Neuroeconomics Research Paper

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Decision making is a fundamental part of human behavior. We all make decisions every day that influence our health, well-being, finances, and future prospects, among other things. Researchers have become increasingly interested in why we make the decisions we do, especially when, in many cases, these decisions do not appear to be rational or beneficial to us in the long run. While neoclassical economics has traditionally looked at how people should behave, other disciplines such as psychology and cognitive science have tried to answer the question of why people act the way they do.

A new discipline, referred to as neuroeconomics, has sought to meld theory and methodology from diverse areas such as economics, psychology, neuroscience, and decision theory to create a model of human behavior that not only explains but also predicts how people make decisions (Glimcher & Rustichini, 2004). Neuroeconomics research examines how people make choices and attempts to determine the underlying neural basis for these choices and decisions. This research paper examines some of the seminal studies in neuroeconomics, highlighting the public policy implications and offering areas of future research where neuroeconomics could be applied.

Theory

Traditional economic theory has maintained that humans are rational decision-making entities, that each individual has a clear sense of his or her own preferences, tries to maximize his or her own well-being, and makes consistent choices over time (Huang, 2005). However, this model is more often violated than upheld as people and animals attempt to outwit evolution and destiny. Neuroscience gives researchers the opportunity to look into the “black box” of cognitive processing to reveal empirical indications of how the brain really processes choice, risk, and preferences. The goal is to create “a complete neu-roeconomic theory of the brain” (Glimcher, 2003).

Decision theory integrates mathematics and statistics to better understand how decisions such as choices between incommensurable commodities, choice under uncertainty, intertemporal choice, and social choice are made. It has been assumed that agents respond rationally in forming their choices and preferences. This theory finds that any “normal” preference relation over a finite set of states can be expressed as an expected utility equation.

However, the introduction of prospect theory, which suggests the possibility that other factors may affect behavioral decision making for the individual, has generated an interest in understanding the underlying mechanisms of preference, judgment, and choice (Kahneman & Tversky, 1979). The significance of these findings can have important implications for the marketing discipline. To this end, a better understanding of the decision-making processes used by people is important to understanding the critical drivers of economic behavior.

Psychology has sought to investigate the inner workings of the human mind (Camerer, Loewenstein, & Prelec, 2005; Loewenstein, Rick, & Cohen, 2008). Cognitive psychology, and more recently cognitive neuroscience, has introduced new tools that allow researchers to capture and measure data from brain activity related to a specific function and behavior. This new type of data has led to new directions of research that combine neuroscience, psychology, and decision theory to better understand the complexities of human decision making.

Neuroscience looks at the structure, function, and development of the nervous system and brain, while cognitive neuroscience investigates how behavior and the nervous system work together in humans and animals. In other words, cognitive neuroscience is the study of the neural mechanisms of cognition (Gazzaniga, 2002). At the nexus of neuroscience, economics, and psychology, there is an area that has tentatively been coined neuroeconomics, which uses neuroscience techniques to look specifically at how human subjects make choices. Neuroeconomics is interested not only in exposing brain regions associated with specific behavior but also in identifying neural circuits or systems of specialized regions that control choice, preference, and judgment (Camerer et al., 2005; Loewenstein et al., 2008).

Techniques borrowed from neuroscience include brain imaging methods that may reveal how humans and animals use the neural substrates of the brain to process and evaluate decisions, weigh risk and reward, and learn to trust others in transactions. Brain imaging techniques that can be used on human subjects include electroencephalography (EEG), positron emission tomography (PET), magnetoen-cephalography (MEG), and functional magnetic resonance imaging (fMRI). EEG and MEG measure changes in electrical brain currents by placing electrodes on the scalp to measure electrical waves emitted from the cortex. PET scans measure changes in blood flow by capturing images of radioactive isotopes injected in the bloodstream. fMRI measures blood flow to neural regions by relying on the magnetic properties of oxygenated and deoxygenated blood in the brain.

Another technique borrowed from neuroscience is transcranial magnetic stimulation (TMS), which is used to produce a magnetic pulse that can temporarily interfere with normal brain activity. For example, TMS can produce sudden movements in motor areas. While it has not yet been used for neuroeconomic studies, TMS has been used successfully for cognitive neuroscience studies and could potentially be used in the future to study decision making. In addition, there are invasive techniques of monitoring brain activity in animals, including single-cell recording, wherein an electrode is passed through the skull into the brain and neural activity is recorded. Neuroeconomics studies can also use nonimaging techniques. For example, some studies have been conducted using patients with brain lesions that disable specific parts of the brain. In addition, to determine central nervous system (CNS) response, studies can measure hormone levels, galvanic skin response, sweat gland activity, and heart rate (Carter & Tiffany, 1999; Frackowiak et al., 2004).

