Thinking like a trader selectively reduces individuals’ loss aversion Peter Sokol-Hessnera, Ming Hsub, Nina G. Curleya, Mauricio R. Delgadoc, Colin F. Camererd, and Elizabeth A. Phelpsa,1 aDepartment of Psychology, New York University, 6 Washington Place, New York, NY 10003; bBeckman Institute for Advanced Science and Technology, Department of Economics, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801; cDepartment of Psychology, Rutgers University, 101 Warren Street, Newark, NJ 07102; and dDivision of the Humanities and Social Sciences, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125 arousal ͉ emotion regulation ͉ decision-making W e are not at the whim of our emotions—rather, research on emotion regulation suggests we have a degree of control over our affective state and can reduce or enhance the emotional impact of a given stimulus in real time (1). We are able to do this intentionally, and when doing so, we not only report decreased negative affect (1–3) but also show signs of decreased physiological responding (4, 5) and decreased activity in brain areas that are closely linked to emotions and affect (1–3). Emotion regulation research so far has primarily used pictures (1–5), but any stimulus that results in an emotional response could theoretically be the target of regulation. We propose to examine a specific role for emotions in economic choice behavior and to observe the effects of an intentional cognitive regulation strategy on both behavior and associated emotional responses. It is widely acknowledged that emotion plays a role in decisionmaking, drawing on evidence from numerous behavioral studies using emotional stimuli as well as physiological, neuroimaging, and lesion studies. For example, one study demonstrated that irrelevant emotional states induced by film clips could eliminate or even reverse the endowment effect (higher selling than buying prices) in subsequent choices (6). Another study on consumption behavior of drinks showed that the subliminal presentation of emotional faces not only altered participants’ ratings of various drinks but also the actual amount they drank and the price they were willing to pay for the drink (7). These startling results clearly demonstrate an effect of emotional stimuli on decisions, even when these stimuli are irrelevant or below awareness. www.pnas.org͞cgi͞doi͞10.1073͞pnas.0806761106 Self-reports of affect have been used to explore the effect of subjective feelings on choices (8, 9), widening the possible measures of the affective experience. Neuroimaging studies (10–13) and studies with brain-damaged patients (11, 14–16) have repeatedly demonstrated the involvement and necessity of brain regions including the amygdala and insula in decisionmaking, although these particular areas are arguably best known for their association with a range of tasks involving emotion and physiological responding (17–20). This overlap suggests there are some common underlying mechanisms involved in reward, choice, and emotion. For example, a now-classic study using the Iowa Gambling Task illustrated the close relationship between physiological arousal and choices in normal participants but showed that brain-damaged patients, who did not show normal arousal responses, also did not show normal choice patterns (14). A similar study with the same patients (and others) showed behavior consistent with diminished sensitivity to losses (16), further establishing the necessity of emotion-related brain regions in mediating aspects of decision-making. The current study builds on this research by using behavioral models and physiological measures to investigate a specific and quantifiable role for emotional responses in risky monetary decision-making. Given the aforementioned work suggesting emotions may play a central role in the anticipation and processing of losses, the phenomenon of loss aversion is of obvious interest. In 1979, Kahneman and Tversky (21) suggested that losses loom larger than equivalent gains, a property called ‘‘loss aversion.’’ Loss aversion subsequently came to be conceptualized as a multiplicative overweighting of losses relative to gains represented by a parameter (21). Laboratory studies have since demonstrated that humans can show loss aversion for objects such as mugs (22), money (23), and simulated investments (24, 25). This work has been supported by analyses of real world data that show similar behavior in, among other situations, stock markets (26–28), the pricing and purchasing of consumables (29, 30) and condominiums (31), and the choice of work hours by cabdrivers (32). It has been suggested that loss aversion might have a specific, evolutionarily conserved neurobiological basis (as opposed to being epiphenomenal or cultural in origin). Supporting this claim, work with primates has shown that our genetic cousins also exhibit loss aversion in a fiat currency economy (33). Loss aversion appears to exist across both domains and species, and because decisionmaking in the context of possible losses has been linked to emotional responses, loss aversion is an excellent candidate Author contributions: P.S.-H., M.H., M.R.D., C.F.C., and E.A.P. designed research; P.S.-H. and N.G.C. performed research; M.H. and C.F.C. contributed new reagents/analytic tools; P.S.-H., M.H., and N.G.C. analyzed data; and P.S.-H., C.F.C., and E.A.P. