Neuroeconomics foundational issues and consumer relevanc

47 Neuroeconomics Foundational Issues and Consumer Relevance GIOVANNA EGIDI HOWARD C NUSBAUM JOHN T CACIOPPO University of Chicago Two cornerstones of classic economic theory are the assumptions that individuals are rational decision makers and have purely self-regarding preferences (Camerer & Fehr, 2006) These assumptions fly in the face of most theories of consumer behavior, where individuals are depicted as characterized by bounded rationality and bounded self-interests In the 1950s, a popular television show called The Millionaire was based on the premise that an unseen multimillionaire would dispatch his associate carrying a tax-free cashier's check for $1,000,000 made out to "random" individuals on condition that they never attempt to discover who sent it, or reveal where the money came from, except to their husband or wife The new millionaires would accept the check and experience a series of dramatic events, the details of which would vary with each episode There was no drama in whether they would accept $1,000,000 from a stranger without context or explanation and who demanded total secrecy Classic economic theory tells us that a rational person would accept the offer, and the intuition of the television audience was in agreement However, if a complete stranger were to actually approach you on the street and offer you $1,000,000, with no explanation or preamble, would you or the average person truly just accept the offer without some kind of deliberation or consideration of the situation? You might consider the norm of reciprocity and what future obligations to this person you were incurring, or you might hesitate remembering the aphorism that "if it looks too good to be true, it usually is." In fact, work in psychology and behavioral economics suggests that most people would respond with a kind of bounded rationality (cf Gigerenzer & Selten, 2002; Kahneman, 2003; Simon, 1982) reflecting a greater knowledge of people and behavior Perhaps even a strictly Bayesian response might be modulated by the highly improbable nature of this event, which in this case makes this kind of outof-the-blue offer extremely suspicious Imagine a more likely scenario: Someone approaches you to offer $100 that was money you had already spent Perhaps spending this money was not even originally your choice you had to spend the money for a tax or a fee or being overcharged Imagine the person approaching you is a known public figure Under these circumstances, who would not accept the return of their own money? 1177 I I S I I(,II>I,IRAV,1Rl)( NI'UVU:vI, 1NI)JOIIN I.C V IO1'PO lust as in the case of V ic Millionaire, it appears intuitive that no one would be so irrational as to rcjcct the money Certainly Bill Frist, the Senate Republican Majority Leader in 2006, thought that taxpayers would readily accept a $100 "rebate," given the soaring price of gas This was an offer to return $100 to taxpayers with no strings attached The response was completely unexpected: Senators received direct feedback from taxpayers rejecting the offer Constituents were angry and insulted by the (,tIer of $100 How can an offer of $100 with no immediate or apparent quid pro quo be viewed as insulting? Perhaps the average taxpayer considered the broader and longer term nature of the energy price problem that this proposal could not truly address and was insulted In some sense, the failed Senate Republican proposal has the structure of a simple game that is used in behavioral economics research, the ultimatum game (e.g., Guth, Schmittberger, & Schwartze, 1982; see Thaler, 1988) In this game, two players are apprised of the size of a pot of money One player (the Allocator) makes an offer about how to split the pot the other player (the Responder) de.: ides whether to reject or accept the offer If the offer is accepted, both players get their respecti1 e parts of the pot If the offer is rejected, neither player gets anything From the perspective of a rationalist economist (e.g., Rubenstein, 1982), the best offer for the Allocator is a penny (or the smallest offer possible) regardless of the size of the pot, and the Responder should accept this offer WW' by give up a penny and get nothing when you can keep a penny? However, people not usually act in this fashion In general, most Allocators make much larger (and therefore irrational) Offers than predicted (see 'Thaler, 1988), and Responders reject offers that seem insulting relative to some standard of fairness (e.g., Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003) Furthermore, brain imaging studies indicate that the brain system involved in reward is activated when Responders (in a similar game) spend money to punish unfair partners (de Quervain et al., 2004) This finding has been interpreted as signifying "altruistic punishment," meaning that people are deriving personal pleasure from foregoing their rational self interests and pursuing what is in the interest of the collective '[hat is, the behavioral and neuroimaging evidence suggest the operation of bounded rationality and bounded self-interests the use of brain imaging to investigate processes of economic decision illustrates a new approach to the study of economic behavior termed "neuroeconomics." Neuroeconomics is an interdiscipliniIry field with the goal of building a neural model of decision making in economic environments To achieve this goal, investigators typically examine brain function when individuals formulate or cKpress preferences, evaluate decisions, categorize risks and rewards, or make choices to test economic hypotheses Our goal in the present chapter is to review foundational issues in neuroeconomi(s to permit readers to better understand and evaluate the burgeoning literature in this +ield and its possible relevance to consumer behavior We will draw from the literature on neurocWnomics to illustrate specific points, but space precludes a complete review of neuroeconornic I;?od-Js per se lXI l.AINING ECONOMIC DECISIONS lie apparent irrationality of the average economic decision maker has been well studied and docuInente_ed (s contrary to the assumptions of standard economics (e.g., Tversky & hahnenian, 1981) Rational choice theorists have continued to hew to the coldly rationalist model of the individua decision maker in part by questioning the generalizahility of those results to actual economica d.leo si(_m settings, in part by reasoning that what appears to he irrational behavior is in fact coldl} r tional but reflective of utility functions that were incorrectly or incompletely specified, and it Parr by noting that economic theory makes predictions about aggregate, not individual behavior NEUROECONOMICS 1179 and irrationality does not have an impact on aggregate human behavior For instance, if the irrationality describing individual behavior is unsystematic, then the departure from rationality will sum to zero as one aggregates over individuals, leaving the rational choice theory of economic behavior intact Behavioral economists, psychologists, and consumer behaviorists have contested each of these defenses of rational choice theory Federal Reserve Chairman Alan Greenspan's use of the term "irrational exuberance" in 1996 to describe what he viewed as an overvalued U.S stock market was an acknowledgement that irrationality was not confined to university experiments The suggestion that incompletely specified utility functions accounted for what appeared to be irrational decision making, although plausible, is not falsifiable And the notion that irrationality is haphazard and, therefore, sums to zero when aggregated is not supported by the extant data A half century ago, McGuire (1960) proposed a theory of belief, which addresses how a change in one belief can impact inferential changes in beliefs and preferences, on which deviations from rationality are systematic and predictable In McGuire's (1960, 1981) theory, beliefs are conceptualized as having a syllogistic structure, with a major premise ("Time magazine has the best movie reviews of any weekly publication"), a minor premise ("Weekly movie reviews are very valuable"), and a conclusion ("Time magazine is valuable") McGuire reasoned that an individual's belief in a premise or conclusion is not necessarily all or none, so a probability or confidence statement can be assigned to each premise and conclusion The probability of the conclusion can be designated as p(B), the probability of the minor premise can be designated as p(A), and the major premise can be designated as p(B/A) Bayes theorem provides the probability that the conclusion will be held, based on pure logic, given an individual's other beliefs (Wyer & Goldberg, 1970) By a simple extension, changes in beliefs about the major or minor premises produce point predictions about the resulting changes in an individual's endorsement of the conclusion (i.e., preferences) Empirical research on McGuire's theory has produced evidence for bounded rationality Evidence for rationality, termed "logical consistency" in the model, refers to the finding that people's endorsement of conclusions is in general accord with predictions derived