Analytic thinking reduces impact bias in affective forecast

doi:10.3724/SP.J.1041.2020.01168

Abstract

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.

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

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

Figures/Tables 6

Figure 1. The effect of priming analytic thinking on impact bias under the condition of acceptance/rejection

Figure 2. The mediating role of the intensity of affective forecast under acceptance condition.
Note. 0 = control group, 1 = priming group.

Figure 3. The mediating role of the intensity of affective forecast under rejection condition.
Note. 0 = control group, 1 = priming group.

Table 1 Independent sample t-test of negative emotion prediction, emotion experience and impact bias under different experimental conditions

Dependent variables	Priming group	Control group	t	df	p	95% CI	d
Affective forecasting	2.19 ± 0.81	2.78 ± 0.99	-2.36	50	0.02	[-1.09, -0.87]	0.65
Emotional experience	1.94 ± 0.54	2.09 ± 1.09	- 0.65	50	0.52	[-0.63, 0.33]	0.17
Impact bias	0.26 ± 0.65	0.69 ± 0.83	- 2.11	50	0.04	[-0.85, -0.02]	0.58

Figure 4. The mediating role of the intensity of affective forecast in the influence of analytic thinking on impact bias
Note. 0 = control group, 1 = priming group.

Table 2 Independent-sample t-test was used to test the predictive power of analytic thinking on positive and negative emotions

Dependent variables	Analytic Thinking	Affective forecasting	t	df	p	95% CI	d
Positive emotions	Analytic Thinking	4.54 ± 1.63	2.02	106	0.046	[0.01, 1.27]	0.39
Positive emotions	Control Group	5.18 ± 1.66	2.02	106	0.046	[0.01, 1.27]	0.39
Negative emotion	Analytic Thinking	2.98 ± 1.32	-1.01	106	0.314	[-0.86, 0.28]	-0.19
Negative emotion	Control Group	2.69 ± 1.65	-1.01	106	0.314	[-0.86, 0.28]	-0.19

