Neuroeconomics is an emerging interdisciplinary study combining the fields of neuroscience, economics, and psychology to establish a common and unified framework for better understanding and describing the processes of decision making, from stimulus detection through to choice, evaluation and learning [1]. Neurobiology serves as a methodological and analytical foundation by which economic models and theories can be empirically validated. Conversely, economics provides neurobiology with testable theories that can contextualize the underlying patterns of neural activity associated with different phases and domains of decision making [2]. Early research in neuroeconomics was largely focused on identifying the neural correlates of economic variables, such as value and probability, as described by neoclassical economics [2-3]. Confining the operationalization of variables to neoclassical models imposed constraints on the ability of neuroscience to generate successful predictions about choice-behaviours. Subsequently, neuroeconomics largely abandoned neoclassical approaches in favour of those of behavioural economics, a field of economics embracing the principles of psychology [3]. These models incorporated psychological variables that better captured how humans actually make decisions. Integrating a broader set of variables and new populations of study into the neuroeconomic framework led to many developments and a large expansion of the field [2].

Neuroeconomics, however, faces a number of technical hurdles and epistemological challenges. For instance, do decisions, such as those made by large social groups (e.g. political elections), reach a level of complexity (i.e. emergence) that is irreducible to neurobiological principles? Despite such challenges and potential limitations, neuroeconomics continues to provide fresh insights into the neurobiology underpinning various domains of decision-making, including decisions under risk and uncertainty, pathological or dysregulated decision making (e.g. addiction), social and moral decisions, and the interaction between memory systems and choice-behaviour [4-8].

1. Murawski, C. Neuroeconomics: Investigating the neurobiology of choice. Aust. Econ. Rev. 44, 215-224 (2012).
2. Polister, P. Neuroeconomics: A Guide to the New Science of Making Choices. (Oxford Univ. Press, New York, 2008).
3. Platt, M.L. & Glimcher, P.W. Neural correlates of decision variables in parietal cortex. Nature 400, 233-238 (1999).
4. Glimcher, P.W. Decisions, Uncertainty and the Brain: The Science of Neuroeconomics. (MIT Press, Cambridge, 2003).
5. Takahashi, T. A neuroeconomic theory of bidirectional synaptic plasticity and addiction. Med. Hypo. 75, 356-358 (2010).
6. Rilling, J. et al. A neural basis for social cooperation. Neuron 35, 395–405 (2002)
7. Shenhav, A. & Greene, J.D. Moral judgments recruit domain-general valuation mechanisms to integrate representations of probability and magnitude. Neuron 67, 667-677 (2010).
8. Delgado, M. R. & Dickerson, K. C. Reward-related learning via multiple memory systems. Biol. Psychiatry. 72, 134-141 (2012).

A Multiple Memory Systems Approach to Economic Decision Making

main article: A Multiple Memory Systems Approach to Economic Decision Making
author: Erik Friesen

Multiple Memory Systems
Image Unavailable
An illustration of how memory stores are commonly divided in psychology

Much of human choice behavior, especially that in economic situations, is perceived as highly hedonistic and self-motivated, i.e., our decision making process is underpinned by a calculus of what action will elicit the greatest personal gain. For this reason, it is intuitive that many neuroeconomic investigations into the neural circuitry underlying choice behaviors have focused on ‘pleasure’ centers in the brain, as well as those substrates involved in reward-mediated learning and memory. Much of these had been already been identified throughout the 20th century, and the role of areas including the ventral tegmental area, the basal ganglia, and the dopaminergic/cholinergic diffuse modulatory systems in reward-conditioned behavior had been well documented [1]. For this reason, these nuclei and pathways became a focal point for neuroeconomic studies of brain substrates involved in choice behaviour [2]. Through the use of animal models, fMRI studies, and investigations into the effect of various dopaminergic/striatal pathologies (ex. Parkinson's Disease) on decision-making, two things became clear: (1) these brain regions play a role an important role, but (2) cannot fully account for the high degree of complexity and variability surrounding human choice behavior [2].

