Article | Open | Published:

COMT Val158Met polymorphism interacts with stressful life events and parental warmth to influence decision making

Scientific Reports volume 2, Article number: 677 (2012) | Download Citation


Both genetic and environmental factors have been shown to influence decision making, but their relative contributions and interactions are not well understood. The present study aimed to reveal possible gene-environment interactions on decision making in a large healthy sample. Specifically, we examined how the frequently studied COMT Val158Met polymorphism interacted with an environmental risk factor (i.e., stressful life events) and a protective factor (i.e., parental warmth) to influence affective decision making as measured by the Iowa Gambling Task. We found that stressful life events acted as a risk factor for poor IGT performance (i.e., high reward sensitivity) among Met carriers, whereas parental warmth acted as a protective factor for good IGT performance (i.e., higher IGT score) among Val/Val homozygotes. These results shed some new light on gene-environment interactions in decision making, which could potentially help us understand the underlying etiology of several psychiatric disorders associated with decision making impairment.


Decision making is a complex process that involves weighing alternative outcomes' desirability and their probabilities1,2. Affective decision making is associated with differing probabilities of reward and punishment, as well as their induced emotional responses3. It is an important type of decision making in our daily life, which has been initially described and studied in patients with frontal lobe damages4,5. Some of the original decision-making tasks that have been developed to detect and study decision-making impairments in brain lesion patients, as well as patients with neuropsychiatric disorders, include the Iowa Gambling Task (IGT)6,7 and the Cambridge Gambling Task4,8,9,10.

Affective decision-making is a complex process that depends on an anatomical circuitry that includes several brain regions11. It has been hypothesized that this neural circuitry, and consequently decision-making capacities in risky and uncertain situations, is influenced by a variety of neurotransmitter systems, including dopamine (DA)11. The influence of processes involving DA-related reward, risk and uncertainty5,7,12 on individuals' decision-making has become especially significant in light of the impressive advances in research on the role of the mesolimbic DA in mechanisms of reward prediction errors13,14,15,16,17,18,19,20,21,22,23. While pathologies in these anatomical and pharmacological systems lead to a wide range of decision-making impairments such as those manifested in clinical populations, including patients with brain damage or some neuropsychiatric disorders, the fact remains that decision-making capacities can vary among normal individuals. Understanding the variations in decision-making capacities among normal individuals is especially important in light of the argument that these differences can serve as underlying biological markers for rendering some individuals more vulnerable than others to certain conditions associated with poor decision-making, such as addiction24, depression25,26, and schizophrenia27. Indeed, previous studies have shown that individual differences in affective decision making have both genetic28,29 and environmental sources30. However, their interactions are not well understood.

One of the frequently studied genes associated with human decision making process is the Catechol-o-methyl transferase (COMT) gene located on the 22nd chromosome. The COMT gene encodes COMT enzyme, one of the major enzymes to degrade DA in the prefrontal cortical areas31. The most common variation of the COMT gene is the Val158Met polymorphism (rs4680) in which a single G/A base-pair substitution leads to a valine (Val) to methionine (Met) substitution at codon 15832. This Met substitution reduces the activity of COMT enzyme to one-quarter of what is originally encoded by the Val allele32. Thereby, Met carriers have higher extracellular DA level in the prefrontal cortex33, a region that is vital for affective decision making, as suggested by both lesion5,7,34,35 and fMRI studies36,37,38,39,40.

Three studies have examined the influence of COMT polymorphism on affective decision making measured by the IGT41,42,43 and a monetary decision making task44. The results are mixed. In a pioneering study, Roussos et al.43 reported that the G allele at COMT rs4818 polymorphism, which is in high linkage disequilibrium with the Val allele at rs4680, was associated with better performance of the IGT in healthy males. This result was confirmed by van den Bos et al.42 who directly examined the COMT Val158Met polymorphism and found that subjects with the Val/Val genotype chose more advantageously than the Met allele homozygotes. These results suggested that Met allele carriers, who have increased levels of tonic DA and reciprocal reduction of phasic DA in subcortical regions45, tended to have lower IGT scores. In a placebo-controlled pharmacological study, Farrell et al.44 found that a COMT inhibitor (Tolcapone) made Met subjects more risk seeking but Val subjects more risk averse in a monetary decision making task, suggesting decision making can be altered by COMT inhibitors. However, in another study, Kang et al.41 failed to find a significant correlation between COMT Val158Met polymorphism and IGT performance. Several factors might have contributed to the discrepancy in the literature. In most of the existing studies, the sample size is relatively small (no more than 200 subjects), and the effect size is usually small. In addition, these studies have not considered the environmental factors and possible gene-environment interactions, which have gained increasing attention in recent literature.

Cumulative evidence has suggested that environmental factors, such as stress and parental warmth, can influence affective decision making. On the one hand, both acute and chronical stress have been widely recognized as risk factors that influence affective decision making46. For example, Gray47 showed that students who reported high stress due to impending exams earned less money in a monetary decision making task compared with students who did not report high stress when faced with impending exams and students without impending exams. On the other hand, family relationship has been shown to be a protective factor against poor affective decision making30. Children in better parent-child relationships showed significant improvements on IGT scores in a one-year longitudinal study30.