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. Freely available online through the PNAS open access option. 1To whom correspondence should be addressed. E-mail: firstname.lastname@example.org. This article contains supporting information online at www.pnas.org/cgi/content/full/ 0806761106/DCSupplemental. PNAS Early Edition ͉ 1 of 6 NEUROSCIENCE Research on emotion regulation has focused upon observers’ ability to regulate their emotional reaction to stimuli such as affective pictures, but many other aspects of our affective experience are also potentially amenable to intentional cognitive regulation. In the domain of decision-making, recent work has demonstrated a role for emotions in choice, although such work has generally remained agnostic about the specific role of emotion. Combining psychologically-derived cognitive strategies, physiological measurements of arousal, and an economic model of behavior, this study examined changes in choices (specifically, loss aversion) and physiological correlates of behavior as the result of an intentional cognitive regulation strategy. Participants were on average more aroused per dollar to losses relative to gains, as measured with skin conductance response, and the difference in arousal to losses versus gains correlated with behavioral loss aversion across subjects. These results suggest a specific role for arousal responses in loss aversion. Most importantly, the intentional cognitive regulation strategy, which emphasized ‘‘perspective-taking,’’ uniquely reduced both behavioral loss aversion and arousal to losses relative to gains, largely by influencing arousal to losses. Our results confirm previous research demonstrating loss aversion while providing new evidence characterizing individual differences and arousal correlates and illustrating the effectiveness of intentional regulation strategies in reducing loss aversion both behaviorally and physiologically. PSYCHOLOGY Edited by Edward E. Smith, Columbia University, New York, NY, and approved February 4, 2009 (received for review July 19, 2008) 2 of 6 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0806761106 LAMBDA (λ) PARAMETER ESTIMATE measure for examining the effect of intentional regulation strategies on emotion-related aspects of choice behavior. In determining a strategy that might affect loss aversion, we return to research on intentional emotion regulation with a focus on reinterpretation (often termed ‘‘reappraisal’’) (1, 2, 34). As opposed to other kinds of emotion regulation techniques, reinterpretation is distinguished by changing the meaning of a stimulus with the goal of altering the resulting affective state. The stimulus remains physically identical, but the perceiver thinks about it in a different way, perhaps focusing on different aspects of it, taking a different perspective, or putting it in some greater context that changes its immediate meaning. Such reinterpretation of a disturbing image of injured people could include imagining that the people in the image are just actors with makeup performing a stunt, or recognizing that even a small cut can sometimes bleed quite a bit, making things look much worse than they actually are. In the context of monetary decisions, reinterpretation of a particular outcome could include putting it in a greater context as one of many outcomes (35) or taking a different perspective on a choice, perhaps imagining that oneself is an experienced professional trader, rather than an excitable amateur investor. These kinds of strategies are sometimes recommended to investors in articles (36) or investment guides. For example, one investment company reminded their clients that ‘‘it is the return of the entire portfolio that matters, not the individual parts. Stay focused on how your investments are performing as a whole, rather than each one, to get over the inevitable bumps in the road toward reaching your goals.’’ These reinterpretations are not in the spirit of denial (‘‘it does not exist, look away, think of something else’’) but rather focus on the affect-inducing object and attempt to change its meaning for the participant. In the current study, we examine loss averse behavior, its physiological correlates, and the impact of an intentional regulation strategy on these variables. Emotion is a complex construct, and one commonly accepted theoretical approach is to consider emotion as consisting of multiple component processes (37), including facial and vocal expression, subjective feelings, action tendencies, bodily responses, and cognitive appraisals. For the following study, we focus on the latter 3 components. Participants’ choices are our objective measure of action tendencies, modeled on an individual participant basis with quantitative parametric behavioral models conventionally used in economics. We measure participants’ skin conductance to quantify bodily arousal responses, and relate such responses to behavior. Finally, cognitive appraisal is operationalized as the intentional cognitive regulation strategy that we instruct participants to use. This strategy is similar to other emotion regulation strategies in its reinterpretive nature, despite its content being more relevant to economic decisions. We observe both behavioral and physiological consequences of the strategy, suggesting that emotional responses are related