Models of decision-making usually focus on cognitive, situational, and socio-cultural variables in accounting for human perfor- mance. However, the emotional component is rarely addressed within these models. This paper reviews evidence for the emotional aspect of decision-making and its role within a new framework of investigation, called neuroeconomics. The new approach aims to build a comprehensive theory of decision-making, through the uniﬁcation of theories and methods from economics, psychology, and neuroscience. In this paper, we review these integrative research methods and their applications to issues of public health, with illustrative examples from our research on young adults’ safe sex practices. This approach promises to be valuable as a comprehen- sively descriptive and possibly, better predictive model for construction and customization of decision support tools for health professionals and consumers.
Decision-making is a ﬁeld of interest for philosophers, economists, psychologists, and neuroscientists, among others. A fundamental question that drives research in this area is why do people who are presented with the same options make diﬀerent choices? What is it about the cogni- tive and neurological processes that lead people to diﬀerent outcomes? Why do rational models such as those used in economics and the classical decision-making theory not always accurately predict an individual’s behavior? These questions and others are particularly true of decision-making under risk, uncertainty, and ambiguity. These questions are addressed by the emerging ﬁeld of neuroeconomics which is the combination of the diﬀerent perspectives and theories of psychology, economics, and neuroscience. This is given in Fig. 1.
Our objective in this paper is to explore the role of emotion in decision-making and to introduce theories and methods employed in the emerging ﬁeld of neuroeconomics in this context. We ﬁrst present a review of theories and research methods for studying decision-making, including classical decision theories, the cognitive naturalistic approach, and the neurological basis of behavior. We then present a summary of the role of emotion in decision-making, deﬁning the dichotomy of emotional and rational systems, research on risk assessment, and ﬁndings from neuroscience. The following section introduces a new methodology for researching and explaining decision-making behavior, the neuroeconomic approach. Our discussion focuses on the integration of the separate disciplines of economics, psychology, and neuroscience in this new approach, and a summary of work already conducted in this vein. We then propose an extension to the neuroeconomic model, cognitive neuroeconomics, which highlights the primacy of both emotion and cognition in decision- making under risk and uncertainty. We illustrate how this approach can be applied to issues of public health, namely sexual decision-making, with an example from our research on young adults’ safer sex practices, which have implications for the spread of HIV. The apparent limitation of this review is that our study did not utilize neuroscience methods, which restricts our discussion to possible future research using these methods. In our conclusions, we discuss the relevance of this new approach to medical informatics, and the implications for the construction of decision support tools for health professionals and consumers.
In our illustration, we focus on decision-making by the lay public, speciﬁcally to understand how and under what conditions the public makes ‘‘near misses’’ and risky deci- sions about their health. Despite public health campaigns, educational programs in schools, and other interventions, the rate of HIV infection among the younger ‘‘heterosexual’’ population is on the rise. According to the World Health Report 2004, the dominant mode of HIV transmission is unprotected sexual intercourse . Young adults engage in unprotected intercourse even though they are aware that they risk HIV infection by doing so, and they recognize that condom use is an eﬀective means of protection against infection. In light of this information, we need to address this public health issue and the promotion of safer sex practices, especially in young adults—individuals who are often high risk takers.
We conducted a study of decision-making about risky sexual behavior, using a group of 60 heterosexual young urban men and women . These young adults were recently sexually active, and had a moderate knowledge of HIV on a standard knowledge assessment. We collected daily journals chronicling each young adult’s sexual behavior, which were supplemented by in-depth interviews concerning sexual histories and attitudes towards a variety of related sexual-health topics. Cognitive analysis of the data led us to pay particular attention to condom use as a target decision point during the sexual encounter. In an immediate decision situation during a sexual encounter, there are only two options from which to proceed: (1) use condoms and thus greatly minimize the risk of contracting a STD or pregnancy, or (2) not use condoms and thus greatly increase the risk of pregnancy or contracting a STD. Classical decision theory and economic models of decision-making propose that the best option for the individual is to use condoms during sexual intercourse, but this is not usually the case in practice.
In the cognitive naturalistic model, there are cognitive (memory, knowledge, inferences, strategies), socio-cultural (group norms), and situational (environmental) constraints on the decision-making process. The cognitive factors are past experiences, beliefs and assumptions, perceptions, and actions. These factors interact with socio-cultural standards and the environment, resulting in some behavior. Using this framework, we identiﬁed four distinct patterns of lifetime condom use, an indicator of safe sex behavior:
- mostly consistent condom use (35.0%), (2) mostly inconsistent condom use (16.7%), (3) shifting from consistent to inconsistent condom use (35.0%), and (4) shifting from inconsistent to consistent condom use (13.3%) .
However, we were not able to fully account for all behavior using the cognitive model alone. An analysis of the data using temporal and thematic coding, as well as semantic relations, strongly suggests that emotion is a key factor in understanding the variation in behavior. Extending our model to include the role of emotion, as well as its interaction with cognition, increases our ability to account for decision-making under risk.
Fig. 1. Pyramid outline.
Perspectives on decision-making
Classical decision theories in psychology and economics
According to Classical Decision Theory (CDT), making decisions involve choosing a course of action among a ﬁxed set of alternatives with a speciﬁc goal in mind. The three components of a decision are (1) options or courses of action, (2) beliefs and expectancies of the options in achieving the goal, and (3) outcome expectancies (negative or positive) . CDT focuses on how and why decisions deviate from a certain standard of rationality, which is based on optimality. According to this theory, the aim in making a decision is to maximize the gains, or expected value of the outcome, and use information in a way that would accomplish this goal. The expected value is a linear model that expresses a multiplicative relationship between probability and utility. This assumes that individuals are aggregating and weighing information accurately and consistently and that they have the ability to make logical and empirically correct judgments . However, CDT has failed to explain behavior and decision-making in practical, real world situations. This theory is limited in descriptive power because it treats all decisions as essentially the same, comparing them to a normative standard. However, individuals have not been found to make decisions following a normative model .
The established paradigm for studying medical decision- making is the normative comparative approach, based on CDT . Traditionally, research has been conducted in controlled laboratory settings and the focus is on the nature of the decision outcome and how it deviates from a normative standard. Under this model, experts are subject to the same standards as laypeople. There is also less emphasis on domain speciﬁc knowledge.
Decision researchers have also looked at judgment under uncertainty and its inﬂuences on decisions and behavior. According to the normative model, uncertainty reﬂects the judgment of the likelihood of an event in a particular situation (a probability). The theories that fall under this normative approach are the expected utility and subjective expected utility theories, which assert that decisions are made to maximize one’s gains (ratio of chance taken by amount of payoﬀ), and the conditional probability theory, based on the Bayesian perspective (Laplace-Bayes theorem) . The theories all suppose the optimal decision is chosen in situations of uncertainty. The strengths of these models are that they provide a standard from which to compare and ﬁnd ways to improve human behavior, and well deﬁned mathematical models of rational decisions. However, these models usually perform better than humans do and humans do not usually reason in accord with the premises of these models . For example, after assessing late adolescents’ and young adults’ (16–21 years) perceived personal risk of HIV, risk associated with six sexual activities, and perceived likelihood of and recent experience engaging in each type, strong representations of sexual risk were not found to be useful predictors of actual sexual behavior . In another study, young adult men (21–33 years) were found to be only marginally guided by risks associated with unprotected sexual activity and by perceived prevalence of HIV .
Tversky and Kahneman  have deﬁned the use of heuristics, biases, and framing eﬀects in aﬀecting the decision maker. They have been found to produce decisions that systematically deviate from the normative standard in an attempt to compensate for lack of knowledge. These heuristic strategies, although sometimes appropriate and eﬃcient, often result in poor decisions.
