Abstract
By the late 1990s, several converging trends in economics, psychology, and neuroscience had set the stage for the birth of a new scientific field known as “neuroeconomics”. Without the availability of an extensive variety of experimental designs for dealing with individual and social decision-making provided by experimental economics and psychology, many neuroeconomics studies could not have been developed. At the same time, without the significant progress made in neuroscience for grasping and understanding brain functioning, neuroeconomics would have never seen the light of day. The paper is an overview of the main significant advances in the knowledge of brain functioning by neuroscience that have contributed to the emergence of neuroeconomics and its rise over the past two decades. These advances are grouped over three non-independent topics referred to as the “emo-rational” brain, “social” brain, and “computational” brain. For each topic, it emphasizes findings considered as critical to the birth and development of neuroeconomics while highlighting some of prominent questions about which knowledge should be improved by future research. In parallel, it shows that the boundaries between neuroeconomics and several recent sub-fields of cognitive neuroscience, such as affective, social, and more generally, decision neuroscience, are rather porous.
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Notes
It is commonly admitted today that the birth of neuroeconomics coincides with the publication by the neurobiologist Michael Platt and the neurophysiologist Paul Glimcher in Nature of a study on behavior of monkey linked to anticipated “rewards” (in this case, food rewards) (Platt & Glimcher 1999). For the first time, an electrophysiological experiment on a monkey proved that the brain “value” stimuli independently of sensory or motor processes. Thanks to cerebral imaging, this finding was extended to humans in the early 2000s (Berns et al., 2001; Breiter et al., 2001; Delgado et al., 2000; Elliot et al., 2000; Knutson et al., 2000; Knutson et al., 2001). For a first brief history of neuroeconomics, refer to Glimcher & Fehr (2014b) and Serra (2022), chap. 3.
The most basic element of nervous system function is the “action potential” (or “spike”) that arises when a voltage of a neuron’s cell body rises above a particular threshold. Neurophysiologists use changes in firing rate of a neuron as an index of whether a stimulus changes the ongoing information processing with which that neuron is associated. Single-unit recording is a direct measurement of action potentials requiring the insertion of very fine electrodes into the neural tissue immediately adjacent to the neurons of interest. The invasive nature of this technique limits its use to non-human animals (except in the rare cases of human patients with clinically indicated electrodes).
EEG and MEG are non-invasive neurophysiologic techniques. Input to a neuron changes the electrical potential of its cell membrane. If many neurons evince similar changes in their membrane potential, the collective electrical current they generate can be detected by electrodes positioned on the scalp. EEG provides high-temporal-resolution access to the electrical activity of the brain. However, electrical currents, like those generated by dendritic activity of neurons, also give rise to magnetic fields that MEG is able to measure thanks to external sensors.
PET was the first functional imaging technique to gain wide-spread acceptance. It allows measuring brain metabolic activity thanks to emissions made by positrons coming from a radioactive isotope that is injected before or during scanning, depending on the isotope being used. The most salient disadvantage of PET is its invasiveness: safety guidelines restrict how that radioactive material can be created, handled, and administrated. This technique also has very limited temporal resolution.
Since its development in the early 1990s, fMRI has grown to become the dominant functional imaging technique in cognitive neuroscience. Its success comes from the intertwining of the image creation process from MRI with new insights into the metabolic changes associated with brain activity. It is based on magnetic properties of hemoglobin: neural activity in a particular zone induces a stronger demand for oxygenated hemoglobin, and then generates a higher BOLD (blood oxygenation-level-dependent) signal. This technique is a good combination of spatial and temporal resolution. Much of the growth of fMRI in research has been facilitated by the prevalence of high-field scanners for clinical applications. Structural MRI (morphometry), which is effective in discriminating between gray and white matter in the brain, and diffusion tension imaging (DTI), which measures the direction and magnitude of water diffusion in brain tissue, are also used in a few neuroeconomic experiments. Near-infrared spectography is another method recently introduced in experiments.