by Bayes theorem However, there are also significant discrepancies between predicted and observed conclusions, and the discrepancies are systematic Specifically, the desirability of the conclusion to an individual influences the likelihood the individual will endorse the belief This effect, variously termed "wishful thinking" and "hedonic consistency," is especially evident in the behavior of individuals who are less educated (Watts & Holt, 1970) If individual choice is characterized by bounded rationality and bounded self-interest, and if deviations from these bounded conditions are systematic rather than random, then aggregate prediction of economic behavior, including aggregate consumer behavior, can benefit from a better understanding of the individual decision process Although behavioral economists and psychologists have worked to better characterize the psychological process of economic decision making, there are limitations to relying on single behavioral outcomes (i.e., expressed decision) when trying to break down the process into subcomponents (component processes) Of course, additional information can be measured or collected, such as how quickly the decision is made, or how confident or valuable the decision is, or an introspective report of the decision process using a talk-aloud protocol (Waterman & Newell, 1971) But the decision itself reflects the pinnacle of a set of processes and these ancillary measures cannot be parsed into the partial influences of the component processes or mechanisms (cf Small & Nusbaum, 2004) Simply asking participants about the nature of their decision process is not a reliable means of investigating decision making: The conscious introspective parsing of such processes by participants often deviates significantly from objective measures of processing (e.g., Nisbett & 1180 GIOVANNA EGIDI, HOWARD C NUSBAUM, AND JOHN T CACIOPPO Wilson, 1977) and is more influenced by folk psychology and biases than the actual processing that takes place Additive factors methods (Sternberg, 1969) offered some hope for decomposing from a response time measure the component processes underlying the response, but the componential analysis of response times is not without its own limitations and drawbacks (e.g., McClelland, 1979) Although behavioral measures play an important role, any reliable and valid data that bears directly on the component processes as they unfold contributes to the generation of better theories and better means of falsifying theories It is partly for this reason that neuroeconomics has quickly gained traction Within the last decade, the development of neuroimaging methods has offered a new and more powerful extension of an old approach to understanding the mechanisms that underlie behavior (cf Petty & Cacioppo, 1986; Coles, Donchin, & Porges, 1986) As we learn about the functional properties of different parts of the brain, this knowledge can guide understanding of other psychological processes Of course, it is important to note that neuroimaging data are only correlational with behavior and not necessarily the causal basis for behavior (Uttal, 2001) However, there is some reliability across studies of human neuroimaging in terms of the patterns of brain activity, particularly those that correspond to lower level sensory processing (e.g., vision, touch; cf Gazzaniga, 2004) There is no simple one-to-one mapping between activity in a neural region and psychological function (one region is associated with many functions and one function is distributed across several regions), but there is consistency in the patterns of activity across studies suggesting that distributed networks can be associated with psychological processes Therefore, although behaviorally a decision itself is realized generally as a single output (e.g., taking money) with few dimensions of analysis (e.g., vigor and speed of response), neurally the decision is accompanied by a spatial and temporal distribution of activity within the brain (Cacioppo & Nusbaum, 2003) This neuroanatomical distribution of brain activity can be related by careful analysis to the specific nature of psychological processing thereby offering a different kind of window into understanding decision making or any psychological function (cf Small & Nusbaum, 2004) In addition, it is possible to intervene in the neural processing of specific brain regions thereby testing causal hypotheses about the function of those locations Of course, each such intervention has its own set of caveats and the localizationist assumptions about the specificity of any intervention may be overly optimistic However, the lesion model has been used for decades to study the effects of brain damage on neural and psychological processes Today, it is possible to damage focal areas in animal's brains and evaluate the resulting effects In addition, it is possible to excite or inhibit neural processes by applying electrical stimulation directly to human cortical tissue (e.g., Penfield & Roberts, 1959; Ojemann, 1991) or to animal brains (e.g., Bliss & Lomo, 1973) or through transcranial magnetic stimulation of human brains (Fadiga et al., 1999) It is also possible to directly change the neural communication that takes place in the brain through the use of drugs, hormones, and other steroids Thus, bringing the brain into scientific consideration goes beyond just providing a broader picture of the processing underlying economic decision making and offers a new means of testing hypotheses FINDING YOUR WAY AROUND THE BRAINANATOMY, PHYSIOLOGY, AND FUNCTION A basic assumption of neuroeconomics-as in cognitive and social neuroscience-is that the ability to make judgments and decisions depends on the integrated functioning of the nervous system (Camerer, Loewenstein, & Prelec, 2004) Research in this discipline attempts to identify both the NEUROECONOMICS 1181 neural systems in which activity is associated with decision-making and the role of these systems in the process This research is based on a diverse body of research in neural functioning that has examined domains such as motivation, avoidance, drives, and reward among others To understand this literature, it is important to have at least some basic knowledge of the nervous system Therefore, we begin here with a very brief description of neural structure and function, from the cellular level to the most complex level of central neural systems For those with some background and understanding of very general neuroanatomy and physiology, it is safe to skip over this section; for those without this background (see Kandel, Schwartz , & Jessel, 2000), this brief section will lay some foundation for understanding any review of neuroeconomics research Neural Communication- Neurons and Circuits, Neurotransmitters Although the majority of research in neuroeconomics depends on understanding gross brain anatomy and function, we will start with a brief description and review of neural communication, the underlying basis for all neural processes The basic building blocks of the brain are neurons, whose function is to process, store, and transmit information across the nervous system The term "information" is used here in a loose way, since the representation of neural information is not understood well However, it is clear that neurons have internal states that perseverate, that they are modified by inputs from other neurons and from the local chemical and electrical environment in the brain, and that these inputs affect the outputs sent to other neurons (Black, 1994) Very briefly, the primary process by which neurons interact unfolds as follows: each neuron receives input from thousands of other neurons via terminals, called synapses, connected to those neurons Specific synaptic input provided to the neuron may bias it towards or against sending signals through the use of neurotransmitters These neurotransmitters, as inputs to neurons, change the electrical state of the neuron either by exciting or inhibiting it The sum of all inputs (both excitatory and inhibitory) therefore determines whether the neuron fires (produces an output) If the input is sufficiently positive to surpass a certain threshold, an electric action potential is generated inside the neuron body, which causes it to release neurotransmitter Neurons consist of a main body as well as input and output extensions (dendrites and axons) that provide the means for communication with other cells Both body and extensions are protected by a membrane, which consists of channels that allow sodium, potassium, and chlorine ions to enter and exit the cell The permeability of the membrane is variable, that is, in certain conditions (detailed later) the membrane will allow ions to enter and in certain cases it will not The cell body (or soma) contains its nucleus, where the chromosomes with the cell's genetic code are stored and where protein biosynthesis occurs to produce neurotransmitters to stimulate or inhibit other neurons The nucleus also provides for basic maintenance of the cell as well as structural modifications due to experience (e.g., Kandel, Schwartz , & Jessel , 2000) Dendrites continuously grow and transform throughout life: