Balancing Model-Based and Memory-Free Action Selection under Competitive Pressure

Mayr, U.; Kikumoto, A.

doi:10.1101/662700

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bioRxiv · 10.1101/662700

Balancing Model-Based and Memory-Free Action Selection under Competitive Pressure

Mayr, U.·Kikumoto, A.

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Abstract

In competitive situations, winning depends on selecting actions that surprise the opponent. Such unpredictable action can be generated based on representations of the opponents 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 used 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 opponents 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 opponents deviations from randomness while (2) remaining unpredictable for the opponent.

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Mayr, U., Kikumoto, A.. 2019-06-06. Balancing Model-Based and Memory-Free Action Selection under Competitive Pressure. https://doi.org/10.1101/662700

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