Heuristics, biases, and framing eﬀects have been well documented in the context of health-related decisions. For example, researchers have studied conﬁrmation bias and framing eﬀects (survival vs. mortality rates) in the med- ical area . It has been found that positive framing results in more risk-averse choices whereas negative framing increases risk-seeking choices .
There are also studies that have examined the biases and heuristics used by young adults when reasoning about risky situations [12–14] that are relevant to HIV [15–22]. In a laboratory study involving male university students, judg- ments of STD risk potential were lowered signiﬁcantly after viewing photographs of people rated high in sex appeal compared to initial estimates based on sexual histo- ries alone . Individuals consistently demonstrate an optimistic bias with regard to perceptions of their own HIV risk [24,25]. Other researchers have also found false consensus biases whereby individuals rate their peers as similar to themselves in terms of risk, regardless of risk information .
A limitation of the traditional laboratory-based ‘‘heuris- tics and biases’’ approach is that it is constrained by not having data on how people make mistakes at the point that the decision is made, only that they made a mistake . Research using this approach makes the implicit assump- tion that the decision maker has identiﬁed a goal and the method of implementing the decision is not part of the decision-making problem. However, sometimes the use of heuristics is adaptive and facilitates a decision, but research has usually not viewed heuristics and biases from this per- spective .
Cognitive naturalistic decision-making
Naturalistic decision-making (NDM) has emerged mainly out of the frustration with the eﬀorts to apply methods and ﬁndings from the classical decision research in these complex and multifaceted settings. Patel and colleagues  refer to this as an ‘‘emerging new paradigm’’ and suggest that this model of decision-making combines the traditional protocol analytic methods with innovative methods designed to investigate cognition and behavior in realistic settings.
The naturalistic decision-making approach emphasizes descriptive adequacy of its models, which necessitates in-depth qualitative methodologies that complement quantitative ones . Unlike CDT, NDM views expert performance as the gold standard and focuses on the role of expert knowledge organization in performance. The decision process from a problem-solving perspective is a search in the problem space (which is evolving and dynamic), in which the problem solver performs an operation (inference or action) from possible operations in moving toward a solution or goal.
In medicine, this approach has been used to study clinical competency in novices and experts, where the goal is formulation of a diagnosis or treatment plan [28,29]. This research has had an impact on cognitive science and artiﬁcial intelligence-focused research, in studying humans and building computational models. This approach recognizes conceptual knowledge as a resource to aid decisions and a contributor to identifying patterns of misunderstanding, which lead to suboptimal decisions.
Studies of decision-making in the lay public have also shown how the understanding of pediatric illnesses inﬂuenced mothers’ choices of treatment for their children . The study, which was conducted in rural India, showed how mothers interpreted concepts related to bio- medical theories of nutritional disorders. The authors found that traditional knowledge and beliefs played an important role in interpretation of these disorders, which led to decisions that were inﬂuenced by ‘‘non-scientiﬁc’’ traditional ideas. The layperson, who had no formal education, had a clear understanding of the traditional knowledge and described the concept in a connected piece of logic. The person with more formal education used the medical concepts, but the reasoning was still traditional with no change in the underlying thinking . With more education, instead of rejecting traditional explanations, the mothers developed diﬀerent conceptual structures, which they used in an opportunistic manner [32,33].
Furthermore, naturalistic decision-making as it applies to decisions in high stress situations necessitates immediate response behavior, and perceptual cues may play a more prominent role in the decision process. This was corroborated by the work of Klein and colleagues  with regard to the decision process involving work with ﬁre commanders and platoon leaders, employing various methods, such as ﬁeld observations and retrospective accounts of actual emergency events. Commanders had to decide whether to employ a search and rescue, or initiate an oﬀensive attack on the ﬁre or whether to use a more precautionary defen- sive strategy. It was found that commanders acted on the basis of prior experience, immediate feedback and careful monitoring and assessment of the situation. This process involved a serial evaluation of options rather than system- atic selection of pre-determined options. The results indi- cate that expert commanders relied more on strategies of situation recognition with minimal deliberation, whereas novices employed a more deliberative decision-making strategy. This kind of decision-making based on situa- tion-recognition is the characteristic of naturalistic deci- sion-making in dynamic environments.
Leprohon and Patel  studied the decision-making strategies used by nurses in emergency telephone triage set- tings. In this context, nurses are required to respond to public emergency calls for medical help (exempliﬁed by 911 telephone service). The study analyzed transcripts of nurse–patient caller telephone conversations of diﬀerent levels of urgency and complexity and interviewed nurses immediately following their conversations. In decision- making situations such as emergency telephone triage, there is a chronic sense of time urgency—decisions often have to be made in seconds. This may involve the immedi- ate mobilization and allocation of resources. Decisions are always made on the basis of partial and sometimes unreli- able information.
The results were consistent with three patterns of decision-making that reﬂect the perceived urgency of the situation. The ﬁrst pattern corresponds to immediate response behavior as reﬂected in situations of high urgency. In these circumstances, decisions are made with great rapidity. Actions are typically triggered by symptoms or the unknown urgency level in a forward-directed manner. The nurses in this study responded with perfect accuracy (i.e., allocating the proper resources to meet the demands) in these situations. The second pattern involves limited problem solving and typically corresponds to a situation of moderate urgency and to cases that are of some complexity. The behavior is characterized by information seeking and clariﬁcation exchanges over a more extended period of time. These circumstances resulted in the highest percentage of decision errors (mostly false positives). The third pattern involves deliberate problem solving and planning and typically corresponds to low urgency situations. These situations involved evaluating the whole situation and exploring options and alternative solutions, such as identifying the basic needs of a patient and referring the patient to an appropriate clinic. The nurses made fewer errors than in situations of moderate urgency and more errors than in situations requiring immediate response behavior. They could accurately perceive a situation as not being of high urgency. Decision-making accuracy was signiﬁcantly higher in nurses with 10 years or more of experience than nurses with less experience, which is consistent with the acquisition of expertise in other domains.
Most decisions in this study were based on symptoms rather than on diagnostic hypotheses, especially in urgent situations. These decisions rely on prior instances that facilitate rapid schema access, based on minimal information and enable them to represent the situation to gather information and make decisions. This ﬁnding is consistent with the research by Benner and Tanner  who found that nurses respond on the basis of prior experiences in memory and do not decompose decisions into sets of alter- natives or attempt to understand the underlying pathophysiology of a patient problem. Nurses’ training, which focuses on observational skills and detection of abnormal and urgent symptoms, would contribute to the acquisition of this type of decision-making process. Benner also sug- gests that experience-based knowledge forms the basis of much of nurses’ intuitive clinical judgments.
Crandall and Calderwood  studied nurses’ decision- making about patients with sepsis in a neonatal intensive care unit. They employed an interview methodology known as the critical decision method, which involves asking individuals about particularly challenging incidents and probing for cues that resulted in particular decisions. The ﬁndings indicated that experienced nurses rely heavily on perceptually based indicators and ﬁndings not documented in the medical literature. The nurses were very sensitive to subtle changes in an infant’s condition, were able to detect trends early on in the clinical course, and predicted poten- tially adverse outcomes (a worsening septic shock). The researchers elicited much of this information through probes, since nurses had diﬃculty in verbally explaining the perceptual cues.
