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The Drift Diffusion Model can account for the accuracy and reaction time of value-based choices under high and low time pressure

Author

Listed:
  • Milica Milosavljevic
  • Jonathan Malmaud
  • Alexander Huth
  • Christof Koch
  • Antonio Rangel

Abstract

An important open problem is how values are compared to make simple choices. A natural hypothesis is that the brain carries out the computations associated with the value comparisons in a manner consistent with the Drift Diffusion Model (DDM), since this model has been able to account for a large amount of data in other domains. We investigated the ability of four different versions of the DDM to explain the data in a real binary food choice task under conditions of high and low time pressure. We found that a seven-parameter version of the DDM can account for the choice and reaction time data with high-accuracy, in both the high and low time pressure conditions. The changes associated with the introduction of time pressure could be traced to changes in two key model parameters: the barrier height and the noise in the slope of the drift process.

Suggested Citation

  • Milica Milosavljevic & Jonathan Malmaud & Alexander Huth & Christof Koch & Antonio Rangel, 2010. "The Drift Diffusion Model can account for the accuracy and reaction time of value-based choices under high and low time pressure," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(6), pages 437-449, October.
  • Handle: RePEc:jdm:journl:v:5:y:2010:i:6:p:437-449
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    Citations

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    Cited by:

    1. Stefan Scherbaum & Simon Frisch & Susanne Leiberg & Steven J. Lade & Thomas Goschke & Maja Dshemuchadse, 2016. "Process dynamics in delay discounting decisions: An attractor dynamics approach," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 11(5), pages 472-495, September.
    2. Uggeldahl, Kennet & Jacobsen, Catrine & Lundhede, Thomas Hedemark & Olsen, Søren Bøye, 2016. "Choice certainty in Discrete Choice Experiments: Will eye tracking provide useful measures?," Journal of choice modelling, Elsevier, vol. 20(C), pages 35-48.
    3. Arkady Konovalov & Ian Krajbich, 2016. "Revealed Indifference: Using Response Times to Infer Preferences," Working Papers 16-01, Ohio State University, Department of Economics.
    4. Larry G. Epstein & Shaolin Ji, 2017. "Optimal Learning and Ellsberg's Urns," Papers 1708.01890, arXiv.org.
    5. Vaibhav Srivastava & Samuel F. Feng & Jonathan D. Cohen & Naomi Ehrich Leonard & Amitai Shenhav, 2015. "A martingale analysis of first passage times of time-dependent Wiener diffusion models," Papers 1508.03373, arXiv.org, revised Sep 2016.

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