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....
Saved in:
Main Authors: | Katinka van der Kooij, Nina M. van Mastrigt, Emily M. Crowe, Jeroen B. J. Smeets |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/e17f23876a6e43f5bb08ca42b22748b5 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Joint speed feedback improves myoelectric prosthesis adaptation after perturbed reaches in non amputees
by: Eric J. Earley, et al.
Published: (2021) -
G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes
by: Florent Le Borgne, et al.
Published: (2021) -
Increased functional connectivity between prefrontal cortex and reward system in pathological gambling.
by: Saskia Koehler, et al.
Published: (2013) -
A computational reward learning account of social media engagement
by: Björn Lindström, et al.
Published: (2021) -
The influence of attention and reward on the learning of stimulus-response associations
by: Devavrat Vartak, et al.
Published: (2017)