Speciﬁcally in the HIV area, Patel et al.  examined the relationships among knowledge, decision-making strategies, and risk assessment about HIV by youths, using semi-structured interviews, risk assessment questionnaires, and peer group focused discussions using real life HIV scenarios. It was found that youths have diﬃculty interpreting low-risk and negligible-risk probabilities appropriately, but are able to accurately interpret high-risk probabilities. Negotiations and conﬂict resolutions during peer group discussions appeared to shape youths’ understanding about HIV through clariﬁcation of risk factors and justiﬁcation of the conditions under which risks were taken.
Neurological basis of decision-making
Like other executive processes, decision- making involves a wide range of inputs such as multi-modal sensory inputs, conditioning based on past experience, sensory and emotional responses, and the anticipation of future goals. Furthermore, these inputs must be integrated and associated with uncertainties, expectations and outcomes and subsequently processed to make the most appropriate decisions. Investigators have looked at various aspects of decision-making, from the neurological basis of simple binary choice in non-human primates to highly complex analyses of human decision-making by individuals, as well as by groups in applied settings. As a result, a hiatus has developed between the neuropsychological studies of deci- sion-making and other disciplines involved in the ﬁeld. On the one hand, the neural basis of decision-making has been conﬁned to an analysis of the simplest decision processes and has not been applied to complex processes involved in human judgment. On the other hand, theories about high-level decision-making have remained purely descrip- tive with very little neurological underpinnings . With the advent of new technological advances in the ﬁeld of neuroscience, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), it is now possible to attempt to ﬁnd the neurological bases for psychological and economic theories of decision-making.
However, despite the excitement that has been generated about investigation of this ﬁeld, it is important to point out that the challenge is a daunting one. The study of the neu- ral basis of decision-making involves a myriad of neural processes and sub-processes, with an equally complex set of regions and sub-regions in the brain. In spite of the leaps made in understanding these phenomena in the past dec- ade, what we currently have are essentially bits and pieces of information about various regions and sub-regions involved in diﬀerent functionalities. Moreover, these investigations have not always led to consistent results. Fellows  discusses that the lack of a systematic approach has led to diﬃculties in analyzing and interpreting the available data.
It is also important to note that a considerable portion of our current knowledge in this ﬁeld comes from lesion studies and patient with chronic drug abuse (for review, see Krawczyk ). This is a highly valuable source of information about the functionality of the speciﬁc brain regions. However, it is important to note that the complexity of the brain and numerous interconnections and innervations between diﬀerent brain regions can, and often do, result in signiﬁcant confounders that give a less than accurate picture of the processes and regions involved.
One further point is that the concepts and tools of neuroscience are just one of the many useful but incomplete tools for the study of decision-making. Neuroscience has served and continues to serve as a descriptive tool used to shed light on the parts of the brain involved in decision- making, but cannot be used as a full-ﬂedged predictive tool. In other words, while neuroscience techniques can explain how various parts of the brain interact during decision- making, and what that means, it has little predictive power with regard to the course of action taken.
Neural architecture of decision-making
The neural architecture of decision-making is comprised of a highly complex and interconnected circuitry. Table 1 presents some of these regions along with their functional classiﬁcations. However, it is important to note that all areas of the prefrontal cortex (PFC) are heavily intercon- nected, emphasizing the primacy of the region in decision processes. Furthermore, the PFC is highly interconnected with several sub-cortical regions, which have also been implicated in various aspects of decision-making. Evidence has come from lesion studies in animals, studies of humans with PFC damage, and neuro-imaging studies. It should also be noted that this is not an exhaustive list, but rather a useful architectural tool for the present discussion.
Examples from health care domain
The Bechara/Damasio model of decision-making is deﬁned as the ‘‘ability to select the most advantageous response from an array of immediate possible behavioral choices’’ [41,42]. A corollary of this model is that responses in real time are inﬂuenced by multiple cognitive and aﬀective processes. These include retrieval of previous reward/ punishment experiences associated with a response; work- ing memory, in temporary maintenance of information; level of responsiveness to future goals and outcomes; and planning for an optimal future outcome or goal. As dis- cussed in the previous section, each of these processes has an underlying neural substrate within the prefrontal cortex and prefrontal-subcortical circuitry. The model predicts that a deﬁcit in any one of these functions result in impaired decision-making in the form of cognitive impulsivity, deﬁned as a selection biased towards the choices associated with greater immediate reward, irrespective of the future consequences of the choices. These deﬁcits can result from a large number of factors, such as lesions (for review, see ) and drug abuse [43,44]. Maia and McClelland  challenge the interpretation that lesions of this region, particularly those of the ventro-medial prefrontal cortex (VMPC) cause a deﬁcit in the Iowa Gambling Task, through cognitive impulsivity. Rather, they believe that this is a result of the disruption of reversal learning, thus disabling them from adjusting their responses when the reinforcement value of the stimuli is reversed . Therefore, the information with regard to this issue is not conclusive and further work is needed to fully describe and interpret the various neural substrates and processes involved.
The role of emotion in decision-making
Until recently, decision researchers have not focused on the role of emotion as a separate factor in the decision process. Emotion was viewed only as a negative inﬂuence and hindrance to the rational decision process. More recently, some researchers have stressed the importance of environmental, social, and emotional inﬂuences on decision-making [27,46–49]. Fischhoﬀ  emphasizes the eﬀect that context and interpretation can have on decision-making, and Loewenstein  stresses that visceral factors, such as sexual arousal or hunger, can greatly aﬀect decision- making processes. Gold and colleagues [50,51] examined diﬀerences between thinking in ‘‘the cold light of day’’ versus in ‘‘the heat of the moment.’’ Thinking in the cold light of day is more likely to be based on more rational, knowl- edge-based inferences, whereas thinking in the heat of the moment is generally faultier and more likely to contain irrational justiﬁcations for risky, yet personally desired, behavior. Factors such as these reﬂect the need for integra- tion of knowledge and real world experiences across a wide variety of internal and external contexts. These cognitive, environmental, and social factors interact with emotion, especially in risky and uncertain situations.
There has been a continuous debate over the independence of cognition and emotion, and the conﬂict between rational and emotional reasoning. Current theories suggest there are two dominant systems people use to understand and assess risk: the ‘‘analytic system’’ and the ‘‘experiential system.’’ The ‘‘analytic system’’ involves conscious and deliberate cognitive processes that employ various algorithms and normative rules to produce logical, reason-oriented, behavior . In contrast, the ‘‘experiential system’’ uses past experiences, emotion-related associations, and intuitions when making decisions. The experiential system relies more on unconscious rather than conscious processes. Slovic  suggests that these two systems must work in collaboration in order for the decision-maker to reach a rational decision. Most models of decision-making assume the process to be rational, which would exclude the possibility of emotion playing a role, other than of a hindrance. Other models take the valence-based approach and evalu- ate negative and positive aﬀects on behavior, without spec- ifying the emotion . This has led to a limited understanding of how speciﬁc emotions, especially those present in an individual in risky and uncertain situations, contribute to the decision-making process. Risky sexual behavior contains an inherent emotional component that can not be ignored, but must be integrated into a more dynamic model of decision-making.
Metcalfe and Mischel  outline a framework for understanding an individual’s ability to delay gratiﬁcation through willpower. They, similar to Slovic, Gold, and others, describe a cool cognitive system, the ‘‘know’’ system, and a hot emotional system, the ‘‘go’’ system, where the interaction between the two systems is fundamental to self-regulation, speciﬁcally in terms of delaying gratiﬁca- tion. In their model, the hot and cool systems are made up of nodes that can be activated and inhibited by external and internal events and processes (e.g., developmental fac- tors, stress, dispositional factors, and environmental fac- tors). Hot nodes contain the experience of diﬀerent feelings related to speciﬁc events. On the other hand, cool nodes contain information about an event, but are discon- nected and devoid of emotional experience. The interaction of these systems with other external and internal factors results in observed behavior.