to the observed behavior. By combining the above variables and individual level behavioral and physiological analyses, we can explore subtle effects within subjects, and can speak directly to the effects of our strategy on a given individual, rather than being limited to group analysis. Participants made a series of forced monetary choices between a binary gamble (P ϭ 0.5) and a guaranteed amount (P ϭ 1) (Fig. S1). All choice outcomes were realized immediately after decision (e.g., ‘‘you won’’). One hundred and forty choices constituted a ‘‘set,’’ from which we quantified 3 aspects of behavior: the weighting of losses relative to gains (loss aversion, ), attitudes toward chance (risk aversion, ), and consistency over choices (logit sensitivity, ) (Fig. S2). The values in the set were selected a priori to allow accurate estimation of a range of possible values of , , and . The participants completed 2 full sets of choices: one while using the ‘‘Attend’’ strategy, which emphasized each choice in isolation from any context, ‘‘as if it was the only one,’’ 4 3 2 1 0 INDIVIDUAL PARTICIPANTS Fig. 1. Individual loss aversion coefficients () when using the Attend strategy in Study 1. Green indicates Ͻ 1 (gain seeking), blue indicates is not different from 1 (gain-loss neutral), and red indicates Ͼ 1 (loss averse). Error bars are standard error of the mean. and the other using the “Regulate” strategy, emphasizing choices in their greater context, ‘‘as if creating a portfolio’’ (complete instructions included in the SI Text). This allowed separate quantification of Attend and Regulate behavior for each subject. Choices were presented in pseudorandomly ordered blocks of 10 with a given strategy, and block order, gamble order, and gamble outcome were counterbalanced across participants. The conceptual nature of the strategies was emphasized and participants were thoroughly instructed and quizzed on all procedures. In Study 1, the participants were initially endowed with $30 and were paid this sum plus actual gains or losses from 10% of the trials selected at random upon completion of the study. Study 2 had an identical behavioral session as Study 1, but the participants returned for a separate session in which their skin conductance response (SCR, a measure of sympathetic nervous system activity) was recorded during the choice task as a measure of arousal. See Methods and SI Text for more detail. Results Study 1 Results. Attend Results. Mean parameter estimates (with standard errors) were ϭ 1.40 (0.15), ϭ 0.83 (0.04), and ϭ 2.57 (0.29). Because of the multiplicative nature of the loss aversion parameter , taking the log can avoid biases in calculating the mean. The mean log() value was 0.198 (0.09) and was significantly greater than zero (t(29) ϭ 2.113, P Ͻ 0.05), indicating that the group was on average loss averse. Translating that value out of the log scale by raising the constant e to that value gave a mean of 1.22. The range of parameter values were : 0.41–3.91, : 0.37–1.23, and : 0.71–6.53. Individual values are found in Fig. 1. These values indicate that there are 9 gain seeking, 7 gain-loss neutral, and 14 loss averse participants in our sample, where gain seeking is defined as having a significantly less than 1, gain-loss neutral is defined as having a not significantly different from 1, and loss averse is defined as having a significantly greater than 1. Regulate Results. Mean parameter estimates (with standard errors) were ϭ 1.17 (0.15), ϭ 0.87 (.04), and ϭ 2.39 (0.29). The mean log() value was Ϫ0.0005 (0.10), and was not significantly different from zero (t(29) ϭ Ϫ0.005, not significant (n.s.)). This corresponded to a mean ϭ 0.999. Paired t tests with the Attend data were conducted to determine the effect of the cognitive strategy within-subjects on the parameters estimated. An effect was observed for the loss aversion coefficient (t(29) ϭ 3.64 P Ͻ 0.0011), but not for (t(29) ϭ 1.66 P Ͻ 0.11) or (t(29) ϭ 0.79 P Ͻ 0.44). Although 26 out of 30 subjects showed decreases in loss aversion when using the Regulate strategy, there was variability across individuals in the strength of the effect. To capture some Sokol-Hessner et al. ) µS $ 10% 0.01 ( 0% -10% * * * * INDIVIDUAL PARTICIPANTS * * * * Fig. 2. Percent change in loss aversion due to the cognitive strategy in Study 1, expressed as (Regulate Ϫ Attend) as percent of Attend. A negative value indicates less loss aversion during Regulate trials. Participants are in the same order as Fig. 1. Changes in loss aversion exceeding the 0.05 criterion cutoff are marked with red asterisks. of this variability and quantify the strength of this effect, we performed likelihood ratio tests on the parameters (see Methods and SI Text) to identify individuals with differences in parameters between the conditions stronger than a cutoff P value of 0.05. Of 30 subjects, 15 exceeded the criterion for changes in , 3 for changes in estimated values, and 2 for changes in their estimated values. Overall, individuals’ loss aversion as measured during the attend instruction (Attend) was reduced by an average of 16% (standard error 3.09%) during the regulate instruction (Regulate) (See Fig. 2). This effect was independent of Attend (r Ͻ 0.03, n.s.). Counterbalancing (including block order, gamble outcome, and gamble order within block) had no effects on parameter