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Proc Natl Acad Sci U S A. 2014 Feb 18;111(7):2470-5. doi: 10.1073/pnas.1321728111. Epub 2014 Feb 3.

Predicting risky choices from brain activity patterns.

Author information

1
Imaging Research Center, and Departments of Psychology and Neuroscience, University of Texas at Austin, Austin, TX 78712.

Abstract

Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights.

KEYWORDS:

decision-making; fMRI; machine learning

PMID:
24550270
PMCID:
PMC3932884
DOI:
10.1073/pnas.1321728111
[Indexed for MEDLINE]
Free PMC Article

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