ArticleNature communications2019
Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 25 citations in OpenAlex.
- Continuous dynamics of cooperation and competition in social decision-making.Communications psychology · 2025Article
- Human and macaque pairs employ different coordination strategies in a transparent decision game.eLife · 2023Article
- Distinct but correlated latent factors support the regulation of learned conflict-control and task-switching.Cognitive psychology · 2022Article
- Personalized next-best action recommendation with multi-party interaction learning for automated decision-making.PloS one · 2022Article
- Minimal impact of consolidation on learned switch-readiness.Journal of experimental psychology. Learning, memory, and cognition · 2021Article
- Continuous decisions.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Dorsolateral and dorsomedial prefrontal cortex track distinct properties of dynamic social behavior.Social cognitive and affective neuroscience · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Previous studies of strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices. Yet, most real-world social behaviors involve dynamic, coevolving decisions by interacting agents, which poses challenges for creating tractable models of behavior. Here, using a game in which humans competed against both real and artificial opponents, we show that it is possible to quantify the instantaneous dynamic coupling between agents. Adopting a reinforcement learning approach, we use Gaussian Processes to model the policy and value functions of participants as a function of both game state and opponent identity. We found that higher-scoring participants timed their final change in direction to moments when the opponent's counter-strategy was weaker, while lower-scoring participants less precisely timed their final moves. This approach offers a natural set of metrics for facilitating analysis at multiple timescales and suggests new classes of experimental paradigms for assessing behavior.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.