
importance of distinguishing between the magnitude and
the probability of reward, and have tested directly for
their separate influence on reward-related brain activity.
Single-cell recordings from dopamine neurons [9] and
neurons in the orbitofrontal cortex [10,11], striatum [12]
and posterior cingulate cortex [13] have shown that neural
responses scale reliably with reward magnitude. These
results have also been observed in human subjects:
activity changes in many of these same brain areas scale
directly with the magnitude of earned monetary reward
[14–17], the appetitive value of food reward [18,19], as well

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Yes

No

60

30

0

Decision

ConsequencesAlternatives BeliefsTake
umbrella?

50%

50%

50%

50%

Rain

No rain

Rain

No rain
100

TRENDS in Cognitive Sciences

Figure 1. Subjective Expected Utility theory posits two fundamental characteristics of an alternative that must be combined before reaching a decision, namely the value of

the alternative and the probability that this value will be attained. The value of each alternative is weighted by its attendant probability, and the option with the highest

probability-weighted values (or ‘utility’) is then chosen. An illustrative example is shown above. Imagine you are trying to decide whether or not to bring an umbrella as you

walk to work one morning. Utility theory posits that we must weigh the probability (in this case set at pZ0.5, perhaps gleaned from previous experience or from the morning
weather forecast), with the value of each of the four possible outcomes. These four outcomes are assigned a subjective rating (0Zworst outcome, 100Zbest outcome), which
of course varies depending on the individual decision-maker’s preferences, and a calculation is performed multiplying each outcome by its relevant probability, and then

combining across alternatives. Therefore, in this example, the utility for the option of taking the umbrella is [(0.5*60)C(0.5*30)]Z45, whereas the utility for not taking one’s
umbrella is [(0.5*100)C(0.5*0)]Z50. The utility maximizing decision-maker should therefore opt to leave the umbrella behind in these circumstances.

Review TRENDS in Cognitive Sciences Vol.10 No.3 March 2006110
as with more abstract, social, rewards [20]. Furthermore,
brain stimulation experiments offer a precise mechanism
for investigating how reward magnitude might be
encoded, suggesting that value is encoded as the integral
of excitatory inputs to reward-related brain areas [21].

There is mounting evidence that the mesencephalic
dopamine system plays a crucial role in value assessment
by signaling errors in reward prediction, which are used to
augment reward-producing behaviors both by generating
learning signals [22], and by adaptively updating goal
states and attentional focus in working memory [23].
Similarly, it has been suggested that the norepinephrine
(noradrenaline) system regulates the balance between
exploitation (seeking to maximize utility from a given
source of reward) and exploration (seeking new sources)
through its influence on mechanisms of learning and
attention [24,25]. Although computational models have
been developed to describe functions of the dopamine and
norepinephrine systems, their interaction remains rela-
tively unexplored. Neuroscientists could benefit from
interacting with economists who have been empirically
investigating similar issues, and are developing theo-
retical models that provide useful frameworks for inte-
grating these different lines of neuroscientific research
(see [26] on learning from feedback and [27] on the trade-
off between exploitation and exploration).

Although most neuroimaging work to date has focused
on positive rewards, recent findings also suggest that
there are complementary mechanisms for evaluating
negative utility. For example, there is evidence that the
anterior cingulate cortex (ACC) responds to a variety of
signals that indicate negative utility (e.g. performance
costs such as response conflict, errors, negative feedback
and pain [28–34]). Across these results, there is evidence
that increased activity in ACC correlates with the
magnitude of anticipated consequences, and current
efforts are beginning to test the extent to which these
relationships conform to predictions of economic theory
(such as Prospect Theory [35]). For example, one
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important finding from behavioral economics is that
people evaluate the outcome of decisions based on a
flexible reference point. This predicts that the neural
systems responsible for utility assessment should be
responsive to relative gains and losses rather than to
absolute levels of rewarding and punishing stimuli.
Recent studies, using both fMRI [36] and scalp electrical
recordings [37] have provided support for this prediction.
Neural basis of probability estimation

Probability has been less well studied than reward,
although several recent neuroimaging studies have
begun to address this aspect. For example, one study has
shown that activity in medial prefrontal cortex is inversely
related to the probability of obtaining monetary reward
[38]. Other research has observed that activity in (human)
brain reward areas depends on uncertainty in the timing
of reward delivery [39].
Neural basis of the utility signal

