How genes make up your mind: Individual biological differences and value-based decisions

Journal of Economic Psychology 31 (2010) 818–831

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How genes make up your mind: Individual biological differences and value-based decisions Thomas Z. Ramsøy *, Martin Skov Decision Neuroscience Research Group, Copenhagen Business School, Denmark Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Denmark

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Article history: Received 16 February 2008 Received in revised form 16 November 2008 Accepted 16 December 2009 Available online 14 April 2010 JEL classification: D01 PsycINFO classification: 2500 Keywords: Value-based decision making Emotions Imaging genetics Neuroeconomics

a b s t r a c t Neuroeconomics is the multidisciplinary study of value-based decision-making. One of the core topics is how emotions affect decision-making. Developments in economic models of decision-making have been influenced by technological innovations and empirical findings in cognitive neuroscience. Now, a recent approach in cognitive neuroscience, often referred to as ‘‘imaging genetics”, promises to make significant contributions to our understanding of both behavioral and neural aspects of value-based decision-making. Recent work has demonstrated the role of neurotransmitter alterations in clinical states such as Parkinson’s disease, depression and anxiety, and how this may affect decision behavior. However, these insights are limited through their focus on extreme neuropathology, which sheds little light on similar functions in healthy individuals. Here, we present and discuss studies of the role of drug-induced and genetically driven changes in neurotransmitter levels, and their effects on value-based decision-making. Following this, we argue that in healthy subjects, individual variance in decision behavior can be explained by such genetic factors, and gene–environment interactions. We suggest that this development should be used in neuroeconomic research in order to both improve behavioral models, by stressing the biological nature of individual variance, and through the improvement of our general understanding of the brain basis of value-based decision-making. Ó 2010 Elsevier B.V. All rights reserved.

1. Introduction Neuroeconomics is the study of the neurobiology of value-based decision-making. The notion of ‘‘value-based decisionmaking” (VBDM) refers to a suite of functional neural processes involved in representing internal and external states of the organism, in the valuation of possible behavioral options, and in selecting motor actions based on these valuations (Rangel, Camerer, & Montague, 2008). A key assumption of most neuroeconomic models is that VBDM involves both cognitive and emotional components (Bechara, Damasio, & Damasio, 2000; Bechara, Damasio, & Damasio, 2003; Dunn, Dalgleish, & Lawrence, 2006; Fellows, 2007; Loewenstein, Weber, Hsee, & Welch, 2001). Emotions are typically seen as crucial to the generation of motivational states and the valuation of decision options, whereas cognitive processes are assumed to be involved in information processing functions such as memory, executive function and action selection. The interaction between emotional processes and cognitive processes is recognized as being complex, and it has been demonstrated that cognitive representations such as semantic knowledge about an object can modulate the emotional processes involved in the

* Corresponding author. Address: Decision Neuroscience Research Group, AØ, Copenhagen Business School, Solbjerg Plads 3, 2000 Frederiksberg, Denmark. E-mail address: [email protected] (T.Z. Ramsøy). 0167-4870/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.joep.2010.03.003

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computation of its value (Kirk, 2008; Kirk, Skov, Hulme, Christensen, & Zeki, 2009; McClure et al., 2004; Plassmann, O’Doherty, Shiv, & Rangel, 2008). Conversely, early and unconscious affective responses in structures such as the amygdala can affect perceptual processing, and hence influence how cognitive representations are computed (Vuilleumier, Richardson, Armony, Driver, & Dolan, 2004). In general, neuroeconomics assumes that predicted values – what is sometimes referred to as the predicted utility (Kahneman, Wakker, & Sarin, 1997) – of available options determine decision behavior. However, it has been demonstrated that this relationship between predicted value and decision can be dissociated (Weber & Johnson, 2009) and even break down in pathological situations such as addiction or pathological gambling (Berridge & Aldridge, 2008). To date most neuroeconomic research has focused on illuminating how various factors change the workings of the emotional and cognitive processes involved in VBDM. To name just a few examples, decisions have been shown to be influenced by attentional processes, including perceptual scan strategies, assessment of the situation (framing, goal elicitation), and by the selection of information, but research has also shown them to be influenced by the physiological state of the organism (conditioning its behavioral motivation), and the hedonic values of the behavioral options (previous experience, the certainty of the outcome, the temporal availability of the reward, comparison of different possible reward possibilities, etc.). In this research, it is often assumed (implicitly or explicitly) that the neurobiological systems involved in computing these factors are uniform across the population. This is, however, not the case. The molecular make up of both emotional and cognitive processes may vary from individual to individual due to systematic biological factors. This individual variance naturally influences such processes, and have an impact on decision-making. To improve models of VBDM, and make them ecologically valid, the relevant biological factors associated with individual variance need to be included. Here, we wish to draw attention to a number of these factors. We will present and discuss recent insights into the relationship between neurotransmitter functions, emotions and decision-making. Specifically, we focus on how genetic variation causes variations in the molecular biology of neural structures associated with VBDM. Finally, we discuss the implications of such insights on models and assumptions in neuroeconomics. Before embarking on this discussion, however, we will review how different neural structures and processes are thought to be involved in value-based decision-making. 2. Neural structures involved in value-based decision-making Studies of patients with brain lesions provided early indications of the relationship between specific brain regions and decision-making. Since the emergence of neurology in the late 19th century, studies of brain injured patients have demonstrated a link between emotional responses and decision-making behavior. For instance, patients with lesions to the ventromedial prefrontal cortex (vmPfC) demonstrate significant deficits in decision-making tasks including gambling (Bechara, 2003; Bechara & Damasio, 2002; Bechara, Damasio, Tranel, & Anderson, 1998; Bechara, Tranel, & Damasio, 2000b; Cavedini, Riboldi, Keller, D’Annucci, & Bellodi, 2002; Clark et al., 2008). Other kinds of lesions, such as lesions to the amygdala, have been demonstrated to produce similar behavioral deficits (Bar-On, Tranel, Denburg, & Bechara, 2003; Bechara, 2001; Bechara & Damasio, 2002). But it was not until the late 1980s and early 1990s that it was formally suggested that decision-making was the result of an integration of cognitive and emotional processes. In particular, Damasio and colleagues proposed that patients with lesions to the vmPfC performed poorly on gambling tasks due to reduced emotional responses (Bechara, Damasio, Damasio, & Lee, 1999; Bechara, Damasio, Tranel, & Damasio, 1997). On this view, emotions provide somatic markers to guide the choosing between different available options. Bodily responses were used during the decision-making process to help labeling some choices as ‘‘bad”, especially in gambles with unknown, or risky, outcomes (Bechara & Damasio, 2005; Damasio, 1996). Later studies have complicated this initial hypothesis somewhat. For example, patients with amygdala or vmPfC lesions perform better than normal controls on gambling tasks that reward risk taking (Shiv, Loewenstein, Bechara, Damasio, & Damasio, 2005), and patients with bilateral damage to vmPfC make more utilitarian moral judgments than healthy subjects (Koenigs et al., 2007). Nevertheless, the early patient studies demonstrated an advantageous role of emotion in decision-making, and have led to a general acceptance of the idea that brain regions responsible for generating emotional responses serve some fundamental role in the evaluation of stimuli and events, with the purpose of guiding or motivating decisions (Bechara, 2003). Today, there is an immense focus on the role of emotions during decision-making processes. Emotions are often divided into positive and negative responses. However, one should note that the term ‘‘emotion” itself is unclear. Indeed, in neuroeconomics emotional responses may refer to the mental processes during expected outcomes, during the decision-making phase, or during the outcome phase. Analogous to this, in the economic literature, there has been a suggestion that one should distinguish between different forms of processes during the decision-making process, including ‘‘expected utility”, ‘‘decision utility”, ‘‘outcome utility,” and ‘‘experienced utility” (Kahneman, 1994; Kahneman & Tversky, 1979, see also Peterson, 2007). Conversely, in cognitive neuroscience, the term ‘‘emotion” is normally used to refer to the immediate behavioral responses of an organism to stimuli or events that are, e.g., life-threatening or rewarding. Compared to the multiple utility functions provided in economics, the cognitive neuroscience approach seems far too simplistic. In defining emotions, there is indeed a need for a cross-talk between traditional disciplines. In particular, as the emerging multidisciplinary collaboration between economics, psychology and neuroscience continues, such inconsistencies need to be mapped out and resolved into a coherent and common framework. Within the tradition of cognitive neuroscience, positive emotions are normally thought to be rooted in reward and approach behaviors, and are typically associated with neural processes taking place in the mesolimbic and mesocortical pathways (projections from the ventral tegmentum of the midbrain to the frontal lobe), processes that mainly involve neurotransmitters such as dopamine and opioid (Adcock, Thangavel, Whitfield-Gabrieli, Knutson, & Gabrieli, 2006; Adinoff, 2004; Berridge, 2007;

