Affect and Framing
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Running head: AFFECT AND RISK-SEEKING IN THE FRAMING EFFECT
I’m feeling lucky: The relationship between affect and risk-seeking in the framing effect
Elaine Cheung
Cornell University
Affect and Framing
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Abstract
The present research explored the role of affect in the framing effect. In Study 1,
participants provided affect ratings during a gambling task. Positive affect was found to
be associated with risk-seeking in the loss frames; affect was not related to behavior in
the gain frames. In Study 2, participants were either instructed to make their decisions in
the gambling task using emotions (emotion-focused condition) or without using emotions
(emotion reappraisal condition). Participants in the emotion-focused condition displayed
the classic pattern of the framing effect. Conversely, participants in the emotional
reappraisal condition displayed reduced risk seeking in the loss frames and greater risk
aversion in the gain frames. These findings suggest affect is related to risk-seeking in the
framing effect.
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I’m feeling lucky: The relationship between affect and risk-seeking in the framing effect
The way information is presented can largely impact one’s judgments and
decisions. For instance, the presentation of the outcomes of a cancer treatment in terms of
mortality or survival may largely influence one’s decision whether or not to accept the
treatment. It is important, therefore, to understand the processes behind people’s
susceptibility to information presentation and how this may influence their decision
behavior. Decision research has largely focused on cognitive explanations for biases in
judgments and decision making (e.g., Kahneman & Tversky, 1979; Payne, Bettman, &
Johnson, 1993). However, there has been recent attention highlighting the importance of
affect in guiding decision behavior (Loewenstein, Weber, Hsee, & Welch, 2001; Slovic,
Fincuane, Peters, & Macgregor, 2002). In the present research, we seek to explore the
role of affective processes in one notable decision bias, the framing effect.
The “framing effect” (Tversky & Kahneman, 1981) is the tendency for people to
make systematically different decisions based on whether alternatives are framed
positively (i.e., as gains) or negatively (i.e., as losses). When alternatives are framed
positively, individuals are more likely to display risk aversion, preferring to choose
options with certain outcomes over those that are risky. Conversely, when alternatives are
framed negatively, individuals are more likely to display risk-seeking behavior,
preferring risky options over a sure loss. That is, individuals are more likely to choose to
keep a “sure thing” in decisions framed as gains and gamble in decisions frames as
losses, in spite of the decisions being objectively equivalent.
The classic paradigm used to demonstrate the framing effect is the “Asian Disease
Problem”, a hypothetical scenario created by Tversky and Kahneman (1981):
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Imagine that the United States is preparing for an outbreak of an unusual Asian
disease that is expected to kill 600 people. Two alternative programs to combat
the disease have been proposed. Scientific estimates of the consequences of the
programs are as follows:
Problem 1(Positive Frame):
If Program A is adopted, exactly 200 people will be saved.
If Program B is adopted, there is a 1 in 3 probability that all 600 people will be
saved and a 2 in 3 probability that no people will be saved.
Which of the two programs would you favor?
Problem 2 (Negative Frame):
If Program C is adopted, exactly 400 people will die.
If Program D is adopted, there is a 1 in 3 probability that nobody will die and a 2
in 3 probability that all 600 will die.
Which of the two programs would you favor?
When presented with options framed positively (i.e., as lives saved), people
overwhelming choose Program A (i.e., the sure option). Conversely, when presented with
options framed negatively (i.e., as lives lost), people overwhelming choose Program D
(i.e., the risky option). This pattern consistently emerges, despite the fact that the
expected outcomes of all four programs are equivalent.
Prospect Theory (Kahneman & Tversky, 1979) is a cognitive theory of decision
making under risk and uncertainty that has been used to explain framing. According to
Prospect Theory, the value of an option is defined in terms of gains and losses. Rather
than considering the final outcomes of a decision, people tend to assign value to gains
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and losses and use decision weights instead of probabilities to make their decisions.
Moreover, the theory suggests that people tend to underweigh outcomes that are probable
in favor of outcomes that are certain (the certainty effect) and generally discard
components that are shared by all options under consideration (the isolation effect).
These tendencies are represented in the form of an s-shaped value function in which the
function is concave for gains (implying risk aversion), convex for losses (implying risk
seeking), and steeper for losses than for gains (implying loss aversion). Although
Prospect Theory offers a compelling explanation for decision making in the framing
effect, affective processes may also be influencing these decisions. We believe a
consideration of the role of affect in framing may help provide a more comprehensive
understanding of decision making in the framing effect.
