The anterior cingulate cortex (ACC) has been implicated in a number of functions, including performance monitoring and decision-making involving effort. The prediction of responses and outcomes (PRO) model has provided a unified account of much human and monkey ACC data involving anatomy, neurophysiology, EEG, fMRI, and behavior. We explored the computational nature of ACC with the PRO model, extending it to account specifically for both human and macaque monkey decision-making under risk, including both behavioral and neural data. We show that the PRO model can account for a number of additional effects related to outcome prediction, decision-making under risk, gambling behavior. In particular, we show that the ACC represents the variance of uncertain outcomes, suggesting a link between ACC function and mean-variance theories of decision making. The PRO model provides a unified account of a large set of data regarding the ACC.
Bibliographical noteFunding Information:
The authors thank Meghan Castagno, Marc Mancarella, and Caleb Strait for assistance with data collection. They also thank Priya Modak for helpful comments. This research was funded by a National Science Foundation grant (grant number: BCS1253576) received by BYH, and a National Institutes of Health grant (grant number: DA038615) received by BYH.
© 2022, The Psychonomic Society, Inc.
- Computational model
- Prefrontal cortex
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, U.S. Gov't, Non-P.H.S.