Dataset Information

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

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.

SUBMITTER: Kikumoto A

PROVIDER: S-EPMC6812965 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Kikumoto Atsushi A Mayr Ulrich U

eLife 20191002

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 whe ...[more]

PMID: 31577231

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Project description:Recently diverged species typically have incomplete reproductive barriers, allowing introgression of genetic material from one species into the genomic background of the other. The role of natural selection in preventing or promoting introgression remains contentious. Because of genomic co-adaptation, some chromosomal fragments are expected to be selected against in the new background and resist introgression. In contrast, natural selection should favor introgression for alleles at genes evolving under multi-allelic balancing selection, such as the MHC in vertebrates, disease resistance, or self-incompatibility genes in plants. Here, we test the prediction that negative, frequency-dependent selection on alleles at the multi-allelic gene controlling pistil self-incompatibility specificity in two closely related species, Arabidopsis halleri and A. lyrata, caused introgression at this locus at a higher rate than the genomic background. Polymorphism at this gene is largely shared, and we have identified 18 pairs of S-alleles that are only slightly divergent between the two species. For these pairs of S-alleles, divergence at four-fold degenerate sites (K = 0.0193) is about four times lower than the genomic background (K = 0.0743). We demonstrate that this difference cannot be explained by differences in effective population size between the two types of loci. Rather, our data are most consistent with a five-fold increase of introgression rates for S-alleles as compared to the genomic background, making this study the first documented example of adaptive introgression facilitated by balancing selection. We suggest that this process plays an important role in the maintenance of high allelic diversity and divergence at the S-locus in flowering plant families. Because genes under balancing selection are expected to be among the last to stop introgressing, their comparison in closely related species provides a lower-bound estimate of the time since the species stopped forming fertile hybrids, thereby complementing the average portrait of divergence between species provided by genomic data.

Dataset Information

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

Publications

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

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