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RESEARCH PAPER
Individual Differences in Mood Changes
Magdalena Marszał-Wis
´niewska
1
•Magdalena Nowicka
1
Published online: 26 April 2017
The Author(s) 2017. This article is an open access publication
Abstract The study explores the influence of individual tendencies toward mood
improvement/deterioration and the Big Five personality traits on mood changes. Partici-
pants (218 students) completed NEO-FFI and The Mood Regulation Scales. Based on the
within-person structure of individual tendencies toward mood improvement/deterioration
four mood regulative types were distinguished (increasing,decreasing,hot and cool type).
In the experimental stage participants were randomly assigned to one of four group con-
ditions created by experimental factors: (1) induced mood (positive/negative), and (2) the
level of cognitive loading (easy/hard condition). Direct (Mood Adjective Check List) and
indirect (emotional version of Lexical Decision Task) measurements were used to assess
mood changes. The results showed different patterns of mood changes for increasing and
decreasing regulative types. Those differences were visible especially in the positive mood
regulation. While the decreasing type decreased the induced positive mood, the increasing
type was characterized by mood changes manifested in energetic arousal increase and tense
reduction. Moreover, high Neuroticism and low Extraversion in the decreasing type
contributed to a negative mood increment manifested in reduction of energetic arousal. The
same effect was revealed for high Conscientiousness for the increasing type. The results
are discussed in the context of psychological status of mood regulation strategies (auto-
matic/controlled) and with reference to previous research in this area.
Keywords Mood Mood changes Mood regulation strategies Individual
differences in mood changes
&Magdalena Nowicka
nowickamagda@op.pl
1
SWPS University of Social Sciences and Humanities, Chodakowska 19/31, 03-977 Warsaw, Poland
123
J Happiness Stud (2018) 19:1415–1438
https://doi.org/10.1007/s10902-017-9879-5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 Introduction
The subjective well-being (SWB) has been an area of intense research over recent decades.
Researchers have been especially interested in the cognitive and affective factors that help
to explain individual levels of SWB (Arhaud-Day et al. 2005). SWB increases with the
frequency of positive affect and decreases with the frequency of negative affect (Tomyn
and Cummins 2011). However, some researchers speculate that high SWB is not neces-
sarily related with low level of negative affect. Accepting one’s negative emotions (ac-
ceptance) and then trying to seek out positive aspects (positive reappraisal) might be an
optimal strategy for building high life satisfaction (North et al. 2011). In other words,
negative emotions can contribute to SWB especially when accompanied with adaptive
affective regulation strategies. From this point of view a healthy and situation-adequate
pattern of mood regulation strategies seems to be critical for SWB.
1.1 Mood
The basic affective state known as a core affect refers to the feeling tone component of
both emotions and mood (Yik et al. 2011). It is a neurophysiological state experienced
constantly and—most of the time—consciously, although the intensity of this feeling
varies over time. Examples of core affect include bipolar opposites, namely pleasure–
displeasure,tension–relaxation,energy–tiredness. Core affect is not sufficient for a mental
representation of emotions which are defined as intense feelings (going beyond pleasure/
displeasure) directed at someone or something (Feldman-Barrett et al. 2007). Mood is
understood as a prolonged core affect, often relatively mild, with behavior, thoughts, and
motivation. Studies have shown higher correlations of core affect with mood scales than
with emotions scales (e.g. Yik et al. 2011). This suggests that mood contains a very large
component of core affect. Mood determines life satisfaction and an ability to meet envi-
ronmental requirements (Larsen 2000). Comparing to emotion, mood is longer, more
global, more diffuse and usually remote from its cause. Finally, moods particularly
influence cognitive functioning whereas emotions are thought to aid in adaptive reactions.
Different models of mood are usually boiled down to two, mutually dependent, bipolar
dimensions: the hedonic dimension and the activation dimension (Diener 1999; Russell
2003; Watson and Tellegen 1985). The neurophysiological data concerning the specificity
of the hedonic dimension (Panksepp 1993) and the heterogeneity of the activation
dimension (cf. Thayer 1989) led to the formulation of the three–dimensional model of
mood (Matthews et al. 1990). This model presents three correlated bipolar mood factors.
Two of them are associated with subjective arousal, experienced either as positive affect
and energy (Energetic Arousal; energy–fatigue) or as negative affect tension (Tense
Arousal; tension–relaxation). A third dimension of Hedonic Tone contrasts pleasant,
contented mood with sadness and dissatisfaction (pleasure–displeasure). Schimmack and
Grob (2000) showed that this model fits the empirical data best, also semantically, i.e., in
terms of the subjective descriptors of experienced affective states.
1.2 Mood Regulation
Mood regulation as a part of general self-regulation refers to processes directed toward
modifying/maintaining the occurrence, duration, and intensity of mood (Larsen 2000).
Different models of mood regulation explain why (motivation approach) (Isen 1987) and
1416 M. Marszał-Wis
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how (processual approach) (Larsen 2000) people regulate their mood. In motivation
approach attention is focused especially on hedonic principle as the major motive
underlying mood regulation (Isen 1987). In processual approach mood regulation is con-
ceptualized as a series of distinct but interrelated control processes which rely especially on
comparing the desired subjective state (set point) to the current mood (Larsen 2000). When
a discrepancy occurs, regulatory mechanisms are engaged to reduce it (negative feedback
loop). More and more often researchers come to the conclusion that mood regulation
provides a unique route to enhanced well-being and that different functional outcomes of
mood regulation depend on different aspects of this processes (Haga et al. 2009; Kashdan
2007). In other words, subjective well-being depends on how, why and when individuals
engage in specific strategies to modify or alter their subjective experience.
1.2.1 Automatic Versus Controlled Mood Regulation
Classifications of mood regulation strategies are based on two main criteria: (1) the
direction of these strategies (mood improvement/deterioration), and (2) the level of their
controllability (automatic/controlled) (Larsen 2000).
Whereas the flexible use of strategies leading to mood improvement is associated with
enhanced life satisfaction and positive affect, mood deterioration strategies, conversely,
contribute to many negative outcomes (e.g. psychopathology) (Kashdan 2007).
Controlled strategies require conscious effort during initiation and implementation
whereas automatic strategies operate immediately and independently of conscious control,
taking advantage especially of automatic cognitive mechanisms (Larsen 2000). Automatic
strategies are perceived as relatively maladaptive, playing an important role in different
forms of psychopathology (Mauss et al. 2007). This conclusion is somewhat surprising for
two reasons. Firstly, automatic mood regulation is effortless—so probably it is more
flexible. Secondly, it seems that many people manage their moods automatically without
the above-mentioned negative effect. Furthermore, some unique data suggest that auto-
matic mood regulation may promote resilience, recovery from stress and high subjective
well-being (e.g. Coifman et al. 2007). According to dual process-theory automatic and
controlled mood regulation strategies are not mutually exclusive categories, but rather have
porous boundaries and numerous interconnections (Mauss et al. 2007). A complete
understanding of mood regulation processes thus requires a detailed investigation of both
automatic and controlled forms of this process, including their possible interactions and
contradictory effects. As some authors suggest, their combined as well as dissected effects
might be grasped during the procedure of considering the analysis of mood changes in
different cognitive loading conditions (Gyurak et al. 2011).
