The Cognitive Neuroscience Toolkit for the Neuroeconomist:A Functional Overview

Joseph W. KableUniversity of Pennsylvania

This article provides the beginning neuroeconomist with an introductory overview to thedifferent methods used in human neuroscience. It describes basic strengths and weaknessesof each technique, points to examples of how each technique has been used in neuroeco-nomic studies, and provides key tutorial references that contain more detailed information.In addition to this overview, the article presents a framework that organizes humanneuroscience methods functionally, according to whether they provide tests of the associ-ation between brain activity and cognition or behavior, or whether they test the necessity orthe sufficiency of brain activity for cognition and behavior. This framework demonstratesthe utility of a multimethod research approach, because converging evidence from tests ofassociation, necessity, and sufficiency provides the strongest inference regarding brain–behavior relationships. Set against this goal of converging evidence, human neurosciencestudies in neuroeconomics currently rely far too heavily on methods that test association,most notably functional magnetic resonance imaging (MRI).

Keywords: neuroeconomics, methods, functional MRI, lesion studies, noninvasive brainstimulation

A challenge to doing research in interdisciplin-ary fields like neuroeconomics is becoming skilledin the sheer diversity of methods used by thedifferent parent fields. Ideally, a neuroeconomistwould have some working knowledge of the an-alytical tools used by choice theorists, the analyt-ical tools used by computational modelers, exper-imental design in psychology and economics,statistical techniques ranging from those used inneuroimaging to those used in the analysis ofbehavior, and the full suite of neuroscience meth-ods available for investigating cognition. A trulycomprehensive primer on methods would have tobe book-length to cover each of these topics indetail.

The goal of this review is much more mod-est: to provide an introductory overview of

one set of items in the neuroeconomist’s tool-kit, the methods of cognitive neuroscience.This overview is intended for people who arenew to the field and who have very little, ifany, knowledge of cognitive neurosciencetechniques. It is intended as a starting pointfor beginning students and researchers, not asthe last word. Because many techniques aresummarized in a small space, no one methodcan be covered in great detail. Rather, onlythe basics of each method are discussed, andinterested readers are directed to key tutorialreferences for each technique for more de-tailed information. In addition to these refer-ences, beginning researchers will also want toconsult one of several good methodologytextbooks (e.g., Senior, Russell, & Gazzaniga,2006; Toga & Mazziotta, 2002).

Given the constraints of space, this review islimited to only those neuroscience methods thatcan be used in human subjects. This excludes agreat chunk of important research in neuroeco-nomics that uses nonhuman animals as subjects.However, this focus accords with a likely audi-ence for this review: researchers in psychology,economics, or business who work with humansubjects and are curious about incorporatingneuroscience data in their research.

This work was supported by the National Institutes ofHealth (R01-DA029149 to Joseph W. Kable). Khoi Voworked tirelessly to collect and organize the citations inNeuroeconomics and the abstracts from the Society forNeuroeconomics conferences. Stav Atir, Annika Hill-ebrandt, and Jessica Stump provided helpful comments onprevious drafts of the article.

Correspondence concerning this article should be ad-dressed to Joseph W. Kable, Department of Psychology,University of Pennsylvania, 3720 Walnut Street, Philadel-phia, PA 19104. E-mail: kable@psych.upenn.edu

Journal of Neuroscience, Psychology, and Economics © 2011 American Psychological Association2011, Vol. 4, No. 2, 63–84 1937-321X/11/$12.00 DOI: 10.1037/a0023555

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In addition to introducing the techniques ofcognitive neuroscience, this review also tries toanswer two broad questions about methodologyin neuroeconomics. First, given the plethora ofneuroscience techniques available, what kind ofmethodological approach should research inneuroeconomics take? While several introduc-tions to the field recommend a multimethodapproach (Camerer, 2007; Camerer, Loewen-stein, & Prelec, 2004; Camerer, Loewenstein, &Prelec, 2005), this overview emphasizes a partic-ular strength of using multiple methods. Neuro-science methods differ according to the type ofinference they provide concerning the relationshipbetween brain and behavior. Broadly, methodscan test the association between brain activity andbehavior, the necessity of brain activity for behav-ior, or the sufficiency of brain activity for behav-ior. The most solid claims about brain–behaviorrelationships are based on converging evidencefrom these three kinds of tests. Neuroeconomicresearch programs would therefore do well toemploy a combination of methods that can ad-dress questions of association, necessity, andsufficiency.

Second, given the strengths of a multimethodapproach, does current research in neuroeconom-ics actually use this approach? Two sources ofdata are consulted to quantify what methods arecurrently being used in the field. These data sug-gest that, at this early stage, the field as a wholefalls short of its stated goal to gather convergingevidence from multiple methodologies. The vastmajority of studies to date rely on tests of associ-ation, with most of these using a single tech-nique—functional MRI (fMRI). While fMRI hasseveral important strengths that justify its wide-spread use, neuroeconomic research programswould be strengthened by greater inclusion ofcomplementary techniques that can address theinferential limitations of fMRI.

What Kind of Methodological ApproachShould Research in Neuroeconomics Use?

The cognitive neuroscience toolkit is justthat—a set of tools. A logical way to organizeany set of tools is according to their function.This introductory overview therefore organizeshuman neuroscience methods functionally, ac-cording to the kinds of inferences each methodprovides about brain– behavior relationships.This organization differs from the canonical

presentation of cognitive neuroscience methods,which emphasizes the spatial and temporal res-olution of each technique (Churchland & Se-jnowski, 1988). The motivation for the currentframework arises from answering the question,“What kind of methodological approach shouldresearch in neuroeconomics use?” Or, in otherwords, “What different kinds of evidenceshould we gather to allow the strongest infer-ences about brain function?”

This discussion takes it as given that someneuroeconomists are interested in building theo-ries about the relationship between the brain andcognition or behavior. Of course, some econo-mists and experimental psychologists are only in-terested in neuroscience findings and techniquesinasmuch as these can inform theories of cogni-tion or of human economic behavior. The questionof how neuroscience research can inform theoriesin economics and psychology is an important one,which has been extensively debated (Camerer etal., 2005; Glimcher, 2010; Gul & Pesendorfer,2008; Harrison, 2008; McCabe, 2008). However,the methodological framework and methods over-view below stress the role of each technique inelucidating brain–behavior relationships. Theconcluding section returns to the question ofhow neuroscience methods, and the knowledgeof brain–behavior relationships gained throughthem, can inform theories in economics andpsychology.

So, what different kinds of evidence combine tomake the strongest inferences regarding the map-pings between brain systems and cognitive func-tions or behaviors? To answer this question, wecan examine the evidence underlying neurosci-ence claims that have achieved the status of broadagreement. Below, two such claims are consid-ered. Both are routinely taught in introductoryneuroscience lectures. Both were initially tested inanimal models. One concerns the relationship be-tween a specific brain region and a specific cog-nitive function, while the other concerns the rela-tionship between a specific neurochemical systemand a specific kind of social behavior.

As a first example, take the statement that“Area MT (or V5) subserves visual motion per-ception.” This claim is often taught in introductorylectures on the visual system, and MT is oftenused as a paradigmatic example of specializationin cortical organization. What is the evidence thatMT is important for motion perception?

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This claim has been most systematicallytested in the model system of the Rhesus ma-caque (Macaca mulatta) monkey (for a detailedreview, see Newsome, 1997; Parker & New-some, 1998). (MT is so named because of its“middle temporal” location in this animal.) Sin-gle neurons in MT fire action potentials whenmotion is present at a particular retinotopic lo-cation. This response is strongest when the mo-tion is in a particular direction, and falls off asthe direction of motion deviates from the “pre-ferred” direction of that neuron. For example,an MT neuron might respond most when right-ward motion is presented and least when left-ward motion is presented. You can calculatehow well a neuron’s responses distinguish be-tween two different directions of motion, andmany neurons match or exceed the sensitivity ofthe animal to differences in motion direction.When discriminating between motion directionsis difficult, so that the animal makes differentresponses to repetitions of the same stimulus,the activity of MT neurons predicts which de-cision the animal will make. Neurons with sim-ilar preferred directions are located next to eachother in MT, and stimulating a group of MTneurons biases a monkey’s perception of mo-tion direction toward the preferred direction ofthose neurons. Lesions of MT impair motionperception, without affecting other aspects ofvisual perception.

