Losses loom larger than gains in the brain : Neural loss aversion predicts behavioral loss aversion

One of the most robust phenomena in behavioral studies of decision making is loss aversion, the tendency for people to exhibit greater sensitivity to losses than to equivalent sized gains. We measured brain activity while individuals decided whether to accept or reject gambles without feedback. This design isolated activity reflecting decisions without contamination by the anticipation or experience of impending monetary gains or losses. A broad neural network (including midbrain dopaminergic regions and their limbic and cortical targets) showed increasing activity as the potential gain increased, whereas an overlapping set of regions showed decreasing activity as the potential loss increased. Thus, potential losses did not engage a separate set of emotional brain systems, but were instead represented by decreasing activity in several gainsensitive areas. Moreover, these regions exhibited neural loss aversion as shown by their greater sensitivity to losses than gains. Finally, individual differences in behavioral loss aversion were predicted by a measure of neural loss aversion in several regions including ventral striatum and prefrontal cortex. These results provide the first neuroscientific evidence that risk aversion is driven by the brain’s greater sensitivity to losses than gains. Main text Many decisions, such as whether to invest in the stock market or whether to accept a new job, involve the possibility of either gaining or losing relative to the status quo. When faced with such decisions, most people are strikingly risk averse. For instance, when deciding whether to accept gambles that offer a 50/50 chance of gaining or losing money, people typically only accept gambles in which the amount that could be gained is at least twice the amount that could be lost (e.g., a 50/50 chance to either gain $100 or lose $50) (1). Prospect theory, the most successful behavioral model of decision making under risk and uncertainty (1, 2), explains risk aversion for “mixed” (gain/loss) gambles using the concept of loss aversion: People are more sensitive to the possibility of losing objects or money than they are to the possibility of gaining the same objects or amounts of money (1, 3, 4). Thus, people typically require a potential gain of at least $100 to make up for exposure to a potential loss of $50 because the subjective impact of losses is roughly twice that of gains. Loss aversion also applies to decisions that involve no risk (5). For example, in one study, students who were randomly assigned to receive a coffee mug (so that giving up the mug would be perceived as a loss) subsequently set a median selling price of $7.12 for that mug, whereas students who were randomly assigned an opportunity to receive either an identical mug or money (so that acquiring the mug would be perceived as a gain) valued the mug at only $3.12 (6). Outside the laboratory, loss aversion has been invoked to help explain a wide range of economic behaviors, such as why consumer demand for products is more sensitive to price increases than decreases (7) and why investors require a much higher average return to invest in stocks than bonds (8). Moreover, loss aversion has been documented in the trading behavior of children as young as age five (9) and capuchin monkeys (10), suggesting that the tendency for losses to loom larger than gains may reflect a fundamental feature of how potential outcomes are assessed by the primate brain. The neural basis of loss aversion has not been directly investigated to date. Previous neuroimaging studies of responses to monetary gains or losses have focused on activity associated with the anticipation of immediate outcomes (“anticipated” utility) (11, 12) or the actual experience of gaining or losing money (“experienced” utility) (11, 13, 14). However, to our knowledge, no previous studies have addressed the question of which brain systems represent potential losses versus gains when a decision is being made (“decision” utility). Behavioral researchers have shown that evaluations of outcomes at these different points in time (i.e., “decision”, “anticipated”, and “experienced”, utilities) often diverge in dramatic ways, which raises the possibility that the corresponding brain systems involved may also differ (15). In the current study we aimed to isolate activity associated with the evaluation of a gamble when choosing whether or not to accept it (i.e., “decision” utility). This allowed us to test whether the neural response during the evaluation