| 分析思维降低情感预测影响偏差 |
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| 引用本文： | 耿晓伟,刘丹,牛燕华. 分析思维降低情感预测影响偏差[J]. 心理学报, 2020, 52(10): 1168-1177. DOI: 10.3724/SP.J.1041.2020.01168 |
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| 作者姓名： | 耿晓伟 刘丹 牛燕华 |
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| 作者单位： | 鲁东大学教育科学学院, 山东 烟台 264011 |
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| 基金项目： | * 国家自然科学基金项目(71401068);国家自然科学基金项目(71971104);教育部人文社科一般项目(19YJA190002);山东省高等学校青创科技支持计划(2019RWF001) |
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| 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。
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| 关 键 词： | 分析思维 情感预测 影响偏差 双系统理论 |
| 收稿时间： | 2019-07-04 |
Analytical thinking reduces impact bias in affective forecast |
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| GENG Xiaowei,LIU Dan,NIU Yanhua. Analytical thinking reduces impact bias in affective forecast[J]. Acta Psychologica Sinica, 2020, 52(10): 1168-1177. DOI: 10.3724/SP.J.1041.2020.01168 |
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| Authors: | GENG Xiaowei LIU Dan NIU Yanhua |
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| Affiliation: | School of Education Science, Ludong University, Yantai 264011, China |
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| 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. |
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| Keywords: | analytic thinking affective forecast impact bias dual-process theory |
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