Neuroeconomics is an interdisciplinary field that seeks to explain human decision making, the ability to process multiple alternatives and to follow a course of action. It studies how economic behavior can shape our understanding of the brain, and how neuroscientific discoveries can constrain and guide models of economics.
It combines research from neuroscience, experimental and behavioral economics, and cognitive and social psychology. As research into decision-making behavior becomes increasingly computational, it has also incorporated new approaches from theoretical biology, computer science, and mathematics. Neuroeconomics studies decision making by using a combination of tools from these fields so as to avoid the shortcomings that arise from a single-perspective approach. In mainstream economics, expected utility (EU) and the concept of rational agents are still being used. Many economic behaviors are not fully explained by these models, such as heuristics and framing.
Behavioral economics emerged to account for these anomalies by integrating social, cognitive, and emotional factors in understanding economic decisions. Neuroeconomics adds another layer by using neuroscientific methods in understanding the interplay between economic behavior and neural mechanisms. Using tools from various fields, neuroeconomics works toward an integrated account of economic decision making.
The field of decision making is largely concerned with the processes by which individuals make a single choice from among many options. These processes are generally assumed to proceed in a logical manner such that the decision itself is largely independent of context. Different options are first translated into a common currency, such as monetary value, and are then compared to one another and the option with the largest overall utility value is the one that should be chosen. While there has been support for this economic view of decision making, there are also situations where the assumptions of optimal decision making seem to be violated.
The field of neuroeconomics arose out of this controversy. By determining which brain areas are active in which types of decision processes, neuroeconomists hope to better understand the nature of what seem to be suboptimal and illogical decisions. While most of these scientists are using human subjects in this research, others are using animal models where studies can be more tightly controlled and the assumptions of the economic model can be tested directly.
For example, Padoa-Schioppa & Assad tracked the firing rates of individual neurons in the monkey orbitofrontal cortex while the animals chose between two kinds of juice. The firing rate of the neurons was directly correlated with the utility of the food items and did not differ when other types of food were offered. This suggests that, in accordance with the economic theory of decision making, neurons are directly comparing some form of utility across different options and choosing the one with the highest value. Similarly, a common measure of prefrontal cortex dysfunction, the FrSBe, is correlated with multiple different measures of economic attitudes and behavior, supporting the idea that brain activation can display important aspects of the decision process.
Neuroeconomics studies the neurobiological along with the computational bases of decision-making. A framework of basic computations which may be applied to Neuroeconomics studies is proposed by A. Rangel, C. Camerer, and P. R. Montague. It divides the process of decision making into five stages implemented by a subject. First, a representation of the problem is formed. This includes analysis of internal states, external states and potential course of action. Second, values are assigned to potential actions. Third, based on the valuations, one of the actions is selected. Fourth, the subject evaluates how desirable the outcome is. Final stage, learning, includes updating all of the above processes in order to improve future decisions.
Major research areas
Decision making under risk and ambiguity
Most of our decisions are made under some form of uncertainty. Decision sciences such as psychology and economics usually define risk as the uncertainty about several possible outcomes when the probability of each is known. When the probabilities are unknown, uncertainty takes the form of ambiguity. Utility maximization, first proposed by Daniel Bernoulli in 1738, is used to explain decision making under risk. The theory assumes that humans are rational and will assess options based on the expected utility they will gain from each.
Research and experience uncovered a wide range of expected utility anomalies and common patterns of behavior that are inconsistent with the principle of utility maximization – for example, the tendency to overweight small probabilities and underweight large ones. Daniel Kahneman and Amos Tversky proposed prospect theory to encompass these observations and offer an alternative model.
There seem to be multiple brain areas involved in dealing with situations of uncertainty. In tasks requiring individuals to make predictions when there is some degree of uncertainty about the outcome, there is an increase in activity in area BA8 of the frontomedian cortex as well as a more generalized increase in activity of the mesial prefrontal cortex and the frontoparietal cortex. The prefrontal cortex is generally involved in all reasoning and understanding, so these particular areas may be specifically involved in determining the best course of action when not all relevant information is available.