Many studies suggest that human behavior is determined by the combination of genes and environments (i.e., gene-environment interaction). For example, Caspi et al.48 found that COMT Val158Met polymorphism interacted with cannabis use to influence the development of psychotic symptoms and schizophreniform disorder. However, potential gene-environment interactions on affective decision making are largely unknown. Biologically, COMT Val158Met polymorphism mainly influences the activity of the COMT enzyme, and leads to different DA levels in the prefrontal cortex33,49. Animal studies have suggested that both stress and maternal care could alter brain DA levels. For example, Abercrombie et al.50 found that intermittent tail-shock stress increased extracellular DA levels relative to the baseline by 25% in the striatum, 39% in the nucleus accumbens, and 95% in the medial frontal cortex. In a later study, Gambarana et al.51 found that chronic stress decreased DA level in the nucleus accumbens. Hall et al.52 found that maternal deprivation was associated with increased mesolimbic DA level. The present study analyzed the main effects of COMT Val158Met polymorphism and two environmental variables (parental warmth and stressful life events) as well as their interactions on affective decision making measured by the IGT.


Out of the 556 subjects, 310 were Val allele homozygotes (Val/Val), 210 were heterozygotes (Val/Met), and 36 were homozygous for the Met allele (Met/Met). The distribution is consistent with Hardy-Weinberg Equilibrium (χ2 (1) = .003, p = .96). There was no gender difference in the genotype distribution. The allele frequencies in the present study are comparable to other studies with Chinese participants66,67,68. Because of the limited number of subjects in the Met/Met group, we grouped the Val/Met and Met/Met subjects into the Met-carrier group in all subsequent analysis.

In our sample, the mean score for the Stressful Life Events Scale (SLES) was 9.84 (SD = 5.42) and the mean score for the Parent Warmth Scale (PWS) was 53.08 (SD = 7.45). Neither SLES (t(531) = 1.09, p = .28) nor PWS (t(531) = 1.49, p = .14) showed significant gender difference. These two environmental measures were negatively correlated (Spearman r(533) = −.20, p < .01). Because neither environmental measures was normally distributed (SLES: K-S test of normality = 0.064, p < .001; PWS: K-S test of normality = 0.105, p < .001), we split the subjects into two groups by group median. After splitting, there were 278 (52.2%) participants in the low stress group (mean SLES score 6.16, SD = 2.14) and 255 (47.8%) in the high stress group (mean SLES score 13.84, SD = 5.05). There were 295 (55.3%) participants in the low parental warmth group (mean PWS score 47.93, SD = 5.64) and 238 (44.7%) in the high parental warmth group (mean PWS score 59.46, SD = 3.34). COMT genotype did not affect either stress grouping (χ2 (1) = .33, p = .57) or parental warmth grouping (χ2 (1) = 1.67, p = .20).

According to traditional IGT analysis6,34,35, the mean scores (SD) for each block was −4.81 (6.58), −.47 (7.53), 2.29 (9.35), 3.52 (10.36) and 4.58 (10.62), suggesting that subjects learned to select more from the advantageous decks as the task progressed. There was no gender difference in IGT score (t(531) = .99, p = .32). The revised expectancy valence model61 worked well in our sample as the mean model fit was adequate (4.71 on average). The mean (SD) for reward sensitivity was .65 (.30). There was no gender effect on W (t(531) = 1.18, p = .24). Reward sensitivity (W) was negatively correlated with IGT scores (r (533) = −.43, p < .01).

The mean scores and SD of the dependent variables (IGT scores and reward sensitivity) by genotype and environmental variables are shown in Table 1. Three-way ANCOVA (minimum N = 46 in each cell) was performed on reward sensitivity (W) with COMT, stress, and parental warmth as independent variables and three IQ measures as covariates. Results showed that there was a significant interaction between COMT genotype and stress (F(1,522) = 6.57, p = .01). Further analysis on COMT by stress interaction showed that Met carriers with higher stress were more sensitive to gains (reward sensitivity, W) than those with lower stress (Figure 1A, t(239) = 2.78, p < .01, Cohen's d = .36), but no difference was found for Val/Val homozygotes (t(290) = .38, p = .70). There was no main effect of COMT genotype (F(1,522) = .34, p = .56), stress (F(1,522) = 2.28, p = .13), or parental warmth (F(1,522) = 1.42, p = .23). The other two two-way interactions (F(1,522) = 2.51, p = .11 for COMT by parental warmth; and F(1,522) = .60, p = .44 for stress by parental warmth) and the three-way interaction (F(1,522) = 2.84, p = .09) were not significant.

Table 1: Means (SD) of the dependent variables by independent variables
Figure 1: Results of Gene-Environment interactions.
Figure 1

COMT genotype interacted with stress to influence reward sensitivity (A), and interacted with parental warmth to influence the IGT scores (B). The number on each bar denotes the number of subjects in each group. Error bars indicate standard errors. **: p < .01.