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Pfaus, 2009; Schott et al., 2008), and more recently the neuropeptide orexin/hypocretin (Aston-Jones, Smith, Moorman, & Richardson 2009; Harris & Aston-Jones 2006). The structures implied in the dopaminergic network include the ventral striatum (VS), amygdala, orbitofrontal cortex (OfC) and anterior cingulate (ACC), and the role of this system can be tentatively described as the computing of reward prediction and of the experienced hedonic value of the actual reward outcome (Berridge, 2007; Berridge & Aldridge, 2008; Cooper & Knutson, 2008; McClure, Daw, & Montague, 2003). Several studies have uncovered the brain basis of some of the factors influencing reward based economic behavior, including risky choices, time discounting, as well as co-operation and altruism (Sanfey & Chang, 2008; Sanfey, Loewenstein, McClure, & Cohen, 2006; van ‘t Wout, Kahn, Sanfey & Aleman, 2006; Vorhold, 2008; Zak, 2004; Zak & Fakhar, 2006; Zak, Kurzban, & Matzner, 2004). Negative emotions, on the other hand, are thought to be related to aversion and avoidance behaviors, and mainly driven by responses of the amygdala, lateral OfC, insula, and hypothalamus (Charney, 2003; Fischer, Andersson, Furmark, & Fredrikson, 2000; Lane et al., 1997; Price, 1999; Royet et al., 2000). The neurotransmitter serotonin is thought to mediate functions of this circuit, as alterations in the levels of serotonin has been linked to affective functions, with high levels generally being associated with increased vigilance, fear and anxiety, and low levels generally associated with reduced vigilance, depression and increased pain sensitivity (Charney, 2003; Hariri et al., 2002; Hurlemann et al., 2009; Surguladze et al., 2008; Williams et al., 2009). A currently unresolved controversy concerns the exact role played by positive and negative emotions in decision-making processes. Many decisions seem to involve both positive and negative dimensions. For instance, choosing to buy a chocolate bar both includes the (expected and experienced) reward of eating the chocolate and the negative consequences associated with future health and obesity considerations. Similarly, gambling situations are by nature composed of positive and negative factors, such as the expected likelihood of winning or losing a bet. Gambling decisions are therefore influenced both by positive emotions such as reward expectancy and negative emotions such as aversion to loss, risk or outcome ambiguity. As a result, one may suggest that decision-making is a tug-of-war between positive and negative emotional processes. However, this idea has recently been challenged. For example, in a recent neuroimaging study Tom, Fox, Trepel, and Poldrack (2007) found that both potential rewards and potential losses were related to a scaled response in midbrain dopaminergic regions and their targets, such as the VS. Increased activation in these regions was found for higher potential gains, and reduced VS activation was found for potential losses. Surprisingly, no separate activations were specific for potential losses, as one might have expected from the models positing a separate neuroarchitecture of the aversion system. This suggests that expectation of monetary losses and gains might be fully processed by a unitary (appetitive) system, centered on the striatum. Only a few reports have shown increased activations to experienced or expected financial loss, such as the amygdala (Yacubian et al., 2006) and insula cortex (Knutson & Bossaerts, 2007; Knutson, Rick, Wimmer, Prelec, & Loewenstein, 2007; Kuhnen & Knutson, 2005). Furthermore, Clark et al. (2008) recently demonstrated that patients with lesions to vmPfC and insula displayed selective and distinctive disruptions of betting behavior. On the one hand, vmPFC damage was associated with increased betting regardless of the odds of winning, while insular damage was related to a failure to adjust bets by the odds of winning. This study clearly demonstrates the necessary role of structures implied in aversive responses, such as the insula, in decision-making under risk. These conflicting findings may in part be explained by the lack of common definitions, in particular regarding the different kinds of utility involved in valuation and decision-making. In particular, while Tom et al. (2007) focused on decision utility, Knutson et al. (2007) focused on expected utility. Yet other studies, such as Clark et al. (2008) could not distinguish between different stages of the decision-making process. Such discrepancies need to be resolved when comparing studies, but also provides new possibilities for comparing different stages of the valuation and decision-making process. The lack of consistent labeling and definition across disciplinary borders, is further fueled by findings that break with the traditional dichotomy between reward and aversion structures. For example, Levita et al. (2009) recently reported that the VS responded just as strongly – or even stronger – to the expectation of punishment or pain, as to reward expectation. Furthermore, studies have suggested that dopamine and the VS is more involved with outcome expectancy rather than purely with liking and hedonic pleasure (O’Doherty et al., 2002; Suri, 2002), and that the opioid system and structures such as the OfC may be involved in the experience of hedonic reward (O’Doherty et al., 2002; Peciña, Smith, & Berridge, 2006; Petrovic et al., 2008). Similarly, findings imply that the amygdala is also involved in generating positive emotions (Murray, 2007). Indeed, these studies demonstrate that structures such as the amygdala and VS in some situations demonstrate a non-linear U-shape response function, with high activation levels for positive and negative emotions, and low activation for neutral events. While the nature of such findings are yet poorly understood, it is possible that these findings may in part be explained by complex excitatory and inhibitory interconnections between the amygdala and VS. For now, the neuroimaging studies cited here imply that there is as of yet no clear distinction in the neural architecture between positive and negative emotions. Furthermore, the distinctions between different forms of utility, as used in economics, may be informative. It is possible that structures such as the amygdala may be selectively important for some stages of the VBDM process. Whether this is the case, or whether these structure–function relationships are more general in nature, remains to be seen. The debate between ‘‘seperatists” and ‘‘unitarists” may be informed by on-going developments in neuropharmacology and in the combination of cognitive neuroscience, neuroimaging and genetics – often referred to as imaging genetics (Hariri, Drabant, & Weinberger, 2006; Meyer-Lindenberg & Weinberger, 2006). In the rest of this paper we present and discuss studies that relate neurotransmitters to gambling behavior, by first introducing the link between neurotransmitter levels and pathological states of emotion and behavior. Furthermore, we will look at how artificially induced changes in levels of dopamine and serotonin may affect emotion and decision-making. We then present studies of the genetic influence on individual