Affect in Risky Decision Making
Affect has been shown to be instrumental in guiding decisions. Loewenstein et al.
(2001) assert the importance of affect in risky decision making through their “risk as
feelings” model. According to this model, individuals react to the prospect of risk at both
a cognitive level and an emotional level. Although the two levels are interrelated, they
can diverge from one another. When this divergence occurs, affective reactions often
supersede cognitive interpretations in influencing risky decision behavior.
Similarly, the “affect heuristic”, proposed by Slovic et al. (2002), asserts the
importance of using affect in guiding decisions. The authors argue that using affective
impressions to make judgments can be more quick and efficient than using a deliberative
strategy such as weighing the pros and cons of each option, particularly in complex and
uncertain circumstances.
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The authors extend the affect heuristic to risky decision making suggesting that an
individual’s affective impression of an option should directly inform his or her judgments
of risk and benefit in the option (Slovic, Finucane, Peters, & MacGregor, 2004).
Specifically, a positive view of an option should lead an individual to judge the risks of
that option as low and the benefits of that option as high whereas a negative view of an
option should lead an individual to judge the risks of the option as high and the benefits
of the option as low.
Accordingly, affect likely plays an important role when making framing
decisions. For example, people may have different affective reactions to the prospect of
risk in alternatives framed as gains or losses, and these reactions may lead to different
choices by frame. Additionally, people’s affective reactions to each option may influence
their judgments of both the risks and benefits in the options. These judgments of risk and
benefit may also lead them to different choices based on frame. Thus the pattern of riskseeking in loss frames and risk aversion in gain frames is likely influenced by different
affective responses to the gain and loss frames.
Evidence for Affect in the Framing Effect
There is some preliminary empirical evidence for affective processes in framing
behavior. De Martino, Kumaran, Seymour, and Dolan (2006) conducted a neuroimaging
study of a monetary gambling task and found that behavior consistent with framing effect
(specifically risk-averse behavior in gain frames and risk-seeking behavior in loss
frames) was associated with greater activity in the amygdala, a brain region associated
with emotional processes. This association between framing and neural activity in the
amygdala suggests that affect may play an important role in the framing effect.
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Moreover, the authors found that greater activity in the orbital and medial prefrontal
cortex, brain regions associated with deliberative processes, was associated with a
reduced display of framing. Although these findings offer support for an association
between affect and the framing effect, a direct test is needed to elucidate the specific
nature of how affect may influence decision making in the framing effect.
Current Project
The purpose of the present research was to investigate the role of affect in the
framing effect. In Study 1, participants provided affect ratings when making framing
decisions in a computerized gambling task. Study 2 sought to determine whether
increasing or decreasing reliance on emotion would influence framing. Participants in this
study were given instructions manipulating their reliance on emotion when making
decisions in the computerized gambling task.
Study 1
In the present study, we sought to determine the role of affect in the framing
effect. Participants in this study completed the monetary gambling task from the
aforementioned De Martino et al. (2006) study adapted to include a measure of affect.
We chose to use a monetary gambling task as opposed to hypothetical vignettes such as
the Asian Disease Problem because we believe that the decisions within a monetary
gambling task would be less abstract, more familiar, and more personally relevant to
participants. In the control condition, participants completed the original task from De
Martino et al. (2006). In the affect-probe condition, participants completed the same task
with the addition of providing affect ratings before each decision.
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We predicted that participants in both the affect-probe and the control conditions
would display classic framing (i.e., risk aversion in the gain frames and risk seeking in
the loss frames). We also expected affect ratings to be a significant predictor of behavior
consistent with the framing effect. As this is the first direct test to our knowledge of the
relationship between affect and the framing effect, we did not include specific hypotheses
regarding affect valence, and its interaction with frame, in influencing decisions in the
framing effect.
Method
Participants
Sixty-four undergraduate students (age range: 18-23; M=19.27, 38 females and 26
males) participated in exchange for course credit.
Measures
Positive and Negative Affect Schedule- State Version (PANAS-S; Watson, D.,
Clark, L. A., & Tellegen, A., 1988). The PANAS-S is a twenty-eight item measure of
current state affect. Participants were given instructions to rate the extent they were
currently feeling each of the emotions listed on a 5-point scale from 1 (very slightly or
not at all) to 5 (extremely). Averages of positive and negative items were calculated to
obtain a mean positive score and mean negative score for each participant.