Studies have also begun to examine the interaction
between value and probability in the computation of
utility and the execution of decision-making behavior.
This work builds on the recent discovery of brain areas
that appear to be directly related to decision-making,
including the lateral intraparietal area (LIP) and regions
of frontal cortex. Neurons in these areas closely track the
dynamics of decision-making in simple two alternative
forced choice tasks: response-selective neurons exhibit a
progressive increase in firing rate following stimulus
presentation, and the time at which they cross a threshold
of activity predicts the timing of the behavioral response.
This has been interpreted as evidence that these neurons
integrate choice-relevant information and implement a
fundamental decision-making mechanism [40–43] that
approximates the optimal algorithm [44–46]. These
neurons are also directly sensitive to manipulations of
utility, exhibiting combined effects of value and prob-
ability in a manner predicted by the Expected Utility

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Review TRENDS in Cognitive Sciences Vol.10 No.3 March 2006 111
model [47]. Additionally, there have been human imaging
studies testing for activity that scales directly with
expected utility, manipulating both reward magnitude
and probability [36,38]. This research has found that
activation in striatal reward areas correlates with
expected utility, demonstrating that utility-like measures
are found outside motor preparation areas such as LIP.

The investigations described above reflect the influence
that economic theory is already having on neuroscience
studies. An independent metric for assessing reward value
is crucial for a system that must often choose between
rewards delivered in different modalities (e.g. do we take
extra vacation time or an additional paycheck?), and the
neural systems outlined above may well provide the basis
for such a signal.

The view from neuroscience: multiple systems

Psychology and neuroscience also bring much to the
neuroeconomics table, contributing a rich tradition of
empirical research and increasingly precise methods for
studying behavior and the neural mechanisms by which it
is governed. Of particular relevance to economics is the
growing insight into mechanisms that are responsible for
the assessment of utility and execution of decision-making
behavior, as outlined in the previous section. However,
perhaps the single most important perspective that
neuroscience brings is to challenge the core assumption
in economics that behavior can be understood in terms of
unitary evaluative and decision-making systems.

How neuroscience can inform economics: the benefits of

a multiple-system approach

As noted in the first section of this article, economic theory
assumes that people choose between alternative courses of
action based on a rational evaluation of the consequences,
and economists have developed detailed theoretical
models for dealing with many decision situations: for
example, the Expected Utility model (described above)
for decisions under risk, and the Discounted Utility model
for decisions with consequences spread over time. These
models have the virtue that they are formally explicit,
analytically tractable, and can be used to make quanti-
tatively precise predictions about decision-making in a
wide variety of circumstances. As such, they have
provided a strong and unifying foundation for the
development of theory about decision-making, with an
assumption that decisions reflect the operation of a
unitary all-purpose information processor.

However, psychological research on judgment and
decision-making has produced a wealth of evidence
demonstrating that, in practice, these models do not
provide a satisfactory description of human behavior [48].
There is a long legacy of research within psychology,
strongly supported by findings from neuroscience, to
suggest that human behavior is not the product of a single
process, but rather reflects the interaction of different
specialized subsystems. Although most of the time these
systems interact synergistically to determine behavior, at
times they compete, producing different dispositions
towards the same information. A major cause of these
observed idiosyncrasies of behavior that have been used to
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challenge the standard economic model might be that
these decisions do not emerge from a unitary process, but
rather from interactions between distinguishable sets
of processes.

Multiple processes in decision-making

The most general distinction, and the most important for
neuroeconomics, is one that psychologists make between
automatic and controlled processes [49,50]. Automatic
processes are fast and efficient, can often be carried out in
parallel, but are highly specialized for domain-specific
operations and therefore relatively inflexible. They are
thought to reflect the operation of highly over-trained
(and, in some cases, possibly ‘hardwired’) mechanisms.
However, humans also have a capability for controlled
processing underlying our higher cognitive faculties.
Controlled processes are highly flexible, and thus able to
support a wide variety of goals, but are relatively slow to
engage and rely on limited capacity mechanisms – that is,
they can support only a small number of pursuits at a
time. Furthermore, the operations involved in controlled
processes (such as reasoning) are often accessible to
introspective, explicit description, whereas those involved
in automatic processes (such as recognizing a face) are
usually much less so. An example commonly given of the
distinction between these processes is driving a stick-shift
car: the novice is thought to rely on controlled processing,
requiring focused concentration on a sequence of oper-
ations that can be articulated but that are effortful and
easily disrupted by distraction. By contrast, the well-
practiced driver, relying on automatic processes, can carry
out the same task efficiently while engaged in other
activities (e.g. conversing), but might no longer be able to
articulate clearly the individual operations involved.

