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Integrating Models of Interval Timing and Reinforcement Learning
- Source :
- Trends in cognitive sciences. 22(10)
- Publication Year :
- 2018
-
Abstract
- 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.
- Subjects :
- 0301 basic medicine
business.industry
Cognitive Neuroscience
Internal model
Experimental and Cognitive Psychology
Basis function
Interval (mathematics)
Models, Psychological
03 medical and health sciences
030104 developmental biology
0302 clinical medicine
Neuropsychology and Physiological Psychology
Action (philosophy)
Reward
Time Perception
Reinforcement learning
Humans
Artificial intelligence
business
Reinforcement
Psychology
Representation (mathematics)
Set (psychology)
Reinforcement, Psychology
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 1879307X
- Volume :
- 22
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- Trends in cognitive sciences
- Accession number :
- edsair.doi.dedup.....bc8313f2ac73164ff20337bb0a195f28