Overcorrection for Social-Categorization Information Moderates Impact Bias in Affective Forecasting

Abstract

Plural societies require individuals to forecast how others—both in-group and out-group members—will respond to gains and setbacks. Typically, correcting affective forecasts to include more relevant information improves their accuracy by reducing their extremity. In contrast, we found that providing affective forecasters with social-category information about their targets made their forecasts more extreme and therefore less accurate. In both political and sports contexts, forecasters across five experiments exhibited greater impact bias for both in-group and out-group members (e.g., a Democrat or Republican) than for unspecified targets when predicting experiencers’ responses to positive and negative events. Inducing time pressure reduced the extremity of forecasts for group-labeled but not unspecified targets, which suggests that the increased impact bias was due to overcorrection for social-category information, not different intuitive predictions for identified targets. Finally, overcorrection was better accounted for by stereotypes than by spontaneous retrieval of extreme group exemplars.