As a second example, take the statement that“the neuropeptides oxytocin and vasopressinplay a critical role in affiliative behavior.”Again, students often learn this in an introduc-tory behavioral neuroscience class. What is theevidence that oxytocin and vasopression areimportant for affiliative behavior?

This claim has been most systematicallytested in the model systems of the prairie andmontane voles (for a detailed review, see Insel& Fernald, 2004; Insel & Young, 2001). Theprairie vole (Microtus ochrogaster) and mon-tane vole (Microtus montanus) are both smallrodent species found in central and westernNorth America. Despite many similarities be-tween the species, prairie voles and montanevoles differ dramatically in mating behavior.Prairie voles form monogamous breeding pairs,with a male and female sharing parental care oftheir offspring. In contrast, montane voles matepromiscuously, and male montane voles are notinvolved in the care of their offspring. The

expression of oxytocin and vasopression recep-tors differs dramatically in the two species.Oxytocin receptors are densely expressed in thenucleus accumbens of female prairie voles, andvasopression receptors are densely expressed inthe ventral pallidum of male prairie voles. Inmontane voles, there is not a similar expressionof oxytocin or vasopression receptors. In prairievoles, injections of oxytocin (for females) orvasopressin (for males) increase affiliative be-havior toward one’s mating partner. The act ofmating has a similar effect on affiliative be-havior in prairie voles, and injecting drugsthat block oxytocin (for females) or vasopres-sin (for males) receptors block this effect ofmating. By overexpressing the vasopressinreceptor in the ventral pallidum, male mon-tane voles can be genetically engineered torespond to vasopression in the same manneras male prairie voles do.

What can we learn from these two examples?In both cases, we see the combined use of threebroad kinds of evidence (Sarter, Berntson, &Cacioppo, 1996; Silva, 2007).1 First, there aretests of association. These involve observing orexperimentally manipulating psychologicalstates or behavior, simultaneously measuringneural activity, and examining the correlationbetween the two.2 In probabilistic terms, tests of

1 The framework used here borrows from Silva (2007) andSarter, Berntson, and Cacioppo (1996). Silva’s (2007) “Con-vergent Four” hypothesis argues that the strongest way to testa hypothesis concerning the connection between two phenom-ena is to gather converging evidence from theoretical work andthree different kinds of experiments: nonintervention (herecalled association), negative alteration (necessity), and posi-tive alteration (sufficiency). In their comment on functionalimaging techniques, Sarter, Berntson, and Cacioppo (1996)distinguish between the ability of neuroimaging to test P(���)and the ability of other techniques to test P(���).

2 Despite the name, tests of association can in some casesprovide causal evidence (Weber & Thompson-Schill, 2010),depending on the particulars of the experiment. For exam-ple, neuroimaging experiments typically control the stimulipresented to the subjects and measure the subject’s re-sponses and brain activity. At one end of the spectrum,when examining the relationship between stimuli and brainactivity, causal inference is clearly warranted. At the other,when examining the neural activity linked to different be-havioral responses to the same stimuli, causal inference isclearly not warranted. Note that the causal inference pro-vided is only ever about how changes in the environment orpsychological states cause changes in brain activity, whiletests of necessity and sufficiency are interested in the op-posite direction of causality.

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association mostly focus on the probability of aneural state (�) given a psychological state (�),P(���), though in some cases the analysis shiftsthis focus to the probability of a psychologicalstate given a neural state, P(���). Examples oftests of association include the correlation be-tween neuronal activity in MT and the directionof motion perceived by the subject, and thecorrelation between receptor expression andmating behavior across prairie and montanevoles.

Second, there are tests of necessity. Theseinvolve disrupting neural activity and showingthat this manipulation impairs a specific behav-ioral or psychological function. In probabilisticterms, tests of necessity reduce the probabilityof a neural state and see how this changesusbthe probability of a psychological state,P(���2�).3 Examples include the impairmentin motion discrimination caused by lesions toMT and the reduction in affiliative behaviorcaused by oxytocin and vasopression antago-nists.

Third, there are tests of sufficiency. Theseinvolve enhancing neural activity and show-ing that this manipulation results in a specificbehavioral or psychological state. In probabi-listic terms, tests of sufficiency increase theprobability of a neural state and see how thischanges the probability of a psychologicalstate, P(�� �1�). Examples include thechanges in motion perception induced by mi-crostimulation of MT, and the increase inaffiliative behavior induced by oxytocin andvasopression agonists.

As illustrated by the two case studiesabove, successful research programs in neu-roscience employ a multimethod approachthat combines tests of association, necessityand sufficiency. This combination providesstrong evidence for the causal influence ofneural states on psychological states. In testsof association, neural activity is measured,while in tests of necessity and sufficiency,neural activity is manipulated. The first kindof test establishes an important preconditionfor causality, that occurrences of the respec-tive neural and psychological states are cor-related. The second and third tests then testcausality directly, by establishing whether theneural states are necessary and sufficient con-ditions for the psychological states.

What Neuroscience Methods ProvideWhich Kinds of Inference?

This methodological framework— distin-guishing between tests of association, necessityand sufficiency—provides a way to functionallyorganize the methods of human neuroscience(Tables 1 and 2). Table 1 uses this framework tocategorize methods that link regional neural ac-tivity to mental function. Table 2 similarly cat-egorizes methods that link neurochemical sys-tems to mental function. The following sectionsprovide a brief description of each techniquelisted in Tables 1 and 2, organized by the cate-gories used in these tables. For each technique,tutorial references dedicated to that techniqueare listed, which the interested reader shouldconsult for additional information.

Methods for Linking Regional NeuralActivity to Mental Function

Tests of Association Between RegionalNeural Activity and Mental Function

fMRI. fMRI is the most widely usedmethod in neuroeconomics, and in cognitiveneuroscience more generally (for more informa-tion on fMRI, see Bandettini, 2009a, 2009b;Huettel, Song, & McCarthy, 2004; Jezzard,Matthews, & Smith, 2001). Like all MRI, fMRIinvolves measurements made with an MRIscanner. MRI scanners have a large static mag-netic field. For a typical research scanner with afield strength of 3 Tesla, the field is 60,000times the strength of the Earth’s gravity. Signalsbased on the properties of nuclear magneticresonance are then measured by making small,local modulations in the strength of this field. InfMRI, the dominant source of these signalscomes from protons in water molecules. Differ-ent kinds of MR images have different con-trasts—that is, they are sensitive to differentproperties of the measured tissue. The kind ofcontrast used in most fMRI is referred to as

3 Technically, the probabilistic framework is not con-cerned with causality, and it does not matter whether thechange in neural state is because of an experimental manip-ulation or is simply observed. The only distinction is be-tween P(���) and P(���), so P(���), P(���2�), andP(���1�) are formally the same. The important point toemphasize is that tests of association are often of P(���),while tests of necessity and sufficiency are of P(���).

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T2� contrast (“T-2-star,” so named in compari-son to T1 or T2 contrast, both of which are usedin anatomical imaging). T2� is sensitive to,among other things, the oxygenation level of theblood. This is because deoxygenated hemoglo-bin has magnetic properties, and thereforecauses local susceptibility effects that decreaseT2� contrast. Oxygenated hemoglobin does nothave magnetic properties. Because local in-creases in neural activity are associated withincreases in cerebral blood flow, and these in-creases in blood flow are associated with in-creases in blood oxygenation, local increases inneural activity are associated with increases inthe fMRI signal. This fMRI signal is commonlyreferred to as the blood oxygenation level de-pendent, or BOLD, signal.

