Specialized neural circuits supporting reinforcement learning
Abstract/Contents
- Abstract
- A fundamental challenge facing organisms is to learn how to act in order to earn rewards. Theoretical and empirical work has characterized distinct neural systems that learn different classes of information and assist learning in distinct behavioral contexts. In this thesis, I argue that these sources of information are integrated within the striatum, a region of the basal ganglia known to be critical for associative learning. Neurons in the striatum store the motivational value of stimuli in the environment and support the expression of habitual behavioral responses to these stimuli. Based on anatomy, the striatum is also known to receive direct afferent input from diverse cortical and subcortical sites. I report the results of two studies designed to characterize the contribution of two such afferent regions, the inferior frontal cortex and the hippocampus, to reward learning. Subjects in my experiments performed tasks in which they were required to learn the relationship between sets of visual features and motor responses. These tasks were designed to emphasize different types of relationships that draw on the computational properties known to be uniquely supported by the inferior frontal sulcus and hippocampus. I developed computational models of learning adapted to the demands of the tasks and used functional magnetic resonance imaging (fMRI) to relate brain activity to the variables in these models. In Study 1, I show that interactions between the striatum and the inferior frontal cortex support the learning of abstract rules. In Study 2, I show that interactions between the striatum and the hippocampus support the learning of conjunctive relationships. These results provide novel evidence that different learning systems interact in cases where the problem draws on multiple types of representations.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ballard, Ian Connors |
|---|---|
| Degree supervisor | McClure, Samuel M |
| Degree supervisor | Wagner, Anthony David |
| Thesis advisor | McClure, Samuel M |
| Thesis advisor | Wagner, Anthony David |
| Thesis advisor | Goodman, Noah |
| Thesis advisor | Newsome, William T |
| Thesis advisor | Poldrack, Russell A |
| Degree committee member | Goodman, Noah |
| Degree committee member | Newsome, William T |
| Degree committee member | Poldrack, Russell A |
| Associated with | Stanford University, Neurosciences Program. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ian Connors Ballard. |
|---|---|
| Note | Submitted to the Neurosciences Program. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Ian Connors Ballard
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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