Before these technologies made it possible to examine the neural mechanisms of cognition, much of economic theory relied on the rational choice model. This model posits that individuals have stable preferences and a clear understanding of the options facing them. Thus, people are assumed to make their choices based on careful, unemotional calculations that maximize the benefits and minimize the costs that they will incur. However, current models of decision making only partially explain real human behavior. Neuroeconomics examines higher level cognitive functions of personal choice and decision making, demonstrating how these are expressed at the neuronal and biochemical levels. The analysis of this newest form of data that more closely examines brain processing promises to bring us closer to answering questions as to why people consume, have addictions, save, and hoard; what drives preference and choice; and what makes people happy, risk seeking or risk adverse, and trusting or trustworthy.

Over the past 50 years, scientists have experimented with a number of hypothetical game scenarios to determine models of how people make choices in economic situations. Before imaging technology, it was not possible to accurately investigate the influence of emotions and cognition on these economic models of decision making. However, behavioral economists have begun to challenge the assumptions of the rational agent and have found that psychological and emotional factors do indeed play an important role in people’s economic decision-making process. Essentially, neuroeconomics looks at two branches of choice: solitary choice and strategic choice (Zak, 2004).

In their article on neuroeconomics, Camerer et al. (2005) argue for the fundamental insights that neuroscience could offer economics. They maintain that economic theory has assumed that agents can “mentalize,” or infer from the actions of others, what their preferences and beliefs are. However, accumulating evidence from individuals with autism, Asperger’s syndrome, and brain lesions shows that mentalizing is a specialized skill modularized in specific brain regions. More important, the ability to mentalize exists in varying degrees from person to person.

Applications and Empirical Evidence

The Neuroscience of Game Playing

Games give neuroeconomists a useful way to isolate decision and choice variables in experimental studies. Most of these studies look at either behavior, autonomic reactions (such as hormone levels or heart rate), or brain activity while subjects are engaged in strategic games, thus revealing how the neuronal system processes fairness, reward, loss, trust, distrust, revenge, discounting, and choice. Specific brain regions have been implicated in how judgments are made about perceptual stimuli received from our environment (Adolphs, 2003). Some of these brain regions involved in judgment include the amygdala, which is central in the processing and memory of emotions; the insula, believed to be involved with feelings of disgust and unease; and the anterior cingulate cortex, which is implicated in reward anticipation, decision making, and empathy (Frackowiak et al., 2004).

Neural Calculations of Decisions

The idea that people seem prone to violate expected utility theory has led to the development of alternative models on how choices are made under risk. One such alternative, prospect theory, exhibits a series of effects that alter the value assigned to gains and losses (Kahneman & Tversky, 1979). Phenomena such as the certainty effect, which states that people are prone to undervalue probable versus certain outcomes, or the isolation effect, which finds inconsistent preferences for identical outcomes based on how the outcomes are framed, challenge the notion that utility theory holds in real-life cases of human judgment (Kahneman & Tversky, 1982). Interestingly, Camerer et al. (2005) make clear in their article on neuroeconomics that all the violations of the utility theory that humans commit have been replicated in animal studies. For example, rats have also committed the same patterned violations in addition to other expected utility properties (Kagel, Battalio, & Green, 1995). Probability is how animals and humans calculate associations between events and predict outcomes critical to survival and understanding their environment. For example, there is evidence that dopamine neurons of the primate ventral midbrain may act to predict reward by specifically coding errors (Fiorillo, Tobler, & Schultz, 2003). It was found that dopamine levels increase during gambling, which indicates that uncertainty may be the mechanism that induces this dopamine rush. This may explain the reward people feel when gambling, which cannot be explained by the monetary gain of gambling because losses usually outnumber gains.

Trust and Cooperation

Imaging studies have revealed more about how social interaction shapes neural response, allowing us to choose mutual cooperation and shared gains over self-interested choices to create a sense of stability in longer-term game scenarios (McCabe, Houser, Ryan, Smith, & Trouard, 2001). Increased activity in players who were more trusting and cooperative was shown in a brain area believed to be the locus of mentalizing, as well as in the limbic system, where emotions are believed to be processed (Camerer et al., 2005).