A study on consumer decision-making investigated the interaction between cognition and emotion . In the research, participants chose between two options: one of the options was associated with a more favorable aﬀect, but with less favorable cognitions and the other option was associated with a less favorable aﬀect, but with more favorable cognitions. One of the ﬁndings was that when processing resources are higher and easily accessible, cogni- tion has a greater eﬀect on choice than aﬀect. This study supported the work of Puri , who distinguished between ‘‘impulsives’’ (high on consumer impulsivity) and ‘‘prudents’’ (low on consumer impulsivity). The distinguishing characteristic between these two groups was the degree of accessibility of cognitions related to impulsive behavior. It was also found that ‘‘prudents’’ spent more time thinking about the consequences of their actions than did ‘‘impulsives’’, and therefore this group was more easily able to access related cognitions.
Emotions also inﬂuence our attitudes and judgments, which in turn, inﬂuence the decisions we make. Zajonc  proposed that emotion is an independent, primary, and dominant inﬂuence of people’s responses to social situations. However, it can have indirect eﬀects on our behavior through implicitly shaping our attitudes and judgments (cognitive representations of the world). In addition, emo- tion may have diﬀerent eﬀects depending on the negative or positive valence of the emotion , or speciﬁc negative or positive emotions, such as anger or fear, or happiness and pleasure .
In the context of health-related decisions, aﬀect has been found to be an important predictor of engaging in health- protective behaviors (e.g., safer sex practices, exercise, healthy diet) through its eﬀects on self-eﬃcacy (perceived ability to manage one’s health) . However, the exact psychological, and possible neurobiological, mechanisms for this relationship are not fully understood.
Another factor that inﬂuences the decision-making process is arousal. This is particularly relevant in sexual situations, and may have implications for decisions to use safer sex practices, such as condom use. Arousal theories are mainly concerned with the independent eﬀect of emotionality and arousal on cognitive performance, whereas atten- tion theories focus on the cognitive component of emotions and its primarily detrimental eﬀect on cognition . However, the problem is more complex because arousal can be beneﬁcial or harmful, depending on the situation. Also, arousal can trigger diﬀerent appraisals of the felt emotion, depending on the context of the situation .
The relationship between arousal and performance has also been explored and an inverted U-curve relationship has been found. This phenomenon is known as the Yerkes–Dodson law [63,64]. Easterbrook’s theory (1959)  explains this phenomenon in that arousal aﬀects performance by regulating the breadth of an individual’s focus of attention. Under situations of low arousal, the focus of attention is too broad, and the individual has to sort through relevant and irrelevant information. Contrastingly, under situations of high arousal, the focus of attention is too narrow, and important information may be omitted. The optimal situation is moderate arousal, where there is a balance of the focus of attention.
Neuroscience research on emotion
The study of the nature of human emotions as well as their neural bases has been a subject of contention for over half a century. Given the subjective nature of this topic it has been diﬃcult to form a comprehensive list of what counts as an emotion and even more diﬃcult to identify the neural substrates of this vast and complex phenomenon. In this section of the paper, we will attempt to give an overview of the neural circuitry involved in emotional processing and then go on to discuss the relevance of these substrates to decision-making.
Historically, it has been believed that the limbic system in its entirety is responsible for processing emotional responses in humans and other animals. However, the more recent view puts particular emphasis on the amygdale (Fig. 2) as the key to emotional experiences. This region and its circuitry are believed to be involved in processing of emotional stimuli, organization of overt responses to these stimuli and the visceral or internal responses of the body’s organs .
The basolateral nuclei of the amygdala are thought to attach emotional signiﬁcance to a stimulus. The processed information from this region is subsequently sent to the cingulate gyrus, temporal pole, the medial orbitofrontal cortex, and the medial prefrontal cortex; all of which have been implicated in cognitive processing of emotional stimuli . This region of the amygdala also projects to the hippocampal formation in the limbic system, which is thought to be important in learning the emotional signiﬁcance of complex stimuli or the context in which emotion- ally charged stimuli are experienced. Another important projection of the basolateral amygdala neurons is to the central amygdala nuclei, which are believed to be important in mediating the behavioral responses to emo- tional stimuli.
Two other functions of the limbic system are worth mentioning. First, the central nuclei of the amygdala process and mediate the visceral consequences of emotions through their projections to the autonomic nervous system. In other words, this system is considered to be involved in the bodily sensations, such as arousal, produced in response to emotional stimuli. Second, the overt behavioral signs of emotion, such as ‘‘ﬁght or ﬂight’’ reactions and engagement in sexual activity, are considered to be mediated by the actions of the limbic system on the somatic motor systems.
The limbic system exerts inﬂuence throughout the brain through all major neurotransmitters. The most important of these is the midbrain dopaminergic projections, which synapse onto the regions involved in emotional possessing, such as the amygdala, and the cingulate gyrus. They also project onto the hypothalamus, which exerts a direct control over the hormonal aspects of emotion, particularly in terms of fear and arousal. The serotonergic projections are mostly involved in mediating mood and the feelings of anxiety and depression.
Following this discussion, the natural question is: What is the relevance of this information from a neural perspective to the process of decision-making? Throughout the paper, we have discussed the role of emotion in decision- making; a fact that has been emphasized by the somatic marker hypothesis (SMH) [68,69]. The SMH has been generally interpreted as evidence that emotional factors, out- side of consciousness, are suﬃcient and imperative for advantageous decision-making. However, it is important to note that despite the success of the SMH since its publication by Bechara, Damasio and colleagues in 1997 , it has recently come under scrutiny. In a signiﬁcant work done by Maia and McClelland , the authors reported ﬁndings that raise signiﬁcant questions about the interpre- tation of the results that led to the SMH. Although the work by Maia and McClelland does not completely rule out the somatic marker hypothesis, it certainly tries to set a standard for what can be interpreted as emotional and sub-conscious inﬂuences on decision-making. This point is further pursued by Sanfey and Cohen  as to the extent in which emotional processes play a primary role in decision-making and whether it is possible that emotion may be one of the many, albeit important, factors that gov- ern the process of decision-making.
From the above discussion we can observe that many of the projections from the limbic system synapse onto the regions of the brain that are involved in decision-making, particularly the prefrontal cortex. Moreover, experimenters have also demonstrated that other regions of the brain, which receive input from the amygdala, and other limbic regions are also involved in various aspects of decision- making. Sanfey and colleagues  demonstrated that unfair oﬀers in the ultimatum game (an economic game described in Section 4.1.1) activate the anterior insula, an
overlapping region of the frontal, temporal and parietal cortex that is known to be invoked during negative emotions such as pain and disgust [71,72]. It was also demonstrated that negative oﬀers also increased the activity of the anterior cingulated cortex, another region that receives projections from the limbic system and has been known to be involved in conﬂict and ambivalence [73,74].
Given the interconnections within the brain and the commonality of pathways, the interplay of emotion and cognition in decision-making should come as no surprise. Nevertheless, there is still considerable work to be done, in order to better understand the interactions of various neural substrates for emotions and their impact on decision-making.
Fig. 2. Nuclei and projections of the amygdale .
Neuroeconomics: its evolution and current status
Up to this point, we have reviewed diﬀerent theoretical perspectives of decision-making, as well as the methods employed in these domains. We then provided evidence for the role of emotion in decision-making, and its changing deﬁnitions over time and across domains of research. A recurrent issue is how we can extend the existing models of decision-making, speciﬁcally the cognitive naturalistic model, to account for diﬀerences in decisions and behavior due to emotion. As has been contended by other decision researchers, a single, comprehensive theory that integrates methods from various domains is needed to increase our understanding of the complex interplay of emotional and cognitive processes in decision-making. Work towards this goal is currently underway, speciﬁcally under the umbrella of the new approach of neuroeconomics. As implied in its name, this approach is concerned with identifying the neu- ral mechanisms that underlie decision-making behavior .
