Predicting risky choices from brain activity patterns
Abstract
Previous studies have examined the neural correlates of risk, but it is unknown if patterns of brain activity can predict choices in risky decision-making. We used functional MRI data to predict choice behavior in subjects while they performed a naturalistic risk-taking task. We found choices on subsequent trials could be predicted with high accuracy when condensing each individual's brain activity to two values, indicating that choice behavior is encoded even in coarse activation patterns. A searchlight analysis demonstrated that choices can also be predicted based on localized activity patterns within neural networks involved in cognitive control. These regions show greater activation prior to safe choices than risky choices, suggesting that control systems play a key role in inhibiting risky choices.
- Publication:
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Proceedings of the National Academy of Science
- Pub Date:
- February 2014
- DOI:
- 10.1073/pnas.1321728111
- Bibcode:
- 2014PNAS..111.2470H