BOLD fMRI has several advantages thatmake it a preferred technique in human neuro-science. Although many methods measure cor-relates of neural activity noninvasively in hu-mans, fMRI provides the best combination ofspatiotemporal resolution and anatomical cov-erage. Functional MRI provides a simultaneousmeasurement of neural activity across almostthe entire brain, including subcortical regions(the exceptions being a few regions hamperedby technical signal-dropout artifacts, such asorbitofrontal cortex and the temporal pole: Oje-mann et al., 1997; Weiskopf, Hutton, Josephs,& Deichmann, 2006). This whole-brain cover-age is achieved at a high spatial resolution (typ-ically 3 mm) and at a good temporal resolution(typically a measurement every 2 s). On the

Table 1Functional Overview of Human Neuroscience Methods, Part 1: Methods That Link Regional NeuralActivity to Mental Processes

Type of testMethod linking regional neural

activity to mental function Example(s) using this method

Tests of association Functional MRI Kable & Glimcher (2007); Plassmann et al. (2007), and Tomet al. (2007) demonstrated utility-like value signals duringhuman choice, consistent with those posited in decisiontheory.

Positron emission tomography([15O] water, etc.)

de Quervain et al. (2004) showed that increased dorsalstriatal activity was associated with the willingness toincur costs to punish defectors in an economic exchange.

Electroencephalography Yeung & Sanfey (2004) demonstrated that the valence andmagnitude of outcomes had differential effects on ERPresponses.

Magnetoencephalography Ambler, Braeutigam, Stins, Rose, & Swithenby (2004)recorded magnetic activity during simulated shopping.

Near-infrared spectroscopy Luu & Chau (2009) decoded subjective preferences fromhemodynamic activity in prefrontal cortex.

Anatomical imaging Bjork, Momenan, & Hommer (2009) found that increasedvolume of dorsolateral prefrontal cortex was associatedwith reduced discounting of delayed rewards.

Invasive recordings Zaghloul et al. (2009) demonstrated that human substantianigra neurons respond to unexpected outcomes, similar todopaminergic responses in other species.

Tests of necessity Lesion studies Bechara et al. (1997) documented decision making deficits inpatients with ventromedial prefrontal damage; Fellows &Farah (2007) observed that similar damage led to moreintransitive preferences.

Transcranial magneticstimulation

Figner et al. (2010) found that disruption of left lateralprefrontal cortex resulted in increased impatience on anintertemporal choice task.

Transcranial direct currentstimulation (cathodal)

Knoch et al. (2008) demonstrated that disruption of rightlateral prefrontal cortex led to increased acceptance ofunfair offers in the Ultimatum Game.

Tests of sufficiency Transcranial direct currentstimulation (anodal)

Fregni et al. (2008) showed that stimulation of lateralprefrontal cortex reduced craving in smokers.

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practical side, many institutions have MRI scan-ners and the technical expertise necessary to runthem, and several good software options foranalyzing fMRI data are freely available, whichlowers the practical barriers to starting an fMRIresearch project.

The basic analysis of fMRI data involvescomparing BOLD activity under two differentconditions, with this statistical comparison usu-ally being performed independently at each spa-tial location. By designing the right set of com-parisons between stimuli or tasks, one can askquestions about where regional brain activity isassociated with different mental functions.

Because fMRI has good spatiotemporal res-olution and the possibility for many repeatedtrials, more sophisticated experimental designsand analyses are also possible. Parametric de-signs look for BOLD activity that changes withparametric variations in stimuli or task vari-ables, rather than categorical changes, whichcan strengthen inferences regarding the causesof neural activity (Aguirre & D’Esposito, 1999;Buchel, Wise, Mummery, Poline, & Friston,1996). Model-based fMRI (O’Doherty, Hamp-

ton, & Kim, 2007) builds on parametric designs,by testing for activity that is correlated withparametrically varying hidden variables that areestimated in a statistical model of the subject’stask behavior. Both parametric and model-based fMRI designs identify not just where neu-ral activity changes, but also how activity inthese regions changes with experimental vari-ables. Adaptation and repetition suppression de-signs (Grill-Spector & Malach, 2001; Henson &Rugg, 2003; Weigelt, Muckli, & Kohler, 2008)capitalize on the finding that repeated stimuliare associated with reduced BOLD activity, us-ing this effect to ask what stimuli are treated asthe same in a given neural population. Multi-variate techniques such as pattern classification(Haynes & Rees, 2006; Norman, Polyn, Detre,& Haxby, 2006) ask how the spatial pattern ofneural activity changes under different condi-tions. Both adaptation and pattern classificationtechniques tap into neural coding at the sub-voxel level, and by testing what stimuli a regioncan distinguish between and which it cannotdistinguish, these techniques show how infor-mation is encoded in that region. Finally, other

Table 2Functional Overview of Human Neuroscience Methods, Part 2: Methods That Link NeurochemicalSystems to Mental Processes

Type of testMethod linking regional neural

activity to mental function Example(s) using this method

Tests of association Positron emission tomography([11C] raclopride, etc.)

Zald et al. (2004) showed that variable rewards wereassociated with increased dopamine transmission in thestriatum; Cools et al. (2009) found that individualsexhibiting greater dopamine synthesis were better atreversal learning.

Genetics Frank, Moustafa, Haughey, Curran, & Hutchison (2007)observed a triple dissociation between the effects ofthree different dopaminergic genes on reinforcementlearning.

Tests of necessity Pharmacology (antagonists) Rogers, Lancaster, Wakeley, & Bhagwagar (2004) showedthat beta-adrenergic blockade reduced the impact oflosses during risky decision making; Pessiglione,Seymour, Flandin, Dolan, & Frith (2006) showed thatdopaminergic blockade impaired learning from gains.

Neurotransmitter depletion Crockett, Clark, Tabibnia, Lieberman, & Robbins (2008)found that serotonin depletion increased rejection ofunfair offers in the Ultimatum game.

Tests of sufficiency Pharmacology (agonists) Kosfeld, Heinrichs, Zak, Fischbacher, & Fehr (2005)demonstrated that administration of oxytocin increasedtrust during economic exchange; Pessiglione et al.(2006) demonstrated that dopaminergic stimulationimproved learning from gains.

Neurotransmitter loading Schweighofer et al. (2008) tested the effect of serotoninloading and depletion on reward discounting.

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statistical techniques have been used to test forinteractions (“functional” or “effective” con-nectivity) between different brain regions, usingthe correlations or coherences between time-series, and how those interactions change withexperimental manipulations (Friston, 1994;Friston et al., 1997; McIntosh, Bookstein,Haxby, & Grady, 1996; Sun, Miller, &D’Esposito, 2004). All of these designs andanalyses go beyond localization, or a simplemapping of the regional brain activity associ-ated with specific stimuli or tasks, to ask moredetailed and textured questions about how brainactivity is related to mental activity.

Functional MRI does have both theoreticaland practical limitations. Perhaps the most im-portant theoretical limitation is the source of thefMRI signal. The BOLD signal reflects massactivity across the more than one million neu-rons in a voxel, and may be more closely linkedto changes in synaptic conductances than neu-ronal spiking (Logothetis, 2008; Logothetis,Pauls, Augath, Trinath, & Oeltermann, 2001).This means that the signal cannot easily distin-guish bottom-up versus top-down signals orneuromodulatory versus other inputs (Logothe-tis, 2008). The BOLD signal does not provideinformation about neural dynamics that occur ata fast time scale, and may be insensitive to otherneural dynamics as well. Practically, the re-quirement of being in an MRI scanner con-strains the kind of experiments that are possible.For example, experiments that require any headmovement or large body movements are notpossible. There are also restrictions on the kindsof subjects that can be tested. Subjects who arecontraindicated for MRI (e.g., have metal intheir body) or those who cannot lie motionlessin a relatively small space (e.g., young children,claustrophobics) cannot be tested. FunctionalMRI is also more expensive than behavioraltesting or several of the other techniques dis-cussed below.

Positron emission tomography (PET).PET predates fMRI as a technique for noninva-sively mapping changes in cerebral blood flow(for more information on PET, see Raichle,1998). In PET, a radioactive tracer is injectedinto the blood stream. For measurements ofcerebral blood flow, [15O] water is used. Thedecay of this tracer in the brain is then mea-sured. This radioactive decay involves the cre-ation of positrons, and these positrons are

quickly annihilated when they react with nearbyelectrons, thereby creating two photons thatleave the point of annihilation in exactly oppo-site directions. PET scanners are designed todetect the coincident arrival of these photonpairs. The location of their origin, and thus thepoint of radioactive decay, can then be esti-mated from these measurements. In the case of[15O] water, differences in the amount of radio-active decay across regions indicate differencesin cerebral blood flow.