There are three major types of theories of decision-making: (1) normative theories that tell us how a rational individual should behave, (2) descriptive theories that characterize how individuals do behave, and (3) prescriptive theories that tell us how to behave under our own cognitive and other limitations. Classical decision theories in psychology and economics are normative theories, and they do not explain how individuals behave in the ‘‘real world.’’ Cognitive naturalistic decision-making is descriptive and details how individuals behave in ‘‘real world’’ set- tings, and can be predictive. Neuroscience theories are also descriptive, and characterize the mechanisms of human behavior, but are usually not predictive. Prescriptive theo- ries should tell us how to behave under our own limita- tions, such as those in cognitive as well as emotional processing.
Separately, the ﬁelds of economics, psychology, and neuroscience only have a limited perspective and a limited explanatory power. Economics is the science of decision- making, and its models can be applied to a variety of behaviors. It is concerned with modeling individuals’ valuing of rewards and making decisions out of a set of options.
Psychology is the science of the mind, and its models can also be applied to a wide range of behaviors. The cognitive processes involved include ﬁltering and encoding information from the environment, accessing memories of similar events, and using heuristics and biases in interpreting the information. Neuroscience is concerned with measurements and modalities in characterizing a limited set of behaviors . However, research in this ﬁeld is narrowly focused at the cellular level and there is no explanation of how or why decisions are made.
Ultimately, ‘‘The goal of the emerging neuroeconomic program will have to be a mechanistic, behavioral, and mathematical explanation of choice that transcends the explanations available to neuroscientists, psychologists, and economists working alone ’’. In this new approach, economists and psychologists provide the conceptual tools for understanding and modeling behavior, while neuroscientists provide the tools for the study of the mechanisms of behavior. In this section, we review the evolution of neuroeconomics and ﬁndings that attempt to account for the role of emotion to answer why when given the same infor- mation, people do not always make the same decisions and why one individual does not always make decisions in a consistent manner.
We present an example of how a decision-making phenomenon called the St. Petersburg Paradox  would be explained according to the perspectives of economics, psychology, and neuroscience, respectively. In order to under- stand this paradox, we will deﬁne some economic terms. An expected value is the sum of the probability of each possible outcome in a game multiplied by its payoﬀ (value). In other words, it is the average amount one ‘‘expects’’ to win per bet if bets with identical odds are repeated many times. However, the value might not be ‘‘expected’’ and may be unlikely or even impossible. Utility represents the anticipated payoﬀ of the game. Personal utility may vary with the personal degree of risk aversion. Risk aversion refers to the willingness to accept a lower expected payoﬀ for a more predictable outcome.
The game is then presented like this:
In a game of chance with a ﬁxed entrance fee, a fair coin is tossed repeatedly until a ‘‘head’’ ﬁrst appears. You win 1 cent if a head appears on the ﬁrst toss, 2 cents if on the second, 4 cents if on the third, 8 cents if on the fourth, etc. The prize doubles with every toss. In other words you win 2k-1cents if the coin must be tossed k times.
How much would an individual be willing to pay to enter the game? According to economic theory, the game has an inﬁnite expected value, which means that on aver- age, one can expect to win an inﬁnite amount of money when playing the game. An economist would explain that the desirability of money grows more slowly as the total amount of the stake increases. This is based on Bernoulli’s notion of subjective value/utility, which states that as the objective value of the gain increases, subjective desirability grows more slowly . Humans are typically risk averse, so it is assumed that the player will multiply the probability of winning on each ﬂip by the utility of the amount won on that ﬂip. Therefore, it doesn’t matter how much one pays to enter, because you will make up for it in the long run. However, the probability of an extremely large payoﬀ is very small, even though the probability of success at each ﬂip (P (T) = 0.5) is the same as the probability of failure (P (H) = 0.5). In reality, this is not how individuals behave. No reasonable person would pay more than a few cents to enter. Why is that so? Economic theory is a normative theory explaining optimal behavior, but not taking into account the constraints inherent in the ‘‘real world.’’
A psychologist would explain the paradox via a mechanism that accounts for human risk aversion, which shows that human beings are more sensitive to monetary losses than to monetary gains. Human beings have a strong fear of losing, and they evaluate the value of all possible gains and losses relative to a particular frame of reference. Another psychologist could explain the paradox according to the use of heuristics. A neuroscientist would explain the paradox through a detailed description of a stimulus–re- sponse mechanism, and the resulting propagation of syn- apses to the sensory and motor regions of the brain.
All of these explanations attempt to justify the same observed behavior. So, what would distinguish the correct explanation from the incorrect ones, or are all of the explanations correct at the same time? To resolve this conﬂict, an integration of these three perspectives propels the neuroeconomic approach.
A concept that exists in economics, psychology, and neuroscience is subjective desirability, or preferences. It is agreed that subjective desirability is essential to the decision-making process. The central concept in modern economics is subjective utility, where preferences are described as subjective properties of the chooser. Psychologists, Kahneman and Tversky  assert that subjective utilities are computed with regard to a reference frame. The concept of preferences, speciﬁcally that they are represented in the nervous system, are subjective, and inﬂuence behavior, has only been incorporated recently into the ﬁeld of neuroscience. Research by Newsome
 on perceptual decision-making in monkeys, who viewed ambiguous sensory stimuli, showed that there is no single or deﬁnite nerve path from stimulus to response. In other words, the same stimulus does not always produce the same response across subjects, nor does it produce the same response within the same subject across diﬀerent situations. From a neurological point of view, the posterior parietal cortex’s role in decision-making may be the encoding of the desirability of making partic- ular movements. Thus, neuroscientiﬁc evidence for the economic and psychological concept of preferences supports and substantiates its role in decision-making and its origins in the brain. This example illustrates how neural mechanisms can be found to correspond with economic and psychological constructs.
Decision-making under risk, uncertainty, and ambiguity: evidence from neuroeconomics
We are faced with situations of risk, uncertainty, and ambiguity on a daily basis, and we are continually making decisions under these conditions. In such conditions (e.g., time pressure, arousal), we often rely on emotions to help guide our choices. When evaluating options in making a decision, people often rely on their own perceptions of risks and beneﬁts and not necessarily the actual risks and bene- ﬁts involved in the decision. Further, perceived risks and beneﬁts are often inﬂuenced by one’s emotional state. Risk exists when there is a possibility of loss or other negative outcomes associated with a decision option. It is largely subjective (assessed by the individual) and varying in degrees. Risks are usually viewed relative to possible bene- ﬁts or positive outcomes associated with a decision option. Uncertainty refers to an individual’s doubt as to the cor- rect or best option to choose when making a decision. To this individual, the outcome is largely unknown, which heightens the individual’s focus on their current emotional state and other situational factors. The presence of uncer- tainty also inﬂuences an individual’s perception of the risks involved with each potential option; increasing the perceived risk in the situation.
Ambiguity occurs when information is missing that could be essential to the decision-making process. The Ells- berg paradox  is a well known example of decision- making under ambiguity, and in general, is used as evidence of ambiguity aversion. Ambiguity aversion refers to the idea that individuals would rather choose the option in which the probabilities of winning, or of obtaining a positive outcome, are known. In the Ellsberg paradox, this is essentially what occurs; an individual chooses the option with the known probability in all situations, even if there is the possibility that the unknown probability is higher. This paradox is important because it shows how individuals’ choices violate the core assumptions of economics’ expect- ed utility theory, and states that individuals label ambigu- ous situations and options as high in risk, and therefore, prefer to avoid this risk.
