We study the impact of manipulating the attention of a decision-maker who learns sequentially about a number of items before making a choice. Under natural assumptions on the decision-maker's strategy, forcing attention toward one item increases its likelihood of being chosen.

We study maximum-likelihood estimation and updating, subject to computational, cognitive, or behavioral constraints. We jointly characterize constrained estimates and updating within a framework reminiscent of a machine learning algorithm. Without frictions, the framework simplifies to standard...