Need for cognition predicts the accuracy of affective forecasts

Abstract

Affective forecasting involves predicting our emotional response to a future event. Previous research has found that individuals tend to overestimate the intensity of their emotional reactions, a phenomenon referred to as impact bias. To explore individual differences in impact bias, this study draws on dual-process models that propose that cognition can be divided into analytical processing and an intuitive processing. The present study evaluates whether the magnitude of impact bias is moderated by an individual's need for cognition (NFC) as well as the situational demands under which the forecast is made. Results suggest that high NFC individuals make more accurate forecasts overall, but this depends on the situational demands present at the time of the forecast, with high NFC participants making more accurate forecasts when told to rely on their intuition, but less accurate forecasts when told to use visualization. The findings suggest that NFC may be an important determinant of affective forecasting accuracy, but the situational demands need to be considered in combination with individual differences.