TMS stimulates neurons by means of electromagnetic induction. It uses a magnetic field which can pass easily through the skull, to generate an electrical current inside the brain. This electric current acts on the underlying neurons and triggers action potentials in axons that cross the field at appropriate orientations (e.g., perpendicular). This means that some locations in the cortex are easier to stimulate than others using this technique. The artificial and temporary lesion of the target zone allows identifying the behavioral effect. TMS is often applied repeatedly for changing induced neuronal excitability beyond the moment of stimulation (rTMS).
tDCS is a more recent non-invasive electrostimulation tool able to change cortical excitability thanks to electrodes that are wrapped in sponges soaked in saline solution and mounted to the head. It can be used in two modes: anodal tDCS to upregulate and cathodal tDCS to downregulate neural processing in a brain region. tDCS has an additional advantage: it helps to avoid a problem that may arise when using rTMS in social neuroeconomic experiments; e.g., to study “social preferences”. The issue is that each player must face a series of one-shot stranger-matching games sequentially with the behavioral study focusing on the participant playing second. This poses an implementation problem, because each participant will be faced with a high number of protagonists and there is a great temptation to deceive the participants and to confront them with prefabricated options. Yet, in experimental economics, it is well known that it is strongly recommended not to deceive participants to keep their trust in the experimentalist. As tDCS is inexpensive, it can be administered simultaneously too many interacting subjects. Deep brain stimulation, microstimulation, and optogenetic are invasive stimulation methods reserved for animal experiments or for patients with chronic and severe neurological disorders (Parkinson’s disease, epilepsy, and obsessive compulsive disorder).
At least 60 different neurotransmitters have been identified. Some of them increase the probability that the postsynaptic cell will transmit an action potential (“excitatory” neurotransmitters), while others decrease this probability (“inhibitory” neurotransmitters). The main excitatory neurotransmitter is glutamate and the main inhibitory one is GABA. Some neurotransmitters, known as neuromodulators, act mainly by modulating the activity of glutamate and GABA releasing neurons. Examples of neuromodulators include dopamine, serotonin, and noradrenaline/norepinephrine.
Charles Darwin was one of the first scholars to study emotions through facial expressions (Darwin 1872).
Using these relatively simple and inexpensive tools in neuroeconomic experiments rather than the complex and very expensive neuroimaging is actively encouraged par Axel Rubinstein, an economist rather skeptical about usefulness of neuroeconomics for economists without totaling rejecting this approach (Rubinstein 2008). Reuter & Montag (2016, Part VII) give a scholarly introduction into the constellation of methods and techniques relevant to neuroeconomics.
In short, the argument is that if a phenomenon is already well known in psychological and behavioral terms, knowledge of neural correlates and mechanisms would be useless for economists (e.g., Harrison 2008a, 2008b; Rubinstein 2008; Smith 2008). In addition to this issue of interest for economists and beyond the philosophical issue of the “mindless economics” argument (Gul & Pesendorfer 2008), controversial debates about neuroeconomics bear on reliability of findings, in relation to the non-trivial statistical analysis of fMRI data and particularly with the so-called reverse inference “fallacy”. The reverse inference problem, which questions the validity of the rationale underpinning neuroimaging methods—namely inferring thought processes from brain activity—is a practical issue also found in cognitive psychology experiments that rely on neuroimaging to infer particular cognitive functions (memory, attentiveness, language…). On this topic, see Poldrack (2006, 2011, 2018); Harrison (2008b); Harrison & Ross (2010); Ross (2010); Bourgeois-Gironde (2010); Poldrack et al. (2017); Serra (2021). Remark that recent progress in the development of methods for decoding human neural activity as measured with fMRI should lead to bypassing the reverse inference problem. We know that fMRI studies focused on associating brain zones with mental functions. The introduction of decoding using the so-called “multivariate pattern analysis” (MVPA) has revolutionized fMRI research by changing the questions that are asked. Instead of asking what a zone’s function is, in terms of a single brain state associated with global activity, we can now ask what information is represented in a zone, in terms of brain states associated with distinct patterns of activity, and how that information is encoded and organized (see, e.g., Normann, Polyn, & Haxby 2006; Haxby, Connoly, & Guntupalli 2014; Efron & Hastie 2016).
In the same time, neuroeconomics results are viewed as useful in psychiatry for analyzing a constellation of mental and neurological disorders including frontotemporal dementia, obsessive–compulsive disorder, and drug addiction (see, e.g., Millan, 2013; Schutt et al., 2015; Conn 2016; Lis & Kirsch 2016; Dreher & Tremblay, 2017; Alos-Ferrer 2018).
However, there is a significant difference between neuroeconomic choice models and random utility models. While the latter posit that preferences are in essence stochastic and that choices always reflect these underlying preferences, neuroscience research suggests that the choice process itself might be systematically biased and sub-optimal (we shall return to this point in Sect. 5).
