Cargando…
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies
Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been us...
Autores principales: | Piray, Payam, Dezfouli, Amir, Heskes, Tom, Frank, Michael J., Daw, Nathaniel D. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581260/ https://www.ncbi.nlm.nih.gov/pubmed/31211783 http://dx.doi.org/10.1371/journal.pcbi.1007043 |
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) -
Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes
por: Keramati, Mehdi, et al.
Publicado: (2011) -
Linear reinforcement learning in planning, grid fields, and cognitive control
por: Piray, Payam, et al.
Publicado: (2021) -
Bayesian hierarchical modelling for inferring genetic interactions in yeast
por: Heydari, Jonathan, et al.
Publicado: (2015)