References 34

[1]	Eggleston, C. M., Wilson, T. D., Lee, M., & Gilbert, D. T. (2015). Predicting what we will like: Asking a stranger can be as good as asking a friend. Organizational Behavior and Human Decision Processes, 128, 1-10.
[2]	Emanuel, A. S., Updegraff, J. A., Kalmbach, D. A., & Ciesla, J. A. (2010). The role of mindfulness facets in affective forecasting. Personality and Individual Differences, 49(7), 815-818.
[3]	Evans, J. S. B. T. (2003). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Sciences, 7(10), 454-459. doi: 10.1016/j.tics.2003.08.012 URL pmid: 14550493
[4]	Evans, J. S. B. T. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59(1), 255-278.
[5]	Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). Gpower 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods*, 39(2), 175-191. URL pmid: 17695343
[6]	Franks, A. S., & Scherr, K. C. (2017). Analytic thinking reduces anti-atheist bias in voting intentions. The International Journal for the Psychology of Religion, 27(3), 129-140.
[7]	Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19(4), 25-42.
[8]	Geng, X. W., & Jiang, H. Y. (2017). Influence of regulatory focus and regulatory fit on impact biases in affective forecast. Acta Psychologica Sinica, 49(12), 1537-1547. doi: 10.3724/SP.J.1041.2017.01537 URL
[9]	Geng, X. W., & Zhang, F. (2015). The impact bias in affective forecasting: Focalism or unforeseen adaptation? Journal of Psychological Science, 38(5), 1201-1206.
[10]	Gervais, W. M., & Norenzayan, A. (2012). Analytic thinking promotes religious disbelief. Science, 336(6080), 493-496. doi: 10.1126/science.1215647 URL pmid: 22539725
[11]	Gilbert, D. T., Pinel, E. C., Wilson, T. D., Blumberg, S. J., & Wheatley, T. P. (1998). Immune neglect: A source of durability bias in affective forecasting. Journal of Personality & Social Psychology, 75(3), 617-638. doi: 10.1037//0022-3514.75.3.617 URL pmid: 9781405
[12]	Gilbert, D. T., & Wilson, T. D. (2007). Prospection: Experiencing the future. Science, 317(5843), 1351-1354. doi: 10.1126/science.1144161 URL pmid: 17823345
[13]	Gilbert, D. T., & Wilson, T. D. (2009). Why the brain talks to itself: Sources of error in emotional prediction. Philosophical Transactions: Biological Sciences, 364(1521), 1335-1341.
[14]	Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression- based approach. New York: Guilford.
[15]	Hoerger, M., Quirk, S. W., Lucas, R. E., & Carr, T. H. (2010). Cognitive determinants of affective forecasting errors. Judgment and Decision Making, 5(5), 365-373. URL pmid: 21912580
[16]	Hong, P. M., Lishner, D. A., Vogels, E. A., & Ebert, A. R. (2016). The effect of a mindfulness practice and dispositional mindfulness on affective forecasting. Basic and Applied Social Psychology, 38(3), 153-165.
[17]	Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93(5), 1449-1475.
[18]	Kong, D. T. (2015). The role of mindfulness and neuroticism in predicting acculturative anxiety forecasting error. Mindfulness, 6(6), 1387-1400. doi: 10.1007/s12671-015-0409-4 URL
[19]	Lang, Z., Li, S., Li, Y. M., & Liu, C. J. (2007). Impact bias in happiness prediction. Chinese Mental Health Journal, 21(10), 693-695.
[20]	Morewedge, C. K., & Buechel, E. C. (2013). Motivated underpinnings of the impact bias in affective forecasts. Emotion, 13(6), 1023-1029. doi: 10.1037/a0033797 URL
[21]	Robinson, M. D., & Clore, G. L. (2002). Belief and feeling: Evidence for an accessibility model of emotional self-report. Psychological Bulletin, 128(6), 934-960. doi: 10.1037/0033-2909.128.6.934 URL pmid: 12405138
[22]	Schwarz, N., & Xu, J. (2011). Why don't we learn from poor choices? The consistency of expectation, choice, and memory clouds the lessons of experience. Journal of Consumer Psychology, 21(2), 142-145.
[23]	Stagnaro, M. N., Ross, R. M., Pennycook, G., & Rand, D. G. (2019). Cross-cultural support for a link between analytic thinking and disbelief in God: Evidence from India and the United Kingdom. Judgment and Decision Making, 14(2), 179-186.
[24]	Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8(3), 220-247. doi: 10.1207/s15327957pspr0803_1 URL pmid: 15454347
[25]	Sun, Y., Li, S., & Yin, X. L. (2007). Two systems in decision-making and reasoning: Heuristic system and analytic system. Advances in Psychological Science, 15(5), 721-845.
[26]	Swami, V., Voracek, M., Stieger, S., Tran, U. S., & Furnham, A. (2014). Analytic thinking reduces belief in conspiracy theories. Cognition, 133(3), 572-585. doi: 10.1016/j.cognition.2014.08.006 URL pmid: 25217762
[27]	Ubel, P. A., Loewenstein, G., Hershey, J., Baron, J., Mohr, T., Asch, D. A., & Jepson, C. (2001). Do nonpatients underestimate the quality of life associated with chronic health conditions because of a focusing illusion? Medical Decision Making, 21(3), 190-199. doi: 10.1177/0272989X0102100304 URL pmid: 11386626
[28]	Ubel, P. A., Loewenstein, G., & Jepson, C. (2005). Disability and sunshine: Can hedonic predictions be improved by drawing attention to focusing illusions or emotional adaptation? Journal of Experimental Psychology: Applied, 11(2), 111-123. doi: 10.1037/1076-898X.11.2.111 URL pmid: 15998183
[29]	Wilson, T. D., & Gilbert, D. T. (2003). Affective forecasting. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 35, pp.345-411). San Diego, CA: Academic Press.
[30]	Wilson, T. D., Wheatley, T., Meyers, J. M., Gilbert, D. T., & Axsom, D. (2000). Focalism: A source of durability bias in affective forecasting. Journal of Personality and Social Psychology, 78(5), 821-836. doi: 10.1037//0022-3514.78.5.821 URL pmid: 10821192
[31]	Wirtz, D., Kruger, J., Scollon, C. N., & Diener, E. (2003). What to do on spring break? The role of predicted, on-line, and remembered experience in future choive. Psychological Science, 14(5), 520-524. URL pmid: 12930487
[32]	Xu, J., & Jiang, D. (2009). Expectations, experiences, and memories: When consumers fail to learn from experiences. Acta Psychologica Sinica, 41(8), 745-752.
[33]	Yilmaz, O., Karadöller, D. Z., & Sofuoglu, G. (2016). Analytic thinking, religion and prejudice: An experimental test of the dual-process model of mind. The International Journal for the Psychology of Religion, 26(4), 360-369.
[34]	Zhang, Y. P. (2005). Evaluations on ultimatum game. Economic Perspectives(6), 83-86.