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Our decisions are guided by outcomes that are associated with decisions made in the past. However, the amount of influence each past outcome has on our next decision remains unclear. To ensure optimal decision-making, the weight given to decision outcomes should reflect their salience in predicting future outcomes, and this salience should be modulated by the volatility of the reward environment. We show that human subjects assess volatility in an optimal manner and adjust decision-making accordingly. This optimal estimate of volatility is reflected in the fMRI signal in the anterior cingulate cortex (ACC) when each trial outcome is observed. When a new piece of information is witnessed, activity levels reflect its salience for predicting future outcomes. Furthermore, variations in this ACC signal across the population predict variations in subject learning rates. Our results provide a formal account of how we weigh our different experiences in guiding our future actions.

Original publication




Journal article


Nat Neurosci

Publication Date





1214 - 1221


Adolescent, Adult, Bayes Theorem, Brain Mapping, Decision Making, Female, Gyrus Cinguli, Humans, Image Processing, Computer-Assisted, Learning, Magnetic Resonance Imaging, Male, Oxygen, Pattern Recognition, Visual, Photic Stimulation, Probability, Reaction Time, Reinforcement (Psychology)