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 methods 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. By using tools from various fields, some scholars claim that neuroeconomics offers a more integrative way of understanding decision making.
- 1 Introduction
- 2 Major research areas in neuroeconomics
- 3 Methodology
- 4 Notable theorists
- 5 Experiments
- 6 Neuroeconomic Programs
- 7 Related Fields
- 8 Criticism
- 9 Neuromarketing
- 10 See also
- 11 References
- 12 Further reading
- 13 Journal
- 14 External links
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 higher 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.
Major research areas in neuroeconomics
Decision making under risk and uncertainty
Most of our decisions are made under some conditions of risk. Decision sciences such as psychology and economics usually define risk as the uncertainty about several possible outcomes when the probability of each is known. 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 human tendency to be risk-averse or risk-seeking. Also, the tendency to overweigh small probabilities and underweigh large ones. Daniel Kahneman and Amos Tversky proposed the prospect theory to encompass these observations and offers 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 uncertainty, 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.
One interesting aspect of human decision making is a strong aversion to potential loss. For example, 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 is driven by a single neural system that directly compares options and decides between them or whether there are competing systems, one responsible for a reasoned comparison among options and another more impulsive and emotional system driven by an aversion to potentially negative outcomes.
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 there are areas in the brain that respond specifically to potential loss or whether loss aversion is the byproduct of more general reasoning processes.
Another 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 it seems that losing a particular amount of money is experienced more strongly than gaining the same amount.
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 EU 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. Interestingly, 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 situations 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. Your likelihood of cooperating with another individual is directly related to how much you trust them to cooperate with you; if you expect the other individual to defect against you, there is no reason for you to cooperate with them. Trust behavior seems to 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.
Behavioral economics experiments record the subject's decision over various design parameters and use the data to generate formal models that predict performance. Neuroeconomics extends this approach by adding observation of the nervous system to the set of explanatory variables. The goal of neuroeconomics is to inform the creation and contribute another layer of data to the testable hypotheses of these models.
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 valid reasons for the presence of 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) 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 of one dollar and 50% chance of nothing. 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' tools may be useful not only to describe experiments involving rewarding but may also be applied in order to describe the psychological behavior of common psychiatric syndromes involving addiction as well as delusion. (Download)
Several universities are conducting direct research in Neuroeconomics, such as MIT, Caltech, New York University, Carnegie Mellon University, Duke University, and George Mason University. A few schools offer a degree in Neuroeconomics. Claremont Graduate University was the first institution to offer a PhD in Neuroeconomics; it remains one of the few Neuroeconomics institutes in the United States. Caltech started its Behavioral and Social Neuroscience (BSN) PhD in conjunction with its Computation and Neural Systems and Social Science programs, mixing economic theory, neurobiology, computational neuroscience, dynamic causal modeling and neuroscientific techniques.
Starting in 2010, the Department of Economics at the University of Zurich in Zurich/Switzerland began offering a degree-awarding PhD program in Neuroeconomics. Students in this program take dedicated neuroeconomics courses and conduct research within the research groups at the Department's Laboratory for Social and Neural Systems Research (SNS-Lab).
Maastricht University (Netherlands) offers a 2-year research master program in Neuroeconomics. The program is a joint venture of the Department of Economics and the Faculty of Psychology and Neuroscience. It offers core courses at PhD level in economics, cognitive and social neuroscience and hands-on training in experimental and neuroscience methods. A listing of most degree programs can be found on the Society for Neuroeconomics Website.
Neuroeconomics have also opened the door for other new emerging fields like Neurofinance, Nueroinvesting and Neurotrading. These new fields of study focus on the cognitive processes engaged in acquiring and processing information in financial decision making. According to Elise Payzan, portfolio managers and traders have to process information on the spot in rapidly changing environments. Little is known about how to tailor organizational and individual decision-making processes to help people process information efficiently in such contexts. By identifying environmental factors improving efficient information processing, it is hoped that research in neurofinance will produce practical results on how to improve investment and trading decisions, at both individual and organizational levels. As for now, there is also a new concept of using nootropics (smart drugs) to help investors/traders enhance their cognitive acuteness while trading the market. The company leading this field is Trubrain Trading.