of potential outcomes is similar to patterns previously reported in studies of anticipated and experienced outcomes. One important question is whether loss aversion reflects cognitive or emotional processes. Decisions have been shown to vary systematically depending on whether options are described in terms of gains from one reference point or losses from another (16), demonstrating that cognitive representations are involved in loss aversion. However, this cognitive account alone does not explain why losses should loom larger than gains. Alternatively, it has been suggested that enhanced sensitivity to losses is driven by negative emotional responses, such as fear or anxiety (e.g, 17). This notion predicts that exposure to increasing potential losses should be associated with increased activity in brain systems involved in negative emotions (such as amygdala or anterior insula, cf., (18, 19)). Alternatively, loss aversion could reflect an asymmetric response to losses versus gains within a single system that codes for the subjective value of the potential gamble, such as ventromedial/orbital prefrontal cortex and ventral striatum (11, 20, 21). Imaging decision utility. To examine the neural systems that process decision utility, we collected functional magnetic resonance imaging (fMRI) data while participants decided whether to accept or reject “mixed” gambles that offer a 50/50 chance of either gaining one amount of money or losing another amount (see Figure 1) (22). To encourage participants to reflect on the subjective attractiveness of each gamble rather than revert to a fixed decision rule, we asked that they indicate one of four responses to each gamble (strongly accept, weakly accept, weakly reject, and strongly reject). In order to estimate the neural response to gains and losses separately, the sizes of the potential gain and loss were manipulated independently, with gains ranging from $10 to $40 (in increments of $2) and losses ranging from $5 to $20 (in increments of $1). These ranges were chosen because previous studies indicate that people are, on average, roughly twice as sensitive to losses as to gains (1, 23); thus, we expected that for most participants this range of gambles would elicit the entire range of attitudes, from strong acceptance to indifference to strong rejection. All combinations of gains and losses were presented (see Figure 1), with trials distributed over three fMRI scanning runs. During scanning, participants evaluated each gamble without receiving feedback. In addition to allowing for the isolation of decision utility from anticipated or experienced utility, it prevented early trial outcomes from influencing a participant’s decision making on later trials. Sixteen participants were endowed with $30 at least one week prior to the scanning session. The endowment was provided during a separate behavioral testing session in order to minimize the potential increase in risk seeking behavior that can occur when individuals feel that they are gambling with “house money” (24). Participants were asked to bring $60 in cash to the scanning session, and told they could win up to an additional $120 or lose up to $60. At the outset of the scanning session, participants were told that one of their decisions from each of the three scanning runs would be selected at random, and each selected gamble would be played for real money if they had accepted it during scanning. Outcomes were summed across all “played” trials and ranged from -$12 to +$70, with an average total gamble outcome of $23 (leading to an average payout of $53, including the endowment). ---Figure 1 about here ---Behavioral loss aversion. Behavioral sensitivity to gains and losses was assessed by fitting a logistic regression to each participant’s acceptability judgments collected during scanning, using the size of the gain and loss as independent variables. Based on this analysis, a measure of loss aversion (λ) was computed as the ratio of the (absolute) loss response to the gain response. The observed level of loss aversion (median λ = 1.93; range: 0.99-6.75) is consistent with the fact, shown in Figure 1B, that participants were roughly indifferent to gambles in which the potential gain was twice the potential loss (i.e., gambles that fall along the main diagonal of the gain/loss matrix in Figure 1). This finding also accords well with results reported by other researchers (1, 23). Neural response to potential gains and losses. The imaging data were first analyzed to identify regions whose activation correlated with the size of the potential gain or loss, using