With the development of fMRI, the use ofPET in cognitive neuroscience has waned.Functional MRI offers several advantages overPET. Functional MRI does not involve radioac-tivity, which restricts the number of measure-ments you can make in a given subject. Func-tional MRI also offers better spatial (3 mm vs. 1cm) and temporal (2 s vs. 1 min) resolution. Atypical PET study involves 1 to 2 measurementsper subject per condition, with each measure-ment reflecting neural activity averaged overone minute. This restriction limits the kind ofdesigns that can be used in PET compared tofMRI; for example, “event-related” designs arenot possible with PET. In addition, fMRI is lessexpensive and more widely available than PET.

There is still a role for PET, however. Inparticular, PET can measure blood flow in ab-solute terms (mL/g/min), while BOLD fMRIonly provides a relative measure. Absolute mea-sures of blood flow permit comparisons acrosssubjects, across sessions, and across brain re-gions. PET can also measure quantities otherthan blood flow. For example, other tracers canbe used to measure cerebral metabolism (Fox &Raichle, 1986), or, as discussed below, differentcomponents of specific neurotransmitter sys-tems (Zald et al., 2004).

Electroencephalography (EEG). Asidefrom lesion studies, EEG is one of the oldesthuman neuroscience techniques (for more infor-mation on this technique, see Handy, 2005;Luck, 2005). In EEG, electrodes are placed onthe scalp to measure electrical changes that re-sult from brain activity. All neuronal signalingis electrochemical, and electrical signals withinneurons are accompanied by simultaneous ex-tracellular changes. These electrical signals canpropagate through the brain and skull and bedetected on the scalp. The size of these signalsat the scalp is very small, 10 to 100 �V, anorder of magnitude smaller than at their source.

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Classically in EEG studies, the time-lockedresponses to many repetitions of a stimulus areaveraged. The spatial and temporal profile ofthis average response, called the event-relatedpotential or ERP, is then investigated. Morerecent statistical techniques have focused ondecomposing the ERP so that varying responseson single trials can be investigated (Debener,Ullsperger, Siegel, & Engel, 2006). In additionto the ERP, EEG can also be used to measurechanges in oscillatory activity that accompanytask performance.

One of the advantages of EEG is great tem-poral resolution (�1 ms). This allows investi-gations of the timing and evolution of neuralactivity at the timescale at which cognition oc-curs. It also permits studies of neural signalsthat occur at a very fast time scale, such assynchronized or oscillatory activity. Anotheradvantage is that EEG is less expensive andmore widely available than fMRI. The EEGsetup also places fewer constraints on experi-mental design (e.g., no loud noises or restrictingsubjects’ movement) and can be altered to beportable, so that measurements could be takenoutside of a laboratory.

An important disadvantage of EEG is that itcannot spatially localize activity in the mannerthat fMRI or PET can. This is because of the“inverse problem” (Michel et al., 2004). EEGmeasures electric potentials at the scalp, and thequestion of what configuration of brain sourcescould have led to the observed data is an ill-posed problem—an infinite number of differentconfigurations are possible in principle. Thus,calculating an inverse solution is only possibleby making certain assumptions, and the validityof this inverse solution is complicated to test.Recent efforts have focused on developing newstatistical techniques for calculating the inversesolution, developing new hardware such asdense electrode nets that help further constrainthis solution, and conditionalizing the solutionon other data such as fMRI activations (Michelet al., 2004).

Magnetoencephalography (MEG). MEGmeasures fluctuations in the magnetic field atthe scalp that are caused by changes in neuralactivity (for more information on MEG, seeLounasmaa, Hamalainen, Hari, & Salmelin,1996). The source of this signal is similar to thatof EEG signals. Neuromagnetic fields have astrength of 50 to 500 fT, which is many orders

of magnitude smaller than the Earth’s magneticfield (0.5 mT) or fluctuations because of noisein the environment (1 nT–1 �T). Detecting neu-romagnetic signals therefore requires very sen-sitive detectors called SQUIDs (superconduct-ing quantum interference devices), as well assophisticated noise cancellation techniques. In atypical MEG setup, a fixed helmet containingmore than 100 detectors is placed on top of thesubject’s head.

The advantages and disadvantages of MEGcan be compared to those of EEG. Like EEG,MEG provides great temporal resolution (�1ms), but poor spatial resolution. Solutions to theinverse problem in MEG are somewhat morereliable and accurate, because the magnetic per-meability of the head is more uniform than itselectrical conductivity, which greatly simplifiesthe calculation of the MEG signal pattern pro-duced by a given neural current source (Lounas-maa et al., 1996). On the downside, MEG equip-ment is much more expensive than that used forEEG. MEG equipment is also not portable.

Near-infrared spectroscopy (NIRS).NIRS (for more information on NIRS, see Grat-ton & Fabiani, 2001; Hoshi, 2005) is anothernoninvasive imaging method that can be used inhumans. NIRS uses a light source and detectorto measure the absorption and scatter of near-infrared light by the skull and underlying tissue.In its most widely used form, NIRS detectschanges in absorption due to changes in oxy-genated and deoxygenated hemoglobin, andtherefore is sensitive to the same kind of hemo-dynamic signals measured by BOLD fMRI.There are also reports that NIRS can be sensi-tive to faster signals arising because of changesin scatter associated with neuronal firing (Grat-ton & Fabiani, 2001), but these signals are muchweaker than the hemodynamic signals that aretypically assessed (Obrig & Villringer, 2003).

Despite measuring a similar signal, NIRSprovides much poorer spatial localization thanfMRI, and is restricted to measuring the dorsalcortical surface directly underneath the skull.Recent technical developments are aimed at im-proving spatial localization, by using differenttypes of measurement (time- or frequency-resolved in addition to continuous wave) or byusing imaging techniques that combine multiplesources and detectors (Hoshi, 2005; Obrig &Villringer, 2003). NIRS does have some advan-tages relative to fMRI, including much higher

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temporal resolution, as well as specific mea-surements of both oxygenated and deoxygen-ated hemoglobin. One of the biggest advantagesof NIRS is that the measurement devices areportable, unobtrusive and lower-cost. NIRS cantherefore be used in populations that cannoteasily be scanned, such as infants and youngchildren, claustrophobics and others contraindi-cated for fMRI, and specific patient popula-tions. It is also possible to construct wearableNIRS systems that allow measurements whilesubjects move around (Hoshi, 2005).

Anatomical imaging. The above methodsall test for dynamic changes in regional neuralactivity associated with the engagement of par-ticular mental functions. There are also tech-niques for testing for static differences in brainanatomy associated with differences in mentalfunction. These techniques test for associationsacross (rather than within) subjects, using anindividual differences approach. The relevantindividual differences might be in the structure(e.g., size, cortical thickness, or gray/white mat-ter composition) of brain regions as measuredwith anatomical MRI (Ashburner & Friston,2000) or in the structure of white matter tractsas measured with diffusion-tensor imaging(DTI; Johansen-Berg & Rushworth, 2009; Mori& Zhang, 2006).

Invasive recordings. In addition to theabove noninvasive techniques, there are alsorare opportunities to measure neural activitymore directly in the human brain (for moreinformation on invasive recordings, see Engel,Moll, Fried, & Ojemann, 2005). These oppor-tunities are only available in specific patientpopulations where invasive neural recordingsare already being performed as part of a clinicaltreatment. Examples include patients with Par-kinson’s disease undergoing surgery to implanta deep brain stimulator, and patients with epi-lepsy undergoing invasive monitoring and sur-gery to detect and remove epileptic tissue. Insuch patients, neural recordings might occuracutely during surgery or more chronically(over days) as part of monitoring. Obviously,because any research study is secondary to thepatient’s treatment, these studies have manypractical limitations. Also, by definition, thesubjects in these studies all have some kind ofbrain pathology, which could limit generaliz-ability of the data. Nevertheless, these studiesprovide a rare source of data from the human

brain that is comparable to that obtained innonhuman animal models.