Upon further investigation, an array of different functional brain regions became implicated in choice behavior, thus complicating our understanding of it. The medial temporal lobes (MTL), sites crucial for the consolidation of emotional and declarative memory, were identified as being a key component in choice behavior [3]. In addition, aspects of the frontal lobes involved in working memory and executive function were also implicated. Other regions have been identified as well, but will not be discussed here [2].

To add further complexity, the role that these different functional regions play in the choice behaviors appears to be far from consistent, varying dramatically with the context of the decision to be made. This finding has lead to current investigations into the nature of the connections between these multiple memory systems, and further, how the nature of the connection is influenced by context [2].

This portion of the ‘Neuroeconomics’ neurowiki will focus on the basic anatomy and physiology of these different neural memory systems and how they relate to choice behavior, with an emphasis put on (1) the basal ganglia, and (2) the medial temporal lobes. In addition, it will introduce the emerging theories pertaining to the relationship between these multiple memory systems, and how this relationship culminates into human choice behavior.

1. Bear M.F., Connors B.W., Paradiso M.A. (2007) Neuroscience: Exploring the Brain. 3rd ed. Baltimore (MD): Lippincott Williams & Wilkins.
2. Delgado, M.R., Dickerson, K.C. (2012) Reward-Related Learning via Multiple Memory Systems. Biol Psychiatry 72(2), 134-41.
3. Buckner, R.L., Carroll, D.C. (2007) Self-projection and the Brain. Trends Cogn Sci. 11(2), 49-57.

Decisions under Risk and Uncertainty

main article: Decisions under Risk and Uncertainty
author: Raymond MacNeil
In this world nothing can be said to be certain, except death and taxes. ~ Benjamin Franklin

In unravelling the causal neural mechanisms that underpin decision making, neuroeconomics formulates the very function and evolution of the nervous systems in terms of decision making [1-2]. Neuroeconomic theory posits that evolutionary processes shape the nervous system to generate choice-behaviours that maximize an organism’s fitness [3]. As Glimcher [1] notes, “The goal of behaviour is to make the right choices; to choose the course of action that maximizes the survival of an organism’s genetic code.”

Of course, phyletic and architectural evolutionary constraints will limit the nervous system from being able to select and execute truly optimal behaviours. Put differently, the sensori-motor and perceptual capacities of an organism restrict the knowledge it can have about the world and thus, its ability to detect and respond optimally to stimuli [2]. Furthermore, environmental stochasticity produces unpredictable changes to the environmental conditions that an organism confronts. This being said, we should expect to observe mechanisms in the brain that evolved for governing choice-behaviours which reflect the uncertainty that is inherent to an organism’s perception and the environment in which it navigates [4]. How, then, would these mechanisms operate in order to select the right choices in the face of imminent risk and uncertainty? Economic models have already proven useful in addressing this question [5]. If we can formulate a plausible mechanism and specify a mathematical model for it, then we can form some clue as to what we should be looking for in the underlying neural activity of the brain. This is the essence of neuroeconomics.

1. Glimcher, P.W. Decisions, Uncertainty and the Brain: The Science of Neuroeconomics. (MIT Press, Cambridge, 2003).
2. Polister, P. Neuroeconomics: A Guide to the New Science of Making Choices. (Oxford Univ. Press, New York, 2008).
3. Zak, P. J. Neuroeconomics. Phil. Trans. R. Soc. Lond. B 359, 1737–1748 (2004).
4. Glimcher, P. W. Foundations of Neuroeconomic Analysis (Oxford Univ. Press, Oxford, 2011).
5. Platt, M. L. & Glimcher, P. W. Neural correlates of decision variables in parietal cortex. Nature 400, 233-238 (1999).

