Volatile Bandit Experiment
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Background
The Volatile Bandit experiment is a classic paradigm in decision-making research that examines how people learn and adapt their choices in uncertain environments. This task is particularly useful for studying reinforcement learning, cognitive flexibility, and decision-making under uncertainty.In this experiment, participants are presented with two options (represented by blue and green shapes) and must learn through trial and error which option is more likely to provide rewards. The experiment consists of two phases: a static phase where one option is consistently better, and a volatile phase where the better option changes over time.
Method
Phase 1 (Static): 120 trials
- One color (blue or green) has a 75% chance of being correct
- The other color has a 25% chance of being correct
- Participants learn which color is generally better
Phase 2 (Volatile): 170 trials
- The better color switches every 30-40 trials
- Each color still has 60% vs 40% probability when it's the "better" option
- Participants must adapt to these changes
Scoring
- Correct choices earn points equal to the number displayed on the chosen shape
- Incorrect choices earn 0 points
- Goal is to reach the target line on the progress bar
References
- Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 1214-1221. 10.1038/nn1954
- Daw, N. D., O'Doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006). Cortical substrates for exploratory decisions in humans. Nature, 441(7095), 876-879. 10.1038/nature04766
- Nassar, M. R., Wilson, R. C., Heasly, B., & Gold, J. I. (2010). An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment. Journal of Neuroscience, 30(37), 12366-12378.
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Frequently Asked Questions
Have another question? Let us know.
What does the volatile bandit test?
Learning when reward probabilities change unpredictably — the reinforcement-learning logic of foraging in a changing environment.
How do good learners behave?
They track recent reward history and re-commit quickly after reversals, balancing stickiness (trusted evidence) against flexibility (detecting change).
What models describe this?
Delta-rule and Kalman-filter-style learners with dynamic learning rates; human adjustments are often well described by such models in computational psychiatry research.
What do my choices indicate?
Results describe your behavior in this session: how much your accuracy drops and recovers around reversals (adaptation speed) and how much reward you harvested. Single sessions are noisy, and no single strategy is best across all volatility regimes.
About This Experiment
This experiment is an educational adaptation for demonstration purposes. It is not a clinical diagnostic tool. Results reflect your performance in this specific session and should not be interpreted as an assessment of your cognitive abilities.
Data Privacy
Your experiment data is processed and stored locally in your browser. No data is uploaded to any server unless you explicitly export it.