estimates or the effectiveness of the Regulate strategy. See SI Text for more details. Study 2 Results. Behavioral Results. The behavioral results in Study 2 confirmed those of Study 1. Mean Attend parameter estimates (with standard errors) were ϭ 1.31 (0.13), ϭ 0.88 (0.03), and ϭ 2.97 (0.28). Mean Regulate parameter estimates (with standard errors) were ϭ 1.15 (0.12), ϭ 0.92 (0.03), and ϭ 2.60 (0.23). Paired t tests between the Attend and Regulate conditions confirmed a strong effect for the loss aversion coefficient (t(28) ϭ 6.91 P Ͻ 1.6 ϫ 10Ϫ7). There was no significant effect on (t(28) ϭ 1.82 P Ͻ 0.08), or (t(28) ϭ 1.40 P Ͻ 0.17). Physiological Results. Because amounts of money won or lost varied both within participants and between participants, SCR at outcome in units of microsiemens (S; square-root transformed to reduce skewness) were normalized by the amount of money won or lost on a given trial, giving each participant an average Gain SCR score and average Loss SCR score with units of ͌S/$. In addition to the Gain and Loss SCR scores, we created an SCR difference score of Loss Ϫ Gain as a physiological measure of loss aversion. On average, losses were more arousing than gains in the Attend condition (Loss Ϫ Gain ϭ 0.0092 ͌S/$, t(28) ϭ 1.89, P Ͻ 0.035 one-sample t test, one-tailed), but not in the Regulate condition (Loss Ϫ Gain ϭ 0.0017 ͌S/$, t(28) ϭ 0.24, P Ͻ 0.40 one-sample t test, one-tailed). However, although 28 of the 29 subjects in Study 2 showed a decrease in loss aversion with the regulation strategy, there was extensive variability in the strength of the effect. To characterize that variability, we separated participants using likelihood ratio tests. We divided them based on whether they exceeded the cutoff for changes in their coefficient between Attend and Regulate, creating 2 groups: Regulators (P Ͻ 0.05), and Nonregulators (P Ͼ 0.05). Only Regulators showed a significant decrease in the Loss Ϫ Gain Sokol-Hessner et al. -0.01 * “ATTEND” “REGULATE” ALL SUBJECTS -0.02 * 0.02 B 0.01 0 -0.01 -0.02 “ATTEND” “REGULATE” REGULATORS “ATTEND” “REGULATE” NON-REGULATORS Fig. 3. Average Loss Ϫ Gain (͌S/$) SCR difference scores from Study 2. (A) Across all subjects, arousal is greater per dollar to losses than gains, but only in the Attend condition. (B) Regulators (exceeded cutoff for decrease in ) show a significant decrease in the difference score, while Nonregulators (did not exceed cutoff for decrease in ) do not. Error bars are standard error of the mean. SCR difference score from Attend to Regulate (Attend ϭ 0.0102 ͌S/$, Regulate ϭ Ϫ0.0102 ͌S/$, t(13) ϭ 2.04, P Ͻ 0.031 one-tailed paired t test), whereas Nonregulators showed a nonsignificant increase (Attend ϭ 0.0089 ͌S/$, Regulate ϭ 0.0128 ͌S/$, t(14) ϭ 0.49, P Ͻ 0.64 two-tailed paired t test) (see Fig. 3). We also conducted ANOVAs on the Gain and Loss SCR scores. A 2 ϫ 2 ϫ 2 repeated measures ANOVA with factors of Outcome (Loss, Gain), Strategy (Attend, Regulate), and Group (Regulators, Nonregulators), revealed a trending interaction between all 3 factors (F (1, 27) ϭ 3.70 P Ͻ 0.065), but no other interactions nor main effects (all P’s Ͼ 0.29). To examine what might be driving the 3-way interaction, we conducted separate 2 ϫ 2 repeated measures ANOVAs on the Gain and Loss SCR scores. The Loss SCR score ANOVA showed a trending interaction between Strategy and Group (F(1,27) ϭ 3.791 P Ͻ 0.062, all other P’s Ͼ 0.37), but an identical ANOVA on just the Gain SCR score had no significant main effects or interactions (all P’s Ͼ 0.48), illustrating that the initial 3-way interaction was driven in large part by changes in the Loss SCR score. Individuals’ Loss Ϫ Gain SCR difference scores also positively correlated with their respective behavioral loss aversion () coefficients in both the Attend condition (r(27) ϭ 0.394, P Ͻ 0.035) (See Fig. 4), and the Regulate condition (r(27) ϭ 0.403, P Ͻ 0.031). Model Estimation. To test the significance of the estimated pa- rameters, we performed a likelihood ratio test for each individual in each condition, contrasting the likelihood of the data given the estimated parameters for that condition with the likelihood of the data given a random choice model. This showed that for subjects of Study 1 in both conditions (60 sets of parameters total), all estimated models had a P Ͻ 5 ϫ 10Ϫ5, and 56 out of 60 models had a P Ͻ 1 ϫ 10Ϫ10 (Participants’ models in Study 2 had similar significance levels). For more details on validity tests of the model, see Methods. PNAS Early Edition ͉ 3 of 6 PSYCHOLOGY * NEUROSCIENCE -50% * * ) -40% * µS $ * 0 ( * LOSS - GAIN -20% -30% * 0.02 A 20% LOSS - GAIN PERCENT CHANGE IN LAMBDA (λ) 30% LOSS - GAIN ( S/$) “ATTEND” 0.12 0.10 0.08 0.06 0.04 0.02 0 -0.02 -0.04 0 1 2 3 4 LAMBDA (λ) “ATTEND” Fig. 4. Individuals’ physiological SCR difference score Loss Ϫ Gain (͌S/$) plotted against their behavioral loss aversion coefficient in the Attend condition in Study 2. Removal of candidate outliers strengthens the correlation. Discussion Our results support the idea that taking a perspective similar to that of a trader can alter choices and arousal responses related to loss aversion. Building upon the evidence that emotions have a role in decision-making (6–9, 12), and that there might be an important role in decision-making for the anticipation of emotional responses to losses (16), we show that loss aversion is linked to physiological arousal responses to loss outcomes relative to gain outcomes and that these measures are reliably and systematically affected by perspective-taking. In Study 1, we showed that an intentional reinterpretive regulation strategy had a specific and strong effect in decreasing individuals’ initial levels of loss aversion. No other measurements of behavior showed significant changes as a result of using the strategy. Study 2 demonstrated that behavioral loss aversion was correlated with a physiological arousal measure, specifically the SCR per dollar to loss outcomes relative to gain outcomes. Furthermore, Study 2 showed that only the individuals most successful at reducing their degree of loss aversion by taking a different perspective had a corresponding reduction in the physiological arousal response to loss outcomes. One goal of this study was to find an ecologically plausible reinterpretive strategy that could lead to a change in the emotional significance of some of the components of decisionmaking. In this context, it appears that ‘‘thinking like a trader’’ may reduce the subjective impact of loss outcomes. Just as recent work demonstrating that individuals’ anticipation of loss may shift their choices (38), it appears that participants in our study similarly anticipated their responses to gains and losses and chose accordingly (39). Given the correlational nature of this study, however, future manipulations that alter arousal directly will be necessary to demonstrate causality. This is not the first study to show the effect of perspectivetaking on loss-averse behavior. For example, a study by Thaler et al. (25) applied an ecologically plausible situational manipulation (based on the frequency of feedback for risky investments) in a between-subjects design. They showed that temporally bracketing choices decreased the occurrence of behavior consistent with loss aversion [similar to results found by Gneezy and Potters (24)]. Other studies have hypothesized that emotional attachment and cognitive perspective might modulate loss aversion and, more specifically, that having the intention to trade some good or currency would reduce loss aversion for that item, potentially through affective and/or cognitive means (40–42). This study builds upon these ideas, combining intentional reg4 of 6 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0806761106 ulation, cognitive perspective taking, and physiological measurements of arousal. We have shown that not only do different individuals’ perspectives alter their choices but also that within an individual, choosing to take a different perspective can reliably reduce their loss aversion. In addition, our demonstration of changes in arousal due to the intentional regulation strategy coincides with evidence from studies of the cognitive regulation of emotion illustrating significant behavioral (1–3), physiological (4, 5), and neural (1–3) changes associated with the intentional use of regulation strategies to reappraise emotional stimuli. Because the ‘‘trader perspective,’’ or portfolio approach, that our regulation strategy encourages is similarly reinterpretive, it is possible that a related mechanism is at work. In that context, this study may provide some insight into what separates professional traders and gamblers from amateurs. It is possible that professionals and amateurs are fundamentally different people from the start, but it is also possible that professionals have learned not just facts about investments, but strategies for addressing the normal emotional responses that might prevent amateurs from making the same decisions, given the same information (36, 43, 44). Indeed, professional sports card dealers (45), condominium investors (rather than owners) (31), and experienced cab drivers (32) show less apparent response to loss than less experienced agents. Our results also shed light on a simmering debate about the nature of loss aversion (42, 46): do losses hurt as much as our decisions to avoid them suggest, or are we overzealous at the time of decision in predicting that losses will hurt disproportionately, when in fact they are not any worse than gains are good? In other words, is loss aversion due to a basic hedonic property of our reaction to losses, as are simple basic preferences for food, sleep, sex, and warmth? Or is it a kind of error in judgment caused by an exaggerated fear of losses relative to their actual impact (47), perhaps due to an underappreciation of our capacity for emotional adaptation to negative events (48)? Our results support the former, ‘‘hedonic,’’ interpretation, that losses do hurt more than gains feel good, because differential physiological arousal responses are linked to actual feedback about loss and gain, and therefore, at least to some degree, loss aversion may not be a judgment error. However, our results also support the latter, ‘‘judgmental error,’’ interpretation to some extent by demonstrating that cognitive strategies can systematically reduce loss aversion behaviorally and physiologically; so whatever ‘‘fear of loss’’ may exist is not so basic as to be immutable, but is instead subject to regulation. At least it appears there is some hope for the ‘‘amateur’’ decision-maker, in that a simple reinterpretation might mitigate one dimension