Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Translating upwards: linking the neural and social sciences via neuroeconomics



The social and neural sciences share a common interest in understanding the mechanisms that underlie human behaviour. However, interactions between neuroscience and social science disciplines remain strikingly narrow and tenuous. We illustrate the scope and challenges for such interactions using the paradigmatic example of neuroeconomics. Using quantitative analyses of both its scientific literature and the social networks in its intellectual community, we show that neuroeconomics now reflects a true disciplinary integration, such that research topics and scientific communities with interdisciplinary span exert greater influence on the field. However, our analyses also reveal key structural and intellectual challenges in balancing the goals of neuroscience with those of the social sciences. To address these challenges, we offer a set of prescriptive recommendations for directing future research in neuroeconomics.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: The disciplinary connectivity of neuroeconomics research.
Figure 2: Interdisciplinary research groups occupy more central positions in the community of neuroeconomists.
Figure 3: Literature usage of 'neuroeconomic*' and 'decision neuroscience'.
Figure 4: Knowledge domains in the neuroeconomics literature.


  1. 1

    Geschwind, D. H. & Konopka, G. Neuroscience in the era of functional genomics and systems biology. Nature 461, 908–915 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  2. 2

    Shonkoff, J. P., Boyce, W. T. & McEwen, B. S. Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. JAMA 301, 2252–2259 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  3. 3

    Ariely, D. & Berns, G. S. Neuromarketing: the hope and hype of neuroimaging in business. Nature Rev. Neurosci. 11, 284–292 (2010).

    CAS  Article  Google Scholar 

  4. 4

    O'Connor, C., Rees, G. & Joffe, H. Neuroscience in the public sphere. Neuron 74, 220–226 (2012).

    CAS  PubMed  Article  Google Scholar 

  5. 5

    Farah, M. J. Neuroethics: the practical and the philosophical. Trends Cogn. Sci. 9, 34–40 (2005).

    PubMed  Article  PubMed Central  Google Scholar 

  6. 6

    Zeki, S. Essays on science and society. Artistic creativity and the brain. Science 293, 51–52 (2001).

    CAS  PubMed  Article  Google Scholar 

  7. 7

    Clithero, J. A., Tankersley, D. & Huettel, S. A. Foundations of neuroeconomics: from philosophy to practice. PLoS Biol. 6, e298 (2008).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  8. 8

    Glimcher, P. W. & Rustichini, A. Neuroeconomics: the consilience of brain and decision. Science 306, 447–452 (2004).

    CAS  PubMed  Article  Google Scholar 

  9. 9

    Loewenstein, G., Rick, S. & Cohen, J. D. Neuroeconomics. Annu. Rev. Psychol. 59, 647–672 (2008).

    PubMed  Article  Google Scholar 

  10. 10

    Sanfey, A. G., Loewenstein, G., McClure, S. M. & Cohen, J. D. Neuroeconomics: cross-currents in research on decision-making. Trends Cogn. Sci. 10, 108–116 (2006).

    PubMed  Article  Google Scholar 

  11. 11

    Fehr, E. & Rangel, A. Neuroeconomic foundations of economic choice - recent advances. J. Econ. Perspect. 25, 3–30 (2011).

    Article  Google Scholar 

  12. 12

    Rushworth, M. F., Noonan, M. P., Boorman, E. D., Walton, M. E. & Behrens, T. E. Frontal cortex and reward-guided learning and decision-making. Neuron 70, 1054–1069 (2011).

    CAS  PubMed  Article  Google Scholar 

  13. 13

    Montague, P. R. & Berns, G. S. Neural economics and the biological substrates of valuation. Neuron 36, 265–284 (2002).

    CAS  Article  Google Scholar 

  14. 14

    Rangel, A., Camerer, C. & Montague, P. R. A framework for studying the neurobiology of value-based decision making. Nature Rev. Neurosci. 9, 545–556 (2008).

