American Economic Association
Neuroeconomic Foundations of Economic Choice—Recent Advances
Author(s): Ernst Fehr and Antonio Rangel
Reviewed work(s):
Source: The Journal of Economic Perspectives, Vol. 25, No. 4 (Fall 2011), pp. 3-30
Published by: American Economic Association
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- Volume
4- Fall 2011- Pages3-30
25, Number
Perspectives
JournalofEconomic
Foundations
Neuroeconomic
Economic
Ernst
Fehr
Choice
and
Antonio
-
Recent
of
Advances
Rangel
brain controls human behavior. Economic choice is no exception.
induced variationin neural
Recentstudieshave shownthatexperimentally
The
in specificregionsof the brain changes people's willingnessto pay
activity
forgoods, rendersthemmore impatient,more selfish,and more willingto violate
social norms and cheat their tradingpartner (Camus et al., 2009; Figner et al.,
2010; Knoch, Pascual-Leone,Meyer,Treyer,and Fehr,2006; Baumgartner,Knoch,
Hotz, Eisenegger,and Fehr,2011; Ruff,Ugazio, and Fehr,2011; Knoch, Schneider,
Schunk,Hohmann, and Fehr,2009) . These studiesuse noninvasivebrain stimulation techniquessuch as transcranialmagneticstimulation(TMS) and transcranial
direct currentstimulation(tDCS), which enable the researcherto exogenously
in specificregionsof the cortexbeforesubjects
increaseor decrease neural activity
make decisions in experimentaltasksthat elicit theirpreferences.Such findings
demonstratethat neural activitycausallydetermineseconomic choices, and they
provide motivationfor studyingthe neurobiological and computationalmechanismsat workin economic behavior.
Neuroeconomics combines methods and theories from neuroscience,
economics,and computerscience to investigatethreebasic questions:
psychology,
1) What are the variablescomputed by the brain to make differenttypesof decisions,and how do theyrelateto behavioraloutcomes?2) How does the underlying
■ ErnstFehris Professor
Antonio
, University
, Zurich
, Switzerland.
ofEconomics
ofZurich
is
Neuroscience
and
Institute
Economics,
,
Rangel Professor
of
California
of Technology
Pasadena, California.Theire-mailaddresses
are {ernst,
and
eh)
(rangel@hss
[email protected].
.caltech.edu).
fToaccesstheAppendices,
visit
articles.
25.4.3.
http://www.aeaweb.
org/
php?doi=10.1257/jep.
doi=10.1257/jep.25.4.3
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4
Perspectives
JournalofEconomic
neurobiologyimplementand constrainthesecomputations?3) Whatare the implications of this knowledgefor understandingbehavior and well-beingin various
contexts:economic, policy,clinical,legal, business,and others?The ultimategoal
is to produce detailed computationaland neurobiologicalaccountsof the decisionmakingprocess thatcan serveas a common foundationforunderstandinghuman
behavioracrossthe naturaland social sciences (Wilson,1999).
Traditionally,economists have not been interestedin the neural processes
underlyinghuman choice. This lack of interestis drivenby the theoryof revealed
preference,which is one of the triumphsof twentiethcenturyeconomics. Most
economic models assume that individuals make choices as if maximizing a
prespecifiedutilityfunction,subject to feasibilityand informationalconstraints.
The revealedpreferenceviewis based on a well-knownresult:as long as observed
some basic consistencyaxioms,such as theWeakAxiom of Revealed
choices satisfy
function
Preference,theyare consistentwiththemaximizationofsome latentutility
(Houthakker,1950; Samuelson,1938). As a result,traditionaleconomic models are
"as if,"as opposed to "as is,"descriptionsof decision making.
In contrast,neuroeconomistsare interestedin the actual computationaland
neurobiologicalprocessesbehind human behavior.The neuroeconomicapproach
or "as is" models of decision making.Because neuroeconomics
aimsfor"structural"
sound structuralmodel of how the brain
is a veryyoung discipline,a sufficiently
available.
choices
is
not
makes
However,the contoursof such a computational
yet
model have begun to arise. Furthermore,
giventhe rapid progressthathas already
been made, thereis reason to be hopefulthatthe fieldwilleventuallyput together
structuralmodel.
a satisfactory
This article has two main goals. First,we provide an overviewof what has
been learned about how the brain makes choices in twotypesof situations:simple
choices among small numbersof familiarstimuli(like choosing betweenan apple
or an orange), and more complex choices involvingtradeoffsbetweenimmediate
and futureconsequences (like eating a healthyapple or a less-healthychocolate cake). In each case, we describe the emergentcomputationalmodel of the
underlyingchoice process as well as the neuroeconomic experimentsthat test
the differentcomponents of the model.1 Second, we show thateven at thisearly
stage in the field,insightswithimportantimplicationsforeconomics have already
been gained.
We willshowbelow,forexample, thatone importantimplicationis the prevalence of systematicmistakesin economic choices. Neural activityis stochasticby
its verynature and thus the neural computationsnecessaryfor making choices
1Given
that
research
areasofneuroeconomics
wedonotdiscuss
thescopeofthisessay,
many
important
of
toassign
learns
howthebrain
toeconomists,
should
beofinterest
values,theneurobiology
including
offinancial
decision
theneurobiology
ofstrategic
theneurobiology
socialpreferences,
choice,
making,
in onlineAppendix
forthesetopicsis available
A listofcitations
andneuralmechanism
A,
design.
available
with
thispaperat(http://ejep.org).
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ErnstFehrand AntonioRangel 5
are stochastic.For thisreason, neuroeconomics can provide a neural foundation
for random utilitymodels. However,while random utilitymodels assume that
preferencesare stochasticand thatchoices alwaysreflectunderlyingpreferences,
neuroeconomic research suggeststhat the choice process can be systematically
biased and suboptimal. For example, the computation and the comparison of
decision values underlyinggoal-directedbehaviormaybe biased because decisionmakersmayfail to take into account the relevantattributesof experienced utility.
In fact,as we willshowbelow,neuroeconomic researchindicatesthatconsumption
choices can be biased bysimplemanipulationsof subjects'visualattentionand the
opportunitycostsof time,thus providinginsightsinto how marketingactions can
affectthe probabilityof mistakes.The observedpatternof the neuronal encoding
of decision values also implies thatchoices willfail to satisfythe independence of
irrelevantalternatives,which is at odds withthe axioms of many (random) utility
models. In addition, the pattern of the neuronal encoding of decision values
implies thatmistakesare more likelyto occur if the range of values thatsubjects
need to consider is bigger.One consequence of this line of research is that one
cannotsimplyuse revealedpreferencesto measurewelfarebut thatmore elaborate
proceduresare required. Taken together,these findingsand implicationssuggest
thatneuroeconomics contributesto a positivetheoryof the mistakesthatpeople
make in theirchoices, withpotentiallyimportantconsequences for positiveand
normativeeconomics.
Simple Choices: Computational Model and Neuroeconomic
Evidence
Simple choices are the simplestinstance of economic decision making that
can be studiedusing the neuroeconomicsapproach. They involvechoices between
a small number of familiargoods, with no informationalasymmetries,strategic
considerations,or self-control
problems.A typicalexample is whetherto choose an
or
an
for
dessert.
apple
orange
At firstsight, these choices are not terriblyinterestingfor an economist.
However,theyare invaluable for neuroeconomics because theyallow us to study
the computationaland neurobiological basis of decision making in the absence
of complicatingfactors.The hope is that the principles and insightslearnt in
the simple case will also be at work in more complicated and interestingproblems. Indeed, even at our currentlimited level of understanding,the insights
that have already been obtained about simple choice have useful implications
foreconomics.
We begin the discussion by describing the five key components of the
computationalmodel of simple choice that is arisingfromthe neuroeconomics
literature.Closelyrelatedversionsof the model have been proposed byGlimcher
(2010), Kable and Glimcher (2009), Padoa-Schioppa (2011), and Rangel and
Hare (2010).
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6
Perspectives
JournalofEconomic
1. The braincomputesa decisionvalue signalforeach optionat thetimeof choice.
In thismodel, economic choice is drivenbythe computationand comparison
of "decisionvalue" signals.In particular,the model assumesthatthe decisionvalues
are computed fromthe instantthe decision process starts(for example, when a
choice pair is displayedto a subject on a computer screen) to the moment the
choice is made (forexample, when the subject indicatesa choice by,say,pressing
a button). Decision values should be thoughtof as signalscomputed at the timeof
choice thatforecastthe eventualhedonic impact of takingthe differentoptions.