To rule out the possibility that parental warmth could mediate the interaction between COMT genotype and stress, a two-way ANCOVA was performed on W with COMT genotype and Stress as independent variables and the three IQ measures as well as the standardized parental warmth score as covariates. The interaction of COMT by stress on W was still significant (F(1,525) = 4.97, p < .05), and no significant main effect was found (both ps > .10).

Using the IGT scores as the dependent variable, the three-way ANCOVA found a significant COMT by parental warmth interaction (F(1,522) = 4.77, p = .029). Further analysis showed that Val/Val homozygotes with higher parental warmth chose more advantageously than those with lower parental warmth (Figure 1B, t(290) = 2.68, p < .01, Cohen's d = .32), but no difference was found for Met carriers (t(239) = .44, p = .66). There was no significant main effect of COMT genotype (F(1,522) = 1.22, p = .27), stress (F(1,522) = 2.86, p = .09), or parental warmth (F(1,522) = 1.17, p = .28). The other two-way interactions, COMT by stress (F(1,522) = .02, p = .89) and stress by parental warmth (F(1,522) = .44, p = .51), and the three-way interaction (F(1,522) = 1.61, p = .21) were not significant.

Similarly, to rule out the possibility that stress could mediate the interaction between COMT genotype and parental warmth, a two-way ANCOVA was performed on the IGT scores with COMT genotype and parental warmth as independent variables and the three IQ measures as well as the normalized stress scores as covariates. The interaction of COMT by stress on IGT score was still significant (F(1,525) = 4.39, p < .05), although none of the main effects was significant (both ps > .10).


The present study showed that COMT polymorphism not only interacted with a protective factor (i.e., parental warmth) but also with a risk factor (i.e., stressful life events) to influence affective decision making. COMT Met carriers showed more reward sensitivity (i.e., more sensitive to gains) if they experienced high stress, whereas Val/Val homozygotes showed better IGT performance if they experienced high parent warmth. Previous studies reported mixed findings about the association between COMT Val158Met polymorphism and human decision making. Two studies42,43 showed that the Val (or Val equivalent G allele at rs4818) allele was associated with higher IGT scores, but another study41 failed to replicate this result. In addition, van den Bos et al.42 revealed no effect of COMT Val158Met polymorphism on any components of the Expectancy Valence Model61. This discrepancy might have been due to their omission of relevant environmental factors and/or the relatively small sample sizes. Using a large sample, the present study found gene-environment interactions, suggesting that the effect of genotype depends on environmental factors.

Stress is usually considered as a risk factor for many psychiatric disorders69 as well as unhealthy lifestyle behaviors, such as smoking, drinking or unhealthy diet46. Both cross-sectional70 and longitudinal71 studies have shown that recent stressful life events are associated with self-reported psychotic experiences. Many studies have also examined the effect of stress on decision making in normal subjects46 and drug abusers72,73,74. It is suggested that the current stress level is related to heightened reward sensitivity and lowered punishment sensitivity in normal subjects46, and is related to drug craving and relapse in addicts72,73,74. In the Expectancy Valence Model, reward sensitivity (W) is a motivation parameter with small values denoting strong attention to losses, and large values denoting heightened attention to gains. In the IGT, a high W thus indicates a preference for the high-gain, disadvantageous decks, and therefore poor affective decisions. For example, Yechiam et al.75 showed that drug offenders, sex offenders, dangerous drivers, chronic cocaine abusers, and theft criminals were characterized by high reward sensitivity (W). In the present study, we found that stressful life events interacted with COMT Val158Met polymorphism to influence decision making: COMT Met carriers were more reward sensitive (i.e., more sensitive to gains over losses) if they had experienced more stressful events. Although our study is the first to document a COMT by stress interaction on affective decision making, a number of previous studies have demonstrated a similar interaction between COMT Val158Met polymorphism and stress on other behaviors. For example, several studies showed that COMT Val allele was more susceptible to the effect of stress on psychosis or paranoia76,77. Other studies suggest that the COMT Met allele is more susceptible to the effect of stress on psychosis78,79. A recent fMRI study by Ursini et al.49 suggested that COMT and stress interacted through methylation, and this interaction tended to modulate prefrontal activities in a working memory task. It is not clear, however, why the effect of stress was evident for subjects with one allele in some studies but for subjects with a different allele in other studies. More research is needed to clarify these relations.

Parental warmth is usually considered as a protective/promotional factor for many behaviors. For example, researchers have revealed that parental warmth significantly predicted later social adjustment and school achievement80, and that it is associated with less adolescent problem behaviors and lower levels of depressed mood58. In the area of decision making, Xiao et al.30 found that children in better parent-child relationship showed significant improvement on the IGT scores in a one-year longitudinal study. Other studies suggested that parental warmth acted as a protective factor in gene-environment interaction. For example, Propper et al.81 found that higher parental warmth was associated with decreased externalizing behavior only for African American children possessing the short polymorphism of the DRD4 gene. The present study found that among Val/Val homozygotes, higher parental warmth was associated with higher IGT scores. Although previous studies have found that Val/Val homozygotes tend to have higher IGT score42,43, we showed that when paired with higher parental warmth, this effect was larger.

