Neuron,Vol.47,763–770,September1,2005,Copyright©2005by Elvier Inc.DOI10.uron.2005.08.008 The Neural Basis of Financial Risk Taking
Camelia M.Kuhnen1,3,*and Brian Knutson2,3,*
1Stanford Graduate School of Business
518Memorial Way,S479
Stanford,California,94305
2Department of Psychology
Building420,Jordan Hall
Stanford University
Stanford,California94305
Summary
Investors systematically deviate from rationality when making financial decisions,yet the mechanisms re-sponsible for the deviations have not been iden-tified.Using event-related fMRI,we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task.We characterized two types of deviations from the optimal investment strategy of a rational risk-neu-tral agent as risk-eking mistakes and risk-aversion mistakes.Nucleus accumbens activation preceded risky choices as well as risk-eking mistakes,while anterior insula activation preceded riskless choices as well as risk-aversion mistakes.The findings suggest that distinct neural circuits linked to anticipa-tory affect promote different types of financial choices and indicate that excessive activation of the cir-cuits may lead to investing mistakes.Thus,consider-ation of anticipatory neural mechanisms may add pre-dictive power to the rational actor model of economic decision making.
Introduction
Individual investors systematically deviate from optimal behavior,which could influence ast valuation(Daniel et al.,2002;Hirshleifer,2001;Odean,1998).The caus of the deviations have not been established,but emotion may have some influence.While some re-arch has examined the role of emotion in decision making(Camerer et al.,2005;Loewenstein et al.,2001) and economists ha
ve begun to incorporate emotion into models of individual choice(Bernheim and Rangel, 2004;Caplin and Leahy,2001),scientists still lack a mechanistic account of how emotion might influence choice.Understanding such mechanisms might help theorists to specify more accurate models of individual decision making,which could ultimately improve the design of economic institutions so as to facilitate opti-mal investor behavior.
Here,we sought to examine whether neural activa-tion linked to anticipatory affect would predict financial choices.At least two hypothes have been put forth regarding the role of affect in decision making.Accord-*Correspondence:camelia@stanford.edu(C.M.K.);knutson@psych. stanford.edu(B.K.)
3The authors contributed equally to this work.ing to one account,undifferentiated arousal might be related to both risk eking and risk aversion(Lo and Repin,2002).However,according to a cond account, positive aroud feelings associated with anticipation of ,“excitement”)may promote risk taking, whereas negative aroud feelings associated with an-ticipation of ,“anxiety”)may promote risk aversion(Knutson et al.,2005;Paulus et al.,2003). Recent evidence from human brain imaging implies that affect evoked by the anticipation of gain and loss may carry distinct neural signatures.Specifically,the nucleus accumbens(NAcc)of the ventral striatum
冬瓜鸡蛋汤shows proportional activation during anticipation of monetary gains(Breiter et al.,2001;Knutson et al.,2001), and this activation correlates with positive aroud affect (Bjork et al.,2004;Knutson et al.,2005;Martinez et al., 2003).Neural markers of anticipatory negative affect have not been as clearly delineated,but the anterior insula provides a candidate substrate for a number of reasons.First,brain imaging studies have consistently reported activation of the anterior insula during antici-pation of physical pain,which correlates with lf-reported state anxiety(Buchel and Dolan,2000;Chua et al.,1999;Ploghaus et al.,1999).Second,the anterior insula shows activation during anticipation of aversive visual stimuli(Simmons et al.,2004).Third,the anterior insula shows activation during risky choice in games involving nonmonetary incentives,which correlates with subquent risk-aversion and trait measures of negative aroud affect(Paulus et al.,2003).Although the anterior insula is also nsitive to attentional and other demands(Phan et al.,2002),a recent review sug-gests that activation in this region is more common un-der negative than positive affective circumstances(Wa-ger et al.,2003).
The goals of this experiment were,first,to determine whether anticipatory activity in the NAcc and anterior insula would differentially predict risk-eking versus risk-aver choices and,cond,to examine whether activation in the regions would precede both subop-timal and optimal choices.T
wo studies have correlated anticipatory neural activation with choice,but both in-volved choices that occurred in the context of social interactions(which might prove more susceptible to af-fective bias)rather than financial decisions(Fehr et al.,2004;Sanfey et al.,2003).Another study demon-strated a correlation between neural activation and im-mediate versus delayed reward choices,but did not in-vestigate risky choices(McClure et al.,2004).
