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ATTITUDES AND SOCIAL COGNITION

Hedonic Tone and Activation Level in the Mood–Creativity Link:
Toward a Dual Pathway to Creativity Model

Carsten K. W. De Dreu, Matthijs Baas, and Bernard A. Nijstad
University of Amsterdam

To understand when and why mood states influence creativity, the authors developed and tested a dual
pathway to creativity model; creative fluency (number of ideas or insights) and originality (novelty) are
functions of cognitive flexibility, persistence, or some combination thereof. Invoking work on arousal,
psychophysiological processes, and working memory capacity, the authors argue that activating moods
(e.g., angry, fearful, happy, elated) lead to more creative fluency and originality than do deactivating
moods (e.g., sad, depressed, relaxed, serene). Furthermore, activating moods influence creative fluency
and originality because of enhanced cognitive flexibility when tone is positive and because of enhanced
persistence when tone is negative. Four studies with different mood manipulations and operationaliza-
tions of creativity (e.g., brainstorming, category inclusion tasks, gestalt completion tests) support the
model.

Keywords: mood, creativity, cognitive flexibility, emotions, arousal

What enables scientists to make notable contributions, engineers
to develop innovative products, and work teams to creatively solve
their problems? What hinders stand-up comedians from being
funny and refrains poets from being original? When are people
creative, and why? What hinders creativity, and when? Partly
because of the importance of creativity for human progress and
adaptation, these questions are as old as the human sciences
(Simonton, 2003). Apart from its obvious, problem-solving func-
tion (Mumford & Gustafson, 1988), creative ideation allows indi-
viduals to remain flexible (Flach, 1990), giving them the capacity
to cope with the advantages, opportunities, technologies, and
changes that are a part of their day-to-day lives (Runco, 2004).
Accordingly, creativity is studied in a variety of disciplines, in-
cluding psychology, organizational behavior, and communication
sciences.

Creativity is usually defined as the generation of ideas, insights,
or problem solutions that are new and meant to be useful (Amabile,
1983; Paulus & Nijstad, 2003; Sternberg & Lubart, 1999). Among
the many variables that have been shown to predict creativity,

mood stands out as one of the most widely studied and least
disputed predictors (e.g., George & Brief, 1996; Isen & Baron,
1991; Mumford, 2003). For example, Ashby, Isen, and Turken
(1999) noted that

It is now well recognized that positive affect leads to greater cognitive
flexibility and facilitates creative problem solving across a broad
range of settings. These effects have been noted not only with college
samples but also in organizational settings, in consumer contexts, in
negotiation situations . . . and in the literature on coping and stress. (p.
530)

In a similar vein, Lyubomirksy, King, and Diener (2005) con-
cluded that people in a positive mood are more likely to have
richer associations within existing knowledge structures, and thus
are likely to be more flexible and original. Those in a good mood
will excel either when the task is complex and past learning can be
used in a heuristic way to more efficiently solve the task or when
creativity and flexibility are required. (p. 840)

Although many studies support the idea that positive mood
states trigger more creative responses than do neutral mood control
conditions, studies in which positive and negative mood states
were compared appear to be less conclusive: “There is also a large
literature on negative affect, which indicates that the impact of
negative affect is more complex and difficult to predict than is the
case for positive affect” (Ashby et al., 1999, p. 532). Indeed,
whereas some studies suggest that positive mood states trigger
more creativity than do negative mood states (e.g., Grawitch,
Munz, & Kramer, 2003; Hirt, Levine, McDonald, Melton, &
Martin, 1997; Hirt, Melton, McDonald, & Harackiewicz, 1996),
other studies report similar levels of creativity (Bartolic, Basso,
Schefft, Glauser, & Titanic Schefft, 1999), and still other studies

Carsten K. W. De Dreu, Matthijs Baas, and Bernard A. Nijstad, De-
partment of Psychology, University of Amsterdam, Amsterdam, the Neth-
erlands.

We thank Joyce Jacobs for help in coding the data of Study 4 and
Gerben van Kleef, Mark Rotteveel, and Richard Ridderinkhof for com-
ments and suggestions.