    CAS  Article  Google Scholar 

  15. 15

    Glimcher, P. W. Foundations of Neuroeconomic Analysis (Oxford Univ. Press, 2011).

    Google Scholar 

  16. 16

    Camerer, C., Loewenstein, G. & Prelec, D. Neuroeconomics: how neuroscience can inform economics. J. Econ. Lit. 43, 9–64 (2005).

    Article  Google Scholar 

  17. 17

    Glimcher, P. W. Decisions, Uncertainty, And The Brain: The Science Of Neuroeconomics (MIT Press, 2003).

    Book  Google Scholar 

  18. 18

    Mirowski, P. More Heat Than Light: Economics As Social Physics, Physics As Nature's Economics (Cambridge Univ. Press, 1989).

    Book  Google Scholar 

  19. 19

    Hodgson, G. M. Economics And Evolution: Bringing Life Back Into Economics (Univ. of Michigan Press, 1993).

    Book  Google Scholar 

  20. 20

    Wasserman, S. & Faust, K. Social Network Analysis: Methods And Applications (Cambridge Univ. Press, 1994).

    Book  Google Scholar 

  21. 21

    Borner, K. & Scharnhorst, A. Visual conceptualizations and models of science. J. Informetrics 3, 161–172 (2009).

    Article  Google Scholar 

  22. 22

    Klavans, R. & Boyack, K. W. Toward a consensus map of science. J. Am. Soc. Inf. Sci. Technol. 60, 455–476 (2009).

    Article  Google Scholar 

  23. 23

    Rosvall, M. & Bergstrom, C. T. Maps of random walks on complex networks reveal community structure. Proc. Natl Acad. Sci. USA 105, 1118–1123 (2008).

    CAS  PubMed  Article  Google Scholar 

  24. 24

    Rafols, I., Porter, A. L. & Leydesdorff, L. Science overlay maps: a new tool for research policy and library management. J. Am. Soc. Inf. Sci. Technol. 61, 1871–1887 (2010).

    Article  Google Scholar 

  25. 25

    Porter, A. L. & Rafols, I. Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics 81, 719–745 (2009).

    Article  Google Scholar 

  26. 26

    Bernheim, B. D. On the potential of neuroeconomics: a critical (but hopeful) appraisal. Am. Econ. J. Microecon. 1, 1–41 (2009).

    Article  Google Scholar 

  27. 27

    Gul, F. & Pesendorfer, W. in The Foundations Of Positive And Normative Economics (eds Caplin, A. & Schotter, A.) 3–39 (Oxford Univ. Press, 2008).

    Google Scholar 

  28. 28

    Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  29. 29

    Daw, N. D. & Shohamy, D. The cognitive neuroscience of motivation and learning. Soc. Cogn. 26, 593–620 (2008).

    Article  Google Scholar 

  30. 30

    Schweber, S. S. Darwin and the political economists: divergence of character. J. Hist. Biol. 13, 195–289 (1980).

    CAS  PubMed  Article  Google Scholar 

  31. 31

    Camerer, C. Behavioral Game Theory: Experiments In Strategic Interaction (Russell Sage Foundation, 2003).

    Google Scholar 

  32. 32

    Lee, D. Game theory and neural basis of social decision making. Nature Neurosci. 11, 404–409 (2008).

    CAS  PubMed  Article  Google Scholar 

  33. 33

    Currie, J. Healthy, wealthy, and wise: socioeconomic status, poor health in childhood, and human capital development. J. Econ. Lit. 47, 87–122 (2009).

    Article  Google Scholar 

  34. 34

    Glimcher, P., Camerer, C., Poldrack, R. & Fehr, E. (eds) Neuroeconomics: Decision Making And The Brain (Academic Press, 2008).

    Google Scholar 

  35. 35

    Becker, G. S. Altruism, egoism, and genetic fitness — economics and sociobiology. J. Econ. Lit. 14, 817–826 (1976).

