More affect, less forecast: How mood contributes to affective forecasting.

摘要

The affective forecasting literature explores emotion predictions that are made when people imagine affective reactions to future events. This dissertation examines the possible role of Mood-as-Information (MaI) in affective forecasting. This idea is explored in four studies. Replicating and extending the methodologies used in existing affective forecasting research, Study 1 examines whether completion of typical affective forecasting tasks alters mood and assesses whether such mood alterations are a plausible mediator of affective forecasts. Study 2 assesses whether a diary task commonly used to reduce the extremity of affective forecasts similarly alters mood. Study 3 examines whether mood causally influences affective forecasts: Each participant's mood is manipulated experimentally, and the effects of such manipulations on affective forecasts are assessed. Drawing on research suggesting that knowledge of the source of one's moods can blunt MaI effects, Study 4a manipulated both mood and the salience of the mood's source. When mood source salience is low, one might expect typical MaI effects to emerge in affective forecasts; when mood source salience is high, such effects should not emerge. Study 4b replicated study 4a using a stronger mood salience manipulation: Neither study yielded the expected moderation effects, but both showed that induced moods alter affective forecasts.