Cargando…
A sensorimotor paradigm for Bayesian model selection
Sensorimotor control is thought to rely on predictive internal models in order to cope efficiently with uncertain environments. Recently, it has been shown that humans not only learn different internal models for different tasks, but that they also extract common structure between tasks. This raises...
Autores principales: | Genewein, Tim, Braun, Daniel A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3486689/ https://www.ncbi.nlm.nih.gov/pubmed/23125827 http://dx.doi.org/10.3389/fnhum.2012.00291 |
Ejemplares similares
-
Structure Learning in Bayesian Sensorimotor Integration
por: Genewein, Tim, et al.
Publicado: (2015) -
Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences
por: Peng, Zhen, et al.
Publicado: (2014) -
Risk-Sensitivity in Bayesian Sensorimotor Integration
por: Grau-Moya, Jordi, et al.
Publicado: (2012) -
Paradigm Shift in Sensorimotor Control Research and Brain Machine Interface Control: The Influence of Context on Sensorimotor Representations
por: Zhao, Yao, et al.
Publicado: (2018) -
Risk-Sensitivity in Sensorimotor Control
por: Braun, Daniel A., et al.
Publicado: (2011)