    Google Scholar 

  36. 36

    Robson, A. J. The biological basis of economic behavior. J. Econ. Lit. 39, 11–33 (2001).

    Article  Google Scholar 

  37. 37

    Rangel, A. & Hare, T. Neural computations associated with goal-directed choice. Curr. Opin. Neurobiol. 20, 262–270 (2010).

    CAS  PubMed  Article  Google Scholar 

  38. 38

    O'Doherty, J. P. Contributions of the ventromedial prefrontal cortex to goal-directed action selection. Ann. NY Acad. Sci. 1239, 118–129 (2011).

    PubMed  Article  Google Scholar 

  39. 39

    Montague, P. R., Dolan, R. J., Friston, K. J. & Dayan, P. Computational psychiatry. Trends Cogn. Sci. 16, 72–80 (2012).

    Article  Google Scholar 

  40. 40

    Crockett, M. J., Clark, L. & Robbins, T. W. Reconciling the role of serotonin in behavioral inhibition and aversion: acute tryptophan depletion abolishes punishment-induced inhibition in humans. J. Neurosci. 29, 11993–11999 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 41

    Venkatraman, V., Huettel, S. A., Chuah, L. Y., Payne, J. W. & Chee, M. W. Sleep deprivation biases the neural mechanisms underlying economic preferences. J. Neurosci. 31, 3712–3718 (2011).

    CAS  PubMed  Article  Google Scholar 

  42. 42

    Samanez-Larkin, G. R. et al. Anticipation of monetary gain but not loss in healthy older adults. Nature Neurosci. 10, 787–791 (2007).

    PubMed  Article  CAS  Google Scholar 

  43. 43

    Winecoff, A., Labar, K. S., Madden, D. J., Cabeza, R. & Huettel, S. A. Cognitive and neural contributors to emotion regulation in aging. Soc. Cogn. Affect. Neurosci. 6, 165–176 (2011).

    PubMed  Article  Google Scholar 

  44. 44

    Kanai, R. & Rees, G. The structural basis of inter-individual differences in human behaviour and cognition. Nature Rev. Neurosci. 12, 231–242 (2011).

    CAS  Article  Google Scholar 

  45. 45

    Cohen, M. X., Schoene-Bake, J. C., Elger, C. E. & Weber, B. Connectivity-based segregation of the human striatum predicts personality characteristics. Nature Neurosci. 12, 32–34 (2009).

    CAS  PubMed  Article  Google Scholar 

  46. 46

    Locke, H. S. & Braver, T. S. Motivational influences on cognitive control: behavior, brain activation, and individual differences. Cogn. Affect. Behav. Neurosci. 8, 99–112 (2008).

    PubMed  Article  Google Scholar 

  47. 47

    Venkatraman, V., Payne, J. W., Bettman, J. R., Luce, M. F. & Huettel, S. A. Separate neural mechanisms underlie choices and strategic preferences in risky decision making. Neuron 62, 593–602 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. 48

    De Martino, B., Kumaran, D., Seymour, B. & Dolan, R. J. Frames, biases, and rational decision-making in the human brain. Science 313, 684–687 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  49. 49

    Hariri, A. R. The neurobiology of individual differences in complex behavioral traits. Annu. Rev. Neurosci. 32, 225–247 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  50. 50

    Bogacz, R., Brown, E., Moehlis, J., Holmes, P. & Cohen, J. D. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev. 113, 700–765 (2006).

    PubMed  Article  Google Scholar 

  51. 51

    Busemeyer, J. R. & Townsend, J. T. Decision field-theory — a dynamic cognitive approach to decision-making in an uncertain environment. Psychol. Rev. 100, 432–459 (1993).

    CAS  PubMed  Article  Google Scholar 

  52. 52

    Ratcliff, R. & McKoon, G. The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 20, 873–922 (2008).

    PubMed  Article  PubMed Central  Google Scholar 

  53. 53

    Dekel, E., Lipman, B. L. & Rustichini, A. Temptation-driven preferences. Rev. Econ. Stud. 76, 937–971 (2009).