To investigate the influence of anticipatory neural ac-tivation on financial risk taking,we combined a dy-namic investment task with event-related fMRI.We compared subjects’actual investment choices during the task to tho of a rational risk-neutral agent who maximizes expected utility.Suboptimal choices were defined as deviations from this model and included both“risk-eking mistakes”(in which people take risks when they should not)and“risk-aversion mis-takes”(in which people do not take risks when they should).
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Figure 1.Trial Structure 2s per
panel.
We designed a task to elicit a range of investment behaviors,including risk-eking and risk-aver fi-nancial choices.The Behavioral Investment Allocation Strategy (BIAS)task consisted of 20blocks of 10trials each (e Figure 1).During each trial,subjects first saw two stocks and a bond (Anticipation)and then cho one when the word “Choo”appeared above the as-ts (Choice).Then subjects waited for a brief period (Wait),after which their earnings for that trial and tota
l earnings were displayed (Outcome).The were fol-lowed by a display of the outcomes of all asts on that trial (Market)and a fixation cross (Fixation;e Figure 1).At the beginning of each block (indicated by a cue),one of the two stocks was randomly assigned to be the “good”stock,while the other was assigned to be the “bad”stock,without the subject’s knowledge.The good stock dominated the bad stock in the n of first-order stochastic dominance (Huang and Litzen-berger,1988).Specifically,outcomes of the good stock (i.e.,+$10with 50%probability,+$0with 25%prob-ability,and −$10with 25%probability)were better than outcomes of the bad stock (i.e.,+$10with 25%prob-ability,+$0with 25%probability,and −$10with 50%probability)on average for each trial.The bond paid $1with 100%probability on each trial.Earnings were drawn independently from the distributions for each trial,and subjects were informed about the distribu-tions before performing the task.
Bad on prior rearch,we first predicted that gain versus loss outcomes would activate the NAcc and me-sial prefrontal cortex (MPFC)(Knutson et al.,2003)and that loss versus gain outcomes would instead activate the anterior insula (Paulus et al.,2003).We then exam-ined whether NAcc activation preceded both optimal and suboptimal stock (i.e.,risky)choices,as well as whether anterior insula activation instead preceded both optimal and suboptimal bond (i.e.,riskless)choices.Results
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Analys of brain imaging data focud on changes in activation during outcome,market,and anticipation periods prior to a given choice.Analys proceeded through two stages.In the first “localization”stage,we constructed group statistical maps to identify foci of
interest and then verified the predicted patterns of acti-vation with multivariate regressions.In the cond “pre-diction”stage,we ud activation extracted from the foci during the anticipation period to predict both opti-mal and suboptimal subquent investment choices with logit regression models.
In localization analys of the outcome period,stock gain versus loss outcomes were associated with NAcc and MPFC activation at both the small volume-cor-rected and global thresholds,as predicted (Knutson et al.,2003)(e Table 1and Figure 2).Although the ante-rior insula did not show significant deactivation at the global threshold,bilateral foci did show the only deacti-vations in the brain for this contrast that pasd the small volume-corrected threshold (TC =−39,19,7;Z =−2.99;TC =38,19,11;Z =−2.99).Other regions that pasd the global threshold included right orbitofrontal cortex,left anterior cingulate,left precuneus,and left posterior cingulate,replicating prior findings (Knutson et al.,2003).Multiple regression of VOI data (hemody-namic lag =4s)verified that,after prior stock choice,gain outcomes were associated with incread NAcc and MPFC activation (all p values <0.05;e Table S1in the Supplemental Data available online).
In analys of the market period,relative gain out-comes (i.e.,larger difference between the outcome of the chon versus unchon stock)were also associ-ated with MPFC activation at the small volume-cor-rected and global thresholds,as predicted (e Table 2and Figure 2).Other areas that pasd the global threshold included left middle frontal gyrus,bilateral caudate,left putamen,and dorsomedial thalamus.Mul-
Table 1.Activation Foci for Choice Outcome:Contrast of Gain versus Loss following Stock Choice Region Z Score Talairach Coordinates L MPFC 5.34−3,56,4L MPFC 5.47−3,49,0R OFC 3.8922,36,−8R NAcc 6.4111,12,−3L NAcc 5.82−13,8,−4L Ant.Cing 4.07−1,−1,34L Precuneus 4.71−1,−33,43L Post.Cing.