In this respect, as suggested by Huettel (2010), neuroeconomics may be viewed as a subfield of decision neuroscience which deals with both perceptual and VBD decisions. Yet, some scholars do not distinguish between neuroeconomics and decision neuroscience by opposing them to molecular neuroscience (e.g., Montague 2007).
Notice that the different neural regions referred to in the text often include only a part of the BAs mentioned in bracket.
The anterior cortex (or frontopolar cortex) (BA 10) is the most rostral zone of the frontal lobe. It performs a function of cognitive control in the most complex situations; it is involved to monitor completely unknown situations or forcing the subject to think about one’s own thoughts (i.e., metacognition). The dorsolateral PFC (BA 8, 9, 46) corresponds to the superior part of the frontal lobe exterior. It is seen as the most “rational” part of the brain.
The cingulate cortex is an internal zone located along the interhemispheric fissure above the corpus callosum. It is divided into an anterior (ACC) (BA 24, 32, 25) and a posterior (PCC) (BA 23, 31) parts. The ACC has long been known to play a role in decision-making, especially when subjects made errors in simple decision-making tasks and detected those errors. It is traditionally known as mainly implicated in the monitoring of internal conflicts, namely when conflicting signals are sent by several neural areas and that selection of an action may be tricky. The rostral ACC is known as the paracingulate cortex. The PCC (BA 7, 40) is typically known as devoted to several high-level cognitive functions, including attention, working memory, and more broadly, “external consciousness”, but its ventral part seems to show a functional integration with the whole areas belonging to the cerebral “default mode” (i.e., the brain’s intrinsic activity when it is undertaking no task whatsoever); this network is supposed to accommodate what some authors called “internal subjective consciousness”. The TPJ (BA 22, 40) is a part of the temporal cortex at the edge of the parietal cortex. It is implicated both in reorienting of attention and social cognition.
All vertebrates (fish, amphibians, reptiles, birds, and mammals) possess such a neural structure, of one form or another. It consists of a set of functionally diversified nuclei embedded in cerebral hemispheres depth, behind the frontal lobes and encircling the thalamus, including the striatum. The striatum includes itself three structures connected to different neural regions: the caudate nucleus, the putamen, and the nucleus accumbens (NAcc). They receive extensive inputs from the frontal cortex and send almost all of their outputs to two other nuclei in the basal ganglia (the globus pallidus and the substantia nigra pars reticula). Today, many researchers simply divide the striatum into two sections: the ventral striatum (the NAcc and lower parts of the caudate and putamen), interacting with regions engaged mainly in emotion and motivation, and the dorsal striatum (the upper parts of the caudate and putamen), interacting with regions implicated in movement and memory.
The amygdala corresponds to a group of nuclei in the medial temporal lobe in front of the hippocampus. This structure plays a central place in emotion and motivational processing, and is implied both in the emotional component of sensorial stimuli and emotional stimuli memorization. The hippocampus, with near structures with whom it is closely connected, is related to memory in general and spatial memory and is crucial for complex spatial representations; it is part of a “human navigation network”.
In the wide orbitomedial region of the PFC (the region encompassing all internal and orbital neural areas), several specific zones are identified, but not all researchers agree on their boundaries. By moving up from the zone located just above the orbits to the top of the skull, are typically defined the orbitofrontal cortex (OFC) (whose medial/caudal/lateral parts are differentiated) (BA 11, 14 / 13 / 47/12), ventromedial PFC (BA 10, 11, 14, 32), and dorsomedial PFC (BA 9, 8, 32) (sometimes named globally medial PFC). The ventromedial PFC very often is defined as including the medial OFC.
The insula (or insular cortex) is a part of the cortex moved in depth of the lateral sulcus, at the junction between the frontal and temporal lobes. The insula is sometimes called the “paralimbic structure”. Its anterior part is strongly involved in emotion expressing: it is acting as a monitoring system that informs the brain about high-risk or unpleasant situations that may be a source of danger, harm, or pain. Some authors call this structure the “interoceptive” cortex, because it is implicated in the processing of internal representations signals of body states.
Psychologists distinguish another notion, “mood”, considered as an affective state more diffuse, less intense but more durable than emotion. The term “affect” often is used as a generic term that involves both emotion and mood (e.g., Scherer 2005).
The locus coerulus, located in the cerebral pons, is in close contact with the amygdala. It is associated with noradrenaline/norepinephrine, a chemical substance related to adrenaline considered as neurotransmitter; it is seen as active in waking, sleeping, and feeding behavior, but it also interplays with cortical regions for modulating attention.