    Article  Google Scholar 

  54. 54

    Gul, F. & Pesendorfer, W. Temptation and self-control. Econometrica 69, 1403–1435 (2001).

    Article  Google Scholar 

  55. 55

    Hare, T. A., Camerer, C. F. & Rangel, A. Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324, 646–648 (2009).

    CAS  Article  Google Scholar 

  56. 56

    Hyman, S. E., Malenka, R. C. & Nestler, E. J. Neural mechanisms of addiction: the role of reward-related learning and memory. Annu. Rev. Neurosci. 29, 565–598 (2006).

    CAS  Article  Google Scholar 

  57. 57

    Brass, M. & Haggard, P. To do or not to do: the neural signature of self-control. J. Neurosci. 27, 9141–9145 (2007).

    CAS  PubMed  Article  Google Scholar 

  58. 58

    Heatherton, T. F. & Wagner, D. D. Cognitive neuroscience of self-regulation failure. Trends Cogn. Sci. 15, 132–139 (2011).

    PubMed  Article  PubMed Central  Google Scholar 

  59. 59

    Chen, E., Cohen, S. & Miller, G. E. How low socioeconomic status affects 2-year hormonal trajectories in children. Psychol. Sci. 21, 31–37 (2010).

    PubMed  Article  Google Scholar 

  60. 60

    Hackman, D. A. & Farah, M. J. Socioeconomic status and the developing brain. Trends Cogn. Sci. 13, 65–73 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  61. 61

    Hackman, D. A., Farah, M. J. & Meaney, M. J. Socioeconomic status and the brain: mechanistic insights from human and animal research. Nature Rev. Neurosci. 11, 651–659 (2010).

    CAS  Article  Google Scholar 

  62. 62

    Bossert, J. M. et al. Ventral medial prefrontal cortex neuronal ensembles mediate context-induced relapse to heroin. Nature Neurosci. 14, 420–422 (2011).

    CAS  PubMed  Article  Google Scholar 

  63. 63

    Kober, H. et al. Prefrontal-striatal pathway underlies cognitive regulation of craving. Proc. Natl Acad. Sci. USA 107, 14811–14816 (2010).

    CAS  PubMed  Article  Google Scholar 

  64. 64

    Harrison, G. W. Neuroeconomics: a critical reconsideration. Econ. Philos. 24, 303–344 (2008).

    Article  Google Scholar 

  65. 65

    Wilson, E. O. Consilience: The Unity Of Knowledge (Knopf, Random House, 1998).

    Google Scholar 

  66. 66

    Falk, A. & Heckman, J. J. Lab experiments are a major source of knowledge in the social sciences. Science 326, 535–538 (2009).

    CAS  PubMed  Article  Google Scholar 

  67. 67

    DellaVigna, S. Psychology and economics: evidence from the field. J. Econ. Lit. 47, 315–372 (2009).

    Article  Google Scholar 

  68. 68

    Smith, V. L. Experimental economics: induced value theory. Am. Econ. Rev. 66, 274–279 (1976).

    Google Scholar 

  69. 69

    Ashraf, N., Karlan, D. & Yin, W. Tying Odysseus to the mast: evidence from a commitment savings product in the Philippines. Q. J. Econ. 121, 635–672 (2006).

    Article  Google Scholar 

  70. 70

    Karlan, D. S. Using experimental economics to measure social capital and predict financial decisions. Am. Econ. Rev. 95, 1688–1699 (2005).

    Article  Google Scholar 

  71. 71

    Ochsner, K. N. & Gross, J. J. The cognitive control of emotion. Trends Cogn. Sci. 9, 242–249 (2005).

    Article  Google Scholar 

  72. 72

    Plott, C. R. & Smith, V. L. Experimental examination of 2 exchange institutions. Rev. Econ. Stud. 45, 133–153 (1978).

