Integrating Models of Interval Timing and Reinforcement Learning.

Published

Journal Article (Review)

We present an integrated view of interval timing and reinforcement learning (RL) in the brain. The computational goal of RL is to maximize future rewards, and this depends crucially on a representation of time. Different RL systems in the brain process time in distinct ways. A model-based system learns 'what happens when', employing this internal model to generate action plans, while a model-free system learns to predict reward directly from a set of temporal basis functions. We describe how these systems are subserved by a computational division of labor between several brain regions, with a focus on the basal ganglia and the hippocampus, as well as how these regions are influenced by the neuromodulator dopamine.

Full Text

Cited Authors

  • Petter, EA; Gershman, SJ; Meck, WH

Published Date

  • October 2018

Published In

Volume / Issue

  • 22 / 10

Start / End Page

  • 911 - 922

PubMed ID

  • 30266150

Pubmed Central ID

  • 30266150

Electronic International Standard Serial Number (EISSN)

  • 1879-307X

International Standard Serial Number (ISSN)

  • 1364-6613

Digital Object Identifier (DOI)

  • 10.1016/j.tics.2018.08.004

Language

  • eng