However, several meta-analyses showed that often there are differences in response intensity of a same structure depending on the emotion: e.g., both fear and happiness active the amygdala, but the activation level is significantly stronger with fear than with happiness, or both disgust and anger actives insula, but the activation level is significantly stronger with disgust than with anger. Hemispheric lateral effects also were observed, e.g., the right amygdala is more involved in negative emotions and the left in positive.
Consider Plato’s famous metaphor where the mind is seen as a chariot pulled by two horses. The rational brain is the charioteer who guides the horses. One of the horses is well bred and well behaved, while even the best charioteer has difficulty controlling the other horse; this obstinate horse represents negative, destructive emotions. The charioteer’s task is to keep both horses moving forward. Through that simple metaphor, the mind was seen as conflicted, torn between reason and emotion. This dual division of the mind is one of the most enshrined ideas in Western culture. A large set of influential philosophers, from René Descartes to Sigmund Freud, and including Francis Bacon, Auguste Comte, and Emmanuel Kant, all embraced various forms of this duality, which continues through to the modern brain–computer metaphor proposed by cognitive psychology that sees emotions as antagonists of rationality. Aristotle in The Nicomachean Ethics is seen as an exception by claiming that rationality is not always in conflict with emotion. Another widely known exception is Spinoza, a contemporary of Descartes, Antonio Damasio highlights this opposition between Descartes and Spinoza in the titles of two of his books. Descartes’ Error: Emotion, Reason, and the Human Brain (Damasio 1994) and Looking for Spinoza: Joy, Sorrow, and the Feeling Brain (Damasio 2003).
We know that in economics, the experience of regret in decision-making was initially introduced by Bell (1982) and Loomes & Sugden (1982). In this theory, we suppose that, for each decision, the agent is taking account her/his utility and the potential degree of regret/satisfaction, i.e., the comparison with what she/he could have obtained.
It was back in 1994 that Damasio depicts for the first time the now famous history of this young American railway worker named Phineas Gage who, in 1948, was suffering a serious injury in the brain (a crowbar of 6 kg was going through his brain), an accident whose consequences, against all odds, were not physical but behavioral (for further detail see Macmillan 2000). Interested in pathological consequences of patients with frontal lobe lesions, Damasio had the opportunity to observe subjects like Gage: Elliot history, a patient suffering from a benign brain tumor, is now as famous as Gage history (Damasio 1994).
Of course, this is not to say that emotions are only beneficial effects for subjects. Damasio himself acknowledges that the participation of emotion to reasoning process may be advantageous or detrimental according to both the decision circumstances and the decision-maker’s past history. There is compelling evidence that the perception of emotionally salient stimuli and the experience of emotional states can profoundly alter cognition and promote specific harmful behavioral tendencies (see, e.g., Okon-Singer et al., 2015; Engelman & Hare 2018).
Over the years, several studies have questioned the somatic marker hypothesis (e.g., Dunn et al., 2006). Nevertheless, this hypothesis has played a central role in affective neuroscience in that it was one of the first which links emotional responses and brain systems to behavioral decision patterns.
A lot of neuroscientific studies show that the emotion of regret also is implicated in several clinical disorders such as schizophrenia, depression, obsessive–compulsive disorder, and “chasing” behavior in pathological gambling.
This example indirectly refers to understanding consumer behavior in terms of “mental accounting” as proposed in behavioral economics (Thaler 1985, 1999). This very general mental process is analyzed by distinguishing two often simultaneous phases: a “framing” phase, which is concerned with the external description of events that is given to an agent, and an “editing” phase, which is concerned with the internal process whereby the agent analyses the information. These neuroeconomic experiments focus on the editing phase.
Furthermore, the work of the American financial journalist Jason Zweig (Zweig 2007) aimed at the general public uses a broad range of examples from the history of finance to show the potential of neuroeconomics to elucidate and guide financial choices.
See Frederick, Loewenstein & O’Donoghue (2002) and Camerer & Loewenstein (2004) who distinguish this “choice tasks” method from other popular experimental methods such as the “matching tasks” method.
The experiment was repeated with food rewards in McClure et al. (2007) with the consumption of a fruit juice being either immediate or delayed (offset by 10 min or several minutes more). Unlike financial rewards, the emotional mechanism was activated only in the immediate consumption option, suggesting that time scales are perceived differently by the brain according to the nature of the reward.