parametric regressors (for details on model specification, see Supplementary Materials and Methods). This analysis found a network of regions responsive to the size of potential gains when evaluating gambles (averaging over levels of loss) (see Figure 2). The gain-responsive network included regions previously shown to be associated with the anticipation and receipt of monetary rewards, including dorsal and ventral striatum, ventromedial prefrontal cortex (vmPFC), ventrolateral PFC (vlPFC), anterior cingulate (ACC), orbitofrontal cortex (OFC), and dopaminergic midbrain regions (see Supplementary Figure 2). There were no regions that showed decreasing activation as gains increased. ---Figures 2 and 3 about here ---If loss aversion is driven by a negative affective response (e.g., fear, vigilance, discomfort), then one would expect increasing activity in brain regions associated with these emotions as the size of the potential loss increases. Contrary to this prediction, no brain regions showed significantly increasing activation during evaluation of gambles as the size of the potential loss increased (averaging over all levels of gain). Instead, a network of regions including the striatum, vmPFC, ventral ACC, and medial OFC, most of which also coded for gains, showed decreasing activity as the size of the potential loss increased (see Figure 2 and Supplementary Figure 3). A conjunction analysis between increasing activity for gains and decreasing activity for losses demonstrated joint sensitivity to both gains and losses in a set of regions, including the dorsal and ventral striatum and vmPFC (see Figure 3 and Supplementary Table 1). In order to ensure that potential loss-related responses were not being obscured by the overall positive expected value of the gambles, we compared activity evoked by the worst possible gambles (gain $10-$16, loss $17-$20) and the best possible gambles (gain $34-$40, loss $5-8). In a whole-brain analysis, there were no regions that showed significantly more activity for the worst gambles compared to the best (corrected p > 0.4 in all voxels using randomization tests). Given the specific prediction regarding lossrelated activity in amygdala and insula based on prior studies of experienced utility and risk aversion (11, 19), we performed further analyses focused on these areas. Even at a very liberal uncorrected threshold of p < 0.01, there were no significant voxels in the amygdala, and only two single unconnected voxels in the insula. By comparison, at the same threshold there were large clusters of activation for the best versus the worst gambles in the ventral striatum and vmPFC. These results strongly support the conclusion that losses and gains are coded by the same mechanism rather than two separate mechanisms. Moreover, this aggregate representation of decision utility appears to be represented by the same neural circuitry that is engaged by experienced rewards such as the receipt of money (11), the consumption of cocaine (25) or fruit juice (26), and the viewing of attractive faces (27). These results are also consistent with previous studies showing increased and decreased activity in striatum, respectively, for experienced monetary gains and losses (11, 13). Correlating behavioral and neural loss aversion. As noted above, there was substantial variability in behavioral loss aversion (i.e., reluctance to gamble) across individuals in the present study. We next investigated whether individual differences in brain activity during decision making were related to these individual differences in behavioral loss aversion, using whole-brain analyses to identify regions where the neural response to gains or losses was correlated with behavioral loss aversion. Surprisingly, greater loss aversion was associated with greater sensitivity to both not only losses but also gains. For increasing gains, correlation with behavioral loss aversion was only observed in the sensorimotor cortex and superior frontal cortex (see Supplementary Figure 4). On the other hand, as potential losses increased, an extensive set of areas showed a more rapidly decreasing response to mounting losses in individuals who were more loss averse (see Supplementary Figure 5). Notably, these regions encompassed many of the areas that showed an overall decrease in neural activity with increasing potential loss. The association of decreased loss aversion with decreased neural responses to losses and gains during decision making is consistent with the longstanding notion that some forms of risk-taking may have their roots in sensation-seeking by individuals