Article

On the computational principles underlying human exploration

2023-05-24

Abstract excerpt

<p>Adapting to new environments is a hallmark of animal and human cognition, and Reinforcement Learning (RL) models provide a powerful and general framework for studying such adaptation. A fundamental learning component identified by RL models is that in the absence of direct supervision, when learning is driven by trial-and-error, exploration is essential. The necessary ingredients of effective exploration have b...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8d97d476-8d5e-565f-9265-6da5df9245de
DOI: 10.31234/osf.io/s96b2

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

On the computational principles underlying human explorationDOI 10.31234/osf.io/s96b2Read

Connecting Exploration, Generalization, and Planning in Correlated TreesSemantic proximity Read

Generalization guides human exploration in vast decision spacesSemantic proximity Read

The Value of AbstractionSemantic proximity Read

Human Exploration in Complex Problem-Solving Tasks: More Effortful Interaction Leads to Higher EfficiencySemantic proximity Read

Curiosity-driven exploration: Diversity of mechanisms and functionsSemantic proximity Read

Finding structure in multi-armed banditsSemantic proximity Read

Reinforcement learning under uncertainty: expected versus unexpected uncertainty and state versus reward uncertaintySemantic proximity Read

Rational decisions in multi-step environments with few rolloutsSemantic proximity Read

Learning generalizable representations through efficient codingSemantic proximity Read