In situations that involve known risk rather than ambiguity, the insular cortex seems to be highly active. For example, when subjects played a ‘double or nothing’ game in which they could either stop the game and keep accumulated winnings or take a risky option resulting in either a complete loss or doubling of winnings, activation of the right insula increased when individuals took the gamble. It is hypothesized that the main role of the insular cortex in risky decision making is to simulate potential negative consequences of taking a gamble.
In addition to the importance of specific brain areas to the decision process, there is also evidence that the neurotransmitter dopamine may transmit information about uncertainty throughout the cortex. Dopaminergic neurons are strongly involved in the reward process and become highly active after an unexpected reward occurs. In monkeys, the level of dopaminergic activity is highly correlated with the level of uncertainty such that the activity increases with uncertainty. Furthermore, rats with lesions to the nucleus accumbens, which is an important part of the dopamine reward pathway through the brain, are far more risk averse than normal rats. This suggests that dopamine may be an important mediator of risky behavior.
Individual level of risk aversion among humans is influenced by testosterone concentration. There are studies exhibiting correlation between the choice of a risky career (financial trading, business) and testosterone exposure. As markers for prenatal (“organizational”) testosterone transfer, 2D:4D ratio and Baron-Cohen test can be used, whereas circulating (“activational”) testosterone can be measured directly via its concentration in saliva. It appears that lower digit ratio preconditions a longer and more successful career in financial trading, especially for markets of high volatility. In addition, daily achievements of traders with lower digit ratio are more sensitive to circulating testosterone. A long-term study of risk aversion and risky career choice was conducted for a representative group of MBA students. It revealed that females are in average more risk averse, but the difference between genders vanishes for low organizational and activational testosterone exposure leading to risk-averse behaviour. Students with high salivary testosterone concentration and low digit ratio, disregarding the gender, tend to choose risky career in finance (e.g. trading or investment banking).
Serial and functionally localized model vs distributed, hierarchical model
In 2017 March, Laurence T. Hunt and Benjamin Y. Hayden argued an alternative viewpoint of the mechanistic model to explain how we evaluate options and choose the best course of action. Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. The component processes typically include the evaluation of options, the comparison of option values in the absence of any other factors, the selection of an appropriate action plan and the monitoring of the outcome of the choice. They emphasized how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organization of timescales for information processing across the cortex.
One aspect of human decision making is a strong aversion to potential loss. Under loss aversion, the cost of losing a specific amount of money is higher than the value of gaining the same amount of money. One of the main controversies in understanding loss aversion is whether the process truly exists in the brain and manifested in the neural representation of positive and negative outcomes or whether it is a side effect of other neural effects, such as increased attention and arousal with losses. Another issue is whether one could find loss aversion as a response for neural sub-systems, such as an impulsive and emotional system driven by an aversion to potentially negative outcomes, whose responses are monitored and controlled by a system responsible for a reasoned comparison among options.
A basic controversy in loss aversion research is whether losses are actually experienced more negatively than equivalent gains or merely predicted to be more painful but actually experienced equivalently. Neuroeconomic research has attempted to distinguish between these hypotheses by measuring different physiological changes in response to both loss and gain. Studies have found that skin conductance, pupil dilation and heart rate are all higher in response to monetary loss than to equivalent gain. All three measures are involved in stress responses, so one might argue that losing a particular amount of money is experienced more strongly than gaining the same amount. On the other hand, in some of these studies there was no behavioral loss aversion, which can suggest that the effect of losses is merely on attention (what is known as loss attention); such attentional orienting responses also lead to increased autonomic signals.
Brain studies have initially suggested that there is increased mid-prefrontal and anterior cingulate cortex rapid response following losses compared to gains, which was interpreted as a neural signature of loss aversion. However, subsequent reviews have noticed that in this paradigm individuals do not actually show behavioral loss aversion casting doubts on the interpretability of these findings. With respect to fMRI studies, while one study found no evidence for an increase in activation in areas related to negative emotional reactions in response to loss aversion another found that individuals with damaged amygdalas had a lack of loss aversion even though they had normal levels of general risk aversion, suggesting that the behavior was specific to potential losses. These conflicting studies suggest that more research needs to be done to determine whether brain response to losses is due to loss aversion or merely to an alerting or orienting aspect of losses; as well as to examine if there are areas in the brain that respond specifically to potential losses .