An adequate mood changes measurement is another important methodological chal-
lenge when studying mood regulation (Lischetzke et al. 2011). To date mood changes have
usually been assessed by direct, self-report questionnaires measuring only momentary
mood state and relying on the ability of respondents to report on the construct of interest
(Larsen 2000). Meanwhile, mood regulation processes, especially automatic ones, may
produce changes which are not always easily accessible for self-insight. Thus, a more
precise analysis of automatic and controlled mood changes would be possible, using
simultaneously both direct and indirect mood measures (Lischetzke et al. 2011). Indirect
measures capture information processing by measuring continuously performance that is
relevant to the targeted characteristic and, hence, do not require self-insight. Moreover,
they allow to analyze not only momentary states but also the whole course of their changes.
Individual Differences in Mood Changes 1417
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The research presented here expands the existing data by proposing an experimental
design aimed at analyzing both automatic and controlled mood regulation and providing
direct as well as indirect measures of mood changes. We measured positive and negative
mood changes by assessing the intensity of three mood dimensions twice during the
experiment by means of a self-report questionnaire. To provide an alternative to the self-
report we proposed an emotional variant of lexical decision task (LDT) in which partici-
pants are asked to categorize verbal stimuli as word or non-word (Niedenthal and Set-
terlund 1994). The literature regarding affective influences in the LDT showed that moods
would facilitate lexical decisions about words specifically related to the their valence (e.g.
Halberstadt et al. 1995). According to this foundation sad-induced individuals would
respond faster to sad words than to happy words whereas happy-induced individuals would
respond faster to happy than to sad words. During this task we decided to manipulate the
level of cognitive loading arguing that hard condition (with additional cognitive task)
might lead to a greater likelihood of engaging in automatic mood regulation whereas easy
condition (without additional task) might promote automatic as well as controlled mood
regulation. In developing this procedure we reasoned that mood changes caused by mood
regulation processes would be associated with changes in: (1) subjective intensity of
different mood dimensions assessed before and after LDT using direct mood measurement
(self-report questionnaire), as well as, in (2) time reactions for affective stimuli in LDT
(indirect, continuous measure).
1.2.2 Individual Differences in Mood Regulation
Individuals differ from each other in their abilities or propensities for engaging in mood
regulation (Larsen 2000). Individual differences in mood regulation are related with (1)
direction of preferred practices (positive/negative mood increase/decrease) and their psy-
chological status (automatic/controlled, cognitive/behavioral), (2) the frequency of real use
of specific strategies, and (3) the individual effectiveness of used practices. In this article
we focus on individual tendencies toward mood improvement or deterioration (Larsen
2000; Wojciszke 2003). High tendency toward mood improvement leads to the more
frequent use of strategies leading to positive mood improvement/negative mood deterio-
ration, whereas high tendency toward mood deterioration leads to the more frequent use of
positive mood deterioration/negative mood improvement.
There is still a shortage of empirical data on stable individual differences in mood
regulation (Larsen 2000). So far researchers have explored how often people with different
mood regulation tendencies use cognitive strategies leading to negative mood repair and
positive mood deterioration. For example, Rusting and De Hart (2000) found that fol-
lowing a negative mood induction, those high in individual tendency to negative mood
regulation expectancies experienced more positive memories and recalled more positive
words. People with high mood deterioration tendency use positive mood deterioration and
negative mood improvement strategies more often (Joormann 2006). Strategies leading to
positive mood increase/maintenance are less frequently analyzed empirically (Tugade and
Fredrickson 2004). Although these findings begin to stretch the existing conceptualizations
of mood regulation to include positive mood improvement/maintenance processes, we still
don’t know exactly why and how people do it. What is probably more important, there is a
lack of research providing experimental evidence supporting the impact of different pat-
terns of mood regulation tendencies on real mood changes (Larsen 2000). Meanwhile, such
data could serve as an answer to the following ambiguities. Firstly, we still do not know
whether different patterns of mood regulation tendencies produce general changes in mood
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or induce effects that are selective to some dimensions. Previous studies revealed that
specific changes of different mood dimensions are modified by different temperamental
and motivational traits (Matthews et al. 1990; Zajenkowski et al. 2012). Secondly, the
existing evidence supports a model implying that individuals with low mood improvement
tendencies use more rarely negative mood repair/positive mood improvement strategies
whereas people characterized by low mood deterioration tendencies do not engage in
positive mood decrease/negative mood improvement strategies (cf. Rusting and De Hart
2000). What about people with the concomitance of low levels of these traits? Does it
means that their moods are more stable? Or maybe the root of their mood changes is in
more spontaneous, automatic forms of mood regulation? Thirdly, studies suggest that high
tendency toward mood deterioration is definitely maladaptive, whereas high tendency
toward mood improvement leads to positive psychological outcomes (Haga et al. 2009).
Meanwhile, the combination of these two traits may be quite hazardous. On the one hand,
they may activate contradictory strategies leading to negative affective consequences and
psychopathology. On the other, their synergistic working can result in more flexible mood
regulation and higher SWB.
The present research attempts to contribute to previous data by exploring the impact of
different patterns of mood regulation tendencies on automatic and controlled mood
changes. We decided to begin our investigation by determining the within-person structure
of individual tendencies toward mood improvement/deterioration through a cluster anal-
ysis. We assumed that this method would allow an individual to fall into naturally
occurring groups rather than requiring the artificial creation of all possible combinations of
variables (Gohm 2003). The process of forming relatively homogenous groups makes data
more manageable and facilitates communications. Moreover, the cluster analysis gives the
opportunity to make a greater contribution to the area of SWB through the identification of
specific groups that might best benefit from different types of interventions concerning
mood management.
1.3 Personality and Mood Variables
Although many variables (i.e., subjective well-being, emotional intelligence etc.) may be
meaningful for examining individual differences in affective functioning, researchers
suggest that the relation between personality traits and mood seems to be the most
important (Augustine and Larsen 2015; Gross 1998). Mood is essential for our under-
standing of numerous personality and individual differences variables. Affective states can
be inputs, components, or outputs of the personality system. Some results suggest that
personality predicts more than one-third of the variance along the dimension of Pleas-
antness–Unpleasantness and less than a third of the variance along the dimension of
Activation–Deactivation (Augustine and Larsen 2015; Yik and Russell 2001).
