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 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.
Balancing model-based and memory-free action selection under competitive pressureOpen Science Foundation, https://osf.io.
- Ulrich Mayr
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: The entire study protocol and consent forms were approved by the University of Oregon's Human Subjects Review Board (Protocol 10272010.016).
- Daeyeol Lee, Johns Hopkins University, United States
© 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.