Because choices are made by computing and comparing decision values, these
signalscausallydrivethe choices thatare made: options thatare assigneda higher
decisionvalue willbe more likelyto be chosen.2
The existenceofdecisionvalue signalsat the timeofchoice mightbe thesingle
testedhypothesisin neuroeconomics,as well as the mostsystematimostfrequently
callyreplicatedfindingthusfar.3Multiplehuman studiesusingfunctionalmagnetic
resonance imaging (fMRI) and electro-encephalography
(EEG), as well as single
neuron recordingsin nonhuman primates,have shown thatneural activityin an
area of the ventromedialprefrontalcortexincreaseswithbehavioralmeasures of
the decisionvalues assignedto optionsat the timeof choice.
Since these typesof studiesare unfamiliarto economists,it is usefulto begin
byexplainingtheirbasic logic. How does one testthatthe brain encodes a certain
variable- say,decision values- at a particulartime in the choice process? The
typicalexperimenthas threemain components.First,some formof behavioraldata
is used to estimatethe value thatthe brain assignedto the signalof interest.These
behavioral data are sometimesobtained in a separate task: for example, in the
bids for
case of decision values,byaskingsubjectsto provideincentive-compatible
each option used in an experiment,or to provide"liking"ratings.In otherexperimentaldesigns,thevalues can be inferreddirectlyfromthe patternof choices (for
example,Chib, Rangel,Shimojo,and O'Doherty,2009; Padoa-Schioppa and Assad,
is takenduringthe choice process
2006) . Second, a measurementof neural activity
in particularbrain areas. The three most popular techniques used in neuroeconomic studiesinclude fMRI,EEG, and (in animal studies) single neuron in vivo
duringthe
recordings.4Third,statisticalmethodsare used to testifneural activity
2Itis
sincethere
ortautological,
ofthemodelisnotempty
toemphasize
thatthiscomponent
important
For
values.
decision
andcomparing
makechoices
thebrainmust
isnoa priori
reason
bycomputing
why
whena red
associations
couldbe madeusinglearnt
choices
(forexample,
stimulus-response
example,
oftheoptions
ispresent,
(forexample,
properties
presstheleftlever)orbasedontheperceptual
light
alternadriven
belargely
Infact,
behavior
thehighest
visual
with
choosetheitem
contrast).
bythese
may
suchasnematodes.
insufficiently
tivetypes
ofprocesses
simple
organisms
arecomputed,
values
decision
where
andwhen
totheliterature
listofreferences
Fora detailed
showing
with
thispaperat(http://e-jep.org).
seeonline
B,available
Appendix
of
measures
fMRIisnoninvasive,
anddisadvantages.
haverelative
Themethods
provides
advantages
butit
of0.5-3.0mm3),
inrelatively
neural
(intheorder
regions
anatomically
specific
activity
aggregate
anditprovides
about0.5Hz).EEGisalsononinvasive,
resolution
haspoortemporal
extremely
(typically
unit
thanfMRI.
orspatial
resolution
anatomical
butithasmuchpoorer
finetemporal
resolution,
Single
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- RecentAdvances 7
Foundations
Neuroeconomic
ofEconomicChoice
period of interestis modulated by the signal (or signals) of interest.If the neural
relatedto the signalof interest,then thisis taken
is statistically
activity
significantly
to be evidence consistentwiththe hypothesisthatactivityin thatneural substrate
encodes the signal.
An example ofa paper providingevidencefortheexistenceofdecisionvaluesis
Plassmann,O'Doherty,and Rangel (2007). Hungrysubjectswereshowna pictureof
a familiarfood snackin everytrialand had to decide how much to bid forthe right
was measuredwithfMRI.
to eat it at the end of the experiment,whileneural activity
The bids providea behavioralmeasure of the decision values computed in every
in the area of the brain under studyduringthe
trial.The studyfound thatactivity
choice period correlatedwiththe bids,whichprovidesevidence forthe hypothesis
thatthebraincomputesdecisionvalues at the timeof choice. This findinghas been
replicatedin multiplestudiesusing distinctchoice objects (lotteries,foods,donations to charity,trinkets),distinctvaluation paradigms (price purchase decisions,
auctionformats,
binarychoices,likingratings), and distinctchoice speeds (fromone
to severalseconds). Follow-upstudieshaveshownthatdecisionvaluesare encoded in
cortexin morecomplexchoice settings:
thesame area oftheventromedialprefrontal
choices among gambles (Levy,Snell, Nelson, Rustichini,and Glimcher,2010; Tom,
Fox,Trepei,and Poldrack,2007); delayedmonetarypayments(Kable and Glimcher,
2007); and charitabledonations (Hare, Camerer,Knoepfle,and Rangel,2010).
A relatedclassofstudieshas askedifthesame area oftheventromedialprefrontal
cortexencodes the decision values forchoices among both appetitiveand aversive
items(Litt,Plassmann,Shiv,and Rangel,2010; Plassmann,O'Doherty,and Rangel,
2010; Tom, Fox, Trepei, and Poldrack,2007). The distinctionbetween appetitive
and aversiveitemsis unfamiliarto economists,but is importantin psychologyand
neuroscience.An itemis called "appetitive"ifan animal would workto consume it
(forexample,sugarwhen hungry)and "aversive"ifan animal would workto avoid
it (for example, an electricshock). A common hypothesisin psychologyis that
choices among appetitiveitems,sometimescalled approach choice, and choices
among aversiveitems,sometimescalled avoidance choice, involveseparatesystems
(Larsen, McGraw,Meilers, and Cacioppo, 2004). These studies are important
because theyshow that,at least in the case of simple choice, the same area of the
brain seems to encode the decision value forboth typesof choices, thusproviding
evidence againstthe multiplesystemhypothesis.5
allowthemeasurement
ofactivity
insingle
neurons
with
but
resolution,
recordings
very
hightemporal
thismethod
isextremely
is pervasive
inanimalstudies,
itis rare
invasive.
themethod
Thus,although
inhumans.
Anadditional
offMRI
andEEGoversingle
unitrecordings
isthatthey
allowthe
advantage
simultaneous
ofactivity
intheentire
iscritical
forstudying
howthecomputameasurement
which
brain,
tions
carried
outindifferent
brainregions
affect
eachother.
' This
isalsoimportant
inareasofthebrain
becauseitrulesoutthepossibility
thattheactivity
finding
toencodedecision
which
arealtervalues
canbeattributed
toattention
orsaliency
thought
responses,
natesignals
andthathavebeenfound
inother
cortical
thatincrease
with
theabsolute
valueoftheitems
areas(Litt,
andRangel,
andOlson,2004).
Plassmann,
Shiv,
2010;Roesch
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8
Perspectives
JournalofEconomic
Clearly,these findingsconstituteonlypreliminaryevidence,and furthertests
mustbe carried out. Of particularinterestis establishingthat the decision value
signals causally affectchoices. The existingevidence suggeststhat the decision
value signals are precursors,and not consequences, of the choice process. The
ventromedialprefrontalcortex seems to encode decision values for all options
being considered before the choice is made, and the signals do not depend on
which option is chosen (Hare, Schultz,Camerer,O'Doherty,and Rangel, forthcoming; Padoa-Schioppa and Assad, 2006; Wunderlich,Rangel, and O'Doherty,
2010). Also, individualswith damage in the relevantareas of the ventromedial
prefrontalcortexare unable to make consistentchoices, which suggeststhatthis
partof the brainplaysa necessaryrole in computingreliabledecisionvalue signals
itis possibleto investigate
(Fellowsand Farah,2007) . In addition,althoughdifficult,
the causalityof these signals by experimentallymanipulatingthe value signal in
cortexand examiningtheresultingbehavioralchanges.
theventromedialprefrontal
In a recentstudy,for example, Baumgartner,Knoch, Hotz, Eisenegger,and Fehr
thevalue signalin theventromedialprefrontalcortex
(forthcoming)down-regulate
using transcranialmagneticstimulationin the dorsolateralprefrontalcortex.This
of theventromedialprefrontalcortexless sensimakesthe activity
down-regulation
tiveto inputs,whichrendersthe value signalsencoded here weakerand produces
sizable changes in behavior.6
2. The braincomputesan experiencedutilitysignalat the timeof consumption.
The brain needs to keep trackof the consequences of its decisions to learn
how to make choices in thefuture.A keycomponentofsuch learningis thecomputationof an experienced utilitysignal at the timeof consumptionthatreflectsthe
actual consequences forthe organismof consumingthe chosen option.7
We emphasize that decision values are distinctfromthe experienced utility
signal:decisionvalues are forecastsabout the experiencedutilitysignalthatwillbe
computed at the time of consumption.8Indeed, decision values and experienced
utilityneed not agree witheach other.It is a prioripossible thata person might
6
causaleffects:
andcanthushaveimportant
braincircuitry
alsoaffect
manipulations
Pharmacological
behavior
hasbeenshownto increase
Theneuropeptide
Zak,
Heinrichs,
(Kosfeld,
trusting
oxytocin
in theultioffers
increases
testosterone
andFehr,
Fischbacher,
2005).Thesexhormone
bargaining
inanhonesty
andFehr,
matum
Snozzi,
Heinrichs,
2010)andhonesty
game(Wibral,
game(Eisenegger,
serotonin
neurotransmitter
of
the
The
and
Weber,
Falk,
Dohmen,
2011).
depletion
Kingmüller,
intheultimatum
rateofresponders
increases
therejection
Lieberman,
Clark,
Tabibnia,
game(Crockett,
et
rate(Gospic
reduces
therejection
ofbenzodiazepines
andRobbins,
2008)whiletheadministration
al.,2001).