5.11
−3,−34,27
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Figure 2.Effect of Actual and Relative Out-comes on Neural Activation
The top panels depict the contrast of large gains versus large loss during the Out-come period following stock choice.The bottom panels depict the contrast of chon versus unchon outcomes during the Mar-ket period following stock choice.n =
19.
tivariate regression of VOI data verified that,after a stock choice,relative gain outcomes incread NAcc and MPFC activation.Converly,relative loss out-comes incread anterior insula activation (e Table S2).After a bond choice,relative gain outcomes (i.e.,either of the stocks performed wor than the bond)incread MPFC activation (e Table S3).
While not the focus of this study,uncertainty corre-lated maximally and negatively with bilateral anterior cingulate foci,easily exceeding the global threshold (TC =+416,45;Z =−5.37;TC =−4,16,45;Z =−6.99).Further analysis of anticipatory activation extracted from the foci revealed that activation was not great-est with maximal uncertainty (i.e.,uncertainty =0.5,corresponding to minimal information about which stock to choo),but rather with maximal conflict (i.e.,uncertainty =0.3,corresponding to minimal information about whether to choo the stock or the bond).Specif-ically,activation in this region was −0.08±0.01(mean ±SEM,n =2100)when uncertainty was less than 0.25;−0.05±0.01(n =868)when uncertainty was between 0.25and 0.35;and −0.15±0.02(n =832)when uncer-tainty was greater than 0.35.Additionally,anterior cin-gulate anticipatory activation robustly predicted sub-jects’subquent reaction time [t(3718)=7.92,R 2=0.15in a linear regression model that included subject fixed effects].Thus,anticipatory anterior cingulate acti-vation correlated most ro
bustly not with uncertainty,which was greatest when it was unclear which stock to choo,but rather with conflict,which was greatest when it was unclear whether to choo a stock or the bond.However,anticipatory anterior cingulate activa-
Table 2.Activation Foci for Market Outcome:Contrast of Chon Stock versus Unchon Stock Value Region Z Score Talairach Coordinates L MFG 3.93−3,56,8L MPFC 4.26−3,49,−5L Caudate 4.46−7,19,8R Caudate 4.597,19,8L Putamen 4.14−20,9,−2DM Thalamus
5.00
−1,−7,12
tion did not correlate with subquent choice,as de-scribed below.
In prediction analys,we included anticipatory NAcc,MPFC,and anterior insula activation (lag =4s)in logistic regression models of subquent choice,after incorporating relevant behavioral variables (e Tables 3–5).Adding activation from control regions (i.e.,bilat-eral anterior cingulate,orbitofrontal cortex,medial cau-date,and amygdala)did not increa explanatory power,and so,data from the regions were not in-cluded in subquent prediction analys.
Logistic regressions indicated that anticipatory NAcc and anterior insula activation were correlated with sub-quent choice and that the associations critically depended upon prior choice.For all choices,anticipa-tory NAcc activation incread the likelihood of choos-ing a stock only when the prior choice was a bond (a 0.1%increa in NAcc activation led to a 0.06%in-crea in the odds of choosing a stock;p <0.05).When the prior choice was a stock,anticipatory anterior in-sula activation incread the likelihood of choosing the bond (a 0.1%increa in anterior insula activation led to a 0.08%increa in the odds of choosing a bond;p <0.05;e Table 3and Figure 3).MPFC activation did not correlate with subquent choice.Thus,high NAcc activation preceded switching to risk-eking choices,while high anterior insula activation preceded switching to risk-aver choices.
