Skip to main content
SpringerLink
Log in

Does Neuroeconomics Really Need the Brain?

  • Chapter
  • First Online:
Quantifying and Processing Biomedical and Behavioral Signals (WIRN 2017 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 103))

Included in the following conference series:

  • 582 Accesses

Abstract

The systematic study of biological basis of behavior and of the process involved in economical choices has outlined a new paradigm of research: neuroeconomics. Now the intersection between neuroscience, psychology and economics, neuroeconomics presents itself as an alternative to the neoclassical vision on economics, according to which the homo oeconomicus acts within the bonds of a formalizing rationality tending to the maximization of the anticipated utility. Brain imagining methods have shown that the decision-making processes activate the frontal lobe and the limbic system above all, a big circonvolution running through the callous body on the medial surface of the hemispheres, extending itself down, responsible for the regulation of emotional phenomena. Reinforcing such a tendency, we find the injury paradigm. It was observed that frontal lobe injuries harm the capacity of making advantageous decisions either in one’s own behalf or in others, as well as decisions according to the social conventions. In this paper, we will try to show that if, by the one hand, the neuro visual methods have given us a great amount of data, on the other hand, using them uncritically, with the recurrent confusion between “correlation” and “causal relation”—contemporary microevents indicate only simple correlations, and no cause-effect relation—risks to stress the relevant explanatory gap regarding the abstract ideal of understanding the nature of the brain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Maldonato, N.M., Dell’Orco, S.: How to make decisions in an uncertain world: heuristics, biases, and risk perception. World Futures 67(8), 569–577 (2011)

    Article  Google Scholar 

  2. Maldonato, N.M., Dell’Orco, S.: Toward an evolutionary theory of rationality. World Futures 66(2), 103–123 (2010)

    Article  Google Scholar 

  3. Maldonato, N.M., Dell’Orco, S.: Making Decision Under Uncertainty: Emotions, Risk and Biases. Smart Innovation, Systems and Technologies, vol. 37, pp. 293–302. Springer, Berlin (2015)

    Google Scholar 

  4. Gigerenzer, G., Selten, R.: Bounded Rationality: The Adaptive Toolbox. MIT Press, Cambridge, MA (2002)

    Google Scholar 

  5. Gazzaniga, M.S.: Who’s in Charge? Free Will and the Science of the Brain. HarperCollins, New York (2011)

    Google Scholar 

  6. Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1947)

    MATH  Google Scholar 

  7. Tversky, A., Kahneman, D.: The framing of decisions and the psychology of choice. Science 211(448), 453–458 (1981)

    Article  MathSciNet  Google Scholar 

  8. Scott, S., Bruce, R.: Decision-making style: the development of a new measure. Educ. Psychol. Measur. 5, 818–831 (1995)

    Article  Google Scholar 

  9. Tversky, A., Sattah, S., Slovic, P.: Contingent weighting in judgment and choice. Psychol. Rev. 95(3), 371–384 (1988)

    Article  Google Scholar 

  10. Maldonato, N.M., Dell’Orco, S.: The predictive brain. World Futures 68(6), 381–389 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Ogawa, S., Tank, D.W., Menon, R., et al.: Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. U.S.A. 89, 5951–5955 (1992)

    Article  Google Scholar 

  13. Dunn, B.D., Dalgleish, T., Lawrence, A.D.: The somatic marker hypothesis: a critical evaluation. Neurosci. Biobehav. Rev. 30, 239–271 (2006)

    Article  Google Scholar 

  14. Damasio, A.R.: The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philos. Trans. R. Soc. Lond. 351(1346), 1413–1420 (1996)

    Article  Google Scholar 

  15. Damasio, A.R., Tranel, D., Damasio, H.: Individuals with sociopathic behavior caused by frontal damage fail to respond autonomically to social stimuli. Behav. Brain Res. 41(2), 81–94 (1990)

