Advertisement

Neural Correlates of Purchasing Decisions in an Ecologically Plausible Shopping Scenario with Mobile fNIR Technology

  • Murat Perit ÇakırEmail author
  • Tuna Çakar
  • Yener Girişken
  • Ari K. Demircioğlu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)

Abstract

In this paper we present our preliminary findings for the neural correlates of purchasing decisions made in a computerized setting as well as in an ecologically plausible supermarket environment. Participants who were randomly recruited from a database of typical customers maintained by a marketing consultancy company were given a specific budget and asked to make purchasing decisions for basic grocery items in two separate conditions. In the first condition, participants made their decisions in a computerized scenario, where in each trial a single product and its price were displayed for a fixed duration of time, and then the participants clicked on buttons to specify which products they wish to purchase. In the second experiment, participants made similar purchasing decisions while wandering around a custom-made grocery aisle with shelves including physical products. In both conditions participants’ brain activities in their prefrontal cortices as well as their eye movements were recorded wıth a wireless fNIR device and a glass eye tracker respectively. In both conditions we observed higher mean oxygenation levels for the purchase decisions at the left dorso-medial prefrontal cortex. Despite the limited sample size, the oxygenation trends were similar in both purchasing situations. Our preliminary findings suggest that fNIR can effectively be employed to investigate neural correlates of purchasing behavior in ecological settings.

Keywords

Decision making Neuroergonomics Neuroeconomics Optical brain imaging Mobile fNIR 

Notes

Acknowledgments

The authors would like to thank Dr. Hasan Ayaz for his guidance and help during the analysis and processing of fNIR signals. This research and development project was supported by The Scientific and Research Council of Turkey, TUBITAK-1501 grant to ThinkNeuro (Project No: 3140565).

