分析思维降低情感预测影响偏差

doi:10.3724/SP.J.1041.2020.01168

摘要/Abstract

摘要：

人们在决策前需要对决策可能带来的结果进行预测。人们往往会高估未来事件对情感的影响, 这被称为影响偏差。本研究从双系统理论出发, 考察了分析思维是否会降低情感预测影响偏差。实验1(采用图片启动)和实验2(采用语言流畅性任务)考察了分析思维对影响偏差的影响, 并分析了情感预测程度的中介作用。实验3在现场中以真实的决策(生育二孩)为例, 考察了分析思维启动对情感预测的影响。结果发现:分析思维会降低情感预测强度, 进而降低影响偏差。

关键词: 分析思维, 情感预测, 影响偏差, 双系统理论

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 analytical processing. System 2 often overrides the input of System 1 when analytical thinking is activated. Thus, we here hypothesize that analytical 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 analytical thinking or geometric figures as a control condition. The results showed that analytical 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 score on a memory test exceeded 90% or not before they took 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 analytical thinking to prime analytical thinking or to a verbal fluency task with words not related to analytical thinking as a control condition. The results showed that analytical 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 analytical thinking or geometric figures as a control condition. Results showed that analytical 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 analytical 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 promote satisfaction with those decisions. Limitations and further research are here discussed as well.

Key words: analytic thinking, affective forecast, impact bias, dual-process theory

中图分类号:

耿晓伟, 刘丹, 牛燕华. (2020). 分析思维降低情感预测影响偏差. 心理学报, 52(10), 1168-1177.

GENG Xiaowei, LIU Dan, NIU Yanhua. (2020). Analytical thinking reduces impact bias in affective forecast. Acta Psychologica Sinica, 52(10), 1168-1177.

图/表 6

图1 接受/拒绝条件下启动分析思维对情感预测影响偏差的影响

图2 接受条件下情感预测强度在启动分析思维对影响偏差影响中的中介作用注:启动分析思维为虚拟变量, 0 = 控制组, 1 = 启动组。

图3 拒绝条件下情感预测强度在启动分析思维对影响偏差影响中的中介作用注:启动分析思维为虚拟变量, 0 = 控制组, 1 = 启动组。

表1 不同实验条件下消极情感预测、情感体验、影响偏差的独立样本t检验

因变量	启动组	控制组	t	df	p	95%置信区间	d
情感预测	2.19 ± 0.81	2.78 ± 0.99	-2.36	50	0.02	[-1.09, -0.87]	0.65
情感体验	1.94 ± 0.54	2.09 ± 1.09	-0.65	50	0.52	[-0.63, 0.33]	0.17
影响偏差	0.26 ± 0.65	0.69 ± 0.83	-2.11	50	0.04	[-0.85, -0.02]	0.58

图4 情感预测强度在启动分析思维对影响偏差影响中的中介作用注:启动分析思维为虚拟变量, 0 = 控制组, 1 = 启动组。

表2 启动分析思维对积极和消极情感预测的独立样本t检验

因变量	分析思维	情感预测	t	df	p	95%置信区间	d
积极情感	分析思维	4.54 ± 1.63	2.02	106	0.046	[0.01, 1.27]	0.39
积极情感	控制组	5.18 ± 1.66	2.02	106	0.046	[0.01, 1.27]	0.39
消极情感	分析思维	2.98 ± 1.32	-1.01	106	0.314	[-0.86, 0.28]	-0.19
消极情感	控制组	2.69 ± 1.65	-1.01	106	0.314	[-0.86, 0.28]	-0.19

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