Computational Neural Models of Risk
Abstract
The central aim of the project was to develop computational models of how individual decision-makers learn in real time to anticipate and take into account the risks and potential consequences of their actions. The main focus was on the medial prefrontal cortex (mPFC), an area of the brain known to signal mistakes as well as the level of difficulty or conflict facing the decision-maker. The research effort involved iteratively developing computational models and testing their predictions with fMRI, leading to further refinements of the model. The original goal of developing a model of risk prediction was achieved. Further effort yielded a more general model of how both good and bad potential consequences are learned and anticipated. The model predictions were validated by numerous behavioral and fMRI studies, and the effort also yielded an exact recursive model of hyperbolic temporal discounting. The results overall provide a new and relatively simple computational model of consequence prediction that accounts for and predicts a wide array of empirical data and is well-grounded in the known neurobiologically.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 24, 2010
- Accession Number
- ADA515423
Entities
People
- Joshua W. Brown
Organizations
- Indiana University