In addition to risk preference, another central concept in economics is intertemporal choices which are decisions that involve costs and benefits that are distributed over time. Intertemporal choice research studies the expected utility that humans assign to events occurring at different times. The dominant model in economics which explains it is discounted utility (DU). DU assumes that humans have consistent time preference and will assign value to events regardless of when they occur. Similar to EU in explaining risky decision making, DU is inadequate in explaining intertemporal choice.
For example, DU assumes that people who value a bar of candy today more than 2 bars tomorrow, will also value 1 bar received 100 days from now more than 2 bars received after 101 days. There is strong evidence against this last part in both humans and animals, and hyperbolic discounting has been proposed as an alternative model. Under this model, valuations fall very rapidly for small delay periods, but then fall slowly for longer delay periods. This better explains why most people who would choose 1 candy bar now over 2 candy bars tomorrow, would, in fact, choose 2 candy bars received after 101 days rather than the 1 candy bar received after 100 days which DU assumes.
Neuroeconomic research in intertemporal choice is largely aimed at understanding what mediates observed behaviors such as future discounting and impulsively choosing smaller sooner rather than larger later rewards. The process of choosing between immediate and delayed rewards seems to be mediated by an interaction between two brain areas. In choices involving both primary (fruit juice) and secondary rewards (money), the limbic system is highly active when choosing the immediate reward while the lateral prefrontal cortex was equally active when making either choice. Furthermore, the ratio of limbic to cortex activity decreased as a function of the amount of time until reward. This suggests that the limbic system, which forms part of the dopamine reward pathway, is most involved in making impulsive decisions while the cortex is responsible for the more general aspects of the intertemporal decision process.
The neurotransmitter serotonin seems to play an important role in modulating future discounting. In rats, reducing serotonin levels increases future discounting while not affecting decision making under uncertainty. It seems, then, that while the dopamine system is involved in probabilistic uncertainty, serotonin may be responsible for temporal uncertainty since delayed reward involves a potentially uncertain future. In addition to neurotransmitters, intertemporal choice is also modulated by hormones in the brain. In humans, a reduction in cortisol, released by the hypothalamus in response to stress, is correlated with a higher degree of impulsivity in intertemporal choice tasks. Drug addicts tend to have lower levels of cortisol than the general population, which may explain why they seem to discount the future negative effects of taking drugs and opt for the immediate positive reward.
Social decision making
While most research on decision making tends to focus on individuals making choices outside of a social context, it is also important to consider decisions that involve social interactions. The types of behavior that decision theorists study are as diverse as altruism, cooperation, punishment, and retribution. One of the most frequently utilized tasks in social decision making is the prisoner’s dilemma.
In this situation, the payoff for a particular choice is dependent not only on the decision of the individual but also on that of another individual playing the game. An individual can choose to either cooperate with his partner or defect against the partner. Over the course of a typical game, individuals tend to prefer mutual cooperation even though defection would lead to a higher overall payout. This suggests that individuals are motivated not only by monetary gains but also by some reward derived from cooperating in social situations.
This idea is supported by neural imaging studies demonstrating a high degree of activation in the ventral striatum when individuals cooperate with another person but that this is not the case when people play the same prisoner’s dilemma against a computer. The ventral striatum is part of the reward pathway, so this research suggests that there may be areas of the reward system that are activated specifically when cooperating in social situations. Further support for this idea comes from research demonstrating that activation in the striatum and the ventral tegmental area show similar patterns of activation when receiving money and when donating money to charity. In both cases, the level of activation increases as the amount of money increases, suggesting that both giving and receiving money results in neural reward.