Glenn W. Harris and Emanuel Donchin have criticized the emerging field. Example of critics have been that it is "a field that oversells itself"; or that neuroeconomic studies "misunderstand and underestimate traditional economic models". A critical 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
- Loewenstein,G., Rick,S., & Cohen, J. (2008). Neuroeconomics. 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: 672–677. doi:10.1016/j.conb.2009.09.012.
- 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: 95–102. doi:10.1038/nn2020.
- Spinella M., Yang B., Lester D. (2008). "Prefrontal cortex dysfunction and attitudes toward money: A study in neuroeconomics". Journal of Socio-Economics 37: 1785–1788. doi:10.1016/j.socec.2004.09.061.
- Mohr M., Biele G., Hauke R. (2010). "Neural Processing of Risk". e Journal of Neuroscience 30 (19): 6613–6619. doi:10.1523/jneurosci.0003-10.2010.
- 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. PMID 12814578.
- 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.
- 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. PMID 15888656.
- 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.
- 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.
- Fiorillo C.D., Tobler P.N., Schultz W. (2003). "Discrete coding of reward probability and uncertainty by dopamine neurons". Science 299 (5614): 1898–1902. doi:10.1126/science.1077349. PMID 12649484.
- 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.
- 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. doi:10.1126/science.1134239. PMID 17255512.
- 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. doi:10.1073/pnas.0910230107. PMC 2840433. PMID 20142490.
- 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. doi:10.1073/pnas.0806761106.
- 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: 140–156. doi:10.1002/bdm.692.
- 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. 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. 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.
- 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.
- Takahashi T (2004). "Cortisol levels and time-discounting of monetary gain in humans". NeuroReport 15: 2145–2147. doi:10.1097/00001756-200409150-00029.
- 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: 2539–2543. doi:10.1097/00001756-200411150-00022.
- 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. doi:10.1073/pnas.0604475103.
- Kosfeld M., Heinrichs M, Zak P.J., Fischbacher U., Fehr E. (2005). "Oxytocin increases trust in humans". Nature 435 (7042): 673–676. doi:10.1038/nature03701. PMID 15931222.
- 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). doi:10.1093/schbul/sbr068.
- Neuroeconomics The MIT Neuroeconomics Center
- Here is a Review of Neuroeconomic Programs in the United States
- "Caltech: Humanities and Social Sciences". Hss.caltech.edu. 2010-01-29. Retrieved 2010-07-28.
- Society for Neuroeconomics
- "fMRI: Not the Only Way to Look at the Human Brain in Action". Observer. 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. Advances in economics and econometrics: theory and applications, ninth World Congress. Cambridge, UK: Cambridge University Press. ISBN 0-521-87153-0. Retrieved 2010-01-01.
- Gul, Faruk; Pesendorfer, Wolfgang (2008). "A Case for Mindless Economics". In Schotter, Andrew; Caplin, Andrew. The Foundations of Positive and Normative Economics: A Handbook (Handbooks in Economic Methodologies). Oxford University Press, USA. pp. 3–42. ISBN 0-19-532831-0. Retrieved 2009-03-04.
- Paul W. Glimcher (2008). "Neuroeconomics - Scholarpedia". 3(10):1759, revision #50592. Retrieved 2009-03-04.
- 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.
- David Krueger (2009) The Secret Language of Money, McGraw-Hill Professional. ISBN 978-0-07-162339-1
- De Martino B, Camerer CF, Adolphs R (February 2010). "Amygdala damage eliminates monetary loss aversion". Proc. Natl. Acad. Sci. U.S.A. 107 (8): 3788–92. doi:10.1073/pnas.0910230107. PMC 2840433. PMID 20142490. PDF
- Paul W. Glimcher; Colin Camerer; Russell A. Poldrack; Ernst Fehr; Neuroeconomics: Decision Making and the Brain, Academic Press, 2008.
- Camerer CF (November 2008). "Neuroeconomics: opening the gray box". Neuron 60 (3): 416–9. doi:10.1016/j.neuron.2008.10.027. PMID 18995815.