intellectual activity promotes their growth, but diseases that bring about mental retardation or senility are usually characterized by reduced dendritic length Drugs such as cocaine and amphetamine can also lead to an increase in dendritic branching in regions associated with high level cognitive functioning Such increased branching can in turn lead to long-term behavioral effects and can result from changes in experience (e.g., Withers & Greenough, 1989) The terminal endings of the dendrites contain chemical receptors, providing the basis for receiving input from other neurons In absence of stimulation, the electric potential inside the membrane is negative with respect to environment outside the cell (at approximately -70 mV, called resting potential) due to a concentration of negatively charged sodium ions inside the cell and positively charged potassium ions outside 1182 GIOVANNA EGIDI, HOWARD C NUSBAUM, AND JOHN T CACIOPPO the cell When a neurotransmitter binds at a receptor, ions either enter or exit via the membrane: positively charged sodium ions or negatively charged chlorine ions pass the membrane inward and positively charged potassium ions pass the membrane outward When positive sodium ions pass the membrane inward, the membrane is depolarized, i.e., its voltage tends to zero When positive potassium ions pass the membrane outward or negative chlorine ions pass the membrane inward, the internal negative voltage of the membrane increases so that it is hyperpolarized When the membrane depolarizes to about -50mV, it becomes completely permeable to positive sodium ions until the voltage reverses to about +40 or 50 mV At the same time, the membrane lets out positively charged potassium ions, thus balancing the inflow of positive sodium ions This process occurs within half a millisecond, after which the membrane again becomes impermeable to positive sodium ions and its resting potential is restored This sudden reversal of polarity and the restoration of the resting potential is the action potential When the sum of the stimulation received at the dendritic level exceeds the threshold, the action potential is initiated at the cell body and it travels on the axon to the synapses to transmit a signal to other neurons (and the neuron is said to fire or discharge) Action potentials are an allor-none phenomenon and not vary in amplitude or intensity, but can vary in frequency Thus neurons fire an action potential each time the electric potential internal to the membrane reaches threshold Most neurons fire fewer than 100 times per second, but they can reach a maximum of 1,000 times per second For neuroscientists, having the ability to detect the changes in electrical activity caused by action potentials (either via single cell recordings or recording of field potentials) is one of the main tools for studying brain function The primary means of communication among neurons is generally taken as the firing rate of the neuron Electrodes inserted into the brain can record these electrical impulses from single neurons, groups of neurons, or from entire population responses These aggregate population responses can also be recorded from electrodes placed on the scalp and are the basis for electroencephalography (EEG) and the measurement of event-related potentials (ERPs) The detection of these population responses at the scalp in ERP experiments is limited to neurons whose axons are oriented perpendicularly to the surface of the cortex In addition, magnetoencephalography (MEG) can detect changes in the magnetic fields induced by fluctuation in voltage Moreover, just as electrodes can record brain electrical activity, electrodes can also deliver electrical stimulation to neurons simulating aspects of neural firing More recently, transcranial magnetic stimulation (TMS) has been used induce electrical current in neural tissue to excite or inhibit neural population responses These latter two approaches make it possible to test causal hypotheses about neural function by changing the inputs to neural tissue without damaging the brain The ability to rapidly generate action potentials within the neuron body is matched by a mechanism that assures rapid propagation of the electrical impulse through the axon so that neurotransmitters are released quickly after generation of an action potential The axons of most neurons are coated with myelin sheaths, lipidic insulating shells that leave the axon exposed in only specific areas (called nodes of Ranvier) thus speeding the axon's electrical conduction In fact, since the electrical impulse can only travel on the exposed part of the axon's membrane, it "jumps" from one node of Ranvier to the next These myelinated lengths of axon form the basis for tissue that is detected in diffusion tensor imaging (DTI) which allows the tracing of connections within the brain Since axons terminals and dendrites are not in direct contact, when a neuron fires it releases its neurotransmitters in the synaptic cleft, a thin break between the cells about 20 nm wide After the neurotransmitters bind to the receptors on the dendrites they are removed from the cleft by digestion by an enzyme, by being taken back into the neuron (reuptake), or by being broken down and rendered inactive (or the combination of the last two) The receptors that come in contact with the NEUROECONOMICS 1183 neurotransmitters are responsible for the change in the membrane's permeability to the ions Some receptors also initiate a sequence of chemical changes within the cell, which may further regulate ion inflow The chemicals involved in this process are called secondary messengers Ligand binding studies allow researchers to radioactively tag neurotransmitter receptors and detect, using positron emission tomography (PET), the distribution of these receptors that form the basis for neural communication Moreover, iontophoretic application of neurotransmitter agonist or antagonists can increase or decrease specific neurotransmitter effects in particular brain areas (Williams & Goldman-Rakic, 1995) allowing causal tests of hypotheses about neurotransmitter effects (e.g., the role of dopamine in working memory) Similarly, the oral administration of precursors to neurotransmitters, selective serotonin reuptake inhibitors (SSRIs), transmitter agonists and antagonists, and other pharmacologic agents (e.g., amphetamine) can change neurotransmitter function in the brain (Cooper, Bloom, & Roth, 2003), which allows testing for specific cognitive or social effects (e.g., Luciana & Collins, 1997) When studying how neural activity is associated with behavior, social and cognitive neuroscientists typically study groups, or populations of neurons These populations are typically determined by anatomical criteria (e.g., cytoarchitectonic criteria), such as being formed of the same cell types with similar connectivity Neighboring neurons tend to respond to the same or similar stimuli and to connect to the similar neurons or sets of neurons forming networks When these groups are interconnected or have convergent or divergent connections, they are said to form neural circuits Brain atlases such as the Talairach and Tournoux (1988) standard allow neuroscientists to use gross anatomical landmarks (such as bumps and turns in gyri or sulci-ridges and valleys) to identify specific anatomical regions Such anatomical regions have been traditionally assumed to have specific functions For example, damage to the frontal gyrus (inferior frontal) was identified by Broca (1861) as producing a loss of expressive language function or aphasia While many such function-region identifications have been made over the years (e.g., see Gazzaniga, 1987), there is now a greater appreciation of the many-to-many mapping between psychological functions and brain regions and the need to consider broader networks in the brain (e.g., Haxby et al., 2001; Nyberg & McIntosh, 2001) However, in recent years there has also been an attempt to identify "functional" regions of the brain (e.g., Kanwisher, McDermott, & Chun, 1997) under the assumption that anatomical criteria may not be as good a demarcation of anatomical specialization as the function itself Functional regions are identified by using a neuroimaging method that registers changes in neural processing during a specific task The metabolic activity that accompanies neural processing is detected as functional neural activity in PET (see Posner & Raichle, 1994), and changes in blood oxygenation level resulting from this metabolic activity (see Logothetis, Pauls, Augath, Trinath, & Oeltermann, 2001) are detected as functional neural activity in functional magnetic resonance imaging (fMRI) So called "localizer tasks" can identify brain regions that respond to a particular kind of stimulus or psychological process such as preferential responses to faces relative to other body parts or objects (e.g., Kanwisher, 2000) This functionally defined region can serve as the basis for subsequent study A major advance in cognitive and social neuroscience comes from being able to identify patterns of brain activity with anatomical regions whether these regions are defined by purely anatomical criteria or functional criteria