    Article  Google Scholar 

  16. Damasio, A.R.: The frontal lobes. In: Heilman, K.M., Valenstein, E. (eds.) Clinical Neuropsychology. Oxford University Press, New York (1979)

    Google Scholar 

  17. Schmitt, W.A., Brinkley, C.A., Newman, J.P.: Testing Damasio’s somatic marker hypothesis with psychopathic individuals: risk takers or risk averse? J. Abnorm. Psychol. 108(3), 538–543 (1999)

    Article  Google Scholar 

  18. Maldonato, N.M.: Undecidable decisions: rationality limits and decision-making heuristics. World Futures 63(1), 28–37 (2007)

    Article  Google Scholar 

  19. Bechara, A., Damasio, H.: Decision-making and addiction (part I): impaired activation of somatic states in substance dependent individuals when pondering decisions with negative future consequences. Neuropsychologia 40, 1675–1689 (2002)

    Article  Google Scholar 

  20. Fix, J.D.: Theory of Games and Economic Behavior. Neuroanatomy. Lippincott Williams & Wilkins, Philadelphia (2008)

    Google Scholar 

  21. Finger, S.: Defining and Controlling the Circuits of Emotion. Origins of Neuroscience: A History of Explorations into Brain Function. Oxford University Press, Oxford (2001)

    Google Scholar 

  22. Al-Najjar, N.I., Weinstein, J.: The ambiguity aversion literature: a critical assessment. Econ. Philos. 25, 249–284 (2009)

    Article  Google Scholar 

  23. Maldonato, N.M., Dell’Orco, S., Sperandeo, R.: When intuitive decisions making, based on expertise, may deliver better results than a rational, deliberate approach. In: Esposito, A., Faundez-Zanuy, M., Morabito, F.C., Pasero, E. (eds.) Multidisciplinary Approaches to Neural Computing. Springer, Cham (2018)

    Google Scholar 

  24. Poline, J.B., Worsley, K.J., Evans, A.C., Friston, K.J.: Combining spatial extent and peak intensity to test for activations in functional imaging. NeuroImage 5(2), 83–96 (1997)

    Article  Google Scholar 

  25. Ramnani, N., Owen, A.M.: Anterior prefrontal cortex: insights into function from anatomy and neuroimaging. Nat. Rev. Neurosci. 5, 184–194 (2004)

    Article  Google Scholar 

  26. Glimcher, P.W., Camerer, C., Poldrack, R.A., Fehr, E.: Neuroeconomics: Decision Making and the Brain. Academic Press, Cambridge (2008)

    Google Scholar 

  27. Schonberg, T., Bakkour, A., Hover, A.M., Mumford, J.A., Nagar, L., Perez, J., Poldrack, R.A.: Changing value through cued approach: an automatic mechanism of behavior change. Nat. Neurosci. 17(4), 625–630 (2014)

    Article  Google Scholar 

  28. Maldonato, N.M., Dell’Orco, S.: The natural logic of action. World Futures 69(3), 174–183 (2013)

    Article  Google Scholar 

  29. Davis, T., LaRocque, K.F., Mumford, J.A., Norman, K.A., Wagner, A.D., Poldrack, R.A.: What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis. Neuroimage 97, 271–283 (2014)

    Article  Google Scholar 

  30. Park, M., Koyejo, O., Ghosh, J., Poldrack, R., Pillow, J.: Bayesian structure learning for functional neuroimaging. In: Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, pp. 489–497 (2013)

    Google Scholar 

  31. Koyejo, O., Patel, P., Ghosh, J., Poldrack, R.A.: Learning predictive cognitive structure from fMRI using supervised topic models. In: International Workshop on Pattern Recognition in Neuroimaging (PRNI), pp. 9–12 (2013)

    Google Scholar 

  32. Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., Pezzulo, G.: Active inference and epistemic value. Cogn. Neurosci. 6(4), 187–214 (2015)

    Article  Google Scholar 

  33. Ogawa, S., Lee, T.M., Nayak, A.S., Glynn, P.: Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Mag. Res. Med. 14, 68–78 (1990)

