The Role of Executive Function in Shaping Reinforcement Learning.

By Milena Rmus, Samuel David McDougle, A. G. Collins. 2020

57 citations recorded by the source corpus.

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 information in complex environments.. Instead, these aspects of instrumental behavior are assumed to be supported by the brain's executive functions (EF). We review recent findings that highlight the importance of EF in learning. Specifically, we advance the theory that EF sets the stage for canonical RL computations in the brain, providing inputs that broaden their flexibility and applicability. Our theory has important implications for how to interpret RL computations in the brain and behavior.

Subfields: Cognitive Neuroscience

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In Cognitive Neuroscience

1. Doina Precup
2. Irina Rish
3. Khimya Khetarpal
4. Matthew Riemer

Citations in the corpus

• Towards Continual Reinforcement Learning: A Review and Perspectives (2020, background): “Finally, it has been recently suggested that many sophisticated aspects of human learning cannot be explained by RL, and that these aspects of behavior may be supported by the brain’s executive functions (Rmus et al., 2020).”