Logistic regressions also indicated that anticipatory NAcc and anterior insula activation were correlated with the types of mistakes that subjects made.When the prior choice was riskless (i.e.,the bond),anticipa-tory NAcc activation incread the likelihood of making a risk-eking mistake (a 0.1%increa in NAcc activa-tion led to a 0.07%increa in the odds of making a risk-eking mistake;p <0.05).Also,anticipatory NAcc activation decread the likelihood of making a risk-aversion mistake (a 0.1%increa in NAcc activation led to a 0.06%decrea in the odds of making a risk-aversion mistake;p <0.05).When the prior choice was risky (i.e.,a stock),anterior insula activati
on incread the likelihood of making a risk-aversion mistake (a 0.1%increa in insula activation led to a 0.11%increa in odds of making a risk-aversion mistake;p <0.05;e
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Table3.Logit Estimation of the Probability of Choosing a Stock or Bond in Trial t
Previous Previous
Choice Was Choice Was
a Stock the Bond
All Data StockChoice t Coef Coef Coef
lNAcc t ANT−0.04980.58890.3192
人工智能的理解(0.24)(3.21)***(2.70)*** lMPFC t ANT−0.0461−0.0222−0.0137
(0.26)(0.15)(0.14) linsula t ANT−0.78750.1910−0.2359
(3.04)***(0.89)(1.69)* RelEarnings t-1−0.05500.0447−0.0360
艾滋病阻断药(5.18)***(4.08)***(6.65)*** Outcome t-1−0.0253−0.0452
(1.88)*(4.65)*** Uncertainty t−4.7256−8.8818−8.1441
(7.68)***(12.89)***(21.42)*** CumEarnings t-1−0.0036−0.0017−0.0031
(3.43)***(1.99)**(5.51)*** Constant 2.7542 1.8624 2.7986
(7.37)***(5.30)***(12.33)*** Obrvations157815953367 Pudo R-sq0.270.310.33 Robust Z statistics are in parenthes.*significant at10%; **significant at5%;***significant at1%.The dependent variable, StockChoice t,is an indicator variable equal to1if a stock was chon and0if the bond was chon on trial t.lNAcc t ANT, lMPFC t ANT,and linsula t ANT are activations in the left NAcc,MPFC, and anterior insula in the Anticipation period of trial t.RelEarnings t is equal to the difference between the dividends on trial t of the stock not chon and tho of the chon stock.If the ast chon in trial t was the bond,RelEarnings t is equal to the maximum dividend paid by the two stocks on that trial.Outcome t is equal to the earnings made on trial t.Uncertainty t is the uncertainty of the choice and defined as min(Pr{Stock T=Good |History},Pr{Stock R=Good|History}).CumEarnings t is wealth accumulated during the task up to and including trial t.Subject fixed effects are included,with robust standard errors.Inclusion of brain variables increas R-sq by1%in each regression.
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Tables4and5and Figure3).MPFC activation was not correlated with subquent mistakes.Thus,anticipa-tory neural activation correlated with both optimal and suboptimal subquent choices,even after controlling for behavioral variables that should have been the pri-mary determinants of tho choices.
Finally,we investigated whether individual differ-ences in average anticipatory activation correlated with subquent choice,after establishing that average an-ticipatory activation varied across individuals.Becau regression of anticipatory NAcc activation on subject fixed effects yielded no significant differences,relation-ships between individual differences in anticipatory NAcc activation and choice were not examined further. On the other hand,regression of anticipatory anterior insula activation on subject fixed effects did yield sig-nificant differences in8(all p values<0.05)of19sub-jects,suggesting some individual differences in antici-patory insula activation.Individual differences in average anterior insula activation during anticipation were sig-nificantly correlated with the frequency of choosing a bond after having chon a stock[t(17)=2.14,p<0.05; R2=0.21].Additionally,individual differences in average anterior insula activation during anticipation were also Table4.Logit Estimation of the Probability of Making a Risk-Aversion Mistake in Trial t
Previous Previous
Choice Was Choice Was
a Stock the Bond
All Data RAM t Coef Coef Coef
lNAcc t ANT0.2962−0.5787−0.1973
(1.11)(2.34)**(1.21) lMPFC t ANT−0.1224−0.1361−0.1578
(0.52)(0.61)(1.11) linsula t ANT 1.09850.10270.4973
(3.22)***(0.34)(2.56)** RelEarnings t-10.0474−0.05110.0384
(3.45)***(3.20)***(5.02)*** Outcome t-10.04950.0497
(2.47)**(3.89)*** Uncertainty t 3.933311.612211.7142
(2.25)**(7.52)***(11.86)*** CumEarnings t-10.00190.00160.0026
(1.40)(1.58)(3.67)*** Constant−2.3645−2.4798−3.3136
(5.27)***(5.11)***(10.64)*** Obrvations10156941857 Pudo R-sq0.260.210.25 Robust Z statistics are in parenthes.*significant at10%; **significant at5%;***significant at1%.The dependent variable, RAM t(Risk-Aversion Mistake),is an indicator variable equal to1if the bond was chon on trial t while the optimal choice was one of the stocks.lNAcc t ANT,lMPFC t ANT,and linsula t ANT are activations in the left NAcc,MPFC,and anterior insula in the Anticipation period of trial t.StockChoice t is an indicator variable equal to1if a stock was chon and0if the bond was chon on trial t. RelEarnings t is equal to the difference between the dividends on trial t of the stock not chon and tho of the chon stock.If the ast chon in trial t was the bond,RelEarnings t is equal to the maximum dividend paid by the two stocks on that trial.Outcome t is equal to the earnings made on trial t.Uncertainty t is the uncertainty of the choice and defined as min(Pr{Stock T=Good |History},Pr{Stock R=Good|History}).CumEarnings t is wealth accumulated during the task up to and including trial t.Subject fixed effects are included,with robust standard errors.Inclusion of brain variables increas R-sq by1%in each regression.