At the same time, there has been a substantial shift in research from studying single brain regions associated with specific psychological functions to identifying complex networks associated with these processes (e.g., Haxby et al., 2001; Nyberg & McIntosh, 2001) In order to understand these networks it is necessary to understand the general structure of the brain Although it is the neural processes associated with small neuron populations that provide the physical and physiological basis for measuring brain responses, in human neuroscience, the 1184 GIOVANNA EGIDI, HOWARD C NUSBAUM, AND JOHN T CACIOPPO majority of studies focus on the grosser organization of these units into anatomical and functional regions Understanding how these regions are organized is important to understanding the results of cognitive and social neuroscience research The next section provides a thumbnail sketch of the overall organization of the anatomical regions of the brain most relevant to understanding neuroeconomics research The Big Brain Picture-Cortical and Subcortical Parts There are a number of different ways that neuroscience research describes the landscape of the brain Given the assumption of some relationship between function and structure, it is important to be able to identify which parts of the brain are involved (active) during different kinds of psychological processing In order to refer to different parts of the brain, a number of different taxonomic schemes are used in research, with different strengths and weaknesses However, it is important to be able to recognize which scheme is being used and to have some idea of how to interpret the intended reference Brodmann (1909) numbered brain areas according to cellular composition (cytoarchitecture) and this numbering scheme is still widely used to identify different brain regions, although there have been criticisms raised of this work (e.g., von Bonin & Bailey, 1925) Brain regions are numbered from to 52 with subareas identified with letters For example, Brodmann area refers to primary motor cortex and 22 to a part of the superior temporal gyrus A different approach is provided in the stereotactic coordinate system described by Talairach and Tournoux (1988) In many studies, brain areas are referred to by a set of x, y, z coordinates that specify position in the left-right dimension, the anterior-posterior dimension, and the ventraldorsal dimension (down vs up) These coordinates are relative to an anatomical landmark called the anterior commissure or AC Software such as the Talairach Daemon (http://ric.uthscsa.edu/ projects/tdc/) maps between this coordinate system and more conventional anatomic designations such as the caudate or superior frontal gyrus For example, entering Talairach coordinates such as 38, 40, 33 will identify this as the right superior frontal gyrus and Brodmann area In neuroimaging studies, results are often identified by tables of Talairach coordinates that specify the center of mass of brain activity Beyond these taxonomic conventions for referring to specific parts of the brain, it is useful to think of the brain in terms of overall structural organization The more central (medial) parts of the brain are generally viewed as important for regulation of the most basic and essential bodily functions, whereas more lateral parts of the brain, including the cerebral cortex, are mainly involved in higher-level mental functions Following this general distinction, the brain can be partitioned into subcortical and cortical regions In terms of evolutionary history, the subcortical systems, physically located underneath the cerebral cortex, developed earlier than the cortex In what follows, we describe the subcortical areas according to their location in the brain, following a path that begins at the base of the head, where it connects to the spinal cord, and that ends at the cortex At the same time, we also trace the path of the subcortical systems main functions from the most basic ones to the gradually more complex ones Attached to the spinal cord is the hindbrain, a system responsible for regulating involuntary musculature and basic bodily functions such as digestion, breathing, heart rate, and blood pressure Posterior and lateral to the hindbrain is the cerebellum, responsible for the ability to coordinate functionally related muscles, execute smooth movements, and integrate motor commands with sensory feedback, although it has been viewed more recently as being important for certain cognitive functions (e.g., Bischoff-Grethe, Ivry, & Grafton, 2002; Justus, Ravizza, Fiez, & Ivry, 2005) NEUROECONOMICS 1185 Superior to the brainstem is the midbrain, which receives projections from the retina and the r and therefore contributes to the initial processing of visual and auditory stimuli and the control visually and auditorily related behaviors Additionally, neurons in the midbrain that utilize the ,urotransmitter dopamine (called dopaminergic neurons) have been shown to play a pivotal role in ocessing motivations and rewards (Wise, 1982; Montague, Dayan, & Sejnowski, 1996) Superior to the midbrain is the diencephalon, a system that includes two structures that play an Iportant role in behavior and perception: the hypothalamus and the thalamus The hypothalamus gulates body growth, body temperature, hunger, thirst, sexual activity, and endocrine functions e., the production and circulation of hormones in the body) The thalamus, a bulb located cenally superior to the hypothalamus, allows the passage of sensory information (excluding smell) am the sensory organs to the cortical regions and is important for the attentional control of sen,ry information processing (e.g., Shipp, 2004) Both systems are involved in almost all aspects of -havior and higher functions Superior to the diencephalon, located centrally, are the basal ganglia, structures associated with e initiation and termination of action and with learning Finally, wrapped around the thalamus id approximately in the center of the brain, is the limbic system, a ring-shaped set of structures at perform several related functions The most frontal parts of the limbic system include the nygdala, a structure that has a main role in the regulation of emotional responses and memory r emotional events More posterior, structures such as the hippocampus and the mamillary bodies e pivotal to our ability to form new memories These structures are immediately adjacent to the ,rtical areas, or cortex The cortex is the part of the brain that has most expanded during evolution and now comprises ,out 80% of the human brain The surface has grown in volume and has become more and more nvoluted with evolutionary development Now it covers a surface of about 2500 cm2, about 1.5 to mm thick Its folded structure is necessary to fit such a high area in a proportionally small skull Ie external surfaces of these convolutions are called gyri, and the dips are called sulci Particularly rge sulci are called fissures More than two thirds of the cortex is in the sulci The cortex consists of layers of neuronal bodies, arranged in groups of different cell types The -nsity of the neurons' distribution varies depending on the region and the layer of the cortex The rtex, particularly the regions responsible for the processing of sensory information, has a vertical ganization, in that groups of cells at different levels form neural circuits with vertical afferent and :erent connections to groups of cells in other layers The activity in cortical regions is influenced their reciprocal connectivity with other regions (called reentry) and by feedback loops with bcortical regions as well Within the cortex the two largest anatomical structures are the two hemispheres-left and ht-which are roughly symmetrical and separated in all their length by a fissure, called longidinal fissure However, they are connected inferiorly, near the limbic system, by a large band of ers called corpus callosum by which most inter-hemispheric communication occurs The thalaus, the basal ganglia, and the limbic system are also connected with both hemispheres, thus owing passage of information as well, though less directly Due to its high connectivity, the rtex is well designed to process properties of stimuli across different sensory modalities and can rform higher-level processing than the subcortical parts e Cortical Terrain of the Mind ch cortical hemisphere is considered as divided in four lobes: the occipital lobe in the inferior and ,sterior part of the head, the parietal lobe in the superior and posterior part, the temporal lobe on 1186 GIOVANNA EGIDI, HOWARD C NUSBAUM, AND JOHN T CACIOPPO the lateral sides, at the level of the temples, and the frontal lobe, in the superior and frontal part, behind the eyes and forehead The occipital, parietal, and temporal lobes contain regions that have direct interactions with thalamic regions and therefore perform the primary and more basic processing of sensory information These areas are called primary sensory cortex In these areas, sensory information is organized in maps that preserve the initial organization in the sensory organs For example, the occipital lobes host the primary visual