who have a diminished physiological response to stimulation (28). It is also worth remarking that previous work has shown that individuals with high levels of the “harm avoidance” personality trait (who are presumably more loss averse) show greater activation of the ventral striatum during risky decision making (cf., 29). Initial examination of regions of interest in the striatum and vmPFC from the gain/loss conjunction analysis (as shown in Figure 3) revealed that these regions exhibit a pattern of “neural loss aversion”; that is, the (negative) slope of the decrease in activity to increasing losses was steeper than the slope of the increase in activity for increasing gains in a majority of participants (striatum: median loss/gain =1.88, loss > gain for 14/16 participants, sign test p = .004; vmPFC: median loss/gain = 1.82, loss > gain for 13/16 participants, p = .021). Therefore, whereas loss averse participants showed more neural sensitivity to both potential gains and losses compared to less loss averse participants, it appears that this neural sensitivity was disproportionately greater for losses than for gains. In order to more directly assess the relationship between behavioral loss aversion and neural loss aversion, we defined a neural loss aversion measure as the difference between the (negative) slope of the parametric loss responses and the slope of the parametric gain responses at each voxel. Whole-brain analysis of the correlation between neural and behavioral loss aversion (see Supplementary Figure 6 and Supplementary Table 2) showed significant correlations in several regions, including bilateral lateral and superior (pre-SMA) PFC, bilateral ventral striatum, right inferior parietal cortex, and lateral occipital/cerebellum. Activation maps and scatterplots for a subset of these regions are shown in Figure 4. These analyses show exceedingly strong correlations between behavioral and neural loss aversion, and these associations are highly significant using robust regression, which prevents undue influence of outliers. ---Figure 4 about here ---Loss aversion is mediated by sensitivity to losses. The foregoing analysis demonstrates a direct relation between neural and behavioral loss aversion. We further investigated whether this relationship was driven more by the neural processing of potential gains or losses in each of the clusters identified in the foregoing analysis. Whereas all of the regions had significant negative correlations between behavioral loss aversion and the (negative) neural loss response, only one region showed a significant relationship between behavioral loss aversion and the neural gain response (see Supplementary Table 2). Thus, in agreement with the whole-brain analysis above, participants who were less behaviorally loss averse (i.e., more risk seeking) were less neurally sensitive to the size of the potential loss. In order to more directly characterize the relative roles of gain and loss responses in behavioral loss aversion, the data from each cluster were entered into a mediation analysis, which revealed that the effect of the neural gain response on (log) behavioral λ was mediated by the neural loss response in five of the eight clusters (including bilateral lateral PFC, pre-SMA, and ventral striatum). Thus, differences in behavioral loss aversion across individuals seem to be driven primarily by the degree to which those individuals show decreasing neural activity to potential losses. Conclusions. The present study replicates the common behavioral pattern of risk aversion for mixed gambles that offer a 50/50 chance of gaining or losing money, and shows for the first time that this pattern of behavior is directly tied to the brain’s relative sensitivity to potential losses versus gains. These results provide striking evidence in favor of one of the fundamental claims of prospect theory (1, 2), namely that the function mapping money to subjective value is steeper for losses than gains. Importantly, mediation analysis suggests that individual differences in risk attitudes (as measured by the behavioral coefficient of loss aversion) are driven primarily by individual differences in the degree to which activity in the brain’s reward circuitry is attenuated by potential losses. Although the present study focused on loss aversion in the context of mixed gambles, recent work has found that the coefficient of loss aversion (i.e., the ratio of sensitivity to losses versus gains) is highly correlated across risky and riskless contexts (30). Therefore, we surmise that a similar mechanism may contribute to other