In the next two sections, we will review research on the Ultimatum game and gambling tasks, which provide evidence for a role of emotion in decision-making under risk, uncertainty, and ambiguity. In a typical study using the neuroeconomic approach, neuro-imaging techniques are used to measure brain activity while participants play economic games.
Unfairness in the Ultimatum game
An economic game evaluating decision-making under ambiguous circumstances is the Ultimatum game . In this game, two players have the task of splitting a sum of money. The ﬁrst player, the ‘‘proposer’’, makes an oﬀer of how the money should be split. The second player, the ‘‘responder’’, accepts or rejects the oﬀer. If the responder accepts the oﬀer, then the money is split as proposed. However, if the ‘‘responder’’ rejects the oﬀer, then neither one wins the game and neither one gets any money. The stan- dard economic solution is that some money is better than no money, so one should always accept the oﬀer. In reality, behavioral research has shown that low oﬀers (20% of total) have a 50% chance of being rejected. Based on par- ticipant reports, they rejected low oﬀers because of anger (negative emotion) felt due to the unfairness of the oﬀer, and they wanted to punish the other player in some way. The unfair oﬀers induced conﬂict between the cognitive motive to accept the oﬀer and the emotional motive to reject the oﬀer.
Sanfey et al.  performed functional magnetic reso- nance imaging (fMRI) on the ‘‘respondent’’ participants while they played the Ultimatum game against either a computer or a human. The results suggested that there was a stronger emotional reaction to unfair oﬀers made by a human than those same oﬀers made by a computer. The brain areas with greater activation for unfair oﬀers compared to fair oﬀers were bilateral anterior insula, dor- solateral prefrontal cortex, and anterior cingulate cortex. The bilateral anterior insula is associated with negative emotional states, whereas the dorsolateral prefrontal cortex is associated with cognitive processes, such as goal mainte- nance and executive control. Further research has shown that the level of activity in the anterior insula can reliably predict whether or not a player will reject an unfair oﬀer . These two areas may be competing with each other for dominance in the decision-making process. The anteri- or cingulate cortex is usually associated with cognitive con- ﬂict. Thus, activation in this area may reﬂect the conﬂict between the cognitive motive to accept the oﬀer and the emotional motive to reject the oﬀer and resist unfairness .
Immediate versus delayed gratiﬁcation
When making decisions, there exists a tension between a desire for immediate gratiﬁcation and delayed gratiﬁcation. This has been seen through many diﬀerent studies across diﬀerent domains . Impulsivity consists of giving in to temptation and desire for immediate gratiﬁcation, where the longer term consequences might be ignored or not eval- uated. Patience consists of knowledge of beneﬁts of delayed gratiﬁcation and ability to wait for these beneﬁts, perhaps to avoid undesirable consequences. McClure et al.  found that all kinds of inter-temporal choices, not only the ones involving immediate rewards, activate the lateral prefrontal areas of the brain, including associated parietal areas. This ﬁnding supports the idea that emotion greatly inﬂuences our choices. A competition within the brain between lower level processes, such as those governed by the limbic system, and the higher level processes, governed by the prefrontal area has been suggested .
Of particular importance in this regard is the role of the limbic system in the brain’s reward circuit. The main centers of the reward circuit are located along the medial fore- brain bundle, consisting of the ventral tegmental area (VTA) and the nucleus accumbens (NACC). Also included in the circuitry, are several other sections such as the sep- tum, the amygdale, the prefrontal cortex and certain parts of the thalamus. All of these sections innervate the hypothalamus, which in turn acts not only on the ventral tegmental area but also on autonomic and endocrine functions of the body, through the pituitary gland. An example of this neuro-endocrinological regulation is the production, secretion and regulatory function of oxytocin, a neuro-peptide, which acts as both a hormone in the blood stream and a neurotransmitter within the central nervous system .
Recently, research on oxytocin, a neuropeptide associated with social attachment and aﬃliation in non-human mammals, and its inﬂuence on trust and decisions in social interactions have provided a concrete example as to the implications of understanding the neural bases of behavior and the need for a more comprehensive cross-disciplinary theory of decision-making. Kosfeld and colleagues  investigated the eﬀect of oxytocin on trusting behavior in humans in a double-blind study, where participants were administered either one dose of oxytocin or placebo, via a nasal spray. They found that those participants who received oxytocin exhibited more trusting behavior than those participants who received the placebo. The authors conclude that oxytocin causes an increase in trust in humans, aﬀecting ‘‘an individual’s willingness to accept social risks arising through interper- sonal interactions (p. 673)’’ . These ﬁndings are one illustration of a practical implication of understanding the interaction between social interactions, cognition, and neuroscience.
Extension of the model: cognitive neuroeconomics
In this paper, we have reviewed various perspectives of decision-making as well as many diﬀerent theories and methods that have been developed to account for deci- sion-making behavior. We have attempted to strengthen and validate the idea that emotion plays a prominent role in decision-making. The theories and methods we have reviewed so far do not represent an exhaustive list, but highlight the eﬀorts of many researchers in accounting for emotion in an individual’s decision-making behavior. The current challenge is to integrate these various theories into one cohesive and general model of decision-making, which can then be applied to a variety of decision situations and customized based on the context.
In this next section, we propose the Cognitive Neuroeco- nomic model, which highlights the roles of emotion and cognition in decision-making under risk. Fig. 3 illustrates this model, which can be applied to a variety of decision- making situations. The model is read from left to right, showing the progression of time across the decision-making situation. External to the individual are environmental factors, which refer to the physical environment, and socio- cultural factors, which include various social inﬂuences (e.g., family, peer) and group norms. Within the individual, one’s neurophysiology supports two main processes: emo- tional (i.e., arousal) and cognitive (i.e., past experiences, beliefs and assumptions, perceptions). There is a bidirec- tional link between these two processes to indicate the mutual inﬂuence that they have on each other. Both envi- ronmental and socio-cultural factors inﬂuence the individual’s emotional and cognitive processes. In other words, an individual’s cognitions and emotions are not isolated but take into account external social and environmental inﬂuences. Moving to the next box on the right, an individual’s temporal focus moderates the relationship between emotions and cognitions and perceived risks and beneﬁts. In other words, the individual perceives a diﬀerent set of risks and beneﬁts depending on whether they are focusing on the short term (in the immediate situation; the present) or the long term (after the immediate situation; the future). This set of perceived risks and beneﬁts then inﬂuences the indi- vidual’s behavior in the immediate situation. In turn, the actual behavior inﬂuences subsequent emotions and cogni- tions, which leads to an evaluation period. This evaluation post-decision inﬂuences the pre-decision processes during the next decision-making situation.
This model is dynamic, and is sensitive to changes in emotion, cognition, and behavior over time and with accumulated experience. Fig. 4 illustrates the bidirectional inﬂuences of the stability of perceptions and emotion as well as the consistency of behavior over time. In addition, an unexpected event, which occurs externally to the individual, inﬂuences both the stability of perceptions and the stability of emotions, which in turn, inﬂuences the consistency of behavior.
Fig. 3. Cognitive neuroeconomic model of decision-making.
Fig. 4. Changes in cognitions, emotions, and behavior over time.