Biologically, compared to the COMT Met allele, the COMT Val allele is associated with increased phasic and reduced tonic DA transmission subcortically and decreased DA concentrations cortically45. This tonic-phasic difference of DA caused by COMT Val/Met polymorphism results in better cognitive flexibility for subjects with the Val allele and better cognitive stability for those with the Met allele. Although more research is needed to reveal the underlying biological process involved in our finding of gene-environment interactions, some existing studies have already pointed to several possibilities. For example, many studies showed that stress was associated with increased DA level in both cortical (e.g., the medial frontal cortex) and subcortical (e.g., the striatum and nucleus accumbens) regions50,82,83, although the opposite pattern has been reported by one study51. Reward sensitivity has been suggested to be triggered by DA signals in the striatum, a subcortical nucleus vital to reward and decision making17,84. Because of the positive relationship between DA and reward63,64, elevated tonic DA in the striatum plus increased DA when under stress will cause COMT Met allele carriers to show higher attention to reward/gain45. In terms of the interaction between parental warmth and COMT, previous studies have shown that parental warmth is associated with altered DA levels in rats52 and elevated cognitive flexibility in children85. Consistent with these results, we found that when paired with higher parental warmth, COMT Val/Val homozygotes showed higher cognitive flexibility and had higher IGT scores.

The exact mechanisms underlying the gene-environment interactions revealed in the present study still remain to be explored. Although the importance of the DA neurotransmitter system is particularly emphasized in the present study, other neurotransmitter systems could also play a role in decision-making11,86. For example, many studies have suggested that decision making was also influenced directly by the serotonin system29,87,88,89,90,91 or its interaction with the DA system42. In particular, Rogers86 summarized evidence from pharmacological experiments in humans, and provided a detailed review on the role of DA and serotonin in decision making. More research is needed to clarify their specific roles.

In conclusion, the present study showed that COMT Val158Met polymorphism interacted with parental warmth and stressful life events to influence affective decision making. Stressful life events seemed to cause COMT Met carriers to pay too much attention to gains (i.e., reward sensitivity), whereas high parent warmth seemed to cause Val/Val homozygotes to obtain high IGT scores. These results showed that the of two environmental factors on affective decision making depended on the genotype of the individual. These interaction effects are likely to occur at the level of DA regulation in the brain. A fundamental question in addiction research is always whether cognitive changes that one observes are a consequence, or a precedent to substance abuse. We have argued that poor decision-making linked to sub-clinically hypo-functioning prefrontal cortex may serve as a predisposing factor that heightens the vulnerability of an individual to succumb to drug abuse12,24,92. Indeed not every person who visits a casino turns into a pathological gambler, or every person who has a drink turns into an alcoholic, or a person who experiments with drugs turn into an addict. Only a small percentage of the population succumbs to addiction when conditions are met. We have suggested that since the function of the prefrontal cortex is influenced by neurotransmitter systems11, then genetic factors that impact the level of activity in these systems can in turn serve as a predisposing factor for poor decision-making, and consequently addiction12,24,92. We have also suggested that since the maturity of the prefrontal cortex is delayed until early twenties, environmental factors, such as early life stress, could also lead to alterations in the normal development of decision-making capacities12,24,92. The current results provide support for these notions. As such, the results help advance our understanding of some of the underlying mechanisms of certain psychiatric disorders involving decision making deficits, such as addiction, schizophrenia, or depression, and perhaps leading to novel ways of looking into the problems and the strategy for their treatment.



Participants were a subsample of a large-scale gene-brain-behavior project29, which recruited 572 (312 females, aged from 17 to 27 years old, with a mean of 20.47 years, SD = 1.01) Han Chinese undergraduate students. They had normal or corrected-to-normal vision, and had no history of neurological or psychiatric problems according to self-report. Only 4 subjects were identified to have high levels of alcohol problems (scored 16 or higher on the Alcohol Use Disorders Identification Test53). The results remained virtually the same after excluding these subjects. No subject was found to show high (6–7) or very high (8 and above) dependence on nicotine according to the Fagerström Test for Nicotine Dependence54. Informed written consents were obtained from all participants and the study was approved by the Beijing Normal University Institutional Review Board.

Out of the 572 participants, 556 (306 females) were genotyped (16 participants failed to be genotyped due to technical errors) for COMT Val158Met polymorphism (rs4680). Genotyping was done on a Gel Doc 2000 imaging system (Bio-Rad Laboratories Ltd, UK) following a polymerase chain reaction (PCR) protocol55. All 556 participants completed Stressful Life Events Scale (SLES) and Parental Warmth Scale (PWS). However, only 533 (298 females) subjects completed the IGT and IQ tests, so all analysis in the present study were based on those 533 subjects.

