Ancient Greek philosopher Socrates allegedly said of greed that ‘He who is not contented with what he has, would not be contented with what he would like to have’. Interestingly, recent development of greed research also emphasizes two parallel facets in the operational definition of greed personality trait: the desire of always wanting more and the dissatisfaction of not having enough (Krekels and Pandelaere, 2015; Mussel and Hewig, 2016; Mussel et al., 2015; Seuntjens et al., 2015). Greed has long been considered a double-edged sword: it motives people to compete and outperform for better well-being, but is also associated with negative stereotypes such as immorality (Livingstone and Lunt, 1992; Lunt and Livingstone, 1991; Piff et al., 2012; Rokeach, 1973; Seuntjens et al., 2016; Tickle, 2004; Vohs et al., 2006; Zandi, 2008) and excessive risk-taking which has been accused for insurmountable debt level and recent meltdown in the finance industry (Aquino et al., 2009; Lo et al., 2005). Understanding how greed might influence human risk-taking behavior, therefore, has far-reaching implications in cognitive and social sciences.

Although anecdotes and field studies generally suggest a potential link between greed personality trait and individual risk-taking, current research demonstrating this association is much less comprehensive and the results are inconsistent (Mussel et al., 2015; Seuntjens et al., 2015). For example, it has been shown that the self-assessed greed personality score was not related to risk-taking in a gain-only environment (Seuntjens et al., 2015), while other studies tended to suggest that greed personality trait significantly influences individual’s risky taking behavior. In addition, recent Electroencephalography (EEG) studies also showed reduced feedback-related negativity-difference to losses relative to gains in human subjects high in trait-greed (Mussel and Hewig, 2016; Mussel et al., 2015). In light of these findings, one potential hypothesis to reconcile the conflicts in the extant literature is that greed personality trait might influence risk-taking along specific dimension that is not ubiquitous to all the risky decision-making tasks. For instance, subjects’ risk-taking is believed to be driven by their risk attitudes in the gain or loss only gambling tasks, but is influenced by both risk attitude and relative sensitivity towards gains versus losses (loss aversion sensitivity) in a gain-loss mixed gambling task. This hypothesis can be formalized under the theoretical framework of prospect theory, where risk attitude (α) and loss aversion coefficient (λ) together determine the degree of risk-taking in a value-based gambling task (Barkley-Levenson et al., 2013; Kahneman and Tversky, 1979; Köbberling and Wakker, 2005; Tversky and Kahneman, 1992). Therefore, extant literatures raise an intriguing possibility that individual greed personality trait might be related to the degree of loss aversion, but not risk attitude. It further predicts that greed personality would only affect risk-taking in mixed gamble tasks, where loss aversion plays a role (Mussel et al., 2015), but not in gain/loss only tasks, where loss aversion is not involved or canceled out (Seuntjens et al., 2016; Seuntjens et al., 2015). Here using functional magnetic resonance imaging (fMRI) with a classic gambling task, we directly tested this hypothesis and investigated whether and how greed personality trait might influence subjects’ risky choice behavior. Previous research suggested that the activities of a network of brain structures, including ventral striatum, amygdala, and OFC, might encode a ‘neural loss aversion’ signal (Canessa et al., 2013; De Martino et al., 2010; De Martino et al., 2009; Park et al., 2011; Plassmann et al., 2010; Tom et al., 2007). We thus focused on these brain structures and examined how their responses were related to subjects’ trait-greed, risk attitude and loss aversion. Finally, given that one significant concomitant personality trait often associated with greed is impulsivity (Seuntjens et al., 2015) and impulsivity has been implicated to influence risk-taking (Baumann and Odum, 2012; Lejuez et al., 2003; Pack et al., 2001; Zaleskiewicz, 2001), we also measured subject’s impulsivity trait score to dissociate it from the effect of trait-greed in risk-taking behavior.