The distinction between controlled and automatic
processing is probably best thought of as a continuum,
rather than a qualitative dichotomy [51,52]. Nevertheless,
it has proven extremely useful in characterizing the
dynamics of behavior involving competing processes.
This distinction has appeared prominently in the field of
decision research, where investigators refer to two types of
evaluative systems: System 1 and System 2 [53,54].
System 1 is automatic and heuristic-based; quickly
proposing intuitive answers to problems as they arise.
System 2, which corresponds closely with controlled
processes, monitors the quality of the answer provided
by System 1 and sometimes corrects or overrides
these judgments.

Behavioral evidence for multiple systems

The description of System 2 bears a close resemblance to
the rational, general-purpose processor presupposed by
standard economic theory. However, a bounty of exper-
imental findings suggest that controlled processing
accounts for only part of our overall behavioral repertoire,
and in some circumstances can face stiff competition from
domain-specific automatic processes.

There is now an extensive literature describing the
many ways in which human decision-making violates the
principles of rationality as defined by the Expected Utility
(EU) [55] and Discounted Utility (DU) [56] models. Early

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Review TRENDS in Cognitive Sciences Vol.10 No.3 March 2006112
work in economics revealed situations (e.g. Ellsberg and
Allais paradoxes) whereby behavior violated key axioms of
the EU model. More recently, the ‘Heuristics and Biases’
approach in psychology has documented many instances
of deviations from economic rationality [57]. For example,
most people are reluctant to play a gamble with 50%
chance of winning $25 and 50% chance of losing $20,
despite the gamble’s overall positive expected value. This
illustrates the phenomena of ‘loss aversion’, whereby
people often place disproportionate weight on losses
relative to gains of similar absolute value [58].

When it comes to choice over time, there is also ample
evidence of violations of the DU model. Perhaps most
importantly, there is strong evidence that discounting is
much steeper for short time delays than for longer delays, a
phenomenon known as ‘hyperbolic time discounting’ [59].
For example, offered a choice between $10 today and $11 in a
week, many people are likely to choose the immediate $10.
However, offered the choice between $10 in a year and $11 in
a year and a week, most people would chose the $11, now
considering the extra week of wait inconsequential. From the
economist’s perspective, however, this implies a reversal of
preference (i.e. whether an extra dollar is worth a week’s wait
or not), and therefore does not conform to the rational
model [56].

Responding to the documented limitations of EU and DU,
a variety of alternative models of decision-making under
uncertainty and intertemporal choice have been constructed
(including hyperbolic time discounting and Prospect The-
ory). These models have made notable progress in describing
real-world decision-making [60], and even in designing
policy interventions to change behavior [61].

Additionally, economists have begun to construct
models that play on the automatic vs. controlled distinc-
tion. For example, Benhabib and Bisin (J. Benhabib and
A. Bisin, unpublished) propose a model of decision-making
in which automatic processes are initially allowed to
determine behavior, but controlled processes are activated
whenever the costs from letting the automatic processes
carry on become too large. They apply this framework to a
dynamic saving-consumption model, and describe how its
predictions differ from traditional economic theory. The
key insight here is that, rather than thinking about
human behavior as being governed by a unitary, general
purpose mechanism, it can often be better described in
terms of the interaction – and sometimes competition –
between different subsystems that might favor different
alternatives for a given decision.

However, although these new models of decision-
making have made great strides towards providing
descriptively realistic models of behavior, as yet they
have not provided insight into the actual mechanisms
responsible for the deviations from the normative models.
Recently, neuroimaging studies have begun to identify the
contribution that different systems make to decision-
making, in particular when this involves competition
between controlled processing and emotional responses –
a special case of automatic processes that we consider in
the next section.
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Neural evidence for multiple systems

With the advent of neuroeconomics, discoveries about the
neural mechanisms involved in perception, attention,
learning and action selection have begun to drive the
development of new, mechanistically explicit models of
decision-making. In several instances, where these models
have been expressed formally, they have also begun to
make contact with economic theory [62]. In some cases,
this has provided validation of some of the basic principles
of economic theory (see previous section), whereas in
others it has begun to provide insight into how and why
human behavior deviates from optimality as defined by
economic models. Psychological research, buttressed by
recent neuroscientific findings, has begun to identify
separable systems that contribute to decision-making
and behavior, systems that for the most part work
cooperatively, but sometimes compete. Recent neuroscien-
tific research has begun to characterize the engagement
and disposition of these neural systems under a variety of
conditions in which behavior seems to deviate from the
expectations of economic theory.
Systems for emotion and deliberation. Perhaps the
distinction with the greatest immediate ramifications for
economic theories is between systems supporting emotion
and those supporting deliberation, which closely parallels
the distinction between automatic and controlled processes.
The nature of emotions has been the subject of intense
inquiry and debate for entire fields of science, a full consi-
deration of which is well beyond the scope of this article. For
present purposes, we will use ‘emotion’ …

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