The trust game is often used in neuroeconomics studies and mimics the relationship between an investor and broker. The game is played in multiple rounds where a player is given an amount of money (e.g., $10) and then must decide how much of the money, if any, to send to a second player. The amount is tripled, and then the second player decides how much to send back to the first player. One study found that subjects who received the first “investment” violated the rational response, which would be to accept any amount of money offered to them by the first player or “investor” (McCabe, 2003). Interestingly, when a small amount of money was offered by a computer player instead of a human, the response by the investor was not as extreme. In other words, players were upset about receiving a low return only if they believed that another person was trying to take advantage of them. If they thought the small amount of money was from an impartial computer, investors were not as emotionally sensitive. In addition, half of the subjects in the study were characterized as cooperators and had a common pattern of divergent activation in the prefrontal cortex where simultaneous attention to mutual gains and inhibition of immediate gratification allow for cooperative choices.

Studies of fMRI brain scans found that when responders were offered low monetary amounts (e.g., $1 out of a maximum of $10), there was more activation in the prefrontal cortex, anterior cingulate, and the insula cortex (Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003). When monetary offers were low, the receivers had increased activity in the insula, which is often associated with feelings of pain and disgust (Wright, He, Shapira, Goodman, & Liu, 2004). The anterior cingulate cortex receives input from a number of other areas and is thought to resolve conflict among these areas. A player refusing an offer could be predicted by the level of activation in the insula. The author speculates that the insula may be the neural area responsible for distaste for inequality or unfair conditions.

The relationship between trust and hormones is another area of interest for researchers. Hormonal response in people was investigated during a series of trust games to determine whether there was a specific hormone that could be connected to feelings of trust and distrust (Zak, Kurzban, & Matzner, 2004). Participants’ blood was tested after each round for levels of oxytocin, which has been associated in facilitating social behaviors, social recognition, maternal attachment, pair bonding, and the feeling of falling in love. The study found that when money was returned to the first player, oxytocin did indeed increase to twice the levels of the random draw. This means that if people felt they were being trusted, increased oxytocin levels made them more likely to trust back. Interestingly, ovulating women were less likely than nonovulating women or men to give money back even if they received the full amount from the other player. This, Zak (2004) believes, is due to the fact that progesterone, which increases during ovulation, binds with oxytocin to inhibit its affect. In looking at distrust, Zak looked at dihydrotestosterone and testosterone in both men and women to see if levels increased during low-trust games. The study found that testosterone did not significantly increase in either women or men, and dihydrotestosterone levels did not increase for women. However, there was a significant increase in the level of dihydrotestosterone in men when the other player signaled distrust. Zak hypothesizes that this may be related to the increased feelings of aggression that men reported when engaged in a low-trust game.

While cooperation is an important component in human society, the desire to punish is the flip side of cooperation, which may be how society is able to enforce social norms.

An interesting study that looked at the neural basis for altruistic punishment or revenge found that people feel a sense of satisfaction when punishing those who break what are considered social norms (de Quervain et al., 2004). Using PET scans, researchers found greater activation in the striatum, which is usually “implicated in the processing of rewards that accrue as a result of goal-directed actions.” In addition, those with the strongest responses in the striatum were more likely to take on higher costs for the right to mete out punishment to those who deviate from societal norms.

Fairness

Humans tend to reject inequality even if it means walking away from a reward (Powell, 2003). In a study that looked at the neural substrates of cognitive and emotional processing, specifically fairness and unfairness activated during the ultimatum game, 19 subjects were scanned using fMRI (Sanfey et al., 2003). The ultimatum game is based on one player offering the other a split of a sum of money that the responder can either reject or accept. Players were paired with others who offered various split amounts of $10. The responders were scanned as they decided whether they would choose fair or unfair proposals. Previous behavioral research on the ultimatum game found that low offers are rejected 50% of the time even though a rational maximizing solution would be for the responder to accept any amount of money because some money should be better than no money. Subjects usually report that low offers are often rejected because it provokes an angry response. In Sanfey et al.’s (2003) study, brain imaging revealed that unfair offers activated the bilateral anterior insula, dorsolateral prefrontal cortex, and anterior cingulate cortex. The anterior insula is often implicated in negative emotional responses, more specifically in disgust (Krolak-Salmon et al., 2002; Wright et al., 2004). The dorsolateral prefrontal cortex is often implicated in executive function and goal maintenance, which may stem from the responder actively maintaining the cognitive goal of acquiring as much money as possible. Increased activation of the insula was biased toward rejection of the offer, and increased activation of the dorsolateral prefrontal cortex was biased to accepting the offer. The anterior cingulate cortex has been implicated in cognitive conflict and may be a result of conflict between emotional and goal motivation during the game. Interestingly, the experiment was also run with both human and computer partners who acted in offering the split. The response in these brain areas was stronger when unfair offers were made by the human partner versus the computer, suggesting that the response was not just to the monetary amount offered but also to the contextual factor that the unfair offer was made by another human.

