Generalization guides human exploration in vast decision spaces

2018/11/06 by Charley M. Wu, Eric Schulz, Maarten Speekenbrink +2 · 39 citations
Computer Science · #Gaussian Processes and Bayesian Inference #Advanced Multi-Objective Optimization Algorithms #Reinforcement Learning in Robotics

paper · doi:10.1038/s41562-018-0467-4

Citations

• On a Measure of the Information Provided by an Experiment

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• Discovery of hierarchical representations for efficient planning. [europepmc]
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• Children perform extensive information gathering when it is not costly. [europepmc]
• Human complex exploration strategies are enriched by noradrenaline-modulated heuristics. [europepmc]
• The dynamics of explore-exploit decisions reveal a signal-to-noise mechanism for random exploration. [europepmc]
• Search for solutions, learning, simulation, and choice processes in suicidal behavior. [europepmc]
• Promises and challenges of human computational ethology. [europepmc]
• Mixing memory and desire: How memory reactivation supports deliberative decision-making. [europepmc]
• Asymmetric reinforcement learning facilitates human inference of transitive relations. [europepmc]
• Lower Levels of Directed Exploration and Reflective Thinking Are Associated With Greater Anxiety and Depression. [europepmc]
• Time pressure changes how people explore and respond to uncertainty. [europepmc]
• Humans can navigate complex graph structures acquired during latent learning. [europepmc]
• Exploration heuristics decrease during youth. [europepmc]
• Humans adaptively resolve the explore-exploit dilemma under cognitive constraints: Evidence from a multi-armed bandit task. [europepmc]
• Value-free random exploration is linked to impulsivity. [europepmc]
• Developmental changes in exploration resemble stochastic optimization. [europepmc]