ISSN 0439-755X
CN 11-1911/B

Acta Psychologica Sinica ›› 2020, Vol. 52 ›› Issue (10): 1168-1177.

• Reports of Empirical Studies •

Analytic thinking reduces impact bias in affective forecast

GENG Xiaowei(), LIU Dan, NIU Yanhua

1. School of Educational Science, Ludong University, Yantai 264011, China
• Received:2019-07-04 Published:2020-10-25 Online:2020-08-24
• Contact: GENG Xiaowei E-mail:fengandwei@126.com
• Supported by:
National Natural Science Foundation of China(71401068);National Natural Science Foundation of China(71971104);the Foundations of Humanities and Social Sciences of the Ministry of Education(19YJA190002);Science and Technology Support Plan for Youth Innovation of Universities in Shandong Province(2019RWF001)

Abstract: People overestimate the intensity and duration of their affective reactions to events in the future. This is called impact bias (Wilson & Gilbert, 2003). Impact bias influences individuals’ satisfaction with their decision making. Few studies have shed light on how to reduce impact bias in affective forecast based on dual-process theories. According to dual-process theories of human thinking, there are two distinct but interacting systems for information processing. System 1 relies on frugal heuristics and produces intuitive responses, while System 2 relies on deliberative analytic processing. System 2 often overrides the input of System 1 when analytic thinking is activated. Thus, we hypothesize that analytic thinking reduces the impact bias in affective forecasting.
In experiment 1, a total of 240 undergraduates were assigned to play an ultimatum game as proposers and asked to predict how they would feel when their proposals were accepted or rejected by responders. At random, they were told their proposals were accepted or rejected. As soon as they knew the result, they were asked to report how they felt. Before the ultimatum game began, participants were randomly assigned to view pictures of The Thinker to prime analytic thinking or geometric figures as a control condition. The results showed that analytic thinking reduced impact bias in affective forecasting by reducing the intensity of predicted emotions.
In experiment 2, a total of 52 undergraduates took part in a memory test. They were asked to predict how they would feel if their scores on a memory test exceeded 90% or not before the test. As soon as they knew the result that they did not exceed 90%, they were asked to report how they felt. Before taking the memory test, participants were randomly assigned to perform a verbal fluency task with words related to analytic thinking to prime analytic thinking or a task not related to analytic thinking as a control condition. The results showed that analytic thinking reduced impact bias in affective forecasting by reducing the intensity of predicted emotions.
In experiment 3, a total of 111 women who had only one child were asked to predict how they would feel if they had a second. Before predicting their feelings, they were randomly assigned to view pictures of The Thinker to prime analytic thinking or geometric figures as a control condition. Results showed that analytic thinking reduced the positive affect of having the second child but not the negative affect of having the second child.
In sum, the present research shows that analytic thinking reduces impact bias in affective forecasting by reducing the intensity of predicted emotions. It can help us reduce impact bias in affective forecasting when making decisions and be satisfied with those decisions. Limitations and further research are discussed as well.