An important aspect of social interactions such as the prisoner’s dilemma is trust. The likelihood of one individual cooperating with another is directly related to how much the first individual trusts the second to cooperate; if the other individual is expected to defect, there is no reason to cooperate with them. Trust behavior may be related to the presence of oxytocin, a hormone involved in maternal behavior and pair bonding in many species. When oxytocin levels were increased in humans, they were more trusting of other individuals than a control group even though their overall levels of risk-taking were unaffected suggesting that oxytocin is specifically implicated in the social aspects of risk taking. However this research has recently been questioned.
One more important paradigm for neuroeconomic studies is ultimatum game. In this game Player 1 gets a sum of money and decides how much he wants to offer Player 2. Player 2 either accepts or rejects the offer. If he accepts both players get the amount as proposed by Player 1, if he rejects nobody gets anything. Rational strategy for Player 2 would be to accept any offer because it has more value than zero. However, it has been shown that people often reject offers that they consider as unfair. Neuroimaging studies indicated several brain regions that are activated in response to unfairness in ultimatum game. They include bilateral mid-anterior insula, anterior cingulate cortex (ACC), medial supplementary motor area (SMA), cerebellum and right dorsolateral prefrontal cortex (DLPFC). It has been shown that low-frequency repetitive transcranial magnetic stimulation of DLPFC increases the likelihood of accepting unfair offers in the ultimatum game.
Another issue in the field of neuroeconomics is represented by role of reputation acquisition in social decision making. Social exchange theory claims that prosocial behavior originates from the intention to maximize social rewards and minimize social costs. In this case approval from others may be viewed as a significant positive reinforcer – i.e., a reward. Neuroimaging studies have provided evidence supporting this idea – it was shown that processing of social rewards activates striatum, especially left putamen and left caudate nucleus, in the same fashion these areas are activated during the processing of monetary rewards. These findings also support so-called “common neural currency” idea, which assumes existence of shared neural basis for processing of different types of reward.
Sexual decision making
Regarding the choice of sexual partner, research studies have been conducted on humans and on nonhuman primates. Notably, Cheney & Seyfarth 1990, Deaner et al. 2005, and Hayden et al. 2007 suggest a persistent willingness to accept fewer physical goods or higher prices in return for access to socially high-ranking individuals, including physically attractive individuals, whereas increasingly high rewards are demanded if asked to relate to low-ranking individuals.
The neurobiological basis for this preference includes neurons of the lateral intraparietal cortex (LIP), which is related to eye movement, and which is operative in situations of two-alternative forced choices.
Behavioral economics experiments record the subject’s decisions over various design parameters and use the data to generate formal models that predict performance. Neuroeconomics extends this approach by adding states of the nervous system to the set of explanatory variables. The goal of neuroeconomics is to help explain decisions and to enrich the data sets available for testing predictions.
Furthermore, neuroeconomic research is being used to understand and explain aspects of human behavior that do not conform to traditional economic models. While these behavior patterns are generally dismissed as ‘fallacious’ or ‘illogical’ by economists, neuroeconomic researchers are trying to determine the biological reasons for these behaviors. By using this approach, we may be able to find explanations for these seemingly sub-optimal behaviors.
Neurobiological research techniques
There are several different techniques that can be utilized to understand the biological basis of economic behavior. Neural imaging is used in human subjects to determine which areas of the brain are most active during particular tasks. Some of these techniques, such as fMRI or PET are best suited to giving detailed pictures of the brain which can give information about specific structures involved in a task. Other techniques, such as ERP (event-related potentials) and oscillatory brain activity are used to gain detailed knowledge of the time course of events within a more general area of the brain.
In addition to studying areas of the brain, some studies are aimed at understanding the functions of different brain chemicals in relation to behavior. This can be done by either correlating existing chemical levels with different behavior patterns or by changing the amount of the chemical in the brain and noting any resulting behavioral changes. For example, the neurotransmitter serotonin seems to be involved in making decisions involving intertemporal choice while dopamine is utilized when individuals make judgments involving uncertainty. Furthermore, artificially increasing oxytocin levels increases trust behavior in humans while individuals with higher cortisol levels tend to be more impulsive and exhibit more future discounting.
