Reliability of Consumer Choices for Conflicting Price Promotions

  • Amanda SargentEmail author
  • Jan Watson
  • Yigit Topoglu
  • Hongjun Ye
  • Wenting Zhong
  • Hasan Ayaz
  • Rajneesh Suri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 953)


Decisional conflict arises when all options of a multiple dimensional decisions task has equal or close to equal expected utility, causing additional mental effort. In this study, we investigated the potential of capturing decisional conflict related mental effort in a realistic complex decision-making task. In a binary choice paradigm, participants made decisions related to electricity supply plans. The study presented in each trial a choice with two different utility plans where variables related to fixed rate or time-of-use plans with peak-rate value and duration were compared against each other. We monitored the anterior prefrontal cortex of participants during binary decision-making, to assess the level of conflict using functional near infrared spectroscopy (fNIRS). Results indicate that fNIRS is able to measure the difference between conflict and no conflict decision making processes consistent with the neural efficiency hypothesis.


Functional near infrared spectroscopy Consumer behavior Price promotions Decisional conflict 


  1. 1.
    Tremblay, S., Sharika, K.M., Platt, M.L.: Social decision-making and the brain: a comparative perspective. Trends Cogn. Sci. 21(4), 265–276 (2017)CrossRefGoogle Scholar
  2. 2.
    Yoon, C., et al.: Decision neuroscience and consumer decision making. Mark. Lett. 23(2), 473–485 (2012)CrossRefGoogle Scholar
  3. 3.
    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(4), 593–602 (2009)CrossRefGoogle Scholar
  4. 4.
    Rouault, M., Drugowitsch, J., Koechlin, E.: Prefrontal mechanisms combining rewards and beliefs in human decision-making. Nat. Commun. 10(1), 301 (2019)CrossRefGoogle Scholar
  5. 5.
    Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A.: The neural basis of loss aversion in decision making under risk. Science 315(5811), 515–518 (2007)CrossRefGoogle Scholar
  6. 6.
    Barkley-Levenson, E.E., Van Leijenhorst, L., Galvan, A.: Behavioral and neural correlates of loss aversion and risk avoidance in adolescents and adults. Dev. Cogn. Neurosci. 3, 72–83 (2013)CrossRefGoogle Scholar
  7. 7.
    Harrison, Y., Horne, J.A.: The impact of sleep deprivation on decision making. A review. J. Exp. Psychol. Appl. 6(3), 236–249 (2000)CrossRefGoogle Scholar
  8. 8.
    Xue, G., Lu, Z., Levin, I.P., Weller, J.A., Li, X., Bechara, A.: Functional dissociations of risk and reward processing in the medial prefrontal cortex. Cereb. Cortex 19(5), 1019–1027 (2009)CrossRefGoogle Scholar
  9. 9.
    Di Domenico, S.I., Rodrigo, A.H., Ayaz, H., Fournier, M.A., Ruocco, A.C.: Decision-making conflict and the neural efficiency hypothesis of intelligence: a functional near-infrared spectroscopy investigation. Neuroimage 109, 307–317 (2015)CrossRefGoogle Scholar
  10. 10.
    Franceschini, M.A., Boas, D.A.: Noninvasive measurement of neuronal activity with near-infrared optical imaging. NeuroImage 21(1), 372–386 (2004)CrossRefGoogle Scholar
  11. 11.
    Izzetoglu, M., Izzetoglu, K., Bunce, S., Ayaz, H., Devaraj, A., Onaral, B., Pourezzaei, K.: Functional near-infrared neuroimaging. IEEE Trans. Neural Syst. Rehabil. Eng. 12(2), 153–159 (2005)CrossRefGoogle Scholar
  12. 12.
    Shimokawa, T., Misawa, T., Suzuki, K.: Neural representation of preference relationships. NeuroReport 19(16), 1557–1561 (2008)CrossRefGoogle Scholar
  13. 13.
    Knutson, B., Rick, S., Wimmer, G.E., Prelec, D., Loewenstein, G.: Neural predictors of purchases. Neuron 53(1), 147–156 (2007)CrossRefGoogle Scholar
  14. 14.
    Plassmann, H., Kenning, P., Deppe, M., Kugel, H., Schwindt, W.: How choice ambiguity modulates activity in brain areas representing brand preference: evidence from consumer neuroscience. J. Consum. Behav. 7(4–5), 360–367 (2008)CrossRefGoogle Scholar
  15. 15.
    Deppe, M., Schwindt, W., Kugel, H., Plassmann, H., Kenning, P.: Nonlinear responses within the medial prefrontal cortex reveal when specific implicit information influences economic decision making. J. Neuroimaging 15(2), 171–182 (2005)CrossRefGoogle Scholar
  16. 16.
    Gonzalez, C., Dana, J., Koshino, H., Just, M.: The framing effect and risky decisions: examining cognitive functions with fMRI. J. Econ. Psychol. 26(1), 1–20 (2005)CrossRefGoogle Scholar
  17. 17.
    Fleming, S.M., Huijgen, J., Dolan, R.J.: Prefrontal contributions to metacognition in perceptual decision making. J. Neurosci. 32(18), 6117–6125 (2012)CrossRefGoogle Scholar
  18. 18.
    Bang, D., Fleming, S.M.: Distinct encoding of decision confidence in human medial prefrontal cortex. Proc. Natl. Acad. Sci. 115(23), 6082–6087 (2018)CrossRefGoogle Scholar
  19. 19.
    Bechara, A., Damasio, A.R.: The somatic marker hypothesis: a neural theory of economic decision. Games Econ. Behav. 52(2), 336–372 (2005)CrossRefGoogle Scholar
  20. 20.
    Ayaz, H., Izzetoglu, M., Izzetoglu, K., Onaral, B.: The use of functional near-infrared spectroscopy in neuroergonomics. In: Neuroergonomics, pp. 17–25. Academic Press (2019)Google Scholar
  21. 21.
    Ayaz, H., Shewokis, P.A., Curtin, A., Izzetoglu, M., Izzetoglu, K., Onaral, B.: Using MazeSuite and functional near infrared spectroscopy to study learning in spatial navigation. J. Vis. Exp. 56, 3443 (2011)Google Scholar
  22. 22.
    Curtin, A., Ayaz, H.: The age of neuroergonomics: towards ubiquitous and continuous measurement of brain function with fNIRS. Jpn. Psycholo. Res. 60(4), 374–386 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Amanda Sargent
    • 1
    Email author
  • Jan Watson
    • 1
  • Yigit Topoglu
    • 1
  • Hongjun Ye
    • 2
  • Wenting Zhong
    • 2
  • Hasan Ayaz
    • 1
    • 3
    • 4
    • 5
  • Rajneesh Suri
    • 2
    • 3
  1. 1.School of Biomedical Engineering, Science and Health SystemsDrexel UniversityPhiladelphiaUSA
  2. 2.LeBow College of Business, Drexel UniversityPhiladelphiaUSA
  3. 3.Drexel Business Solutions Institute, Drexel UniversityPhiladelphiaUSA
  4. 4.Department of Family and Community HealthUniversity of PennsylvaniaPhiladelphiaUSA
  5. 5.Center for Injury Research and Prevention, Children’s Hospital of PhiladelphiaPhiladelphiaUSA

Personalised recommendations