People often make repeated decisions from experience. In such scenarios, persistent biases of choice can develop, most notably the “hot stove effect” (Denrell & March, 2001) in which a prospect that is mistakenly believed to be negative is avoided and thus belief-correcting information is never obtained. In the existing literature, the hot stove effect is generally thought of as developing through interaction with a single, stochastic prospect. Here, we show how a similar bias can develop due to people’s tendency to selectively attend to a subset of features during categorization. We first explore the bias through model simulation, then report on an experiment in which we find evidence of a decisional bias linked to selective attention. Finally, we use these computational models to design novel interventions to “de-bias” decision-makers, some of which may have practical application.