A significant portion of the research to date has focused on the relation between basic
mood components and Big Five personality traits. Extraversion and Neuroticism are the
two dimensions most predictive of momentary affect (Diener 1999; Rusting and Larsen
1995; Watson and Clark 1997). Extraversion is linked strongly and positively to levels of
positive affect and less negative affect variance, whereas neuroticism—to level of negative
affect and less positive affect variance. However, data concerning the tripartite model of
affect suggest that the relation between major personality dimensions and mood is not as
clear as it appears to be at first glance. For instance, Matthews et al. (1990,2003) reported
a correlation only of around .25 between neuroticism and tense arousal, and .13 between
extraversion and positive affect. This group of results relates to experimental studies where
Individual Differences in Mood Changes 1419
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measuring mood forms a part of a broader procedure and usually occurs prior to perfor-
mance. It may indicate that the relationships between major personality traits and mood
dimensions are not stable and vary across situations (Zajenkowski et al. 2012). In other
words, the demands of a situation (e.g. cognitive demands) may be another factor that
influences the relation between personality and mood as well as between personality and
the specificity of mood changes. However, studies which brought these interesting data
were based only on self-reports instruments of analyzed mood changes (e.g. Matthews
et al. 1990) and considered only two measurements of mood (before and after the stimulus
presentation).
Although there are probably many possible consequences of Big Five personality traits
on affective experience, the current research focuses on the consequences for mood
changes processes, including both positive and negative mood. So far, researchers have
found that mood deterioration strategies (e.g. rumination) correlate positively with neu-
roticism and negatively with extraversion, whereas mood improvement strategies (e.g.
cognitive engagement) correlate positively with extraversion (Augustine and Larsen 2015;
John and Gross 2007). Conscientiousness predicts positively the use of strategies involving
behavioral engagement and negatively—waiting strategies (Augustine and Larsen 2015).
Those high in agreeableness are more likely to engage in affect repair in social settings
whereas those high in openness are more likely to use strategies involving active dis-
traction, behavioral engagement and rumination (Tobin et al. 2000). Some authors suggest
that specific effects of personality traits on mood regulation may also depend (like per-
sonality-mood relation) on the specific features of the situation in which regulatory efforts
takes place (John and Gross 2007). This area still requires a definite answer. Moreover, we
still do not know whether personality traits have an analogical impact on controlled and
automatic mood regulation strategies. Some data suggest that personality traits composed
structurally of affective experience modify both automatic and controlled mood regulation,
whereas these non-affective in structure—only controlled strategies (Gyurak et al. 2011).
Such relationships have not been studied more precisely yet.
The current project focuses on the impact of Big Five personality traits on automatic
and controlled changes of both positive and negative mood. Moreover, this study expands
the existing data by examining the possible interactional effects of Big Five personality
traits and different patterns of mood regulation tendencies on real mood changes. As the
existing data have shown, relations between these constructs are rather modest in size
(Gross and John 2003). It suggests that mood regulation tendencies converge with, but did
not duplicate, broader personality dimensions. Thus, they may contribute differently to
predict different aspects of well-being and life satisfaction (Haga et al. 2009).
1.4 The Aims and Hypotheses of the Present Study
The first important aim of the current study was to analyze the influence of different
patterns of individual tendencies toward mood improvement/deterioration on automatic
and controlled changes of both positive and negative mood. The second aim was to learn
how Big Five personality traits would modify mood changes. We posed three hypotheses.
H1 Mood changes related with high tendency toward mood improvement consist in
positive mood increasing and negative mood decreasing.
H2 Mood changes related with high tendency toward mood deterioration rely on
positive mood decreasing and negative mood increasing.
1420 M. Marszał-Wis
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H3 Mood changes for high neurotic individuals consist in positive mood decreasing
(after positive mood induction) and negative mood increasing (after negative mood
induction), whereas for high extraverts—in positive mood increasing (after positive
mood induction) and negative mood decreasing (after negative mood induction).
Moreover, we attempted to contribute to the previous research by formulating the
following empirical question:
Q1 How do interactions of individual tendencies toward mood improvement/deterio-
ration and Big Five personality traits modify automatic and controlled mood changes?
2 Methods
2.1 Participants
A total of 218 undergraduate psychology students of SWPS University participated in the
study. The sample consisted of 132 female and 86 male participants with a mean age of
24.3 years (SD =4.14). Participants were required to be fluent in Polish, to have no
history of psychiatric disorders, to be right-handed, and to have normal or corrected-to-
normal vision. All participants were volunteers and they participated in the research
individually and anonymously. They provided a written consent according to the proce-
dures of the Ethics Committee of SWPS University.
2.2 The Design and Procedure of the Study
The presented research comprised two stages:
1. A questionnaire based study in which individual tendencies toward mood improve-
ment/deterioration and the Big Five personality traits were measured.
2. An experiment (one week after the 1st stage) used to test changes of positive and
negative mood in two levels of cognitive loading.
In the experimental stage (see Fig. 1), participants were randomly assigned to one of
four between-groups conditions created by the factorial combination of induced mood
(positive/negative) and the level of cognitive loading (low/high cognitive loading condi-
tion). Subsequent steps of the experiment were as follows. First, in order to measure their
current mood state, participants completed Mood Adjective Check List,MACL (control
measurement before mood induction). Secondly, they were trained in lexical decision task,
LDT. Training, typically applied in this type of procedure, served to practically acquaint
the participants with instruction, fixation point, and the pattern of responding (Niedenthal
and Setterlund 1994). By using training we tried to ensure that stimulus identification speed
MACL –
control
measurement
LDT -
training
Mood
induction:
positive
or
negative
MACL –
1st
measurement
LDT
or
LDT +
parrallel
task
MACL –
2nd
measurement
Fig. 1 The scheme of the experimental procedure
Individual Differences in Mood Changes 1421
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in the main part of experiment was mediated especially by affective information processing
and not by disturbances related to technical aspects of procedure.
Following the training, the participants were exposed to mood manipulation. They were
reading (for 7 min) about 250-word long, sad (excerpt of the testimony of a homeless
person) or happy story (an excerpt of a funny family tale) presented on the computer screen
while listening to background musical pieces (happy/sad). Next, to assess the effects of this
procedure on their mood state the participants rated their current mood by MACL (1st
direct mood measurement). It was used firstly, before LDT, as a momentary, subjective,
requiring self-insight and more precise mood measure. Levels of three mood dimensions
assessed by MACL in 1st measurement served as a baseline for assessing mood regulation
processes taking place during the next part of the experiment. Moreover, this measurement
possibly increased the situational clarity of feelings, which is indispensable in order to
activate different mood regulation processes (Larsen 2000; Lischetzke et al. 2011). After
the 1st direct mood measurement by MACL participants completed an emotional version
of LDT which was used as an indirect continuous measure of mood changes. In hard
condition participants were also asked to perform a parallel task (automatic mood regu-
lation condition). LDT (in easy as well as in hard condition) took approximately 7-9 min to
complete. The last direct mood assessment by MACL took place after the lexical decision
test (2nd measurement). This measurement served as an endpoint for mood regulation
processes.
