Explanatory Biases in Social Categorization

Stereotypes are important simplifying assumptions we use for navigating the social world, associating traits with social categories. These beliefs can be used to infer an individual’s likely social category from observed traits (a diagnostic inference) or to make inferences about an individual’s unknown traits based on their putative social category (a predictive inference). We argue that these inferences rely on the same explanatory logic as other sorts of diagnostic and predictive reasoning tasks, such as causal explanation. Supporting this conclusion, we demonstrate that stereotype use involves four of the same biases known to be used in causal explanation: A bias against categories making unverified predictions (Exp. 1), a bias toward simple categories (Exp. 2), an asymmetry between confirmed and disconfirmed predictions of potential categories (Exp. 3), and a tendency to treat uncertain categorizations as certainly true or false (Exp. 4).