manifestations of loss aversion. Neural loss aversion was observed throughout, though not strictly limited to, the targets of the mesolimbic and mesocortical dopamine (DA) systems (specifically, the ventral striatum and orbital, medial, and lateral prefrontal cortices). Lesions to some of these regions (particularly the core region of nucleus accumbens in the ventral striatum and medial PFC) have been previously associated with increased risk aversion in animal models (31), and disruption of the DA system can also result in shifts of risk attitudes in humans (32). It is tempting to speculate that the individual differences in behavioral and neural loss aversion observed in the present study may be related to naturally occurring differences in dopamine function, perhaps due to genetic variability in dopamine signaling or metabolism. This is an appealing hypothesis, though the relation between genetic variation in the DA system and personality traits such as impulsivity and risktaking remains largely unknown (33), and it is possible that other neurotransmitter systems (e.g., serotonin and noradrenaline) are also involved. Previous studies have shown that anticipated or experienced losses give rise to activation in regions that have been associated with negative emotions, such as amygdala or anterior insula (11, 18, 19). In contrast, the present study demonstrates that, in the context of decision making, potential losses are represented by decreasing activity in regions that seem to code for subjective value rather than by increasing activity in regions coding for negative emotions. This difference between present and prior results reinforces the importance of distinguishing between experienced, anticipated, and decision utility in economic theories of choice (15). It is possible that the engagement of the amygdala for experienced losses reflects negative prediction error (11, 34) rather than negative value per se, whereas the lack of immediate outcomes in the present study precludes the computation of prediction errors. The present results also illustrate how neuroimaging can be used to directly test predictions stemming from behavioral theories, and thereby complement evidence provided by behavioral studies and studies of lesion patients. Further, the diminished neural sensitivity to losses among less loss averse (i.e., more risk seeking) individuals may shed light on a number of neuropsychiatric and behavioral disorders, such as substance abuse, pathological gambling, and antisocial personality disorder, that are associated with increased risk-taking and impulsive behavior. The involvement of dopamine in these disorders, and the overlap of our results with the dopamine system and its targets, suggest that greater traction on these disorders could be obtained by integrating the methods and models of behavioral economics with the tools of cognitive neuroscience. Acknowledgments This work was supported by NSF DMI-0433693 (C. Fox & R. Poldrack, PIs), and by NIH P20 RR020750 (R. Bilder, PI). The authors thank Adam Aron, Robert Bilder, Michael Frank, Adriana Galvan, Marisa Geohegan, Eric Johnson, Matthew Lieberman, Raj Raizada, and Elena Stover for helpful comments, Kristopher Preacher for assistance with mediation analysis, and Ajay Satpute and Jessica Cohen for assistance in data collection. Figure Captions Figure 1. A. An illustration of the event-related task design. During each trial, the participant was presented for 3 seconds with a display showing the size of the potential gain (in green) and loss (in red). After the acceptability response, a variable interval was presented to allow for optimal deconvolution of fMRI responses to each trial (35). Gambles were not resolved during scanning. The values of gain and loss for each trial were sampled from the gain/loss matrix, as shown here for two example gambles; a gamble from each cell in this 16×16 matrix was presented during scanning, but the data were collapsed into a 4×4 matrix for analysis. B. Color-coded heatmap of probability of gamble acceptance at each level of gain/loss (red: high willingness to accept the gamble, blue: low willingness to accept the gamble). Participants’ willingness to accept gambles increased as the size of the gain increased, and decreased as the size of the loss increased. C. Color-coded heatmap of response times (red: slower response times, blue: faster response times). Performance was slowest on trials that were closest to the point of indifference between acceptance and rejection. Figure 2. Whole-brain analysis of parametric responses to size of potential gain (left) or