The Laibson model (Laibson 1997) that uses quasi-hyperbolic discounting is however criticized, because it is incompatible with the notion of self-control. Thus, Ainslie (2012) prefers the original hyperbolic approach (Ainslie 1975, 1991), but introduces a recursive process of self-prediction by the subjects themselves at the different expected timeframes, which may imply stronger commitment from the subjects towards themselves or, on the contrary, a progressive disengagement.
In the beginning, much research in social neuroscience has been driven by mental illnesses, because many of them often involve a breakdown of the “social” brain (in particular, schizophrenia). Remember that, likewise, the study of brain lesions has been a starting point for much of the early progress in neuroscience. Yet, in the last 15 years, research in social neuroscience has increasingly focused on the social behavior of mentally healthy decision-makers, encompassing many social phenomena as social interactions.
It was recognized that ability to mentalize is severely delayed in autism. That could explain observed failure in communication and social interaction by most autistic children. Today, the autistic brain is at the heart of social neuroscience, because it helps to clarify the missing links between brain and social behavior (Frith 2001). Temple Grandin (an American professor in animal science) was one of the first high-functioning autistic woman (people with Asperger syndrome) whose brain was scanned by fMRI toward the end of the 1980s. Like Gage and Elliot cases, mentioned by Damasio (1994), Grandin case is become paradigmatic in cognitive neuroscience (Sacks 1995).
For a systematic confrontation between theory of mind and game theory, see Schmidt & Livet (2014). It would also be interesting to parallel the mentalizing approach with the various informational requirements posit by normative economic in which ethical principles are conditioned by the existence of either interpersonal comparisons of utility (i.e., ability to put yourself in others’ shoes, with their preferences)—e.g., utilitarianism, welfarist social choice—or only intrapersonal comparisons of utility (ability to put yourself in others’ place, with our own preferences)—e.g., theories of equity and fairness, non-welfarist social choice (on this literature on theory of utility and ethics, see, e.g., Roemer 1996; Mongin & d’Aspremont 1998).
Some authors introduce additional distinctions. For example, Blomm (2017) adds to cognitive and affective empathy two other senses of empathy: “emotional contagion”, understood as sharing the feelings of those in your immediate vicinity while for affective empathy others does not have to be present or even exist, and “compassion”, “kindness”, or “sympathy”, that would replace affective empathy as a moral motivation. When one empathizes with another person, there does not have to be a prosocial motivation attached to it; when one sympathizes or shows compassion for another person, there is. However, in general, empathy is viewed as a first necessary step in the process that begins with affect sharing, which motivates other-related concern and finally engagement in helping behavior. Empathy and prosocial behavior are closely linked (Singer & Tusche, 2014).
Although the unique features of human social cognition are often emphasized, there is now evidence that they may depend on more basic social cognitive processes present in other primates and sometimes even in other mammals, including monitoring the actions of others, assigning importance to others, and orienting behavior toward or away from others (for a survey, see Rushworth, Mars, & Sallet 2013).
Two participants are randomly and anonymously matched, one as investor (player I) and one as trustee (player T), and play a one-shot game. Both participants are endowed with an amount of money. Player I can send some, all or none of her endowment to player T. Every amount sent by player I is tripled. Player T observed the tripled amount send, and can send some, all or none of the tripled amount back to player I. The amount send by the investor is view as a measure of trust; the amount returned by the trustee is view as a measure of trustworthiness.
As is well known, Prisoner’s Dilemma (PD) games are used to study “social dilemmas” that arise when the welfare of a group conflicts with the narrow self-interest of each individual group member. In a typical two-player PD, each player can choose either to cooperate or defect. Payoffs are symmetric, and chosen, so that the sum of the payoffs is greatest when both choose to defect. However, each player earns the most if she chooses to defect when the other cooperate.
In the simplest variant of the game, each player simultaneously chooses a number P between 0 and 100. The person whose number is closest to 2/3 times the average of all chosen numbers wins a fixed amount of money; others receive noting; ties are broken randomly.
This game, originally discussed as “guessing game” by Moulin (1986), is an ideal tool for assessing where the chain of iterated dominance reasoning breaks down in a strategic-form game. It was studied experimentally by Nagel (1995). This game is also called a “beauty contest” (Camerer 1997), because it captures the importance of iterated reasoning that John Maynard Keynes (1936) described in his famous analogy for stock market investment. Keynes speaks about a newspaper contest in which people guess what faces others will guess are most beautiful, and compares that contest with the stock market investment. Like people selecting the prettiest picture, each subject in the beauty contest game must guess what average number other subjects will prefer, then pick the fraction P of that average, knowing that everybody is doing the same as her/him. The P-beauty contest game is a workhorse example for the cognitive hierarchy approach in strategic thinking, such that developed by several models of bounded rationality in behavioral game theory, including rationalizability, level-K, or cognitive hierarchy models (Camerer, Ho, & Chong 2004a, 2004b). In these models, players use various levels of strategic thinking, and high-level thinkers distinguish themselves by correctly anticipating what players using fewer levels of thinking will do. It seems that limits of strategic thinking arise in particular from limits on working memory. For an overview of these models, see Cartwright (2016); Serra (2017).
