Motivated underpinnings of the impact bias in affective forecasts.

Emotion ◽  
2013 ◽  
Vol 13 (6) ◽  
pp. 1023-1029 ◽  
Author(s):  
Carey K. Morewedge ◽  
Eva C. Buechel
Author(s):  
John A. Aitken ◽  
Seth A. Kaplan ◽  
Olivia Pagan ◽  
Carol M. Wong ◽  
Eric Sikorski ◽  
...  

2010 ◽  
Author(s):  
Carey Morewedge ◽  
Eva Buechel ◽  
Joachim Vosgerau

2005 ◽  
Vol 14 (3) ◽  
pp. 131-134 ◽  
Author(s):  
Timothy D. Wilson ◽  
Daniel T. Gilbert

People base many decisions on affective forecasts, predictions about their emotional reactions to future events. They often display an impact bias, overestimating the intensity and duration of their emotional reactions to such events. One cause of the impact bias is focalism, the tendency to underestimate the extent to which other events will influence our thoughts and feelings. Another is people's failure to anticipate how quickly they will make sense of things that happen to them in a way that speeds emotional recovery. This is especially true when predicting reactions to negative events: People fail to anticipate how quickly they will cope psychologically with such events in ways that speed their recovery from them. Several implications are discussed, such as the tendency for people to attribute their unexpected resilience to external agents.


2013 ◽  
Vol 105 (5) ◽  
pp. 749-756 ◽  
Author(s):  
Linda J. Levine ◽  
Heather C. Lench ◽  
Robin L. Kaplan ◽  
Martin A. Safer
Keyword(s):  

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 41 ◽  
Author(s):  
Zahra Sahlaoui ◽  
Soumia Mordane

This study focused on investigating the impact of gauge adjustment on the rainfall estimate from a Moroccan C-band weather radar located in Khouribga City. The radar reflectivity underwent a quality check before deployment to retrieve the rainfall amount. The process consisted of clutter identification and the correction of signal attenuation. Thereafter, the radar reflectivity was converted into rainfall depth over a period of 24 h. An assessment of the accuracy of the radar rainfall estimate over the study area showed an overall underestimation when compared to the rain gauges (bias = −6.4 mm and root mean square error [RMSE] = 8.9 mm). The adjustment model was applied, and a validation of the adjusted rainfall versus the rain gauges showed a positive impact (bias = −0.96 mm and RMSE = 6.7 mm). The case study conducted on December 16, 2016 revealed substantial improvements in the precipitation structure and intensity with reference to African Rainfall Climatology version 2 (ARC2) precipitations.


2018 ◽  
Vol 106 ◽  
pp. 37-46 ◽  
Author(s):  
Kimberly A. Arditte Hall ◽  
Jutta Joormann ◽  
Matthias Siemer ◽  
Kiara R. Timpano

2011 ◽  
Author(s):  
Carey K. Morewedge ◽  
Eva C. Buechel ◽  
Joachim Vosgerau

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