We first ran a pilot online study asking participants to fulfill the Dispositional Greed Scale (DGS) (Seuntjens et al., 2015) and Barratt Impulsiveness Scale (BIS-11) (Patton et al., 1995) to measure greed personality trait (GPT) and impulsivity personality trait (IPT) and tested the potential relationship between their GPT and IPT scores, as previous study suggested (Seuntjens et al., 2015). In the fMRI experiment, in addition to the completion of GPT and IPT measurements (see Materials and methods for details), in order to examine whether the greed personality trait scores significantly influence individuals’ risky behavior, each subject completed a gambling task consisting of gain, loss and mixed trials while their brain activities were simultaneously monitored in the fMRI scanner. In each trial, subjects were asked to make a choice between options of a risky gamble and a guaranteed sure amount (see experiment procedure in Figure 1 and gamble sets in Figure 1—figure supplement 1).

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GPT and task performance

Since impulsivity was also considered a potential factor influencing people’s risky choices (Clark et al., 2013; Studer and Clark, 2011), especially among adolescents, pathological gamblers and drug addicts (Barkley-Levenson and Galván, 2014; Chandler et al., 2009; Pehlivanova et al., 2018; Verdejo-García et al., 2008), we ran the simple regression analysis between GPT and IPT scores both in the pilot and fMRI datasets. Consistent with previous studies (Seuntjens et al., 2015), we found subjects’ GPT and IPT scores significantly correlated with each other in the pilot questionnaires (r = 0.272, p = 0.018) as well as in the fMRI dataset (r = 0.543, p = 0.005; Figure 2A), suggesting that IPT might be a confounding factor in identifying the specific effect of trait-greed (Supplementary file 2). Therefore, unless mentioned otherwise, we controlled the effect of subject’s impulsivity by including the IPT score as a covariate of no interest in the following behavioral and imaging analyses.

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We tested whether subjects’ GPT scores were related to their gambling propensities among three different gamble types (gain, loss, and mixed trials). Our data showed that the GPT was significantly correlated with the percentage of risky choices in the mixed gambles (r = 0.440, p = 0.032; Figure 2B), but not in gain (r = 0.236, p = 0.267; Figure 2C) nor loss only gambles (r = −0.002, p = 0.992; Figure 2D). Furthermore, corroborating previous research that impulsivity was mainly related to the preference bias in the delay discounting tasks, our subjects’ IPT scores were not correlated with the percentages of risky choices in all three-gamble contexts (see Figure 2—figure supplement 1). Importantly, we replicated these results by recruiting an independent cohort of subjects and asked them to perform the same behavioral task (Figure 2—figure supplement 2). The discrepancy of correlation results between GPT and gambling percentages among three tasks implied that the GPT might impact subjects’ risk-taking only when both prospective gains and losses were presented (mixed trials) but not when single valence trials were involved (gain and loss only).

It is possible that above results were mainly driven by the fact that there were more trials in the mixed condition than the gain/loss only conditions and therefore led to a more robust estimation in the mixed condition. To exclude this possibility, we adopted a more rigorous model-based approach by specifying a prospect theory model to capture individual subject’s behavior (Kahneman and Tversky, 1979; Sokol-Hessner et al., 2013; Sokol-Hessner et al., 2009; Tversky and Kahneman, 1992). Importantly, according to the prospect theory model, individual risk-taking was driven solely by the risk attitude parameter (α, a constant representing valence sensitivity) in the gain and loss only trials, but by the interplay of both risk attitude (α) and loss aversion (λ, a parameter capturing the asymmetrical weighting of loss relative to gain) parameters in the mixed condition (see Materials and methods for details). We took the hierarchical Bayesian analysis (HBA) approach in model fitting to obtain the estimated model parameters for individual subjects (Ahn et al., 2013). Consistent with previous literature suggesting that people weigh losses more than gains (Chib et al., 2012; De Martino et al., 2010; Sokol-Hessner et al., 2009; Tom et al., 2007), our results showed that the average loss aversion parameter (λ; mean = 1.561) across all subjects was significantly larger than 1 (t = 3.874, p = 0.001), implicating general loss aversion across subjects. Average risk attitude was not significantly different from 1 (mean = 1.033, t = 0.812, p = 0.425), indicating risk neutrality (see Supplementary file 1). Additionally, risk attitude and loss aversion parameters were not correlated (r = 0.020, p = 0.925).

