Dunovan, Kyle
(2017)
A biologically motivated synthesis of accumulator and reinforcement-learning models for describing adaptive decision-making.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Cognitive process models, such as reinforcement learning (RL) and accumulator models of decision-making, have proven to be highly insightful tools for studying adaptive behaviors as well as their underlying neural substrates. Currently, however, two major barriers exist preventing these models from being applied in more complex settings: 1) the assumptions of most accumulator models break down for decisions involving more than two alternatives; 2) RL and accumulator models currently exist as separate frameworks, with no clear mapping between trial-to-trial learning and the dynamics of the decision process. Recently I showed how a modified accumulator model, premised off of the architecture of cortico-basal ganglia pathways, both predicts human decisions in uncertain situations and evoked activity in cortical and subcortical control circuits. Here I present a synthesis of RL and accumulator models that is motivated by recent evidence that the basal ganglia acts as a site for integrating trial-wise feedback from midbrain dopaminergic neurons with accumulating evidence from sensory and associative cortices. I show how this hybrid model can explain both adaptive go/no-go decisions and multi-alternative decisions in a computationally efficient manner. More importantly, by parameterizing the model to conform to various underlying assumptions about the architecture and physiology of basal ganglia pathways, model predictions can be rigorously tested against observed patterns in behavior as well as neural recordings. The result is a biologically-constrained and behaviorally tractable description of trial-to-trial learning effects on decision-making among multiple alternatives.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
| Title | Member | Email Address | Pitt Username | ORCID |
|---|
| Committee Chair | Fiez, Julie | | | | | Committee CoChair | Verstynen, Timothy | | | | | Committee Member | Tokowicz, Nataha | | | | | Committee Member | Luna, Beatriz | | | | | Committee Member | Rubin, Jonathan | | | |
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| Date: |
20 January 2017 |
| Date Type: |
Publication |
| Defense Date: |
6 December 2016 |
| Approval Date: |
20 January 2017 |
| Submission Date: |
9 December 2016 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
106 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Psychology |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
decision-making; accumulator models; reinforcement learning; basal ganglia; computational modeling |
| Date Deposited: |
20 Jan 2017 19:32 |
| Last Modified: |
21 Jan 2017 06:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/30628 |
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A biologically motivated synthesis of accumulator and reinforcement-learning models for describing adaptive decision-making. (deposited 20 Jan 2017 19:32)
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