Uncertainty plays a critical role in reinforcement learning and decision making. However, exactly how it influences behaviour remains unclear. Multi-armed bandit tasks offer an ideal test-bed, since computational tools such as approximate Kalman filters can closely characterize the interplay between trial-by-trial values, uncertainty, learning, and choice. To gain additional insight into learning and choice processes we obtained data from subjects' overt allocation of gaze. The estimated value and estimation uncertainty of options influenced what subjects looked at before choosing; these same quantities also influenced choice, as additionally did fixation itself. A momentary measure of uncertainty in the form of absolute prediction errors determined how long participants looked at the obtained outcomes. These findings affirm the importance of uncertainty in multiple facets of behaviour, and help delineate its effects on decision making.
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