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differences in dopamine and serotonin levels, and how this has been found to affect emotions and decisions. Intriguingly, as we will show, changes in levels of neurotransmitters such as serotonin and dopamine have different effects on approach and avoidance behavior.

3. Neurotransmitter pathology and decision-making The role of neurotransmitters in VBDM is clearly demonstrated by studies of pathological conditions. Although the link between neurotransmitter levels and behavior has been debated for decades, studies of mental conditions have consistently linked high and low levels of neurotransmitters such as dopamine and serotonin, to changes in mental states and behaviors. For instance, as dopamine levels decrease, due to pathological neurodegeneration of dopamine producing regions such as the brainstem substantia nigra, a person will develop symptoms of Parkinson’s disease (PD). Beside progressive loss of voluntary and involuntary muscle control, PD patients demonstrate a number of secondary symptoms, such as depression and anxiety. In particular, PD is associated with marked frontal lobe impairment which gives rise to behavioral problems associated with anticipation, planning, and the regulating and directing of purposeful behavior (Azuma, Cruz, Bayles, Tomoeda, & Montgomery, 2003). Treating PD by pharmacologically increasing dopamine levels has – apart from improving the primary symptoms – also been related to significant alterations in decision-making behaviors in some patients, in particular the increased likelihood of developing pathological gambling and hyper-sexual behaviors (Cabrini et al., 2009; Merims & Giladi, 2008; Stamey & Jankovic, 2008). Thus, there seems to be a link between increased dopamine levels and decision-making behaviors and executive functions, including pathological gambling. The nature of this link to gambling behavior could be the result of increased risk taking, or reduced responsiveness to losses. Studies of pathological gambling in dopamine agonist treatment in PD show an increase in reward driven behaviors and diminished impulse control, rather than changes in responses to losses or aversion in general (Cilia et al., 2008; Evans, Strafella, Weintraub, & Stacy, 2009; Ferrara & Stacy, 2008; Ondo & Lai, 2008; Steeves et al., 2009; Torta & Castelli, 2008). According to the suggested link between dopamine levels and risk taking, pathological states leading to abnormally high levels of dopamine should demonstrate a similar pattern. Schizophrenia is a mental disorder with increased dopamine levels, or at least increased dopaminergic transmission in some brain regions (Meyer-Lindenberg et al., 2002), that is characterized by distorted perception and thoughts, and abnormalities in the understanding and expression of reality (Antonova, Sharma, Morris, & Kumari, 2004; Frith & Dolan, 1996; Gallhofer, Lis, Meyer-Lindenberg, & Krieger, 1999). Recently, researchers have demonstrated decision-making impairments in schizophrenia (Desai & Potenza, 2009; Yip, Sacco, George, & Potenza, 2009). In particular, it has been suggested that schizophrenics display an impaired ability to integrate affective and cognitive information (Heerey, Bell-Warren, & Gold, 2008) and that schizophrenic patients display many of the same characteristics as pathological gamblers (Borras & Huguelet, 2007). Pharmacological adjustments of dopamine levels are known to affect psychiatric and neurological symptoms: one of the side effects of dopamine antagonism treatment of schizophrenia is Parkinsonism, while side effects of increasing dopamine levels in PD include psychosis. In addition, behavioral symptoms of affective responses and decision-making seem to follow a similar scaling effect with dopamine levels. These results suggest that increased levels of dopamine affect decision-making behavior through the uncoupling of affective and cognitive elements. In healthy subjects, decision-making has been found to be based on the integration of emotional and cognitive components. This link is thought to be represented in the ventromedial prefrontal cortex, as lesions to this region affect normal decision-making (Bechara, 2001; Bechara, 2003; Bechara, Tranel, & Damasio, 2000a; Clark et al., 2008). Conversely, studies of low levels of dopamine have been less conclusive. Untreated PD patients (with low dopamine levels) show increased anxiety or depression, and one may expect that this will affect their decision-making, i.e., by making them more risk averse or generally more responsive to aversive outcomes. Also, pharmacological adjustments of dopamine levels to improve PD and schizophrenia seem to suggest that dopamine is a scaling variable for decision-making behavior. The clinical observation that a side effect of dopamine antagonism in schizophrenia is parkinsonism (both primary and secondary symptoms), while side effects of increasing dopamine levels in PD include psychosis, pathological gambling and hyper-sexuality, seems to support this notion. Brain regions involved in aversion have been shown to be modulated by the neurotransmitter serotonin (Kalbitzer et al., 2009; Watson, Ghodasra, & Platt, 2009; Zuckerman, Ballenger, & Post, 1984). The role of serotonin in emotional processing has been demonstrated in a range of studies during the past several decades. All have consistently related changes in serotonin levels to emotional disturbances such as anxiety, depression, obsessive–compulsive disorder and impulse control disorders (e.g., de Win et al., 2004; Kalbitzer et al., 2009; Merens, Willem Van der Does, & Spinhoven, 2007; Nugent et al., 2008; Reimold et al., 2008; Riedel, Klaassen, & Schmitt, 2002; Ruhé, Mason, & Schene, 2007; Savitz, Lucki, & Drevets, 2009; Young & Leyton, 2002). Drugs that increase the amount of synaptic serotonin, such as the selective serotonin re-uptake inhibitor (SSRI), lead to reduced depression and anxiety (Gorman & Kent, 1999; Sokolski, Conney, Brown, & DeMet, 2004; Spillmann et al., 2001). Such treatments also improve performance on decision-making tasks, possibly because decisions are now less driven by over-activation of the aversion circuit (Dannon, Lowengrub, Gonopolski, Musin, & Kotler, 2005; Figgitt & McClellan, 2000; Hollander et al., 2000). This suggests that serotonergic transmission influences emotional processing and decisionmaking, and that it may serve as an important factor influencing VBDM. Altered levels of serotonin have thus been linked to mood disorders such as depression and anxiety, although this link between disorder and neurotransmitters may be too simplistic. For example, depressed patients demonstrate both global