Other games with very different logical structures are also concerned by this specificity of subjects’ behavior when they know (or believe to know) that they are interacting with humans and not with computers. For instance, in one of the first PET experiments, Gallagher et al. (2002) showed that in the well-known rock-paper-scissors game, the paracingulate cortex (rostral ACC) was strongly more activated when subjects thought they were playing against another human player rather than against a computer (in reality, they always were faced with random choices). For a review of neuroeconomic works dealing with strategic thinking, see Camerer & Hare (2014).
The structure of public good (PG) games is similar to that of prisoner’s dilemma (PD) games, but they are typically played in larger groups. In a typical PG game, each member of a group of four people is allocated an amount of money, say 10 dollars. Group members simultaneously decide how to allocate their endowment between two “accounts”, one private and one public. The private account returns one dollar to the subject for each dollar allocated to that account. In contrast, every dollar invested in the public account doubles, but is then split equally among the four group members (0.50 dollar each). Thus, like the PD game, group earnings are maximized at 80 dollars if everybody cooperates and contributes everything to the public account, in which case each of the four participants will earn 20 dollars. However, if three subjects contribute 10 dollars each, and the fourth free-rides and contribute nothing, then the free-rider will earn 25 dollars. Like the PD game, each group member has the private incentive to contribute nothing (free-riding). In on another side, we know that the funding of public goods is a balancing act, both voluntary and involuntary mechanisms. In general, modern societies rely much more on taxation than on voluntary giving to provide public goods. However, for specific goods (e.g., the arts or some kinds of medical research), voluntary giving can be quite important. The goal of charitable donations games is to experimentally study altruistic giving in a PG framework.
PG games with punishment are sequential PG games where players have the option to punish non-contributors and to reward the highest contributors after a round of the game.
Two participants are randomly and anonymously matched, one as proposer (player P) and one as responder (player R), and told that they will play a one-shot game. Player P is endowed with an amount of money, and suggests a division of that amount between herself and player R. Player R observes the suggestion and then decides whether to accept or reject. If the division is accepted, then both earn the amount implied by the player P’s suggestion. If rejected, then both players earn nothing for the experiment. It is a simple take-it-or-leave-it bargaining environment. Remark that in ultimatum games, the act of rejection of the Proposer’s offer by the Responder represents an act of costly punishment, because both players suffer a cost.
Several forms of social punishment are identified, including second-party or third-party punishment. “Parochial “altruism, namely a preference for altruistic behavior towards in-group members and mistrust or even hostility towards out-group members (e.g., one’s ethnic, racial, or any other social group), is a pervasive feature in human society. Parochial altruism involves a third-party punishment behavior. Recent evidence from fMRI studies suggested that areas involved in social cognition (including dorsomedial PFC and bilateral TPJ) must play a role in differentiating in-group and out-group members in behavior (Baumgartner et al., 2012), while Baumgartner et al. (2014) showed that the transient disruption of the right (but not the left) TPJ reduces parochial punishment with real social group.
For a brief presentation of these tools, refer to paragraph 2.1.1.
These studies complete the rare experiments that study in a game-theoretic framework the social behavior of patients with prefrontal damage. Krajbich et al. (2009), in particular, found that patients with damage to the ventromedial PFC show a specific insensibility to guilt.
However, it turns out that oxytocin inhalation does not affect the loyalty of the trustees. To explain this asymmetry between investors and trustees, the authors highlight the difference between “pure” trust found in investors (that can only be generated by a certain empathy) and the “calculated” trust of trustees (as it is a function of their experience during the game).
A more complete panorama of this neuropharmacology literature, that also includes the effects of chemical substances on time and risk preference, can be found in Crockett & Fehr (2014).
It should be noted that social neuroscience literature covers a much broader thematic domain than questions of social cognition. A lot of studies concern in particular what is called “moral dilemmas”, which differ from “social dilemmas” by the fact that all solutions of a given problem generate a not morally desirable outcome (e.g., the famous “trolley problem”) (Christensen & Gomila 2012).
