Article

The Role of Executive Function in Shaping Reinforcement Learning

2020-06-02

Abstract excerpt

<p>Reinforcement learning (RL) models have advanced our understanding of how animals learn and make decisions, and how the brain supports some aspects of learning. However, the neural computations that are explained by RL algorithms fall short of explaining many sophisticated aspects of human decision making, including the generalization of learned information, one-shot learning, and the synthesis of task informat...

Topics

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

Identifiers and source

Literature Corpus work: aab25f03-4c1c-5aac-9ec5-a8dc2af66fea
DOI: 10.31234/osf.io/9cvw3

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

The Role of Executive Function in Shaping Reinforcement LearningDOI 10.31234/osf.io/9cvw3Read

Context-dependent outcome encoding in human reinforcement learningSemantic proximity Read

How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis.Semantic proximity Read

Compositional generalization in multi-armed banditsSemantic proximity Read

Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision MakingSemantic proximity Read

From Reinforcement Learning to Cognitive Psychology: with Navigational Strategies in MindSemantic proximity Read

Data Driven Equation Discovery Reveals Non-linear Reinforcement Learning in HumansSemantic proximity Read

Confidence phenotypes: a unified computational account of value and decision certainty in reinforcement learningSemantic proximity Read