Citation
Gallo Aquino, Tomas (2022) Single Neuron Correlates of Learning, Value, and Decision in the Human Brain. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/has2-gk35. https://resolver.caltech.edu/CaltechTHESIS:03042022-195852401
Abstract
In this thesis, I present several new results on how the human brain performs value-based learning and decision-making, leveraging rare single neuron recordings from epilepsy patients in vmPFC, preSMA, dACC, amygdala, and hippocampus, as well as reinforcement learning models of behavior. With a probabilistic gambling task we determined that human preSMA neurons integrate computational components of stimulus value such as expected values, uncertainty, and novelty, to encode an utility value and, subsequently, decisions themselves. Additionally, we found that post-decision related encoding of variables for the chosen option was more widely distributed and especially prominent in vmPFC. Additionally, with a Pavlovian conditioning task we found evidence of stimulus-stimulus associations in vmPFC, while both vmPFC and amygdala performed predictive value coding, establishing direct evidence for model-based Pavlovian conditioning in human vmPFC neurons. Finally, in a Pavlovian observational learning paradigm, we found a significant proportion of amygdala neurons whose activity correlated with both expected rewards for oneself and others, and in tracking outcome values received by oneself or other agents, further establishing amygdala as an important center in social cognition. Taken together, our findings expand our understanding of the role of several human cortical brain regions in creating and updating value representations which are leveraged during decision-making.
Item Type: | Thesis (Dissertation (Ph.D.)) | ||||||||||
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Subject Keywords: | reinforcement learning, human single neurons, electrophysiology, reward learning, decision making, amygdala, preSMA, hippocampus, vmPFC, Pavlovian conditioning, exploration, uncertainty, novelty, observational learning | ||||||||||
Degree Grantor: | California Institute of Technology | ||||||||||
Division: | Biology and Biological Engineering | ||||||||||
Major Option: | Computation and Neural Systems | ||||||||||
Thesis Availability: | Public (worldwide access) | ||||||||||
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Defense Date: | 20 January 2022 | ||||||||||
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Record Number: | CaltechTHESIS:03042022-195852401 | ||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechTHESIS:03042022-195852401 | ||||||||||
DOI: | 10.7907/has2-gk35 | ||||||||||
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Default Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||
ID Code: | 14512 | ||||||||||
Collection: | CaltechTHESIS | ||||||||||
Deposited By: | Tomas Gallo Aquino | ||||||||||
Deposited On: | 12 Mar 2022 00:05 | ||||||||||
Last Modified: | 13 Jul 2022 18:34 |
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