We then examined whether the loss aversion and risk attitude parameters were associated with GPT scores across individuals using linear regression. Our results showed that subjects’ loss aversion coefficients (λ) were negatively correlated with their GPT scores (r = −0.479, p = 0.018; Figure 3A), but no correlation was found between risk attitude coefficients (α) and GPT scores (r = 0.062, p = 0.775; Figure 3B). Thus, our model-based analyses confirmed previous behavioral results (Figure 2B–D) and suggested that individual GPT score was specifically tied to loss aversion coefficient but not subject’s risk attitude. In contrast, participants’ IPT scores were not correlated with α nor λ parameters of the model (Figure 3—figure supplement 1; Supplementary file 1). Furthermore, we found a significant negative correlation between loss aversion coefficient and the percentage of risky gamble choice across all trials (r = −0.919, p < 0.001), and individual subject’s loss aversion coefficient (λ) fully mediated the correlation between GPT score and risky choice percentage across subjects (indirect effect: a × b = 0.024, 95% CI [0.007 0.045]; direct effect: c’=−0.003, 95% CI [−0.014 0.008]) (Figure 3—figure supplement 2) (Preacher and Hayes, 2008; Zhao et al., 2010). Together, our behavioral results showed that subjects’ greed personality traits were associated with their risky choice preference and suggested loss aversion as the potential mediator for such effect (Similar results were found in the behavioral replication study, see Figure 3—figure supplements 3–4).

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Brain–behavior relationship

For imagining analyses, we first examined how prospective gains and losses were represented in the brain activities. We constructed a general linear model (GLM) at the onset of option revelation and entered the magnitudes of potential gain and loss in the gamble option and the outcome of sure option as parametric modulators for each subject. By design, the correlation between the magnitudes of potential gain and loss in the gamble option was not significant across trials (r = 0.011, p = 0.867), allowing us to investigate their corresponding neural activities separately. Consistent with previous studies (Canessa et al., 2013; Park et al., 2011; Plassmann et al., 2010; Tom et al., 2007), we found a neural network including bilateral orbitofrontal cortex (OFC), bilateral parietal cortex, and left posterior cingulate, whose activities positively correlated with potential gains of the gambles (z = 3.1, p < 0.05 false discovery rate (FDR) cluster correction; Figure 4A). Similarly, brain activities in the right OFC, bilateral striatum, bilateral insula and bilateral inferior parietal gyrus were found negatively correlated with the magnitudes of the potential loss associated with the gamble (z = 3.1, p < 0.05 FDR correction; Figure 4B; Supplementary file 3). Furthermore, our conjunction analysis identified that the medial OFC (mOFC) activity was related to the representation of both gain and loss prospects of the gamble (Figure 4C). Consistent with previous literature (Tom et al., 2007), this conjunction analysis also revealed an overlapping area in the ventral striatum, albeit at a more lenient threshold (z = 2.3, p < 0.05 FDR correction; Figure 4—figure supplement 1).

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Previous literatures focusing on brain-behavior correlation suggested that the neural structures related to loss aversion should be sensitive to both gain and loss prospects and indicated limbic structures such as ventral striatum and OFC to encode individual behavioral loss aversion (Canessa et al., 2013; Tom et al., 2007). To test this hypothesis, we performed a region of interest (ROI) analysis using the mOFC mask obtained from the aforementioned conjunction analysis and tested whether BOLD responses in the mOFC correlated with behavioral loss aversion across subjects. Indeed, neural loss aversion in the mOFC, defined as the difference between mOFC activities towards losses versus gains (-β_loss - β_gain) (Tom et al., 2007), showed a significant positive correlation with behavioral loss aversion coefficients (ln (λ); r = 0.450, p = 0.024) even after correction for potential outliers (robust regression, t = 2.203, p = 0.038; Figure 4D). Moreover, as expected, such neural loss aversion signal also negatively correlated with individual subject’s GPT score (r = -0.449, p = 0.028; robust regression, t = -2.239, p = 0.035), echoing our behavioral results of the association between GPT scores and behavioral loss aversion (ln (λ); r = -0.434, p = 0.034). Finally, activities in the ventral striatum, also identified from conjunction analysis above, did not exhibit significant correlation with behavioral loss aversion coefficients (ln (λ); r = -0.012, p = 0.954) nor with GPT score (r = -0.239, p = 0.262).

