1. Running head: EFFECTS OF EMOTIONS ON DECISION-MAKING 1
Effects of Emotions on Decision-Making
Pablo A. Jimenez and Gabriela Rosales
Texas A&M University

2. EFFECTS OF EMOTIONS ON DECISION-MAKING 2
Abstract
The purpose of this study is to examine the effects of emotions on positive and negative
prediction errors during decision-making. The study also measures conductance responses for
different groups in depressive subjects versus non-depressive subjects and high-anxiety subjects
versus low anxiety subjects. The two choice uncertainty task study first introduced subjects (N =
40) to questionnaires for self-reporting narcissism, positive mood, negative mood, worry,
depression, state anxiety, and trait anxiety. Subjects are then introduced to the monetary
decision and equal rewards condition task. A correlational analysis showed that participants
higher in depressed mood, negative mood, and state and trait anxiety responded faster, while
participants with a more future-oriented perspective responded slower on the task. The analysis
showed that more narcissistic individuals chose the more certain, low noise option more than
those lower in narcissism. Similarly, higher positive mood was associated with choosing the
more certain option. The analysis showed that individuals higher in depressive mood, negative
mood, and state and trait anxiety earned fewer points on the task, while individuals with a future-
oriented perspective earned more points on the task. The analysis showed that individuals with
more depressed mood had higher skin conductance responses. The analysis showed no
relationship between skin conductance response and uncertainty preference or points earned on
the task. A linear regression model showed that positive mood, state anxiety, and trait anxiety
were all marginally significant predictors of uncertainty selection in choosing the more uncertain
option.

3. EFFECTS OF EMOTIONS ON DECISION-MAKING 3
Effects of Emotion on Decision-Making
One aspect of psychology involves measuring cognitive processes. Those cognitive
processes can be automatic or unconscious processes. The automatic or unconscious processes in
the study are decision making tasks. Decision making is required for a variety of behaviors and
often involves consideration of multiple alternatives and reasoning about distant future
consequences (Crone et al., 2004). Decision making has been researched for different kinds of
emotions. Those emotions are narcissism, positive mood, negative mood, worry, depression,
state anxiety, and trait anxiety. Empirical support for the operation of autonomic signals come
from studies using a gambling task, which mimic real-life decisions in the way it factors reward,
punishment, and uncertainty of outcomes (Crone et al., 2004). In the gambling task study,
subjects were required to choose from four decks in each trial. Depending on the subject’s
decision, each deck in the gambling task may result in different gain/punishment outcome. Two
decks would result in immediate high gain and accompanied by a large delayed punishment. One
of these two decks would be accompanied by a frequent small punishment (50%) while the other
deck was accompanied with a large infrequent punishment (10%). The other two decks would
result in an immediate low gain with one deck accompanied by a frequent small delayed
punishment (50%) and the other deck accompanied by a large infrequent (10%) delayed
punishment. These last two decks with an immediate low gain were the advantageous decks
because they would result in a net gain in the long run while the other two decks with an
immediate high gain were the disadvantageous decks because they resulted in a net loss. Crone et
al. study showed a distinction in performance, resulting in one third of the participants that were
less likely to avoid the disadvantageous decks, one third that improved moderately throughout
the task, and one third that avoided disadvantageous decks relatively fast (2004). Research has

4. EFFECTS OF EMOTIONS ON DECISION-MAKING 4
shown that an individual’s positive and negative emotions can alter their performance on a
decision making task. Under negative mood people’s perceptions, thoughts, and judgments are
often distorted toward greater negativity and thereby, tainting people’s judgments (Raghunthan
& Pham 1999). In this study Raghunthan and Pham wanted to test whether the same negative
affective states, sadness and anxiety, influenced decision making in gambling the same way or
not. Their results showed that individuals in a sad or anxious mood preferred different gambling
options. Individuals in an anxious mood tended to prefer the lower-risk-lower payoff gamble,
while the individuals in a sad mood tended to prefer the higher risk- higher pay off gamble. This
was due to the participant’s inclination to assess the feeling implications of their decisions,
asking themselves, “What would I feel better about . . . ?” (Raghunthan & Pham 1999). Sad
individuals would feel better by choosing the high-risk option and the anxious individuals would
feel better by choosing the low-risk option. Therefore, showing how negative moods can affect
the decision making process and that different negative moods can result in different strategies
and judgments.
Studies researching the effect of positive moods on decision making showed that subjects in a
more pleasant mood spent more time deliberating and used more decision making related
information than subjects in a less pleasant mood (Mano 2001). In this study, it is argued that
individuals in a more pleasant mood take more time to come up with an elaborate strategy and
see the decision making process as a fun experience that makes them challenge themselves
cognitively. Meanwhile, individuals in a less pleasant mood perceive the cognitive challenge as a
task they need to get rid of as soon as possible. Therefore, leading individuals in a pleasant mood
to make better decisions in a decision making task than those in an unpleasant mood.

