We present a model of market competition in which consumers' attention is drawn to the products' most salient attributes. Firms compete for consumer attention via their choices of quality and price. Strategic positioning of a product affects how all other products are perceived. With this attention externality, depending on the cost of producing quality some markets exhibit "commoditized" price salient equilibria, while others exhibit "de-commoditized" quality salient equilibria. When the costs of quality change, innovation can lead to radical shifts in markets, as in the case of decommoditization of the coffee market by Starbucks. In the context of financial innovation, the model generates the phenomenon of "reaching for yield".

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.

How do people form beliefs about novel risks, with which they have little or no experience? Motivated by survey data on beliefs about COVID we collected in 2020, we build a model based on the psychology of selective memory. When a person thinks about an event, different experiences compete for retrieval, and retrieved experiences are used to simulate the event based on how similar they are to it. The model predicts that different experiences interfere with each other in recall and that non-domain-specific experiences can bias beliefs based on their similarity to the assessed event. We test these predictions using data from our COVID survey and from a primed-recall experiment about cyberattack risk. In line with our theory of similarity-based retrieval and simulation, experiences and their measured similarity to the cued event help account for experience effects, priming effects, and the interaction of the two in shaping beliefs.