    Article  Google Scholar 

  34. Jezzard, P., Matthews, P.M., Smith, S.M.: Functional Magnetic Resonance Imaging. An Introduction to Methods. Oxford University Press, Oxford (2003)

    Google Scholar 

  35. Raichle, M.E.: Behind the scenes of functional brain imaging: a historical and physiological perspective. Proc. Natl. Acad. Sci. U.S.A. 95(3), 765–772 (1998)

    Article  Google Scholar 

  36. Friston, K.: Functional integration and inference in the brain. Prog. Neurobiol. 68(2), 113–143 (2002)

    Article  Google Scholar 

  37. Friston, K., Kahan, J., Razi, A., Stephan, K.E., Sporns, O.: On nodes and modes in resting state fMRI. Neuroimage 99, 533–547 (2014)

    Article  Google Scholar 

  38. Forsting, M., Jansen, O.: MR Neuroimaging: Brain, Spine, Peripheral Nerves. Thieme, New York (2017)

    Google Scholar 

  39. Riitta, H., Puce, A.: MEG-EEG Primer. Oxford University Press, Oxford (2017)

    Google Scholar 

  40. Friston, K.: The history of the future of the Bayesian brain. Neuroimage 62–248(2), 1230–1233 (2012)

    Article  Google Scholar 

  41. Friston, K.: A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 815–836 (2005)

    Article  Google Scholar 

  42. Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11(2), 127–138 (2010)

    Article  Google Scholar 

  43. Brakewood, B., Poldrack, R.A.: The ethics of secondary data analysis: considering the application of Belmont principles to the sharing of neuroimaging data. Neuroimage 82, 671–676 (2013)

    Article  Google Scholar 

  44. Davis, T., Poldrack, R.A.: Measuring neural representations with fMRI: practices and pitfalls. Ann. N. Y. Acad. Sci. 1296, 108–134 (2013)

    Article  Google Scholar 

  45. Maldonato, N.M., Dell’Orco, S.: Adaptive and evolutive algorithms: a natural logic for artificial mind. In: Esposito, A., Jain, L.C. (eds.) Toward Robotic Socially Believable Behaving Systems, vol. II. Springer, Cham (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Neuroscienze e Scienze Riproduttive ed Odontostomatologiche, Università di Napoli Federico II, Naples, Italy

    Nelson Mauro Maldonato

  2. Dipartimento di Economia, Management, Istituzioni, Università degli Studi di Napoli Federico II, Naples, Italy

    Luigi Maria Sicca

  3. Istituto nazionale di Geofisica e Vulcanologia, Sezione di Napoli, Osservatorio Vesuviano, Naples, Italy

    Antonietta M. Esposito

  4. Sipgi, Scuola di Psicoterapia Gestaltica Integrata, Torre Annunziata, Italy

    Raffaele Sperandeo

Authors
  1. Nelson Mauro Maldonato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luigi Maria Sicca
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonietta M. Esposito
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Raffaele Sperandeo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nelson Mauro Maldonato .

Editor information

Editors and Affiliations

  1. Dipartimento di Psicologia, Università della Campania “Luigi Vanvitelli”, Caserta, Italy

    Anna Esposito

  2. Fundació Tecnocampus, Pompeu Fabra University, Mataro, Barcelona, Spain

    Marcos Faundez-Zanuy

  3. Department of Civil, Environmental, Energy, and Material Engineering, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dipartimento di Elettronica e Telecomunicazioni, Laboratorio di Neuronica, Politecnico di Torino, Turin, Italy

    Eros Pasero

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Maldonato, N.M., Sicca, L.M., Esposito, A.M., Sperandeo, R. (2019). Does Neuroeconomics Really Need the Brain?. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Quantifying and Processing Biomedical and Behavioral Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-319-95095-2_13

Download citation

Publish with us

Policies and ethics

Search

Navigation