Stimuli
Gambling Task. The computerized, monetary gambling task adapted from De
Martino et al. (2006) consisted of 96 gambling trials: 32 gain frames, 32 loss frames, and
32 catch trials. Each trial in the study consisted of four components. Participants were
first given a hypothetical endowment of money, ranging from $25 to $100 in increments
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of $25. Then, they were presented a choice situation in which they had to choose between
a sure option and a gamble option: they could either choose to lose/keep a certain amount
for sure (the sure option) or choose to gamble on a set probability of retaining the full
amount (the gamble option). Both options were displayed simultaneously on the
computer screen with the sure option always positioned on the left and the gamble option
always positioned on the right.
In the gain frames, sure options were presented in terms of keeping a set
proportion of the initial endowment; conversely in the loss frames, sure options were
presented in terms of losing a set proportion of the initial endowment. The gamble
options were presented as pie charts depicting the probability of keeping or losing the full
endowment. Probabilities in the gamble option ranged from 20% to 80% in increments of
20%. Notably, the expected outcomes of the sure option and the gamble option were
always equal. (See Figure 1.).
The catch trials were identical to the framing trials with the exception that the
expected outcome of the sure option and the gamble option were not held constant;
rather, the expected outcome was skewed so that there was a clear “correct” response. In
these trials, the sure options were always presented as keeping/losing 50% of the initial
endowment and the probabilities in the gamble options were always either a 95% or 5%
chance of keeping/losing the entire endowment. An example catch trial would be a choice
between keeping $50 of the $100 endowment for sure versus gambling with a 95%
chance of keeping the entire $100 endowment. The purpose of these trials was to ensure
that participants were engaged in the task.
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Before making each decision, participants in the affect-probe condition responded
to the probe “How do you feel about this decision?” by indicating their response on a 7point Likert-type scale from -3 (very negative) to +3 (very positive) on labeled keys on
the keyboard. The choice situation was presented on the screen as participants responded
to the affect probe. Participants in the control condition were not presented with any
probe.
Finally, all participants made their choice of a sure option or a gamble option by
pressing ‘z’ for the sure option and ‘m’ for the gamble option. Again, the choice situation
remained on the screen as participants made their decision.
Apparatus
The gambling task was presented to participants on a 17” LCD screen using a
Dell Optiplex GX270 desktop with E-Prime experimental software. Participant responses
were recorded via a standard keyboard.
Procedure
The complete duration of the experiment lasted 30 minutes. Participants were run
in groups ranging from one to four persons. Upon arrival, participants were seated in
front of a computer and told they would be completing a computerized gambling task.
Before beginning the task, participants completed the PANAS-S measure of their
baseline affective state. Participants then completed the aforementioned gambling task.
Each participant was randomly assigned to either the affect-probe condition or the control
condition. In order to ensure the task was personally meaningful, participants were
informed that they would receive a sum proportional to their total winnings at the end of
the study.
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Upon completion of the task, participants once again completed a PANAS-S
measure of their current affective state. All participants were then rewarded their
“winnings”. Participants were then debriefed about the study and thanked for their time.
Results
The dependent variable for this study was calculated as the percentage of trials
participants chose the gamble option in the gain and loss frames. We used 50% as an
indicator for risk neutrality, with percentages significantly above 50% indicating riskseeking behavior and the percentages significantly below 50% indicating risk-averse
behavior.
Effects of Condition
To determine whether participants differed between the affect-probe and control
conditions in the percentage of trials they chose the gamble option, we conducted a 2
(condition: affect-probe vs. control) by 2 (frame: gain vs. loss) Analysis of Variance
(ANOVA) on the percentage of trials participants chose the gamble option. We found
that there was no significant difference by condition in the percentage of trials the gamble
option was chosen (F(1, 62) = .115, ns, ƞ² = .001). There was a significant effect of
frame, such that the gamble option was chosen less frequently in the gain frames (42.5%)
than the loss frames (57.6%) across both conditions (F (1, 62) = 18.2, p < .001, ƞ² =
.128). Importantly, there was no significant frame by condition interaction (F (1, 62) =
.002, ns, ƞ² = .000). (See Figure 2.). Seeing as both groups did not differ in the
percentage of trials participants chose the gamble option in the gain frames and the loss
frames, we can assume that the affect-probe did not alter decision making in this task.
