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Neural Processing of Risk

Author

Listed:
  • Peter N. C. Mohr
  • Guido Biele
  • Hauke R. Heekeren

Abstract

In our everyday life, we often have to make decisions with risky consequences, such as choosing a restaurant for dinner or choosing a form of retirement saving. To date, however, little is known about how the brain processes risk. Recent conceptualizations of risky decision making highlight that it is generally associated with emotions but do not specify how emotions are implicated in risk processing. Moreover, little is known about risk processing in non-choice situations and how potential losses influence risk processing. Here we used quantitative meta-analyses of functional magnetic resonance imaging experiments on risk processing in the brain to investigate (1) how risk processing is influenced by emotions, (2) how it differs between choice and non-choice situations, and (3) how it changes when losses are possible. By showing that, over a range of experiments and paradigms, risk is consistently represented in the anterior insula, a brain region known to process aversive emotions such as anxiety, disappointment, or regret, we provide evidence that risk processing is influenced by emotions. Furthermore, our results show risk-related activity in the dorsolateral prefrontal cortex and the parietal cortex in choice situations but not in situations in which no choice is involved or a choice has already been made. The anterior insula was predominantly active in the presence of potential losses, indicating that potential losses modulate risk processing.

Suggested Citation

  • Peter N. C. Mohr & Guido Biele & Hauke R. Heekeren, 2010. "Neural Processing of Risk," SFB 649 Discussion Papers SFB649DP2010-065, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2010-065
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    Citations

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    Cited by:

    1. Renata S Suter & Thorsten Pachur & Ralph Hertwig & Tor Endestad & Guido Biele, 2015. "The Neural Basis of Risky Choice with Affective Outcomes," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-22, April.
    2. Plamen Nikolov, 2018. "Time Delay and Investment Decisions: Evidence from An Experiment in Tanzania," Economics Bulletin, AccessEcon, vol. 38(2), pages 1124-1137.
    3. Mohr, Peter N. C. & Heekeren, Hauke R. & Rieskamp, Jörg, 2017. "Attraction Effect in Risky Choice Can Be Explained by Subjective Distance Between Choice Alternatives," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7, pages 1-10.
    4. Martin S. Shapiro & Paul C. Price & Edward Mitchell, 2020. "Carryover of domain-dependent risk preferences in a novel decision-making task," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(6), pages 1009-1023, November.
    5. Casado-Aranda, Luis-Alberto & Liébana-Cabanillas, Francisco & Sánchez-Fernández, Juan, 2018. "A Neuropsychological Study on How Consumers Process Risky and Secure E-payments," Journal of Interactive Marketing, Elsevier, vol. 43(C), pages 151-164.
    6. Alena MyÅ¡iÄ ková & Song Song & Piotr Majer & Peter N.C. Mohr & Hauke R. Heekeren & Wolfgang K. Härdle, 2011. "Risk Patterns and Correlated Brain Activities. Multidimensional statistical analysis of fMRI data with application to risk patterns," SFB 649 Discussion Papers SFB649DP2011-085, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. repec:cup:judgdm:v:15:y:2020:i:6:p:1009-1023 is not listed on IDEAS
    8. Chen Ying & Härdle Wolfgang K. & He Qiang & Majer Piotr, 2018. "Risk related brain regions detection and individual risk classification with 3D image FPCA," Statistics & Risk Modeling, De Gruyter, vol. 35(3-4), pages 89-110, July.
    9. Piotr Majer & Peter Mohr & Hauke Heekeren & Wolfgang Karl Härdle, 2014. "Portfolio Decisions and Brain Reactions via the CEAD method," SFB 649 Discussion Papers SFB649DP2014-036, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Li, Yingxing & Huang, Chen & Härdle, Wolfgang K., 2019. "Spatial functional principal component analysis with applications to brain image data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 263-274.
    11. Hytönen, Kaisa & Baltussen, Guido & van den Assem, Martijn J. & Klucharev, Vasily & Sanfey, Alan G. & Smidts, Ale, 2014. "Path dependence in risky choice: Affective and deliberative processes in brain and behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 566-581.
    12. Wei-Hsiang Lin & Justin L Gardner & Shih-Wei Wu, 2020. "Context effects on probability estimation," PLOS Biology, Public Library of Science, vol. 18(3), pages 1-45, March.
    13. Rustichini, Aldo & DeYoung, Colin G. & Anderson, Jon E. & Burks, Stephen V., 2016. "Toward the integration of personality theory and decision theory in explaining economic behavior: An experimental investigation," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 64(C), pages 122-137.
    14. Yingxing Li & Chen Huang & Wolfgang Karl Härdle, 2017. "Spatial Functional Principal Component Analysis with Applications to Brain Image Data," SFB 649 Discussion Papers SFB649DP2017-024, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    15. Terence C. Burnham & Jay Phelan, 2022. "Ordinaries 10," Journal of Bioeconomics, Springer, vol. 24(3), pages 181-202, October.
    16. El Mouden, Claire, 2013. "The Sciences Of Risk: Implications For Regulation Of The Financial Sector," INET Oxford Working Papers 2013-01, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    17. Lucia Reis Peixoto Roselli & Adiel Teixeira Almeida & Eduarda Asfora Frej, 2019. "Decision neuroscience for improving data visualization of decision support in the FITradeoff method," Operational Research, Springer, vol. 19(4), pages 933-953, December.

    More about this item

    Keywords

    XX JEL Classification: C00;

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General

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