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and regional decreases in brain serotonin levels (Meyer et al., 2001; Meyer et al., 2004). Depression is a mental disorder wellknown for being characterized by a low mood, low self-esteem, and a loss of interest or pleasure in normally enjoyable activities. A fundamental aspect of depression is a general reduced sensibility and response to rewards (Foti & Hajcak, 2009). Given these findings, one may assume that depressed subjects will display altered decision-making behavior. Such a relationship has indeed been found. Studies have reported reduced sensitivity to rewards and biased expectation to negative outcome in depressed subjects (Foti & Hajcak, 2009). Consistent with this view, yet perhaps surprisingly, depressive subjects perform better on the Iowa gambling task (IGT) (Smoski et al., 2008). In this card drawing task, choices with high payoff are also associated with rare but significant losses, with a cumulated net loss. Conversely, alternate options are associated with relatively lower immediate payoff but also lower losses, leading to a cumulated net win. Depressed subjects were faster at learning to avoid risky responses (with net losses) than healthy subjects, and were even found to earn more money than healthy subjects. As noted, depressed subjects respond less to rewards. This could mean that depressed subjects, compared to healthy subjects, are less responsive to reward size, and thus do not fall prey to a ‘‘gambler’s fallacy” effect of going for large yet rare rewards (Burns & Corpus, 2004; Erev, 1998). Another mood disorder influenced by serotonin, anxiety disorder, is defined as an unpleasant emotional state, for which the cause is either not readily identified, or perceived to be uncontrollable or unavoidable. Anxiety is a heterogenous disease, but can in general be seen as a future oriented cognitive and emotional state/trait characteristic involving several components, such as anxious apprehension, worry, affective or behavioral conflicts, and altered approach/avoidance behaviors. Social anxiety, the experience of emotional discomfort regarding social situations, has been related to global and regional decreases in brain serotonin levels (5-HT1A) (Lanzenberger et al., 2007), and has also been demonstrated for panic disorder, characterized by recurring severe panic attacks (Nash et al., 2008). However, the exact role of serotonin in anxiety overall is still unclear. Patients with anxiety demonstrate increased vigilance and fear responsiveness, and studies have demonstrated that anxious subjects also demonstrate increased amygdala responses to aversive stimuli (Birbaumer et al., 1998; De Bellis et al., 2000; Rauch, Shin, & Wright, 2003; Stein, Goldin, Sareen, Zorrilla, & Brown, 2002). Taken together, such results suggest a link between serotonin and aversive responses, but are as of yet inconclusive. Can anxiety influence decision-making in situations that include factors such as risk, ambiguity or loss? Indeed, this has been demonstrated in recent studies of decision-making in anxious subjects. For example, in a neuroimaging study, subjects with high trait anxiety performed equally well compared with subjects with normal trait anxiety on a decision-making task with either a high or low error rate. However, during the low error rate condition, when the likelihood of incorrect responding was very low, high trait anxiety subjects demonstrated higher activation of the anterior cingulate and medial PfC. This suggests that anxious subjects devote more time and processing resources to decision-making at times when chances of errors are very low (Paulus, Feinstein, Simmons, & Stein 2004). Possibly, anxiety influences VBDM through both the regulation of general mood and vigilance as well as an increased attention to errors, and aversive responses. When taken together, the studies discussed above suggest a role for serotonin in VBDM. Increased levels of serotonin may be related to increased anxiety, leading decisions to be influenced by fear of losing, risk or even ambiguity. Conversely, low levels of serotonin are related to depression, in which responses to rewards and losses are generally lower, thus leading decisions to be generally less influenced by emotional factors. Besides psychiatric disease, these findings hint that induced changes in serotonin levels may also influence value-based decision-making in healthy subjects. This will be demonstrated in the next section, where we look at pharmacological interventions that influence neurotransmitter levels.

