Analytic thinking reducing impact bias in affective forecasting
【Abstract】People overestimate the intensity and duration of their affective reactions to events in the future. This is called impact bias (Wilson and Gilbert, 2003). Impact bias influences individuals’ satisfaction with their decision making. Few studies have shed light on how to reduce impact bias in affective forecasting based on the dual-process theory. According to the dual-process theory 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. Randomly, 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 the second child. 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 promote satisfaction with those decisions. Limitations and further research are discussed as well.
【Keywords】 analytic thinking; affective forecasting; impact bias; dual-process theory;
.  In this experiment, 1 means very unhappy/unhappy/unpleasant, and 5 means very happy/happy/pleasant. The higher the score is, the stronger the positive emotion is; and the lower the score is, the stronger the negative emotion is. Under the acceptance condition, the bias of emotion prediction is positive. The greater the value is, the greater the impact bias is. Under the rejection condition, the bias of emotion prediction is negative. The smaller the value is, the greater the impact bias is. For convenience of understanding, the degree of impact bias is expressed by absolute value. [^Back]
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