Even monkeys seem to adhere to this notion of fairness. Brosnan and de Waal (2003) found that cooperation may have developed through evolution where individuals must compare their own efforts to the payoff they receive with those of others. Brown capuchin monkeys responded negatively when offered unequal rewards from experimenters and even refused to participate when they witnessed other monkeys receiving more attractive rewards for the same amount of effort. The researchers posit that this inequity aversion may have an evolutionary origin in our neurological development.

Reward and Loss

Kahneman and Tversky (1979) found that loss is judged by people as being more painful than an equivalent gain is pleasurable, as is evidenced in the convex utility curve for losses and concave utility curve for gains in the value function. How valuation of gain and loss is calculated in the brain is an area under investigation by neuroscientists. Montague and Berns (2002) have looked at a number of experiments to develop a computational model referred to as the predictor valuation model, which anticipates neural responses in the orbit frontal cortex and striatum. Other brain imaging studies have found that the brain processes gains and losses differently (K. Smith, Dickhaut, McCabe, & Pardo, 2002). PET imaging has revealed that there are two separate but functionally integrated choice systems, both in anatomical structure and in processing, each sensitive to loss. The dorsomedial system processes loss when deliberating risky gambles. When subjects make a choice that results in loss, there is a greater use of the dorsomedial system, which serves to calculate the loss versus the visceral representations in the more primitive ventromedial system, which animals most likely use to make decisions. Choice processing seems to be centered in the more medial structures, with more ventral than dorsal distribution.

Animal studies, mainly using monkeys, are revealing new information about how animals estimate the value of specific actions. For example, in a series of experiments, Schultz (1998; Schultz, Dayan, & Montague, 1997) looked at the neuronal response in the substantia nigra and the ventral tegmental area of the monkey brain to determine activity when a monkey pressed levers for juice rewards. Another animal study looked at whether specific neuronal activation can be correlated to the probability that the animal expects gain (Platt & Glimcher, 1999). Another animal study looking at reward valuation has found that reward valuation in monkeys can be predicted in a model based on reward history that duplicates foraging behavior (Sugrue, Corrago, & Newsome, 2004).

How we respond to monetary reward has also been investigated using fMRI. The neural substrates of financial reinforcement overlap with areas that deal with primary reinforcers, such as food (Elliott, Newman, Longe, & Deakin, 2003). Gold (2003) has made an argument for reward expectation to be linked to the basal ganglia. Breiter, Aharon, Kahneman, Dale, and Schizgal (2001)

have used fMRI to analyze the neural response to expectation and experience of gains and loss. The study found that that there may be a common circuitry of neurons that processes different types of rewards. In studies conducted with monkeys, it was found that the rhinal cortex was important for creating the associations between visual stimuli and their motivational significance (Liu, Murray, & Richmond, 2000; Liu & Richmond, 2000). Monkeys whose rhinal cortex had been removed were not able to adjust their motivation to changes in a reward schedule, while unaffected monkeys were able to adjust their motivation. The complexity of how motivation works to cause action is not clearly understood, but it is believed that a limbic-striatal-pallidal circuit forms the basis for the translation of motivation into action (Liu et al., 2000).

The neural substrate association with time discounting was investigated using fMRI, and it was found that human subjects use different regions in the brain to calculate short- and long-term monetary rewards (McClure, Laibson, Loewenstein, & Cohen, 2004). The limbic system associated with dopamine production tended to be activated when decisions that would bring immediate gratification were contemplated. On the other hand, the lateral prefrontal cortex and posterior parietal cortex were activated regardless of whether there was short or long intertemporal delay. There was greater frontal parietal cortex activity only when the choice made by subjects was longer term.