- Clithero JA, Tankersley D, Huettel SA (November 2008). Ashe, James, ed. "Foundations of Neuroeconomics: From Philosophy to Practice". PLoS Biol. 6 (11): e298. doi:10.1371/journal.pbio.0060298. PMC 2586372. PMID 19067493.
- Deppe M, Schwindt W, Pieper A et al. (July 2007). "Anterior cingulate reflects susceptibility to framing during attractiveness evaluation". NeuroReport 18 (11): 1119–23. doi:10.1097/WNR.0b013e3282202c61. PMID 17589310.
- De Martino B, Kumaran D, Seymour B, Dolan RJ (August 2006). "Frames, Biases, and Rational Decision-Making in the Human Brain". Science 313 (5787): 684–7. doi:10.1126/science.1128356. PMC 2631940. PMID 16888142.
- Colin Camerer, George Loewenstein, Drazen Prelec, "Neuroeconomics: How neuroscience can inform economics", Journal of Economic Literature, 2005
- Deppe M, Schwindt W, Kugel H, Plassmann H, Kenning P (April 2005). "Nonlinear responses within the medial prefrontal cortex reveal when specific implicit information influences economic decision making". J Neuroimaging 15 (2): 171–82. doi:10.1177/1051228405275074. PMID 15746230.
- Daniel Houser; Kevin McCabe; Neuroeconomics, Advances in Health Economics and Health Services Research v. 20, Emerald Group Publishing Limited, Bingley West Yorkshire
- Peter Kenning, Hilke Plassmann, "Brain Research Bulletin - Special Issue on NeuroEconomics", 2005
- Paul Glimcher, Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics, MIT Press, 2003.
- Montague PR, Berns GS (October 2002). "Neural economics and the biological substrates of valuation". Neuron 36 (2): 265–84. doi:10.1016/S0896-6273(02)00974-1. PMID 12383781. PDF
- Zak PJ, Kurzban R, Matzner WT (December 2004). "The neurobiology of trust". Annals of the New York Academy of Sciences 1032: 224–7. doi:10.1196/annals.1314.025. PMID 15677415. alternative source
- Kosfeld M, Heinrichs M, Zak PJ, Fischbacher U, Fehr E (June 2005). "Oxytocin increases trust in humans". Nature 435 (7042): 673–6. doi:10.1038/nature03701. PMID 15931222.
- Zak PJ, Kurzban R, Matzner WT (December 2005). "Oxytocin is associated with human trustworthiness". Horm Behav 48 (5): 522–7. doi:10.1016/j.yhbeh.2005.07.009. PMID 16109416.
- John Cassidy, "Mind Games. What neuroeconomics tells us about money and the brain", New Yorker, 2006 September.
- Fließbach K., Weber B, Trautner P, Dohmen T, Sunde U, Elger CE, Falk A.; Social Comparison Affects Reward-Related Brain Activity in the Human Ventral Striatum, Science, 2007, 318(5854):1305-8.
- Weber B, Rangel A, Wibral M, Falk A. The medial prefrontal cortex exhibits money illusion., PNAS, 106(13):5025-8.
- Hardy-Vallée, B. (forthcoming). "Decision-making: a neuroeconomic perspective". Philosophy Compass.
- Takahashi T, Hadzibeganovic T, Cannas SA, Makino T, Fukui H, Kitayama S (2009). "Cultural neuroeconomics of intertemporal choice". Neuro Endocrinol. Lett. 30 (2): 185–91. PMID 19675524.
- Zak PJ, Stanton AA, Ahmadi S (2007). Brosnan, Sarah, ed. "Oxytocin Increases Generosity in Humans". PLoS ONE 2 (11): e1128. doi:10.1371/journal.pone.0001128. PMC 2040517. PMID 17987115.
- Sanfey AG (October 2007). "Social decision-making: insights from game theory and neuroscience". Science 318 (5850): 598–602. doi:10.1126/science.1142996. PMID 17962552.
- Scholarpedia neuroeconomics
- Research Master in Neuroeconomics at Maastricht University
- The Center for Economics and Neuroscience at University of Bonn
- Doctoral Program in Neuroeconomics, University of Zurich
- The Center for Neuroeconomic Studies at Claremont Graduate University
-  The Spanish Society for Neuroeconomics.