Learning the value of information in an uncertain world.

Journal article

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.

Authors: Behrens, T; Woolrich, M; Walton, M; Rushworth, M

• Publication status: Published
• Journal: Nature neuroscience
• Volume: 10
• Issue: 9
• Pages: 1214-1221
• Publication date: 2007-09-01
• DOI: 10.1038/nn1954
• EISSN: 1546-1726
• ISSN: 1097-6256
• Language: English
• Keywords: Bayes Theorem; Adult; Humans; Adolescent; Oxygen; Image Processing, Computer-Assisted; Reinforcement (Psychology); Pattern Recognition, Visual; Reaction Time; Magnetic Resonance Imaging; Brain Mapping; Female; Gyrus Cinguli; Learning; Probability; Decision Making; Male; Photic Stimulation