cortex, a fissure located inferiorly near the cerebellum (called calcarine fissure), where there are different neurons that respond-among other things-to the orientation, the length, and the movement of visual stimuli These neural groups preserve the spatial organization of an image initially projected by the light on the eyes' retina in a retinotopic map This kind of topographic representation is typical of representation in many parts of the brain from somatosensory cortex (e.g., Buonomano & Merzenich, 1998) to motor cortex (e.g., Penfield & Jasper, 1954) The temporal lobes are the location of the primary auditory cortex, a gyrus on the superior convolution, neighboring to the frontal lobe (called transverse temporal gyrus), in which neurons are sensitive to different sound frequencies The parietal lobes host the somatosensory cortex, a superior gyrus bordering the frontal lobe (called postcentral gyrus), which is the main receptive area of basic somatic sensations and perceptions The frontal lobe hosts the primary motor cortex, a superior gyrus adjacent to the primary somatosensory area in the parietal lobe and the upper section of the connected sulcus (called precentral gyrus and anterior central sulcus), whose neurons are responsible for the movement of the body parts Adjacent to the primary sensory and motor cortices are areas that perform higher level processing of sensory stimuli, called association cortex Some of these regions receive projections from the primary regions and perform subsequent and more specialized processing the stimuli coming from them (these regions are called secondary cortex) For example, in the occipital lobe, the secondary visual cortex (also extrastriate cortex), located around the primary, consists of different neurons that process-among other things-color, form, and movement of objects Association cortices include regions connected to both primary and secondary areas, as well as to other lobes These regions combine information from more than one system when performing complex functions For example, the parietal-temporal-occipital association area includes parts of the parietal, the temporal, and the occipital lobes It integrates visual information with sensory and auditory information for higher-level functions such as multimodal language comprehension and spatial orientation Indeed, a number of areas such as the superior temporal sulcus integrate information from different modalities and are active in during a wide range of psychological processes from understanding biological motion (Pelphey, Morris, Michelich, Allison, & McCarthy, 2005; Thompson, Clarke, Stewart, & Puce, 2005) to spoken language (Belin, Zatorre, Lafaille, Ahad, & Pike, 2000; Binder, 2000) Among the more complex functions that the brain performs are psychological operations sometimes referred to as executive functions that involve reasoning, planning, and making judgments and decisions Performing these functions widely involves the prefrontal cortex, the anterior regions of the frontal lobes These areas play a major role in evaluating future consequences of current actions, monitoring current behavior, choosing more preferable actions over less preferable ones, controlling impulses and socially unacceptable behaviors, pursuing goals, evaluating options, general problem solving and socialization skills Damage to these areas can result in impulsive behavior, can impair the ability to plan complex sequences of actions, and can inhibit the ability to make a behavior change following changes in the social environment Perhaps the most often cited and notable example of the effects of damage to the prefrontal regions is the case of Phineas Gage, a railroad worker who in a dynamite blast was hit by a metal 1200 GIOVANNA EGIDI, HOWARD C NUSBAUM, AND JOHN T CACIOPPO experiences along with the context of their occurrence, ventromedial prefrontal cortex can bias cognitive determinations in other cortical regions through projections of various types Clearly, the connection between the ventral striatum and prefrontal areas that are active in the McClure and colleagues' (2004a) study in response to near-term reward is an important part of understanding the concept of utility However, the role of the prefrontal regions may not be in the affective appreciation of an anticipated reward but rather in biasing action in service of receiving that reward At the present time, there are thus several views about the function of prefrontal regions including direct participation in the hedonic value of reward, estimation of the probability of reward, and a biasing signal anticipating reward in response to choice Beyond their implication in monetary reward in neuroeconomics studies, the striatum and its midbrain dopaminergic interactions are clearly an important part of the neural reward system (Schultz, 2000) The striatum responds to sexual arousal (Karama et al., 2002), humor (Mobbs, Greicius, Abdel-Azim,Menon, & Reiss, 2003), drug hedonic value (Wise & Hoffman, 1992; Robinson & Berridge, 1993) and is generally viewed as part of a complex system that includes prediction of reward and the reinforcing outcome of reward As a result it is clear why it would be a target system for understanding the neural mechanisms underlying the concept of utility in economics It should also be clear that there are more components to the system than would be postulated by a model that only takes into account probability and value For example, the dorsal striatum may be more involved with the prediction of reward while the ventral striatum may be more involved with maintenance of outcome information to aid in biasing choices (e.g., O'Doherty et al., 2004) In this case, prediction of rewards and biasing selection are very different operations from appraising value and estimating probability of outcome as a more standard economic view might suggest Moreover, there is neural evidence that the attributed utility of a reward (money earned) is different depending on whether the reward is contingent on behavior or not (earned vs simply received)the striatum is more active for an earned reward (Zink, Pagnoni, Martin-Skurski, Chappelow, & Berns, 2004) suggesting that it is less responsive to the face value of a choice than all the anticipated and integrated hedonic dimensions However, at this point it seems plausible that there will be no single part of the brain that is simply registering utility and no single part registering probability THE NEUROSCIENCE OF CHOICE From an economic perspective, understanding utility should be sufficient to explain choice behavior This would suggest a relatively straightforward neural model of consumer decisions Items that produce greater levels of activity within the limbic areas identified with utility evaluation should be selected in a choice Indeed, Prelec, Knutson, Loewenstein, Rick, and Wimmer (2006) reported that activation of the nucleus accumbens correlated positively with participants' willingness to spend money on a purchase and activation of the insula correlated negatively with it These patterns of activity predicted purchases independently from self-report variables As noted previously, the nucleus accumbens is usually active for anticipation of rewards whereas the insula is active for anticipation of pain Thus two different hedonic valuation systems play off against each other in making purchasing decisions However, the neural networks involved in choice go well beyond evaluation of the pain and pleasure of a purchase For example, consider the basic consumer choice among different sodas Presumably a consumer will choose the soda that produces the highest activity within those areas of the limbic system identified as relevant to utility To test this, a group of consumers participated in a blind tasting of decarbonated Pepsi and Coke while being scanned using fMRI (McClure et al., 2004b) The participants' preference for a particular soda was associated with increasing activity NEUROECONOMICS 1201 in the ventromedial prefrontal cortex This is one area that has been identified with determination of the probability of an outcome (e.g., Knutson et al., 2005) but not the evaluation of the magnitude of a reward, which would seem to be associated more with activity in the ventral striatum (e.g., Knutson et al., 2005; Schultz, 2000) However this study did not report any striatum activity associated with tasting the soda when participants did not know which soda was being tasted, nor was there any striatum activity related to preferences expressed before the scanning session One could, in principle, correlate activity in the ventromedial prefrontal cortex with hemodynamic measurements made in the ventral striatum to assess whether the variation in striatum activity was associated with prefrontal activity even if the striatal activity did not exceed threshold The ventromedial prefrontal activity is consistent with Bechara et al.'s (1997) suggestion that this area may bias choices In real life, consumer choices are often affected as much by culturally induced expectations and advertisement as by the actual taste of a product For example, McClure and colleagues (2004b) found that people prefer Coke