Adaptive Integration of Perceptual and Reward Information in an Uncertain World

Perceptual uncertainty and salience both impact decision-making, but how these factors precisely impact trial-and-error reinforcement learning is not well understood. Here, we test the hypotheses that (H1) perceptual uncertainty modulates reward-based learning and that (H2) economic decision-making is driven by the value and the salience of sensory information. For this, we combined computational modeling with a perceptual uncertainty-augmented reward-learning task in a human behavioral experiment (N = 98). In line with our hypotheses, we found that subjects regulated learning behavior in response to the uncertainty with which they could distinguish choice options based on sensory information (belief state), in addition to the errors they made in predicting outcomes. Moreover, subjects considered a combination of expected values and sensory salience for economic decision-making. Taken together, this shows that perceptual and economic decision-making are closely intertwined and share a common basis for behavior in the real world.