significantly correlated with the frequency of risk-aver-sion mistakes after having chon a stock[t(17)=2.10, p<0.05,R2=0.21].Thus,individual differences in an-ticipatory anterior insula activation were related to making subquent riskless choices and risk-aversion mistakes.
Discussion
While NAcc activation preceded both risky choices and risk-eking mistakes,anterior insula activation pre-ceded both riskless choices and risk-aversion mis-takes.The findings are consistent with the hypothe-sis that NAcc reprents gain prediction(Knutson et al., 2001),while anterior insula reprents loss prediction (Paulus et al.,2003).One of the contributions of this paper is the BIAS task,as it provides a way to opera-tionalize optimal choices,which by extension allows the identification of suboptimal choices.According to financial models,one can define risk-neutral choices bad on Bayesian updating as rational and deviations from the choices as irrational.The results therefore
Neural Basis of Financial Risk Taking
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Figure 3.Association of Anticipatory Neural Activation with Sub-quent Choice
The left panel indicates a significant effect of anterior insula activa-tion on the odds of making riskless (bond)choices and risk-aver-sion mistakes (RAM)after a stock choice (Stock t-1).The right panel indicates a significant effect of NAcc activation on the odds of making risk-aversion mistakes,risky choices,and risk-eking mis-takes (RSM)after a bond choice (Bond t-1).The odds ratio for a given choice is defined as the ratio of the probability of making that choice divided by the probability of not making that choice.Per-cent change in odds ratio results from a 0.1%increa in NAcc or anterior insula activation.Error bars indicate the standard errors of the estimated effect.*coefficient significant at p <0.05.
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indicate that,above and beyond contributing to rational choice,anticipatory neural activation may also promote irrational choice.Thus,financial decision making may require a delicate balance—recruitment of distinct cir-cuits may be necessary for taking or avoiding risks,but excessive activation of one mechanism or the other may lead to mistakes.
While the obrvation that NAcc activation is corre-lated with subquent risk taking and risk-eking mis-takes agrees with a gain prediction account of NAcc function (Knutson et al.,2001),the c
urrent findings are not as consistent with alternative accounts.Motor preparation accounts predict equal activation prior to motor acts of equal force (Mogenson et al.,1980)and so cannot explain the NAcc’s prediction of risk-eking but not risk-aver choices,since both required active choices indicated by button press.Similarly,a sali-ency account predicts equal activation during anticipa-tion of both large gains and loss (Zink et al.,2003)and so cannot account for the NAcc’s prediction of risk-eking but not risk-aver choices.Finally,a be-havioral switching account predicts that NAcc activa-tion will increa prior to any switch from a repeated behavior to a novel behavior (Robbins et al.,1986).While the influence of the NAcc in biasing choice was most pronounced when subjects switched from risk-aver to risk-eking choices,NAcc activation did not predict switches in the opposite direction (from risk-eking to risk-aver choices).The same arguments apply in rever to the anterior insula predicting risk-aver choices.In either ca,theories that fail to in-clude the anticipated subjective value of an outcome cannot easily account for the obrved pattern of re-sults.