more often when they are comparing it to a soda that could be either Pepsi or Coke (but which is actually Coke), than when they are blindly tasting and comparing Pepsi and Coke Participants' experience of the beverage in the taste test was affected by their knowledge and expectations about the brand Previous knowledge about a product can clearly override the sensory information people received from the stimulus This kind of knowledge and expectation may influence decision making by modulating activity in reward-sensitive brain regions (Erk, Spitzer, Wunderlich, Galley, & Walter, 2002) More interestingly, a second neural network seems to be involved in shaping these kinds of consumer choices compared with the apparent solitary activation of ventromedial prefrontal cortex in blind tasting of sodas The relative engagement of these two systems depends on whether the type of information available to the person is only sensory (such as when people are blindly tasting Pepsi and Coke) or it is accompanied by brand name information (such as when people know that one of the drinks is Coke) McClure et al showed that when brand information was available the second system was active, including the dorsolateral prefrontal cortex and the hippocampus Given the important role of the hippocampus in memory, it is possible that it is involved in recalling the information associated to the brand name (McClure et al., 2004b) Dorsolateral prefrontal cortex is implicated in a number of cognitive processes such as working memory and selective attention The hippocampus is implicated in cognitive processes such as episodic memory While both of these areas are implicated in other kinds of functions as well, these cognitive functions seem well suited to the use of past knowledge and experience in shaping attention and expectations about the taste of a soda However, these are not areas that are typically implicated in the research on utility as we have discussed it previously This suggests that it is important to broaden our consideration of the neural mechanisms involved in consumer decisions At present there are no models proposed that purport to integrate the diverse set of brain areas active in neuroeconomics Indeed there are few models of complex psychological processing at all However there is one exception which is a model of the interaction of affective responses, cognitive processes, and choice processing that involves many of the brain regions we have discussed so far Davidson and his colleagues (Davidson et al., 2000; Davidson et al., 2002) have discussed a view of affective and behavioral control that involves three sets of brain regions: (1) the limbic structures such as the striatum and amygdala, (2) the dorsolateral prefrontal cortex and the anterior cingulate and hippocampus, and (3) the ventromedial and orbitofrontal cortex In their discussions, the primary concern is with serotonergic systems that are implicated in aggression and depression, but in neuroeconomics, it appears that dopaminergic systems are more relevant 1202 GIOVANNA EGIDI, HOWARD C NUSBAUM, AND JOHN T CACIOPPO Nonetheless the sets of brain structures involved in economic choice and depression and aggression seem closely related In an overly simple caricature of these brain areas, we can think about them as related to (1) the expectation and evaluation of utility in structures like the striatum, (2) the biasing and regulation of choice, and (3) expectations, attention, and memory Each of these systems is complex and multipartite and none is truly independent But in considering how expectations and past experience and expected utility can bias choices, this model has the functional elements that are needed to develop a neural model of choice behavior One prediction of such a model might be that focal brain damage in limbic regions might potentiate a more rationalist basis for making economic decisions Shiv, Lowenstein, Bechara, Damasio, and Damasio (2006) found that winning or losing money led to changes in investment strategy that were more conservative for normal participants than for patients with damage to the limbic system These patients were less affected by the outcome of previous trials As a result, patients with limbic system damage actually made better investment decisions over the course of the experiment than normal control participants While a very simple model of utility-in-the-limbic-system might predict a failure of investment performance given a reduction in functionality in the limbic system, a more complex model positing that affective responses and cognitive responses interact, puts the role of limbic system activity in a different perspective Indeed, such a model can also account for tradeoffs that occur when cognitive information (e.g., brand information) is ambiguous or noninformative, thus allowing for an increase of affective responses versus activation of prefrontal areas (Plassmann, 2006) This model reflects a substantial amount of research in affective neuroscience on the one hand and cognitive neuroscience on the other As a model of economic choice, this model grounds decision making in two disciplines that have longer histories and considerably more research than neuroeconomics itself By treating utility, choice, and expectation within this framework, it is possible to make predictions about neural processing during economic decisions in a way that extends beyond the relatively simple prediction that the limbic system will be involved in utilityrelevant decisions It should be possible to make predictions about the modulation of brain activity in one region (e.g., limbic areas) based on activation in other areas (e.g., dorsolateral prefrontal cortex) that can be tested using structural equation models and covariance analysis (e.g., Buchel & Friston, 1997; Nyberg & McIntosh, 2001) Moreover, by using repeated transcranial magnetic stimulation (rTMS), it is now possible to directly change the neural activity in a brain area and, guided by this kind of model, test how this affects economic decisions In a recent study (Knoch, Pascual-Leone, Meyer, Treyer, & Fehr, 2006) rTMS was applied to either the right or left dorsolateral prefrontal cortex (DLPFC) to affect decisions about unfair offers The results showed that rTMS to right but not left DLPFC increased the rate of accepting unfair offers Changing activation in one part of the network can actually change the speed and probability of accepting unfair offers, even though the offers are still considered unfair Thus using a model of economic decision making, the functional role of different brain areas can be directly tested by causal intervention in the processing of those areas Although this model provides a richer theoretical framework for investigating and understanding the neural processing of consumer behavior, it may not be sufficient to account for the breadth of situations in which consumers actually make economic decisions THE SOCIAL NEUROSCIENCE OF ECONOMIC EXCHANGE In the ultimatum game, discussed previously as used by behavioral economists, decisions to accept or reject an offer seem relatively straightforward For economists (and homo economicus in gen- NEUROECONOMICS 1203 eral), if the offer exceeds zero, there is a benefit, so the offer should be accepted For everyone else, if the offer seems fair, the offer should be accepted Of course, this begs the question of what defines fair and the behavioral economists have provided the empirical answer (e.g., see Thaler, 1988): A fair offer is a little under half of the pool, letting the offerer keep a little more than half for the effort of making the offer How participants in the game come to that determination? There is no answer at this point, but one could infer a highly rational computational process that reasons about the role of the offerer and recipient and estimates reasonable portions based on a purely logico-deductive process This would appeal to traditional economists and behavioral economists since it is a rational process that assigns utility differently than postulated by traditional economists Within the context of the neural framework for a decision-making model we have just discussed, there are elements that are entirely consistent with this explanation The recipient's goals of earning money and being fairly treated (using lateral frontal brain regions involved in attention and working memory for holding goals in mind) may bias some responses over others (using orbitofrontal and ventromedial prefrontal regions ) depending on the assessed utility of the alternative (through limbic areas such as the midbrain dopaminergic system including the striatum) In the three different views of playing the recipient in the ultimatum game -traditional economist's, behavioral economist's, and neuroeconomist's-there are certainly similarities but there are also important differences The emphasis shifts from monetary value (offer above zero) to contextualized value (offer deemed "fair ") to an interaction of goals, hedonic states and estimates, and response biases The neural model actually makes explicit-because it is part of the measured responses -aspects of the economic decision that no