Correspondence concerning this article should be addressed to Carsten
K. W. De Dreu, Department of Psychology, University of Amsterdam,
Roetersstraat 15, 1018 WB Amsterdam, the Netherlands. E-mail:
[email protected]

Journal of Personality and Social Psychology, 2008, Vol. 94, No. 5, 739–756
Copyright 2008 by the American Psychological Association 0022-3514/08/$12.00 DOI: 10.1037/0022-3514.94.5.739

739

Page 93

centered on the grand mean (partner M � 1.61 before centering,
SD � 1.55; examinee M � 1.76 before centering, SD � 1.65).

Gender. Gender (coded �.5 for men and .5 for women) was
originally included as a covariate, but because it did not affect the
variables of interest (receiving and providing support) and to
simplify the model presented, it was not included in the analyses
reported below.

Moderating Variables

Both potential moderating variables were measured in the back-
ground questionnaire, which both members of the couple com-
pleted approximately 3 weeks before starting the diary portion of
the study (see above for more details about the background ques-
tionnaire administration).

Relationship satisfaction. Overall relationship satisfaction was
measured with one item taken from the Dyadic Adjustment Scale
(Spanier, 1976), on which 0 � extremely unhappy, 3 � happy, and
6 � perfectly happy. Relationship satisfaction was generally high
among these couples (examinees: M � 4.45, SD � 1.04; partners:
M � 4.56, SD � 1.03).

Self-esteem. Self-esteem was measured using the Rosenberg
Self-Esteem scale (Rosenberg, 1965), a 7-item Likert scale ranging
from 0 (low self-esteem) to 4 (high self-esteem; examinees: M �
3.17, SD � 0.59; partners: M � 3.16, SD � 0.60). Alpha reliability
was .86 for examinees and .88 for partners.

Analytic Approach

The goal of the current analysis was to examine the effects of
receiving support from and providing support to one’s partner on
both an individual’s evaluation of the degree of closeness in the
relationship and simultaneously on an individual’s level of nega-
tive mood. We used a multilevel statistical model to investigate
these relationships separately for partners (less stressed) and ex-
aminees (highly stressed). The models had two levels: a within-
individual level (over time) and a between-individuals level. The
model also took into account the fact that outcomes, negative
mood and closeness, were clustered within individuals.7 Using the
multivariate approach described by Raudenbush and Bryk (2002),
we included both closeness and negative mood in a single multi-
level analysis. The multivariate approach allowed us to estimate
the correlation between the random effects for negative mood and
closeness and to examine the frequencies of participants showing
a moderated pattern (see Figure 1, Model 1) and a differential
effects pattern (see Figure 1, Model 2). All analyses were con-
ducted using the MIXED procedure in SAS (SAS Institute, 2003).

The within-individual level of the analysis allowed each indi-
vidual’s relationship closeness and negative mood to be modeled
as a function of receipt of support. We predicted a given day’s
closeness and negative mood for a particular individual; we ad-
justed for either yesterday’s closeness or same-day morning neg-
ative mood, respectively; number of days in the study; and week-
end effects. Given that support transactions may be more likely to
take place on days when an individual experiences stressful events,
a count of daily stressors was included to adjust for the effects of
stressful events as a third variable. The equation was as follows:

Yijk � �Nijk� � �b0ni � b1nYijk�1 � b2nDik � b3nWik � b4nSik

� b5nGik � b6niRik � b7n�Gik � Rik� � eijk]

� �Cijk� � �b0ci � b1cYijk�1 � b2cDik � b3cWik � b4cSik

� b5cGik � b6ciRik � b7c(Gik � Rik) � eijk]. (1)

The dependent variable, Yijk, is the outcome for participant i for
outcome j (when j � 1 it is negative mood; when j � 2 it is
closeness) on day k. Thus, there were two records for each day
within participant, so the maximum number of records that a
participant contributed was 62. When the outcome is negative
mood, Nijk � 1 and Cijk � 0, and the first part of the model is
selected and all of the b coefficients have the subscript n. When the
outcome is closeness, Nijk � 0 and Cijk � 1, and the second part
of the model is selected and each of the b coefficients have a
subscript c. Yijk � 1 is morning negative mood for individual i when
j is equal to 1; Yijk � 1 is yesterday’s closeness for the same
individual i when j is equal to 2; Dik is the number of days in the
study; Wik indicates whether it is a weekend day or not; Sik adjusts
for the number of stressors experienced; Gik is the individual’s
report of providing (giving) support; Rik is the individual’s report
of receiving support; Gik � Rik is the interaction term for providing
and receiving support, and the residual components are represented
by eijk. The coefficient b0ni is the regression intercept for negative
mood for individual i and represents negative mood on the first
weekday of the study when the individual has neither given nor
received support and all other variables are at their projected
average level (as morning mood and daily stressors are grand mean
centered). The coefficient b0ci is the regression intercept for close-
ness for individual i and represents closeness on the first weekday
of the study when the individual has neither given nor received
support and all other variables are at their projected average level
(as yesterday’s closeness and daily stressors are grand mean cen-
tered).

