Learning a reach trajectory based on binary reward feedback
Abstract Binary reward feedback on movement success is sufficient for learning some simple sensorimotor mappings in a reaching task, but not for some other tasks in which multiple kinematic factors contribute to performance. The critical condition for learning in more complex tasks remains unclear....
Enregistré dans:
Auteurs principaux: | Katinka van der Kooij, Nina M. van Mastrigt, Emily M. Crowe, Jeroen B. J. Smeets |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/e17f23876a6e43f5bb08ca42b22748b5 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Joint speed feedback improves myoelectric prosthesis adaptation after perturbed reaches in non amputees
par: Eric J. Earley, et autres
Publié: (2021) -
G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes
par: Florent Le Borgne, et autres
Publié: (2021) -
Increased functional connectivity between prefrontal cortex and reward system in pathological gambling.
par: Saskia Koehler, et autres
Publié: (2013) -
A computational reward learning account of social media engagement
par: Björn Lindström, et autres
Publié: (2021) -
The influence of attention and reward on the learning of stimulus-response associations
par: Devavrat Vartak, et autres
Publié: (2017)