The Framing Effect
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We then investigated whether the percentage of trials participants chose the
gamble option was consistent with the pattern of the framing effect. One sample t-tests
were conducted to explore whether the percentage of trials participants chose the gamble
option in each frame differed significantly from a risk-neutral score of 50%. One-sample
t-tests using 50 as the test-value showed that in the gain frames, participants were
significantly more likely than chance to choose the sure option t(63) = -3.29, p < .01,
suggesting risk-aversion. Furthermore, in the loss frames, participants were significantly
more likely than chance to choose the gamble option, t(63) = 2.86, p < .01, suggesting
risk-seeking behavior. This pattern of risk-aversion in the gain frames and risk-seeking
behavior in the loss frames is consistent with the pattern in the framing effect.
Affect Ratings Predicting Choice in Each Frame
To determine the influence of affect ratings on decision making, we conducted a
Generalized Estimating Equation (GEE) analysis in the gain and the loss frames. The
GEE controls for within-cluster correlation in regression models with binary outcomes. In
this task, participants completed 96 repeated trials; the GEE takes into consideration
repeated measures of binary outcomes within individual participants. Choice in each trial
resulted in a binary outcome of either the sure option or the gamble option; for this
analysis we coded choice of the sure option as 0 and the gamble option as 1. Moreover,
affect ratings were treated as a continuous variable in this model with higher responses
indicating higher levels of positive affect.
In the gain frames, the relationship between affect ratings and the likelihood of
choosing the gamble option was not significantly related, χ² (1, N = 1024) = .071, ns.
Participants displayed equal levels of risk aversion regardless of their affect ratings, β =
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.018, SE = .07 (see Figure 3). However, in the loss frames, the relationship between
affect ratings and the likelihood of choosing the gamble option was significantly related,
χ² (1, N = 1024) = 5.89, p < .05. As seen in Figure 3, the percentage of trials participants
chose the gamble option varied as a function of affect only in the loss frames, such that
greater positive affect was associated with an increased likelihood of choosing the
gamble option, β = .176, SE = .07.
Mean Affect Ratings
Mean affect ratings did not differ depending on frame; participants reported
feeling slightly positive in both gain (M =. 25, SD = .98) and loss (M = .07, SD = .89)
frames, t(62) = .731, ns.
PANAS-S Scores
We calculated mean positive and negative scores for each participant’s pre-andpost task PANAS-S measures. To test for changes in participants’ affective state, we
conducted paired samples t-tests to compare mean PANAS-S scores before and after the
gambling task. Participants’ PANAS-S scores did not differ as a function of condition;
participants in both affect-probe and control conditions felt equally positive before and
after the task, ts(32) = -.23 and -.21 respectively, ns , and participants in both affect-probe
and control conditions felt equally negative before and after the task, ts(32) = -.43 and 1.04 respectively, ns.
Discussion
The purpose of Study 1 was to determine the role of affect in the framing effect.
We found that participants in both the affect-probe and control conditions showed classic
framing, namely, risk aversion in the gain frames (choosing the gamble option below
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50% of the time), and risk-seeking behavior in the loss frames (choosing the gamble
option above 50% of the time). This pattern is consistent with previous findings of the
framing effect (e.g. De Martino et al, 2006; Kahneman & Tversky, 1981, 2000;
Kuhberger, 1998). Moreover, we found that the relationship between affect and framing
was limited to the loss frames, such that risk-seeking behavior in the loss frames is
predicted by positive affect.
However, Study 1 did not allow us to determine the directionality of the
relationship between affect and choice. Although our affect-probe preceded participants’
choice between the sure option and the gamble option, we cannot be certain that they had
not already made their decision, and that their affect ratings were a result of their feelings
about their choice instead of their affective responses to the decision. Thus, it remains
unclear as to whether the affect ratings in this task were influencing participants’
decisions or whether their decisions were influencing affect ratings. We addressed this
question in Study 2 by including a manipulation of emotion reliance in decision strategy.
If framing can be altered by manipulating emotion reliance in decision strategy, then it
suggests that the directionality of the effect should be affect influencing decisions rather
than decisions influencing affect.
Study 2
In the present study, we sought to determine whether framing could be altered by
manipulating participants’ reliance on emotion when making framing decisions.
Participants in this study completed a gambling task identical to the one in Study 1 with
the addition of an emotion reliance manipulation. Namely, participants were instructed to
either use their emotions when making decisions (emotion-focused condition) or to
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reappraise the choice situation such that they made their decisions without using
emotions (emotion reappraisal condition). The strategy used in the emotion reappraisal
condition is derived from Gross (2002)’s description of reappraisal as a cognitive
emotion regulation strategy which involves reinterpreting a potentially emotion-eliciting
situation in non-emotional terms. Based on the findings in Study 1, we predicted that
participants in the emotion-focused condition would display classic framing; we also
predicted that participants in the emotion reappraisal condition would display the classic
pattern of risk aversion in the gain frames, but they would display reduced risk-seeking
behavior in the loss frames.
