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Model-Based Decision Making in the Human Brain


Hampton, Alan Nicolás (2007) Model-Based Decision Making in the Human Brain. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/K9Z8-E248.


Many real-life decision making problems incorporate higher-order structure, involving interdependencies between different stimuli, actions, and subsequent rewards. It is not known whether brain regions implicated in decision making, such as ventromedial prefrontal cortex, employ a stored model of the task structure to guide choice (model-based decision making) or merely learn action or state values without assuming higher-order structure, as in standard reinforcement learning. To discriminate between these possibilities we scanned human subjects with fMRI while they performed two different decision making tasks with higher-order structure: probabilistic reversal learning, in which subjects had to infer which of two choices was the more rewarding and then flexibly switch their choice when contingencies changed; and the inspection game, in which subjects had to successfully compete against an intelligent adversary by mentalizing the opponent’s state of mind in order to anticipate the opponent’s behavior in future. For both tasks we found that neural activity in a key decision making region: ventromedial prefrontal cortex, was more consistent with computational models that exploit higher-order structure, than with simple reinforcement learning. Moreover, in the social interaction game, subjects were found to employ a sophisticated strategy whereby they used knowledge of how their actions would influence the actions of their opponent to guide their choices. Specific computational signals required for the implementation of such a strategy were present in medial prefrontal cortex and superior temporal sulcus, providing insight into the basic computations underlying competitive strategic interactions. These results suggest that brain regions such as ventromedial prefrontal cortex employ an abstract model of task structure to guide behavioral choice, computations that may underlie the human capacity for complex social interactions and abstract strategizing.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:amygdala; lesion; theory of mind
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computation and Neural Systems
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • O'Doherty, John P.
Thesis Committee:
  • Bossaerts, Peter L. (chair)
  • O'Doherty, John P.
  • Adolphs, Ralph
  • Shimojo, Shinsuke
  • Quartz, Steven R.
Defense Date:17 May 2007
Record Number:CaltechETD:etd-05312007-113932
Persistent URL:
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:2337
Deposited By: Imported from ETD-db
Deposited On:31 May 2007
Last Modified:17 Mar 2020 18:27

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