Analytical thinking reduces impact bias in affective forecast

Abstract

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.