Method
Participants
Forty-three undergraduate students (age range: 18-23; M = 19.7, 27 females and
16 males) participated in a gambling study for payment of $5 or course credit.
Measures, Stimuli, and Procedure
The measures, stimuli, and procedure were identical to Study 1 with the
exception of including an emotion reliance manipulation. Participants were randomly
assigned to either an emotion-focused condition or an emotion reappraisal condition.
Before beginning the task, participants were given instructions designed to
manipulate their reliance on emotion when making decisions. In the emotion-focused
condition, participants were given the following instructions:
As you make each decision, let your EMOTIONS guide your choice.
Please use the following steps to make each decision:
First, consider how you POSITIVE or NEGATIVE you FEEL about each option.
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Finally, make your choice using your emotions to guide you.
The instructions for the emotion reappraisal condition were adapted from the reappraisal
instructions given to participants in Gross (1998). The emotion reappraisal instructions
were as follows:
As you make each decision, DO NOT let your emotions influence your choice.
Instead, please use the following steps to make each decision.
First, consider how you feel about each option.
Second, REEVALUATE the options in a manner that reduces your emotions.
Finally, make your choice WITHOUT using your emotions.
After making each decision, all participants responded to the emotion reliance
query, “How much did your emotions influence your decision” by indicating on a 7-point
Likert-type scale their response from 1 (Not at all) to 7 (Very much so) on the keyboard.
Similar to the affect-probe in Study 1, the options remained on the screen while
participants responded to the query. This query was used both as a prompt to reinforce
participants’ assigned emotion reliance strategy and also as a manipulation check.
Results
Effects of Condition
To determine whether participants differed between conditions in the percentage
of trials they chose the gamble option, we conducted a 2 (condition: emotion-focused vs.
emotion reappraisal) by 2 (frame: gain vs. loss) ANOVA on the percentage of trials
participants chose the gamble option. We found that there was a significant difference in
the percentage of trials the gamble option was chosen based on condition (F(1, 41) =
11.71, p < .001, ƞ² = .30). Participants in the emotion-focused condition were
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significantly more likely to chose the gamble option (50.5%) than participants in the
emotion reappraisal condition (38.7%). There was also a significant effect of frame, such
that the gamble option was chosen less frequently in the gain frames (42.5%) than the
loss frames (50.9%) across both conditions (F (1, 41) = 13.23, p < .001, ƞ² = .338).
Lastly, there was no significant frame by condition interaction (F(1, 41) = .075, ns, ƞ² =
.002). (See Figure 4.).
The Framing Effect
The percentage of trials participants chose the gamble option in the gain and loss
frames for both conditions is displayed in Figure 4. To determine whether the
participants’ behavior was consistent with the pattern of the framing effect, we conducted
one-sample t-tests using a risk-neutral score of 50% as the test-value for each condition in
the gain and loss frames.
Participants in the emotion-focused condition were significantly more likely than
chance to choose the sure option in the gain frames t(21) = -2.14, p < .05, and the gamble
option in the loss frames, t(21) = 2.06, p = .05. This pattern of risk-aversion in the gain
frames and risk-seeking behavior in the loss frames provides evidence for classic framing
in the emotion-focused condition.
For participants in the emotion reappraisal condition, we found that participants
were significantly more likely than chance to choose the sure option t(20) = -4.70, p < .01
in the gain frame, suggesting risk-averse behavior. However, in the loss frames,
participants in the emotion-reappraisal condition did not significantly differ from the riskneutral score of 0.5, t(20) = -1.49, ns. Thus, although participants’ risk aversion in the
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gain frames was consistent with the framing effect, their risk neutrality in the loss frames
was inconsistent with the framing effect.
Emotion Reliance Response
After each decision, participants responded to an emotion reliance query asking,
“How much did your emotions influence your decisions?” on a scale from 1 (Not at all)
to 7(Very much so). To determine whether mean responses to this query differed by
condition, we conducted a 2(condition: emotion-focused vs. emotion reappraisal) by
2(frame: gain vs. loss) ANOVA on participants emotion reliance responses. As expected,
participants significantly differed by condition in emotion reliance responses (F(1, 41) =
175.32, p < .001, ƞ² = 182.35). Frame was not a significant predictor of emotion reliance,
(F(1, 41) = .106, ns, ƞ² = .11). Lastly, the interaction between frame and condition were
also not significant (F(1, 41) = .009, ns, ƞ² = .009). (See Table 1).
