A simple model for learning in volatile environments.
Sound principles of statistical inference dictate that uncertainty shapes learning. In this work, we revisit the question of learning in volatile environments, in which both the first and second-order statistics of observations dynamically evolve over time. We propose a new model, the volatile Kalma...
Main Authors: | Payam Piray, Nathaniel D Daw |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2020-07-01
|
Series: | PLoS Computational Biology |
Online Access: | https://doi.org/10.1371/journal.pcbi.1007963 |
Similar Items
-
Linear reinforcement learning in planning, grid fields, and cognitive control
by: Payam Piray, et al.
Published: (2021-08-01) -
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies.
by: Payam Piray, et al.
Published: (2019-06-01) -
Suboptimal Criterion Learning in Static and Dynamic Environments.
by: Elyse H Norton, et al.
Published: (2017-01-01) -
Speed/accuracy trade-off between the habitual and the goal-directed processes.
by: Mehdi Keramati, et al.
Published: (2011-05-01) -
A Simple Test for Causality in Volatility
by: Chia-Lin Chang, et al.
Published: (2017-03-01)