Evaluation of Probabilities and Brain Activity - An EEG-Study

  • Ralf Morgenstern
  • Marcus Heldmann
  • Thomas Münte
  • Bodo Vogt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5819)


This paper focuses on the problem of probability weighting in the evaluation of lotteries. According to Prospect Theory a probability of 0.5 has a weight of smaller than 0.5. We conduct an EEG experiment in which we compare the results of the evaluation of binary lotteries by certainty equivalents with the results of the bisection method. The bisection method gives the amount of money that corresponds to the midpoint of the utilities of the two payoffs in a binary lottery as it has been shown previously. In this method probabilities are not evaluated. We analyzed EEG data focused on whether a probability is evaluated or not. Our data show differences between the two methods connected with the attention towards sure monetary payoffs, but they do not show brain activity connected with a devaluation of the probability of 0.5.


Prospect Theory Probability Weighting Expect Utility Theory Certainty Equivalent Bisection Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kahneman, D., Tversky, A.: Prospect Theory: An Analysis of Decision under Risk. Econometrica 47(2), 263–291 (1979)CrossRefzbMATHGoogle Scholar
  2. 2.
    Tversky, A., Kahneman, D.: Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty 5(4), 297–323 (1992)CrossRefzbMATHGoogle Scholar
  3. 3.
    Heldmann, M., Münte, T.F., Vogt, B.: Relevance without profit: Electrophysiological correlates of two economic methods for defining the utility function. Working Paper (2008)Google Scholar
  4. 4.
    Galanter, E.: The Direct Measurement of Utility and Subjective Probability. The American Journal of Psychology 75(2), 208–220 (1962)CrossRefGoogle Scholar
  5. 5.
    Camerer, C.F., Ho, T.-H.: Violations of the betweenness axiom and nonlinearity in probability. Journal of Risk and Uncertainty 8(2), 167–196 (1994)CrossRefzbMATHGoogle Scholar
  6. 6.
    Tversky, A., Fox, C.: Weighting Risk and Uncertainty. Psychological review 102(2), 269–283 (1995)CrossRefGoogle Scholar
  7. 7.
    Abdellaoui, M.: Parameter-Free Elicitation of Utility and Probability Weighting Functions. Management Science 46(11), 1497–1512 (2000)CrossRefzbMATHGoogle Scholar
  8. 8.
    Gonzalez, R., Wu, G.: On the Shape of the Probability Weighting Function. Cognitive Psychology 38(1), 129–166 (1999)CrossRefGoogle Scholar
  9. 9.
    Loomes, G., Sugden, R.: Regret Theory: An Alternative Theory of Rational Choice Under Uncertainty. The Economic Journal 92(368), 805–824 (1982)CrossRefGoogle Scholar
  10. 10.
    Bell, D.E.: Disappointment in Decision Making under Uncertainty. Operations Research 33(1), 1–27 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Albers, W., et al.: Experimental Evidence for Attractions to Chance. German Economic Review 1(2), 113–130 (2000)CrossRefGoogle Scholar
  12. 12.
    Münte, T.F., et al.: Event-related brain potentials in the study of human cognition and neuropsychology. In: Handbook of neuropsychology, vol. 1, pp. 139–236 (2000)Google Scholar
  13. 13.
    Duncan-Johnson, C.C., Donchin, E.: On quantifying surprise: the variation of event-related potentials with subjective probability. Psychophysiology 14(5), 456–467 (1977)CrossRefGoogle Scholar
  14. 14.
    Johnson, R.: The amplitude of the P300 component of the event-related potential: Review and synthesis. Advances in Psychophysiology 3, 69–137 (1988)Google Scholar
  15. 15.
    Linden, D.E.: The p300: where in the brain is it produced and what does it tell us? Neuroscientist 11(6), 563–576 (2005)CrossRefGoogle Scholar
  16. 16.
    Polich, J.: Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol. 118(10), 2128–2148 (2007)CrossRefGoogle Scholar
  17. 17.
    Picton, T.W.: The P300 Wave of the Human Event-Related Potential. Journal of Clinical Neurophysiology 9(4), 456 (1992)CrossRefGoogle Scholar
  18. 18.
    Pritchard, W.S.: Psychophysiology of P300. Psychological Bulletin 89(3), 506–540 (1981)CrossRefGoogle Scholar
  19. 19.
    Joyce, C.A., Gorodnitsky, I.F., Kutas, M.: Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology 41(2), 313–325 (2004)CrossRefGoogle Scholar
  20. 20.
    Goldstein, R.Z., et al.: Compromised sensitivity to monetary reward in current cocaine users: an ERP study. Psychophysiology 45(5), 705–713 (2008)CrossRefGoogle Scholar
  21. 21.
    Wu, Y., Zhou, X.: The P300 and reward valence, magnitude, and expectancy in outcome evaluation. Brain Res. (2009)Google Scholar
  22. 22.
    Yeung, N., Sanfey, A.G.: Independent coding of reward magnitude and valence in the human brain. J. Neurosci. 24(28), 6258–6264 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ralf Morgenstern
    • 2
  • Marcus Heldmann
    • 1
  • Thomas Münte
    • 1
  • Bodo Vogt
    • 2
  1. 1.Otto-von-Guericke-University Magdeburg, NeuropsychologyMagdeburgGermany
  2. 2.Faculty of Economics and ManagementOtto-von-Guericke-University MagdeburgMagdeburgGermany

Personalised recommendations