Multiple neurophysiological methods areused in these studies. During surgery, record-ings are usually performed with a single (orsmall number) of electrodes. Presurgical moni-toring can be performed with grids of multiplelarge electrodes (3–4 mm) placed on the corti-cal surface, a technique referred to as electro-corticography (ECoG; Miller et al., 2007), orwith depth electrodes that penetrate deep intothe brain, a technique referred to as stereotactic-EEG (SEEG; Lachaux, Rudrauf, & Kahane,2003). Both of these techniques can record os-cillatory activity, at a greatly increased spatialprecision and signal-to-noise compared to non-invasive EEG/ERP. Depth electrodes can alsorecord multiunit activity and can be adapted toinclude microwire extensions for recording sin-gle-unit activity or probes for measuring neu-rotransmitter concentrations.

Tests of the Necessity and Sufficiency ofBrain Regions for Mental Function

Lesion studies. Perhaps the oldest tech-nique in human neuroscience is the neuropsy-chological approach, which examines how dam-age to the brain results in the breakdown ofmental function (for more information on lesionstudies, see Kolb & Whishaw, 2009; Rorden &Karnath, 2004; Shallice, 1988). This was themethod of inquiry in the classic studies of PaulBroca and Carl Wernicke, and reports of strik-ing dissociations after brain damage date backto the ancients (Benton, 1991).

Modern experiments might study brain dam-age arising from many different sources. Stroke(ischemic or hemorrhagic) is most often stud-ied, because it results in a relatively focal lesion.Other sources of focal lesions include neurosur-gery (i.e., tumor removal, temporal lobe resec-tion, corpus callosotomy) and penetrating headinjury (Phineas Gage being a prominent histor-ical example). Anoxia can cause more diffusebut still localizable damage. Degenerative dis-eases that cause more diffuse damage, such asfrontotemporal dementia or Alzheimer’s dis-ease, have also been studied (Grossman et al.,2010). Investigators have also studied Parkin-son’s disease to examine the effects of degen-eration of the dopaminergic system (Rutledge et

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al., 2009; Shohamy, Myers, Grossman, Sage, &Gluck, 2005).

Often, the neuropsychological approach hasnot been used for brain mapping at all, butrather as a tool for dissecting the structure of themind. In the tradition of cognitive neuropsy-chology (Shallice, 1988), for example, the focusis on how a case illuminates the organization ofmental functions, rather than where the brain isdamaged. Central to cognitive neuropsychologyis the search for dissociations. A single disso-ciation occurs when an individual with braindamage exhibits a deficit in one task whileperforming at normal levels in all others. Suchdissociations are informative, but alone couldbe explained by tasks being differentially sen-sitive to brain damage. A double dissociationoccurs when one individual exhibits a deficit onTask A but not Task B, while a second individ-ual exhibits the opposite, a deficit on Task B butnot Task A. Double dissociations cannot beexplained by differential task sensitivity, andtherefore suggest that the two tasks depend onpartially isolable mental processes.

Of course, the neuropsychological approachcan also be used to address the question of whatbrain structures are necessary for a particularmental function. Especially with the advent ofmodern imaging techniques, neuropsychologi-cal studies have increasingly turned in this di-rection. One approach is to perform group stud-ies, where subjects are grouped according to thelocation of their lesion, and the performance ofdifferent groups is compared (e.g., Bechara,Damasio, Tranel, & Damasio, 1997; Fellows &Farah, 2007; Hsu, Bhatt, Adolphs, Tranel, &Camerer, 2005). An alternative is to group sub-jects by their performance, and to compare theoverlap of lesions in subjects with and withoutdeficits (e.g., Adolphs, Damasio, Tranel, Coo-per, & Damasio, 2000; Tranel, Damasio, &Damasio, 1997). A potential drawback to bothof these methods is that they involve a binaryclassification of the brain or behavioral data thatoverlooks any heterogeneity in lesion locationor performance (i.e., a subject is either in or outof a particular lesion group, a subject eitherdoes or does not have a deficit). Methods suchas voxel-lesion symptom mapping (VLSM)overcome these drawbacks by treating both thebrain and behavioral data as continuous mea-sures (Kimberg, Coslett, & Schwartz, 2007;Rorden, Karnath, & Bonilha, 2007).

The strength of lesion studies is the ability toperform tests of necessity in humans, to exam-ine whether loss of a brain region leads to lossof function. One aspect of lesion studies thatcould limit inference is that the damage is notunder experimental control, so it is possible thatthe behavior in question is related to the per-son’s premorbid personality or medical condi-tion rather than to the brain damage. In manycases, this is not a plausible interpretation, butthere are some examples—for one, an experi-menter observing a deficit after temporal loberesection for epilepsy might worry whether thedeficit was caused by the epilepsy rather thanthe resection. Such concerns can be addressedby constructing the appropriate comparisongroups to rule out potential confounds. Otherissues that limit inference in lesion studies are:(a) cortical reorganization during recovery fromstroke, so that intact regions could be compen-sating for damaged ones in chronic patients; (b)great variability across subjects, which canmake it difficult to relate structure-to-functionwith precision; and (c) the often severe nature ofthe damage, which can make it difficult to ex-amine the more subtle functions of a brain re-gion. Finally, on the practical side, conductinglarge-scale lesion studies requires an infrastruc-ture for the recruitment, retention and testing ofsubjects that, although probably less expensivethan neuroimaging, is less widely available(Fellows, Stark, Berg, & Chatterjee, 2008).

Transcranial magnetic stimulation (TMS).TMS is based on Faraday’s principle of electro-magnetic induction (for more information onTMS, see Pascual-Leone, Walsh, & Rothwell,2000; Walsh & Cowey, 2000). A rapidly flux-ing magnetic field is induced by running anelectrical current through the closed loop of theTMS coil. This magnetic field traverses thescalp and skull, and induces an electrical currentin the underlying neural tissue. A typical setupuses a figure-eight shaped coil that induces amagnetic field of �2 T. The depth and width ofthe cortical area stimulated depends on the char-acteristics of the coil and its placement, but arough estimate is that the cortical area stimu-lated is �3 cm2 at a depth of �2 cm. TMStherefore has a relatively focal spatial effect, butcan only be used to affect the dorsal corticalsurface (although specialized coils that canreach deeper tissues are being developed; Wag-ner, Valero-Cabre, & Pascual-Leone, 2007). In

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a typical setup, a stereotaxic system is used totrack the location of the subject’s head and theTMS coil, and in conjunction with an anatom-ical scan of the subject’s brain, to guide theprecise placement of the TMS coil with respectto cortical landmarks.

The electric current induced in neural tissueby TMS is strong enough to cause neural firing.A single-pulse of TMS therefore disrupts neuralprocessing by inducing a burst of nonspecificneural firing, which is then terminated by cor-tical inhibition (Siebner et al., 2009). Becausethe timecourse of this effect is �200 ms, single-pulse TMS has relatively good temporal speci-ficity. Single-pulse TMS can therefore be usedto test the temporal window within which dis-ruption of cortical processing has an effect onbehavior (Pascual-Leone et al., 2000; Walsh &Cowey, 2000).

Repeated pulses of TMS (rTMS) can also beadministered. Here it is critical that users re-main within the established safety guidelines,because adverse effects of rTMS have beenreported outside this range (Wassermann,1998). Short (�1 s), high frequency bursts canbe used if a temporal effect slightly longer thana single pulse is desired. Alternatively, longertrains (�10 min) of pulses can be administered.In this case, rTMS results in a prolonged (15min �1 h) modulation of cortical excitability.Depending on the intensity and frequency ofstimulation, this modulation can either enhanceor suppress cortical activity, although moststudies use low-frequency (�1 Hz) parametersthat result in suppression of cortical activity.

