Real choices are binding consequential commitments to a course of action, like undergoing surgery or putting a down payment on a house. Researchers in all social sciences seek to understand how real choices are made. However, in studying decisions, scientists and policy makers often have to settle for measuring hypothetical statements about what people would choose, rather than observing what people actually do choose.

In marketing research, for example, hypothetical surveys are used to forecast sales of existing products, to test new products by asking consumers what they would buy, and to evaluate promotions (Silk and Urban, 1978; Urban et al., 1983; Infosino, 1986; Jamieson and Bass, 1989; Green and Srinivasan, 1990; Chandon et al., 2004; Raghubir and Greenleaf, 2006; Schlosser et al., 2006). In public economics and political science, survey data are used to establish the dollar value of goods that are not traded in markets (such as clean air or the prevention of oil spills), and to poll likely voters before an election (Crespi, 1989; Diamond and Hausman, 1994; Carson et al., 1996; Mortimer and Segal, 2008). Hypothetical choices are also necessary in some types of psychology and neuroscience experiments in which measuring real choices is impractical or unethical, especially in the domain of distressing moral choices (Greene et al., 2001, 2004; Kühberger et al., 2002; Hariri et al., 2006; Monterosso et al., 2007).

The reliance on hypothetical choice data presumes either that hypothetical choices are a good and legitimate way to forecast real choices, or that there is some knowable relationship between the two types of choices, such that the hypothetical data can be adjusted to forecast real choice data accurately.

However, many studies in behavioral economics have shown a substantial, systematic gap: typically, hypothetical valuations are greater than real valuations (Cummings et al., 1995; Johannesson et al., 1998; List and Gallet, 2001; Ariely and Wertenbroch, 2002; Little and Berrens, 2004; Murphy et al., 2005; Blumenschein et al., 2007; Tanner and Carlson, 2009). To remedy this typical upward “Yes” bias, in marketing research conjoint analysis using hypothetical preference data has been extended and improved through hybrid incentive-aligned methods in which an inferred choice will be implemented for real (Ding et al., 2005; Ding, 2007; Dong et al., 2010). In the moral domain, FeldmanHall et al. (2012a,b) find that subjects say they will sacrifice more money to spare others' from mild electrical shocks than they actually do when the shocks are real.

However, most of these studies comparing hypothetical and real choices used appetitive goods, that is, goods to which people assign a positive value. Our paper is only the second to compare hypothetical and real economic valuations of aversive “bads” (following FeldmanHall et al., 2012b). We do so using functional magnetic resonance imaging (fMRI). The main goal is to see whether there is distinct neural valuation during hypothetical and real choices about aversive bads, with the hope that knowing where those valuations are encoded would lead to new predictions.

In our choice paradigm, subjects choose how much they would pay to avoid having to eat a food that most people find unpleasant (such as pigs' feet, canned oyster, or a large dollop of spicy wasabi; see Table A1 and Plassmann et al., 2010). Eating these foods is, of course, not as dramatic as some naturally-occurring bads that consumers must spend money or effort to avoid, such as regular colonoscopy screenings or protecting against identity theft. However, the advantage of using bad foods is that consenting subjects can actually make these unpleasant real choices in a lab environment. That is, at the end of the experiment they actually eat one food if they don't pay enough to avoid it. Having real choices is crucial, of course, for the comparative study of real and hypothetical choices. It is expected that initial clues from fMRI during unpleasant decisions about bad foods will provide some guidance regarding the neural valuation of more dramatic and unpleasant aversive experiences.

Hypothetical measures are often used to judge the value of aversive bads. One category of bads is environmental damage (Carson et al., 2003; Loureiro et al., 2009; von Stackelberg and Hammitt, 2009; Martin-Ortega et al., 2011). Another category includes a public good that benefits society but harms a host location, such as locating a prison or a toxic waste dump. In studies of medical decision making, patients are often asked to choose between hypothetical medical treatments that could involve serious side effects (Silvestri et al., 1998; Levy and Baron, 2005), or to express valuations of those procedures in numerical terms such as quality-adjusted life years (QALYs) (Zeckhauser and Shepard, 1976). In all these cases, it is difficult or impossible to compare hypothetical choices with real ones.