IQ tests

Two intelligence tests, namely the Raven's Advanced Progressive Matrices (RAPM) and the Wechsler Adult Intelligence Scale-Revised Chinese Version (WAIS-RC), were used to measure subjects' general cognitive abilities29,56. Three measures were generated and used as covariates in analysis of covariance (ANCOVA), including the number of correct responses to the test items of RAPM, and two IQ scores (verbal IQ and performance IQ) of WAIS-RC.

Decision making task

Participants completed the IGT task, a test of decision making under ambiguity and risk. The IGT was originally designed to test decision making deficits frequently seen in patients with ventromedial prefrontal cortex damage6, and had been used extensively on other populations, including healthy adults29, adolescents30 and drug addicts57. A detailed description of the IGT appeared in Bechara et al.7.

Environmental measures

Two environmental measures were used. The Stressful Life Events Scale (SLES) was modified from Beam et al.58 to measure a major risk factor—chronic stress. Chronic stress was chosen because previous studies have shown that naturally occurring stressors have more severe impacts on decision making than laboratory-induced acute stress46. The SLES is a 24-item questionnaire about stressful family and peer events. Example are “the death of a close friend”, and “a parent became seriously ill”. Subjects were asked to indicate if that problem happened to him/her one or more times in three time periods: elementary school, middle and high school, and college. To assess the protective factor, the Parental Warmth Scale (PWS), modified from Greenberger and Chen59 and Greenberger et al.60, was used. The PWS is an 11-item questionnaire regarding subjects' parents, including questions such as “my parents really enjoy spending time with me”. Subjects were asked to rate their agreement with those statements from 1 (strongly disagree) to 6 (strongly agree). Total ratings of all 11 items were added together to represent the parental warmth measure. This measure has been linked to adolescent problem behavior and depressed mood58,59. The Cronbach α was .83 for our sample, suggesting good internal reliability.

Data analysis

The IGT score was calculated by subtracting the total number of selections of the disadvantageous decks (A and B) from the total number of selections of the advantageous decks (C and D). To decompose the cognitively-complex IGT, we used the revised expectancy valence model61 to compute the underlying cognitive and emotional processes that contribute to IGT performance. In particular, the model decomposes IGT performance into three parameters (for detail of the model, see61,62): 1) Reward sensitivity (W), ranging from 0 to 1, with higher values denoting increased attention to gains over losses; 2) Recency (Φ), ranging from 0 to1, with higher values indicating rapid discount of past outcomes; 3) Choice consistency (c), ranging from −5 to 5, with higher values representing converging choices toward the decks with the maximum reward expectancy. This model provides additional information regarding the mechanisms underlying IGT performance. For example, this model could characterize the differential deficits in affective decision making among neurological and psychiatric disorders, although their IGT performance was equally impaired62. In the present study, we focused on the reward sensitivity (W) parameter due to its tight association with DA functions63,64,65. The descriptive statistics on other parameters are available in Supplemental Materials online. Environment measures were split into “high” and “low” groups by group median. For IGT score and reward sensitivity W, three-way ANCOVAs were carried out with COMT genotype and both environment variables as factors and three IQ measures as covariates separately. Effect size (Cohen's d) was calculated for each direct comparison.


  1. 1.

    & Prospect Theory and the Brain. In: Glimcher P. W., Camerer C. F., Fehr E., Poldrack R. A., editors. Neuroeconomics: Decision Making and the Brain. New York: Academic Press. pp. 145–173 (2009).

  2. 2.

    , & Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience. Trends in Cognitive Sciences 15, 11–19 (2010).

  3. 3.

    , , , , et al. The relationship between affective decision-making and theory of mind in the frontal variant of fronto-temporal dementia. Neuropsychologia 45, 342–349 (2007).

  4. 4.

    , , , , et al. Dissociable Deficits in the Decision-Making Cognition of Chronic Amphetamine Abusers, Opiate Abusers, Patients with Focal Damage to Prefrontal Cortex, and Tryptophan-Depleted Normal Volunteers:: Evidence for Monoaminergic Mechanisms. Neuropsychopharmacology 20, 322–339 (1999).

  5. 5.

    , & Emotion, decision making and the orbitofrontal cortex. Cereb Cortex 10, 295–307 (2000).

  6. 6.

    , , & Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50, 7–15 (1994).

  7. 7.

    , & Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain 123 (Pt11), 2189–2202 (2000).

  8. 8.

    , , , & The contributions of lesion laterality and lesion volume to decision-making impairment following frontal lobe damage. Neuropsychologia 41, 1474–1483 (2003).

  9. 9.

    , , , , et al. Differential effects of insular and ventromedial prefrontal cortex lesions on risky decision-making. Brain 131, 1311–1322 (2008).

  10. 10.

    , , , , et al. Decision-making processes following damage to the prefrontal cortex. Brain 125, 624–639 (2002).

  11. 11.

    & The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior 52, 336–372 (2005).

  12. 12.

    Neurobiology of decision-making: risk and reward. Semin Clin Neuropsychiatry 6, 205–216 (2001).