These experiments revealing the role of dopamine in reward system were carried out in non-human primates. However, a similar mechanism was shown to also exist in honeybees, which employ a close chemical homologue of dopamine called octopamine (Real 1991; Montague et al., 1995). As Glimcher points out, “the fact that the same basic system occurs in species separated by something like 500 million years of evolution suggests how strongly evolution has conserved this mechanism” (Glimcher 2011a, p. 302).
Attention allows for the voluntary processing of relevant over irrelevant inputs in line with the current behavioral goal of the organism. Working memory can be conceived as an active process whereby stimulus or internal representations are stored “on-line” to prevent temporal decay or intrusion from competing or distracting stimuli that are outside the current focus of attention. Therefore, dissociating effects of attention from those of working memory is difficult, and in practice, the two processes are interactive (Awh & Jonides 2001). The dopaminergic system is a primary pharmacological target for psychiatric disorders which are associated with attention deficits such as attention deficit, hyperactivity disorder, schizophrenia, and Parkinson’s disease (e.g., Arnsten & Rubia 2012). Note that dopamine is not the only neuromodulator implicated in attention; acetylcholine, noradrenaline, and serotonin also play a role in top–down attentional control (for a recent review, see Thiele & Bellgrove 2018).
Rolls (2014), particularly, agrees that there is evidence for DNs action in encoding of RPE signals and that this could present a problem; according to Rolls, the alternative hypothesis that DNs reflect the effects of many stimuli salience (i.e., a property less dependent to reward) is more consistent with experimental data. This is also explicit in the survey written by Berridge & O’Doherty (2014), in which each co-author has a slightly different point of view: for O’Doherty, dopamine is a prediction-error mechanism of reward learning, while for Berridge, dopamine mediates incentive salience. Indeed, there has been considerable debate over the role of dopamine activity in processing non-rewarding events (i.e., signals related to salient, surprising, and novel events). A lot of studies provide evidence that DNs are more diverse than previously thought. Rather than encoding a single homogeneous motivational signal, they come in multiple types that encode both reward and non-reward events in different manners. Thus, these results pose a problem for general theories that identify dopamine with a single neural signal or motivational mechanism.
Broadly, serotonin is implicated in a variety of motor, cognitive, and affective functions, such as locomotion, sleep–wake cycles, and mood disorders. It was argued that this neurotransmitter would play a role in impulsive behaviors: reduced levels of serotonin would promote impulsive actions (i.e., the failure to suppress inappropriate actions) and choices (i.e., the choice of small immediate rewards over larger delayed rewards) (Dalley et al., 2011).
The fact that the subjective impact of a loss is greater than that of an equivalent gain is one of the general principles underlying the famous prospect theory. This theory has been tested in recent years by numerous neuroeconomic experiments that have corroborated its main hypotheses such as loss aversion and the non-linearity of the probability-weighting function, but reference-dependence in decision-making and framing effects remain unclear (refer to Fox & Poldrack 2014; Louie & De Martino 2014). Glimcher (2011a, 2011b) established a parallel between the idea of reference point introduced by Kahneman and Tversky and a similar concept in neurobiology. It is interesting to note that Kahneman himself was involved in one of the first experiments in neuroeconomics (Breiter et al., 2001). However, the status of the neural data in this experiment is ambiguous. As with all pioneering experiments in the early 2000s, it is claimed that the experiment is set within reward learning theory, yet it is clear that the prospect theory also plays the role of experimental paradigm. Neural data are alternately considered as parameters of the Kahneman–Tversky model (exogenous variables that must be estimated to “calibrate” the model) or explanatory variables (endogenous variables that are progressively corrected by the neural-learning process). This experiment shows clearly the difficulty that must be faced when transposing the “anomalies”, namely the disparities between “ideal” economic rational and observed behavior, into the theoretical framework of reward learning. In neurobiology, irrational behavior is appraised against learning dynamics (Fox & Poldrack 2014).
Today, the “common currency” hypothesis is widely accepted in the neuroscientific community. Yet, there are some rare researchers who do not fully agree with it. They argue that different specific rewards must be represented “on the same scale” but not necessarily converted into a “common currency”. The key difference between the two concepts of common scaling and common currency lies in the specificity with which rewards are represented at the level of single neurons. While a common currency view implies convergence of different types of reward onto the same neurons, a common scaling view implies that different rewards are represented by different neurons with the activity of the different neurons scaled to be in the same value range. Due to the limited resolution of the tool, fMRI studies cannot answer whether the same or different neurons are encoding the value of different rewards; only single neuron recording studies may provide such evidence (Grabenhorst & Rolls 2011; Rolls 2014).
