The document discusses affective forecasting, a concept related to predicting future emotions and feelings, and its influence on decision-making and behaviors. It outlines a study designed to explore the relationship between participants' expectations before solving anagrams and their subsequent feelings of satisfaction, manipulating expectations across three conditions and incorporating a new variable of 'learned helplessness' through practice anagrams. The study aims to determine how these variables interact and affect participant satisfaction following the anagram tasks.

1. 1
AFFECTIVE FORECASTING
AFFECTIVE FORECASTING
Isaac Estrada
Florida International University
AFFECTIVE FORECASTING
Affective Forecasting is also known as hedonic forecasting,

2. 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,

3. 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

4. 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

5. 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
Hypothesis explanation
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

6. 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.
Significant finding: “Using Expectation Condition as our
independent variable (High, Middle, or Low) and recall of how
many anagrams participants were told the average person solves
the event as the dependent variable, we saw a significant effect,
X2(4)=122.97, p<.001. Most participants in the “High”
condition recalled being told that the average person solves 8
out of 10 anagrams (80,5%); most participants in the “Middle”
condition recalled being told that the average person solves 5
out of 10 anagrams (74.4%); and most participants in the “Low”
condition recalled being told that the average person solves 2
out of 10 anagrams (82,90%). Cramer’s V was strong for this
analysis. This indicates that participants saw our
manipulation as intended. “See Table2.
THIS WAS THE ORIGINAL HYPOTHESIS, now, to this
hypothesis we are going to add, that before participants start
solving the anagrams that are part of the study, they are going
to solve 5 that are not part of the study, they are practice
anagrmas, and this variable might set the mood, attitude,
emotions of the participants to complete the actual anagrams
that are part of the study.
ADDING the practice anagrams to the study creates learned
helplessness, which is an independent variable.
Hi class,

7. As you know, we are going with the idea of including learned
helplessness as our new independent variable in study two for
this methods section. That is, in some conditions participants
will complete a series of pre-study practice anagrams that are
really easy while others will complete a series of pre-study
practice anagrams that are very hard. I’ll refer to this
independent variable as the Helplessness condition.
We will continue to focus on the same expectation manipulation
from study one for our first independent variable, but we will
only keep the “High expectations (8 out of 10)” versus “Low
expectations (2 out of 10)” levels of that independent variable
(We will drop the “Middle expectations (5 out of 10)”
condition. It often overlapped with the “Low” expectation group
in study one, so keeping both the “Middle” and “Low”
conditions is needlessly repetitive). I’ll call this the Expectation
condition.
Consider our new independent variable again (Helplessness).
Here we will focus on introducing the idea of “Easy versus
Hard” tickets. That is, after completing the informed consent
form (an electronic version on canvas), students will complete
five “practice” anagrams. They will be told that the purpose of
this practice session is to familiarize them with solving
anagrams. However, there are two different versions for this
independent variable.
1). For the Easy condition, participants will complete five easy
anagrams, all of which have several solutions. For example,
consider the anagram TARSDE, which can be rearranged to
spell the words Stared, Trades, Treads, Daters
In this Easy condition, all five anagrams can be solved, and all
have at least four potential answers to ensure they are easy to
solve
2). For the Hard condition, participants will complete five hard

8. anagrams, only one of which is solvable. For example, consider
the anagram TARSDE, which can be rearranged to spell the
words Stared, Trades, Treads, Daters
Because we do not want to dramatically increase the chance that
participants in the Hard condition will know that there are
unsolvable anagrams in this study, those in the Hard condition
will receive one anagram out of five that is solvable (TARSDE).
The rest will be impossible to “solve”.
This gives us a 2 (Expectation Condition: High versus Low
expectations) X 2 (Helplessness Condition: Easy versus Hard
practice anagrams) factorial design. That is, there will be four
conditions:
Condition #1 – High expectations and Easy practice anagrams
Condition #2 – High expectations and Hard practice anagrams
Condition #3 – Low expectations and Easy practice anagrams
Condition #4 – Low expectations and Hard practice anagrams
As you begin writing your study two literature review for Paper
III, keep this new “learned helplessness” independent variable
in mind. You’ll need to find prior research that looks at scarcity
and use that literature to help support or justify your study
predictions. Good keywords for PsycInfo might be “learned
helplessness”, “performance”, “hopelessness”, “pessimism”, and
the like.
For your hypothesis, remember that you will need to focus on
both main effects (the effect of each independent variable on its
own) and an interaction (the influence of both independent
variables interacting together). Each of your scaled dependent
variables—like “Imagine you solved 5 out of 10 anagrams. Rate
your level of satisfaction with this potential outcome”, which is
on a scale ranging from 0 (I would not feel satisfied) to 10 (I
would feel satisfied)—will need its own main effect and

9. interaction hypotheses. I’ll give you an example below, but you
will need to think about the hypothesis for your second
dependent variable yourself.
1). Main Effect, Expectation Condition (High versus Low). DV
= “Satisfied”
After imagining they solved 5 out of 10 anagrams, participants
in the high expectation condition will be less satisfied with that
potential outcome than participants in the low expectation
condition.
(Note that this is similar to our study one prediction. The only
thing that really differs is the lack of the “middle” condition.
This prediction ONLY looks at the independent variable
“Expectation Condition”)
2). Main Effect, Helplessness Condition (Easy versus Hard). DV
= “Satisfied”
After imagining they solved 5 out of 10 anagrams, participants
in the easy anagram practice condition will be less satisfied
with that potential outcome than participants in the hard
anagram practice condition.
(Note#1: The reasoning behind this prediction is that those who
practiced with easy anagrams will expect an easy “real study”
anagram task. If they expect the task to be easy but imagine
only solving 5 out 10 anagrams, they should feel less satisfied
with that lower anagram solving rate.)
(Note #2: You will write your second literature review with this
prediction in mind – find support to back it up! But again here,
this prediction ONLY looks at the independent variable
“Helplessness Condition”. If your research does not support this
prediction, feel free to alter it, but you do need to justify why

10. you think you might get your predicted outcome using prior
studies in your second literature review).
3). Interaction, Expectation Condition (High versus Low) X
Helplessness Condition (Hard versus Easy). DV = “Satisfied”
After imagining they solved 5 out of 10 anagrams, participants
will feel the least satisfied if their expectations were high and
they had easy practice anagrams than in all other conditions, but
most satisfied if their expectations were low and they had hard
practice anagrams. Participants given both high expectations
and hard practice anagrams as well as those with low
expectations and easy anagrams will give more middling
satisfaction ratings.
(Note that you need to justify this interaction prediction as well
through your literature review. If you disagree with the
prediction, that is fine. You can alter it, but you do need to
justify the predictions that you create given the new
helplessness independent variable).
Keep in mind that each dependent variable you plan to look at
in your study two will need similar main effect and interaction
hypotheses. You also want some overlap between study one and
study two, so you might want to focus your predictions for
study two on the same dependent variables you analyzed in
study one.
Also keep in mind that I gave you some insight into the
“Potential Anagram Performance” satisfaction dependent
variable in the examples above. When we look at the “Actual
Anagram Performance” dependent variables, we probably will
not see satisfaction levels for either the main effect of
expectations, the main effect of helplessness, or the interaction
of expectations X helplessness. But we will see if the data
confirms that outcome when we start analyzing study two!
Good luck as you work on Paper III.