Although the afore-described procedure is quite complicated, it allows to grasp both
automatic and controlled processes related to different aspects of emotion regulation. Data
from previous studies showed that RTs for affective stimulus in LDT reliably predict mood
changes at subsequent measurement occasions in easy and hard condition (Nowicka 2009;
see also Lischetzke et al. 2011).
2.3 Measures
2.3.1 Mood Regulation Scales
In order to measure individual tendencies toward mood improvement/deterioration The
Mood Regulation Scales (MRS; Wojciszke 2003) were used. The MRS comprises two
subscales: Mood Improvement (MIS) and Mood Deterioration (MDS). MIS includes 11
items describing cognitive and behavioral strategies leading to positive mood increase and
4 items related to negative mood decrease (for example; When I feel well,my memory goes
back to pleasant moments,I try to find a positive side of a bad situation). MDS is composed
of 12 items representing activities leading to positive mood decrease and three items
concerning mood increase practices (for example: When I am happy,I start thinking that it
is only an illusion,When I am in a bad mood, I stop doing all pleasant things). The
respondents are requested to answer the question (using a five point Likert scale from never
to always) of how often they use different regulation strategies.
An exploratory factor analysis of the MRS (principal component, Varimax rotation,
Kaiser’s normalization) conducted on the data from two independent samples (Wojciszke
2003) supported two factors solution (tendency to Mood Improvement and tendency to
Mood Deterioration). Scales of MRS are independent (non-significant inter-correlation
coefficients ranging from .03 to -.18) and obtained high internal reliability scores in
validation studies (Cronbach alphas for MIS and MDS are .84 and .90 respectively;
Wojciszke 2003) as well as in presented study (Cronbach alphas for MIS =.82 and
MDS =.87).
1422 M. Marszał-Wis
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Convergent and discriminant validity of the MRS questionnaire was examined in pre-
vious research (Nowicka 2009; Wojciszke 2003) by analyzing the relationship between
two scales of MRS and different measures related to emotion regulation (Emotion Regu-
lation Questionnaire—Gross and John 2003;Cognitive Emotion Regulation Question-
naire—Garnefski et al. 2001), emotional functioning (Trait Meta-Mood Scale—Salovey
et al. 1995;The Positive and Negative Affect Schedule—Watson et al. 1999) and per-
sonality (NEO-FFI, Costa and McCrae 1992;Life Orientation Test-Revised, Scheier et al.
1994). MDS correlated strongly positively with non-adaptive emotion regulation strategies
[e.g. for Suppression r =.28, p\.001; for Catastrophizing r =.57, p\.001], Negative
Affect (r=.39, p\.01) and negatively—with Positive Affect (r=-.35, p\.01). MIS,
in contrast, showed high positive correlation with emotional processing [r=.52,
p\.001], mood repair [r=.63, p\.001], moderate positive correlations—with adaptive
emotion regulation strategies [e.g. for Reappraisal—r=.33, p\.001; for Refocus on
planning r =.54, p\.001], Positive Affect (r=.37, p\.01) and negative moderate
correlation—with Negative Affect (r=-.25, p\.01). Correlation coefficients between
mood regulation tendencies and personality traits were modest in size, indicating that two
scales of MRS converge with, but did not duplicate basic personality dimensions (i.e. Gross
and John 2003). MDS correlates positively with Neuroticism (r=.24, p\.05), whereas
MIS correlates positively with Extraversion (r=.23, p\.05), Openness to Experience
(r=.18, p\.05), Agreeableness (r=.21, p\05), and dispositional Optimism (r=23;
p\.05). Correlation coefficients between Big Five personality traits and MRS scales
obtained in presented study were very similar, ranging from .11 to .23.
Additionally, we analyzed the relation between scales of MRS and NEO-FFI more
precisely by employing the principal factor analysis with Varimax rotation for all the items
of these two questionnaires. It revealed that a seven-factor solution fits the data best. The
seven-factor solution accounted for 59.9% of the total variance. Visual inspection of the
rotated item-factor loadings suggested that the factors represented Neuroticism (15% of the
total variance), Extraversion (11.9%), Agreeableness (11.3%), Openness to Experience
(10.7%), Consciousness, Mood Deterioration (with four high loading items from Neu-
roticism scale; 5.6%), Mood Improvement (with three high loading items from Extraver-
sion scale and two high loading items from Openness to Experience scale; 5.4%). These
results may suggest that Big Five dimensions and mood regulation tendencies are inter-
related but not strongly overlapping constructs.
2.3.2 NEO-FFI Questionnaire
The Big Five personality traits were measured by NEO-FFI questionnaire (Costa and
McCrae 1992) in the Polish adaptation by Zawadzki et al. (1995). The internal reliability
scores of this questionnaire (Cronbach alphas) in the Polish national sample are satis-
factory and achieve values ranging from .64 to .84. Analogical coefficients (ranging from
.68 to .80) were obtained in the present study.
2.3.3 Mood Adjective Check List (MACL)
In the experimental stage mood was directly measured using the Mood Adjective Check
List (MACL) by Matthews et al. (1990) in the Polish adaptation by Gorynska (2005). The
respondent is requested to answer the question Does this adjective describe your present
mood? on a 4-point scale from definitely not to definitely yes. The check list has three scales
corresponding to the three dimensions of mood (Matthews et al. 1990,2003): hedonic tone
Individual Differences in Mood Changes 1423
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(HT), tense arousal (TA), and energetic arousal (EA). The Polish adaptation of the MACL
was standardized on a national representative sample. An exploratory factor analysis
supports three dimensions of mood. The internal reliability scores were satisfactory in the
Polish validation study [Cronbach alphas for HT =.89, TA =.93, and EA =.78 (Go-
rynska 2005)], as well as, in the present study [Cronbach alphas for HT =.86, TA =.80,
and EA =.74].
2.3.4 Mood Induction
In the mood induction procedure participants were asked to read one of two stories dif-
ferentiated by their affective valence (positive or negative), while listening to background
music pieces (happy or sad). They were reading an excerpt of a funny family tale versus an
excerpt of the testimony of a homeless person while listening to background musical
pieces: Scott Joplin’s Pine Apple Rag and Eric Satie’s Gnossienne no 1 for positive and
negative mood, respectively. As the preliminary study showed, both stories as well as both
music pieces had the affective impact of comparable strength (Nowicka 2009).
In the preliminary analysis [three one-way repeated-measures ANOVAs (induced mood
as the factor and a given dimension of mood as dependent repeated variable] we verified
the mood induction procedure by comparing the control mood measurement and the 1st
mood measurement after mood induction. The participants in whom a positive mood was
induced, compared with those in whom a negative mood was induced, had significantly
higher mean scores on the scales of HT (F
(1,216)
=16.82, p\.001, g
2
=.29; M=3.28,
SD =.58 vs. M=2.74, SD =.31, respectively), and of EA (F
(1,216)
=23.37, p\.001,
g
2
=.30; M=3.33, SD =.62 vs. M=2.81, SD =.41, respectively), and significantly
lower mean scores on the scale of TA (F
(1,216)
=32.02, p\.001; g
2
=.33; M=2.74,
SD =.47 vs. M=2.96, SD =.59, respectively).
