One step at a time: analysis of neural responses during multi-state tasks
| dc.contributor.author | Grey, Talora Bryn | |
| dc.contributor.supervisor | Krigolson, Olave E. | |
| dc.contributor.supervisor | Fyshe, Alona | |
| dc.date.accessioned | 2020-04-29T03:35:29Z | |
| dc.date.available | 2020-04-29T03:35:29Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020-04-28 | |
| dc.degree.department | Interdisciplinary Graduate Program | |
| dc.degree.department | School of Exercise Science, Physical and Health Education | |
| dc.degree.department | Department of Psychology | |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | Substantial research has been done on the electroencephalogram (EEG) neural signals generated by feedback within a simple choice task, and there is much evidence for the existence of a reward prediction error signal generated in the anterior cingulate cortex of the brain when the outcome of this type of choice does not match expectations. However, less research has been done to date on the neural responses to intermediate outcomes in a multi-step choice task. Here, I investigated the neural signals generated by a complex, non-deterministic task that involved multiple choices before final win/loss feedback in order to see if the observed signals correspond to predictions made by reinforcement learning theory. In Experiment One, I conducted an EEG experiment to record neural signals while participants performed a computerized task designed to elicit the reward positivity, an event-related brain potential (ERP) component thought to be a biological reward prediction error signal. EEG results revealed a difference in amplitude of the reward positivity ERP component between experimental conditions comparing unexpected to expected feedback, as well as an interaction between valence and expectancy of the feedback. Additionally, results of an ERP analysis of the amplitude of the P300 component also showed an interaction between valence and expectancy. In Experiment Two, I used machine learning to classify epoched EEG data from Experiment One into experimental conditions to determine if individual states within the task could be differentiated based solely on the EEG data. My results showed that individual states could be differentiated with above-chance accuracy. I conclude by discussing how these results fit with the predictions made by reinforcement learning theory about the type of task investigated herein, and implications of those findings on our understanding of learning and decision-making in humans. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/11695 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | neuroscience | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | electroencephalogram | en_US |
| dc.subject | reward positivity | en_US |
| dc.subject | P300 | en_US |
| dc.subject | reinforcement learning | en_US |
| dc.subject | decision making | en_US |
| dc.subject | event-related brain potentials | en_US |
| dc.subject | support vector machines | en_US |
| dc.title | One step at a time: analysis of neural responses during multi-state tasks | en_US |
| dc.type | Thesis | en_US |