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Although both actual and relative gain outcomes in-cread activation in the MPFC,MPFC activation did not predict subquent risk-taking behavior,consistent with its propod role in reprenting gain prediction error rather than gain prediction (Knutson et al.,2003).Gain outcomes also activated other regions implicated
Table 5.Logit Estimation of the Probability of Making a Risk-Seeking Mistake in Trial t
Previous Previous Choice Was Choice Was a Stock补办身份证需要什么手续
the Bond All Data RSM t Coef Coef Coef lNAcc t ANT 0.39980.73950.4868(0.93)(2.63)***(2.69)***lMPFC t ANT
−0.4330−0.1108−0.1210(1.44)(0.50)(0.81)linsula t ANT −0.60240.4430−0.0577(1.19)(1.30)(0.27)RelEarnings t-1−0.08380.0395−0.0152(3.81)***(2.34)**
(1.67)*Outcome t-10.0037−0.0416(0.16)(2.49)**Uncertainty t −12.4172−14.6378−8.8036(6.20)***(5.37)***(8.07)***CumEarnings t-1−0.0089−0.0008−0.0038(4.32)***(0.58)(4.22)***Constant 7.1203 3.1759 2.9538(5.93)***(2.58)***(5.24)***Obrvations 3538741295Pudo R-sq
0.30
0.34
0.25
Robust Z statistics are in parenthes.*significant at 10%;**sig-nificant at 5%;***significant at 1%.The dependent variable,RSM t (Risk-Seeking Mistake),is an indicator variable equal to 1if a stock was chon on trial t while the optimal choice was the bond.lNAcc t ANT ,lMPFC t ANT ,and linsula t ANT are activations in the left NAcc,MPFC,and anterior insula in the Anticipation period of trial t .StockChoice t is an indicator variable equal to 1if a stock was chon and 0if the bond was chon on trial t .RelEarnings t is equal to the difference between the dividends on trial t of the stock not chon and tho of the chon stock.If the ast chon in trial t was the bond,RelEarnings t is equal to the maximum dividend paid by the two stocks on that trial.Outcome t is equal to the earnings made on trial t .Uncertainty t is the uncertainty of the choice (or uncertainty of the environment)and defined as min(Pr{Stock T =Good |History},Pr{Stock R =Good |History}).CumEarnings t is wealth accumulated during the task up to and including trial t .Subject fixed effects are included,with robust standard errors.Inclusion of brain variables increas R-sq by 1%in each regression.
in decision making (e.g.,orbitofrontal cortex,medial caudate,anterior cingulate cortex),but activation in the regions also did not predict subquent risk-taking behavior.While activation in the regions do not correlate with subquent risk taking,the regions may still play other important roles
in decision making (O’Doherty et al.,2003).For instance,anterior cingulate foci showed incread activation under conditions of incread respon conflict,consistent with the pos-tulated role of this region in conflict monitoring (Ridde-rinkhof et al.,2004).
The BIAS task offers a number of advantages in elic-iting financial choice behavior.First,becau the BIAS task utilizes monetary incentives in a dynamic tting,our findings may generalize to real-world trading sce-narios.Second,the BIAS task enables identification of both optimal choices and suboptimal choices.Third,the BIAS task elicits a range of behaviors from each individual,including both risk-eking and risk-aver choices.Fourth,the event-related design of the study allowed us to correlate anticipatory rather than concur-rent neural activation with choice by temporally isolat-
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ing anticipatory activation and controlling for key ante-cedent behavioral ,earnings,uncertainty). While the event-related analys ensured that both anticipatory activation and decision making occurred prior to actual choice,the dynamic nature of the BIAS task leaves open the question of whether anticipatory activation preceded decision making or the rever. Some of the prent findings support the idea that acti-vation preceded decision making.Sp
ecifically,the link between activation and subquent choice critically depended upon prior choice.For example,if NAcc acti-vation simply reflected the decision to pick a stock, then the relationship between NAcc activation and the likelihood of choosing a stock should not depend upon prior choice.However,anticipatory NAcc activation sig-nificantly predicted the likelihood of subquent stock choice only if the bond was picked on the previous trial (e Table3).The same argument also applies to insula activation.Future rearch that specifically manipu-lates anticipatory activation could further establish whether such activation influences decisions.
The dynamic nature of the BIAS task may have ob-scured stable individual differences in NAcc activation, which might influence subquent choice,but are more evident in stationary tasks(Knutson et al.,2005).How-ever,even during this dynamic task,significant indi-vidual differences were evident in insula activation dur-ing anticipation,and the predicted switching from risky to riskless choices as well as the likelihood of making risk-aversion mistakes while doing so.The link between individual differences in anterior insula activa-tion and subquent risk-aver choices replicates and extends prior findings(Paulus et al.,2003).
While experts and nonexperts who differed in terms of prior courwork in finance and statistics did not sig-nificantly differ in behavior in this experiment,future re-arch should also examine the influe
nce of individual differences in trading experience on financial risk ta-king,since psychophysiological evidence suggests that experienced traders may show less emotional re-sponsiveness to market events than inexperienced traders(Lo and Repin,2002).While many psychophysi-ological ,skin conductance,heart rate, pupillary dilation)index anticipatory arousal,the current results suggest that measures that probe anticipatory valence will also be necessary to predict the likelihood of subquent risky choice.