4. Drug altered decisions Value-based decisions are affected by induced alterations of neurotransmitter levels in healthy subjects. Here, two levels of action are found. Active effects are the immediate effects of the drug, with an effect normally lasting for minutes to hours. Long-term effects are the changes to the neurotransmitter system after prolonged and/or repeated exposure to a drug, with a time span of weeks to months and years. As described previously, one side effect of dopamine agonist treatment in Parkinson’s disease include pathological gambling. Similarly, increases in dopamine levels in other subject groups have also been reported to alter decision-making behavior. For example, the psychoactive drug cocaine is known to block dopamine re-uptake, leading to a general increase in synaptic dopamine levels. Active effects of cocaine have been shown to affect decision-making behavior, including increased risk taking during game playing (Hall et al., 2000; Monterosso, Ehrman, Napier, O’Brien, & Childress, 2001; Stalnaker et al., 2007), and reckless behaviors (MacDonald et al., 2008). Furthermore, in a study by Sevy et al. (2006), healthy subjects were given a mixture containing branched-chain amino acids (BCAA), valine, isoleucine and leucine, or a placebo treatment. The BCAA treatment was found to decrease dopamine levels, and to impair performance on the IGT. Specifically, the authors suggest that the BCAA treatment impaired IGT performance through an increased attention to short-term outcomes, at the cost of more long-term learning of outcome contingencies. As the IGT is a decision-making test that relies on normal arousal functions, reduced dopamine levels may lead to impaired emotional decision-making characterized by shortsightedness and impulsivity. Contrary to this, based on the previously noted IGT behavior in depressed subjects, it is possible that the observed behavioral changes following dopamine antagonism can be explained by an increased sensitivity to losses. However, as depressed subjects demonstrated an actual improvement in IGT performance, these results are not directly comparable.

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In the same way, just as models of the role of serotonin may focus on chronic levels of serotonin, as seen in studies of mood disorders, it is also possible to study the effects of acute and induced changes in serotonin function. Studies of serotonin depletion have largely confirmed the studies of VBDM in anxiety and depression. For example, tryptophan depletion (TD) is a non-medical method for lowering the levels of serotonin through a diet with all amino acids except tryptophan. Studies using positron emission tomography (PET) have shown that TD leads to significant decrease in brain 5-HT2 receptor binding in various cortical regions including the insula (Yatham et al., 2001), but no changes in regional serotonin transporter binding potential (5-HTT BP), an index of 5-HTT density and affinity decreased plasma free tryptophan (Praschak-Rieder et al., 2005). Behaviorally, while leaving subjective mood ratings relatively intact, tryptophan depletion may alter emotional processing such as decreased responses to aversive stimuli, including reduced fear and anxiety responses (Harmer, Rogers, Tunbridge, Cowen, & Goodwin, 2003; Kähkänen et al., 2005; Munafò, Hayward, & Harmer, 2006; Roiser, Müller, Clark, & Sahakian, 2007). Given the role of the aversion circuit in the mediation of such responses and behaviors, one would expect the effect of tryptophan depletion to be involved in changes in this network. Indeed, Cools and colleagues (2005b) found that tryptophan depleted subjects showed stronger neural responses in traditional aversion structures such as the amygdala, and that these responses were related to self-reported threat sensitivity. Similar findings have been reported for other reinforcement learning paradigms (Evers et al., 2005; Finger et al., 2007; Rogers et al., 2003). A potent model of chronic serotonin depletion can be found in subjects using recreational drugs that, as a long-term consequence, reduce serotonin levels. Such serotonin level reductions occur through long-term exposure to serotonin agonists. Recreational drugs like 3,4-methylenedioxymethamphetamine (MDMA, commonly known as ecstasy) and hallucinogens (Hal) exert their main psychoactive effects through modulations of the serotonin (5-HT) neurotransmitter system, in particular via the 5HT2A receptor (Montoya, Sorrentino, Lukas, & Price, 2002; Morton, 2005; Parrott, 2001). Recently, we have reported a general in vivo decrease in 5-HT2A in MDMA/Hal users compared to non-users, where the strongest effects were found in the prefrontal cortex, medial temporal lobe (in particular hippocampus and amygdala) and putamen (Erritzøe et al., 2008). Studies of changes in the neural architecture following MDMA/Hal recreational drug use suggest large effects on regions involved in emotional processing. In a comparison of MDMA/Hal recreational users and non-users, we have recently reported (Ramsøy et al., 2009b) that MDMA/Hal users demonstrate significant grey matter volume reductions in the OfC and ACC, as well as reduced white matter the uncinate fasciculus, a structure implicated in emotional processing. In particular, we have also found that MDMA/Hal users show altered emotional responses, as reduced neural responses to aversive faces in the left amygdala and left rhinal cortex, and an increased response in the left medial OfC (Ramsøy et al., 2009a). MDMA/Hal users demonstrate altered decision-making behavior, in particular through a generally elevated behavioral impulsivity, leading to a poorer performance on the IGT (Quednow et al., 2007). This supports the link between serotonin levels and VBDM, in that lower levels of serotonin may lead to increased impulsivity, and higher levels may lead to decisions being more driven by fears and aversions.

5. Genes and rewards In very much the same way that both pathological states and induced neurotransmitter level changes can influence brain function and behavior, there are naturally occurring differences in neurotransmitter levels. As mentioned, imaging genetics is the study of how genetic differences lead to individual differences in the morphology and functions of the brain, and differences in behavior (see, as a first introduction, Canli et al., 2005; Hariri & Holmes, 2006; Hariri et al., 2006; Winterer, Hariri, Goldman, & Weinberger, 2005). Imaging genetics research includes structural and functional brain imaging tools as phenotypic assays to evaluate genetic variation. Put differently, the focus is on how genetic variations, in combination with life experience, lead to measurable changes in cognitive and affective processes, and eventually behavior (including decisionmaking). In practice, studies in imaging genetics combine genetic analysis, behavioral data and neuroimaging to assess how genetic differences influence brain function and behavior. As a principle, genes influence behavior indirectly, through the modulation of the molecular environment of neurons. The coding sequence of a gene leads to the synthesis or function of proteins, and this affects the operation of a cell. Such a protein may, for example, function as a membrane gateway for specific molecules, or may act as a transporter of synaptic neurotransmitters. In this way, genes can code for parts that play specific functions in a neuron. Consequently, individual differences in specific genes may lead to measurable differences in protein synthesis and, through this, to differences in how neurons respond. At larger scales, such changes might lead to differences in brain activations and, ultimately, overt behavior. Genetic variations – so-called polymorphisms – can be situated in genes encoding different proteins important for the neurotransmitter system, such as proteins involved in the synthesis, transport, post-synaptic uptake, pre-synaptic re-uptake and breakdown of the neurotransmitter. As different genes regulate the molecular biology of these processes, individual differences occur at any level of the neurotransmitter process. As described above, studies have linked increased dopamine levels and risk behavior in gambling, and several other studies have demonstrated a close relationship between dopamine levels and impulsivity (Cardinal, Winstanley, Robbins, & Everitt, 2004; Pattij & Vanderschuren, 2008; Takahashi, 2007; Volkow, Fowler, Wang, Baler, & Telang, 2009; Volkow, Fowler, Wang, Swanson, & Telang, 2007). This link would suggest that genetic variance in dopamine levels would influence risk willingness during gambles. Indeed, several studies now suggest this to be the case. Catechol-O-methyl transferase (COMT) is an enzyme that degrades catecholaminergic neurotransmitters such as dopamine, epinephrine, and norepinephrine

