Literature DB >> 27596541

Dopamine dependence in aggregate feedback learning: A computational cognitive neuroscience approach.

Vivian V Valentin1, W Todd Maddox2, F Gregory Ashby3.   

Abstract

Procedural learning of skills depends on dopamine-mediated striatal plasticity. Most prior work investigated single stimulus-response procedural learning followed by feedback. However, many skills include several actions that must be performed before feedback is available. A new procedural-learning task is developed in which three independent and successive unsupervised categorization responses receive aggregate feedback indicating either that all three responses were correct, or at least one response was incorrect. Experiment 1 showed superior learning of stimuli in position 3, and that learning in the first two positions was initially compromised, and then recovered. An extensive theoretical analysis that used parameter space partitioning found that a large class of procedural-learning models, which predict propagation of dopamine release from feedback to stimuli, and/or an eligibility trace, fail to fully account for these data. The analysis also suggested that any dopamine released to the second or third stimulus impaired categorization learning in the first and second positions. A second experiment tested and confirmed a novel prediction of this large class of procedural-learning models that if the to-be-learned actions are introduced one-by-one in succession then learning is much better if training begins with the first action (and works forwards) than if it begins with the last action (and works backwards).
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computational cognitive neuroscience; Dopamine; Parameter space partitioning; Skill learning; Striatal plasticity

Mesh:

Substances:

Year:  2016        PMID: 27596541      PMCID: PMC5077633          DOI: 10.1016/j.bandc.2016.06.002

Source DB:  PubMed          Journal:  Brain Cogn        ISSN: 0278-2626            Impact factor:   2.310


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