The efficiency of mood induction was also verified in the preliminary study (Nowicka
2009). We measured the participants’ mood just before and just after the mood induction
procedure described above (students, N=130, M
age
=26.13, SD =3.56) by means of the
MACL questionnaire. We did not use any additional task. A series of three one-way
repeated measures ANOVAs (induced mood as the factor and a given dimension of mood
as dependent repeated variable) showed significant effects (for HT:F
(1,128)
=18.33,
p\.001, n
2
=.31; for TA:F
(1,128)
=24.33, p\.001; n
2
=.28; for EA: F
(1,128)
=23.45;
p\.01; n
2
=.23). In the second measurement participants in whom a positive mood was
induced, compared with those in whom a negative mood was induced, had significantly
higher mean scores on the scale of HT (M=3.18, SD =.63 vs. M=2.63, SD =.28,
respectively), and of EA (M=3.41, SD =.57 vs. M=2.83, SD =.39, respectively), and
significantly lower mean scores one the scale of TA (M=2.54, SD =.36 vs. M=3.13,
SD =.44 respectively). We also found that the mood induction procedure modified sig-
nificantly the intensity of mood dimensions in each group. After the positive mood
induction the level of HT (M
1
=2.84, SD =.31 vs. M
2
=3.18, SD =.23), and EA
(M
1
=2.77, SD =.16 vs. M
2
=3.03, SD =.29) significantly increased in second mea-
surement whereas the level of TA decreased (M
1
=2.84, SD =.23 vs. M
2
=3.13,
SD =2.51). After the negative mood induction the pattern of results was reversed (for HT:
M
1
=2.74, SD =.16 vs. M
2
=2.51, SD =.21; for EA: M
1
=2.76, SD =.21 vs.
M
2
=2.43, SD =.26; for TA: M
1
=2.61, SD =.36 vs. M
2
=3.18, SD =.34).
1424 M. Marszał-Wis
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123
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2.3.5 Lexical Decision Task
In order to measure mood changes indirectly, an emotional version of the lexical decision
task (LDT) was used (Halberstadt et al. 1995; Niedenthal and Setterlund 1994).
During the main procedure of LDT on each of 108 trials (in training session—18 trials),
participants were presented with strings of letters and were instructed to indicate whether
the presented string was a legitimate Polish word or not (easy condition). Additionally, in
hard condition participants were instructed to perform a parallel task which consisted in
deciding whether the image (arrangement of two straight lines and three stars) presented on
the computer screen was identical with the pattern shown when giving the instructions. We
also manipulated the level of cognitive loading in the course of training. All decisions had
to be done as quickly and as accurately as possible. In the easy condition participants
responded by pressing the ‘‘P’’ key with their right index finger for ‘‘word’’ and the ‘‘Q’’
key with their left index finger for ‘‘non-word’’. In the hard condition individuals
responded by pressing (with the index finger of their right hand) the ‘‘P’’ key for ‘‘word’’ or
the ‘‘L’’ key for ‘‘non-word’’, and simultaneously (with the index finger of their left hand)
the ‘‘Q’’ key, if the image was identical with the pattern or the ‘‘A’’ key if the image was
different. The letter strings consisted of 27 words [nine positive (e.g., delighted), nine
negative (e.g. aggressive), nine neutral (e.g. wooden)] and nine non-words. During the
main procedure all stimuli were grouped into nine experimental blocks each consisting of
three positive, three negative, three neutral and three non-words (pseudo-homophones).
Non-words were pronounceable and closely resembled words, typically differing in two
letters. They were matched on concreteness, frequency, imageability, pronounceability,
and the number of letters. Additionally, all affective stimuli were judged by 15 psychology
students on a scale of -3 (very negative)to?3(very positive). Positive, negative, and
neutral words had mean ratings of 2.56 (SD =.29); -2.02 (SD =.48) and .33 (SD =.11)
respectively. During the training session the stimuli were presented in two blocks, each
consisting of 6 neutral stimuli and 3 non-words.
In the easy condition the onset of each trial was marked by a horizontally and vertically
centered ‘‘plus’’ sign (?), which served as a fixation point. In the hard condition two points
(?) appeared in the middle of the each half of computer screen and were replaced by a
string of letters (right side) and the image (left side). After 500 ms latency, the points was
replaced by the stimuli. The stimuli items disappeared after the participant responded or
following a latency of 10,000 ms, whichever occurred first, and were followed by an inter-
trial interval of 200 ms. All stimuli in trials were presented in a randomized fashion,
controlled by computer software.
In the preliminary study we verified that the above procedure of LDT with training
session (as well as training treated as a separate procedure) not proceeded by mood
induction had no impact on subjective mood state measured just before and just after its
performance. A series of repeated measures ANOVAs [for the whole procedure: the level
of cognitive loading as the factor and a given dimension of mood as dependent repeated
variable; for training: a given dimension of mood as dependent repeated variable] did not
show significant changes for three basic mood dimensions [LDT with training: for HT:
F
(1,156)
=8.12, p=.09, g
2
=.091; M
1
=2.91; M
2
=3.01; for EA:F
(1,156)
=10.37,
p=.11, g
2
=.018, M
1
=2.89; M
2
=2.83, and for TA:F
(1,156)
=8.0, p=.13;
g
2
=.014; M
1
=2.54; M
2
=2.63; for training: for HT:F
(1,157)
=12.12, p=.11,
g
2
=.099; M
1
=3.04; M
2
=3.02; for EA:F
(1,157)
=12.48, p=.24,g
2
=.010,
Individual Differences in Mood Changes 1425
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M
1
=3.01; M
2
=2.97, and for TA:F
(1,157)
=10.0, p=.18; g
2
=.009; M
1
=2.61;
M
2
=2.67].
For each of the stimuli presented in LDT RTs were unobtrusively measured. We
excluded from the analysis data RT outliers (any RTs less than 250 ms or greater than
1.500 ms) and incorrect responds to RTs. It resulted in less than 2% of the original data
being removed. Because of the disturbances of distribution RTs for all stimuli were
transformed using natural logarithm.
3 Results
3.1 Patterns of Individual Tendencies Toward Mood Improvement/
Deterioration
In the first step we analyzed the within-person structure of tendencies toward mood
improvement/deterioration and we developed the typology of persons.
The MRS scores were first standardized across the whole sample. To group participants
according to the profile of their scores on mood regulation scales, K-means cluster analyses
on the Euclidean distances between scores was conducted. The four-cluster solution pro-
vided the maximum amount of distinctiveness among the participants while keeping within
cluster variance to a minimum and the cluster sizes large enough to allow analysis of
different experimental conditions. In Fig. 2, for four groups defined in the cluster analysis,
the profiles of mood improvement and mood deterioration are given.