Transcranial direct current stimulation(tDCS). Like TMS, tDCS is a method fornoninvasive brain stimulation (for more infor-mation on tDCS, see Been, Ngo, Miller, &Fitzgerald, 2007; Wagner et al., 2007). Typi-cally, a constant current (�2 mA) is applied tothe scalp through patch electrodes (25–35 cm2

area) for a time period of up to 20 minutes.Depending on whether the anode or cathode isplaced over the cortical region of interest, tDCSeither increases (anodal tDCS) or decreases(cathodal tDCS) neuronal excitability in a re-gion of the cortical surface beneath the elec-trode. These effects can persist for 15 min to 1 hafter stimulation. In cognitive studies, a behav-ioral task is performed either during or directlyafter tDCS, and the effects of stimulation onbehavior are examined. Sham stimulation,

where the current is ramped up and immediatelyback down rather than applied continuously,can be used as a control condition.

The precise neuronal effects of TMS andtDCS likely differ, so they should not be con-sidered interchangeable techniques. Neverthe-less, it is useful to compare the benefits anddrawbacks of the two methods. Similar to TMS,tDCS enables reversible activation and inacti-vation studies in humans and is restricted tostudies of the dorsal cortical surface directlybelow the skull. In comparison to TMS, theequipment for tDCS is less expensive, moreportable, and more easily scalable to study agroup of subjects at the same time. In addition,no adverse effects of tDCS have been reported,beyond minor headaches or itching beneath theelectrode. TMS, however, permits greater spa-tial and temporal control of stimulation, becauseit can be applied in single pulses and directed tomore localized cortical regions.

Methods for Linking NeurochemicalSystems to Mental Function

Tests of Association BetweenNeurochemical Systems and MentalFunction

PET. As discussed above, PET can beused to obtain measures of regional cerebralblood flow or metabolism. PET can also be usedto probe the function of neurotransmitter sys-tems, using radioactive tracers that bind specificneutrotransmitter receptors, transporters or en-zymes. For example, [11C] raclopride has beenused to measure dopamine D2/D3 receptors(Zald et al., 2004), while [18F] fluorodopa hasbeen used to measure dopamine synthesis(Cools et al., 2009). The main limitation onwhat neurochemical processes can be studied isin the development of appropriate radioactiveligands.

One way to employ these kinds of PET scansis to look for associations between subjects. Forexample, individual differences in dopaminesynthesis in the striatum have been related todifferences in reversal learning (Cools et al.,2009). Another way to employ these kinds ofPET scans is to look for associations withinsubjects, across different tasks. For example,[11C] raclopride has been used to measurechanges in dopamine release under different

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task conditions, by assuming that neurotrans-mitter released endogenously competes with theradioactive tracer (Zald et al., 2004; althoughsee Egerton et al., 2009, for several importantcaveats to this technique).

Genetics. Many techniques are now avail-able for linking genetic differences (genotypes)to differences in behavior or brain function(phenotypes). Because most cognitive neurosci-ence studies incorporating genetics have fo-cused on polymorphisms affecting one or moreneurotransmitter systems, genetics is labeledhere as a method for linking neurochemicalsystems to mental function. It is important toremember, however, that this is only a smallportion of the possible genetic differences thatcould be studied.

Most cognitive neuroscience studies haveused a candidate gene approach, in which asmall number of preidentified genetic polymor-phisms are tested (for more information on thisapproach, see Green et al., 2008). Some widelystudied polymorphisms are in genes that codefor neurotransmitter receptors (e.g., the D2 orD4 dopamine receptors, the A2-adrenergic re-ceptor), neurotransmitter transporters (e.g., thedopamine, norepinephrine, serotonin or acetyl-choline transporters), enzymes involved in neu-rotransmitter synthesis (e.g., dopamine beta-hydroxylase, tryptophan hydroxylase), enzymesinvolved in the breakdown of neurotransmitters(e.g., catechol-O-methyltransferase, monamineoxidase), and proteins involved in neurotrans-mitter receptor signaling cascades (e.g.,DARPP32 protein). Usually, but not always,there is already molecular biological evidencethat the genetic difference studied results inchanges in the function of the associated protein(i.e., the protein is more or less efficient or isexpressed to a greater or lesser degree). In thesestudies, genetic material is obtained from salivaor blood samples, and each subject’s genotypefor the candidate gene is determined. Then be-havioral performance (or neural activity) is cor-related with genotype. Because genotype is notmanipulated by the experimenter, these shouldbe considered tests of association. These arealso between subjects rather than within subjectassociations.

Although most published studies in cognitiveneuroscience have used the candidate gene ap-proach, the most recent and advanced genetictechnologies allow for much larger studies.

These range from using gene chips to examinehundreds of polymorphisms at a time, to per-forming genome-wide association studies(Manolio, 2010). In the latter case, the entiregenome is scanned for loci associated with aparticular phenotype. These larger-scale studiesrequire a greater infrastructure of people andtechnology.

There are several complexities to keep inmind regarding genetic studies (Green et al.,2008). Most (if not all) behaviors are affectedby many genes ( polygenicity), so the effect ofany one gene on the phenotype of interest islikely to be very small. Also, a given polymor-phism is likely to have effects on multiple phe-notypes ( pleiotropy). Furthermore, there couldbe interactions between different polymor-phisms, including interactions between differentpolymorphisms within the same gene. Finally,these genetic differences are present throughoutthe life span, so they may exert effects throughdevelopmental or compensatory changes, andinteractions between the gene and the environ-ment are likely to play an important role.

Tests of Necessity and Sufficiency ofNeurochemical Systems for MentalFunction

Pharmacology. Pharmacological interven-tions can be used to test the necessity and suf-ficiency of neurochemical systems for mentalprocesses (for more information on this ap-proach, see Robbins & Arnsten, 2009). Thesestudies involve administering a drug that blocks(an antagonist) or stimulates (an agonist) a par-ticular kind of neurotransmitter receptor, andcomparing behavioral performance on and offthe drug.

A main limiting factor to these studies is theexistence of a drug that modulates the system ofinterest and can be safely administered to drug-naı̈ve subjects. Another practical limitation isthe possible need for a physician’s oversight.Theoretical interpretation of these studiesshould take into account that: (a) drugs act atmany locations in the brain (and body) simul-taneously, and these effects may interact, (b)most drugs are not perfectly specific and canhave effects at multiple sites (e.g., multiple re-ceptor subtypes), and (c) the same drug canhave different and sometimes opposing effectsat different doses.

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In addition to manipulating neurotransmittersystems pharmacologically, there are also meth-ods for increasing or decreasing the basal levelsof serotonin and dopamine/norepinephrine(Mendelsohn, Riedel, & Sambeth, 2009; Silber& Schmitt, 2010). These methods involve di-etary manipulations that overload or depletetryptophan (the metabolic precursor of sero-tonin), or that overload or deplete phenyalanineand tyrosine (the metabolic precursors of dopa-mine and norepinpehrine).

Methods That Monitor Peripheral NervousSystem Activity

So far, this review has focused on methodsthat link mental functions to regional brain ac-tivity or to the action of diffuse neurochemicalsystems. Several other methods in the neuro-economist’s toolkit do not fit into these twocategories, but can be broadly characterized asmeasurements of peripheral nervous system ac-tivity. This includes indirect indices of auto-nomic (sympathetic/parasympathetic) activity,such as measurements of heart rate, blood pres-sure, respiration rate, pupil diameter, and gal-vanic skin response. These measurements haveshown associations with arousal, learning, anddecision making (Satterthwaite et al., 2007), andare also particularly useful in studies of emotion(Olsson & Phelps, 2007). Other measures, such aseye tracking or facial electromyography (EMG),index peripheral somatomotor activity. Eye track-ing can provide information about attention andinformation processing (Krajbich, Armel, & Ran-gel, 2010), while facial EMG can indicate thepresence of different emotions (Halberstadt,Winkielman, Niedenthal, & Dalle, 2009). All ofthese techniques can provide important data forneuroeconomic studies.

Combining Methods

The functional organization of this methodsoverview stresses how different methods pro-vide different kinds of inference, and thereforehighlights what can be gained by studying thesame decision paradigm with multiple differentmethods. Many of these gains occur even whenthe different methods are used in separate stud-ies. In some cases, though, the simultaneous useof two different methods enables one to ask

novel questions that could not be answered withone technique alone or with the two techniquesused at separate times.