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(Meyer-Lindenberg et al., 2005; Meyer-Lindenberg et al., 2006; Strauss et al., 2004; Volavka, Bilder, & Nolan, 2004). This means that a higher operation level of COMT would lead to greater breakdown rates of dopamine, and thus generally lower levels of synaptic dopamine. A functional polymorphism in the COMT gene, the val158met variation, is the cause of a fourfold increase in enzyme activity (Tan et al., 2007). In particular, the low-activity met allele has been associated with improved working memory performance (Goldberg et al., 2003), while some val allele carriers display an advantageous increase in emotional resilience against anxiety and dysphoric mood (Enoch, White, Harris, Rohrbaugh, & Goldman, 2002; Zubieta et al., 2003). In a recent study, researchers tested subjects with different allelic COMT variations during an inter-temporal choice paradigm (Boettiger et al., 2007). By comparing subjects according to their genotype, the researchers found a positive correlation between the choice of immediate reward and the magnitude of BOLD (blood oxygen level dependent) fMRI (functional magnetic resonance imaging) signal during decision-making in the posterior parietal cortex (PPC), dorsal PfC, and rostral parahippocampal cortex, while the tendency to choose larger, delayed rewards was correlated with activation in lateral OFC. By dividing their subjects into their allelic types of the COMT gene, the researchers were able to look at differences in brain activation and behavior. It was found that the genotype at the val158met polymorphism of the COMT gene predicted both impulsive behavior and activity levels in the dorsal PfC and PPC during decision-making. In particular, this difference was driven by the homozygote val/val genotype compared to homozygote met/met and heterozygote val/met genotypes. That is, val/val subjects more often displayed increased levels of activity in dorsal PfC and PPC, and more often chose immediate rewards. In this sense, individual differences in choosing immediate or delayed rewards are shaped by each individual’s genetic make up. Recent evidence suggests that a synonymous polymorphism within the COMT gene (rs4818 C/G) accounts for a greater variation of COMT activity than the functional val158met polymorphism (Diatchenko et al., 2005). In a study of the C/G rs4818 variance, Roussos, Giakoumaki, Pavlakis, and Bitsios (2008) found that the C/G polymorphism produced strong and differential effects on PfC functions and behavior. The G/G allelic variance is associated with the highest levels of COMT activation, a >18-fold increase, and thus the lowest dopamine levels compared to the C/G and C/C types. By comparing subjects on two problem solving tasks – one relying on high arousal (the IGT) and one low arousal effects (Stockings of Cambridge) – the researchers found a clear dissociation between genotype on the two tasks. On the Stocking of Cambridge task, C/C individuals demonstrated the best performance, G/G the worst and with the C/G individuals as intermediate. However, during the IGT, G/G individuals performed best, while C/C individuals performed the worst and C/G at intermediate levels. This suggests that prefrontal dopamine levels influence task performance differently, and that low levels are disadvantageous for planning in non-arousing problem solving but optimal in arousal based decision-making. Contrary, high prefrontal dopamine levels may be advantageous for non-arousing problem solving, but disadvantageous for problems with high arousal. To sum up, genetic differences in the COMT system produce differences in the availability of dopamine in the brain, and through this variance influences decision-making. Other genetic polymorphisms influence the dopaminergic system, and have been identified as likely contributors to differences in decision-making aspects such as impulsivity (Kreek, Nielsen, Butelman, & LaForge, 2005). Hence, the reward seeking aspect of decision-making under risk (such as gambles) seems to be influenced by dopamine levels. High levels of dopamine may be related to more risky gambles, and poor performance on non-emotional tasks, but may show benefits on other tasks that involve arousal, such as the IGT.

6. Genes and aversion The serotonergic system is also influenced by individual genetic differences at all stages (Hariri et al., 2006). For example, humans show a common variation of a promoter region of the serotonin transporter gene called 5-HTTLPR: a short (s) and a long (l) version. People who have two copies of the long genetic sequence (homozygote l/l) in this region are thought to have less synaptic serotonin, due to a higher pre-synaptic re-uptake of the neurotransmitter. Conversely, people with two copies of the s allele (homozygote s/s) show a lower re-uptake and therefore have higher synaptic serotonin levels. Thus, emotional responses in a population may show individual variance according to 5-HTTLPR types. More specifically, the homozygote s/s allele version is associated with increased activations of structures innervated by the serotonergic system, including the amygdala, lateral OfC, insula, and hypothalamus. In an fMRI study Hariri et al. (2002) compared the activation of the amygdala in the 5-HTTLPR allelic groups during an emotion task (matching of facial expressions compared to matching of geometric figures). This task is well known to produce a robust activation of the amygdala (LeDoux, 1993; LeDoux, 2003; Vuilleumier et al., 2002). By comparing the aversion response in s/s and l/l subjects, the researchers found that amygdala activation differed according to genotype. The s/s group showed a significantly higher signal increase in the amygdala during the emotion task than the l/l group. In other words, the level of amygdala activation was influenced by genotype, through a variation in the availability of synaptic serotonin. The results imply that the higher concentrations of serotonin found in the s/s group lead to higher engagement of the amygdala, and that this elevated activation produces a heightened emotional response to aversive stimuli. In behavioral terms, it may be contended that subjects differ in the decision-making process to aversive stimuli – e.g., aversion for loss or risk – in a way that is influenced by their genetic make up. The question thus arises whether genetic polymorphism of the serotonin system influences decision-making behaviors. Following the studies of how mood disorders, pharmacological challenges and genetic differences affects emotional

