Affective forecasting as an adaptive learning process

Theories propose that human affective forecasting is an adaptive learning process guided by prediction errors. Although this learning process can be formally described by a Kalman filter, human forecasts are suggested to be biased and computationally suboptimal. We compared the accuracy of human affective forecasts to statistical forecasts made using a Kalman filter and explored the differences between these two processes. Participants (from the general population) repeatedly rated current levels of affect and forecasted levels of affect that they would experience 2-3 hr later (Study 1, n = 62), 1 min later (Study 2a, n = 91), and 1-2 hr later (Study 2b, n = 87), in daily life or in experimental settings. Results showed that compared to statistical forecasts, the participants' forecasts showed larger absolute errors in hour-long forecasting (dz = 0.42 and 0.30) but not in minute-long forecasting (dz = 0.17). Relative errors were also evaluated in each study, showing no differences in Studies 1 and 2b (hour-long forecasting in daily life) but more optimistic errors in participants' than statistical forecasts in Study 2a (minute-long forecasting in an experimental setting). Across the three studies, participants exhibited a strong tendency to project their current affective experience onto a new forecast, and this may explain human-specific forecasting errors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).