Clusters 1 (N=52) and 2 (N=60) are mirror images of one another and were labeled
decreasing type (individuals scoring above the mean on mood deterioration and below on
mood improvement) and increasing type (individuals scoring above the mean on mood
improvement and below on mood deterioration) (e.g. Gohm 2003). Participants in cluster 3
(N=41) scored below the mean on two scales and were labeled cool type. Cluster 4
(N=56), named as hot type, consisted of participants who scored above the mean on two
2,12
3,52
2,13
3,35
3,4
2,31
2,26
3,12
1,5
2
2,5
3
3,5
4
Cluster 1/decreasing
type
Cluster 2/increasing
type
Cluster 3/cool type Cluster 4/hot type
MEANS for MRS
mood improvem ent mood deteriorat ion
Fig. 2 Results of K-means cluster analyses
1426 M. Marszał-Wis
´niewska, M. Nowicka
123
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scales. One-way analyses of variance (ANOVAS) were conducted to examine mean dif-
ferences in two types of mood regulation strategies inside each cluster. In line with our
expectations mood deterioration and mood improvement scores differ significantly only in
cluster 1 [F
(1,51)
=16.14; p\.001; g
2
=.022] and 2 [F
(1,59)
=5.38; p\.05; g
2
=.055]
(see Fig. 2). Four regulative types distinguished on the basis of cluster analysis were
included in further analyses.
We verified general pattern of results obtained by individuals representing each cluster
in NEO-FFI questionnaire. The analyses of variance (MANOVAS) showed that the profile
of results differed between clusters [F
(5,197)
=94.01; p\.05; g
2
=.047]. Figure 3dis-
plays personality profiles of each cluster as summarized by mean NEO-FFI Tscores.
Decreasing type was characterized by higher neuroticism and lower extraversion, open-
ness, agreeableness, and conscientiousness scores than hot,cool and increasing type
(ps \.01). The pattern of results obtained in NEO-FFI by increasing and hot type was
incredibly similar. They scored higher on extraversion than cool and decreasing type
(ps\.01) and lower on neuroticism than decreasing type (ps \.01).
3.2 Individual Differences and Automatic and Controlled Mood Changes
To examine the impact of regulative types on mood changes we used the four-way
ANOVAs with direct mood measure (level of HT, EA and TA) and with indirect mood
measure (time reaction in LDT) as dependent repeated variable, and regulative types,
moment of measure, type of mood induction, and the level of cognitive loading as inde-
pendent variables. Analogical five-ways ANOVAs with additional independent variable—
the level of Big Five personality traits were made (separate analysis for each trait of Big
Five). A median split was used to separate people with high and low levels of Big Five
personality traits.
67,4
42,3
36,1
34,2
36,2
46,2
61,1 61 59,9
47,2
44,8
64,1 63,4
57,4
51,3
51,4
45,3
56,5
54,1
56,7
30
40
50
60
70
Neuroticism Extraversion Openess Agreeableness Conscientiousness
Me ans fo r NEO-FFI T scores
populaon norm decraesing type increasing type
hot type cool type
Fig. 3 General pattern of results obtained by individuals representing each cluster in NEO-FFI
questionnaire
Individual Differences in Mood Changes 1427
123
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3.2.1 Patterns of Mood Regulation Tendencies and Mood Changes Measured
Directly
The four-ways ANOVAS for each mood dimension (HT, EA, TA) as dependent repeated
variables [the level of mood after mood induction (1st measurement) and the level of mood
after the task (2nd measurement), and regulative types (increasing, decreasing, hot and
cool), type of mood induction (positive/negative) and the level of cognitive loading (high/
low cognitive loading condition) as independent variables] revealed significant effects of
four way interactions for energetic arousal (EA) [F
(3,202)
=8.06; p\.01; g
2
=.037], and
tense arousal (TA) [F
(3,202)
=5.12; p\.01; g
2
=.018]. Post hoc analyses (see Fig. 4)
pointed at essential differences between increasing and decreasing types, especially after
positive mood induction (F
(2,98)
=7.24; p\.01; g
2
=.023). While the level of EA
decreased for the decreasing type in both high and low cognitive loading condition, it
increased for the increasing type only in easy condition (see Fig. 4). Moreover, for the hot
type energetic arousal increased in both low (p\.05) and high (p\.05) cognitive loading
condition.
2,98 3,01
2,89 3,03
2,54
3,62
3,21
3,09
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
decreasing type increasing type h ot type cool type
Easy condition
Means for EA
3,04 3,01 2,96
2,79
2,42
2,87
3,28 3,06
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
decreasing type increasing type hot type cool type
Hard condition
measurement 1-MACL mood measurement after positive mood induction
measurement 2 - MACL mood measurement after LDT
Means for EA
Fig. 4 Change of energetic arousal after positive mood induction (decreasing, increasing and hot types)
1428 M. Marszał-Wis
´niewska, M. Nowicka
123
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The level of TA increased for the decreasing type in both high cognitive loading
condition and low cognitive loading condition and it decreased for the increasing type only
in easy condition (see Fig. 5).
3.2.2 Patterns of Mood Regulation Tendencies and Mood Changes Measured
Indirectly
The 4 (regulative types) 92 (mood induction) 92 (the level of cognitive loading) mixed
ANOVAs were performed on mean correct RTs data from nine stimuli blocks as dependent
repeated variable independently for positive, negative and neutral stimuli.
In the analysis considering time reactions for negative stimuli (negative words) a sig-
nificant effect of three-way interaction of moment of measure, mood induction and reg-
ulative types was revealed [F
(8,192)
=3.08; p\.05; g
2
=.019]. Generally, the decreasing
type reacted faster for negative words than other groups (ps \.05), but only in hard
condition and after positive mood induction.
2,69
2,81
2,72
2,84
2,92
2,54
2,86 2,99
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
decreasing type increasing type hot type cool type
Eas y conditio n
Means of TA
2,74 2,68
2,84
2,84
3,12 2,79
2,93
3,02
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
decreasing type increasing type hot type cool type
Hard condition
measurement 1- MACL mood measurement after positive mood induction
measurement 2 - MACL mood measurement after LDT
Means of TA
Fig. 5 Change of tense arousal after positive mood induction (decreasing and increasing types)
Individual Differences in Mood Changes 1429
123
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Tests of trends from the analysis of variance showed a highly significant linear trend for
this three way interaction [F
(2,198)
=7.90; p\.001; g
2
=.028]. The analysis of simple
effects showed that the decreasing type decreased time reaction for negative words after
positive mood induction, in low (p\.05) as well as in high (p\.05) cognitive loading
condition (see Fig. 6).
In the analysis considering time reactions for positive stimuli (positive words) signifi-
cant effect of four-way interaction of the moment of measure, mood induction, the level of
cognitive loading and regulative type was revealed [F
(8,192)
=2.57; p\.05; g=.024].