In addition to studying the behavior of normal individuals in decision making tasks, some research involves comparing the behavior of normal individuals to that of others with damage to areas of the brain expected to be involved in certain behaviors. In humans, this means finding individuals with specific types of neural impairment. For example, people with amygdala damage seem to exhibit less loss aversion than normal controls. Also, scores from a survey measuring correlates of prefrontal cortex dysfunction are correlated with general economic attitudes.
Previous studies investigated the behavioral patterns of patients with psychiatric disorders, such as schizophrenia, autism, depression, or addiction, to get the insights of their pathophysiology. In animal studies, highly controlled experiments can get more specific information about the importance of brain areas to economic behavior. This can involve either lesioning entire brain areas and measuring resulting behavior changes or using electrodes to measure the firing of individual neurons in response to particular stimuli.
In a typical behavioral economics experiment, a subject is asked to make a series of economic decisions. For example, a subject may be asked whether they prefer to have 45 cents or a gamble with a 50% chance to win one dollar. The experimenter will then measure different variables in order to determine what is going on in the subject’s brain as they make the decision. Some authors have demonstrated that neuroeconomics may be useful to describe not only experiments involving rewards but also common psychiatric syndromes involving addiction or delusion.
Glenn W. Harris and Emanuel Donchin have criticized the emerging field. Criticisms have included claims that neuroeconomics is “a field that oversells itself”; or that neuroeconomic studies “misunderstand and underestimate traditional economic models”. A salient argument of traditional economists against the neuroeconomic approach is that the use of non-choice data, such as response times, eye-tracking and neural signals that people generate during decision making, should be excluded from any economic analysis.
Neuromarketing is a distinct discipline closely related to neuroeconomics. While neuroeconomics has more academic aims, since it studies the basic mechanisms of decision-making, neuromarketing is an applied field which uses neuroimaging tools for market investigations.
- ^Center for Neuroeconomics Study at Duke University http://dibs.duke.edu/research/d-cides/research/neuroeconomics
- ^Levallois, Clement; Clithero, John A.; Wouters, Paul; Smidts, Ale; Huettel, Scott A. (2012). “Translating upwards: linking the neural and social sciences via neuroeconomics”. Nature Reviews Neuroscience. 13 (11): 789–797. doi:10.1038/nrn3354. ISSN 1471-003X. PMID 23034481.
- ^ Jump up to:ab c d e Loewenstein, G., Rick, S., & Cohen, J. (2008). Annual Reviews.59: 647-672. doi:10.1146/annurev.psych.59.103006.093710
- ^Rustichini A (2009). “Neuroeconomics: What have we found, and what should we search for?”. Current Opinion in Neurobiology. 19 (6): 672–677. doi:10.1016/j.conb.2009.09.012. PMID 19896360.
- ^ Jump up to:ab Padoa-Schioppa C.; Assad J.A. (2007). “The representation of economic value in the orbitofrontal cortex is invariant for changes of menu”. Nature Reviews Neuroscience. 11(1): 95–102. doi:10.1038/nn2020. PMC 2646102. PMID 18066060.
- ^ Jump up to:ab Spinella M.; Yang B.; Lester D. (2008). “Prefrontal cortex dysfunction and attitudes toward money: A study in neuroeconomics”. Journal of Socio-Economics. 37 (5): 1785–1788. doi:10.1016/j.socec.2004.09.061.
- ^Rangel A.; Camerer C.; Montague P. R. (2008). “A framework for studying the neurobiology of value-based decision making”. Nature Reviews Neuroscience. 9 (7): 545–556. doi:10.1038/nrn2357. PMC 4332708. PMID 18545266.
- ^Mohr M.; Biele G.; Hauke R. (2010). “Neural Processing of Risk”. Journal of Neuroscience. 30 (19): 6613–6619. doi:10.1523/jneurosci.0003-10.2010. PMC 6632558. PMID 20463224.
- ^Wakker, Peter P. (2010). Prospect Theory: For Risk and Ambiguity. Cambridge, UK: Cambridge University Press.
- ^Volz K.G.; Schubotz R.I.; von Cramon D.Y. (2003). “Predicting events of varying probability: uncertainty investigated by fMRI”. NeuroImage. 19 (2 Pt 1): 271–280. doi:10.1016/S1053-8119(03)00122-8. hdl:11858/00-001M-0000-0010-D182-2. PMID 12814578.