Overall,the findings suggest that risk-eking choices(such as gambling at a casino)and risk-aver choices(such as buying insurance)may be driven by two distinct neural circuits involving the NAcc and the anterior insula.The findings are consistent with the no-tion that activation in the NAcc and anterior insula,re-spectively,index positive and negative anticipatory af-fective states and that activating one of the two regions can lead to a shift in risk preferences.This may explain why casinos surround their guests with reward ,inexpensive food,free liquor,surpri gifts, potential jackpot prizes)—anticipation of rewards acti-vates the NAcc,which may lead to an increa in the likelihood of individuals switching from risk-aver to risk-eking behavior.A similar story in rever may ap-ply to the marketing strategies employed by insurance companies.
Consideration of risk necessarily involves weighing potential gains against potential loss.The notio
n that distinct neural mechanisms anticipate gain versus loss suggests a novel componential view of risk taking.Com-bined with such a view,the findings provide neural tar-gets for investigating complex risk phenomena such as loss aversion,in which people weigh loss more than gains of equivalent size(Kahneman and Tversky,1979). The findings further imply that neuroeconomic re-arch may foster a more comprehensive theory of in-dividual decision making than the rational actor model and thus may ultimately yield new insights relevant to economic policy and institutional design.
Experimental Procedures
Nineteen healthy volunteers(10females,mean age=27,range= 24–39years,right handed)participated in the study.Prior to enter-ing the scanner,subjects played a practice version of the invest-ment task for at least10min,minimizing learning effects.Subjects were then shown the cash they could earn by performing the task successfully and correctly reported believing that they would re-ceive cash at the end of the experiment contingent upon their per-formance.Subjects received a fixed compensation of$20per hour, as well as a tenth of their total task earnings.They were also in-formed that it was possible to lo money on the task and that any loss would be deducted from their total payment.
To elicit a range of investment behavior,subjects included both “experts”and“nonexperts,”depending on whether they had taken prior graduate courwork in statistics and finance.Experts in-cluded Ph.D.students in finance,economics,or accounting,while nonexperts included Ph.D.students in humanities at Stanford Uni-versity,to equate age,socioeconomic status,education,and intelli-gence.A2(expert versus nonexpert-between)×20(block-within) analysis of variance revealed a main effect of block[F(19,323)= 2.35,p<0.005],indicating that subjects cho the bond more often as the experiment progresd.However,experts and nonexperts did not significantly differ in choice of stocks versus bonds,either overall(54%±6%versus53%±6%)or across blocks.Experts and nonexperts also did not significantly differ in the proportion of risk-eking mistakes[26%±6%versus35%±8%;t(17)=0.88, n.s.]or risk-aversion mistakes they made overall[23%±6%versus 29%±6%;t(17)=0.67,n.s.;calculated as percentage of mistakes made on trials where mistakes of that type were possible],suggest-ing more of a performance continuum than distinct groupings. Since choices and mistakes did not significantly differ between experts and nonexperts,we combined groups in subquent analys.
Behavioral Analysis
In the context of the BIAS task,the optimal strategy of a rational, risk-neutral agent is to pick a stock i
f he or she expects to receive a dividend that is at least as large as the bond earnings.Since the actual monetary amounts at stake in each trial were small(−$1to $1),we ud risk neutrality as the baline model of investor beha-vior(Rabin,2000),a model which assumes that individuals maxi-mize expected return.A rational actor should also update his or her beliefs about the probability of each stock being optimal ac-cording to Bayes’rule.Bad on the assumptions,we derived the optimal portfolio lection strategy,which was the same for all trials(e Supplementary Data).
For each trial,the objective probability of each of the two stocks being dominant can be computed using Bayes’rule.We refer to the minimum of the two probabilities as“uncertainty”for that trial.Uncertainty is highest(and equal to0.5)at the beginning of a block,when the probability of either stock being optimal is50%, and decreas as more information about dividends is revealed, clarifying which stock dominates.On trials where uncertainty was 0.3or lower,the optimal choice was one of the stocks—otherwi, the optimal choice was the bond.Thus,when uncertainty is clo to the threshold value of0.3,it is most difficult for subjects to deter-