    Article  Google Scholar 

  73. 73

    Bossaerts, P., Plott, C. & Zame, W. R. Prices and portfolio choices in financial markets: theory, econometrics, experiments. Econometrica 75, 993–1038 (2007).

    Article  Google Scholar 

  74. 74

    Benjamin, D. J. et al. The genetic architecture of economic and political preferences. Proc. Natl Acad. Sci. USA 109, 8026–8031 (2012).

    CAS  PubMed  Article  Google Scholar 

  75. 75

    Peters, J. & Büchel, C. Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal–mediotemporal interactions. Neuron 66, 138–148 (2010).

    CAS  PubMed  Article  Google Scholar 

  76. 76

    Fehr, E. & Tyran, J. R. Individual irrationality and aggregate outcomes. J. Econ. Perspect. 19, 43–66 (2005).

    Article  Google Scholar 

  77. 77

    Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D. Large-scale automated synthesis of human functional neuroimaging data. Nature Methods 8, 665–670 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  78. 78

    Yarkoni, T., Poldrack, R. A., Van Essen, D. C. & Wager, T. D. Cognitive neuroscience 2.0: building a cumulative science of human brain function. Trends Cogn. Sci. 14, 489–496 (2010).

    PubMed  Article  PubMed Central  Google Scholar 

  79. 79

    Smith, K. Brain imaging: fMRI 2.0. Nature 484, 24–26 (2012).

    CAS  PubMed  Article  Google Scholar 

  80. 80

    Wallis, J. D. Cross-species studies of orbitofrontal cortex and value-based decision-making. Nature Neurosci. 15, 13–19 (2012).

    CAS  Article  Google Scholar 

  81. 81

    Kennerley, S. W., Behrens, T. E. & Wallis, J. D. Double dissociation of value computations in orbitofrontal and anterior cingulate neurons. Nature Neurosci. 14, 1581–1589 (2011).

    CAS  PubMed  Article  Google Scholar 

  82. 82

    Hayden, B. Y., Pearson, J. M. & Platt, M. L. Neuronal basis of sequential foraging decisions in a patchy environment. Nature Neurosci. 14, 933–939 (2011).

    CAS  PubMed  Article  Google Scholar 

  83. 83

    Chang, L. J., Smith, A., Dufwenberg, M. & Sanfey, A. G. Triangulating the neural, psychological, and economic bases of guilt aversion. Neuron 70, 560–572 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  84. 84

    Hare, T. A., Schultz, W., Camerer, C. F., O'Doherty, J. P. & Rangel, A. Transformation of stimulus value signals into motor commands during simple choice. Proc. Natl Acad. Sci. USA 108, 18120–18125 (2011).

    CAS  PubMed  Article  Google Scholar 

  85. 85

    Huettel, S. A., Song, A. W. & McCarthy, G. Functional Magnetic Resonance Imaging (Sinauer Associates, 2008).

    Google Scholar 

  86. 86

    Woolrich, M. W., Ripley, B. D., Brady, M. & Smith, S. M. Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage 14, 1370–1386 (2001).

    CAS  PubMed  Article  Google Scholar 

  87. 87

    Ashby, F. G. Statistical Analysis Of fMRI Data (MIT Press, 2011).

    Book  Google Scholar 

  88. 88

    Ely, J. C. Kludged. Am. Econ. J. Microecon. 3, 210–231 (2011).

    Article  Google Scholar 

  89. 89

    Bordalo, P., Gennaioli, N. & Shleifer, A. Salience theory of choice under risk. Q. J. Econ. 127, 1243–1285 (2012).

    Article  Google Scholar 

  90. 90

    Sarver, T. Anticipating regret: why fewer options may be better. Econometrica 76, 263–305 (2008).

    Article  Google Scholar 

  91. 91

    Boulding, K. E. Economics as a moral science. Am. Econ. Rev. 59, 1–12 (1969).

    Google Scholar 

  92. 92

    Simon, H. A. Reason In Human Affairs (Stanford Univ. Press, 1983).