- ^ Jump up to:ab Volz K.G.; Schubotz R.I.; von Cramon D.Y. (2004). “Why am I unsure? Internal and external attributions of uncertainty dissociated by fMRI”. NeuroImage. 21 (3): 848–857. doi:10.1016/j.neuroimage.2003.10.028. PMID 15006651.
- ^ Jump up to:ab Knutson B.; Taylor J.; Kaufman M.; Peterson R.; Glover G. (2005). “Distributed Neural Representation of Expected Value”. Journal of Neuroscience. 25 (19): 4806–4812. doi:10.1523/JNEUROSCI.0642-05.2005. PMC 6724773. PMID 15888656.
- ^ Jump up to:ab Paulus M.P.; Hozack N.; Zauscher B.; McDowell J.E.; Frank L.; Brown G.G.; Braff D.L. (2001). “Prefrontal, parietal, and temporal cortex networks underlie decision-making in the presence of uncertainty”. NeuroImage. 13 (1): 91–100. doi:10.1006/nimg.2000.0667. PMID 11133312.
- ^ Jump up to:ab Paulus M.P.; Rogalsky C.; Simmons A.; Feinstein J.S.; Stein M.B. (2003). “Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism”. NeuroImage. 19 (4): 1439–1448. doi:10.1016/S1053-8119(03)00251-9. PMID 12948701.
- ^ Jump up to:ab Fiorillo C.D.; Tobler P.N.; Schultz W. (2003). “Discrete coding of reward probability and uncertainty by dopamine neurons” (PDF). Science. 299 (5614): 1898–1902. Bibcode:2003Sci…299.1898F. doi:10.1126/science.1077349. PMID 12649484.
- ^ Jump up to:ab Cardinal R.N.; Howes N.J. (2005). “Effects of lesions of the nucleus accumbens core on choice between small certain rewards and large uncertain rewards in rats”. BMC Neuroscience. 6: 37. doi:10.1186/1471-2202-6-37. PMC 1177958. PMID 15921529.
- ^ Jump up to:ab John M. Coates; Mark Gurnell; Aldo Rustichini (2009). “Second-to-fourth digit ratio predicts success among high-frequency financial traders”. Proceedings of the National Academy of Sciences. 106 (2): 623–628. Bibcode:2009PNAS..106..623C. doi:10.1073/pnas.0810907106. PMC 2626753. PMID 19139402.
- ^ Jump up to:ab Paola Sapienza; Luigi Zingales; Dario Maestripieri (2009). “Gender differences in financial risk aversion and career choices are affected by testosterone”. Proceedings of the National Academy of Sciences. 106 (36): 15268–15273. Bibcode:2009PNAS..10615268S. doi:10.1073/pnas.0907352106. PMC 2741240. PMID 19706398.
- ^Hunt, Laurence T.; Hayden, Benjamin Y. (2017). “A distributed, hierarchical and recurrent framework for reward-based choice”. Nature Reviews Neuroscience. 18 (3): 172–182. doi:10.1038/nrn.2017.7. PMC 5621622. PMID 28209978.
- ^Sokol-Hessner P.; Hsu M.; Curley N.G.; Delgado M.R.; Camerer C.F.; Phelps E.A. (2009). “Thinking like a trader selectively reduces individuals’ loss aversion”. Proceedings of the National Academy of Sciences. 106 (13): 5035–5040. Bibcode:2009PNAS..106.5035S. doi:10.1073/pnas.0806761106. PMC 2656558. PMID 19289824.
- ^Hochman G.; Yechiam E. (2011). “Loss aversion in the eye and in the heart: The autonomic nervous system’s responses to losses”. Journal of Behavioral Decision Making. 24 (2): 140–156. doi:10.1002/bdm.692.
- ^ Jump up to:ab Yechiam, E.; Hochman, G. (2013). “Losses as modulators of attention: Review and analysis of the unique effects of losses over gains”. Psychological Bulletin. 139 (2): 497–518. doi:10.1037/a0029383. PMID 22823738.