Role of emotion and cognition in sexual decision- making: an illustration
In this section, we provide an illustration of how this cognitive neuroeconomic model can be applied to sexual decision-making, a particular type of decision-making under risk. We provide evidence of its applicability through examples from our study on young adults’ safe sex practices. In the beginning of the paper, we explained the cognitive naturalistic model, which we have used to identify patterns of condom use among the young adults in our study. We were not able to account for all of our data using our current model, but extending the model to incorporate the role of emotion has allowed us to explain more varia- tions in behavior than before. It is important to note, how- ever, that our study did not involve the use of any neuroscience techniques. We are, therefore, unable to fully illustrate this particular aspect of the model apropos to our current study.
Our investigations show that, in all patterns of condom use, there are dominant emotions that inﬂuence the process of decision-making. These emotions can be felt before a decision has been made (pre-decision) and after a decision has been made (post-decision), and these emotions may differ from each other and have diﬀerent inﬂuences on subsequent cognitions and behavior. The emotions also interact with cognitions and perceptions of the situation, environ- mental and situational factors, past experiences, and per- ceived risks and beneﬁts of performing the behavior. The young adults in our study were more sensitive to categori- cal changes (from low to high or high to low) in beneﬁts and risks, than actual changes in the exact numerical prob- ability of these beneﬁts and risks. Even if they based their decisions on a number, their estimations of the number are usually inaccurate. Therefore, making a decision according to the classical model will not always result in the best decision. The stability over time of pre- and post-decision emotions can diﬀer depending on an individ- ual’s behavior and the stability of that individual’s percep- tions. The behavior, in turn, can inﬂuence the emotion felt post-decision (after the sexual encounter), which may inﬂu- ence perceptions and behavior during the next sexual encounter.
Pattern A is characterized by consistent condom use. At the beginning of these individuals’ lifetime sexual activity, the dominant pre-decision emotion was fear (negative aﬀect). In addition, these young adults focus on the long-term eﬀects of their behavior. Their choices are to use condoms or not use condoms during sexual intercourse. If they use condoms, the perceived beneﬁts in the long term are high (protection from STDs/HIV and pregnancy) and the perceived risks are low (protec- tion from STDs/HIV and pregnancy). If they do not use condoms, the perceived beneﬁts are high (pleasure), but the perceived risks are also high (no protection from STDs/HIV or pregnancy, if an alternate form of contra- ception is not used). In both situations, the beneﬁts are high, even though they refer to diﬀerent beneﬁts, but if they do not use condoms, the risks are also high. There- fore, they choose to use condoms consistently during sex- ual intercourse, simultaneously ensuring high beneﬁts and low risks. The initial fear that they felt became com- fort and a feeling of safety (positive aﬀect) because their behavior kept them protected from negative consequenc- es. One young woman underscores this point with her response to the interviewer’s question of what has inﬂu- enced her decision to use condoms. She replied, ‘‘Because they’re protective [against] pregnancy. And I feel clean and safe also.’’ After the sexual encounter, these individ- uals feel comfort in the fact that they have protected themselves and satisfaction (positive aﬀect) from engag- ing in the sexual activity. Their emotions remain stable and so do their perceptions, resulting in the consistency of condom use.
On the other end of the spectrum, Pattern D is characterized by inconsistent condom use, in which young adults representing this pattern do not use condoms most of the time. These individuals’ dominant emotions are desire and passion, which stem from sexual arousal. As a result, they only focus on the short-term eﬀects of their behavior in the isolated sexual encounter. Because these young adults sometimes do use condoms, it is important to iden- tify how the circumstances diﬀer during protected and unprotected sexual encounters. One young man (partici- pant #46) gives insight into this issue:
Q: And so, when you have decided to use condoms, what has distinguished that from other times where you haven’t used them?
A: I don’t know it’d be like; the condom is always in my mind. It’s always the ﬁrst thing on my mind to use a condom, it’s like, that’s all that matters, but it’s like I said, like, it, the heat of the moment, I mean, I don’t know, the lust just take over. I like, I forgot, like, I forgot the whole condom issue.
Q: And that doesn’t aﬀect your decision-making the next time you decide to have sex?
A: I mean it does, I mean, I’m using condoms like crazy, so…
Q: How often, what percentage of the times would you say you have used condoms?
A: You talking about, like in my lifetime?
Q: Yeah, or like your recent, like past couple years or last year or so.
A: Not, like last years.. .it’s been like, condom use has been like 15, 20%, real low, and I haven’t really, all my partners be usually, you know, like, people I kin- da.. .like even if you know them, you still really don’t know them so, but it ain’t like total strangers I just meet.
In this case, if they use condoms, the perceived beneﬁts are low (less pleasure) and the perceived risks are low (protection from STDs/HIV and pregnancy). If they do not use condoms, the perceived beneﬁts are high (pleasure, desire, passion), but the perceived risks are also high (no protection from STDs/HIV or pregnancy, if an alternate form of contraception is not used). Because their focus is on each speciﬁc encounter, the perceived risks and beneﬁts are accurate. Congruent with their emotion and desire for plea- sure, these individuals focus on the high beneﬁts incurred from not using condoms, instead of the low risks from using condoms. These factors also interact with the envi- ronmental factor of condom availability. When a condom is not available, it is more likely for them not to use a con- dom based on their emotions and focus on the pleasurable beneﬁts of not using a condom. However, if a condom is available, it is likely that they will decide to use a condom and think about the high risks of not using a condom.
Most times, these young adults do not use condoms, and sometimes experience regret and guilt post-sexual encounter because they did not use a condom and thus, put them- selves at risk. Therefore, their emotions are constantly in ﬂux. These negative post-decision emotions will sometimes aﬀect condom use during the next sexual encounter if that encounter happens soon after, but most decisions are made in the actual sexual situation because they have not formulated a plan beforehand.
Pattern B is characterized by a shift from consistent to inconsistent condom use during a relationship. Initially, these individuals experience insecurity and fear for their safety because of a lack of trust and uncertainty about their partners. However, these young adults want to trust their partners and are usually involved in long-term relation- ships. As with the individuals practicing consistent condom use, these young adults also focus on the long-term eﬀects of their behavior. At the beginning of a new relationship, they think of protection. If they use condoms, the perceived beneﬁts in the long-term are high (protection from STDs/ HIV and pregnancy) and the perceived risks are low (pro- tection from STDs/HIV and pregnancy). If they do not use condoms, the perceived beneﬁts are high (pleasure, trust), but the perceived risks are also high (no protection from STDs or pregnancy, if an alternate form of contraception is not used). As with Pattern A, the behavior, in the initial stages of the relationship, is to use condoms consistently.
Over time, these individuals experience a gradual change in their emotions from insecurity to security due to the presence of trust. Changes in their perception of the rela- tionship rely on changes in their emotions. Thus, their per- ception of the commitment and exclusivity of their partner the relationship may be inaccurate. One young woman talked about how her feelings changed when she and her partner decided to discontinue condom use. She said that she got a ‘‘security feeling’’ and ‘‘I just feel secure, like, I don’t get that feeling (negative feeling) in my stomach.. .’’ These individuals continue to focus on the long-term, but the perceived risks and beneﬁts change. If they use condoms, the perceived beneﬁts are low (less commitment, deception in relationship) and the perceived risks are low (protection from STDs and pregnancy). If they do not use condoms, the perceived beneﬁts are high (pleasure; relationship that is more committed), but the perceived risks are now low (pro- tected because trust that partner is monogamous and free of infection/on birth control). Congruent with their reliance on their feelings of trust, these individuals focus on the high ben- eﬁts of pleasure and commitment and the perceived low risk of not using condoms. Therefore, they decide to terminate condom use. These changes occur over the course of one rela- tionship. If a new relationship is entered, the initial fear and lack of trust is encountered again, and the condom use behavior changes.
