Considering What We Know and What We Don't Know: Expectations and Confidence Guide Value Integration in Value-Based Decision-Making.

When making decisions, we often have more information about some options than others. Previous work has shown that people are more likely to choose options that they look at more and those that they are more confident in. But should one always prefer options one knows more about? Intuition suggests not. Rather, how additional information impacts our preferences should depend critically on how valuable we expect the options to be. Here, we formalize this intuition in a Bayesian sequential sampling model where attention and confidence influence the precision of momentary evidence. Our model makes a key prediction: attention and confidence both increase choice probability for better-than-average options, and both decrease choice probability for worse-than-average options. We confirm this prediction in two experiments in which we independently manipulate value and attention. Our results offer a novel perspective on prior work on the role of attention and confidence in decision-making, showing that people rely on contextual knowledge and uncertainty estimates to adaptively learn about their options and make better decisions.