7The
isan
values
decision
future
areusedbythebraintoupdate
ofhowexperienced
utility
signals
study
ithere.SeeNivand
butwedonotdiscuss
inneuroeconomics,
areaofresearch
andvery
active
important
review.
(2008)foranexcellent
Montague
8
the
valueandexperienced
decision
distinction
between
Theneuroeconomic
parallels
utility
signals
economists
madebybehavioral
thatisoften
andexperienced
between
decision
distinction
utility
utility
andSarin,
Wakker,
1997).
(Kahneman,
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ErnstFehrand AntonioRangel 9
have a higher decision value for apples than for oranges, but that experienced
utilitymightbe the opposite. This should not happen oftenin a well-performing
organism,but it cannot be ruled out in all cases byassumption.Understandingthe
circumstancesunder which the twosignalsare in agreementor disagreementis a
criticalquestion in neuroeconomics.
Where in the brain are the experienced utilitysignalscomputed,and whatare
the differencesbetween the processes involvedin computingdecision values and
those involvedin computingexperienced utilities?The body of evidence here is
more preliminarythan forthe case of the decision values. This is partlydrivenby
a technicaldifficulty:
it is quite difficultto induce controlledconsumptionexperiences in humans while theyare lyinginside an fMRI scanner,and it is difficultto
measureexperiencedutilityreliablyin animals.
Nonetheless, several studies have found that such signals are present in
various partsof the orbitofrontalcortex and the nucleus accumbens at the time
of consuminga varietyof goods including music, liquids, foods, and art (Blood
and Zatorre,2001; de Araujo, Rolls, Kringelbach,McGlone, and Phillips,2003;
Kringelbach,O'Doherty,Rolls, and Andrews,2003; McClure, Li, Tomlin, Cypert,
Montague, and Montague, 2004; Rolls, Kringelbach,and Araujo, 2003; Small,
Gregory,Mak, Gitelman,Mesulam,and Parrish,2003).
Neuroeconomic studies have also begun to characterizesome novel properties of the experienced utilitysignals.Koszegi and Rabin (2006, 2009, 2007) have
proposed that experienced utilitydepends not only on what is consumed, but
also on the extent to which that consumptionwas expected. In particular,they
propose that positive surprisesincrease experienced utility,and that negative
surprisesdecrease it. Bushong, Rabin, Camerer,and Rangel (2011) used fMRI to
testthishypothesisand foundthatactivity
in the same areas of orbitofrontal
cortex
previouslyassociated withexperienced utilitycomputationsexhibitthe predicted
surpriseeffects.Plassmann, O'Doherty, Shiv,and Rangel (2008) used a related
approach to investigatethe extentto which the pleasure derivedfromdrinkinga
wine depends onlyon itsphysiologicalproperties,or whetherthispleasure is also
modulated by beliefs about the price of the wine. Subjects were asked to drink
wines while in the scanner and told the price of each one. Unbeknown to the
subjects,the same wine was described at two differentprices in differenttrials:
the real retailprice and a fictitiousone. They found that activityin the areas of
orbitofrontal
cortexassociated withthe computationof experienced utilitiesalso
increasedwiththe statedwine prices.
3. Choices are made bycomparingdecision values usinga "drift-diffusion
model."
The drift-diffusion
model was developed by psychologistRoger Ratcliffto
explain the accuracyand responsetimesin anytaskinvolvingbinaryresponsesthat
can be elicitedin a handfulof seconds (Ratcliff,1978; Ratcliff
and McKoon, 2008).
of
such
tasks
include
which of twovisual stimuliis largeror
Examples
identifying
model can also be
brighter,or whichof twonumbersis larger.The drift-diffusion
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10
Perspectives
JournalofEconomic
Figure1
Model
GraphicalDescriptionof the Drift-Diffusion
Authors.
Source:
considerthe case of a
applied to the comparisonof decision values. For simplicity,
binarychoice involvingtwooptions,x and y. Krajbichand Rangel (2011) present
a generalizationto multi-itemchoice. As Figure 1 illustrates,a binarychoice is
made bydynamically
computinga relativedecisionvalue signal,denoted byR, that
measures the value differenceof x versusy. The signal startsat zero and at every
instanttevolvesaccordingto the formula
#i+l =
+ 0 x (v(x) - v(?)) + £,,
where Rt denotes the level of the signal at instantt (measured fromthe startof
the choice process), v(x) and v(y) denote the decision value thatis assignedto the
twooptions,в is a constantthataffectsthe speed of the process,and etdenotes an
independentand identicallydistributederrortermwithvariance s2. The process
continuesuntila prespecifiedbarrieris crossed:xis chosen iftheupper barrierat В
and уis chosen ifthe lowerbarrierat В is crossedfirst.
is crossedfirst,
model has severalimportantfeatures.First,since the relaThe drift-diffusion
choices are inherentlynoisy,and
tivedecision value signal evolvesstochastically,
of the
the amount of noise is proportionalto the parameters2. The stochasticity
of neuronal
relativedecision value is a consequence of the inherentstochasticity
Second, the model predictsthatthe probabilityof choosing л:is a logistic
activity.
functionof the differencein the decision value signals [v{x) - v(^)] (Krajbich,
Armel,and Rangel, 2010; Milosavljevic,Malmaud, Huth, Koch, and Rangel, 2010;
of choice, thereis always
and McKoon, 2008). Third,giventhe stochasticity
Ratcliff
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- RecentAdvances 11
Foundations
Neuroeconomic
ofEconomicChoice
a positiveprobabilitythatindividualswill choose the option withthe lowestdeciof the choice (as measured
sion value. This probabilityincreaseswiththe difficulty
and
with
the parameter9 and with
v
v
decreases
small
is
the
value
how
| (x)
(y) |,
by
the heightof thebarriers.Indeed, the model makesspecificpredictionsabout how
of the choice
the shape of the reaction timedistributionvarieswiththe difficulty
and withthe parametersof the model.
model mightseem like an
The algorithmimplementedby the drift-diffusion
maximizationproblem,but
cumbersomesolutionto a straightforward
unnecessarily
thereis a beautifuland deep reason whyit has evolved.From the brain's point of
view,decision values are estimatedwithnoise at any instant.If the instantaneous
decision value signalsare computed withidenticaland independentlydistributed
model implementsthe optimal statistical
Gaussian noise, then the drift-diffusion
solution to the problem,which entails a sequential likelihood ratio test (Bogacz,
Brown,Moehlis, Holmes, and Cohen, 2006; Gold and Shadlen, 2002, 2007). The
The relativedecisionvalue R tcan
intuitionforwhythisis thecase is straightforward.
be thoughtofas theaccumulatedevidencein favorofthehypothesisthatthealternativeXis better(whenR t> 0), or theaccumulatedevidencein favorof thealternative
hypothesis(when R ,< 0). The more extremethesevalues become, the less likelyit
is thatthe evidence is incorrect.The probabilityof a mistakecan be controlledby
changingthe size of the barriersthathave to be crossedbeforea choice is made. As
a result,the drift-diffusion
model implementsan optimalstatisticalsolutionto the
faced
information
bythe brain.
problem
The components of the drift-diffusion
model have empiricallybeen tested
both
behavioral
and
neural
data.