economist would deny, but also that no economist would consider It is this kind of perspective shift (e.g., Kuhn, 1963) that is at the foundation of conceptual advances in a field However, there is still something missing from the neuroeconomic model we have been discussing In all three perspectives, utility is critical to selection of a choice In the neuroeconomics model, expectation can play a role in modulating choice behavior, through an attentional-memory network involving dorsolateral cortex But it is not clear why choice behavior should change if expectations are constant, the face value of a reward is constant, and other aspects of the received value are constant Utility should not change In the neuroeconomic model, the definition of a fair offer in the ultimatum game should be relatively invariant In a recent study, Sanfey et al (2003) had participants play the ultimatum game as recipients with one small change: Sometimes offers were made by a human partner and sometimes offers were made by a computer partner Brain activity was measured using fMRI to assess the neural activity accompanying the economic decisions Sanfey and colleagues reported increased activation in three brain regions when participants were presented with unfair offers compared to fair ones The first, the anterior insula, is a region whose activation has often been found to correlate with negative emotions (e.g., Calder & Lawrence, 2001) such as anger and disgust Its activity here suggests that emotions are playing a role in guiding participants' choice in an area not typically associated with utility Activity in the insula was proportional to the unfairness of the offers Across participants, it showed higher activation for the unfair offers that were rejected Additionally, participants who had rejected the highest number of unfair offers showed higher insula activation (Sanfey et al., 2003) Rejections of unfair offers can be seen as the result of a conflict between satisfaction of two goals: cognitive goals that push the player to accept the even small gain , and emotional goals that push the player to reject the insulting offer (Sanfey et al., 2003) Consistent with this hypothesis, the second region showing more activation for unfair offers was the dorsolateral prefrontal cortex, 1204 GIOVANNA EGIDI, HOWARD C NUSBAUM, AND JOHN T CACIOPPO an area known to be involved in goal maintenance and cognitive control (Miller & Cohen, 2001) The third region was the anterior cingulate cortex, an area involved in the detection of cognitive conflict (Fehr & Schmidt, 1999; Greene et al., 2004) The activity in these two latter regions, while greater for unfair offers, did not vary with the degree of unfairness, in contrast to the pattern found in the insula (Sanfey et al., 2003) These areas are consistent with the "cognitive" regions described in the neuroeconomic model of choice, whereas the insula represents a different kind of affective activity pattern (related to social affective responses directed at a partner) than is represented by the limbic activity related to utility Moreover it is important to note that these results held only when participants were offered unfair shares by human partners When the offers were made by a computer, not only did participants accept unfair offers more often, but the activity in the insula, dorsolateral prefrontal cortex, and anterior cingulate was not significantly increased for unfair offers (Sanfey et al., 2003) Clearly a decision made in a social setting can strongly depend on attributions made about the participants The notion of fairness and the emotional consequences people experience when they are offered unfair options depend on being able to attribute a theory of mind to the agent making the offer (e.g., McCabe, Houser, Ryan, Smith, & Trouard, 2001) Only once such attributions are made can expectations come into play Notice that the social setting need not be complex or realistic A simple and structured interaction such as the one offered by the ultimatum game suffices This adds another kind of dimension to the neuroeconomic model Economic decisions are often made in social settings (including both cooperation and competition, Decety, Jackson, Sommerville, Chaminade, & Meltzoff, 2004; Rilling et al., 2002) and this suggests that we need to consider social neuroscience mechanisms (Lieberman, 2006) in addition to affective and cognitive neuroscience when thinking about the full scope of a neuroeconomic model In particular, we need to consider the possibility that social emotions are involved, sensitivity to acceptance and rejection (see Eisenberger & Lieberman, 2004), the use of theory of mind (e.g., Rilling et al., 2004; Saxe & Kanwisher, 2003), perspective taking (Decety & Sommerville, 2003; Jackson, Meltzoff, & Decety, 2006) and empathy (Jackson et al., 2006; Decety & Jackson, 2006) When an offer is rejected in the ultimatum game neither of the players receives money Therefore, rejecting unfair offers is a (costly) way to punish a player who made an unfair offer This kind of behavior has been termed "altruistic punishment" (de Quervain et al., 2004) because it is costly for the punisher and it is useful for the punished, since it decreases the chances that this person will adopt that behavior again However, a PET study showed that the desire people have to punish a perpetrator of a misdeed is less altruistic than it seems and it actually has a hedonistic component (de Quervain et al., 2004) In this experiment, two players were engaged in a game of trust In order to make money, participants had to entrust money to their partner If they did, the amount of money quadrupled in the partner's hands The partner had then the choice of keeping all of the money or giving half back Thus the payoff for the participants depended on trusting the partner and on the partner being trustworthy If the partner was not trustworthy, participants could decide to punish him by reducing his payoff (de Quervain et al., 2004) Participants were scanned while they were deciding whether to punish traitors The areas that showed activation during this operation were the caudate, the thalamus, ventromedial and orbitofrontal cortices The caudate, a dorsal part of the striatum, showed increased activation when participants had a strong desire to punish traitors, independent of whether this punishment would be costly or not for the participants But when the betrayal was nonintentional, the caudate was deactivated The activation in this area correlated with the strength of selected punishment: it is possible that higher expected satisfaction from the punishment led participants to inflict higher NEUROECONOMICS 1205 punishments (de Quervain et al., 2004) Additionally, the thalamus also showed increased activation when participants inflicted real punishments (vs symbolic) Finally, frontal regions (ventromedial and orbitofrontal) showed higher activation when the punishments were costly for the participants (de Quervain et al., 2004) Interestingly, as in Sanfey et al.'s (2003) study, an attribution of intentionality was necessary for first players to rate traitors as unfair and to feel the desire to punish them When the betrayal was not intentional, partners were perceived as much less unfair and deserving punishment Additionally, the punishments first players imposed on their partners were higher when the betrayal was intentional than when it was unintentional (de Quervain et al., 2004) Trust and betrayal are certainly important elements of social interaction that takes place in economic exchanges In one study, social cooperation resulted in activity in the ventral striatum, and the medial prefrontal cortices (Rilling et al., 2002) consistent with increasing utility and bias for cooperation As two people play a cooperative economic game, and trust between them grows, there is increasing activity within the caudate nucleus, related to increasing expectation of utility (King-Casas et al., 2005) However, with strong prior positive or negative expectations about a potential economic partner, successive interactions have little impact on the ventral striatum or the caudate (Delgado, Frank, & Phelps, 2005a) These studies suggest that patterns of brain activity are sensitive to participants' moment-bymoment experiences Activity in areas involved in affective learning such as the caudate or ventral striatum is greater with increasing trust Similarly, activity in the anterior insula seems to increase with violations of trust Other brain areas such as medial prefrontal cortex, posterior cingulate, and angular gyrus (Greene et al., 2001) as well as posterior superior temporal sulcus (Rilling et al., 2004) may reflect aspects of processing specific to reasoning about other people's thinking (or theory of mind) Other areas in the cingulate seem to reflect aspects of cognitive control (Delgado et al., 2005a; King-Casas et al., 2005) Areas related to theory of mind and empathy have not figured in neuroeconomics studies that seem to mainly find brain networks involved in the reward system, the motor system, and a system relevant to calculation (Cacioppo & Nusbaum, 2003) In part, this may be due to the relative importance