- ^Gehring, W.J.; Willoughby, A.R (2002). “The medial frontal cortex and the rapid processing of monetary gains and losses”. Science. 295 (2): 2279–2282. doi:10.1002/bdm.692.
- ^Tom S.M.; Fox C.R.; Trepel C.; Poldrack R.A. (2007). “The neural basis of loss aversion in decision-making under risk”. Science. 315 (5811): 515–518. Bibcode:2007Sci…315..515T. doi:10.1126/science.1134239. PMID 17255512.
- ^ Jump up to:ab De Martino B.; Camerer C.F.; Adolphs R. (2010). “Amygdala damage eliminates monetary loss aversion”. Proceedings of the National Academy of Sciences. 107 (8): 3788–3792. Bibcode:2010PNAS..107.3788D. doi:10.1073/pnas.0910230107. PMC 2840433. PMID 20142490.
- ^McClure S.M.; Laibson D.I.; Loewenstein G.; Cohen J.D. (2004). “Separate neural systems value immediate and delayed monetary rewards”. Science. 306 (5695): 503–507. Bibcode:2004Sci…306..503M. doi:10.1126/science.1100907. PMID 15486304.
- ^McClure S.M.; Ericson K.M.; Laibson D.I.; Loewenstein G.; Cohen J.D. (2007). “Time discounting for primary rewards”. Journal of Neuroscience. 27 (21): 5796–5804. doi:10.1523/JNEUROSCI.4246-06.2007. PMC 6672764. PMID 17522323.
- ^Mobini S.; Chiang T.J.; Al-Ruwaitea A.S.; Ho M.Y.; Bradshaw C.M.; Szabadi E. (2000). “Effect of central 5-hydroxytryptamine depletion on inter-temporal choice: A quantitative analysis”. Psychopharmacology. 149 (3): 313–318. doi:10.1007/s002130000385. PMID 10823413.
- ^ Jump up to:ab Mobini S.; Chiang T.J.; Ho M.Y.; Bradshaw C.M.; Szabadi E. (2000). “Effects of central 5-hydroxytryptamine depletion on sensitivity to delayed and probabilistic reinforcement”. Psychopharmacology. 152 (4): 390–397. doi:10.1007/s002130000542. PMID 11140331.
- ^ Jump up to:ab Takahashi T (2004). “Cortisol levels and time-discounting of monetary gain in humans”. NeuroReport. 15 (13): 2145–2147. doi:10.1097/00001756-200409150-00029. PMID 15486498.
- ^Plihal W.; Krug R.; Pietrowsky R.; Fehm H.L.; Born J. (1996). “Coricosteroid receptor mediated effects on mood in humans”. Psychoneuroendocrinology. 21 (6): 515–523. doi:10.1016/S0306-4530(96)00011-X. PMID 8983088.
- ^Rilling J.K.; Gutman D.A.; Zeh T.R.; Pagnoni G.; Berns G.S.; Kilts C.D. (2002). “A neural basis for social cooperation”. Neuron. 35 (2): 395–405. doi:10.1016/S0896-6273(02)00755-9. PMID 12160756.
- ^Rilling J.K.; Sanfey A.G.; Aronson J.A.; Nystrom L.E.; Cohen J.D. (2004). “Opposing BOLD responses to reciprocated and unreciprocated altruism in putative reward pathways”. NeuroReport. 15 (16): 2539–2543. doi:10.1097/00001756-200411150-00022. PMID 15538191.
- ^Moll J.; Drueger F.; Zahn R.; Pardini M.; de Oliveira-Souza R.; Grafman J. (2006). “Human fronto-mesolimbic networks guide decisions about charitable donation”. Proceedings of the National Academy of Sciences. 103 (42): 15623–15628. Bibcode:2006PNAS..10315623M. doi:10.1073/pnas.0604475103. PMC 1622872. PMID 17030808.
- ^ Jump up to:ab Kosfeld M.; Heinrichs M; Zak P.J.; Fischbacher U.; Fehr E. (2005). “Oxytocin increases trust in humans”. Nature. 435 (7042): 673–676. Bibcode:2005Natur.435..673K. doi:10.1038/nature03701. PMID 15931222.