Why are aversive choices interesting? Many everyday choices require paying—money, as well as effort—to avoid unpleasant and harmful events. Consider insurance. The purchase of a car or earthquake insurance policy, an AppleCare service package, or an alarm system does not have any appetitive value, per se; instead, it is a payment to avoid future aversive events (similar to paying to avoid unpleasant food). Similarly, going to the doctor or dentist, taking medicines with side effects, and dieting and exercise, are (typically) aversive choices to prevent even worse future outcomes. Political campaigns also use marketing tactics, to persuade voters to accept aversive tradeoffs (such as raising taxes to eliminate California's deficit, or cutting pensions in Greece). Finally, many household purchases might be appetitive for one household member but aversive for others (e.g., one spouse suffering through a summer action movie, or a teenager dragged to a bed-and-breakfast with her parents). If the unfortunate spouse or teenager misforecasts how aversive the activity will really be, during hypothetical planning, then our study could shed some light on how to market such mixed-valence family activities. Our results would have most direct application in situations where decisions are made to avoid viscerally unpleasant outcomes like pain. It remains to be tested whether they can generalize to less visceral decisions like purchasing an extended warranty. We hope that the present study inspires future research that extends to less visceral domains of aversive choice.

Using fMRI in our study establishes tentative findings about whether there are differences in neural circuitry in making hypothetical and real choices involving aversive bads. Our data extend the two previous fMRI studies on this topic, which used appetitive consumer goods (Kang et al., 2011) and distressing moral choice (FeldmanHall et al., 2012b). Our paper also adds to emerging literature on consumer neuroscience (Knutson et al., 2007; Plassmann et al., 2011, 2012; Yoon et al., 2012).

Behaviorally, comparing bads and goods could also illuminate the general mechanisms which create differences in hypothetical and real choices. Note that evaluating goods requires a comparison of the utility from a positively-valued appetitive good with an aversive payment of money. A hypothetical bias could result from either overvaluation of appetitive goods, or undervaluation of the distaste of paying money, or both, during hypothetical choice. Studying only appetitive goods cannot discriminate which type of biased evaluation is occurring.

For aversive bads, overly positive hypothetical evaluation (Tanner and Carlson, 2009) leads to underestimation of two different kinds of disutility—disutility from eating aversive foods and disutility from paying money. If both are underestimated during hypothetical choice, it is unclear which effect is likely to be more dominant a priori, so the difference in hypothetical and real choices is unclear. In fact, there are three possible hypotheses about which effect could predominate and what the sign of the hypothetical-real bias will be.

First, suppose that in hypothetical choice, there is a general underestimation of how bad spending money is (as compared to real payment), and further, that this underestimation is more substantial than the error in the predicted disutility from the consumption of bad food. Then in the aversive bads domain that we study, real willingness-to-pay (WTP) will be lower in magnitude (i.e., closer to zero payment) than hypothetical WTP. This result would unify the findings for appetitive goods and aversive goods; both could then be explained by an insufficient appreciation, in hypothetical situations, for the distasteful spending of money in real choices—the “pain of paying” (Prelec and Loewenstein, 1998). According to this hypothesis 1, dollar values are inflated in hypothetical choice and are deflated toward zero when choices are real (e.g., paying $100 is not too painful when it's not real spending, be it for goods or bads).

Second, an alternative and more plausible hypothesis 2 is that in hypothetical choices, people will underestimate the aversive experience of eating bad foods to a greater extent than they may underestimate the pain of paying. Consider the extreme example of eating a monkey brain (as in the movie “Indiana Jones”) for real. For many people, this would cause an immediate visceral response (e.g., nausea, feeling of disgust). And to be clear, disgust is indeed a visceral, as in physiological, response. For example, Harrison et al. (2010) found that ratings of disgust after watching repulsive food videos led to stronger gastric stomach responses and neural activity in the insula and thalamus.