Given that the neural loss aversion measures in the mOFC correlated with both individuals’ GPT scores and behavioral loss aversion coefficients (ln (λ)), we reasoned that personal greed personality trait (GPT score) might influence subject’s behavioral loss aversion via the mediation of the mOFC activity. To test this hypothesis, we performed a mediation analysis where (ln (λ)) is the task related dependent variable, GPT scores the input variable, and the neural loss aversion in the mOFC the mediator. We controlled for potential confounds by including variables such as individuals’ impulsivity scores (IPT) as covariates of no interest in the regression analysis (Preacher and Hayes, 2008; Zhao et al., 2010) (see Materials and methods for details). Consistent with our prediction, we found the correlation between individual’s trait-greed and behavioral loss aversion could be fully mediated by the neural loss aversion measures in the mOFC (indirect effect: a × b = −0.024, 95% CI [−0.084 –0.001]; direct effect: c’=−0.047, 95% CI [−0.117 0.024]), suggesting higher GPT score (greed personality trait) might lead to lower behavioral loss aversion (task performance) by engaging brain activities in the mOFC, which by itself is a pivotal structure involved in the integration of gain and loss prospects (Figure 5).

Figure 5

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Greed, hailed by some and dismissed by others, has long been recognized as a critical factor influencing people’s risk-taking behavior (Krekels and Pandelaere, 2015; Mussel et al., 2015; Seuntjens et al., 2015). Traditional wisdom and recent developments in social psychology emphasize two components of greed: insatiable hunger for more and dissatisfaction for not having enough. In a standard risky decision making context, different greed components might be related to risk-taking via influencing subject’s risk attitude and/or loss aversion/gain seeking preference (Sokol-Hessner et al., 2009). Indeed, insatiable appetite for more is associated with elevated impulsivity and novelty seeking, which are comorbid with obsessive gambling, substance and behavorial addiction and aberrant behavior in adolescence (Clark, 2014; Robbins and Clark, 2015; Seuntjens et al., 2015; Spear, 2018). On the other hand, never satisfied with what one current has could relate to risk-taking either by ‘nudging’ subjects into the loss domain and all the options are framed as potential or sure losses or by elevated attention to losses (loss aversion) (De Martino et al., 2010; De Martino et al., 2009; Kahneman and Tversky, 1979; Sokol-Hessner et al., 2013; Sokol-Hessner et al., 2009; Thaler and Johnson, 1990). By taking advantage of an experimental design that included gain, loss and mixed gambles, we not only confirmed the association between greed personality trait and risk-taking in general, but also had the opportunity to demonstrate such correlation was selectively mediated by individual’s loss aversion, but not risk attitude (Figure 3—figure supplement 2). In parallel, our neuroimaging results showed that BOLD responses in the mOFC, in addition to representing gain and loss prospects, encoded the neural loss aversion signal which in turn mediated the relationship between individual greed trait score and behavioral loss aversion coefficient (Figure 5). Therefore, our data provide a unique neural link to associate greed personality trait with subject’s risk-taking behavior.

Although it has been suggested that trait-greed might be directly related to risk attitude (Mussel et al., 2015), it is worth noting that in our task greed scores did not correlate with subjects’ risk attitude measurement, and this result also corresponded well with a previous study showing that greed personality was not associated with risk seeking preference in the gain-only gambles (Seuntjens et al., 2015). We reasoned that such discrepancy could be due to the fact that most of our subjects in the fMRI study were young adults (mean age: 21.360 years) and previous studies have suggested that adolescents and young adults tend to have higher risk tolerance (Barkley-Levenson and Galván, 2014; Grubb et al., 2016). Therefore, one possible explanation is that the risk attitudes of the young adults, whom most of our subjects were, might cluster at the ceiling where the greed personality trait related effect on risk attitude is minimal. Indeed, prospect theory model estimated parameters showed that our subjects tended to be risk neutral (mean = 1.033) instead of risk averse as commonly observed in other studies with similar experimental setups (Suzuki et al., 2016). Future studies of subjects with a greater range of age are required to test whether greed personality scores are related to individual risk attitudes.