of the difference between amateurs and professionals. We can change how we decide, and although we may be sensitive to losses, we can make ourselves less so. Methods Subjects. In Study 1, 30 participants (13 male, mean age 22 Ϯ 3 years) completed the experiment. In Study 2, 52 participants (19 male, mean age 21 Ϯ 3 years) completed the behavioral session. Twelve were excluded based on highly imprecise parameter estimation*, 4 for noiseless performance†, 2 for outlier behavior (Ͼ3 SD from the mean), and 2 for instruction-related issues. Of the remaining 32 participants, one was excluded for SCR nonresponding and one could not make a second session. The remaining 30 participants completed the physiological session, in which one participant was dropped for experimenter error. The behavioral and physiological data from the *The measure used at the time to define significance in the model was later replaced with the likelihood ratio test (see the SI Text). †For participants whose decisions can be fit perfectly (with no noise parameter), there is a range of parameter values which fit equally well, and no standard procedure for choosing one of these sets of values over the others. Problems with noiseless data are common in such estimations. Sokol-Hessner et al. Procedure—Study 2 Physiological Session. Participants returned within 2 weeks after the behavioral session for a physiological assessment. The assessment consisted of 2 sessions at least 48 h apart. The endowment and instructions were exactly the same as in the behavioral session, including a $30 endowment, detailed task instructions, a task quiz, and strategy instruction. Over both sessions, participants completed a total of 120 choices between mixed-valence gambles and a guaranteed amount of zero. Sixty choices were completed using the Attend strategy, and 60 with the Regulate strategy. Choice values were selected a priori using participants’ parameter estimates from the behavioral session to equalize the number of win, loss, and guaranteed outcomes. See the SI Text for more details. The choice structure had the 1. Ochsner KN, et al. (2004) For better or for worse: Neural systems supporting the cognitive down- and up-regulation of negative emotion. NeuroImage 23:483– 499. 2. Ochsner KN, Bunge SA, Gross JJ, Gabrieli JDE (2002) Rethinking feelings: An fMRI study of the cognitive regulation of emotion. J Cognit Neurosci 14:1215–1229. 3. Schaefer SM, et al. (2002) Modulation of amygdalar activity by the conscious regulation of negative emotion. J Cognit Neurosci 14:913–921. 4. Eippert F, et al. (2007) Regulation of emotional responses elicited by threat-related stimuli. Hum Brain Mapp 28:409 – 423. Sokol-Hessner et al. SCR. SCR was measured using Ag-AgCl electrodes attached to the crease between the distal and middle phalanges of the first and second digits of the left hand. The SCR data were amplified and recorded with a BIOPAC Systems skin conductance module connected to an Apple computer. Data were recorded at a rate of 200 samples per second. SCR analysis was conducted using AcqKnowledge software (BIOPAC Systems Inc.). SCR (in S) was measured as the trough-to-peak amplitude difference in skin conductance of the largest response in the window 0.5 s after stimulus onset to 4.5 s after stimulus offset. A minimal response criterion was set at 0.02 S, and responses not exceeding this threshold were scored as ‘‘0.’’ SCR data were low-pass filtered (25Hz), smoothed (3 sample kernel), and square-root transformed to reduce skewness. SCRs at outcome were normalized with the dollar amount of the outcome to produce measurements with units of ͌S/$. Model. We used a 3 parameter model to estimate choice behavior. Gains and losses were estimated with Eqs. 1 and 2 respectively, and Eq. 3 (a logit, or softmax function) translated the difference between the subjective value of the gamble and the subjective value of the guaranteed amount (estimated using Eqs. 1 and 2) into a probability of gamble acceptance between 0 and 1. All 3 functions relied on the 3 parameters described below: (the loss aversion coefficient), (the curvature of the utility function), and (the logit sensitivity). u͑xϩ͒ ϭ x  u͑xϪ͒ ϭ Ϫ ϫ ͑Ϫx͒  p͑gamble acceptance͒ ϭ ͑1 ϩ exp͕Ϫ ͑u͑gamble͒-u͑guaranteed͖͒͒͒ Ϫ1  (Fig. S2a) only appears in the equation for the calculation of the utility of losses (Eq. 2), since it refers to the multiplicative valuation of losses relative to gains. When ϭ 1, gains and losses are valued equally (‘‘gain-loss neutral’’), while Ͼ 1 indicates the overvaluation of losses (loss averse), and Ͻ 1 means gains are overvalued relative to losses (gain seeking). (Fig. S2b) represents risk aversion due to the presence of diminishing sensitivity to changes in value as the absolute value increases, and (Fig. S2c) refers to the sensitivity of the participant’s choices to changes in the difference between subjective values of the gamble and the guaranteed amount (see SI Text for more details on the model). For all participants we separately estimated Attend and Regulate , , and values in Mathematica v5.2 using a maximum likelihood estimation procedure. To determine overall model significance on a per subject, per condition basis, we performed a likelihood ratio test against a random model to determine whether the probability of the data was significantly higher given the parameters we estimated. To determine the significance of within-subject changes in any given parameter, we performed a likelihood ratio test of the full model (Attend and Regulate parameters) against a reduced model which was allowed only one value of the parameter in question for both Attend and Regulate. For more details on the estimation and tests, including against alternative models, see SI Text. ACKNOWLEDGMENTS. This research was sponsored by a James S. McDonnell Foundation grant to E.A.P., Moore Foundation and Human Frontier Science Program grants to C.F.C., and a National Science Foundation Graduate Research Fellowship to P.S.H. 5. Jackson DC, Malmstadt JR, Larson CL, Davidson RJ (2000) Suppression and enhancement of emotional responses to unpleasant pictures. Psychophysiology 37:515–522. 