  13. 13.

    , & How the basal ganglia use parallel excitatory and inhibitory learning pathways to selectively respond to unexpected rewarding cues. The Journal of Neuroscience 19, 10502–10511 (1999).

  14. 14.

    , , & BOLD responses reflecting dopaminergic signals in the human ventral tegmental area. Science 319, 1264–1267 (2008).

  15. 15.

    , & Temporal prediction errors in a passive learning task activate human striatum. Neuron 38, 339–346 (2003).

  16. 16.

    , , , , et al. Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304, 452–454 (2004).

  17. 17.

    Reward representations and reward-related learning in the human brain: insights from neuroimaging. Current Opinion in Neurobiology 14, 769–776 (2004).

  18. 18.

    , & Human insula activation reflects risk prediction errors as well as risk. J Neurosci 28, 2745–2752 (2008).

  19. 19.

    , & Neural differentiation of expected reward and risk in human subcortical structures. Neuron 51, 381–390 (2006).

  20. 20.

    & The short-latency dopamine signal: a role in discovering novel actions? Nature Reviews Neuroscience 7, 967–975 (2006).

  21. 21.

    Getting formal with dopamine and reward. Neuron 36, 241–263 (2002).

  22. 22.

    Dopamine neurons and their role in reward mechanisms. Current Opinion in Neurobiology 7, 191–197 (1997).

  23. 23.

    , & A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).

  24. 24.

    Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat Neurosci 8, 1458–1463 (2005).

  25. 25.

    , , , , et al. Trait-related decision-making impairment in the three phases of bipolar disorder. Biological Psychiatry 70, 357–365 (2011).

  26. 26.

    , , , , et al. Impaired decision-making in suicide attempters may increase the risk of problems in affective relationships. Journal of Affective Disorders 99, 59–62 (2007).

  27. 27.

    & Cognitive and neurological impairment in mood disorders. The Psychiatric clinics of North America 27, 19–36 (2004).

  28. 28.

    , , , & Genetic Variation in Preferences for Giving and Risk Taking. Quarterly Journal of Economics 124, 809–842 (2009).

  29. 29.

    , , , , et al. Serotonin transporter gene-linked polymorphic region (5-HTTLPR) influences decision making under ambiguity and risk in a large Chinese sample. Neuropharmacology 59, 518–526 (2010).

  30. 30.

    , , , , et al. Parent–child engagement in decision-making and the development of adolescent affective decision capacity and binge-drinking. Personality and Individual Differences 51, 285–292 (2011).

  31. 31.

    , , , , et al. Catechol-O-methyltransferase-deficient mice exhibit sexually dimorphic changes in catecholamine levels and behavior. Proceedings of the National Academy of Sciences 95, 9991–9996 (1998).

  32. 32.

    , , , , et al. Human catechol-O-methyltransferase pharmacogenetics: description of a functional polymorphism and its potential application to neuropsychiatric disorders. Pharmacogenetics 6, 243–250 (1996).

  33. 33.

    , , , , et al. Functional analysis of genetic variation in catechol-O-methyltransferase (COMT): effects on mRNA, protein, and enzyme activity in postmortem human brain. Am J Hum Genet 75, 807–821 (2004).

  34. 34.

    , , , , et al. Double dissociation of conditioning and declarative knowledge relative to the amygdala and hippocampus in humans. Science 269, 1115–1118 (1995).

  35. 35.

    , , & Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cereb Cortex 6, 215–225 (1996).

  36. 36.

    , , , & The Iowa Gambling Task in fMRI images. Human Brain Mapping 31, 410–423 (2009).

  37. 37.

    , & Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324, 646–648 (2009).

  38. 38.

    , , & The neural basis of loss aversion in decision-making under risk. Science 315, 515–518 (2007).

  39. 39.

    , , & The impact of prior risk experiences on subsequent risky decision-making: The role of the insula. Neuroimage 50, 709–716 (2010).

  40. 40.

    , , , , et al. Functional dissociations of risk and reward processing in the medial prefrontal cortex. Cereb Cortex 19, 1019–1027 (2009).

  41. 41.

    , , , , et al. Influence of BDNF and COMT polymorphisms on emotional decision making. Neuropharmacology 58, 1109–1113 (2010).

  42. 42.

    , , , den,. & The effect of COMT Val158 Met genotype on decision-making and preliminary findings on its interaction with the 5-HTTLPR in healthy females. Neuropharmacology 56, 493–498 (2009).

  43. 43.

    , , & Planning, decision-making and the COMT rs4818 polymorphism in healthy males. Neuropsychologia 46, 757–763 (2008).

  44. 44.

    , , & COMT Val158Met Genotype Determines the Direction of Cognitive Effects Produced by Catechol-O-Methyltransferase Inhibition. Biological Psychiatry 71, 538–544 (2012).

  45. 45.

    , , & The catechol-O-methyltransferase polymorphism: relations to the tonic-phasic dopamine hypothesis and neuropsychiatric phenotypes. Neuropsychopharmacology 29, 1943–1961 (2004).

