Balancing model-based and memory-free action selection under competitive pressure

  1. Atsushi Kikumoto
  2. Ulrich Mayr  Is a corresponding author
  1. University of Oregon, United States

Abstract

In competitive situations, winning depends on selecting actions that surprise the opponent. Such unpredictable action can be generated based on representations of the opponent's strategy and choice history (model-based counter-prediction) or by choosing actions in a memory-free, stochastic manner. Across five different experiments using a variant of a matching-pennies game with simulated and human opponents we found that people toggle between these two strategies, using model-based selection when recent wins signal the appropriateness of the current model, but reverting to stochastic selection following losses. Also, after wins, feedback-related, mid-frontal EEG activity reflected information about the opponent's global and local strategy, and predicted upcoming choices. After losses, this activity was nearly absent-indicating that the internal model is suppressed after negative feedback. We suggest that the mixed-strategy approach allows negotiating two conflicting goals: (1) exploiting the opponent's deviations from randomness while (2) remaining unpredictable for the opponent.

Data availability

Data and analyses are available through OSF (https://osf.io/j6beq/). Specifically, the repository contains for each of the five experiments, all trial-by-trial data files, as well as R codes to conduct the reported analyses. For Experiment 5, we also include all relevant EEG data and analyses codes.

The following data sets were generated

Article and author information

Author details

  1. Atsushi Kikumoto

    Department of Psychology, University of Oregon, Eugene, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2179-2700
  2. Ulrich Mayr

    Department of Psychology, University of Oregon, Eugene, United States
    For correspondence
    mayr@uoregon.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7512-4556

Funding

National Institutes of Health (R01 AG037564- 01A1)

  • Ulrich Mayr

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Daeyeol Lee, Johns Hopkins University, United States

Ethics

Human subjects: The entire study protocol and consent forms were approved by the University of Oregon's Human Subjects Review Board (Protocol 10272010.016).

Version history

  1. Received: May 26, 2019
  2. Accepted: October 1, 2019
  3. Accepted Manuscript published: October 2, 2019 (version 1)
  4. Version of Record published: October 24, 2019 (version 2)

Copyright

© 2019, Kikumoto & Mayr

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Atsushi Kikumoto
  2. Ulrich Mayr
(2019)
Balancing model-based and memory-free action selection under competitive pressure
eLife 8:e48810.
https://doi.org/10.7554/eLife.48810

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https://doi.org/10.7554/eLife.48810

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