The increasing type reacted generally faster for positive words than other groups
(ps \.05), but only in easy condition and after positive mood induction.
A significant linear trend for this four-way interaction was revealed [F
(2,198)
=6,13;
p\.01; g
2
=.019]. As the analysis of simple effects demonstrated, the increasing and hot
7
7,1
7,2
7,3
7,4
7,5
7,6
7,7
7,8
7,9
8
123456789
RT (natural logarithm)
positive mood -hard condition
M - me an reacon mes for negave words in e ach cluster
5
5,5
6
6,5
7
7,5
8
123456789
RT (natural logarithm)
positive mood -easy condition
M - mean reacon mes for negave words in each cluster
Mincreasing=812 (SD=108)
Mhot=748 (SD= 1)
Mcool= 729 (SD=108)
Mdecreasing=684 (SD=112)
Mcool= 642 (SD=145) Mhot=636(SD=111)
Mincreasing=612 (SD=54) Mdecreasing=574 (SD=98)
Fig. 6 Linear trend for negative words after positive mood induction—low and high cognitive loading
condition
1430 M. Marszał-Wis
´niewska, M. Nowicka
123
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types decreased their RT’s to positive words after positive mood induction but only in low
cognitive loading condition (ps \.05; see Fig. 7).
3.2.3 Big Five Personality Traits, Patterns of Mood Regulation Tendencies and Mood
Changes Measured Directly and Indirectly
The five-way ANOVAs with the moment of direct or indirect mood measurement as a
dependent repeated variable and regulative types (increasing, decreasing, hot and cool),
type of mood induction (positive, negative), the level of cognitive loading (high and low
cognitive loading condition) and the level of personality traits as independent variables
were conducted.
The analysis considering direct mood measurement [each mood dimension (HT, EA,
TA) as dependent repeated variable; the level of mood after mood induction (1st mea-
surement) and the level of mood after the task (2nd measurement)] showed significant
effects of the three way interaction of moment of measure, regulative types and personality
traits for energetic arousal. We observed the following:
•A significant interaction of moment of measure, regulative types and Neuroticism,
F
(2,202)
=3.08; p\.05; g
2
=.016. The analysis of simple effects showed that high
level of neuroticism for the decreasing type promoted strong and statistically
significant energetic arousal decrease (p\.05). An analogical change of energetic
arousal was not observed for low neurotic people representing the decreasing type
(p=.11). Neuroticism does not modify tense arousal changes—the level of this mood
dimension decreased for the decreasing type independently of neuroticism level
(p[.05).
•An significant interaction of moment of measure, regulative types and Extraversion,
F
(2,202)
=2.93; p\.05; g
2
=.019. For the decreasing type strong energetic arousal
decrease was observed only for individuals low in extraversion (p\.05), and not for
those high in extraversion (p=.09).
6
6,2
6,4
6,6
6,8
7
7,2
123456789
RT (natural logarithm)
positive mood -easy condition
M - me an reacon mes for posive words in each cluster
M
hot
=664 (SD=77)
M
decreasing
=667 (SD=93)
M
increasing
=657 (SD=108)
M
increasing
=574 (SD=108)
Fig. 7 Linear trend for positive words after positive mood induction—low cognitive loading condition
Individual Differences in Mood Changes 1431
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•A significant interaction of moment of measure, regulative types and Conscientious-
ness, F
(2,202)
=2.87; p\.05; g
2
=.021. High level of this trait for the increasing type
was related to the decrease of energetic arousal (p\.05).
Analogical analyses for RTs for negative and positive words were conducted. No inter-
actional effects of the analyzed independent variables on RTs for affective words were
found (Figs. 8,9,10).
4 Discussion
The aims of the current study were to experimentally analyze the influence of different
patterns of mood regulation tendencies, Big Five personality traits and their interaction on
automatic and controlled mood changes. This is probably a pioneer study in the area of
mood regulation where the criterion variable (mood changes) was measured (1) directly
using self-report (MACL questionnaire), as well as (2) indirectly by means of emotional
version of LDT.
Before referring to our specific hypotheses we would like to point at two important and
more general results obtained in this study. Firstly, in the preliminary analysis we found
that individual tendencies toward mood improvement/deterioration occur in four specific
profiles labeled as increasing, decreasing, hot and cool. The increasing type is charac-
terized by high tendency toward positive mood improvement and negative mood deteri-
oration, whereas the decreasing type—by high tendency toward negative mood
improvement and positive mood deterioration. Hot type individuals declare they often use
strategies leading to mood improvement (positive mood improvement, negative mood
deterioration) as well as mood deterioration (negative mood deterioration, positive mood
improvement), whereas participants representing cool type use those two types of strate-
gies very rarely. This research is the first to investigate these particular combinations of
traits. Secondly, it seems that proposed procedure allowed us to grasp both automatic and
3,06
3,04
2,61
2,86
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
high Neuroticism low Neuroticism
Energetic Arousal for the decreasing type
measurement 1 - MACL mood measurement after positive/negative mood
induction
measurement 2 - MACL mood measurement after LDT
Means for EA
Fig. 8 Change of energetic arousal depending on the level of neuroticism for the decreasing type
1432 M. Marszał-Wis
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123
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controlled mood regulation strategies. Mood changes observed especially for the de-
creasing type were registered in both high and low cognitive loading conditions. They
manifested themselves in changes of the intensity of different mood dimensions as well as
in changes of time reactions for adjectives in an emotional version of LDT. It revealed that
the emotional version of LDT might be useful in examining the specificity of mood
changes in full time dimension. It provides an opportunity to analyze temporal dynamics of
3,07
3,01
2,71
3,21
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
low Extraversion high Extraversion
Energetic Arousal for the decreasing type
measurement 1 - MACL mood measurement after positive/negative mood inductio
n
measurement 2 - MACL mood measurement after LDT
Fig. 9 Change of energetic arousal depending on the level of extraversion for the decreasing and
increasing type
2,97
3,01
3,05
2,73
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
low Conscientiousness high Conscientiousness
Energetic Arousal for the increasing type
measurement 1 - MACL mood measurement after positive/negative mood induction
measurement 2 - MACL mood measurement after LDT
Fig. 10 Change of energetic arousal depending on the level of conscientiousness for the increasing type
Individual Differences in Mood Changes 1433
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mood regulation like time needed for recovery from induced emotions to optimal affective
state without imposing self-insight (Lischetzke et al. 2011). More analyses are needed to
assess validity and other possible applications of this mood measure in experimental
studies.
Our results let us dispel some ambiguities concerning the impact of specific patterns of
mood regulation tendencies on mood changes. The obtained data partially supported our
predictions concerning mood regulation processes related to different mood regulation
tendencies.
