literature overview

Affective Forecasting is also known as hedonic forecasting, which refers to “implicit or explicit forecasts of utility that will be experienced at a later time” (Polyportis, Kokkinaki, Horváth, & Christopoulos, 2020), in this context, ‘utility’ refers to “the quality and intensity of the hedonic experience associated with [an] outcome” (Kahneman & Snell, 1992, p. 188). In other words, it refers to the prediction of emotions and feelings in the future, regarding an specific situation. Human beings tend to forecasting how they are going to feel about situations we consider are important, this is done unconsciously in the daily life. Following this definition Wilson and Gilbert identified four specific components of emotional experience that one may make predictions of Valence (whether the emotions will be positive or negative), specific emotions experience, intensity of the emotions, and duration of the emotions. For example, a college student is about to take a final exam, and he start getting his hands sweaty, and he start feeling nervous. He is predicting to feel fearful to fail the exam.

Affective Forecasting plays an important role in the daily life because it drives decision and behaviors (Dunn and Laham Affective forecasting: a user’s guide to emotional time travel, Psychology Press, London, 2006). Every decision requires a prediction (Barry Schwartz and R. Sommers, 2013). If we think about how we are going to feel about an event, we are going to make decisions to try to obtain desired outcomes, or to change our behavior towards the situation. For example, a woman makes an appointment to see the doctor because she is not feeling good, however, she is afraid of getting examined, so she decides to cancel her doctor appointment, as a result of this bad decision she starts feeling worse. Most of the times we engage in Affective Forecasting we predict wrongly, and we make mistakes.

Researchers suggest that when predicting our future emotions, affecting forecasting error are frequent (Wilson and Gilbert in Adv Exp Soc Psychol 35:345-411,2003). There are several reasons why we may find ourselves making seemingly basic errors when it comes to affective forecasting (Wilson & Gilbert, 2003). For better understanding here an example, a person heads to work and it seems that the day is going to be smooth, not busy, but it ends up being stressful, and very tiring day. The Target of error and Nature of bias are valence, specific emotions, intensity, and duration. In the study we are going to conduct, we can also see how participants report their feelings before the study, expecting good results about their participation in the study, and their target error, after completing the anagrams exercise.

Now that Affective Forecasting was exposed and explained, I am going to present details about the study. Solving anagrams have been used as an IQ measuring method, that is why participants are going to show different levels of satisfaction with their performance solving them. I think this is going to influence a little, so participants put more effort on solving them. Participants must engage in the study, and pay attention to the directions or the results are not going to reflect what we want to prove.

We are also adding the demographic information to this study to determine if there are relevant factors that influence the performance of each participant. We can determine if age is an important factor to have a good performance, of certain type of expectations, another important factor is if the participant speak English as his/her first language.

The principal objective is to demonstrate the difference of participants expectations before solving anagrams, and their feeling after the same exercise. We are going to manipulate their expectations adding three different levels, low expectation, medium expectation, and high expectation, and they have to solve ten anagrams, which only five of them have solution and the remaining five do not have a solution. In other words We have two basic predictions. First, when they were told to imagine the average participant solved 5 out of 10 anagrams, we predicted that if participants were told that most people solved 8 out of 10 anagrams (high expectation condition), then they would expect to feel less satisfied than participants who were told that most people solved 2 out of 10 anagrams (low expectation condition), with those participants who were told that most participants solve 5 out of 10 anagrams (middle expectation condition) falling in between the high and low expectation groups. However, for our second hypothesis, we predicted that there would be no differences in participant satisfaction between the high, low, and middle expectation conditions after participants completed the anagram task.

References

Buchanan, T. M., Buchanan, J., & Kadey, K. R. (2019). Predicting with your head, not your heart: Forecasting errors and the impact of anticipated versus experienced elements of regret on well-being. Motivation and Emotion, 43(6), 971–984. https://doi.org/10.1007/s11031-019-09772-y

Pauketat, J. V., Moons, W. G., Chen, J. M., Mackie, D. M., & Sherman, D. K. (2016). Self-affirmation and affective forecasting: Affirmation reduces the anticipated impact of negative events. Motivation and Emotion, 40(5), 750–759. https://doi.org/10.1007/s11031-016-9562-x

Schwartz, B., & Sommers, R. (2013). Affective forecasting and well-being. Oxford Handbooks Online. https://doi.org/10.1093/oxfordhb/9780195376746.013.0044

Norem, J. K., & Cantor, N. (1986). Anticipatory and post hoc cushioning strategies: Optimism and defensive pessimism in ?risky? situations. Cognitive Therapy and Research, 10(3), 347–362. https://doi.org/10.1007/bf01173471