Behaviorally,
Milosavljevic,Malmaud, Huth,
using
Koch, and Rangel (2010) showed picturesof familiarfood items to subjects,one
at a time,and asked them to decide whethertheywanted to eat them at the end
of the experiment.Before the choice task,theyobtained independent measures
of the decision values by askingsubjectsto rate how much theywould like to eat
themat the end of the experiment.They found thatit was possible to findparameters of the basic drift-diffusion
model that generated a remarkablequantitative
match between the predicted and observed distributionof choices and reaction
times.This matchprovidesstrongbehavioralevidence in favorof thiscomponent
model makes verystrong quantitative
of the model because the drift-diffusion
about
the
of
the
choice
and
predictions
shape
response timecurves,and how they
relate to each other.
Two recentstudieshave used fMRIto identify
areas involvedin comparingdecision values (Basten, Biele, Heekeren, and Fiebach, 2010; Hare, Schultz,Camerer,
O'Doherty, and Rangel, forthcoming).Hare, Shultz, Camerer, O'Doherty, and
Rangel (forthcoming)argue thata brain area involvedin implementingthe driftdiffusionmodel choice process must exhibitthe followingproperties:1) its level
of activity
in each trialat the timeof choice should correlatewiththe totallevel of
drift-diffusion
model; 2) it should receiveas an
activity
predictedbythe best-fitting
inputthe computationsof the area of theventromedialprefrontalcortexassociated
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12
JournalofEconomic
Perspectives
withcomputingdecision values; and 3) it should modulate activityin the motor
cortexin a waythatis consistentwithimplementingthe choice.9They found that
in twopartsofthebrain- thedorsomedialprefrontal
cortexand thebilateral
activity
sulcus satisfiedthe threerequiredpropertiesand thuswas consistent
intraparietal
model.
withthe implementationof the drift-diffusion
informationabout the attributes
4. Decision values are computedbyintegrating
associatedwitheach optionand theirattractiveness.
From a physiologicalperspective,even the simplest of choices involvesa
bundle of multipleattributes.For example, eating an apple has implicationsfor
basic dimensionssuch as taste,caloric intake,vitaminand mineralregulation,as
well as more abstractdimensionssuch as health or self-image.Let d^x) denote the
characteristics
of option x fordimensioni. The model assumesthat
v(x) = J^w¡di(x),
forsome set ofweights
Consider severalaspects of thisassumption.First,the decision values used to
thatare computedforeach optionat thetime
guide choicesdepend on theattributes
of choice. This impliesthatthe decisionvalue signals,and thusthe choice process,
takeintoaccountthevalue ofan attributeonlyto theextentthatthebraincan takeit
intoaccount in the constructionof the decisionvalues.Second, it providesa source
of preferenceheterogeneity
across individuals:some people mightfail to incorporate a particulardimensionin the decision values,not because theydon't value it,
but because theymightnot be able to computeitat the timeof choice.10
Althoughmuch work remains to be done in testingthis component of the
computationalmodel, several studies have provided supportingevidence. Hare,
Camerer,and Rangel (2009) asked hungrysubjectsto make choices about which
foods theywantedto have as a snack.Subjectswereshowna varietyof foods,one at
a time,thatvariedindependentlyin theirhealthinessand taste.Priorto the choice
tasktheycollectedtasteand healthratingsforeach of thefoods.Theyfoundactivity
and thatthe
in the ventromedialprefrontalcortexcorrelatedwithboth attributes,
relativeweightthattheyreceivedin the decision value signalsof the ventromedial
prefrontalcortexwere correlated,acrosssubjects,withthe weightgivento themin
the actual choices.
9More
intheareaofthemotor
increase
theareainvolved
inthecomparison
should
activity
concretely,
ischosen,
when
theleftoption
associated
with
the"left"
choice
cortex
that
controls
thehandmovements
associated
cortex
thatcontrols
thehandmovements
andshouldincrease
intheareaofmotor
activity
with
the"right"
choicewhentheright
ischosen.
option
10It is also
sumoftherelevant
alsoreflects
theweighted
natural
toassumethatexperienced
utility
neednotbethe
with
thesuperscript
иhighlights
thefactthatthese
attributes,
w",where
weights
weights
sameasthoseusedincomputing
decision
values.
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ErnstFehrand AntonioRangel 13
Lim, O'Doherty,and Rangel (2011b) providean additionaltestof thiscomponent of the model. American-bornsubjectswho did not speak a foreignlanguage
witha printedwordon them.The words
wereasked to make choices about T-shirts
were printedin Korean using differentcolors, sizes, and fontsizes, and varied in
meaningfromtheveryappealing (like"love") to theveryunappealing (like"incest").
As a result,the T-shirtsvaried in two importantdimensions:theiraestheticqualities and the semanticmeaning of the words printedon them. These two specific
attributeswere used because previousstudieshave shown thatthe aestheticvisual
propertiesof stimuliare computed in differentareas than the semanticmeaning
of words.Half of the subjectswere taughtthe meaning of the Korean words; the
otherhalfwere not. This allowed the researchersto dissociatethe areas associated
withthe computationof both attributes.In particular,theyfound that activityin
the posteriorsuperiortemporalgyrus,which has been widelyassociated withthe
computationof semanticmeaning,correlatedwiththe value of the semanticattribute but not withthe aestheticvalue. The opposite was trueforan area of fusiform
gyrusthatis knownto be involvedin computingthevisualpropertiesof the stimuli.
in the ventromedialprefrontalcortexcorrelatedwiththe deciIn addition,activity
sion values and receivedinputsfromboth areas.
5. The computationand comparisonof decision values is modulatedbyattention.
Attentionrefersto the brain's abilityto varythe computationalresourcesthat
are deployed in differentcircumstances.For example, the visual systemmight
increase itsinvolvementwhen high-valuestimuliare presentor when perceivinga
physicalthreat,but mighttune offin othercircumstances.This abilityis extremely
usefulbecause the brain's computationalresources are scarce, costlyin termsof
consumingenergy,and in some cases mightinterferewitheach other.
Attentioncan affectthe choice process in two differentways.First,it might
affecthow attributesare computedand how theyare weightedin the decisionvalue
computation.For example, the presence of other individualsmightincrease the
brain'slikelihoodto computeand weightsocial dimensionsof the choice problem.
This can be incorporatedinto the model as follows:Let a be a variabledescribing
the attentionalstate at the time of choice. The computed decision value is then
givenby
v(x) - S wi(a) di(x>a).
Second, attentioncan also affecthow decision values are compared at the timeof
choice. In particular,Krajbich,Armel,and Rangel (2010) have proposed a simple
model in whichthe evolutionof the relativedecision
variationof the drift-diffusion
value signaldepends on thepatternof attention.The model is identicalto thebasic
drift-diffusion
set-upexcept thatthe path of the integrationat anyparticularinstant
now depends on whichoption is being attended to. Thus, forexample, when the
Xoption is being attended,the relativedecision value signalevolvesaccordingto
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14
JournalofEconomic
Perspectives
Rt+i - Rt + в X (ßv(x) - v{y)) + £t,
whereß measuresthe attentionalbias towardsthe attendedoption.We referto this
model." Ifß = 1, themodel is identicalto the
model as the "attentiondrift-diffusion
basic model and choice is independentof attention,but ifß > 1, choices are biased
towardsthe option thatis attendedlonger.
Two propertiesof the model are worthhighlighting.First,it predicts that
exogenous changes in attention(forexample, throughexperimentalor marketing
manipulations)should bias choices in favorof the mostattended option when its
value is positive,but it should have the opposite effectwhen the value is negative.
Second, the model makes strongquantitativepredictionsabout the correlation
between attention,choices, and reaction times- predictionsthat can be tested
usingeye-tracking.
