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) 2023 APA, all rights reserved).

中文翻译：

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情感预测作为一种适应性学习过程。

理论认为，人类情感预测是一个由预测误差引导的自适应学习过程。尽管这个学习过程可以通过卡尔曼滤波器来正式描述，但人类的预测被认为是有偏差的并且计算上不是最优的。我们将人类情感预测的准确性与使用卡尔曼滤波器进行的统计预测进行了比较，并探讨了这两个过程之间的差异。参与者（来自普通人群）反复评估当前的影响水平以及 2-3 小时后（研究 1，n = 62）、1 分钟后（研究 2a，n = 91）和 1 后他们将经历的预测影响水平-2 小时后（研究 2b，n = 87），在日常生活或实验环境中。结果显示，与统计预测相比，参与者的预测在小时预测（dz = 0.42 和 0.30）中显示出较大的绝对误差，但在分钟预测（dz = 0.17）中则没有。每项研究还评估了相对误差，显示研究 1 和 2b（日常生活中长达一小时的预测）没有差异，但参与者的乐观误差比研究 2a（实验环境中长达一分钟的预测）中的统计预测更乐观。在这三项研究中，参与者表现出强烈的倾向，将他们当前的情感体验投射到新的预测中，这可能解释了人类特有的预测错误。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。