Mood and mental effort : informational mood impact on cardiovascular reactivity and the context-dependency of moods
DE BURGO DE LIMA RAMOS, Joana
This thesis goal was to test the role of mood in the mobilisation of mental effort. Based on the Mood-Behavior-Model, study 1 intended to clarify that mood effects on effort mobilization are context-dependent; study 2 was designed to provide a more conclusive test of mood informational impact on behavior-related judgments; and studies 3 and 4 manipulated judgment context itself, while accounting for the context-dependency of moods. Effort mobilization was operationalized as cardiovascular reactivity. Results support that: 1) moods by themselves are not stable motivational states; 2) effort mobilization only occurs in contexts that explicitly demand effort; 3) mood will only be used as information for demand appraisals and effort mobilization if people remain unaware of their affective state; 4) mood's effect on effort mobilization is context-dependent. Thus, moods per se do not involve effort-related autonomic adjustments, but they can impact effort-related autonomic reactivity during task performance and self-regulation.
DE BURGO DE LIMA RAMOS, Joana. Mood and mental effort : informational mood impact on cardiovascular reactivity and the context-dependency of moods. Thèse de doctorat : Univ. Genève, 2009, no. FPSE 430
URN : urn:nbn:ch:unige-169081
DOI : 10.13097/archive-ouverte/unige:16908
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Doctorat en psychologie
These de Joana de Burgo
Intitulee : « Mood and mental effort : Informational mood impact on
cardiovascular reactivity and the context-dependency of moods»
La Faculte de psychologie et des sciences de I'education, sur preavis d'une commission formee par les professeurs : Mireille Betrancourt , Universite de Geneve ; Patrik Vuilleumier, Faculte de Medecine, Geneve ; Rex Wright, Universite d'Alabama, Birmingham, USA.
autorise I'impression de la presente these, sans pretendre par la emettre d'opinion sur les propositions qui y sont enoncees.
GENEVE , Ie 9 octobre 2009
These No 430
N. B. La these doit porter la declaration precedente * et remplir les conditions enumerees dans les
« Recommandations aux etudiants qui presentent une these ».
40 bd du Ponl-d 'Arve -CH-1211 Geneve 4 Ted . 022 379 90 01 -Fax 022 379 90 20 www .unige .ch/fapse
To Prof. Guido Gendolla, who believed in my ability of adaptation to a new environment and gave me the opportunity to integrate his team and research field, in a stimulating and friendly work environment.
To the members of my jury, professors Mireille Betrancourt, Patrik Vuilleumier, and especially Rex Wright, for their interest in my research and theoretical insights and exchanges.
To the many students who participated in my experiments and to the lab collaborators that helped running them.
A special thanks to the thesis’ “editors”: Cristina and Will for the English part, and Rodolphe for the French summary.
To my future team, for their comprehension and patience: Philip, Manuela, Blagena, Andressa, Sabine, Karl and Daniel.
To Nicolas: a peaceful office-‐mate, always available to help and to facilitate my French improvement.
To Michael: a patient teacher, that likes and encourages you to think, and who is a wise colleague and friend.
To Kerstin: the social pillar of our team, who makes everyone feels at ease and welcome. A very, very special “freund”, with whom I have the pleasure to share many important and funny moments.
To my Uni-‐Mail and CISA colleagues’ and friends’, namely: Tobias & Micaela, Etienne, Martjin, Irene, Chiara, Cristina & Will, Nele & Michael, Anne-‐Laure, Marcello, Anna and many others. Without them, my Ph.D. path would have been merely cognitive. Among them I would like to highlight the friends Judith, Céline, and Joanna for their joy, love, constant support, with incentive words and comprehension, and of course, that special hug.
To the other elements of my new “family”, outside the “Psy” world: Regina and her family, Gäelle &
Yanick, Adrien, Matteo & Maja and specially Sérgio and his family. All of them made me fell at home and helped in my acculturation process.
To Dora, Paulo, Maria for the affection and hours of conversation about everything and nothing.
To Ruben, who handles crisis miles away, who knows always what to say, who helped keep my mental sanity.
To these and many other friends, for their continuous presence, although sometimes physically apart.
To my parents, sister, and family: For always believing in me, for letting me be responsible for my own decisions, for supporting my choices, and for all the love and strength that they transmit and taught me.
“For all those times you stood by me For all the truth that you made me see For all the joy you brought to my life For all the wrong that you made right For every dream you made come true For all the love I found in you”
Ultimately, for seeing the best in me, for being there, handling my moods, helping me canalizing my effort, which helped me to function and achieve my goal in a more efficient way.