5. EFFECTS OF EMOTIONS ON DECISION-MAKING 5
Furthermore, emotions can affect decision making in different degrees. As emotions intensify
they exert an ever-increasing influence on behavior and overwhelm cognitive processing and
deliberate decision making altogether (Loewenstein & Lerner 2003). The intense emotion or
emotions override the human body and lead the individual to produce a behavior or judgment
that is maladaptive or incorrect.
Studies have also shown how individuals produce a different galvanic skin response while
completing a gambling decision making task. Participants differ in their autonomic activity
before making a selection between disadvantageous and advantageous decks in a gambling task.
In Crone et al. study, good and moderate performers showed a large galvanic skin response
preceding a disadvantageous choice while bad performers showed a small galvanic skin
response. These large galvanic skin responses serve as a somatic warning signal before a good or
moderate performer makes a risky choice in the gambling task. Therefore, encouraging
advantageous decks and discouraging disadvantageous decks. This is phenomenon is known as
the Somatic Marker Hypothesis (SMH). The SMH proposes that ‘somatic marker’ biasing
signals from the body are represented and regulated in the emotion circuitry of the brain,
particularly the ventromedial prefrontal cortex , to help regulate decision-making in situations of
complexity and uncertainty (Dunn et al. 2006).

6. EFFECTS OF EMOTIONS ON DECISION-MAKING 6
Method
Participants
40 undergraduate students at Texas A&M University, age range 18 – 22 (M=19.28, SD=1.24),
22 females and 18 males completed the study. Undergraduate students served by volunteering
under the Sona-System to receive two credits for an introduction to psychology course. All the
students were English speakers in class levels of freshmen, sophomore, junior and senior.
Measures and Design
In the two-choice uncertainty decision-making task, one option offers less uncertainty about the
rewards given. There is less variance about the mean (SD=2.50). The other option is more
uncertain and varies more (SD=15.00) from the mean reward value. Both options provide equal
rewards on average. Participants complete 100 trials and there was an exclusion for the first ten
trials to avoid skewing the results for novelty preference.
Materials
The equipment used in the study involved computers in a laboratory setting. The computers used
two programs called Matlab and Biopac GSR100C. Matlab program is used for running different
experiments and simultaneously presenting stimuli to participants and recording their responses.
The Biopac program records data through the participant’s galvanic skin response. Biopac
GSR100C also uses electrodes as a physiological measure for the current state of emotions that the
participants are in. The design for the study uses a correlation with a survey method.
Procedure
Participants were signed into the two-choice uncertainty decision-making task by using the
Psychtoolbox and Experiments files for Matlab. Participants then completed questionnaires that
assed their self-reported emotions. The emotions measured were narcissism, positive mood,

7. EFFECTS OF EMOTIONS ON DECISION-MAKING 7
negative mood, worry, depression, state anxiety, and trait anxiety. The participants then
completed the two-choice uncertainty decision-making task. Participants are instructed that they
must repeatedly choose between two options with the goal of reaching 6500 points. As they
completed the two-choice uncertainty decision-making task their galvanic skin response was
measured via electrodes attached to their index and middle fingers on the right hand and was
recorded with a BIOPAC unit.
In the two-choice uncertainty decision-making task, for each trial there was an anticipatory
period where the participants were instructed to “THINK ABOUT YOUR CHOICE” and then
three seconds later were instructed to “MAKE A SELECTION”. Participants were given three
seconds to make their selection, making each trial a total of 6 seconds. If participants failed to
make a selection at the end of the 3 seconds given, the experiment would continue to next trial.
Results
Correlation coefficient analyses were performed to determine if there was a relationship between
personality and reaction time on the uncertainty decision-making task. Reaction time was
positively associated with the CESD depression measure (r=.38, p=.02), negative mood (r=.44,
p=.01), state anxiety (r=.34, p=.03), and trait anxiety (r=.44, p=.01). Thus, individuals higher in
depressed mood, negative mood, and state and trait anxiety responded faster. There was also a
marginally significant negative relationship between reaction time and future perspective, r= -
.25, p=.13, such that individuals with a more future-oriented perspective responded slower on the
task.
Correlation coefficient analyses were conducted to determine a possible association between
personality and uncertainty preference on the decision-making task. A marginally significant