Risk

Aversion to risk is linked to the amygdala and is driven by the ancient fear response (Camerer et al., 2005). Cortical override of the fear response is demonstrated in animal studies using shock. Over time, the response will be “extinguished.” However, when the connections between the amygdala and the cortex are severed in the animal, there is a tendency for the fear response to return. This demonstrates that the amygdala does not “forget” but that the cortex is suppressing the response.

Risky choice is different from risk judgment in that the subject must choose between risky gambles that force an interaction between cognition and affect. Patients with damage to the ventromedial prefrontal cortex seem to suffer from decision-making deficits. In a study that measured performance in gambling tasks, patients continued to make the wrong choice, resulting in higher losses even after knowing the correct strategy (Bechara, Damasio, Damasio, & Tranel, 1997). Normal subjects used the advantageous strategy even before they consciously realized which strategy worked best. In addition, normal subjects developed skin conductance responses when facing a risky choice even before they knew that the choice was actually risky. For the patients with prefrontal damage, these skin conductance responses never developed. This suggests that there might be a nonconscious, autonomic bias that guides risky decision making based in the ventromedial prefrontal cortex, responding even before conscious cognition is aware of the risk. Bechara et al. (1997) hypothesize that this covert bias activation is dependent on past reward and loss experiences and the emotions that go with them, with damage to the ventromedial cortex interfering with access to this knowledge.

A study looked at how emotion affects perceptions of risk in investment behavior. Using patients with lesions in the brain areas associated with emotion, Shiv, Loewenstein, Bechara, Damasio, and Damasio (2005) compared investment decisions over 20 rounds to those made by patients with lesions in areas unrelated to emotion (control) and normal subjects. They hypothesized that the patients with damage to the emotional regions would be able to make better investment decisions because they would not be subject to emotional reactions that could lead to poor choices. This hypothesis was based on the case of a patient with ventro-medial prefrontal damage who was able to avoid an accident on an icy road while others skidded out of control. The patient revealed later that because he felt a lack of fear, he was able to calmly react to the road conditions by thinking rationally about the appropriate driving response (Damasio, 1994). This led the researchers to wonder whether a lack of normal emotional reactions might allow people to make more advantageous decisions.

The study found that normal participants and control patients became more conservative in the investment strategy after a win or loss, whereas the lesion patients took more risk and, as a result, made more money from their investing choices (Shiv et al., 2005). Other studies have found that even low levels of negative emotions can result in loss of self-control, which can have less than optimal outcomes for the subjects. For example, as the result of myopic loss aversion, people exhibit high levels of loss aversion when gambles are presented one at a time rather than all at once (Benartzi & Thaler, 1995).

Addictive Behavior

Closely related to the question of reward is addiction. Neural activity drives the search for food in both animals and humans. These same neuronal networks may also drive behavior to seek other kinds of substances that rate high on the reward evaluation. When the brain is strongly activated by, for example, sugar, food, or drugs, it can lead to abuse, which is often called addiction (Hoebel, Rada, Mark, & Pothos, 1999). A critical issue in behavioral decision theory is the question of why people and animals would choose to engage in behavior that is detrimental or harmful. This issue is related to the question of addictive behavior and the endeavor to understand the neural underpinnings of reinforcement and inhibition of behavior. In an early neuroeconomic paper dealing with this issue, Bernheim and Rangel (2002a, 2002b) proposed a mathematical theory of addiction that sought to explain irrational addictive behavior in terms of decision theory and economics. The model is based on the idea that cognitive processes such as attention can affect behavioral outcomes regardless of initial preference. If a person is subject to “hot cognition” (or affect-laden thinking), for example, he or she may engage in consumption behavior that conflicts with preference because the focus is on usage and “the high” (Bernheim & Rangel, 2002a).

The theory of cue reactivity is another theory that might serve to explain why addiction levels remain high even though subjects self-report that they are striving to quit and they do not enjoy the consumption of their addictive substance (Carter & Tiffany, 1999; Laibson, 2001). In a recent study that used fMRI to analyze neural response in adolescents to alcohol-related imagery, researchers found that adolescents with even a short usage history of alcohol had significantly higher blood oxygen response in areas of the brain associated with reward, affect, and recall (Tapert et al., 2003).

