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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Predicting risky choices from brain activity patterns.

Sarah M Helfinstein1, Tom Schonberg, Eliza Congdon

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

Proceedings of the National Academy of Sciences of the United States of America
|February 20, 2014
PubMed
Summary
This summary is machine-generated.

Brain activity patterns can predict future risky decisions. This suggests that risk-taking may stem from failures in control systems needed for safe choices.

Keywords:
decision-makingfMRImachine learning

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Decision Science

Background:

  • Previous studies identified brain networks involved in risk processing during decision-making.
  • The predictive power of these brain regions for future choices remained unclear.

Purpose of the Study:

  • To investigate if brain activity patterns predict future risky choices.
  • To identify brain regions whose activity predicts risk-taking behavior.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) data from healthy subjects performing a risk-taking task.
  • Classification analysis and searchlight analysis to predict choices and identify predictive brain regions.

Main Results:

  • Choice category (risky vs. safe) was predicted with 71.8% accuracy.
  • A network of brain regions, including control-related areas, showed predictive activity for subsequent risk-taking.
  • Predictive regions were more active when preparing to avoid risk, suggesting control system involvement.

Conclusions:

  • Brain activity patterns can predict future risk-taking behavior.
  • Risk-taking may be partly due to failures in control systems supporting safe choices.
  • Risk-related information is encoded in both coarse and local brain activation patterns.