Building on a textbook description of associative memory (Kahana 2012), we present a model of choice in which a choice option cues recall of similar past experiences. Memory shapes valuation and decisions in two ways. First, recalled experiences form a norm, which serves as an initial anchor for valuation. Second, salient quality and price surprises relative to the norm lead to large adjustments in valuation. The model unifies many well-documented choice puzzles, including the attribution and projection biases, inattention to hidden attributes, background contrast effects, and context-dependent willingness to pay. Unifying these puzzles on the basis of selective memory and attention to surprise yields multiple new predictions.

We study the rationality of individual and consensus forecasts of macroeconomic and financial variables using the methodology of Coibion and Gorodnichenko (2015), who examine predictability of forecast errors from forecast revisions. We find that individual forecasters typically overreact to news, while consensus forecasts under-react relative to full-information rational expectations. We reconcile these findings within a diagnostic expectations version of a dispersed information learning model. Structural estimation indicates that departures from Bayesian updating in the form of diagnostic overreaction capture important variation in forecast biases across different series, yielding a belief distortion parameter similar to estimates obtained in other settings.