Various types of addictive behaviors are under investigation by researchers. A type of consumption addiction involves the dispensation of products. The neural basis of collecting and hoarding in humans was analyzed using patients with prefrontal cortex lesions (Anderson, Damasio, & Damasio, 2005). In addition to judgments of use and consequences of discarding possessions, other cognitive processing going on at the time of the decision to save may be important. Compulsive hoarders appear to have a peculiar perspective with regard to possessions. When deciding whether to discard a possession, they spend most of their time thinking about being without the possession (the cost of discarding) and little time thinking about the cost of saving it or the benefit of not having it. This notion is similar to an observation made by J. P. Smith (1990) about animal hoarding. Smith speculated that the sight of a nut (by a squirrel) puts the squirrel “in touch with” the feeling of being hungry and without the nut. For the hoarders, the sight of the possession puts them “in touch with” the feeling of being without the possession and needing it. This feeling dominates their consideration of whether the possession should be discarded (Frost & Hartl, 1996).

Limitations

The fact that brain science offers insights into economic and behavioral phenomena is not necessarily a new concept. While not universally embraced by all economists, some behavioral economists have been using constructs from psychology to attempt to build more descriptive and realistic models of behavior (Huang, 2005). However, for the first time, imaging technology such as fMRI offers the type of tools that can effectively explore the subtleties of the human brain while being noninvasive, relatively safe for human subjects, and providing results that are robust and revealing.

However, fMRI studies have been questioned by critics because of, for example, use of small sample sizes (typically less than 40 subjects), ambiguity in human neuroanatomy mapping, lag time in the hemodynamic response, image distortion due to signal drop-off, motion artifacts, poor temporal resolution, and the debate over functional definitions of neural areas (Savoy, 1998, 1999; Savoy, Ravicz, & Gollub, 2000; Wald, 2005). Despite the limitations and difficulties in analyzing the results produced by fMRI, significant improvements in brain mapping, imaging power, and resolution (there are now 3T, 4T, and 7 T scanners being used to gain improved imaging resolution) have indicated that at least some of these shortcoming may be reduced with the next generation of equipment.

Policy Implications

An example of how neuroeconomics could be applied to an important research area deals with the question of consumption addiction. This is especially true in developing effective marketing communications for vulnerable consumers such as children and adolescents. Pechmann, Levine, Loughlin, and Leslie (2005) presented evidence from the addiction and neuroscience literature that adolescents were more vulnerable to advertising and promotions due to the unique structure of their neural development. This may indicate that the decision-making process for adolescents is significantly different from that of children and adults. While there has been evidence from empirical social psychology studies to support this assertion, increasing evidence based on neuroimaging studies has been developed by researchers from psychology, neuroscience, and medicine (Pechmann & Pirouz, 2007). This important development could offer a strong basis for research that seeks to investigate how and why adolescents respond to marketing communications and advertising differently. Furthermore, research could begin to develop ways to protect vulnerable adolescents from detrimental product categories such as cigarettes and alcohol, while enhancing the relevance and efficacy of marketing, such as health messages, targeted toward adolescents. In this way, neuroeconomics methods can offer researchers a valuable suite of methods that will allow a more refined and revealing understanding of the neural basis of choice for adolescents—a developmentally unique segment of the population.

Future Directions

A diverse array of questions can be addressed using neuroeconomic techniques and methods. Neuroeconomics could serve as an important new area for tacking many of the fundamental questions about decision making that have been difficult to explain theoreticaIIy. Neuroeconomics offers the potential for insights into the neuroIogicaI processes that underlie human behavior. Using experimental methodologies combined with imaging and other neuroscience tools, neuroeconomics can better help us understand the mechanisms of decision making, including preference, risk behavior, valuation, biases, and conflict. While neuroeconomics as a field of study is in a relatively early phase, a growing number of researchers are establishing new theoretical constructs that could potentially inform economics, behavioral decision theory, management, marketing, and psychology.

Within neuroeconomics, a number of intriguing areas of research have not yet been fully explored and could prove of further interest. Such future areas of research might include the following:

How do neural systems work together to create decision-making behavior?
How does decision making vary for vulnerable populations such as adolescents or the elderly?
What factors influence the development of addictive behavior, and what factors could act to discontinue these addictions?
How can an understanding of the neural systems underlying decision making help people to make better decisions in their lives?

Conclusion

While the application of neuroscientific methods to economics and other related fields may cause continuing controversy and debate among scientists and the public, the results gleaned thus far from neuroeconomic research have revealed valuable insights into the neural substrates that affect human and animal decision making. It seems reasonable to think that these insights may allow for new, more revealing models of decision making that will take into account the underlying neurological mechanisms that drive behavior, emotion, and choice.