In contrast, losing an abstract secondary reward, such as money, is likely to elicit less visceral and more cognitive “pain”.¹ In hypothetical choices that are purely cognitive and have no binding consequences, the brain might make rapid and effortless decisions without fully taking any visceral factors into account. However, in real choices, visceral factors such as disgust are likely to be weighed more heavily, especially for food choices.

The reasoning laid out above is consistent with “hot-cold empathy gaps” (Kühberger et al., 2002). According to Loewenstein (1996, 2005), when making a decision, people underestimate or ignore the effect of visceral factors (generally aversive) such as thirst, fear, and craving for tobacco that are not currently experienced. More specifically, when people are in an affective cold state (e.g., not experiencing thirst or craving at the moment), they do not accurately estimate how much such visceral states (hot states) will change their preference and behavior, hence the term “hot-cold empathy gap.” For example, smokers who were not having a craving for a cigarette underestimated how much they would value a cigarette when they were later in a high craving state (Sayette et al., 2008). A similar gap has been shown for thirst and for embarrassing public performance (e.g., miming) (Van Boven and Loewenstein, 2003; Loewenstein, 2005; Van Boven et al., 2005), in evaluations of impulsivity of others and social pain (Nordgren et al., 2007, 2011), and in heroin addicts (Badger et al., 2007).

A bigger hot-cold empathy gap for food disgust than for money payment implies the opposite of the typical hypothetical > real bias for goods. For real bad-food choices, the aversion to eating a bad food will be strongly adjusted upward and the aversion to paying money will be adjusted upward, by a smaller amount. Real valuations will then be higher than hypothetical valuations (i.e., real WTP > hypothetical WTP), reversing the typical hypothetical bias.

As far as neural activity, we expect that during real choice there will be stronger neural activations in affective areas implicated in disgust and fear processing, such as the insula and amygdala (Whalen, 1998; Craig, 2002, 2009). That is, in this account stronger affective reactions during real choice cause higher bids to avoid eating the foods which are affectively unpleasant. This is an important step because no previous study of hot-cold empathy gaps has compared biological activity in hot and cold states, and shown direct neural evidence consistent with an affective (empathic, or emotional) difference. In addition, based on findings in (Plassmann et al., 2010) we expect higher neural activity in valuation regions such as medial OFC and ventral striatum, when bids are lower. Since these are bids to avoid an unpleasant experience, bidding low means the experience has less negative value.

Note that alternatively, it is possible that affective reactions are only prominent in the real choice when subjects bid a low amount, so they have a high expectation of having to bid the food. That is, in this alternative account, low bids cause stronger affective reactions (rather than the other way around, as in the previous paragraph).

The third and last hypothesis is that the aversion to bad food and the pain of paying are equally underestimated (or not underestimated at all). In this scenario, it is predicted that there will be no significant behavioral difference between real and hypothetical valuations, and fewer neural differences in the two conditions.

The elements of these hypotheses can be mathematically summarized as follows. Let w be an individual's WTP to avoid bad food, x pain of paying, and y disutility from consuming bad food. And let (w_r, x_r, y_r) and (w_h, x_h, y_h) denote the levels of w, x, y in the real and hypothetical conditions, respectively. Suppose that w, x, and y have a functional relationship, w = f(x, y).

Generally, a person will be less willing to pay to avoid aversive bads if he feels more pain of paying. Likewise, all other things being equal, he will be more willing to pay to avoid bad food if the disutility from consuming it increases (e.g., he feels more strongly disgusted by it). These relationships can be mathematically expressed as: (1)∂f∂x<0, ∂f∂y>0 Typical consumers are overly optimistic (Weinstein, 1980, 1987, 1989; Tanner and Carlson, 2009) and are thus expected, in hypothetical situations, to misforecast (i.e., underestimate) the real pain of paying as well as the real badness of consuming bad food. This can be expressed as: (2)dx≡xh−xr<0   dy≡yh−yr<0 where dx and dy denote the changes in x and y between the hypothetical and real conditions.