The assumptionsthatthe computationand comparisonof decision values are
modulatedbyattentionhave been explicitlytested.Withrespectto thecomputation
of decisionvalues,Hare, Malmoud, and Rangel (2011) used a paradigmsimilarto
theextentto which
the"health-taste"
food choice taskdescribedabove to investigate
cortexthatreflected
thedecisionvalue computationsin theventromedialprefrontal
the healthand tastinessof the foodscould be externallymanipulated.In particular,
theyasked subjectsto makefood choices in one of threeconditions:payattentionto
healthconsiderations,pay attentionto tasteconsiderations,or- in a controlcondiIt was emphasizedthatsubjectsshould make choices based
tion- to reactnaturally.
on theirpreferencesand should pay equal attentionin any of the situations.They
found thatthe healthinessof the choices,as well as the extentto whichhealthwas
cortexvalue signals,increasedin thehealth
reflectedin theventromedialprefrontal
attentioncondition.Furthermore,the extent to which the impact of the healthattentioninstructionaffectedbehavior was correlated,across subjects,with the
receivedin ventromedial
extentto whichitaffectedtheweightthathealthattributes
cortex
signals.
prefrontal
Withrespectto thecomparisonofdecisionvalues,Krajbich,Armel,and Rangel
to test the predictionsof the attentionversion of the
(2010) used eye-tracking
model. Theyfound thatthismodel is able to generatea surprisingly
drift-diffusion
accurate quantitativeaccount of the strongpredictionsof the model. They also
found evidence fora substantialattentionbias in the choice process: options that
werefixatedon more,due to randomfluctuationsin attention,were more likelyto
be chosen. In follow-upwork,Krajbichand Rangel (201 1) have shownthata natural
model to the case of threeextensionof the attentionversionof the drift-diffusion
A centralprediction
fit
of
data.11
choice
also
a
way
provides verygood quantitative
of the attentionversionof the drift-diffusion
model is that exogenous increases
11
the
theauthors
show
thatthethree-way
choicedatacanbeexplained
using
quantitatively
Interestingly,
from
thattheunderlying
estimated
thebinary
choicecase.Thisfinding
processes
suggests
parameters
forsmallnumbers
ofitems.
berobust
might
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- Recent
Advances 15
Neuroeconomic
Foundations
ofEconomicChoice
in the amount of relativeattentionpaid to an appetitiveitem should increase the
probabilitythat it is chosen. Consistentwiththis prediction,several studies have
found thatit is possible to bias choices throughexogenous manipulationsof visual
attention(Armel,Beaumel, and Rangel,2008; Milosavljevic,Malmaud,Huth,Koch,
and Rangel,2010; Shimojo,Simion,Shimojo,and Scheier,2003). 12
Economic Implications of the Neuroeconomic
Model of Simple Choice
Prevalentand SystematicMistakesin Economic Choice
One importantimplicationof the neuroeconomic model of simple choice is
that individualscan often make mistakes.In this framework,an optimal choice
is made when the option associated withthe largestexperienced utilitysignal at
consumptionis selected,and a mistakeis made otherwise.There are threepotential sources of mistakes:1) stochasticerrorsin choices thatare embodied in the
drift-diffusion
model; 2) errorsin the computationof decision values, perhaps by
systematically
failingto take into account some attributesthat will affectexperienced utility;and 3) biases due to how attentionis deployed in the computationof
decisionvalues,or in theweightthattheyreceivein the comparisonprocess.
Note thatthe model goes beyond simplypointingout thatmistakesare likely
costof time
and providesinsightsintohow economic variables,like the opportunity
or marketinginterventions,
can affectthe probabilityof mistakes.An important
open question is how large the potentialmistakesin variousdomains are. Preliminaryexperimentalevidence suggeststhatas manyas 20 percentof simple choices
mightbe mistakes,althoughitis likelythatthe proportionof mistakeschangeswith
detailsof the choice situation,such as stakesor cognitiveload (Frydman,Camerer,
Koch,
Bossaerts,and Rangel,2011; Krajbich,Armel,and Rangel,2010; Milosavljevic,
and Rangel,forthcoming).
An implicationof the model is thatone cannot use simpleversionsof revealed
preferenceto measure welfareand that more sophisticatedprocedures that take
these mistakesinto account must be developed. This insightprovides a neurobiological motivationfor the fieldof behavioralwelfareeconomics. For example,
Bernheim and Rangel (2009) have proposed a modified revealed preference
procedure thatmakes it possible to measure experienced utilityfromthe choice
data even when mistakesare possible.A criticalcomponentof theirmethodologyis
the identificationof "suspect"choice situationsin whichthereis reason to believe
12Lim,
andRangel(2011a)haveusedfMRItotestifsomeofthecomputations
O'Doherty,
necessary
toimplement
inthebrain.
thatin
theattentional
drift-diffusion
modelareencoded
Note,inparticular,
thismodel,
choices
aremadebyaddingovertimetheinstantaneous
modulated
relative
attentionally
valuesignal
wereaskedtomakebinary
foodchoices
while
given
by"ßv(x)- v(y)".Subjects
exogenously
their
visual
which
with
this
isa natural
attention.
Consistent
fixations,
controlling
wayofmanipulating
ofthemodel,
foundthattheareaofventromedial
cortex
associated
with
component
they
prefrontal
decision
values
alsocomputed
anattentionally
modulated
valuedifference.
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16
Perspectives
JournalofEconomic
thatthe subjectmighthave made a mistake.(For relatedwork,see Rubinsteinand
Salant,2006; Salant and Rubinstein,2007).
It is importantto emphasize a methodologicalaspect of how neuroeconomics
deals withmistakes.Since measuringthe experiencedutilityassociatedwithparticit is
ular consumptionepisodes using neurometricmethods is stillverydifficult,
not possible to testthe presence of decision-makingmistakesdirectly.However,a
testsof the model using
roundaboutapproach is possible. Suppose thatsystematic
neuroeconomicmethodsestablishitsvalidity.Then, the presence of mistakesand
their relationshipto differentmodel components followsdirectlyfromthe fact
thatthe choices are made using these specificprocesses.In otherwords,once the
computationalprocessesare pinned down, theirimplicationsare also likelyto be
valid,even iftheyare hard to testdirectly.
Neural FoundationsforRandom UtilityModels
Althoughthe basic economic theoryof revealed preferenceis based on the
assumptionof a stable and nonstochasticchoice correspondencefromchoices to
observablevariables,empiricaleconomistsknow thatrandomnessis a factof life.
This motivatedthe developmentof random utilitymodels of choice, which are a
cornerstoneof empiricalresearch (Gul and Pesendorfer,2010, 2006; Luce, 1959;
McFadden, 1974, 2005). The computationalmodel based on the drift-diffusion
model makes behavioralpredictionsthatare highlyconsistentwithrandom utility
models. Thus, the neuroeconomic model providesa neurobiologicalfoundation
for random utilitymodels. However,the two models have one importantdifference. In the drift-diffusion
model, the noise arisesduringthe processof comparing
the computed decision values,and thus it does not reflectchanges in underlying
preferences:it is purelycomputationalor processnoise. In contrast,randomutility
models assume stochasticshocks to the underlyingpreferences.This differenceis
normativepredictionsabout
important,because the twomodelswillmake different
the qualityof choices.
The computationalmodel also makes predictionsabout how contextualand
environmentalvariablesshould affectthe amount of noise in the choice process.
For example, Milosavljevic,Malmaud, Huth, Koch, and Rangel (2010) asked
subjectsto make simple food choices withand withouttime pressure,and found
thattimepressurespeeded up the decisions but also led to noisier choices. Critically,theyalso found thatthe differencesbetweenboth conditionswere explained
model
with high quantitativeaccuracy by a single change in the drift-diffusion
in
illustrated
model
of
the
drift-diffusion
the
barriers
1)
(as
Figure
parameters:
were smallerunder timepressure.Since manyeconomic factorsaffectthe opportunitycost of time,thismodel predictsthatthe qualityof decision makingshould
change with such factors.It also provides a mechanism for whysubjects might
make fewermistakeswhen the stakesare sufficiently
high: in those cases, subjects
in order to slow the choice
size
of
the
barriers
increase
the
significantly
might
mistakes.
and
reduce
process
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ErnstFehrand AntonioRangel 1 7
"Wired"Restrictionsin the Choice Correspondence
The viewpointthatsimpleeconomic choices are made bycomputingdecision
model implies that knowlvalues and comparing them using the drift-diffusion
edge about the systemsinvolvedin the computationof decision values provides
importantclues about the structureof the choice correspondences- that is, how
of the situation.
individualchoices willbe affectedbythe observablecharacteristics
For example, ifwe knowthatthe decision values are wiredto be unresponsiveto a
certainvariable,thenwe knowthatchoices cannot depend on thatvariable.
Consider an illuminatingexample: Padoa-Schioppa has used single neuron
theextenttowhichthedecisionvaluesassigned
recordingsin monkeysto investigate
to a particularoption in ventromedialprefrontalcortexneurons depend on other
optionsin the choice set (Padoa-Schioppa, 2009; Padoa-Schioppa and Assad,2006,
amountsand
2008) . In thissetting,animalsmake binarychoices betweendifferent
typesofjuices. The range of decision values thatanimals need to compute is held
in others.One keyfinding
constantin some experimentsbut varied systematically
of these studies is that the decision value signals exhibit"range adaptation": the
best and worstitemsreceive the same decision value, regardlessof theirabsolute
and the decision value of intermediateitemsis given by theirrelaattractiveness,
tivelocation in the scale. This findingmattersfor economics because it implies
thatthe likelihoodand size of decision mistakesincreaseswiththe range of values
that needs to be encoded. It also means that the probabilisticchoice correspondence failsto satisfy
a findingat odds with
Independence of IrrelevantAlternatives,
the assumptionsof many popular random utilitymodels (for example, Gul and
Pesendorfer,2006; Luce, 1959).