Economical decision making

main article: Economical decision making
author: perezal2
Found pervasively in the neuroeconomic literature are studies demonstrating the surprising subjective value we attribute toward mutual economic cooperation, as well as our drive to altruistically punish those who deviate from equitable norms [1] . Evidence appears to converge on the existence of neural mechanisms which function to preserve empathic economical dynamics whilst regulating the hedonistic, distrusting tendencies that run central to the tenants of economic theory [2]. A growing body of investigations, within which neuroeconomics plays a crucial role, is aiming to reconcile our neural inclinations (to trust reciprocally and punish altruistically) with the decreasingly suitable assumptions classical economic theories posit on the nature of our ability to trust and be trustworthy. With a combination of a variety of neuroimaging and pharmacological techniques, the neuroscientific perspective on reciprocity in economical exchanges can have important ramifications for current economical theories [3]. These ramifications can include revisions to fundamental economic assumptions [2] as well as a push toward a progressive cooperation existing between our designed social-economic infrastructures and the evolutionary mechanisms at play when we interact with one another over finite resources [1].

1. Dawes, Christopher T and Loewen, Peter John et al. Neural Basis of Egalitarian behavior. Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, 04/2012, Volume 109, Issue 17, pp. 6479 – 6483
2. Jang Woo park et al. (2007) “Neuroeconomics Studies”. Analyze and Kritik vol. 29 pg 47-59
3. Dominique de Quervain et al. (2004) “ Neural basis of altruistic punishment”. Science, New Series, Vol. 305. No. 5688
4. Christopher T. Dawes1, James H. Fowler et al. Egalitarian Motives in humans. Nature, ISSN 0028-0836, 04/2007, Volume 446, Issue 7137, pp. 794 - 796
5. Ernst Fehr, Colin F. Camerer et al. Social neuroeconomics: the neural circuitry of social preferences. Trends in cognitive sciences. Volume 11, Issue 10, pp. 419 – 427
6. Frith, Chris D and Dolan, Raymond J et al. Empathic neural responses are modulated by the perceived fairness of others. Nature. ISSN 0028-0836, 2006, Volume 439, Issue 7075, pp. 466 – 469
7. Decety, Jean and Jackson, Philip L. et al. The neural bases of cooperation and competition: an fMRI investigation. NeuroImage, ISSN 1053-8119, 10/2004, Volume 23, Issue 2, pp. 744 – 751
8. Sanfey, Alan G, Rilling, James K et al. The Neural Basis of Economic Decision-Making in the Ultimatum Game. Science, ISSN 0036-8075, 6/2003, Volume 300, Issue 5626, pp. 1755 - 1758
9. Corradi-Dell'Acqua, Corrado, Civai, Claudia et al. Disentangling self- and fairness-related neural mechanisms involved in the ultimatum game: an fMRI study. Social cognitive and affective neuroscience, 04/2013, Volume 8, Issue 4, p. 424
10. Tabibnia, G. et al. The sunny side of fairness – preference for fairness activates reward circuitry. Psychological science, ISSN 0956-7976, 04/2008, Volume 19, Issue 4, pp. 339 - 347
11. Fehr Ernst, Gächter Simon et al. Altruistic punishment in Humans. Nature, ISSN 0028-0836, 01/2002, Volume 415, Issue 6868, pp. 137 – 140
12. Scheele Dirk, Mihov Yoan et al. Amygdala lesion profoundly alters altruistic punishment. Biological psychiatry, 08/2012, Volume 72, Issue 3, p. e5
13. Civai Claudia, Corradi-Dell’Acqua Corrado et al. Are irrational reactions to unfairness truly emotionally-driven? Dissociated behavioural and emotional responses in the Ultimatum Game task. Cognition, ISSN 0010-0277, 2010, Volume 114, Issue 1, pp. 89 - 95

Neuroeconomics of Addiction

main article: Neuroeconomics of Addiction
author: Martin Musiol

Neuroeconomic Approach to Addiction
Image Unavailable
Which mechanisms influence choice under addiction?

