The Attentional Learning Trap and How to Avoid It

Abstract

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.


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