    Google Scholar 

  93. 93

    Barabasi, A. L. et al. Evolution of the social network of scientific collaborations. Physica A 311, 590–614 (2002).

    Article  Google Scholar 

  94. 94

    Bulik-Sullivan, B. K. & Sullivan, P. F. The authorship network of genome-wide association studies. Nature Genet. 44, 113–113 (2012).

    CAS  PubMed  Article  Google Scholar 

  95. 95

    Roth, C. & Cointet, J. P. Social and semantic coevolution in knowledge networks. Social Networks 32, 16–29 (2010).

    Article  Google Scholar 

  96. 96

    Blondel, V. D., Guillaume, J. L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, 1–12 (2008).

    Article  Google Scholar 

Download references


We thank M. Fan and H. Gold for assistance in data collection. For comments and discussions, we thank A. Beaulieu, M. Carter, L. Harris, L. Leydesdorff, P. Mehta, I. Rafols, C. Reeck, M. van Overveld, participants in the annual meetings of the Society for Neuroeconomics and anonymous reviewers. Funding for this research comes from an Incubator Award from the Duke Institute for Brain Sciences (S.A.H), a US National Institutes of Mental Health National Research Service Award F31-086255 (J.A.C.), the Erasmus Research Institute of Management (C.L. and A.S.), and the Open Research Area programme from the European Science Foundation (NESSHI 464-10-029; C.L., A.S. and P.W.).

Author information



Corresponding authors

Correspondence to Ale Smidts or Scott A. Huettel.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information S1 (box)

Method for the overlay of neuroeconomics publications on the map of science (PDF 104 kb)

Supplementary information S2 (box)

Choice of two interdisciplinary fields for comparison with neuroeconomics (PDF 160 kb)

Supplementary information S3 (figure)

Overlay maps of science for two reference fields (PDF 761 kb)

Supplementary information S4 (figure)

Literature usage of “neuroeconomic*” and “decision neuroscience” on ISI Web of Knowledge (PDF 271 kb)

Supplementary information S5 (box)

Literature search in neuroeconomics review articles (PDF 196 kb)

Supplementary information S6 (table)

Neuroeconomics review articles (PDF 141 kb)

Supplementary information S7 (box)

Literature search in economics articles (PDF 154 kb)

Supplementary information S8 (box)

Steps for the semantic network analysis of abstracts (PDF 161 kb)

Supplementary information S9 (figure)

Term map of economic articles making reference to biology (PDF 418 kb)

Supplementary information S10 (box)

Survey of the social network of neuroeconomists (PDF 173 kb)

Supplementary information S11 (box)

Identification of communities in the social network of neuroeconomists (PDF 153 kb)

Related links

Related links


John A. Clithero's homepage

Duke Center for Interdisciplinary Decision Sciences

Erasmus Center for Neuroeconomics


Society for Neuroeconomics


Community detection

(Also known as cluster analysis). The identification of groups of relatively tightly connected nodes in a network on the basis of an algorithmic analysis of the graph formed by the nodes and edges.

Connected component

In a network, a group of nodes that are all connected either directly or through other nodes.

Expected value

The weighted, probabilistic average of all possible values for an uncertain reward.

Natural language processing

A set of methods from computational linguistics to extract meaningful features (such as the language or the topic) of a corpus.

Out-of-sample predictive power

When fitting a model to data, the predictive power — or generalizability — of that model can be tested on data not used to estimate the model (that is, out-of-sample data).

Semantic network analytic

Application of the methods and tools of network analysis to textual data; it creates networks based on semantic relationships or co-occurrences of terms in a text corpus.

Temporal discounting

(Also known as delay discounting).The tendency to reduce the subjective value associated with rewards as the delay until their receipt increases.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Levallois, C., Clithero, J., Wouters, P. et al. Translating upwards: linking the neural and social sciences via neuroeconomics. Nat Rev Neurosci 13, 789–797 (2012).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing