Anticipating Regret: The role of affect vs cognition and construal level on the forecaster’s impact bias
Document Type
Poster
Campus where you would like to present
Ellensburg
Event Website
https://digitalcommons.cwu.edu/source
Start Date
18-5-2020
Abstract
Affective forecasting errors are common, regularly influencing motivation and behavior (Wilson & Gilbert; 2003, 2005). Forecasting errors of regret specifically suggest that more anticipated regret encourages decision avoidance (Zeelenberg & Pieters, 2007). However, recent research delineates two elements of regret: an affective (feelings following a negative outcome) and cognitive (thoughts/decisions related to the outcome) component, which predict maladaptive and advantageous outcomes, respectively (Buchanan et al., 2016). Across 3 studies, we examined forecasting errors of these components of regret. In Study 1, using the Regret Element Scale (Buchanan et al., 2016) to measure each component, we examined anticipated and experienced regret in response to a manufactured negative outcome. Supporting our hypotheses, participants anticipated more overall/affective regret than was experienced (impact bias). No forecasting errors occurred for the cognitive component. Therefore, in Study 2, we examined if focusing on cognitive aspects of regret could reduce forecasting errors. Participants either experienced failure (as in Study 1) or forecasted failure while focusing on affective or cognitive aspects of the situation. Replicating Study 1, forecasters anticipated more regret than experiencers felt. However, errors were eliminated for forecasters focused on cognitive aspects. In Study 3, we examined the role of construal level in explaining the elimination of forecasting errors by replicating Study 2 while adding a state measure of construal level. Although previous research suggests abstract construals contribute to forecasting errors (Ayton, Pott, & Elwakili, 2007), we found no significant difference in construal level between experimental conditions. Implications and applications of this work will be discussed.
Recommended Citation
Zimmerman, Annie, "Anticipating Regret: The role of affect vs cognition and construal level on the forecaster’s impact bias" (2020). Symposium Of University Research and Creative Expression (SOURCE). 117.
https://digitalcommons.cwu.edu/source/2020/COTS/117
Department/Program
Psychology
Additional Mentoring Department
https://cwu.studentopportunitycenter.com/2020/04/anticipating-regret-the-role-of-affect-vs-cognition-and-construal-level-on-the-forecasters-impact-bias/
Anticipating Regret: The role of affect vs cognition and construal level on the forecaster’s impact bias
Ellensburg
Affective forecasting errors are common, regularly influencing motivation and behavior (Wilson & Gilbert; 2003, 2005). Forecasting errors of regret specifically suggest that more anticipated regret encourages decision avoidance (Zeelenberg & Pieters, 2007). However, recent research delineates two elements of regret: an affective (feelings following a negative outcome) and cognitive (thoughts/decisions related to the outcome) component, which predict maladaptive and advantageous outcomes, respectively (Buchanan et al., 2016). Across 3 studies, we examined forecasting errors of these components of regret. In Study 1, using the Regret Element Scale (Buchanan et al., 2016) to measure each component, we examined anticipated and experienced regret in response to a manufactured negative outcome. Supporting our hypotheses, participants anticipated more overall/affective regret than was experienced (impact bias). No forecasting errors occurred for the cognitive component. Therefore, in Study 2, we examined if focusing on cognitive aspects of regret could reduce forecasting errors. Participants either experienced failure (as in Study 1) or forecasted failure while focusing on affective or cognitive aspects of the situation. Replicating Study 1, forecasters anticipated more regret than experiencers felt. However, errors were eliminated for forecasters focused on cognitive aspects. In Study 3, we examined the role of construal level in explaining the elimination of forecasting errors by replicating Study 2 while adding a state measure of construal level. Although previous research suggests abstract construals contribute to forecasting errors (Ayton, Pott, & Elwakili, 2007), we found no significant difference in construal level between experimental conditions. Implications and applications of this work will be discussed.
https://digitalcommons.cwu.edu/source/2020/COTS/117
Faculty Mentor(s)
Tonya Buchanan