Cargando…
Linear reinforcement learning in planning, grid fields, and cognitive control
It is thought that the brain’s judicious reuse of previous computation underlies our ability to plan flexibly, but also that inappropriate reuse gives rise to inflexibilities like habits and compulsion. Yet we lack a complete, realistic account of either. Building on control engineering, here we int...
Autores principales: | Piray, Payam, Daw, Nathaniel D. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8368103/ https://www.ncbi.nlm.nih.gov/pubmed/34400622 http://dx.doi.org/10.1038/s41467-021-25123-3 |
Ejemplares similares
-
A simple model for learning in volatile environments
por: Piray, Payam, et al.
Publicado: (2020) -
A model for learning based on the joint estimation of stochasticity and volatility
por: Piray, Payam, et al.
Publicado: (2021) -
Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning
por: Gustafson, Nicholas J., et al.
Publicado: (2011) -
Offline replay supports planning in human reinforcement learning
por: Momennejad, Ida, et al.
Publicado: (2018) -
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies
por: Piray, Payam, et al.
Publicado: (2019)