Some good examples involve the simultane-ous combination of fMRI with another method.Simultaneous EEG and fMRI allows one tolook for BOLD activity that varies on a trial-by-trial basis with the amplitude or timing of anevent-related potential, providing a strongerway to link data from the two methods (Debeneret al., 2006). Simultaneous TMS and fMRI canmap the functional interactions between brainregions, by testing how brain regions distantfrom the target respond to TMS pulses, or bytesting how distant regions compensate duringtask performance when the targeted region isdepressed (Siebner et al., 2009). CombiningfMRI with genetics can identify intermediatephenotypes—neural responses that vary withgenotype and that might prove more reliablethan behavioral phenotypes (Canli & Lesch,2007; Green et al., 2008; Hariri, Drabant, &Weinberger, 2006). Combining fMRI withpharmacology can allow one to better pinpointthe location of any pharmacological effects(Leslie & James, 2000; Shah & Marsden, 2004).Finally, peripheral measures can be combinedwith fMRI to study the central correlates ofperipheral effects (Gray et al., 2009).

Is Research in Neureconomics Using aMultimethod Approach?

A natural follow-up question to this overviewof cognitive neuroscience techniques is, “Whichmethods are most neuroeconomists using?”Furthermore, given that the strongest inferencesregarding brain-behavior relationships requireconverging evidence from tests of association,necessity and sufficiency, does research in neu-roeconomics actually employ this approach?This is an important question in assessing neu-roeconomics at this early stage of the field’sdevelopment. Most introductions to the fieldstress the benefits of employing multiple meth-odologies (Camerer, 2007; Camerer et al., 2004;Camerer et al., 2005). Furthermore, proponentsof neuroeconomics point to this multimethodapproach in defending the field from its critics,criticizing detractors for failing to recognizethat claims in neuroeconomics are based onmore than just functional imaging results (Cam-erer, 2008). But does this description coincide

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with what most neuroeconomists are actuallydoing?

To get a sense of what methods are beingused in neuroeconomics research, we can exam-ine two sources of data. The first textbook forthe field, Neuroeconomics: Decision makingand the brain (Glimcher, Camerer, Fehr, & Pol-drack, 2009), provides one source of data. Thereferences for this volume presumably containthe most influential early research in neuroeco-nomics. What do these references show aboutwhat methods are being used? Of the total num-ber of references (1,744), a minority (260) wereresearch articles that collected neurosciencedata in human subjects. Other references werereviews (256), theoretical papers (220), papersusing nonhuman subjects (438), or papers con-cerning behavioral phenomena in human sub-jects (254). We can classify the human neuro-science papers according to method used, ex-cluding a subset that had primarily clinical aims(e.g., anatomical or functional imaging to iden-tify differences between patients and controls).By far most (70.0%) of the human neurosciencepapers used fMRI. The second largest group(9.6%) studied subjects with brain damage.Overall, 81.7% of the cited human neurosciencepapers provided tests of association, 12.5% testsof necessity, 2.1% tests of sufficiency, and 3.8%a combination of these (Figure 1).

A second source of data is the abstracts fromthe Society for Neuroeconomics conferences inthe 5-year window, 2005–2009 (http://www.neuroeconomics.org/conference/annual-conference). Here, close to 60% of the totalnumber of abstracts reported neuroscience stud-ies in human subjects (200/344), with the re-mainder composed of behavioral studies in hu-man subjects (60), studies using theoretical orcomputational approaches (26), and studies us-ing nonhuman subjects (58). Again we can clas-sify the human neuroscience abstracts accord-ing to method used, excluding a small numberthat had primarily clinical aims. Here the pre-dominance of fMRI was even more pro-nounced: 85.3% of the abstracts used fMRI,with no other method represented in more than3% of the abstracts. Accordingly, 93.4% of thehuman neuroscience abstracts provided tests ofassociation, 2.5% tests of necessity, 1.5% testsof sufficiency, and 2.5% a combination of these(Figure 1).

These data illustrate a bias toward using testof association, and particularly fMRI. But howsignificant is this bias? One comparison point isthe field of cognitive neuroscience as a whole.Fellows and colleagues note that publicationsusing functional imaging increased dramaticallyafter the development of fMRI and began tooutnumber those using lesion methods between1997 and 2001 (Fellows et al., 2005). In theeditorial accompanying the Fellows paper,Chatterjee identifies a wider gap in 2005 whenconsulting abstracts for the Cognitive Neurosci-ence Society meeting (Chatterjee, 2005). Thosedata show a ratio of about six-to-one betweenstudies using functional imaging (fMRI, PET,ERPs) and those using the lesion method orTMS. The bias toward tests of association in theNeuroeconomics references is of roughly thesame size, while that in the conference abstractsis much larger.4 In terms of gathering converg-ing evidence from multiple methods, it appearsneuroeconomics is certainly not doing betterthan field of cognitive neuroscience as a whole,and may be doing much worse.

It is important to recognize that there areseveral strong theoretical reasons to rely heavilyon fMRI. There are few techniques that testnecessity and sufficiency, and each has signifi-cant limitations. As a test of association, fMRIhas several important strengths. It is currentlyunparalleled in the ability to image the entirehuman brain at high spatial resolution. Re-searchers have also developed several advancedfMRI design and analysis techniques, whichallow one to ask questions that are much moresophisticated than mere localization. This in-cludes repetition suppression designs, model-based fMRI analyses, pattern classificationmethods, and functional and effective connec-

4 There are several potential explanations for the differencesbetween the citations and conference abstracts data. One pos-sible explanation is that the two datasets differ because theysample different time periods. This explanation can be ruledout, because restricting the citations data to 2005–2009 doesnot dramatically change the overall percentages (associa-tion, 81.1%; necessity, 13.5%; sufficiency, 1.8%; com-bined, 3.6%). Other possible explanations include that the onesource of data is more representative of the field as a whole, orthat, compared to studies using other methods, fMRI studieshave a greater chance of not being published and/or not beingcited.

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tivity techniques. All of these go beyond askingwhether there is an association between in-creased neural activity in brain area X andpsychological process Y, to investigate the re-lationship between the pattern of neural activitywithin and across regions and the pattern ofinstantiations of the psychological process.

There are also practical concerns contributingto the heavy reliance on fMRI. Most researchinstitutions now have an MRI scanner, and user-friendly software is readily available for theanalysis of fMRI data. There are very few bar-riers to beginning fMRI research, even thoughfMRI is more expensive and technically chal-lenging than many of the other methodologiesin human neuroscience. As one important con-trast, access to subjects with brain damage hashistorically been limited to clinicians and theircollaborators, even though in principle creatingthe support structure for patient-based researchwould require (at most) comparable resources toestablishing an imaging center.

An important question for neuroeconomistsis whether we should reduce some of thepractical barriers to using methods other thanfMRI, or otherwise encourage a greater de-gree of multimethod research. There are sev-eral reasons for doing so. A stated standard ofthe field is that claims should be defendedwith evidence gathered using multiple meth-

odologies (Camerer, 2008). Furthermore, asdiscussed above, the strongest arguments inneuroscience rely on converging data thatspeak not just to association, but also to ne-cessity and sufficiency. To the extent thatresearchers in the field want findings in neu-roeconomics to have similar impact, theyshould follow the same approach.

5 From the articles cited in Neuroeconomics, the tests ofassociation included fMRI (168), PET measuring bloodflow (17), EEG (2), anatomical studies (3), invasive record-ings (1), PET measuring neurotransmitter receptors (4), andgenetics (1). The tests of necessity included lesion or patientstudies (23), TMS (4), cathodal tDCS (1), and antagonistpharmacology (2). The tests of sufficiency included anodaltDCS (2) and agonist pharmacology (3). Combinations in-cluded neuroimaging studies in subjects with brain damage(5) and neuroimaging combined with pharmacology (4).

6 From the Society for Neuroeconomics abstracts, thetests of association included fMRI (168), PET (1), EEG (5),NIRS (1), anatomical studies (1), invasive recordings (2),hormone measurements (1), and genetics (5). The tests ofnecessity included lesion or patient studies (3) and TMS (2).The tests of sufficiency included agonist pharmacology (3).Combinations included neuroimaging studies in subjectswith brain damage (2), neuroimaging combined with phar-macology (2), and patient studies combined with pharma-cology (1).

