When and why people misestimate future feelings: Identifying strengths and weaknesses in affective forecasting.

Abstract

People try to make decisions that will improve their lives and make them happy, and to do so, they rely on affective forecasts-predictions about how future outcomes will make them feel. Decades of research suggest that people are poor at predicting how they will feel and that they commonly overestimate the impact that future events will have on their emotions. Recent work reveals considerable variability in forecasting accuracy. This investigation tested a model of affective forecasting that captures this variability in bias by differentiating emotional intensity, emotional frequency, and mood. Two field studies examined affective forecasting in college students receiving grades on a midterm exam (Study 1, N ϭ 643), and U.S. citizens after the outcome of the 2016 presidential election (Study 2, N ϭ 706). Consistent with the proposed model, participants were more accurate in forecasting the intensity of their emotion and less accurate in forecasting emotion frequency and mood. Overestimation of the effect of the event on mood increased over time since the event. Three experimental studies examined mechanisms that contribute to differential forecasting accuracy. Biases in forecasting intensity were caused by changes in perceived event importance; biases in forecasting frequency of emotion were caused by changes in the frequency of thinking about the event. This is the first direct evidence mapping out strengths and weaknesses for different types of affective forecasts and the factors that contribute to this pattern.

Predicted and remembered emotion: tomorrow’s vividness trumps yesterday’s accuracy

“…This study was part of a larger investigation of the accuracy with which people forecast different features of their emotional experience. Hypotheses for the larger project are addressed in another paper (Lench et al, 2019). The main hypotheses for the current investigation were described in a published review (Levine et al, 2018) but analyses were not preregistered.…”

“…First, participants both predicted and remembered the intensity of their feelings about Trump's election fairly accurately. Past research has also shown high accuracy when people predict or remember the intensity of their feelings about events (e.g., Doré et al, 2016;Kaplan et al, 2016;Lench et al, 2019;Levine et al, 2012). Far less accuracy is found when people predict or remember their general emotional experience, a judgment that encompasses multiple features of emotion including intensity, duration, and mood (e.g., Wilson et al, 2000).…”

Incorporating emotion into cue-based political judgment modeling.

“…This second prompt specifically references the emotion-eliciting event and is consistent with wording in affective forecasting measures (e.g., Kermer et al, 2006;Wilson & Gilbert, 2003, 2005Wiener et al, 2014). To address the methodological issues that Lench et al (2019) raised, all participants completed measures that asked them how the policy 6 HOLLOWAY AND WIENER (affects/will affect) their overall mood, and how frequently they will think about the policy (right now/if the government pursues it). By asking about the effects of the policy, itself, this study avoided methodological concerns in the affective forecasting literature pertaining to question wording biasing results (Lenchet al, 2019).…”

Negative valence effect in affective forecasting: The unique impact of the valence among dispositional and contextual factors for certain life events

“…Recently, some authors have raised concerns about the usual means of measuring and interpreting affective forecasting inaccuracies, challenging the significance and validity of the intensity bias ( Lench et al, 2019 ; Levine et al, 2012 , 2013 ; Mathieu & Gosling, 2012 ).…”