Furthermore, we conducted a Generalized Estimating Equation (GEE) analysis in
the gain and the loss frames for each condition to determine whether reliance on emotion
was related to the likelihood of choosing the gamble option. Similarly to Study 1, we
coded choice of the sure option as 0 and the gamble option as 1. Moreover, emotion
reliance responses were treated as a continuous variable with higher responses indicating
increased emotion reliance. Emotion reliance responses were significantly related to the
likelihood of choosing the gamble option for participants in the emotion-focused
condition in the gain and loss frames, χ² (1, N = 704) = 37.40 and 13.42 respectively, ps <
.001, such that higher emotion reliance responses were associated with a greater
likelihood of choosing the gamble option in both the gain and loss frames, βs = .41 and
.26, SEs = .07 and .07 respectively. Likewise, in the emotion reappraisal condition,
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emotion reliance responses were significantly related to the likelihood of choosing the
gamble option in the gain and loss frames, χ² (1, N = 672) = 16.01 and 10.14 respectively,
ps ≤ .001; higher emotion reliance responses were also associated with a greater
likelihood of choosing the gamble option in both the gain and loss frames, βs = .27 and
.18, SEs = .07 and .06 respectively. (See Figure 5.).
PANAS-S Scores
We calculated mean positive and negative scores for each participant’s pre-andpost task PANAS-S measures. To test for changes in participants’ affective state, we
conducted paired samples t-tests to compare mean PANAS-S scores before and after the
gambling task. Similar to Study 1, participants’ PANAS-S scores did not differ as a
function of condition; participants in both conditions felt equally positive before and after
the task, ts(41) = 1.16 and .05 respectively, ns , and participants in both conditions felt
equally negative before and after the task, ts(41) = -1.56 and -.78 respectively, ns.
Discussion
The purpose of Study 2 was to explore the effect of manipulating emotion
reliance in decision strategy on framing behavior. We found that participants in the
emotion-focused condition showed classic framing behavior, namely, risk-aversion in the
gain frames and risk-seeking behavior in the loss frames. This is consistent with our
findings in Study 1, as well as with previous studies of the framing effect (e.g. De
Martino et al, 2006; Kahneman & Tversky, 1981, 2000; Kuhberger, 1998).
Consistent with our hypothesis, participants in the emotion reappraisal did not
display framing behavior in the loss frames; the percentage of trial participants chose the
gamble option was not significantly different from a risk-neutral score of 50%. This
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finding that risk-seeking behavior in the loss frame is not present when making decisions
without using emotions provides further support for Study 1’s finding that the primary
role of affect in the framing effect lies in the loss frame.
However, participants in the emotion reappraisal condition displayed significantly
greater risk-aversion than participants in the emotion-focused condition; because of this
difference in the gain frames between conditions, we cannot assume that the risk-averse
behavior in the emotion reappraisal condition is truly consistent with classic framing. An
alternative explanation for our findings could be that by decreasing the reliance on
emotion when making decisions, we increased overall risk aversion in the emotion
reappraisal condition for both the gain frames and the loss frames. This second
explanation is further supported by our finding that higher emotion reliance responses
were associated with increased risk seeking in both conditions in the gain frames and the
loss frames.
A limitation of the present study is that we did not include a control condition
without any instructions. However, based on our findings in Study 1, increasing the
salience of affect (through an affect probe) did not influence participants’ decisions in the
gambling task. Similarly, we believed that a reliance on emotion when making decisions
would not alter participants’ decisions in this task. We attempted to address this
limitation by comparing the results in the present study with the results from Study 1. We
found that the emotion-focused condition did not differ from either the affect-probe or
control conditions in Study 1, suggesting that an emotion-focused strategy is relatively
comparable to participants’ naturalistic strategy when making framing decisions.
Furthermore, the emotion reappraisal condition, when compared with the affect-probe
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and control conditions in Study 1, showed similar findings as when compared with the
emotion-focused condition in Study 2. The emotion reappraisal condition was marginally
significantly different from the affect and control conditions in Study 1 for the gain
frames and significantly different from the affect and control conditions in Study 1 for the
loss frames. Nevertheless, we acknowledge that this study would have been strengthened
with the addition of a control condition without any instructions.
General Discussion
The present research found a relationship between affect and risk-seeking
behavior in the framing effect. Study 1found evidence for a relationship between positive
affect and risk-seeking behavior in the loss frames of the framing effect. This finding is
consistent with Slovic et al. (2004)’s explanation that positive affective appraisals should
lead to lower perceptions of risk and higher perceptions of benefits in an option.