As Bolger and Shrout (2007) discussed, the mixed-model ap-
proach can be specified to acknowledge that the residuals on
adjacent days are likely to be correlated, and we used this speci-
fication in the analysis we report here. This specification allowed
us to account for dependency between outcomes in individuals and
within individuals across time.

The between-individual level of the analysis allows us to model
possible individual differences in the coefficients specified in
Equation 1. We fit a model that considered intercepts for both
closeness and negative mood to be random (i.e., to vary across
persons) and the effect of support receipt on each of the two
outcomes. The formal specification of these models involves the
inclusion of random effects in the Level 2 equation. These have a
mean of zero but variance that is assumed to be nonzero. For
example, the between-individuals level of the model for the inter-
cepts involves the sum of overall means (�) and random effects
(u). Our analytic model also allowed the random effects for the
intercepts and the support receipt effects to be correlated across

7 Kenny, Kashy, and Bolger (1998) provided a general description of
multilevel statistical models. Raudenbush and Bryk (2002) showed that
these models can be influenced by both within- and between-individuals
variation. When within-person and between-person effects are predicted to
be the same, the multilevel analysis that combines the effects is recom-
mended.

830 GLEASON, IIDA, SHROUT, AND BOLGER

Page 94

both effect type and outcome variable. For those interested in the
details of this analysis, the syntax used is available from Marci
E. J. Gleason. The Level 2 equations were

b0ni � �0n � u0ni

b0ci � �0c � u0ci

b6ni � �6n � u6ni

b6ci � �6c � u6ci. (2)

In addition, we tested the moderation hypothesis in two separate
multivariate, multilevel analyses. The same Level 1 equation de-
scribed in Equation 1 was used for each analysis. The Level 2
equations were modified when testing for moderation to include
the moderators (self-esteem or relationship satisfaction), resulting
in an additional predictor in each of the four equations. We did not
alter the specifications of the random effects for the moderation
tests.

Results

Support Patterns

Examinees reported receiving support on 50% of days, whereas
partners reported receiving support on only 40% of days. Exam-
inees reported giving support on 37% of days, whereas partners
reported giving support on 53% of days. Examinees’ support
receipt increased over time (r � .09, p .01), and their reports of
giving support decreased (r � �.03, p .01). The opposite
pattern is observed for partners, that is, partners received less and
provided more support as the bar exam approached (receiving, r �
�.03, p .05; giving, r � .11, p .01).

Fixed Effects

Table 1 presents the fixed-effect results for both outcomes for
partners and examinees. Only the variables of interest are reported
here. The main effect of support receipt was significant for both
negative mood, partners: b6n � 0.075, t(289) � 3.54, p .001;
examinees: b6n � 0.037, t(292) � 2.04, p .05, and closeness,
partners: b6c � 0.248, t(289) � 6.37, p .001; examinees: b6c �
0.411, t(292) � 12.19, p .001. The main effect of giving support on
negative mood was significant for examinees, but not for partners,
partners: b5n � �0.001, t(289) � �0.10, ns; examinees: b5n �
�0.048, t(292) � �2.24, p .05. The main effect for giving support
was significant for both partners and examinees on closeness, part-
ners: b5c � 0.245, t(289) � 8.73, p .001; examinees: b5c � 0.319,
t(292) � 8.63, p .001. For negative mood, these effects have to be
interpreted in the context of a significant interaction between receipt
and provision (see Table 1). When one takes the interaction into
account, the current findings replicate those of Gleason, Iida, et al.
(2003), in which it was found that supportive equity days (days in
which support is both received and provided) are associated with the
lowest levels of negative mood and that receipt-only days are asso-
ciated with the highest levels of negative mood (see Figure 2). As the
figure shows, the receipt of support is detrimental to negative mood,
but only on days in which the recipient of support did not also provide
support to his or her partner.