References

  1. 1.
    Politser, P.: Neuroeconomics: A Guide to the New Science of Making Choices. OUP, New York (2008)CrossRefGoogle Scholar
  2. 2.
    Rangel, A., Clithero, J.: The computation of stimulus values in simple choice. In: Glimcher, P., Fehr, E. (eds.) Neuroeconomics: Decision Making and the Brain, 2nd edn, pp. 125–148. Academic Press, New York (2014)CrossRefGoogle Scholar
  3. 3.
    Glimcher, P.W., Fehr, E. (eds.): Neuroeconomics: Decision making and the brain. Academic Press, New York (2014)Google Scholar
  4. 4.
    Smith, D.V., Huettel, S.A.: Decision neuroscience: neuroeconomics. Wiley Interdiscip. Rev. Cogn. Sci. 1(6), 854–871 (2010)CrossRefGoogle Scholar
  5. 5.
    Schultz, W.: Behavioral theories and the neurophysiology of reward. Annu. Rev. Psych. 57, 87–115 (2006)CrossRefGoogle Scholar
  6. 6.
    Knutson, B., Taylor, J., Kaufman, M., Peterson, R., Glover, G.: Distributed neural representation of expected value. J. Neurosci. 25(19), 4806–4812 (2005)CrossRefGoogle Scholar
  7. 7.
    Knutson, B., Rick, S., Wimmer, G.E., Prelec, D., Loewenstein, G.: Neural predictors of purchases. Neuron 53, 147–156 (2007)CrossRefGoogle Scholar
  8. 8.
    Levy, I., Lazzaro, S.C., Rutledge, R.B., Glimcher, P.W.: Choice from non-choice: predicting consumer preferences from blood oxygenation level-dependent signals obtained during passive viewing. J. Neurosci. 31(1), 118–125 (2011)CrossRefGoogle Scholar
  9. 9.
    Metereau, E., Dreher, J.C.: The medial orbitofrontal cortex encodes a general unsigned value signal during anticipation of both appetitive and aversive events. Cortex 63, 42–54 (2015)CrossRefGoogle Scholar
  10. 10.
    Holper, L., ten Brincke, R.H., Wolf, M., Murphy, R.O.: fNIRS derived hemodynamic signals and electrodermal responses in a sequential risk-taking task. Brain Res. 1557, 141–154 (2014)CrossRefGoogle Scholar
  11. 11.
    Ogawa, A., Onozaki, T., Mizuno, T., Asamizuya, T., Ueno, K., Cheng, K., Iriki, A.: Neural basis of economic bubble behavior. Neuroscience 265, 37–47 (2014)CrossRefGoogle Scholar
  12. 12.
    Tobler, P.N., Christopoulos, G., O’Doherty, J., Dolan, R.J., Schultz, W.: Risk-dependent reward value signal in human prefrontal cortex. PNAS 106(17), 7185–7190 (2009)CrossRefGoogle Scholar
  13. 13.
    McClure, S.M., Li, J., Tomlin, D., Cypert, K.S., Montague, L.M., Montague, P.R.: Neural correlates of behavioral preference for culturally familiar drinks. Neuron 44(2), 379–387 (2004)CrossRefGoogle Scholar
  14. 14.
    Kumagai, M.: Extraction of personal preferences implicitly using NIRS. In: Proceedings of IEEE SICE Annual Conference (SICE 2012), pp. 1351–1353 (2012)Google Scholar
  15. 15.
    Shimokawa, T., Misawa, T., Suzuki, K.: Neural representation of preference relationships. NeuroReport 19, 1557–1561 (2008)CrossRefGoogle Scholar
  16. 16.
    Mitsuda, Y., Goto, K., Misawa, T., Shimokawa, T.: Prefrontal cortex activation during evaluation of product price: a NIRS study. In: Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference (2012)Google Scholar
  17. 17.
    Obrig, H., Wenzel, R., Kohl, M., Horst, S., Wobst, P., Steinbrink, J., Villringer, A.: Near-infrared spectroscopy: does it function in functional activation studies of the adult brain? Int. J. Psychophysiol. 35(2), 125–142 (2000)CrossRefGoogle Scholar
  18. 18.
    Bunce, S.C., Izzetoglu, M., Izzetoglu, K., Onaral, B., Pourrezaei, K.: Functional near-infrared spectroscopy. IEEE Eng. Med. Biol. Mag. 25(4), 54–62 (2006)CrossRefGoogle Scholar
  19. 19.
    Jobsis, F.F.: Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 198(4323), 1264–1267 (1977)CrossRefGoogle Scholar
  20. 20.
    Cope, M., Delpy, D.T., Reynolds, E.O.R., Wray, S., Wyatt, J., Van der Zee, P.: Methods of quantitating cerebral near infrared spectroscopy data. In: Mochizuki, M., Honig, C.R., Koyama, T., Goldstick, T.K., Bruley, D.F. (eds.) Oxygen Transport to Tissue X, vol. 215, pp. 183–189. Springer, New York (1988)CrossRefGoogle 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), e3443 (2011). doi: 10.3791/3443
  22. 22.
    Ayaz, H.: Functional Near Infrared Spectroscopy based Brain Computer Interface. Ph.D. Thesis, Drexel University, Philadelphia, PA (2010)Google Scholar
  23. 23.
    Izzetoglu, M., Izzetoglu, K., Bunce, S., Ayaz, H., Devaraj, A., Onaral, B., Pourrezaei, K.: Functional near-infrared neuroimaging. IEEE Trans. Neural Syst. Rehabil. Eng. 13(2), 153–159 (2005)CrossRefGoogle Scholar
  24. 24.
    Ayaz, H., Izzetoglu, M., Shewokis, P.A., Onaral, B.: Sliding-window motion artifact rejection for functional near-infrared spectroscopy. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2010, 6567–6570 (2010)Google Scholar
  25. 25.
    Ayaz, H., Shewokis, P.A., Bunce, S., Izzetoglu, K., Willems, B., Onaral, B.: Optical brain monitoring for operator training and mental workload assessment. Neuroimage 59(1), 36–47 (2012)CrossRefGoogle Scholar
  26. 26.
    Çakir, M.P., Çakar, T., Girişken, Y.: Neural Correlates of Purchasing Behavior in the Prefrontal Cortex: An Optical Brain Imaging Study. Paper presented at CogSci 2015, Annual Meeting of the Cognitive Science Society, Pasadena, CA, USA (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Murat Perit Çakır
    • 1
    Email author
  • Tuna Çakar
    • 2
  • Yener Girişken
    • 3
    • 4
  • Ari K. Demircioğlu
    • 4
  1. 1.Graduate School of InformaticsMiddle East Technical UniversityAnkaraTurkey
  2. 2.Biostatistics and Medical InformaticsAcibadem UniversityIstanbulTurkey
  3. 3.Graduate School of Marketing CommunicationsIstanbul Bilgi UniversityIstanbulTurkey
  4. 4.ThinkNeuro Market Research Co.IstanbulTurkey

Personalised recommendations