We present a theory of choice in which attention to the features of options is determined by the decision maker’s categorization of the current problem in a set of problems she solved in the past. Categorization depends on goal-relevant and contextual problem-level features. The model yields heterogeneity in attention and choice in a given problem based on different past experiences and instability when changes in irrelevant context cause recategorization. We show that heterogeneous and unstable representations of a choice problem unify major biases in judgment and decision making.

For standard statistical problems, we provide new evidence documenting (1) multimodality and (2) instability in probability estimates, including from irrelevant changes in problem description. The evidence motivates a model in which, when solving a problem, people represent each hypothesis by attending to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used. The model unifies biases in judgments about i.i.d. draws, such as the Gambler's Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions for how changes in the salience of specific features jointly shapes known biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Salience-driven attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of Bayes' rule.

We embed diagnostic expectations in a workhorse neoclassical model with heterogeneous firms and risky debt. A realistic degree of overreaction estimated from US firms’ earnings forecasts generates realistic credit cycles. Good times produce economic and financial fragility, predicting future disappointment of expectations, low bond returns, and investment declines. To generate the size of spread increases observed during 2007–2009, the model requires only moderate negative shocks. Diagnostic expectations offer a realistic, parsimonious way to produce financial reversals in business cycle models.