Related Experiment Video
Updated: Dec 21, 2025
13:04
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
12.3K
Representation of probabilistic outcomes during risky decision-making
Giuseppe Castegnetti1,2,3, Athina Tzovara4,5,6,7,8, Saurabh Khemka4,5
1Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland. g.castegnetti@gmail.com.
Nature Communications
|May 17, 2020
Summary
This study shows that the brain retrieves potential rewards and losses sequentially when making decisions under risk. This sequential outcome retrieval influences the final action choice.
Area of Science:
- Neuroscience
- Cognitive Science
- Decision Science
Background:
- Goal-directed behavior relies on evaluating potential action outcomes.
- Previous research suggests sequential outcome retrieval for deterministic choices.
- The process for probabilistic outcomes remains less understood.
Purpose of the Study:
- To investigate if sequential retrieval applies to integrating multiple probabilistic outcomes.
- To decode neural representations of outcomes during risky decision-making.
Main Methods:
- Magnetoencephalography (MEG) was used in human participants performing a risky foraging task.
- Machine learning classifiers decoded MEG patterns representing reward and loss outcomes.
- Decoded patterns were analyzed during the deliberation phase.
Main Results:
- Outcome representations showed a temporal, alternating structure, suggesting sequential retrieval.
- The likelihood of representing an outcome depended on loss magnitude, not probability.
- Neural activity patterns predicted the participant's chosen action.
Conclusions:
- Decodable, sequentially structured outcome representations exist during probabilistic decision-making.
- These representations are influenced by task features like loss magnitude.
- Neural decoding of outcomes predicts subsequent action selection.
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