One explanation for the association between positive affect and risk-seeking
behavior could be that the gamble option in the loss frames felt more positive to
participants relative to choosing a sure loss. Additionally, choosing the gamble option
could also elicit positive emotions such as excitement; however, we did not find positive
affect to be associated with increased risk-seeking in the gain frames. A final explanation
could be that there was no true risk of loss to participants in this task; the worst possible
outcome would be for participants not to win anything.
Furthermore, we did not expect the finding in Study 1 that affect did not predict
risk aversion in the gain frames. Perhaps risk-aversion in the gain frames is not driven by
affective processes. Another explanation could be that the role of affect in the gain
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frames is more subtle in nature than that of the loss frames, and that a more nuanced
approach than self-reported affect ratings is necessary.
In Study 2, we found that participants in the emotion-focused condition (relying
on emotions when making decisions) displayed classic framing. Conversely, participants
in the emotion reappraisal condition (making decisions without using emotions)
displayed increased risk aversion in the gain frames and reduced risk-seeking in the loss
frames. The pattern displayed by the emotion reappraisal condition suggests that by
decreasing participants’ emotion reliance in decision strategy, we increased risk-aversion
in their overall decision making. Furthermore, we found that higher emotion reliance
responses were associated with increased risk seeking behavior. Collectively, these
findings suggest that the role of affect in the framing effect may be such that affect drives
the risk-seeking behavior in this tendency.
In the present research, we used a gambling task instead of hypothetical vignettes
such as the classic “Asian Disease Problem” (Tversky & Kahneman, 1981). We chose the
gambling task because we thought monetary gambling decisions would be less abstract,
more familiar, and more personally relevant to participants. However, as the gambling
task consisted of 96 consecutive trials, one limitation may be the possibility that
participants’ decision making may have changed over the course of the 96 trials.
Additionally participants’ decisions in each trial may have been influenced by the
previous trials they have been exposed to; we attempted to minimize order effect s by
randomizing the order of trials. Nevertheless, future research may benefit from studying
the role of affect in framing across different framing paradigms.
Affect and Framing
23
One limitation of the present research is the lack of external validity in the
gambling task; the task did not offer much potential gain or risk of loss to participants.
While we attempted to make the experience personally meaningful to the participants by
informing them that they would receive a sum proportional to their total winnings at the
end of the study, the risk of loss to the participants was relatively benign. Participants did
not risk losing anything in this study; the worst possible outcome would be to not receive
any reward. Moreover, as this was a laboratory experiment, participants may have
suspected that their potential gains were limited. Thus, the affect elicited in this
experiment may not necessarily be comparable to affect elicited by true losses and gains
outside of an experimental setting. This is consistent with our finding in Study 1 that
mean affect, regardless of frame, did not differ a great deal from 0 (neutral). The fact that
we still found significant effects for the role of affect within this experimental design is
promising as one would expect the affective experience to be limited in this design
relative to a real-world setting with authentic gains and losses.
Another limitation is that participants were not provided feedback as to whether
they “won” or “lost” each trial, yet instead made repeated decisions without receiving
feedback until the end of the study. While providing feedback immediately after each
trial would be more naturalistic, the feedback of “winning” or “losing” each trial would
likely influence participants’ affect and their future choices, making it very difficult to
study the pure mechanisms behind the framing effect. For example, Gehring and
Willoughby (2002) found that when participants were provided feedback in a gambling
scenario, participants that were told they ‘lost’ a trial became increasingly risky in
subsequent trials to make up for their losses. Thus, we believe our decision not to include
Affect and Framing
24
feedback, while less naturalistic, allows for a more controlled test of the processes in the
framing effect.
Future research should elucidate the role of discrete emotions underlying the
framing effect. Lerner and Keltner (2000) found that fear and anger, two discrete
emotions of the same valence, resulted in different appraisals of the risk perception in
future events. Specifically, they found that fearful individuals judged the risk of future
events as high whereas angry individuals judged the risk of future events as low.
Likewise, future research may discover how discrete positive and negative emotions
differentially influence risk perception in framing decisions.
Moreover, the risk as feelings model (Loewenstein et al., 2001) distinguishes
between anticipatory and anticipated emotions in risky decision making. Anticipatory
emotions are immediate, visceral reactions to risk and uncertainties whereas anticipated
emotions are emotions that are expected to be experienced in the future. Distinguishing
between anticipatory and anticipated emotions in the gain and loss frames may also help
clarify the influence of emotional processes in framing.