In summary, I am grateful to ALL who made this dissertation possible and made it even a richer experience.
The importance of mood in daily life and in particular on an individual’s self-regulation is unquestionable: mood has determinant influences on one’s preferences for action goals and resource (or effort) mobilization. There is evidence that moods can be used as information for experiences of task demand and effort regulation. The resulting effort for self-regulation of behaviors and experiences corresponds to the motivational intensity of behavior. A link between mood and motivation is commonly accepted, although their relation is not clear. In particular, mixed evidence arises regarding the motivational implications of mood: Do moods have stable motivational implications or, by contrast, are they rather context-dependent? Further, how can moods be informative and have an impact on effort mobilization and consequently on behavior? In order to answer these questions, the present research is based on the Mood-Behavior model (MBM) by Gendolla (2000), which is an integrative model that establishes a link between mood, behavior and effort mobilization. More specifically, the MBM explains how moods can influence behavior-related judgments and consequently interact with task characteristics in the energy mobilization process. Data from Gendolla and collaborators showed that the effort invested in a task depends on task characteristics and subjective task demand.
The above enounced theoretical framework sustained the four experiments presented in this thesis. Study 1 intended to clarify the basic assumption of the MBM that mood effects on resource mobilization are context-dependent. Half of the participants were exposed to an explicit demand to perform a task while the other half did not have any instructions. It was expected that no mobilization of resources would occur if no explicit instruction was given to mobilize resources. Study 2 was designed to provide a more conclusive test of mood informational impact on behavior-related judgments, since previous studies only assessed informational impact without manipulating it. Thus, in addition to mood manipulation, task difficulty was also manipulated. We anticipated that mood’s diagnostic value would be reduced if people receive a cue that their mood has been manipulated. By discounting the mood informational impact, only task difficulty would be evident on resource mobilization. Finally Studies 3 and 4 continue to test the informational mood impact – as conceptualized by the MBM – by directly manipulating judgment context itself, while accounting for the context-dependency of moods. The behavior-related judgment was manipulated through two “effort-rules”: participants were instructed to either evaluate the task’s pleasantness (“enjoy-rule”) or to consider the invested effort (“enough-rule”) for determining the amount of effort to be invested; in addition, a control “no-rule” condition was included. The experimental paradigm was similar across studies: it consisted of three main parts during
mood manipulation and (3) a mental demanding task – either a concentration or a memory task.
The results supported our predictions. In particular, higher systolic reactivity was observed in a negative mood than in a positive mood when: (1) there was an explicit task demand, (2) participants did not have a cue of their mood manipulation, (3) the underlying effort-related judgment was the “enough- rule” (which corresponds to the default “no-rule”), (4) and when task difficulty was not fixed (a “do your best” task) or when the task was easy. Participants in a positive mood showed higher systolic blood pressure reactivity when: (1) the task was difficult, (2) participants did not have a cue of their mood manipulation, (3) and when they had in mind the pleasantness of the task (“enjoy-rule”).
Our data contradict a long tradition of research that defends stable motivational implications of moods. Results corroborate the recent research on mood’s context-dependency and evidence that mood has an informational impact as conceptualized in the MBM. Accordingly, moods per se do not involve effort-related autonomic adjustments, but they can impact effort-related autonomic reactivity during task performance and self-regulation. These findings have important implications for different domains and contribute to new directions of research.