6. Lerner JS, Small DA, Loewenstein G (2004) Heart strings and purse strings: Carryover effects of emotions on economic decisions. Psychol Sci 15:337–341. 7. Winkielman P, Berridge KC, Wilbarger JL (2005) Unconscious affective reactions to masked happy versus angry faces influence consumption behavior and judgments of value. Pers Soc Psychol Bull 31:121–135. 8. Mellers BA, Schwartz A, Ho K, Ritov I (1997) Decision affect theory: Emotional reactions to the outcomes of risky options. Psychol Sci 8:423– 429. PNAS Early Edition ͉ 5 of 6 PSYCHOLOGY Procedure—Study 1 and Study 2 Behavioral Session. Participants were endowed with $30 immediately following completion of informed consent. They were told the money was theirs to risk during the study and were asked to place it in their wallets or purses. At the end of the study, the endowment was adjusted by the actual value of the outcome of 28 randomly selected trials (10% of all trials), given their choices. Participants could lose a maximum of $30 (returning the entire endowment) and win a theoretical maximum of $572. All participants also received a $15 subject fee upon completion of the study. Participants were thoroughly instructed and quizzed on task details and strategy use. See SI Text for more details. There were 2 cognitive regulation strategies (for the complete wording, see SI Text). For the Attend strategy, participants were instructed to consider each monetary choice in isolation from all other choices, to make each of those decisions as if it was the only choice they were making for the study, and to let any emotions or thoughts occur naturally, without trying to control them. We conceived of this instruction to mirror the everyday approach to decisions for most people—that is, one at a time, individually. For the Regulate strategy, participants were instructed to consider each monetary choice in the context of the other choices in that category, as if they were creating a portfolio. The instruction included phrases like “imagine yourself [as] a trader,” “you do this all the time,” and “treat it as one of many monetary decisions, which will sum together to produce a ‘portfolio.’” This strategy was intended to be what a professional trader might do when making many portfolio-style decisions. The conceptual nature of the strategy was emphasized by asking participants to not keep a running total of their previous outcomes. We were not concerned with isolating the efficacious parts of our instructions, but with observing effects given an ecologically relevant general approach of considering choices in their context. Future research could unpack the effects of these various strategic components. The presented choices were identical for both instructed strategies, except for the random outcomes of the risky gambles. Each set of 140 choices consisted of 120 choices between mixed-valence gambles (positive and negative possible outcomes) and guaranteed amounts of zero, and 20 choices between gain only gambles (positive and zero possible outcomes) and positive guaranteed amounts. Each decision was resolved immediately after choice with the outcome of the gamble or the guaranteed amount, depending on participants’ choices (see SI Text for the exact monetary amounts). Participants completed choices in blocks of 10, using one cognitive strategy during each block. The blocks were pseudorandomly ordered such that no strategy ever occurred more than 3 times in a row. Participants completed one of 4 task orders, which were independently randomized along the following dimensions: order of condition blocks (Attend, Regulate), gamble outcomes (‘‘win,’’ ‘‘lose’’), and gamble order within each condition. Before each block of 10 trials, the regulation instruction was displayed for 5 s. Each trial consisted of the presentation of a monetary choice (4 s), a response period (2 s), and the choice outcome (1 s), with a 1 s inter-stimulus interval between response and outcome and a 1–3 s variable inter-trial interval. following changes: each monetary decision consisted of an instruction (1 s) indicating which strategy to use, the presentation of a monetary choice (2 s), a response period (2 s), and the choice outcome (1 s). Because of the lagged nature of the skin conductance response, variable periods of fixation (8 –11s) were inserted before and after outcomes to allow isolation of the responses to each outcome. Trial order and win/lose outcomes were randomly ordered for each subject. NEUROSCIENCE remaining 29 participants is presented. The experiment was approved by the University Committee on Activities Involving Human Subjects at New York University. 