A computational psychiatry approach identifies how alpha-2a noradrenergic agonist guanfacine affects feature-based reinforcement learning in the macaque
Publication year
2017Author(s)
Number of pages
19 p.
Source
Scientific Reports, 7, (2017), pp. 1-19, article 40606ISSN
Publication type
Article / Letter to editor

Display more detailsDisplay less details
Organization
Neuroinformatics
Journal title
Scientific Reports
Volume
vol. 7
Page start
p. 1
Page end
p. 19
Subject
NeuroinformaticsAbstract
Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework.
This item appears in the following Collection(s)
- Academic publications [202914]
- Electronic publications [101091]
- Faculty of Science [31885]
- Open Access publications [69755]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.