  46. 46.

    & Decision making under stress: A selective review. Neurosci Biobehav Rev 36, 1228–1248 (2012).

  47. 47.

    A bias toward short-term thinking in threat-related negative emotional states. Personality and Social Psychology Bulletin 25, 65–75 (1999).

  48. 48.

    , , , , et al. Moderation of the effect of adolescent-onset cannabis use on adult psychosis by a functional polymorphism in the catechol-O-methyltransferase gene: longitudinal evidence of a gene X environment interaction. Biological Psychiatry 57, 1117–1127 (2005).

  49. 49.

    , , , , et al. Stress-related methylation of the catechol-O-methyltransferase Val158 allele predicts human prefrontal cognition and activity. The Journal of Neuroscience 31, 6692–6698 (2011).

  50. 50.

    , , & Differential Effect of Stress on In Vivo Dopamine Release in Striatum, Nucleus Accumbens, and Medial Frontal Cortex. Journal of Neurochemistry 52, 1655–1658 (1989).

  51. 51.

    , , , , et al. A chronic stress that impairs reactivity in rats also decreases dopaminergic transmission in the nucleus accumbens: a microdialysis study. Journal of Neurochemistry 72, 2039–2046 (1999).

  52. 52.

    , , & Maternal deprivation of neonatal rats produces enduring changes in dopamine function. Synapse 32, 37–43 (1999).

  53. 53.

    , , , & Development of the Alcohol-Use Disorders Identification Test (Audit) - Who Collaborative Project on Early Detection of Persons with Harmful Alcohol-Consumption .2. Addiction 88, 791–804 (1993).

  54. 54.

    , , & The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict 86, 1119–1127 (1991).

  55. 55.

    , , , , et al. Family-based and case-control association studies of catechol-O-methyltransferase in attention deficit hyperactivity disorder suggest genetic sexual dimorphism. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 118B, 103–109 (2003).

  56. 56.

    , , , , et al. Individual differences in false memory from misinformation: Cognitive factors. Memory 18, 543–555 (2010).

  57. 57.

    , , , , et al. Affective decision-making deficits, linked to a dysfunctional ventromedial prefrontal cortex, revealed in 10th-grade Chinese adolescent smokers. Nicotine Tob Res 10, 1085–1097 (2008).

  58. 58.

    , , & Adolescent problem behavior and depressed mood: Risk and protection within and across social contexts. Journal of Youth and Adolescence 31, 343–357 (2002).

  59. 59.

    & Perceived Family Relationships and Depressed Mood in Early and Late Adolescence: A Comparison of European and Asian Americans. Developmental Psychology 32, 707–716 (1996).

  60. 60.

    , & The Role of “Very Important” Nonparental Adults in Adolescent Development. Journal of Youth and Adolescence 27, 321–343 (1998).

  61. 61.

    & A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological assessment 14, 253–262 (2002).

  62. 62.

    , , & Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits. Psychol Sci 16, 973–978 (2005).

  63. 63.

    & Brain dopamine and reward. Annual Review of Psychology 40, 191–225 (1989).

  64. 64.

    Predictive reward signal of dopamine neurons. Journal of neurophysiology 80, 1–27 (1998).

  65. 65.

    & The dopamine hypothesis of reward: past and current status. Trends in neurosciences 22, 521–527 (1999).

  66. 66.

    , , , , et al. Association analysis of brain-derived neurotrophic factor Val66Met polymorphisms with Alzheimer's disease and age of onset. Neuropsychobiology 49, 10–12 (2004).

  67. 67.

    , , , , et al. Association of functional catechol O-methyl transferase (COMT) Val108Met polymorphism with smoking severity and age of smoking initiation in Chinese male smokers. Psychopharmacology 190, 449–456 (2007).

  68. 68.

    , , , , et al. Association analysis of the DRD4 and COMT genes in methamphetamine abuse. Am J Med Genet B Neuropsychiatr Genet 129B, 120–124 (2004).

  69. 69.

    , & Psychosocial stress and psychosis. A review of the neurobiological mechanisms and the evidence for gene-stress interaction. Schizophrenia Bulletin 34, 1095–1105 (2008).

  70. 70.

    , , , , et al. Prevalence and correlates of self-reported psychotic symptoms in the British population. The British Journal of Psychiatry 185, 298–305 (2004).

  71. 71.

    , , , , et al. Self-reported psychotic symptoms in the general population. The British Journal of Psychiatry 188, 519–526 (2006).

  72. 72.

    Chronic stress, drug use, and vulnerability to addiction. Annals of the New York Academy of Sciences 1141, 105–130 (2008).

  73. 73.

    , & Stress-induced craving and stress response in cocaine dependent individuals. Psychopharmacology 142, 343–351 (1999).

  74. 74.

    How does stress increase risk of drug abuse and relapse? Psychopharmacology 158, 343–359 (2001).

  75. 75.

    , , , , et al. Neurocognitive deficits related to poor decision making in people behind bars. Psychon Bull Rev 15, 44–51 (2008).

  76. 76.