Attention,
Marketing,and BehavioralPublic Policy
In the neuroeconomicmodel, exogenous shiftsin attentioncan bias choices in
systematic
ways.In particular,cues and framesthatdirectattentiontowardscertain
attributesshould increase the weightthattheyreceivein the computationof decision values,and thusin choice. This providesa neurobiologicalfoundationforthe
effectiveness
of some marketingand behavioralpublic policies.
As one example, manymarketinginterventions
are centeredon changingthe
visualsaliencyand attractiveness
of packages. Milosavljevic,Malmaud, Huth, Koch,
and Rangel (2010) testedthe effectiveness
of these typesof interventionsusing a
binarychoice paradigm in which theyvaried the relative"visualcontrast"of the
images of the options. They found that this manipulationhad a sizable effectin
attentionpaid and thatit biased the choices as predicted.A criticalquestion for
futureresearch is to understand how these marketingtechniques interactwith
traditionaleconomic variablessuch as price and familiarity.
As another example, the neuroeconomic model suggeststhatenvironmental
- like health
cues thatdirectattentiontowardsthe long-termfeaturesof the stimuli
in the case of food or smoking- may lead to healthierdecisions. An example of
thisclass of policies is the mandatoryplacementof pictureson cigarettecontainers
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18
JournalofEconomic
Perspectives
consequences ofsmoking.These
depicting,in highlyemotionalform,thelong-term
associated
with
indicators
of
are
policies
higher
smokingcessationthan less-salient
in capturing
textwarningsthatconveysimilarinformationbut maybe less effective
attention(Borland et al., 2009; White,Webster,and Wakefield,2008). The neuroeconomic model proposed here readilyexplains this phenomenon: the pictures
decrease the decision value of cigarettesbecause theyincrease the extentto which
healthconsiderationsare takeninto account in computingthem.
Other candidates for how attentionplays a criticalrole in economic choice
include cultural norms that affect memory retrievaland cognitive patterns;
educational interventionsthathave a similareffect;and manyof the "nudge" or
"libertarianparternalistic"
policies thathave been advocated bybehavioraleconomists(Bernheimand Rangel,2004, 2007; Kling,Congdon, and Mullainathan,2011;
Thaler and Sunstein,2003, 2008). Indeed, psychologicaland behavioraleconomics
- framing
researchhas identifieda large and diverseset of noneconomic factors
effects,attentioneffects,saliency effects,subliminal primes that affectchoice
behavior.Unfortunately,
to date, economics lacks a model capable of providinga
a speculativeone
of
these
effects.
A naturalhypothesis,
account
admittedly
unifying
at this point, is that manyof these phenomena mightoperate by changing how
decisionvalues are computedthroughattentionaleffects.
Novel Insightsabout ExperiencedUtility
Psychologistsand behavioral economists have speculated that experienced
- mightbe modulated by variables that are
- that is, subjectivewell-being
utility
not traditionally
considered to be sources of well-being,like the extentto which
consumptionwas anticipated,the price at which the item was purchased, and
beliefsabout the propertiesof the stimulusbeing consumed. For example, pain
stimulationexperimentshave manipulatedsubjects' beliefsabout the strengthof
the electricshocksgivento the subjectsand have found thatthe beliefsmodulate
in areas thatare knownto correlate
reportsof experienced pain as well as activity
with subjectivepain reports.Some behavioral economics models incorporating
these typesof assumptionshave been proposed (Koszegi and Rabin, 2006, 2009,
2007). Although these models make testablebehavioral implications,it is often
to disentanglethemfromcompetingexplanationsusingonlychoice data.
difficult
Neuroeconomic methodsprovidean alternativemethodologyto address this
problem: measure neural activityin areas thatare knownto encode experienced
are present.This research
and testtheextenttowhichthehypothesizedeffects
utility
modulated bysurprise,
that
can
be
has
shown
already
experienced utility
program
and
and
beliefs
Camerer,
Rabin,
Rangel, 2011; de Araujo, Rolls,
(Bushong,
prices,
and
Cayeux, 2005; McClure, Li, Tomlin, Cypert,
Kringelbach,Velazco, Margot,
Montague,and Montague,2004; Nitschkeet al., 2006; Plassmann,O'Doherty,Shiv,
otherimportant
and Rangel,2008). It is likelythatsimilarexperimentswillidentify
sources of experienced utilityin the future.An importantcaveat forthisresearch
program,emphasized by Bernheim (2009a), is thatequating economic well-being
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- Recent
Foundations
Advances 19
Neuroeconomic
ofEconomicChoice
withthe experienced utilitysignalscomputed at a particularinstantis an approximatelyvalid assumptionin the simple choice contextbut not in the case of more
complex choices thathave consequences over extended periods of time.
De GustibusEst Disputandum
Many economists view preferences as exogenously given features of an
economic model. Economics does notyethave a satisfactory
theoryofwherepreferences come fromor how theymightbe modulated by other economic variables.
The neuroeconomicapproach providesnovel potentialexplanations.Consider an
example: because decision values depend on how individualscompute and weight
different
attributes,
people maymake selfishchoices not because theydo not care
about others,but because it is hard forthem to understandanother'sperspective.
if
This hypothesismakes the predictionthatsubjectswillbehave more altruistically
is exogenouslyprovidedbyotherpeople or byinstitutions.
thenecessaryinformation
Hare, Shultz, Camerer, O'Doherty, and Rangel (forthcoming) provide
evidence directlyrelatedto thisissue. They foundwithfMRIevidence thatsubjects
who donate more to charitiesactivatemore stronglythe posteriortemporalsulcus
at the time of choice and thatthe responsesin thisarea modulate activityin the
areas ofventromedialprefrontalcortexthatcomputedecisionvalues.The posterior
temporalsulcus has been shownto playa criticalrole in characterizingthe mental
statesof others (Saxe and Kanswisher,2003; Saxe and Wexler,2005). This finding
suggeststhatthe posteriortemporalsulcus mightbe importantforsome typesof
social choices because it computesinformationabout the perceivedneed of others
thatis passed to the ventromedialprefrontalcortexto compute the decision value
of giving.In addition,thereis significant
individualvariationin the abilityto assess
another'sstateof mind. This suggeststhatsome of the observedindividualdifferences in theamountofaltruismmightbe due to cognitivelimitationsand not to the
absence of an altruisticcomponentin experienced utility.
More Complex Decisions:
Norm Compliance
Self Control, Social Preferences, and
Examples of more complex choices include intertemporalchoices involving
financialdecisionsin complexenvironments
monetaryor pleasure-healthtradeoffs;
such as thestockmarket;choices involvingsocial preferences;and compliancewith
prevailingsocial norms.An activeresearchagenda in neuroeconomicsis devotedto
characterizinghow the mechanismsat workin simplechoice change in thesemore
complex situationsand whichadditionalprocessescome intoplay.Not surprisingly,
giventhatthese are the earlydaysof the fieldand the factthatthese domains are
more complicated,it is not possible yetto providefullcomputational
significantly
models of complex choice. Nevertheless,as we discussedhere,some existingresults
have implicationsthatshould be of special interestto economists.
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20
Perspectives
JournalofEconomic
IntertemporalChoice
Here we illustratehow the neuroeconomicsliteratureis attackingthe problem
of complex choice by focusingon the problem of intertemporalchoices. In the
basic versionof the problem,individualschoose betweentwooptions,x and y,in
the present,and their choices have consequences on multiple dimensions for
extendedperiodsof time.
A basic question is how the computationalmodel describingintertemporal
choices mightdifferfromthe one described above forsimple choices. To a large
extent,the existentevidence suggeststhatall of the keycomponentsof the model
for simple choice are also at workhere: choices are made by assigningdecision
values to each option at the timeof choice; these decision values are computedby
and weightingattributes;decision values are compared using a driftidentifying
diffusionmodel; and all of theseprocessesare modulatedbyattention.
Withrespectto the computationof decision values, severalempiricalstudies
have shown that the same areas of ventromedialprefrontalcortex that encode
them in simple choices also do so in more complex situationsinvolvingdietary
choices (Hare, Malmoud, and Rangel, 2011; Hare, Camerer,and Rangel, 2009;
Hutcherson,Plassmann,Gross, and Rangel, 2011) and intertemporalmonetary
choices (Kable and Glimcher,2007; McClure, Laibson, Loewenstein,and Cohen,
2004; McClure, Ericson, Laibson, Loewenstein,and Cohen, 2007). Interestingly,
the decision values based on ventromedial prefrontalcortex activitydo not
For example, in Hare,
depend on the extentto which the subjectsact virtuously.
in
the
ventromedial
and
Camerer,
prefrontalcortexcorreRangel (2009), activity
bad
in
and
well
values
with
the
decision
lates
dieters,and in Kable
good
equally
and Glimcher (2007) the correlationdoes not depend on the extent to which
delayed payoffsare discounted.