As we predicted in hypothesis 1, mood changes related to high tendency toward mood
improvement consisted in positive mood increasing—they lead to increased energetic
arousal and decreased tense arousal only after positive mood induction and only in low
cognitive loading condition. Thus, the specificity of positive mood changes characteristic
for the increasing type involved psycho-physiological energy mobilization and tension
reduction. Analogical mood changes indices for participants representing this type were
obtained also on indirect mood measure—emotional version of the LDT. Positive mood
improvement for the increasing type was expressed by the linear decrease of time reactions
to positive target words. Unexpectedly, our analyses suggested that high mood improve-
ment tendency in the increasing type was not related to negative mood deterioration
processes after negative mood induction.
The obtained results partially supported hypothesis 2. Mood changes observed for the
decreasing people after positive mood induction and in both cognitive loading conditions
manifested themselves in decreased energetic arousal and increased tense arousal. Thus the
specificity of positive mood changes characteristic for this type involved especially
physiological energy reduction and tension increase. Positive mood deterioration for the
decreasing type was also expressed by the linear decrease of time reactions to negative
target words during the LDT. Contrary to hypothesis 2, we did not find any results pointing
that high mood deterioration tendency in the decreasing type might lead to negative mood
improvement processes. No specific patterns of time reactions were observed after negative
mood induction for this type.
Taking into account the above results, we are able to draw some interesting conclusions.
On the one hand, high mood deterioration tendency is not always definitely maladaptive, as
it does not lead individuals to negative mood deterioration, whereas high mood
improvement tendency is not always unequivocally positive, as it does not provoke neg-
ative mood repair. On the other hand, it may be possible that negative mood induced in this
study was not sufficiently explicit or intensive to trigger mood regulation repair. Moreover,
mood regulation processes directed toward negative mood increase are probably more
strongly regulated by situational factors or/and characteristics of psychopathology (Larsen
2000). Such hypotheses should be explored more closely in future research.
The obtained data suggest that the concomitance of low levels of mood regulation
tendencies is related with more stable mood experience. We did not find any statistically
significant mood changes predicted in for the cool type (characterized by low level of
tendency toward mood improvement as well as mood deterioration). Moreover, our study
did not reveal any clear pattern of mood changes predicted in hypothesis 1 and 2 for the hot
type (characterized by high level of tendency toward mood improvement as well as mood
deterioration). Only one result suggests that individuals representing this cluster experi-
enced mood improvement processes after positive mood induction—their level of ener-
getic arousal increased in both high and low cognitive loading condition. It is possible that
for individuals in the hot cluster mood changes diverge simultaneously in different
1434 M. Marszał-Wis
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123
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directions or the specificity of mood changes depends more on situational factors. The
consequence of being hot should be explored in future research.
Our study also explored relationships between Big Fiver personality traits and the
specificity of mood changes. In hypothesis 3 we expected that mood changes for high
neurotic persons would consist in positive mood deteriorating (after positive mood
induction) and negative mood improving (after negative mood induction) whereas for high
extraverts—in positive mood improving (after positive mood induction) and negative
mood deteriorating (after negative mood induction). What emerged, however, was a little
bit more complicated pattern of results. As predicted in hypothesis 3, high Neuroticism was
conducive to a strong energetic arousal decrease after positive and negative mood
induction. Surprisingly, this effect was observed only in participants representing the
decreasing type. We did not find any results indicating that high level of Extraversion
contributed to positive mood improving or/and negative mood deterioration. Conversely,
our results suggest that introverts representing the decreasing type are much more vul-
nerable to strong energetic arousal decrease after positive as well as after negative mood
induction. Additionally, we found that for the increasing type high level of Consciousness
is related with energetic arousal decrease. We may assume that this personality trait
‘‘weakens’’ the adaptive meaning of high tendency toward mood improvement leading to
the reduction of psycho-physiological energy.
In the present study we asked also about the specific impact of individual tendencies
toward mood improvement/deterioration and Big Five personality traits on automatic and
controlled mood changes (Q1). As our study showed, the status of mood changes (auto-
matic versus controlled) is related especially to individual tendencies toward mood reg-
ulation. We found that mood improvement processes characteristic for the increasing type
were observed only in low cognitive loading condition. Thus, they require the use of more
motivated and more effortful but maybe not always strictly controlled mood management
skills. We may suppose that mood deterioration processes—observed for the decreasing
type in both cognitive loading conditions—are constituted especially from automatic
mechanism starting up/maintaining cycle of negative affectivity. Future research should
analyze more precisely why people representing the decreasing type have problems with
‘‘switching up’’ to mood improvement processes. It may be related with another important
implication of our study. Results obtained from emotional version of LDT suggest that
individual tendencies toward mood regulation are related to differences in reactivity to
affective stimuli. The existing literature implicates different cognitive resources as a
potential mechanism of mood regulation. Our experiment showed that mood regulation
processes observed in different regulative types manifested themselves in changes of
reaction times for words specifically related to optimal affective state (negative words for
the decreasing type, positive words for the increasing type). It is possible that, as a result of
greater experience engaging mood improvement action, those representing the increasing
type have greater reactivity to positive stimuli and (likely) greater cognitive resources
available for mood improvement. An analogical relation appears probably between the
decreasing type and negative stimuli.
Our findings may delineate a new area of research concerning different contributors of
well-being. As our results showed the objective of SWB is not always happiness (see also
North et al. 2011). People may differ in stable tendencies toward mood regulation and
these tendencies may promote different moods (positive or negative) as desired. In other
words, different mood regulation tendencies seems to be related with well-being to the
extent that they lead to some beneficial outcomes, which are not always related with
Individual Differences in Mood Changes 1435
123
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positive mood and which are maintained and pursued with flexibility. This assumption
needs further clarification and research.
The results presented here may have some practical implications. Many classical as well
as modern theories of psychotherapy try to identify relations between non-adaptive patterns
of affective regulation and psychopathology (e.g. Leahy et al. 2011). An essential part of
treatment is helping individual to overcome non-adaptive schemes of mood regulation by
eliminating strategies leading to mood deterioration (Leahy et al. 2011) or maintaining
positive feeling (Tugade and Fredrickson 2004). Our findings suggest that therapeutic
techniques should concentrate on a wide variety of mood regulation strategies (not only
related with mood improvement) and matched to the individual characteristic of the
patient.
Although the results of the present study are promising, in the following we will discuss
some limitations. One may be related with mood regulation tendencies measurement. To
put it simply, are people always consciously aware of using strategies to influence their
own mood? It is possible that some of the strategies assessed in the MRS scale, particularly
those more popular, are used relatively habitually (Gross 1998) and people may not be
explicitly aware that they are using such behaviors in order to regulate affect? Moreover,
our conclusions might be limited by the procedure complexity which could interfere with
the process of automatic as well as controlled mood regulation. Thus, we cannot explicitly
conclude whether all observed mood changes were due to mood regulation processes or
procedural factors.
Acknowledgements The research was a part of Project ‘‘Mood: their regulative and informative functions’’
supported by the Committee of Scientific Research in Poland (Grant No. 1410/B/H03/2009/37), and
accepted by the Committee of Research Ethics of SWPS University.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Inter-
national License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
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