Of course, this evolutionary advantage may become a disadvantage in some environments where the structure emphasizes likely utilities and rewards in the very short term. However, the flaw lies in the environment and not in the individual (Ainslie 1992).
For example, where reward is concerned, eat any food within reach in a buffet regardless of how hungry you are; where punishment is concerned, cross the road at the sight of a suspicious-looking individual to avoid a possible attack.
Pavlovian learning is known to be present in vertebrates, including humans, as well as many invertebrates, including insects such as drosophila.
For example, where reward is concerned, drink a cup of coffee every morning as a stimulant regardless of the specific need felt on that particular day; where punishment is concerned, select the same route every day to drive to work regardless of any foreseeable traffic jam on that particular day.
For example, where reward is concerned, select the film at the cinema according to your taste to make it the most pleasurable experience possible; where punishment is concerned, decide to jog regularly to minimize the risk of obesity.
Lengyel & Dayan (2007) advance the hypothesis of a fourth “episodic” system managed by the hippocampus. More recently, O’Doherty et al (2017) review evidence that an additional system would guide inference concerning the hidden states of other agents, such as their beliefs, preference, and intentions, in a social context.
For Pavlovian systems, Dayan et al. (2006) have proposed some hypothesis. More recently, Clark et al. (2012) review first evidence of the existence of multiple parallel Pavlovian valuation systems. Interaction between habitual and goal-directed systems, and particularly the situation when habits come to dominate behavior, has become a topic of great interest in neuropsychology of addiction and others psychiatric disorders involving compulsive behaviors, such as obsessive compulsive disorder (Daw & O’Doherty 2014).
Glimcher’s model is more widely dealing with VBD (i.e., it is supposed to also include habitual decisions), but the switch among the two neural systems is not explicitly mentioned.
Other distinctions are developed in the neuroeconomic literature. Bossaerts, Preuschoff & Hsu, (2009), in particular, mention “true” preferences (what individuals want) and “revealed” preferences (what individuals do), while Berridge & O’Doherty (2014) separate what is “wanting” and “liking” for an outcome: “it is possible to want what is not expected to be liked, not remembered to be liked, as well as what is not actually liked when obtained” (Berridge & O’Doherty 2014, p. 242).
For instance, the PCC is more active in response to a reward of 100 cents than 1 dollar, while the ventromedial PFC and striatal responses to these rewards are indistinguishable.
The costs’ nature issue in encoding of decision is addressed somewhat differently by Grabenhorst & Rolls (2011) (see also Rolls 2014). These authors draw a distinction between “extrinsic” costs (such as action costs, time delay, and risk in getting reward) and “intrinsic” costs (such as motivation state, impulsiveness, risk, and ambiguity attitude of the subject).
The most obvious evidence provides from a decision system with which neurophysiologists are familiar, the monkey visio-saccadic system, which for widely technic reasons was above all studied since the 1980s for understanding the sensorimotor control in general. The core of this frontoparietal network, that is playing a critical role for oculomotor tasks, involves areas known as the lateral intraparietal area (LIP) (in the intraparietal sulcus), the frontal eye field (FEF) (in the PFC), and the superior colliculus (in the midbrain). These findings were generalized later to body movements; it has been shown that the primary motor cortex, some anterior areas of the parietal cortex, and supplementary motor area, are playing an equivalent role.
It is only fair to recognize that the declared ambition of the researchers in the “behavioral economics in the scanner” program was quickly limited to “simply improving the understanding of the decision-making process” (see in particular the review by Sanfey et al., 2006, only a year after the survey of Camerer, Loewenstein, & Prelec 2005).
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Acknowledgements
The author would like to thank Sacha Bourgeois-Gironde, Guillaume Herbet, Philippe Mongin, Pierre Livet, Olivier Oullier, Christian Schmidt, Nicolas Vallois, and Marc Willinger for useful comments and conversations over the years related to the topics of this paper. He would also like to thank Thierry Blayac and Guillaume Cheikbossian for extremely useful comments and suggestions on both earlier and current versions of the paper.
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Serra, D. Decision-making: from neuroscience to neuroeconomics—an overview. Theory Decis 91, 1–80 (2021). https://doi.org/10.1007/s11238-021-09830-3
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DOI: https://doi.org/10.1007/s11238-021-09830-3