Figure 1. Percentage of studies in the field of neuroeconomics that use tests of association,necessity, and sufficiency. (a) Data from articles cited in the textbook Neuroeconomics:Decision making and the brain (Glimcher et al., 2009).5 (b) Data from abstracts presented atthe Society for Neuroeconomics conference.6

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How Can Neuroscience Methods Be Usedto Inform Theories in Economics and

Psychology?

Many economists are skeptical that researchincorporating neuroscience methods can betterinform theories in economics (Camerer, 2007;Camerer et al., 2004; Camerer et al., 2005).They argue that a better understanding of theneural mechanisms of decision making will notnecessarily lead to better predictions about eco-nomic behavior. This critique is similar to ear-lier critiques of cognitive neuroscience(Coltheart, 2004). As cognitive psychologistshave said, “Why should we study the brain inorder to understand the mind?” This is an im-portant debate, and many neuroeconomists haveoutlined how research in the field can informeconomics and psychology as well as neurosci-ence (Camerer, 2007; Glimcher, 2010; McCabe,2008). Many neuroeconomists believe that amultilevel investigation of brain-behavior rela-tionships, aimed at building mechanistic theo-ries of decision making, will ultimately lead toa coherent integration at the border of neurosci-ence and economics/psychology (Glimcher,2010; Kable & Glimcher, 2009). While somescholars of decision making may doubt thispossibility, a similar integration has already oc-curred in perception, and arguably in other do-mains of cognitive psychology (language, mem-ory) as well.

Setting aside the prospects for a broad inte-gration, we can consider specific ways that theneuroscience methodologies reviewed abovecan be used to inform research questions ineconomics and psychology. As most of the re-search in the field has used fMRI, the examplesdiscussed here reference that technique, butcould apply similarly to other tests of associa-tion. One place to start, though, is to note oneway that fMRI data are (commonly) used thatprovides poor inference regarding psychologi-cal processes. This involves making an assump-tion about the function of a brain region basedon past studies, and inferring based on activa-tion in that region that the task the subjectperformed involved a particular psychologicalprocess. For example, one might find that deci-sions under risk are associated with activity inthe amygdala, note that the amygdala has pre-viously been implicated in the emotion of fear,

and then conclude that decisions under risk in-volved fear. This kind of reasoning is called“reverse inference,” and reverse inference isgenerally problematic (Aguirre & D’Esposito,1999; Poldrack, 2006). The central problem isthat it assumes there is only one psychologicalprocess associated with activity in a given brainregion (e.g., the amygdala is only active whenyou experience fear). Such a one-to-one map-ping is unlikely. Poldrack works through anexample in probabilistic terms to show that re-verse inferences are unlikely to provide verystrong information, at least at the spatial scale ofbrain activity and coarseness of the cognitiveontology that are typically used (Poldrack,2006; though see Ariely & Berns, 2010, for amore promising example).

There are several more promising ways,though, that fMRI (and other tests of associa-tion) might be used to inform economic andpsychological theories. First, model-basedfMRI can provide information about what vari-ables and quantities are encoded in the brainwhen an individual makes decisions. One ex-ample of this is the demonstration of predictionerror signals in the human brain, consistent withthose posited in reinforcement learning models(McClure, Berns, & Montague, 2003;O’Doherty, Dayan, Friston, Critchley, & Dolan,2003). Current work is using these signals totest competing learning models (Hampton,Bossaerts, & O’Doherty, 2006). Another exam-ple is the demonstration of utility-like valuesignals during human choice (Kable & Glim-cher, 2007; Plassmann, O’Doherty, & Rangel,2007; Tom, Fox, Trepel, & Poldrack, 2007),consistent with those posited in decision theory.Current work is illuminating the inputs to thosesignals and the domains in which they operate(Hare, Camerer, & Rangel, 2009; Smith et al.,2010; Wunderlich, Rangel, & O’Doherty,2009). Second, fMRI can demonstrate whethertwo different kinds of decisions use similar ordifferent neural processes, and thus whetherthey are likely to use similar or different cog-nitive processes. Examples include prominentdebates in the field over whether decisions un-der risk and ambiguity involve similar or dif-ferent mechanisms (Hsu et al., 2005; Levy,Snell, Nelson, Rustichini, & Glimcher, 2010),and whether decisions about immediate and de-layed rewards involve similar or different mech-anisms (Kable & Glimcher, 2007; McClure,

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Laibson, Loewenstein, & Cohen, 2004). Third,in addition to identifying similarities and differ-ences across tasks, fMRI can also be used to testwhether different individuals perform the samedecision in different ways. Venkrataman andcolleagues provide one example of this withregards to decision strategy (Venkatraman,Payne, Bettman, Luce, & Huettel, 2009), andHouser and colleagues discuss how the discov-ery and validation of different types of decisionmakers could improve econometric predictions(Houser, Schunk, & Xiao, 2007). Fourth andfinally, directly incorporating neural measuresinto models could improve predictions of eco-nomic behavior. Early neuroeconomic studiesdemonstrated that neural activity could be usedto predict simultaneously measured choices(Knutson, Rick, Wimmer, Prelec, & Loewen-stein, 2007). More recent studies have shownhow neural activity can predict individual be-havior outside of the scanner (Levy, Lazzaro,Rutledge, & Glimcher, 2011), and how neuralmeasures can predict what products will be suc-cessful (Berns, Capra, Moore, & Noussair,2009).

Because it is the most widely used techniquein the field, this discussion has so far focused onfMRI. However, considering how neurosciencemethods can best inform theories in economicsand psychology provides yet another rationalefor a multimethod approach. In tests of neces-sity and sufficiency, behavior is the dependentvariable. Because this is also the variable econ-omists and psychologists are usually most in-terested in, these techniques could provide morecompelling evidence to them. Consider thequestion of whether two kinds of decisions in-volve similar or different cognitive processes.The search for double dissociations in neuro-psychology is entirely motivated by these kindsof concerns. Though this may not be how theyare commonly used, TMS, tDCS, and pharma-cology can uncover similar dissociations. In-deed the question “Why should we study thebrain in order to understand the mind?” mighthave seemed strange to earlier human neurosci-entists. For more than a hundred years beforethe advent of modern imaging techniques, hu-man neuropsychologists studied the effects ofbrain damage in order to elucidate mental struc-ture. In addition to providing evidence about thecausal role of brain structures in behavior, testsof necessity and sufficiency, since they treat

behavior as the dependent variable, can alsoprovide evidence that further strengthens theneuroeconomist’s answer to why studying thebrain enlightens us about how people makedecisions.

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Members of Underrepresented Groups:Reviewers for Journal Manuscripts Wanted

If you are interested in reviewing manuscripts for APA journals, the APA Publicationsand Communications Board would like to invite your participation. Manuscript reviewersare vital to the publications process. As a reviewer, you would gain valuable experiencein publishing. The P&C Board is particularly interested in encouraging members ofunderrepresented groups to participate more in this process.

If you are interested in reviewing manuscripts, please write APA Journals atReviewers@apa.org. Please note the following important points:

• To be selected as a reviewer, you must have published articles in peer-reviewedjournals. The experience of publishing provides a reviewer with the basis for preparinga thorough, objective review.

• To be selected, it is critical to be a regular reader of the five to six empirical journalsthat are most central to the area or journal for which you would like to review. Currentknowledge of recently published research provides a reviewer with the knowledge baseto evaluate a new submission within the context of existing research.

• To select the appropriate reviewers for each manuscript, the editor needs detailedinformation. Please include with your letter your vita. In the letter, please identify whichAPA journal(s) you are interested in, and describe your area of expertise. Be as specificas possible. For example, “social psychology” is not sufficient—you would need tospecify “social cognition” or “attitude change” as well.

• Reviewing a manuscript takes time (1–4 hours per manuscript reviewed). If you areselected to review a manuscript, be prepared to invest the necessary time to evaluate themanuscript thoroughly.

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