9. Mellers BA, Schwartz A, Ritov I (1999) Emotion-based choice. J Exp Psychol 128:332– 345. 10. Gottfried JA, O’Doherty J, Dolan RJ (2003) Encoding predictive reward value in human amygdala and orbitofrontal cortex. Science 301:1104 –1107. 11. Hsu M, Bhatt M, Adolphs R, Tranel D, Camerer CF (2005) Neural systems responding to degrees of uncertainty in human decision-making. Science 310:1680 –1683. 12. Murray EA (2007) The amygdala, reward and emotion. Trends Cogn Sci 11:489 – 497. 13. Paulus MP, Rogalsky C, Simmons A, Feinstein JS, Stein MB (2003) Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism. NeuroImage 19:1439 –1448. 14. Bechara A, Damasio H, Tranel D, Damasio AR (1997) Deciding advantageously before knowing the advantageous strategy. Science 275:1293–1295. 15. Clark L, et al. (2008) Differential effects of insular and ventromedial prefrontal cortex lesions on risky decision-making. Brain 131:1311–1322. 16. Shiv B, Loewenstein G, Bechara A, Damasio H, Damasio AR (2005) Investment behavior and the negative side of emotion. Psychol Sci 16:435– 439. 17. Critchley HD, Wiens S, Rotshtein P, Ohman A, Dolan RJ (2004) Neural systems supporting interoceptive awareness. Nat Neurosci 7:189 –195. 18. LeDoux JE (2000) Emotion Circuits in the Brain. Annu Rev Neurosci 23:155–184. 19. Morris JS, et al. (1996) A differential neural response in the human amygdala to fearful and happy facial expressions. Nature 383:812– 815. 20. Whalen PJ, et al. (1998) Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. J Neurosci 18:411– 418. 21. Kahneman D, Tversky A (1979) Prospect theory—Analysis of decision under risk. Econometrica 47:263–291. 22. Kahneman D, Knetsch JL, Thaler RH (1990) Experimental tests of the endowment effect and the coase theorem. J Polit Econ 98:1325–1348. 23. Tversky A, Kahneman D (1992) Advances in prospect-theory— cumulative representation of uncertainty. J Risk Uncertain 5:297–323. 24. Gneezy U, Potters J (1997) An experiment on risk taking and evaluation periods. Q J Econ 112 2:631– 645. 25. Thaler RH, Tversky A, Kahneman D, Schwartz A (1997) The effect of myopia and loss aversion on risk taking: An experimental test. Q J Econ 112:647– 661. 26. Heisler J (1994) Loss aversion in a futures market: An empirical test. Review of Futures Markets 13:793– 826. 27. Haigh MS, List JA (2005) Do professional traders exhibit myopic loss aversion? An experimental analysis. Journal of Finance 60:523–534. 28. Benartzi S, Thaler RH (1995) Myopic loss aversion and the equity premium puzzle. Q J Econ 110:73–92. 29. Putler DS (1992) Incorporating reference price effects into a theory of consumer choice. Marketing Science 11:287–309. 6 of 6 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0806761106 30. Hardie BGS, Johnson EJ, Fader PS (1993) Modeling loss aversion and reference dependence effects on brand choice. Marketing Science 12:378 –394. 31. Genesove D, Mayer C (2001) Loss aversion and seller behavior: Evidence from the housing market. Q J Econ 116:1233–1260. 32. Camerer C, Babcock L, Loewenstein G, Thaler RH (1997) Labor supply of new york city cabdrivers: One day at a time. Q J Econ 112:407– 441. 33. Chen MK, Lakshminarayanan V, Santos L (2006) How basic are behavioral biases? Evidence from capuchin monkey trading behavior. J Polit Econ 114:517–537. 34. Gross JJ (1998) Antecedent- and response-focused emotion regulation: Divergent consequences for experience, expression, and physiology. J Pers Soc Psychol 74:224 – 237. 35. Read D, Loewenstein G, Rabin M (1999) Choice bracketing. J Risk Uncertain 19:171–197. 36. O’Connell P, ed. (2001) Emotion, the Enemy of Investing BusinessWeek Online (Dec. 6). Available at http://www.businessweek.com/investor/content/dec2001/ pi20011268886.htm. 37. Scherer KR (2005) What are emotions? And how can they be measured? Social Science Information 44:695–729. 38. Delgado MR, Schotter A, Ozbay E, Phelps EA (2008) Understanding overbidding: Using the neural circuitry of reward to design economic auctions. Science 321:1849 –1852. 39. Loewenstein G, Weber EU, Hsee CK, Welch N (2001) Risk as feelings. Psychol Bull 127:267–286. 40. Ariely D, Huber J, Wertenbroch K (2005) When do losses loom larger than gains? Journal of Marketing Research 42:134 –138. 41. Novemsky N, Kahneman D (2005) The boundaries of loss aversion. Journal of Marketing Research 42:119 –128. 42. Novemsky N, Kahneman D (2005) How do intentions affect loss aversion? Journal of Marketing Research 42:139 –140. 43. Lo AW, Repin DV, Steenbarger BN (2005) Fear and greed in financial markets: A clinical study of day-traders. The American Economic Review 95:352–359. 44. Lo AW, Repin DV (2002) The psychophysiology of real-time financial risk processing. J Cognit Neurosci 14:323–339. 45. List JA (2003) Does market experience eliminate market anomalies? Q J Econ 118:41– 71. 46. Camerer C (2005) Three cheers—psychological, theoretical, empirical—for loss aversion. Journal of Marketing Research 42:129 –133. 47. Kermer DA, Driver-Linn E, Wilson TD, Gilbert DT (2006) Loss aversion is an affective forecasting error. Psychol Sci 17:649 – 653. 48. Wilson TD, Gilbert DT (2005) Affective forecasting—Knowing what to want. Curr Dir Psychol Sci 14:131–134. Sokol-Hessner et al.
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