Impact bias or underestimation? Outcome specifications predict the direction of affective forecasting errors

Area of Science:

• Psychology
• Cognitive Science
• Behavioral Economics

Background:

• Affective forecasting guides decisions by predicting future emotional responses.
• Forecasters may inaccurately estimate the hedonic impact of future events.
• Sensitivity to event specifications (duration, magnitude, etc.) influences forecasting accuracy.

Purpose of the Study:

• To investigate how outcome specifications of events influence affective forecasting errors.
• To determine if event specifications can predict overestimation or underestimation of hedonic impact.
• To explore the role of affect-richness in forecasting and experiencing events.

Main Methods:

• Experiments manipulating outcome specifications (e.g., prize size, event duration, temporal distance).
• Comparing affective forecasts with actual experienced hedonic impact.
• Analyzing the correlation between specification values and forecasting errors.

Main Results:

• Forecasters overestimated the impact of high-specification events when positively correlated with hedonic impact.
• Forecasters underestimated the impact of low-specification events when positively correlated with hedonic impact.
• Errors reversed when specifications were negatively correlated with hedonic impact; affect-richness explained discrepancies.

Conclusions:

• Outcome specifications significantly predict affective forecasting errors.
• Differences in sensitivity to event details, influenced by affect-richness, explain impact bias and its reversal.
• Understanding these mechanisms can improve future affective forecasting accuracy.