    , , , , et al. COMT Val158Met moderation of stress-induced psychosis. Psychological Medicine 37, 1651–1656 (2007).

  77. 77.

    , , , , et al. Subtle gene–environment interactions driving paranoia in daily life. Genes, Brain and Behavior 8, 5–12 (2009).

  78. 78.

    , , , , et al. COMT Val158Met–Stress Interaction in Psychosis: Role of Background Psychosis Risk. CNS Neuroscience & Therapeutics 17, 612–619 (2011).

  79. 79.

    , , , , et al. Evidence that the COMTVal158Met polymorphism moderates sensitivity to stress in psychosis: An experience-sampling study. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 147, 10–17 (2008).

  80. 80.

    , & Parental warmth, control, and indulgence and their relations to adjustment in Chinese children: A longitudinal study. Journal of Family Psychology 14, 401–419 (2000).

  81. 81.

    , , , & Parenting quality, DRD4, and the prediction of externalizing and internalizing behaviors in early childhood. Developmental Psychobiology 49, 619–632 (2007).

  82. 82.

    , & Increased dopamine and norepinephrine release in medial prefrontal cortex induced by acute and chronic stress: effects of diazepam. Neuroscience 64, 619–628 (1995).

  83. 83.

    , , , , et al. Psychological stress increases dopamine turnover selectively in mesoprefrontal dopamine neurons of rats: reversal by diazepam. Brain Research 557, 154–161 (1991).

  84. 84.

    , & The role of the dorsal striatum in reward and decision-making. The Journal of Neuroscience 27, 8161–8165 (2007).

  85. 85.

    , , & Affective quality of the mother–child relationship: Longitudinal consequences for children's school-relevant cognitive functioning. Developmental Psychology 23, 210–215 (1987).

  86. 86.

    The roles of dopamine and serotonin in decision making: evidence from pharmacological experiments in humans. Neuropsychopharmacology 36, 114–132 (2010).

  87. 87.

    , , , , et al. Tryptophan depletion alters the decision-making of healthy volunteers through altered processing of reward cues. Neuropsychopharmacology 28, 153–162 (2003).

  88. 88.

    , , & The effect of polymorphism at the serotonin transporter gene on decision-making, memory and executive function in ecstasy users and controls. Psychopharmacology 188, 213–227 (2006).

  89. 89.

    , , , , et al. The influence of four serotonin-related genes on decision-making in suicide attempters. Am J Med Genet B Neuropsychiatr Genet 144B, 615–624 (2007).

  90. 90.

    , , , & Robbins TW. Serotonin modulates behavioral reactions to unfairness. Science 320, 1739–1739 (2008).

  91. 91.

    , , , & Cognitive inflexibility after prefrontal serotonin depletion. Science 304, 878–880 (2004).

  92. 92.

    Risky business: emotion, decision-making, and addiction. J Gambl Stud 19, 23–51 (2003).

Download references


This research was supported by the 111 project (B07008) to Qi Dong, the National Science Foundation of China (31130025) to Gui Xue, and by research grants from NIDA R01DA023051 and NCI R01CA152062 to Antoine Bechara. We would also like to thank all the lab members who helped with the data collection.

Author information


  1. Brain and Creativity Institute, University of Southern California, Los Angeles, California 90089, USA

    • Qinghua He
    •  & Antoine Bechara
  2. National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China

    • Qinghua He
    • , Gui Xue
    • , Chunhui Chen
    • , Xuemei Lei
    • , Yuyun Liu
    • , Jin Li
    • , Bi Zhu
    •  & Qi Dong
  3. Department of Psychology and Social Behavior, University of California, Irvine, California 92697, USA

    • Chuansheng Chen
    •  & Xuemei Lei
  4. Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH 43210, USA

    • Zhong-Lin Lu
  5. Department of Biological Chemistry, University of California, Irvine, California 92697, USA

    • Robert K. Moyzis
  6. Department of Neurology, University of Iowa, Iowa City, Iowa 52242, USA

    • Antoine Bechara
  7. Clinical Research Division, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada

    • Antoine Bechara


  1. Search for Qinghua He in:

  2. Search for Gui Xue in:

  3. Search for Chuansheng Chen in:

  4. Search for Zhong-Lin Lu in:

  5. Search for Chunhui Chen in:

  6. Search for Xuemei Lei in:

  7. Search for Yuyun Liu in:

  8. Search for Jin Li in:

  9. Search for Bi Zhu in:

  10. Search for Robert K. Moyzis in:

  11. Search for Qi Dong in:

  12. Search for Antoine Bechara in:


Conceived and designed the experiments: Chuansheng Chen, ZL, RKM, QD. Performed the experiments: QH, Chunhui Chen, XL, YL, JL, BZ. Analyzed the data: QH, GX, AB. Wrote the paper: QH, GX, Chuansheng Chen, ZL, AB.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Qinghua He or Qi Dong.

Supplementary information

Word documents

  1. 1.

    Supplementary Information

    Supplemental Tables

About this article

Publication history






By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.