- ^Nave G.; Camerer C.; McCullough M. (2015). “Does Oxytocin increase trust in humans? Critical review of research”. Perspectives on Psychological Science. 10 (6): 772–789. doi:10.1177/1745691615600138. PMID 26581735.
- ^Gabay, Anthony S.; Radua, Joaquim; Kempton, Matthew J.; Mehta, Mitul A. (1 November 2014). “The Ultimatum Game and the brain: A meta-analysis of neuroimaging studies”. Neuroscience & Biobehavioral Reviews. 47: 549–558. doi:10.1016/j.neubiorev.2014.10.014. PMID 25454357.
- ^Knoch, Daria; Pascual-Leone, Alvaro; Meyer, Kaspar; Treyer, Valerie; Fehr, Ernst (3 November 2006). “Diminishing reciprocal fairness by disrupting the right prefrontal cortex”. Science. 314 (5800): 829–832. Bibcode:2006Sci…314..829K. doi:10.1126/science.1129156. ISSN 1095-9203. PMID 17023614.
- ^Izuma K.; Saito D. N.; Sadato N. (2008). “Processing of Social and Monetary Rewards in the Human Striatum”. Neuron. 58 (2): 284–294. doi:10.1016/j.neuron.2008.03.020. PMID 18439412.
- ^Glimcher & Fehr 2014: 248.
- ^Glimcher & Fehr 2014: 249.
- ^Billeke, P.; Zamorano, F.; Cosmeli, D.; Aboitiz, A. (2013). “Oscillatory Brain Activity Correlates with Risk Perception and Predicts Social Decisions”. Cerebral Cortex. 23 (14): 2872–83. doi:10.1093/cercor/bhs269. PMID 22941720.
- ^Billeke, P.; Zamorano, F.; López, T.; Cosmeli, D.; Aboitiz, A. (2014). “Someone has to Give In: Theta Oscillations Correlate with Adaptive Behavior in Social Bargaining”. Social Cognitive and Affective Neuroscience. 9 (12): 2041–8. doi:10.1093/scan/nsu012. PMC 4249481. PMID 24493841.
- ^Chung, Dongil (2013). “Cognitive Motivations of Free Riding and Cooperation and Impaired Strategic Decision Making in Schizophrenia During a Public Goods Game”. Schizophrenia Bulletin. 39 (1): 112–119. doi:10.1093/schbul/sbr068. PMC 3523913. PMID 21705433.
- ^Donchin, Emanuel (November 2006). “fMRI: Not the Only Way to Look at the Human Brain in Action”. Aps Observer. 19 (11). Retrieved 14 October 2014.
- ^Rubinstein, Ariel (2006). “Discussion of “behavioral economics”: “Behavioral economics” (Colin Camerer) and “Incentives and self-control” (Ted O’Donoghue and Matthew Rabin)”. In Persson, Torsten; Blundell, Richard; Newey, Whitney K. (eds.). Advances in economics and econometrics: theory and applications, ninth World Congress. Cambridge, UK: Cambridge University Press. ISBN 978-0-521-87153-2. Retrieved 1 January 2010.
- ^Gul, Faruk; Pesendorfer, Wolfgang (2008). “A Case for Mindless Economics”. In Schotter, Andrew; Caplin, Andrew (eds.). The Foundations of Positive and Normative Economics: A Handbook (Handbooks in Economic Methodologies). Oxford University Press, USA. pp. 3–42. ISBN 978-0-19-532831-8. Retrieved 4 March 2009.
- ^Glimcher, Paul (2008). “Neuroeconomics”. Scholarpedia. 3 (10): 1759. Bibcode:2008SchpJ…3.1759G. doi:10.4249/scholarpedia.1759. Revision #50592.
- ^Lee N, Broderick AJ, Chamberlain L (February 2007). “What is “neuromarketing”? A discussion and agenda for future research”. Int J Psychophysiol. 63 (2): 199–204. doi:10.1016/j.ijpsycho.2006.03.007. PMID 16769143.