关键词: 调节定向, 调节匹配, 情感预测, 影响偏差

Abstract:  Our minor and major decision are often dependent on our predictions of how pleasant or unpleasant these events would make us feel, that is, our affective forecasts (Wilson & Gilbert, 2003). People overestimate the intensity and duration of their affective reactions to the event in focus, which is called the impact bias (Wilson & Gilbert, 2003). Most previous studies have focused on the cognitive sources of impact bias and the individual differences in impact bias. However, few studies have shed light on the motivated underpinnings of impact bias. The present research examined people’s overestimation of the hedonic impact of future events as a form of self -regulation. That is, sometimes people use impact bias to motivate themselves to actualize their goals. Thus, based on the self-regulatory theory, this study aimed to explore the effects of regulatory focus and regulatory fit on the impact biases. The hypotheses were: (i) in forecasting positive affect, individuals with promotive focus would show greater impact bias than those with preventive focus (H1a); (ii) in forecasting negative affect, individuals with preventive focus would show greater impact bias than those with promotive focus (H1b); (iii) regulatory fit would induce impact bias (H2). In Experiment 1, we examined the influences of regulatory focus on impact biases in forecasting positive affect. A total of 61 undergraduates were randomly divided into 2 conditions, specifically, promotive focus priming and preventive focus priming. They were then asked to finish a discriminability test. Before the test, they predicted the happiness of success in the test. After the test, they were told that they had succeeded in the test, and were asked to report their actual happiness of success in the test. Independent-sample t-test showed that participants with promotive focus overestimated the pleasant of success in discriminability test more than those with preventive focus. In Experiment 2, we tested the influences of regulatory focus on impact biases in forecasting negative affect. Participants’ predominant predisposition for a promotion or prevention state was measured first before they performed a memory task. Before the task, they predicted how happy they would be after a failure in the test. Then, they were told that they failed in the memory test and were asked to evaluate how happy they were. Independent-sample t-test showed that participants with preventive focus overestimated the unhappiness of failure in memory test more than those with promotive focus. In Experiment 3, we tested the influence of regulatory fit on impact biases in forecasting negative affect by 2 (Regulatory focus: promotion vs prevention) × 2 (Strategy: eagerness vs vigilance) between-subject design. A total of 120 undergraduates were randomly divided into 4 groups and were asked to finish a memory test. Before the test, they predicted the unhappiness of failure in the test. After the test, they were told that they had failed in the test, and were asked to report their actual unhappiness of the failure in the test. The results showed that participants overestimate the unhappiness of failure in memory test in the condition of regulatory fit more than in the regulatory non-fit condition. In Experiment 4, we tested the effect of regulatory fit on impact bias in forecasting positive affect. A total of 128 undergraduates were randomly divided into 4 groups and were asked to finish a finding differences test. Before the test, they predicted how happy they would be after the success in the test. After the test, they were told that they had succeeded in the test, and were asked to report their actual happiness of the success. The results showed that participants overestimate the happiness in the condition of regulatory fit more than regulatory non-fit condition. The present research investigated the motivated underpinning of impact bias based under the theory of self-regulatory, which helps our further understanding of the mechanism of impact bias. In addition, it can also help us to manage the improvement of the work engagement of employees. Limitations and further research have been discussed as well.

Key words: regulatory focus, regulatory fit, affective forecast, impact bias