8. EFFECTS OF EMOTIONS ON DECISION-MAKING 8
positive correlation between narcissism (NPI) and uncertainty preference was observed, r=.29,
p=.07. More narcissistic individuals chose the more certain, low noise option more than those
lower in narcissism. Similarly, higher positive mood was associated with choosing the more
certain option, r=.32, p=.04.
Correlation coefficient analyses were executed to assess the relationship between personality and
points earned on the uncertainty task. There was a negative relationship between CESD
depression (r= -.37, p=.02), BAI anxiety (r= -.35, p=.03), negative mood (r= -.41, p<.01), state
anxiety (r= -.41, p<.01), trait anxiety (r= -.43, p<.01). These results suggest that individuals
higher in depressive mood, negative mood, and state and trait anxiety earn fewer points on the
task overall. In contrast, however, future perspective was positively associated with performance
on the task (r=.38, p=.02) in which those with a more future-oriented perspective earned more
points.
Finally, correlation coefficient analyses were conducted to determine whether there was a
relationship between skin conductance, personality, total points earned on the task, and
uncertainty preference. There was a trend significant for the relationship between galvanic skin
response and CESD depression, r=.24, p=.14. Individuals with a more depressed mood had
higher skin conductance responses. The results showed no relationship between skin
conductance response and uncertainty preference or points earned on the task.
A linear regression was performed to determine whether personality could predict points earned
on the task and uncertainty option selections. The CESD, BAI, Positive Mood, Negative Mood,
STAI-S, and STAI-T were entered as predictors. Controlling for age and gender, the overall
regression model was significant, R2=.39, F(8, 31)=2.49, p=.03. Positive mood (β=-.31, p=.09),

9. EFFECTS OF EMOTIONS ON DECISION-MAKING 9
state anxiety (β= -.33, p=.10), and trait anxiety (β= -.44, p=.13) were all marginally significant
predictors of uncertainty selection in choosing the more uncertain option.
Finally, we performed a median split (Mdn=2.00) on galvanic skin response to determine
whether independent low and high skin conductance groups could predict total points earned on
the uncertainty decision-making task or uncertainty option preference. An independent samples
t-test showed a statistical trend between skin conductance and uncertainty option preference,
t(38)= -1.39, p=.17. There was no effect of galvanic skin response on total points earned on the
task, however.
Discussion
The study shows the examination of autonomic activity as a function of several different trial
types; following positive versus negative prediction errors, trials that were preceded by switches
and trials that show large changes in uncertainty.

10. EFFECTS OF EMOTIONS ON DECISION-MAKING 10
References
Mano, H. (1990). Emotional States and Decision Making. Advances In Consumer Research,
17(1), 577-584
Loewenstein G, Lerner JS. The role of affect in decision making. In: Davidson R, Goldsmith H,
Scherer K Handbook of Affective Science. Oxford: Oxford University Press; 2003. pp.
619-642.
Raghunathan, R., & Pham, M. T. (1999). All Negative Moods are not Equal: Motivational
Influences of Anxiety and Sadness on Decision Making, 56-77.
Crone, E. A., Somsen, R. J. M., Van Beek, B., & Van, D. M. (2004). Heart rate and skin
conductance analysis of antecendents and consequences of decision making.
Psychophysiology, 41(4), 531-540. doi:http://dx.doi.org/10.1111/j.1469-
8986.2004.00197.x
Barnaby D. Dunn, Tim Dalgleish, Andrew D. Lawrence, The somatic marker hypothesis: A
critical evaluation, Neuroscience & Biobehavioral Reviews, Volume 30, Issue 2, 2006,
Pages 239-271