of the social interaction that, in the participant's mind, is occurring in the economic task This suggests that the participants' expectations about the study (i.e., the task and social interactions) are critical to the way the brain processes information For example, although the same adjectives can be used to describe people or products (e.g., reliable, effective), these adjectives mean different things in those two cases Yoon, Gutchess, Feinberg, and Polk (2006) examined brain activity to judgments about the relevance of an adjective as a description of a person or a product brand The person could either be the participant (self-relevant condition) or a famous person; the product brand could either be relevant to the participant (self-relevant) or not When the adjectives were judged in relation to people, activity was found in the medial prefrontal cortex but when the adjectives were judged in relation to brands, activity was found in the dorsolateral prefrontal region-an area that has been implicated previously in making economic decisions This might suggest that expectations about the nature of a judgment (applied to a person or a product) may lead to different kinds of neural activity, thus showing a separation of neural networks involved in reasoning about people and reasoning about products However, this result may be specific to the kind of judgments used in this study, which are based on the different kind of expectations we have about people and brands If the expectations about people were more focused on the economic implications of the judgments, a different kind of coupling among brain areas might be observed 1206 GIOVANNA EGIDI, HOWARD C NUSBAUM, AND JOHN T CACIOPPO Delgado, Frank, and Phelps (2005) reported that when participants were given clear information about the moral character of their partner before playing a game together, these expectations dramatically reduced the sensitivity of the caudate to moment-by-moment interactions during the game Participants read a biography of their partners in an economic game These biographies depicted partners as morally good, bad, or neutral Once participants had formed opinions, they played a trust game similar to the one described above Although the behavior of the "good partners" was not consistent with prior expectations and many violations occurred, participants kept sharing more money with good partners than with bad This was mirrored by altered neural striatal activity Delgado et al interpret this result as suggesting that the caudate activity in other studies that examine the development of trust may have reflected the accrual of information about partners With prior expectations, this system does not play a large role in the social interaction Delgado et al monitored neural activity in two phases of the experiment: when participants received responses from their partners and when they decided to share or keep their money on the following trial During the first phase, the caudate showed differential activation only for collaborative and defective responses from neutral partners However, no increased activation was found for responses given by good or bad partners During the following phase, the ventral striatum showed differential activation for decisions to share or keep but only when interacting with bad or neutral partners thus showing that the activation of this region is also sensitive to expectations In this phase, cortical areas associated with cognitive control (cingulate cortex and insular cortex) showed increased activation as well However, this activation was limited to those choices that were inconsistent with the bias created by the initial biography (i.e., keeping the money when playing with the good partner and sharing with the bad one) This suggests that, although top-down information influences subcortical regions, it may not alter the functioning of the cortical ones (Delgado et al., 2005) These findings indicate that social interaction systematically modifies aspects of cognitive and affective mechanisms While some aspects of these interactions can be viewed as simply modifying utility, other aspects (such as unexpected unfair offers) appear to invoke affective systems associated with feelings of anger or disgust In this way, neuroscience has broadened our understanding of economic decisions both by making more explicit the kinds of processes involved and by changing the way we think about longstanding theoretical concepts such as utility TOWARDS A SOCIAL NEUROSCIENCE OF CONSUMER BEHAVIOR Behavioral economics has provided a different perspective on consumer behavior than the traditional approach of economics By introducing and emphasizing the role of heuristics and a variety of cognitive effects such as framing that are not apparent from the surface of strict rationality-but are not irrational-our understanding of consumer behavior has changed Neuroeconomics is beginning to have the same effect Utility, as conceived by economists, has been demonstrated in many studies now to depend on a complex set of neural structures that are important in reward assessment and behavioral control Moreover, there are no clear models that can provide a framework that integrates the operation of neural networks subserving "simpler" functions such as working memory or affective states as seen in economic decision making and exchange Davidson and his colleagues (2000, 2002) have described one kind of model that seems to combine three separable systems-a cognitive memory-attention system, a behavioral control system, and an affective reward system Although an intriguing start, it is likely that this model is too simple even to account for the pathologies they were directed at Although one can conceive of depression in mostly affective and cognitive terms, NEUROECONOMICS 1207 additional networks involved in theory of mind and perspective taking (see Cacioppo, Visser, & Pickett, 2005) will need to be incorporated Of course, this will be true for any neural model of economic decision making Models of economic decision making will need to take into account two fundamental principles of human neural systems discussed by Berntson and Cacioppo (in press) First, rather than conceive of neural networks in a strictly hierarchical arrangement of interactions where simpler sensory systems feed up to associative systems, it is important to recognize the heterarchical nature of neural systems Although there may be clear anatomical connections between systems that suggest a kind of organization by which information may feedforward and feedback, it is better to consider the notion that there are long-distance connections in the brain, ascending and descending subcortical connections, and adjacent connections that together can dynamically assemble functional neural networks to meet the moment-by-moment demands of any particular psychological process Second, there is substantial re-representation in the brain for many different kinds of processes Although much neuroscience work is concerned with the kind of population codes and topographical representations that map differences in function to spatial location in the brain (e.g., retinotopic representation or the homunculus in the premotor region), these representations occur at many different levels over and over again for different purposes Thus, there may be any number of different representations of "utility" for different reasons from biasing an immediate response or choice to forming an expectation that will operate in the future From the research published to date, there is good reason to think that the concept of utility should be broadened and should be informed by the differences in these functional mechanisms However, as long as neuroeconomics continues to analyze patterns of brain activity simply by searching for the most reliable peak of activity, it will be difficult to realize the benefits that neuroeconomics can bring to understanding economic decision making Rather than look for disjoint active spots in the brain, researchers need to begin to use more sophisticated techniques to assess the functional assembly of neural networks While some studies have begun to regress behavioral data onto brain activity (e.g., McClure et al., 2004b) and others have examined the interaction between activity in one brain and activity in another brain (King-Casas et al., 2005) to understand joint neural changes in social interaction, it will be necessary to develop more sophisticated analytic models in neuroeconomics that consider the covariance among brain regions rather than simple magnitude of activity To date, the research in neuroeconomics has focused on nomothetic analyses, emphasizing the regions of activation in the brain observed in judgment and decision making in economic contexts Results are in accord with work in consumer behavior indicating that individuals are characterized by bounded rationality, and points to specific component processes that promote systematic deviations from rationality which summate rather than cancel when aggregated across individuals Consumer researchers have long focused also on individual 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