Figure 3 shows the results for closeness. Support receipt’s
positive effects on closeness were evident despite its also being
associated with an increase in negative mood. Although there was
a marginal interaction between receipt and provision on closeness
for partners, suggesting that supportive equity days were particu-
larly positive for partners, the interaction does not diminish the
beneficial effects of support receipt on closeness.

Partners’ effects of receiving and giving support on relationship
closeness did not differ (difference between estimates � 0.002),
t(289) � 0.10, ns. However, examinees’ effect of receiving sup-
port was greater than the effect of giving support (difference
between estimates � 0.09), t(292) � 2.09, p .05. Days on which
support was received and not given were significantly more neg-
ative when compared with the other three types of days for both
partners (difference between estimates � 0.08), t(289) � 4.45, p
.001, and examinees (difference between estimates � 0.08),
t(292) � 5.19, p .001.

Random Effects

The random effects covariance matrix for both partners and
examinees is displayed in Table 2. The model generated random
effects for the intercepts of closeness and negative mood both
between and within level. The within-level random effects provide
information about what is occurring in individuals’ lives that
affects their levels of negative mood and closeness that was not
captured by our models. As can be seen, the variances for both the
intercept for negative mood and closeness are significant, suggest-
ing that the model does not account for all the variation in these
variables. In addition, the evidence for a negative covariance

Table 1
Multilevel Analysis Results Relating Daily Support to Negative
Mood and Closeness for Partners and Examinees: Fixed Effects

Variable

Partners
(n � 290)

Examinees
(n � 293)

� SE � SE

Negative mood
Intercept 0.343** 0.016 0.589** 0.021
Day � 10 0.015* 0.005 0.102** 0.007
Weekend �0.016† 0.009 �0.016 0.011
Daily stressors 0.027** 0.003 0.064** 0.004
Morning negative mood 0.428** 0.012 0.472** 0.011
Receiving emotional support 0.075* 0.021 0.037* 0.018
Giving emotional support �0.001 0.014 �0.048* 0.021
Receiving Emotional

Support � Giving
Emotional Support �0.093* 0.023 �0.080* 0.027

Closeness
Intercept 1.989** 0.037 1.971** 0.046
Day � 10 �0.057** 0.010 �0.048** 0.001
Weekend 0.113** 0.019 0.135** 0.019
Daily stressors �0.043** 0.007 �0.065** 0.007
Yesterday’s closeness 0.318** 0.011 0.140** 0.011
Receiving emotional support 0.248** 0.039 0.411** 0.034
Giving emotional support 0.245** 0.028 0.319** 0.037
Receiving Emotional

Support � Giving
Emotional Support 0.080† 0.046 0.011 0.046

† p .10. * p .05. ** p .001.

831DUAL EFFECTS OF SUPPORT ON PSYCHOLOGICAL OUTCOMES

Page 185

tarianism and conservatism have been used as moderators of the
threat of MS (i.e., Greenberg et al., 1990, 1992; Lavine et al.,
2005; Pyszczynski et al., 2006) are equally compatible with ADT
and TMT. The two theories do have key similarities, but they also
differ in several core respects. For example, the idea in ADT that
we conform to collective norms and authority in an effort to
prevent and relieve anxiety is akin to the TMT concept of adher-
ence to the “social anxiety buffer,” which includes worldview
contents to protect us from existential anxiety. As well, both ADT
and TMT offer a functional account: Worldview defense is acti-
vated or strengthened as needed. But there are apparent differences
in regard to what triggers worldview defense. In ADT, it is
normative threat or any threat to the collective; in TMT, it is MS
and worldview challenges. Nonetheless, the triggers in ADT and
TMT are connected and similar in regard to worldview threats and
challenges, and the trigger of MS can be seen as a catalyst to the
protection of worldview. Such shaded commonality should en-
courage future research in which ADT and TMT are pitted against
each other in critical studies to determine when and how the two
differ in their predictive capacities in various contexts and what
constitutes the boundaries of their operating domains. Ultimately,
the parallels and the divergences should prompt both ADT and
TMT proponents to conduct full and formal comparisons of these
two intriguing theories and the nature of their respective support.

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Received November 29, 2006
Revision received January 8, 2008

Accepted January 15, 2008 �

923DEATH PENALTY SENTENCING

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