Pattern C, is characterized by a shift from inconsistent condom use to consistent condom use, due to a signiﬁcant negative event in their lives, such as contraction of an STD, pregnancy, and/or abortion. Before this negative event, these individuals’ pattern of emotions, perceptions, and behavior follow that of Pattern D (inconsistent condom use). The negative event causes an abrupt change in their emotions from a positive to negative valence. They now have a persistent fear for their safety that is based on this negative experience. In these individuals, past experience is a highly emphasized factor, which is inﬂuential in chang- ing behavior, but can sometimes be magniﬁed and distort- ed due to its importance in their lives and the fear associated with it. These young adults now focus on the long-term eﬀects of their behavior. One young man told of his negative experience and expresses the fear that result- ed from it when asked if he had always used condoms for sexual intercourse. He responded:
.. .there was one time, one of my girlfriends, I didn’t use a condom, and she up and got pregnant, so after that I was like I’m always going to use a condom, because that really scared me because I had to wait six weeks to get a decision whether she was pregnant or not … And that was really stressful for me. I was scared. So after that, I just use a condom every time.
If these young adults use condoms, the perceived bene- ﬁts are high (protection from STDs/HIV and pregnancy) and the perceived risks are low (protection from STDs/ HIV and pregnancy). If they do not use condoms, the perceived beneﬁts are high (pleasure), but the perceived risks are also high (no protection from STDs or pregnancy, if an alternate form of contraception is not used). Due to past experience and negative aﬀect, they are less likely to take risks and more motivated to protect their health. There- fore, they consistently use condoms.
Discussion: merging theory with practice
Through the course of our discussion so far, we have described the theory and model of neuroeconomics and have extended this model to incorporate both the role of emotion and cognition as integral parts of the decision-making process, hence creating a more comprehensive model for the study of decision-making. Moreover, we have used this new cognitive neuroeconomic model to explain some of the observed behavior patterns in the sexual decision-making of young adults. As discussed in the preceding section, emotion seems to play a highly signiﬁcant role in determination and shaping of sexual behavior in young adults. It was shown that some pat- terns of behavior in our participants rendered them vul- nerable to the contraction of sexually transmitted diseases, posing a serious public health concern in young adults.
The ﬁndings from our study and the highly useful and ﬂexible framework of cognitive neuroeconomic decision- making can prove to be important in developing interventional and preventative tools and techniques. It is impor- tant to note that although we have only discussed the application of this model to lay people, it has tremendous consequences in development of highly integrative decision support tools for clinicians, and more importantly, for
public health informaticians. Currently, a number of meth- odologies are used for educating and communicating with young adults about their sexual decision-making. Most of these approaches are based on representation of STD information in a highly statistical and ‘‘hyper-rational’’ method. Individuals are subsequently expected to under- stand, analyze, and process this information and make their decisions accordingly. However, as we have shown in this paper, most individuals do not make decisions in this way. The proposed model will therefore enable us to present the information in an aﬀective framework that is more likely to appeal to and be understood by the individuals.
In addition, we can use a similar approach to provide information on the web. In recent years, the World Wide Web has become an immeasurable source of information for every ﬁeld and medicine is no exception. Most young adults today use the web as the primary source of informa- tion with regard to sexually transmitted diseases. Although these individuals may subsequently consult their primary physicians for further information, the web remains a vital source of information for them, both in terms of initial information seeking as well as, seeking further information with regard to support and intervention. However, the most popular websites such as WebMD and others provide little information in terms of support, education and pre- vention within an aﬀective or emotional framework. We believe that this source of information can prove to be far more inﬂuential and useful, if the aﬀective aspects of the STDs and sexual decision-making are taken into account.
The use of cognitive neuroeconomics will be valuable to the understanding of human behavior and the diverse nature of mechanisms that play a role in making decisions. This could be very important to development and implementation of decision support tools for clinicians and lay people alike.
We have reviewed some evidence for the role of emotion in decision making from diﬀerent domains such as psychology and neuroscience. We introduced an emerging discipline called neuroeconomics which aims to fuse the theories, methods, and principles of psychol- ogy, economics, and neuroscience into a single theory of choice. This new comprehensive approach hopes to provide a broader framework for investigations into the process of decision-making. Using this approach, we reviewed evidence for the role of emotion in decision-making, with a focus on risky decisions and proposed an extension of the neuroeconomic model— cognitive neuroeconomics—to emphasize the roles of both cognition and emotion in decision-making under risk and uncertainty. Simple choice behaviors do not account for the complexity of human decision-making under uncertainty.
The cognitive neuroeconomic model, as illustrated in Fig. 3, takes into account the interplay of the emotional, socio-cultural, environmental, and neurophysiologic factors that are all involved in the process of evaluating choices. At the time of making a decision, an individu- al’s emotional and cognitive state and temporal focus lead the individual to evaluate the perceived risks and beneﬁts of the decision, which ultimately leads to a ﬁnal decision. This process then works as a feedback mecha- nism, aﬀecting each subsequent decision. To illustrate this model, we provided examples of the role of emotion from our research on young adults’ risky sexual decision-making.
Like Biomedical Informatics, neuroeconomics is an integrated ﬁeld consisting of various theories, models and tools, which form a convergent and coherent ﬁeld with widespread applications. It is interesting to note that many of the integral facets of neuroeconomics also constitute important aspects of biomedical informatics. Decision- making, which is the core of the study of neuroeconomics, is also an important ﬁeld within biomedical informatics. Furthermore, imaging techniques, such as fMRI, that have been crucial in the development of neuroeconomics, are one of the major divisions of biomedical informatics. Given these similarities and shared origins, the applications of neuroeconomics within the ﬁeld of biomedical informatics should come as no surprise.
In this paper, we have discussed the various theories, methods and models that together constitute a cognitive neuroeconomic model of decision-making. We believe that this new model provides the ﬂexibility and compre- hensiveness needed to explain some decision-making behaviors. This new approach can provide decision scien- tists with an unprecedented depth of insight into the pro- cess of decision-making, which is needed if we are to provide appropriate decision support, a ﬁeld of interest to medical informaticians. The aim is to capitalize on the theories and methods developed in each of the disci- plines in this model to try and understand how we make decisions. One hopes for a uniﬁed theory to account for decision-making behavior in a complex domain such as health care.
We hope to further expand this model and use it for our future studies, particularly those involving fMRI data on patients. However, it is important to note that the current status of magnetic resonance imaging tech- niques that are available to us hinder the possibility of fully simulating real world situations. This is an issue of particular concern with regard to the complex inter- play of emotion and cognition as it applies to sexual decision-making or other highly emotional and dynamic situations. We will therefore need to design elaborate and comprehensive simulation techniques to recreate the choices and environments faced by these decision makers. Nevertheless, we believe that this model is a step towards a much clearer understanding of various factors that play a role in decision-making.
This research was supported by an NIMH Grant R01 MH65851 to Vimla Patel.
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Journal of Biomedical Informatics 39 (2006) 720–736
© 2006 Elsevier Inc. All rights reserved.
1532-0464/$ – see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.jbi.2006.03.002
* Corresponding author. Fax: +1 212 305 3302.
E-mail address: firstname.lastname@example.org (V.L. Patel).
1 These authors contributed equally to the manuscript.
Lily A. Gutnik 1, A. Forogh Hakimzada 1, Nicole A. Yoskowitz 1, Vimla L. Patel *
Laboratory of Decision Making and Cognition, Department of Biomedical Informatics, Columbia University, New York, NY, USA
Received 14 December 2005
Available online 7 April 2006