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responses, one would certainly expect that genetically induced differences in serotonin levels may lead to measurable changes in VBDM. As demonstrated previously, studies of pharmacological challenges provide strong indications that a major role of serotonin is in determining sensitivity to reinforcement information (Cools et al., 2005a; Evers et al., 2005; Finger et al., 2007; Rogers et al., 2003) including social reinforcement information (Harmer et al., 2003; Marsh et al., 2006). Thus, it would be expected that similar effects should be found for differences in serotonin function that are genetically mediated. In a study of the effects the of 5-HTTLPR genotype, it was found that genotype did indeed modulate long-term decision-making in humans and rodent, including complete and heterozygous knock-out models (Homberg, van den Bos, den Heijer, Suer, & Cuppen, 2008). Using the IGT, the researchers found that subjects homozygous for the short allele of the 5-HTTLPR chose more disadvantageously than subjects homozygous for the long allele. In rodents, similar trends were found in a comparable decision-making task, suggesting that genetically mediated levels of SERT impact decision-making. In particular, the researchers found that the genotype effects could be explained by differences in the responsiveness to outcomes, and how these ultimately guided subsequent choices. This suggests that decisions altered through SERT manipulation occur through changes in emotional responses as well as top–down modulation of emotional responses, such as PfC-amygdala functional connectivity. Indeed, this was recently observed in an fMRI study comparing subjects homozygous for the short vs. long allele of the 5-HTTLPR gene (Roiser et al., 2009), in which subjects were tested on a gambling task that was either framed in terms of gains or losses. Short allele homozygotes demonstrated a stronger framing effect, and demonstrated a higher amygdala response while making choices in accord with the frame. Furthermore, while long allele homozygotes showed a stronger PfC-amygdala coupling for choices countering the frame, no such effect was found in the short allele homozygotes. This suggests that genetic variation in the 5-HTTLPR is responsible for differences in the effect of emotion and emotion regulation on value-based decisions. It should also be noted that this finding, demonstrating a role for the aversion circuit in decision-making, conflicts with the recent claim that VBDM does not involve primary emotional regions such as the amygdala (Tom et al., 2007). However, these studies differ with respect to the aforementioned distinctions between different kinds of utilities in the decision-making process. While the IGT typically conflates many utility forms such as expected utility, decision utility and experienced utility, the Tom et al. study focused particularly on decision utility. Thus, conflicting results may only be so in a superficial way and as a result of differences of measures. Building on the findings from pharmacological challenges, Blair et al. (2008) studied the effects of tryptophan depletion on subjects with allelic variations in the 5-HTTLPR genotype. Studies of the effects of tryptophan depletion have reported considerable variation in individual responses (Booij, Van der Does, & Riedel, 2003), and a genetic factor in the mental and behavioral responses to tryptophan depletion has been reported earlier (Neumeister et al., 2002). It was therefore assumed that a variation in responsiveness to tryptophan depletion on a decision-making task would be influenced by 5HTTLPR genotype. Indeed, tryptophan depletion had a disproportionate effect on the performance in subjects homozygous for the long allelic version, through modulating the response to punishment information. Here, subjects with the long allelic genotype demonstrated significant difficulties in their ability to learn to avoid responding to stimuli that was associated with punishment. In addition to improving our understanding of individual variance in VBDM, and the individual effects of serotonin alteration, this study also questions the claim that such decisions are not related to emotional structures such as the amygdala (Tom et al., 2007).

7. How genes make up your mind Understanding the brain basis of value-based decisions has the potential to improve our ability to grasp, and model, the underlying mechanisms of our behaviors. The current view on the relationship between neurotransmitters and decisionmaking has been heavily influenced by studies of pathology and how large changes in neurotransmitter levels (e.g. as seen in drug abuse) can make decision-making behavior go awry. Such effects may influence our general view of neurotransmitters in VBDM and our understanding of psychopathology, but shed little light on behavior in healthy individuals. Contrary to this, imaging genetics may provide an improvement in our understanding of VBDM in healthy individuals. In particular, imaging genetics studies suggest that individual differences are not randomly distributed, but may follow systematic and qualitative differences in how decisions are made. Before such progress can be made, there is a need for disentangling the different ways in which VBDM are studied and discussed. In particular, the debate on whether positive and negative emotions are separately organized in the brain, and whether they make different contributions to value-based decision-making, need to distinguish between different stages of the decision-making process. Here, there is a need to determine whether the effects of neurotransmitter variations affect value-based decisions globally, or during specific phases of the process. Both scenarios are equally likely: in the case of tryptophan depletion, it is possible that lower serotonin levels affect emotional responses equally during computations of expected utility, decision utility and experienced utility stages. Alternatively, serotonin depletion may affect selected stages of the process, such as experienced utility, without affecting other stages. There is also a different take on the distinction between global and specific effects. In MDMA/Hal users, we have reported both a global decrease in serotonin binding, as well as regional differences in such effects, such as the amygdala. The relative effects of local and global neural effects on emotional responses and decision-making are still unclear. Consequently, this warrants further studies. The insights that imaging genetics affects emotions, value generation and decision-making have two primary effects. First, imaging genetics studies suggest that individual differences are not random or continuous, but systematic and