Given the above formalism, the change in hypothetical and real WTP, denoted dw, can be written as: Note the signs of the terms on the right-hand side from Equations (1, 2); the sign of dw (e.g., the relative size of hypothetical and real WTP) is determined by the relative size of the terms on the right as summarized in Table 1.

The first results compare hypothetical and real choices, and associated response times (RTs) which provide clues about information processing. The second set of results address differences in neural activity established using fMRI, in both hypothetical and real valuations.

This study is the first to compare the willingness-to-pay to avoid aversive consumption outcomes (unpleasant foods), in the two conditions of non-binding hypothetical decision and binding real decision. Previous studies with appetitive stimuli typically find that hypothetical valuations are higher than real valuations. We find the opposite result. Binding, real bids to avoid eating unpleasant foods were larger than hypothetical bids. The within-subject design provides good statistical power to show that the real > hypothetical bias is highly significant across both foods and subjects.

Before proceeding, it is useful at this point to squarely address potential threats to the validity of our scientific inference from the deliberate design choice to elicit both hypothetical and real valuations of the same foods in a fixed order (i.e., two exposures per food, hypothetical then real). We fixed this order out of concern that eliciting real bids first would lead to a mental state for the second hypothetical block fundamentally different than that in most lifelike situations where hypothetical judgments are made.

Many studies have presented the same stimuli multiple times (e.g., Fitzgerald et al., 2009; Hare et al., 2009), and found consistent signals. The biggest threat to validity when stimuli are judged repeatedly is that the repeated judgments are artificially consistent. However, any such effect could lower the capacity of the design to detect a highly significant hypothetical < real difference in willingness-to-pay; and yet, we do find such a difference. (Therefore, it is likely that a between-subjects design could show a much larger difference, both neurally and behaviorally; FeldmanHall et al., 2012b used a between-subjects design and found striking differences.) Furthermore, the biggest concern with neural activity would be possible habituation of a neural signal. If there was habituation of neural activity to the foods over time, we would expect the neural activity to diminish to the baseline activity or (perhaps) be less value-sensitive for the real trials that come after the hypothetical ones. However, we found just the opposite, that is, stronger sensitivity of neural activity to the value, which is not consistent with the possibility of habituation. And behavioral experiments using appetitive goods by Kang et al. (2011) showed that there were real vs. hypothetical differences in both possible treatment orders. Thus, we argue that the potential risks from the within-subject design with fixed treatment order are not strongly evident in the data. In addition, there are many statistical benefits of repeating the same stimuli in the hypothetical and real conditions. Doing so controls for nuisance variables such as physical and psychological aspects of stimuli (e.g., color, shape, experience, memory) that might be correlated with stimulus value, but are not involved in the valuation of aversive stimuli per se.

Returning to the scientific contributions of the current study, the combination of results herein with earlier results from Kang et al. (2011) (and many earlier behavioral studies showing positive hypothetical bias) rejects the hypothesis that real dollar valuations are lower in general, i.e., for both appetitive goods and aversive bads.

While this conclusion is tentative, it is important because a popular theory about hypothetical bias is that people underestimate the value of money when expressing hypothetical values. An example is the influential report (Arrow et al., 1993) by a panel of academic economists on how to best elicit and use “contingent valuation” survey measures to establish reasonable prices for non-market-traded goods and services (such as clean air). They specifically “emphasize[d] the urgency of studying the sensitivity of willingness to pay responses to… reminders of other things on which respondents could spend their money.” The panel's conclusion followed from a conjecture that opportunity cost reminders would lower hypothetical responses because disutility from monetary payment is underestimated in hypothetical choice, so that reminders would lead to better approximate real values (as shown, in fact, by Knoepfle et al., 2009).