loss (right). Statistical maps were projected onto an average cortical surface using multifiducial mapping in CARET (36); coronal slices (y=+10) are included to show ventral striatal activation. All maps are corrected for multiple comparisons at the wholebrain level using cluster-based Gaussian random field correction (37) at p < .05. Figure 3. Conjunction analysis results. Map in left panel shows regions with conjointly significant positive gain response and negative loss response (p < .05, whole-brain corrected, in each individual map). Heatmaps on right were created by averaging parameter estimates versus baseline within each cluster in the conjunction map for eachof the 16 cells (of 16 gambles each) in the gain/loss matrix; color-coding reflects strengthof neural response for each condition, such that dark red represents the strongestactivation and dark blue represents the strongest deactivation.Figure 4. Correspondence between neural and behavioral loss aversion. Left panelpresents statistical maps of the correlation between neural and behavioral loss aversion inwhole brain analysis (whole brain false discovery rate corrected at q < 0.05 [t > 3.7] andcluster extent > 100 voxels) (see also Supplementary Figure 6 and Supplementary Table2). Right panel presents scatterplots of behavioral versus neural loss aversion in severalclusters. Regression lines and p-values were computed using robust regression byiteratively-reweighted least squares, to prevent influence of outliers. MNI coordinates(X/Y/Z center of gravity in mm) for plotted clusters: B ventral striatum (3.6, 6.3, 3.9), Linferior/middle frontal (-48.5, 24.7, 17.0), R inferior frontal (50.2, 14.3, 7.6), R inferiorparietal (47.9, -45.6, 49.4). References1.A. Tversky, D. Kahneman, Journal of Risk and Uncertainty 5, 297 (OCT, 1992).2.D. Kahneman, A. Tversky, Econometrica 4, 263 (1979).3.N. Novemsky, D. Kahneman, Journal of Marketing Research 42, 119 (2005).4.M. Rabin, Econometrica 68, 1281 (2000).5.A. Tversky, D. Kahneman, Quarterly Journal of Economics 106, 1039 (NOV,1991).6.D. Kahneman, J. Knetsch, R. H. Thaler, Journal of Political Economy 98, 1325(1990).7.B. Hardie, E. Johnson, P. Fader, Marketing Science 12, 378 (1993).8.S. Benartzi, R. H. Thaler, Quarterly Journal of Economics 110, 73 (FEB, 1995).9.W. T. Harbaugh, K. Krause, L. Vesterlund, Economic Letters 70, 175 (2001).10.M. K. Chen, V. Lakshminarayanan, L. R. Santos, Journal of Political Economy(in press).11.H. C. Breiter, I. Aharon, D. Kahneman, A. Dale, P. Shizgal, Neuron 30, 619(May, 2001). 12.B. Knutson, C. M. Adams, G. W. Fong, D. Hommer, J Neurosci 21, RC159 (Aug15, 2001). 13.M. R. Delgado, H. M. Locke, V. A. Stenger, J. A. Fiez, Cogn Affect BehavNeurosci 3, 27 (Mar, 2003).14.B. Knutson, G. W. Fong, C. M. Adams, J. L. Varner, D. Hommer, Neuroreport12, 3683 (Dec 4, 2001). 15.D. Kahneman, P. P. Wakker, R. Sarin, Quarterly Journal of Economics 112, 375(MAY, 1997).16.A. Tversky, D. Kahneman, Science 211, 453 (Jan 30, 1981).17.C. F. Camerer, Journal of Marketing Research 42, 129 (2005).18.I. Kahn et al., Neuron 33, 983 (Mar 14, 2002).19.C. M. Kuhnen, B. Knutson, Neuron 47, 763 (Sep 1, 2005).20.B. Knutson, G. W. Fong, S. M. Bennett, C. M. Adams, D. Hommer, Neuroimage18, 263 (Feb, 2003).21.S. M. McClure et al., Neuron 44, 379 (Oct 14, 2004).22.. (Materials and methods are available as supporting materials on ScienceOnline.).23.M. Abdellaoui, H. Bleichrodt, C. Paraschiv. (ENSAM-Paris, France, 2005).24.R. H. Thaler, E. Johnson, Management Science 36, 643 (1990).25.H. C. Breiter et al., Neuron 19, 591 (Sep, 1997).26.G. Pagnoni, C. F. Zink, P. R. Montague, G. S. Berns, Nat Neurosci 5, 97 (Feb,2002). 27.I. Aharon et al., Neuron 32, 537 (Nov 8, 2001).28.M. Zuckerman, D. M. Kuhlman, J Pers 68, 999 (Dec, 2000). 29.S. C. Matthews, A. N. Simmons, S. D. Lane, M. P. Paulus, Neuroreport 15, 2123(Sep 15, 2004).30.E. Johnson, S. Gachter, A. Herrman. (Columbia Business School, 2006). 31.R. N. Cardinal, D. R. Pennicott, C. L. Sugathapala, T. W. Robbins, B. J. Everitt,Science 292, 2499 (Jun 29, 2001). 32.R. Cools, R. A. Barker, B. J. Sahakian, T. W. Robbins, Neuropsychologia 41,1431 (2003).33.E. Congdon, T. Canli, Behav Cogn Neurosci Rev 4, 262 (Dec, 2005).34.C. Büchel, in Neuroimaging and psychological theories of memory. (Marburg,Germany, 2006).35.A. M. Dale, Hum Brain Mapp 8, 109 (1999).36.D. C. Van Essen, Neuroimage 28, 635 (Nov 15, 2005).37.K. J. Worsley, A. C. Evans, S. Marrett, P. Neelin, J Cereb Blood Flow Metab 12,900 (Nov, 1992). +12 -14+30 -7responseinterval(3 secs)variableISI(mean 2.6 secs) Gain/loss matrixPotential gain Potentialloss-510