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discontinuous. This further indicates that an agent – in economics often referred to as a representative agent – cannot be typified or standardized. Thus, while many economic models apply a standardized agent as a model, results from imaging genetics suggest that this view is unwarranted. Instead, a view encompassing individual differences should be applied. This is not to suggest that neuroeconomic models should consider all possible genetic factors and their behavioral effects. This will probably make such models far too complex and particular. Rather, genetic variance may suggest that economic models should contribute to the identification and interpretation of endophenotypes of VBDM. The term endophenotype originally stems from biology (Gottesman & Gould, 2003), and can be understood as the search to elucidate genetic associations with phenotypes (behavioral types) of interest. In this paper we have provided examples of such endophenotypes, through demonstrating how genetic effects in serotonergic and dopaminergic systems can lead to differences in neural processes and behavioral outcomes. Furthermore, as demonstrated in studies on the difference in the COMT gene, the rs4818 C/G polymorphism accounts for significantly more than the val158met polymorphism in the effect of emotion in decision-making. Through the discovery and comparison of single gene effects, neuroeconomic research may gain insights into factors that make significant and systematic impact on behavior, and ultimately improve neuroeconomic models of VBDM. A second main impact of imaging genetics is an improved understanding of the underlying neurobiological mechanisms in VBDM. By gaining insights into the factors regulating neural functions, regional brain responses and how they affect behavior, our general understanding of the brain basis of VBDM will improve significantly. Such insights holds the promise both to improve our theoretical understanding of the mechanisms underlying decision behaviors, and to influence how we assess and treat disorders of decision-making. In terms of the debate on the nature of the brain’s valuation system, data from studies investigating the pharmacological and genetic influence on reward processing and decision-making behavior may help us to rethink the models based on neuroimaging and patient studies. These models have tended to focus on large-scale anatomical structures due to the nature of their data. Hence, the ‘‘separatists” have marshaled evidence of how certain structures appear to be implicated in approach behavior (ventral striatum and medial OfC, for instance) while others seem to subserve avoidance behavior (e.g., amygdala and insula). In contrast, the ‘‘unitarists” focus on evidence suggesting that activity in these structures underlie both approach and avoidance behavior. However, anatomical structures contain a number of distinguishable cell groups associated with different functions, and an increased understanding of their molecular nature – as evidenced by pharmacological or genetic modulations – can help illuminate this more complicated picture. For instance, in animal models it is possible to show how individual cell groups respond to neurotransmitter manipulation through the use of microinjections of agonists or antagonists. For instance, Berridge and colleagues have shown that injection of the mu-opioid agonist DAMGO into the nucleus accumbens (in the VS), leading to an increase in liking reactions to sucrose, sensitizes the cells in the rostrodorsal quadrant of its medial shell, but not cells in other parts of this structure (Peciña & Berridge, 2005). Such results suggest that the molecular ‘‘action” going on in the structures presently identified as part of the brain’s valuation system is much more complex than presently understood. In order to tease out its precise computational details we need to include molecular manipulations in future decision-making studies. 8. Potential research questions There are still many unanswered questions in the role of gene-driven variations in neurotransmitter functions, and their impact on emotions and value-based decisions. In particular, we suggest a few specific research questions that should be explored in the near future. 8.1. What is the interplay between genetic and experience based factors in VBDM behavior? The relative influence of heritage and learning – also called the ‘‘nature–nurture debate” – has been a conundrum for anyone with a keen interest in the relationship between brain, mind and behavior. Historically, the debate has certainly been influenced by the dichotomization between inborn and learned factors: is our behavior determined by our genetic make up, or are we solely determined by our personal learning history? As such dichotomies have softened up during the past decade, and are replaced by a realization that there is no such mutually exclusion, many new and interesting discoveries have been made. More recent studies have now uncovered the relative role of genes and learning in areas such as intelligence (e.g. van der Sluis, Willemsen, de Geus, Boomsma, & Posthuma 2008) and personality (e.g. Meyer-Lindenberg et al. 2006). For example, in the study of personality development, Caspi et al. (2002) published a highly influential article on gene–environment interaction in personality development. Here, they reported that maltreated children would differ in the development of antisocial personality and violent behavior depending upon whether their genotype conferred high or low levels of MAOA expression, a neurotransmitter-metabolizing enzyme. Thus, Caspi et al. showed that a genetic variation may moderate the influence of environmental factors on behavior in a rather dramatic manner. In the study, children with a low-level MAOA genotype only developed an antisocial personality if maltreated (if a child was not maltreated, a low-level MAOA polymorphism did not cause antisocial behavior). At the same time, maltreatment did not affect children with a high-level MAOA polymorphism. Thus, neither genotype nor maltreatment were sufficient causes of antisocial behavior. Only the interaction between genetic and environmental factors was sufficient to produce antisocial behaviors.

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Following this approach, there is a need for studies in VBDM research that focus on the role of genes, environment and gene–environment interaction on emotions, valuation and decision-making processes. In other words, what are the gene– environment interactions that lead to differences in valuation processes and decision-making behavior, such as whether a person becomes risk averse or risk seeking, or how pathological gambling develops? 8.2. What is the role of gene–gene interactions on VBDM behavior? As hinted throughout in this text, there are numerous genetic influences on the function of a single neurotransmitter. As noted earlier, variations in the COMT gene (rs4818 C/G) accounts for a greater variation of COMT activity than the functional val158met polymorphism, and is thus expected to have a stronger impact on COMT-related behavioral effects that the val158met polymorphism. However, one can also assume that there may be additive or subtractive effects of having one particular rs4818 C/G variant, combined with one particular val158met variant. Furthermore, the interactive genetic effects on serotonin and dopamine functions (and other neurotransmitters) need to be explored with respect to VBDM behaviors. Indeed, there is an emerging literature on these interactions (De Luca, Tharmalingam, Sicard, & Kennedy, 2005; Doornbos et al., 2009; Tang et al., 2009), and such approaches hold the promise of providing further understanding of the relative role of genetic effects on emotions, valuation and decision-making. Following this strategy, studying gene–gene interaction may provide the means to better understand the relative roles of neurotransmitter levels on VBDM functions. 8.3. Gene/neurotransmitter effects and consciousness? Decision-making is most often thought of as a conscious and explicit process, but several studies have demonstrated that consciousness during decision-making is limited, and that unconscious processes may have a significant behavioral impact (Rey, Goldstein, & Perruchet, 2009; Soon, Brass, Heinze, & Haynes, 2008; Suhler & Churchland, 2009). Differences in neurotransmitter levels, either stemming from genetic or induced effects, may operate on either a conscious or an unconscious level. Put differently, increasing the level of serotonin may make a person more loss averse at the behavioral level without the person noticing this change on a subjective level. Conversely, as dopamine has been tied to the expectation phase of value-based decision-making, it is possible that natural or induced alterations in dopamine levels may influence subjective expectations without changing overall decision behavior. 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