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Default mode network activity is related to efficiency in a combined motion error and gambling task
Gregory V Chernov1, Mikhail Y Mel'nikov2, Alexis V Belianin3,4
1Department of Economics, University College London, London, WC1H 0AX, UK. g.chernov@ucl.ac.uk.
Scientific Reports
|January 6, 2026
Summary
This study reveals that the Default Mode Network (DMN), not the striatum, is key for successful risky decision-making. Its activity predicts effective learning and exploration in uncertain environments.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Decision Science
Background:
- Decision-making under risk is crucial for strategic actions but its neural basis in trial-and-error learning without explicit risk ratios remains unclear.
- Understanding the brain's mechanisms for navigating uncertainty is vital for fields ranging from economics to artificial intelligence.
Purpose of the Study:
- To investigate the neural correlates of long-term success in repeated risky decision-making tasks.
- To identify brain regions associated with effective risk-taking and learning in the absence of explicit risk information.
Main Methods:
- Utilized functional magnetic resonance imaging (fMRI) combined with a motion detection and a higher-level risk task.
- Participants (N=25) chose task difficulty linked to reward, then performed the task, receiving feedback.
Main Results:
- Positive feedback (gain) activated brain regions including the striatum, executive control areas, and the Default Mode Network (DMN).
- Activity within DMN nodes, associated with metacognitive functions, positively correlated with the exploration index (a measure of effective risk-taking).
- Striatal activity, linked to reward and risk evaluation, did not show this positive relationship with exploration effectiveness.
Conclusions:
- The Default Mode Network (DMN), particularly its anterior node, plays a significant role in high-level decision-making within risky environments.
- DMN activity is highlighted as important for metacognitive control during reward-based learning, suggesting its crucial function in optimizing exploration strategies.
