Conference on Cognitive Computational Neuroscience

San Francisco • August 25 - 28, 2022

P-1.29

Modeling Risk and Reward Expectation and Surprise using Optimal Learning Rates in Human Neuronal Populations to assess Impulsive Choice

Rhiannon Cowan, Tyler Davis, Bornali Kundu, John Rolston, Elliot Smith, University of Utah, United States
Session:
Posters 1 Poster
Track:
Cognitive science
Location:
Pacific Ballroom H-O
Presentation Time:
Thu, 25 Aug, 19:30 - 21:30 Pacific Time (UTC -8)
Abstract:
Impulsive choice is a facet of impulsivity that may lead to one choosing smaller, immediate rewards over larger, delayed rewards. We examined impulsive choice behavior in humans undergoing intracranial seizure monitoring by fitting reinforcement learning models to behavior and broadband high frequency (70-150Hz) local field potentials. We found neural and behavioral differences between more and less impulsive choosers, informing the neural underpinnings of impulsive choices.
Manuscript:
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License:
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI:
10.32470/CCN.2022.1118-0
Publication:
2022 Conference on Cognitive Computational Neuroscience
Presentation
Discussion
Resources
No resources available.
Session P-1
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