An additional direction for future research may be to study the role of affect in the
framing effect in different developmental populations such as children, adolescents, and
older adults. The framing effect is a tendency that only develops as early as late
childhood (Reyna and Ellis, 1994). Studying the role of affect in the development of this
systematic bias may help shed light on both the framing effect and the development of
emotional processes in decision making. Moreover, research suggests that adolescents
may be more likely to consider the risks and benefits of each outcome rather than relying
on heuristic strategies when making risky decisions (Reyna and Farley, 2006). Future
Affect and Framing
25
research may explore how adolescents may differ in the role of affect when making
framing decisions to further understand adolescent risky decision making. Lastly, Mikels
and Reed (in press) found that when completing a monetary gambling task, older adults
did not display the classic pattern of risk aversion in the gain frames and risk-seeking
behavior in the loss frames that young adults displayed. Whereas older adults displayed
similar levels of risk aversion to young adults in the gain frames, older adults did not
display risk-seeking behavior in the loss frames. Investigating the role of affect in older
adults’ decision making in a framing task could help explain these age differences in riskseeking behavior in the loss frames, and also could help further the understanding of the
role of emotional processes in the decision making of older adults.
Conclusions
The present investigation explored the role of affect in the framing effect. We
found that positive affect was a significant predictor of risk-seeking in the loss frames;
affect was not related to risk aversion in the gain frames. Additionally, we found that
relying on one’s emotions when making framing decisions leads to the classic pattern of
risk aversion in the gain frames and risk seeking in the loss frames. However, using a
strategy in which one does not rely on their emotions when making decisions leads to
reduced risk seeking in the loss frames and greater risk aversion in the gain frames.
Collectively, these findings suggest a relationship between affect and risk-seeking
behavior in the framing effect.
Affect and Framing
26
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Acknowledgements
I would like to thank Dr. Joseph Mikels for his feedback and support at every step
of this process. Without his guidance, this work would not be possible. I would also like
to thank Andy Reed and Dr. Marianella Casasola for their continued support and
thoughtful feedback on this thesis. Special thanks to Dr. Thomas Gilovich and Dr.
Valerie Reyna for agreeing to be on my defense committee; I appreciate your ideas and
feedback on this research.
I would like to thank the Emotion and Cognition Lab for making research a fun
and delightful experience. Special thanks to Lee Kaplowitz for his programming
expertise in this study. I am eternally grateful to John Rhee, Tommy Lei, Jon
Hirschberger and Jade Wu for collecting this data.
Finally, I would like to thank Mrs. Marjorie Corwin and Cornell University for
the financial support throughout the completion of this study. This thesis was partially
funded by the Marjorie A. Corwin Undergraduate Research Fellows Endowment.
Affect and Framing
30
Table 1. Mean Emotion Query Responses in the Gain and Loss Frames for the EmotionFocused and Emotion Reappraisal Conditions in Study 2.
Emotion-Focused
Gain
Loss
M
4.89
4.95
SD
1.04
1.06
Emotion Reappraisal
M
1.96
2.05
SD
0.95
1.02
Framing and Affect
31
Figure Captions
Figure 1. Choice presentation screen of a “gain” frame; in this example, participants
received an initial endowment of $25. The sure option is presented on the left side of the
screen. The gamble option is on the right side of the screen with the gamble probability
(80%) depicted in a pie chart.
Figure 2. The percentage of trials participants chose the gamble option in the gain and
loss frames for the affect-probe and control conditions. The reference line indicates a
risk-neutral score of 50%.
Figure 3. The percentage of trials participants chose the gamble option in gain and loss
frames for negative, neutral, and positive affect ratings. The reference line indicates a
risk-neutral score of 50%.
Figure 4. The percentage of trials participants chose the gamble option in gain and loss
frames for the emotion-reappraisal and the emotion-focused conditions. The reference
line indicates a risk-neutral score of 50%.
Figure 5. The percentage of trials participants chose the gamble option for each emotion
reliance response collapsed across conditions. After each trial participants responded to
how much they relied on their emotions when making their decision on a scale from
1(Not at all) to 7 (Very much so). The reference line indicates a risk-neutral score of
50%.
Framing and Affect
Figure 1.
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Framing and Affect
Figure 2.
33
Framing and Affect
Figure 3.
34
Framing and Affect
Figure 4.
35
Framing and Affect
Figure 5.
36