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

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. A...

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Detalles Bibliográficos
Autores principales: Kikumoto, Atsushi, Mayr, Ulrich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6812965/
https://www.ncbi.nlm.nih.gov/pubmed/31577231
http://dx.doi.org/10.7554/eLife.48810
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author Kikumoto, Atsushi
Mayr, Ulrich
author_facet Kikumoto, Atsushi
Mayr, Ulrich
author_sort Kikumoto, Atsushi
collection PubMed
description 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.
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spelling pubmed-68129652019-10-25 Balancing model-based and memory-free action selection under competitive pressure Kikumoto, Atsushi Mayr, Ulrich eLife Neuroscience 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. eLife Sciences Publications, Ltd 2019-10-02 /pmc/articles/PMC6812965/ /pubmed/31577231 http://dx.doi.org/10.7554/eLife.48810 Text en © 2019, Kikumoto and Mayr http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Kikumoto, Atsushi
Mayr, Ulrich
Balancing model-based and memory-free action selection under competitive pressure
title Balancing model-based and memory-free action selection under competitive pressure
title_full Balancing model-based and memory-free action selection under competitive pressure
title_fullStr Balancing model-based and memory-free action selection under competitive pressure
title_full_unstemmed Balancing model-based and memory-free action selection under competitive pressure
title_short Balancing model-based and memory-free action selection under competitive pressure
title_sort balancing model-based and memory-free action selection under competitive pressure
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6812965/
https://www.ncbi.nlm.nih.gov/pubmed/31577231
http://dx.doi.org/10.7554/eLife.48810
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