In the current study, we identified that mOFC, whose activity represented a ‘neural loss aversion’ signal, mediated the effect of greed personality on individual behavioral loss aversion. According to prospect theory, loss aversion is another critical factor influencing subjects’ risk-taking, especially in the mixed gamble environment (Barkley-Levenson et al., 2013; Kahneman and Tversky, 1979; Köbberling and Wakker, 2005; Tversky and Kahneman, 1992). Numerous studies have explored the possible neural bases for loss aversion. It has been hypothesized that loss aversion might be elicited by the interaction of activities in different brain structures. For example, some studies revealed that sensitivity to losses was driven by negative emotions such as anxiety or fear, which was associated with elevated response in the amygdala, whereas gain processing was implemented in the activities of reward pathway structures such as ventral striatum (Camerer, 2005; De Martino et al., 2010; Sokol-Hessner et al., 2013). However, recent studies also found that compared to healthy control subjects, unmedicated anxiety patients exhibited equivalent levels of loss aversion (Charpentier et al., 2017), contradicting the interaction of distributed brain structure hypothesis. The other line of research, however, suggested the possibility of encoding the subjective value of potential gains and losses in the same brain regions, such as ventral striatum and mOFC/ventromedial prefrontal cortex (vmPFC), and the asymmetric activities towards gains and losses within these regions might compose the neural loss aversion signal (Canessa et al., 2013; Tom et al., 2007). A variety of literatures have suggested that the OFC plays a pivotal role in processing rewards and punishments (Knutson et al., 2003; O'Doherty et al., 2001), making it well suited among the candidates of encoding a neural loss aversion signal. For instance, BOLD responses in the mOFC were found to correlate with subjective (both appetitive and aversive) values of different modalities of stimuli, such as smell (Gottfried et al., 2002), snack foods (Plassmann et al., 2010; Suzuki et al., 2017), soda drinks (McClure et al., 2004) and monetary gains and losses (De Martino et al., 2009). Consistent with previous research, we also found mOFC activity correlates with both potential gains and losses and the response asymmetry towards gains and losses in the mOFC was correlated with behavioral loss aversion (Figure 4D). Furthermore, this neural loss aversion representation of mOFC mediated the effect of greed personality on behavioral loss aversion (Figure 5), providing additional support for the critical role of mOFC in integrating potential gains and losses and functioning as a neural network gateway to bias choice behavior (Behrens et al., 2008; Kable and Glimcher, 2007; Kable and Glimcher, 2009). With a more lenient threshold (z = 2.3, p < 0.05 FDR correction), we also observed activities in the ventral striatum that correlated with both gains and losses (Figure 4—figure supplement 1). However, unlike previous research, our ROI analysis didn’t yield a significant correlation between ventral striatum activity and behavioral loss aversion, potentially due to the difference in the experimental designs (Canessa et al., 2013; Tom et al., 2007). Indeed, in our task, options were presented as the two-alternative choice selection task, in contrast to the default option accept and reject task used in earlier studies (Tom et al., 2007). There has been evidence suggesting the way options were presented and framed might have a peculiar effect on value representation and subsequently behavioral risk-taking (Boureau and Dayan, 2011; De Martino et al., 2009; Wright et al., 2012).

Finally, it is worth emphasizing that greed is a multi-faceted personality trait and may manifest as a myriad of behavioral propensities under different contexts. For example, greed might be tied to subjects’ attitude towards ambiguity, the uncertainty of risk itself, or to asymmetrical updating of positive and negative outcomes (Grubb et al., 2016; Hsu et al., 2005). Future research will be required to study whether greed can be traced to unique neuroanatomical or functional representations in the brain and how it might interact with related brain networks to enable specific context dependent behavior.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided in https://osf.io/rpve7/

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Author details

1. Weiwei Li

   Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

   Contribution

   Formal analysis, Investigation, Writing—original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0990-0243

2. Haixia Wang

   School of Management, Jinan University, Guangzhou, China

   Contribution

   Conceptualization, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8897-4819

3. Xiaofei Xie

   School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China

   Contribution

   Conceptualization, Writing—review and editing

   Competing interests

   No competing interests declared

4. Jian Li

   School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China

   Contribution

   Supervision, Funding acquisition, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   leekin@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3941-2622

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