Our results do not support the idea of a general strong devaluation of money during hypothetical valuation (for both appetitive and aversive objects). Instead, the results lend tentative support to a different hypothesis (hypothesis 2) mentioned in the introduction: aversive experience of visceral factors (i.e., disgust) is more strongly underestimated in hypothetical choice than aversive experience of more cognitive factors (i.e., paying money) because people in an affectively “cold” state easily fail to appreciate the influence of a “hot” visceral factor that is not currently experienced upon their preference and behavior (Loewenstein, 1996, 2000, 2005). We call this the “visceral response underestimation” hypothesis.

In valuing appetitive goods hypothetically, “overly optimistic consumers” (Tanner and Carlson, 2009) would probably overestimate benefit from consumptions of goods and to underestimate the pain of paying, leading to hypothetical values that are too high. In valuing aversive goods hypothetically, disutilities from both of spending money and eating unpleasant foods are generally expected to be underestimated. However, disgust of eating unpleasant foods is likely to be stronger in real choice if there is a tendency to more strongly underestimate the influence of the more visceral factor. Our behavioral finding (higher WTP in real choice) is consistent with this account.

The brain imaging results reported also support this account. Stronger encoding of “better” valuation (i.e., lower bids to avoid less aversive foods) during real choice is found in cortical regions that are well-established to encode value (vmPFC, ACC, and VStr). Notably, Plassmann et al. (2007, 2010) find that the vmPFC encodes both increased value for appetitive goods, and decreased distaste (a positive improvement) for aversive goods. Tom et al. (2007) found a similar common encoding in the VStr and vmPFC for both increased potential money gains and decreased money losses.

Most importantly, we find more value-sensitive activity during real choice in the insula, amygdala, and hippocampus. The insula is thought to encode general emotional and visceral discomfort (Craig, 2002, 2009), ingestive disgust (Harrison et al., 2010), risk (Preuschoff et al., 2008; Mohr et al., 2010), and empathy for pain (Singer et al., 2004, 2009; Bernhardt and Singer, 2013). The amygdala responds rapidly to impending threat, creating neural vigilance (among other functions) (Adolphs et al., 1998; Whalen, 1998). The visceral response underestimation hypothesis is consistent with these functional attributions, assuming that anticipation of actually eating unpleasant foods is viscerally uncomfortable or threatening, as compared to merely imagining so as in hypothetical choice.

Keep in mind that insula and amygdala activity is stronger for low-bid (i.e., less aversive) bad foods when choices are real. This direction is also consistent with Plassman et al.'s (2010) finding, that parahippocampal and insula activity is higher response to lower bids to avoid unpleasant foods. Nonetheless, we would not have been surprised by the opposite pattern—i.e., a stronger emotional reaction to the worst foods that people would pay the most to avoid eating. Given our somewhat surprising result, we offer a speculative reverse inference about why the least-bad foods generated the most insula and amygdala activity in real choice (compared to hypothetical). The signal changes displayed in Figure 4B seem to offer a clue. Note that in hypothetical choice, bads which receive higher bids to avoid have higher amygdala activity. Thus, in this “baseline” condition worse foods generate more amygdala reaction, as one might expect if amygdala is reflecting vigilance or negative emotion. However, when the real condition takes place, subjects now think about the prospect of actually eating the foods. If they bid high they won't have to eat the food, and the amygdala reaction to the worst (high-bid) foods is actually much lower than in the hypothetical condition. Oppositely, the least bad (low-bid) foods are ones they are likely to eat—but in the real condition only—since they bid low. We speculate that the heightened amygdala activity encodes a reaction not to the stimulus per se, but to the expected displeasure of having to worry about “really” eating it. That is, part of the switch from hypothetical to real evaluation is a switch from a subjective emotional reaction to the food itself (in the hypothetical case), to a reaction to the prospect of whether the food will have to truly be eaten (in the real, low-bid case).

A way to test this hypothesis is to immediately display the outcome of the BDM mechanism that determines whether the food will be eaten or not, given their bid. If a person bids 3, for instance, but the BDM draw if 4, then they did not bid enough and will have to eat the food. If our reverse inference is correct, then when they are surprised at having to eat the food, even after bidding so high, we will see a strong aversive signal in amygdala (similar, perhaps, to the hypothetical-condition signal.