The “Neuroeconomics of Addiction” refers to the interdisciplinary approach to addiction research that incorporates behavioural economics and neuroscience. The field draws heavily upon behavioural studies and the concept of neuroplasticity to develop and explain theories. The vast majority of neuroscience-based addiction studies are built upon the Hebbian Theory which proposes “cells that fire together, wire together”.[1] Most notably, the Bienenstock-Cooper-Munro (BCM Theory) model of synaptic plasticity builds upon Hebb’s work by proposing that increasing the postsynaptic activity of a specific neuron (ex. by repeatedly engaging in the same behaviour) will increase long-term potentiation while reducing postsynaptic activity will increase long-term depression in the same neuron.[2] The second half of the 20th century saw a sharp increase in the number of economic and psychological models designed to explain addictive behaviour. Numerous early economic addiction theories, such as Becker and Murphy’s canonical Theory of Rational Addiction included functions that rely on quantifying biomarkers (Equation 1). Due to recent advances improvements in molecular biology and neuroimaging techniques, researchers such as Taiki Takahashi and Warren Bickel have begun theoretically and experimentally merging economic and neurobiological theories to create more accurate and informative models of addiction.[3][4][5][6][7]

1. Hebb, D.O. (1949). The Organization of Behaviour. New York. Wiley & Sons.
2. Bienenstock EL, Cooper LN, Munro PW. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J Neurosci (1982); 2(1): 32-48
3. Takahashi, Taiki (2010). A neuroeconomic theory of bidirectional synaptic plasticity and addiction. Medical Hypotheses. (75),356-358
4. Takahashi, Taiki (2004). Cortisol levels and time-discounting of monetary gain in humans. Neuroreport. 15(13), 2145-2147
5. Monterosso J, Piray P, Luo S (2012). Neuroeconomics and the study of addiction. Biol Psychiatry. 72(2), 107-112
6. Bickel WK. Koffarnus MN, Moody L, Wilson AG (2014). The behavioural- and neuro-economic process of temporal discounting: a candidate behavioural marker of addiction. Neuropharmacology.
7. Bickel WK. Jarmolowicz DP. Mueller ET. Gatchalian KM (2011). The behavioural economics and neuroeconomics of reinforcer pathologies: implications for etiology and treatment of addiction. Curr Psychiatry rep. 13(5), 406-415.

Neuroeconomics of Morality

main article: Neuroeconomics of Morality
author: Arnold Son

Image Unavailable
Road signs representing a moral choice
Retrieved April 2, 2014, from

While most studies in neuroeconomics have dealt with game theoretic concepts implicating monetary or resource gain, moral neuroeconomics is a subfield of neuroeconomics which focuses on decisions based around morality.[2] The term morality as referred to in this subfield of neuroeconomics has very little to do with the colloquial term which can vary in definition depending on one’s religious or cultural beliefs. Instead of trying to define what is morally right or wrong, moral neuroeconomics studies how people make decisions that involve harm or benefit to oneself and other individuals. More importantly, the focus lies in extremes where the harm or benefit is defined as life or death all under uncertain circumstances.[2] Similar to how most neuroeconomic concepts are explored, moral neuroeconomics also relies on studies which utilize game theoretic experiments. These game theoretic experiments have involved participants giving an answer to hypothetical moral dilemmas or participating in games such as the ultimatum game (UD) or the dictator game (DG)[1][2].

1. Shenhav, A., and Greene, J.D. (2010) Moral Judgments Recruit Domain-General Valuation Mechanisms to Integrate Representations of Probability and Magnitude. Neuron. 67, 667-677.
2. Kvaran, T., and Sanfey, A.G. (2010) Toward an Integrated Neuroscience of Morality: The Contribution of Neuroeconomics to Moral Cognition. Topics in Cognitive Science. 2, 579-595.

Add a New Comment
Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License