In intertemporal
choice, the decisionvalues seem to continueto be based on a
and attributes
all need to be time-dated,
buttheattributes
weightedsumofattributes,
in
times (whichallowsfortime-discounting
can have different
weightsat different
theweightingofattributes).The dietarychoice studyof Hare, Camerer,and Rangel
(2009) discussed above suggestsdecision values are computed by integratingthe
value of attributesover dimensionsand time. (Recall that hungrysubjectswere
asked to make food choices about whichfoodstheywantedto eat, foodsthatvaried
in theirhealth and tasteproperties.)Consistentwiththisassumption,theyfound
thatactivityin the ventromedialprefrontalcortexcorrelatedwithboth attributes
and thatthe relativeweightthattheyreceivedin theventromedialprefrontalcortex
decisionvalue signalswas correlated,acrosssubjects,withtheweightgivento them
in the actual choices. In addition, their follow-upstudy (Hare, Malmoud, and
Rangel,2011) , also discussedabove,in whichsubjectswereasked to payattentionto
showsthatattentionmodulatesthe integration
eitherthe healthor tasteattributes,
of the attributesinto the decisionvalues.
The assumptionthatthedecisionvalue is based on a sum ofattributes
weighted
over timeraisesquestionsabout whetherthe brain makes such a computationfor
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ErnstFehrand AntonioRangel 21
all attributesat all times.A workingassumptionis that the grid of attributesand
timehorizonscan be partitionedinto twosets: those attributesat giventimesthat
are easilycomputed,and those attributesat giventimesthatare considered onlyif
cognitiveeffortis deployed.Of course,a criticalquestion is whatdetermineswhich
attributesbelong in whichset.
Psychological
experimentsprovidesome usefulhints.For example,itis plausible
thatwhenan attribute
occurssoonerin time,itis morelikelyto be takenintoaccount.
This is suggested,but not fullyestablished,by the multitudeof animal and human
choice (fora review,see
experimentsshowinga strongpresentbias in intertemporal
Frederick,Lowenstein,and O'Donoghue, 2002). However,this typeof behavioral
evidence is not definitivefor our presentpurposes because it cannot distinguish
betweenwhenan attribute(say,in themoredistantfuture)is computedbutreceivesa
lowweight,and a case inwhichitis notconsideredat all.A seriesofclassicexperiments
is also a keydeterminant
WalterMischelsuggestthatphysicalproximity
bypsychologist
(forexample,see Metcalfeand Mischel,1999;Mischeland Moore,1973). In particular,
Mischelinvestigated
children'sabilityto postponeconsumptionof candyin orderto
increasedifthe
get more candyand foundthattheirabilityto waitwas substantially
based on
itemswere not presentor iftheywere presentbut covered.Furthermore,
the behavioral evidence, psychologistshave repeatedlyproposed that emotional
suchas "tastenow,"are morelikelyto be consideredthanlessemotionalones,
factors,
suchas "healthlater"(Libermanand Trope,2008; Metcalfeand Mischel,1999).
The experimentby Hare, Camerer,and Rangel (2009) carried out the first
neurobiologicaltestof thishypothesis.The logic of theirtestgoes as follows.Based
on the behavioral data, theywere able to divide their sample into two groups:
who assigned above-averageweightto the health attributes,and
self-controllers,
who assignedbelow-averageweightsto health attributes.They
non-self-controllers,
in areas of thedorsolateralprefrontalcortexthathave
thenhypothesizethatactivity
been shownto be involvedin implementingthe typeof scarce cognitiveprocesses
describedabove should be more activeat the timeof choice in the self-control
than
in the non-self-control
it
should
be
the
case
that
these
dorsogroup. Furthermore,
lateral prefrontalcortex areas modulate activityin the ventromedialprefrontal
cortexso thattheycan influencethedecisionvalues thatare computed.Theyfound
evidence consistentwiththishypothesis.Furthermore,the dorsolateralprefrontal
cortexmodulated the ventromedialprefrontalcortexdecision value signalsin the
self-control
group but not in the non-self-control
group.
The Problemof ExperiencedUtilityin IntertemporalChoice
In simple choice, it is naturalto thinkof the experienced utilitysignal at the
timeof consumptionas a neurobiological markerof welfare,because the objects
of choice in thatcase (such as listeningto music or sipping a drink) have immediate physiologicaland hedonic consequences at the time of consumption,but a
minimalimpactlateron. Things are significantly
more complicated in the case of
intertemporalchoice since decisions have hedonic consequences over extended
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22
JournalofEconomic
Perspectives
periods of time (for example, eating an unhealthycake affectshealth for indefinite future).This implies thatexperienced utilityat each instantdepends on the
entirehistoryof choices and not on a single consumptionepisode. As a result,it
to measure how specificchoices affectthe experienced utilitysignalsat
is difficult
futuretimes.
No theoreticalor empirical studyhas persuasivelyaddressed how to find a
neuroeconomic marker for measuring experienced utilityin an intertemporal
context.As a result,it is virtuallyimpossible to test directlyfor the presence of
thereis a conceptualworkmistakesin the intertemporalchoice case. Fortunately,
around forthisproblem.In the absence of sound measuresof experienced utility,
it is virtually
impossibleto testdirectlyforthe presence of mistakes.However,it is
stillpossible to use neuroeconomicmethodsto constructand empiricallyvalidate
a computationalmodel of the underlyingcomputationalprocesses. Once we are
confidentthatthe model providesa sufficiently
good descriptionof the forcesat
work,we can conclude thatotherpropertiesimpliedbythe model are also likelyto
hold, includingthe presenceof decision mistakesin certaincircumstances.
CompetingDecision Systemsin Complex Choice
A growingbody of worksuggeststhatbesides the decision-makingprocesses
thebrainsometimesusesothersystems,
likethePavloviansystem
discussedpreviously,
or the habitualcontrolsystem(Balleine, 2005; Balleine,Daw,and O'Doherty,2008;
Daw, Niv,and Dayan, 2005; Rangel,Camerer,and Montague,2008). The Pavlovian
controlleris activatedbystimulithatactivateautomatic"approach or avoid" behaviors.A typicalexample is the common tendencyto move quicklyawayfromstimuli
such as snakesand spiders.In contrastto the systemsdescribedin thispaper,which
can be used to solvemanydecision problems,the Pavloviancontrolleris thoughtto
"hard-wired"
circumstances.
smallnumberofevolutionarily
workonlyin a relatively
Those circumstancesmay sometimesalso apply to economic choices- like food
choices when the food stimuliare presentat a buffettable. For example,Bushong,
King,Camerer,and Rangel (2010) showed thatPavlovianforcescan have a sizable
impactin such choices.
The habitualsystemis more flexiblethanthe Pavloviansystemand less flexible
than the goal-directedone. In particular,the habitual systemlearns to promote
actionsthathave repeatedlygeneratedhighlevelsof experiencedutilityin the past
over those thathave generatedlower levels. However,it is computationallymuch
less sophisticatedthanthe goal-directedsystem:it makes "choices"overactionsbut
not stimuli,and it can onlydo so in domains in whichit has sufficient
experience.
These circumstancesmightapply to some economic choices, like which road to
travelduringa commute,or addiction,but not to others.
The goal-directedcontrolsystemdescribed in this paper may sometimesbe
in conflictwiththe prepotent (that is, latent) responses drivenby the Pavlovian
and the habitual system,and, depending on people's abilityto deploy cognitive
resourcesto controlthe prepotentresponse,the goal-directedsystemmayor may
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- Recent
Neuroeconomic
Foundations
Advances 23
ofEconomicChoice
in the lateralprefrontalcortex
not prevail.It is widelybelieved thatneural activity
in
a
role
the
to
these
ability deploy
plays key
cognitiveresources.