This fMRI evidence of differential insula and amygdala activity is new evidence of a biological encoding of a hot (real choice)-cold (hypothetical choice) empathy gap, in the brain. FeldmanHall et al. (2012b) also find more activity in amygdala during real moral choice, compared to imagined hypothetical choice, and in temporal-parietal junction (TPJ) and right insula. The convergent evidence of stronger activity in amygdala and insula in both studies, in different domains of unpleasant choices (food and pain administration) is encouraging, and invites further studies to establish further robustness.

Speculation about visceral response underestimation also suggests some potential ways to “de-bias” hypothetical choices for future research. For example, it is known that the amygdala responds to fearful or threatening stimuli such as a fearful face (and even fearful eye whites only) (Whalen et al., 1998, 2004) and electric shocks (Phelps et al., 2001). Insula also responds reliably to exogeneous stimuli that are unpleasant. Hence, one future direction is to manipulate amygdala or insula activity by using such stimuli during hypothetical choice. The idea is that stimulation of such regions by external stimuli might “fool” the neural circuitry into making judgments as if it is in a hot state (as in classic arousal misattribution, e.g., Dutton and Aron, 1974). Inducing an artificial hot state could produce hypothetical choices that are better forecasts of actual real choices. Another direction is to tap visceral urges during hypothetical valuation by having subjects inspect and smell real aversive food items, facilitating them to more easily integrate visceral factors into their hypothetical valuations. A third direction is to manipulate the degree of imagination exerted in the hypothetical case [an approach often used in economic surveys and also in FeldmanHall et al. (2012a)] and see how such treatments alter brain activity.

Note that in our study hypothetical bids are (significantly different but) highly correlated with real bids, and that both types of bids are also significantly correlated with disgust ratings. However, despite such high correlation between the two behavioral measures, there is much less neural activity during hypothetical valuation than real valuation (Figures A3, A4). For many purposes, knowing that real and hypothetical behavior covaries strongly across stimuli is good enough to justify relying on hypothetical choice as a guide (especially when real choices are unethical, expensive, or otherwise problematic). However, the weak neural response to bids in the hypothetical condition suggests that researchers in social and consumer neuroscience who want to understand real choices neurally could find that purely hypothetical contexts generate weaker signals, and that making choices real will often add statistical power (and fidelity). There has been growing academic interest in applying machine learning techniques to neuroimaging data in order to predict purchase choices, using either a real choice or a hypothetical choice paradigm (Grosenick et al., 2008; Tusche et al., 2010; Smith et al., 2012). Although such effort has been somewhat successful (in terms of accuracy rates), the findings of the current study suggests that predictive models estimated using hypothetical data are limited in how well they can accurately predict real purchase choice by how different the underlying neural circuitries are.

Finally, we note that many data about how people evaluate aversive experiences are inherently hypothetical, rather than real, particularly if the data are collected to pre-value or anticipate potential averse events. An important domain is medical decision making. Patients often read hypothetical scenarios regarding different stages of a disease and treatment toxicity in order to make decisions about treatments (e.g., with end-stage cancer, choice between chemotherapy that could extend life by 4 months with severe side effects versus supportive care that could only alleviate symptoms) (O'Connor et al., 1987; Malenka et al., 1993; Yellen et al., 1994; Silvestri et al., 1998; Gurmankin et al., 2002; Levy and Baron, 2005). Further, a physician, who is giving medical recommendations, may need to put herself in the hypothetical situation of being in her patients' minds (particularly for a minor or an incapacitated patient). Cancer patients often say that they would not want to receive grueling cancer treatments (e.g., chemotherapy) but then change their minds when they do get cancer (Loewenstein, 2005). Further understanding of how the brain makes both hot and cold (real and hypothetical)decisions could guide people and societies to make these difficult decisions more effectively.

Design: Min J. Kang, Colin F. Camerer. Data collection: Min J. Kang. Data analysis: Min J. Kang. Writing: Min J. Kang, Colin F. Camerer.