Knoch, Pascual-Leone, Meyer,Treyer,and Fehr (2006), Knoch, Schneider,
Schunk,Hohmann, and Fehr (2009), Baumgartner,Knoch, Hotz, Eisenegger,and
Fehr (forthcoming),and Figner et al. (2010) provide causal evidence related to
thisissue in varioustypesof complex decisions.They down-regulateneural activity
byapplyingtranscranialmagneticstimulationof a subject'sdorsolateralprefrontal
cortex,which is hypothesizedto be involved in the cognitivecontrol of prepotentresponsessuch as the impulse to behave selfishlyor to grab a sooner,smaller
rewardinstead of waitingfora later,largerreward.In Knoch, Schneider,Schunk,
of dorsolateralprefrontal
Hohmann, and Fehr (2009), the neural down-regulation
cortexinducessubjectsto cheat theirpartnerin a repeatedtrustgame althoughthey
knowthatthisdecreases the futuretrustof theirpartners.In otherwords,subjects
trading
forgo the long-rungains from acquiring a reputation as a trustworthy
In
et
al.
the
in
of
short-run
from
favor
the
(2010),
cheating.
Figner
gains
partner
choices
cortex
caused
more
of
dorsolateral
prefrontal
impatient
down-regulation
in an intertemporalchoice task.In Knoch, Pascual-Leone,Meyer,Treyer,and Fehr
of dorsolateralprefrontalcortexcauses a large
(2006) the neural down-regulation
decrease in responders'willingnessto reject unfairoffersin the ultimatumgame
although respondersstilljudge low offersas veryunfair.As in Knoch, Schneider,
Schunk, Hohmann, and Fehr (2009), choices are again biased in the direction
of more selfishbehavior.In addition, in Baumgartner,Knoch, Hotz, Eisenegger,
of dorsolateralprefrontalcortexalso
and Fehr (forthcoming)the down-regulation
leads to a down-regulationof the decision value of rejectingan unfairofferin
the ventromedialprefrontalcortex and largelyremoves the neural connectivity
betweenthese tworegions.
These findingssuggestthat the behavioral implementationof fairnessgoals
or social norms depends on the functioningof elaborate cognitiveand neural
machinerythat is dissociable from the knowledge of what constitutesfair or
norm-compliantbehavior.For example, in these experimentsthe subjectswhose
dorsolateralprefrontalcortex had been down-regulatedstillknew the fair thing
to do, but theywere significantly
less likelyto implementfairand norm-obedient
behavior.The dissociationbetween the knowledgeof what is fairand rightfrom
subjects' abilityto behave according to what is fairand rightraises difficultprobthe legal systemin
lems in the attributionof individualresponsibility.
Currently,
mostWesterncountriesholds people responsiblefortheirnormviolationsif they
knowthe social or legal norm;the above resultsindicate,however,thatthe attributionof individualresponsibility
can be considerablymore complicated.
Economic ImplicationsforComplex Choice
The computationalmodel outlinedabove providesa novelexplanationforwhy
makingoptimal choices in an intertemporalchoice contextis oftendifficultand
whythe choice could sometimesresultin decision mistakes,which in psychology
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24
Perspectives
JournalofEconomic
failures.The basic idea is simple: since scarce computaare knownas self-control
tional processes are not alwaysdeployed correctly,and are not even available in
some cases, decision mistakescan result.In this model, an individual'sabilityto
make optimalintertemporalchoices depends on the abilityto deploythe cognitive
controlfacilitatedbydorsolateralprefrontalcortexprocesses.Moreover,a growing
body of evidence suggeststhat other typesof complex behaviors,such as norm
compliance and fairnesschoices, also depend on these processes. Interestingly,
variationin the activity
of
thereare severalwell-knownsources of cross-individual
the dorsolateralprefrontalcortex,which lead to severalimplicationsthatshould
be of interestto economists.
First,the cognitivecontrolprocessed by the dorsolateralprefrontalcortexis
impairedduringstress,sleep deprivation,or intoxication,and it is depleted in the
shorttermwithrepeated use. This predictsthatsubjectsare more likelyto make
decisionsunderstress,tiredness,or drunkenness,or afterhavingmade
short-sighted
severalpreviouschoices requiringcognitivecontrol. This last predictionis wellknownfromsocial psychologyexperiments(Baumeister,Bratslavsky,
Muraven,and
Muraven
and
Baumeister
and
Baumeister,2000; Muraven,
Vohs, 2004;
Tice, 1998;
Tice, and Baumeister,1998; Vohs, Baumeister,Schmeichel,Twenge,Nelson, and
Tice, 2008).
Second, the lateral prefrontalcortex is the last area of the brain to mature
fully,often only when people age into their mid-20s (Casey, Galvan, and Hare,
2005; Casey,Jones,and Hare, 2008; Giedd et al., 1999; Sowell,Thompson,Holmes,
Jernigan,and Togan, 1999; Sowell, Thompson, Tessner,and Toga, 2001; Sowell,
Peterson,Thompson,Welcome,Henkenius,and Toga, 2003) . As a result,themodel
factthatchildrenand teenagersexhibitlowerself-control
predictsthe well-known
and normcompliancelevels.
the areas of dorsolateralprefrontalcortexidenThird,and more speculatively,
been
shown to play a role in cognitiveprocesses
tifiedin these studieshave also
such as workingmemory.This findingraises the intriguingpossibilitythat an
individual'sabilityto postpone gratification
mightbe affectedby cognitiveabilities. Two recent studiesprovide evidence in supportof thishypothesis.Shamosh
et al. (2008) measuredintelligence;workingmemoryspan; responsesof the dorsolateral prefrontalcortex during a workingmemorytask; and discount rates in a
monetarychoice task.Workingmemoryand intelligencewere associatedwithless
discounting,higherintelligencewasassociatedwithstrongerresponsesin thedorsolateralprefrontalcortex,and these strongerresponseswere associated withlower
Bickel,Yi, Landes, Hill, and Baxter (2011)
discounting.Even more intriguingly,
have shown that workingmemorytrainingreduces discountingin a monetary
choice task.This suggeststhattheremightbe a causal relationshipbetweencognitiveabilities,such as workingmemorycapacityand intelligence,and the abilityto
exert self-control.
As a result,this mightprovide a neurobiological link for why
and parentingmightaffectadult economic performance
educationalinterventions
(Heckman, 2006).
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ErnstFehrand AntonioRangel 25
Final Remarks
Neuroeconomics is a nascent field. Much of the basic work remains to be
done, and manyof the details of the computational models of choice described
here are likely to change and evolve over time. However, we hope that this
description of the current frontierof neuroeconomics convinces economists
that a great deal has already been learned about how the brains make choices,
and thatthese findingsalreadyprovide insightsthatare useful in advancing our
understandingof economic behavior in many domains. We also hope that this
paper helps to shiftthe heated debate about the usefulnessof neuroeconomics
research for economics fromthe abstractand methodological (as in Bernheim,
2009b; Camerer, Loewenstein, and Prelec, 2005; Gul and Pesendorfer,forthcoming) to the concrete; in our view,productivefuturedebates should centeron
thevalidityof specificcomputationalmodels and theirimplicationsformodeling
economic behavior.
We conclude the paper byemphasizingsome of the prominentquestionsthat
are yetto be resolved:What is a good computationalmodel of experienced utility
and well-beingin complexsettingswherechoices have consequences overextended
periods of time?Are theremore complex economic choices in whichsome of the
processesidentifiedin the case of simple choice break down?What is the range of
attributescomputedbythe brain at the timeof choice and how are theyintegrated
into the decision value signals?What determineshow attentionis deployed?How
Are therealternaare valuesand attributeslearned overtimeand overthe lifecycle?
tivebehavioralcontrollersthatinfluencechoices in some domains?The answerto
these questionswillprovidea much deeper mechanisticunderstandingof choice,
includingthe circumstancesand factorsthatdrivechoice mistakes,and theirpositiveand normativeimplications.
Clearly,much remainsto be learnt.Maywe livein interestingtimes!
ColinCamerer
■Antonio
;
; Paul Glimcher
, PeterBossaerts,
Rangelthanks
DouglasBernheim
related
over
the
conversations
and
Matthew
Rabin
Loewenstein
,
years
forveryuseful
George
Christian
to thetopicsofthispaper; ErnstFehrthanksToddHare, JohannesHaushofer,
and conversations
on thetopicofthispaper.
forusefulcomments
Ruffand PhilippeTobler
Wealso thankNicholasBarberis
, FarukGul, David
, Daniel Benjamin
, Federico
Echenique
and
Sobel
Levine, Paul Milgrom,ArielRubinstein
,
usefulcomments.
Joel
for extremely
Moore
Foundation
the
and
Gordon
Financialsupport
, and theLipper
fromNSF,NIH,
Betty
Foundationis gratefully
acknowledged.
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All use subject to JSTOR Terms and Conditions
26
Perspectives
JournalofEconomic
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