TABLE OF CONTENTS
THEORETICAL PART ... 10
1. Introduction ... 11
2. Mood ... 13
2.1. Definitions and presentation ... 13
2.1.1. Motivation ... 13
2.1.2. Affect, emotion, and mood ... 14
2.1.3. Self-regulation ... 16
2.2. Mood and psychopathology ... 16
2.3. Natural mood vs. Induced mood... 17
2.3.1. Mood induction: Procedure ... 18
2.3.2. Mood induction: Measures ... 20
2.4. Research on Mood ... 22
3. Moods and judgments ... 23
3.1. Mood as priming ... 23
3.1.1. Other network models ... 26
3.2. Mood and cognitive capacity ... 27
3.2.1. Resource allocation model and cognitive interference ... 27
3.2.2. Self-focus ... 28
3.2.3. Self-regulation ... 29
3.3. Mood as information ... 30
3.4. AIM: Affect Infusion Model ... 32
3.5. Mood as input ... 35
3.6. Mood and information integration ... 37
3.7. Moods and judgments – Summary ... 39
4.1. Definition ... 40
4.2. The cardiovascular system... 41
4.3. Sympathetic and parasympathetic influences ... 43
4.4. Cardiovascular parameters ... 44
4.5. Active coping and effort mobilization ... 45
4.6. Motivational intensity theory ... 46
4.7. The subjective evaluation of task difficulty ... 49
4.8. The electrodermal system ... 51
4.9. Physiological reactivity as an objective measure of effort ... 54
5. The Mood-Behavior Model ... 55
5.1. Basic assumptions ... 55
5.2. Directive mood impact ... 57
5.3. Informational mood impact ... 58
5.3.1. Tasks without performance standard ... 60
5.3.2. Tasks with performance standards ... 62
5.4. The joint effect of informational and directive mood impacts ... 64
5.5. The MBM and other approaches ... 65
5.6. The MBM – A practical theory ... 67
EXPERIMENTAL PART... 68
1. Synopsis of studies and hypotheses ... 69
2. Study 1 ... 73
2.1. The present study... 74
2.2. Method ... 75
2.3. Results ... 77
2.4. Discussion ... 81
3. Study 2 ... 83
3.1. The present experiment ... 85
3.2. Method ... 86
3.3. Results ... 89
3.4. Discussion ... 98
4. Studies 3 and 4 ... 102
4.1. Experiment 1 ... 106
4.1.1. Method ... 106
4.1.2. Results ... 108
4.1.3. Discussion... 112
4.2. Experiment 2 ... 113
4.2.1. Method ... 113
4.2.2. Results ... 116
4.2.3. Discussion... 121
4.3. Experiment 3 ... 122
4.3.1. Method ... 122
4.3.2. Results ... 123
4.3.3. Discussion... 126
4.4. Meta Analysis ... 127
4.5. General discussion ... 127
1. Summary of the results ... 132
1.1. Mood ... 132
1.2. Demand appraisals ... 132
1.4. Diastolic blood pressure reactivity ... 135
1.5. Heart rate reactivity ... 135
1.6. Electrodermal activity ... 136
1.7. Physiological indices ... 136
1.8. Performance ... 136
1.9. Need for cognition ... 137
2. Integrated discussion of the results ... 140
2.1. Our studies – more of the same? ... 140
2.1.1. Mood as motivation... 140
2.1.2. Two-factor theory of emotion ... 140
2.1.3. Gendolla and collaborators ... 141
2.1.4. Martin and collaborators ... 142
2.2. Our results – explainable by other theories?... 143
2.2.1. Mood and resource allocation on mental capacity ... 143
2.2.2. Mood and self-focus ... 144
2.2.3. Mood as priming... 145
2.2.4. Mood as information ... 146
2.2.5. Mood as motivation... 146
2.2.6. Creativity studies ... 147
2.3. Performance: Achievement and Effort... 149
2.4. Moods vs. Emotions ... 149
3. Limitations, implications and future directions ... 152
3.1. Limitations ... 152
3.1.1. Sample and Setting ... 152
3.1.2. Manipulation checks ... 153
3.2. Implications... 154
3.3. Final remarks and future directions ... 157
3.3.1. Sample ... 157
3.3.2. Mood states ... 157
3.3.3. Index of effort... 158
3.3.4. Expanding our experimental designs ... 158
3.3.5. Cognitive processing style ... 158
4. Conclusion ... 159
FRENCH SUMMARY ... 160
REFERENCES ... 174
“Life is a train of moods like a string of beads; and as we pass through them they prove to be many colored lenses, which paint the world their own hue, and each shows us only what lies in its own focus.”
Ralph Waldo Emerson
There are days when our lives seem better (or worse) than others, even though nothing significant – such as a major life event (cf. Holmes & Rahe, 1967) – has happened. Therefore it is certain that people are never “moodless”: every individual is at any moment in a certain mood state that is more or less intense, and more negative or more positive (Morris, 1989). The intensity and/or frequency of a more positive or negative mood establishes and influences a vast number of daily life events and decisions, as well as health. According to World Health Organization’s (WHO) definition: "health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity" (WHO, 1946, p.2). Since subjective well-being (or “well-feeling” – Hudson, 1996) can be expressed as a result of the balance between positive and negative affects (e.g., Andrews & Robinson, 1991; Diener, Suh & Oishi, 1997; Eid & Diener, 2004; Keyes, Shmotkin, & Ryff, 2002; Krueger & Schkade, 2008; Yardley & Rice, 1991), the link between moods and health is strong. When the focus is on positive affect, there are favorable influences on longevity and on symptoms of disease and pain (Pressman &
Cohen, 2005) and it is also strongly correlated with happiness and success (Lyubomirsky, King & Diener, 2005). In general, positive affect has been related to positive outcomes (e.g., Fredrickson, 2004);
nonetheless the extremes of positive affect – which have been less studied – are usually linked to mania (Gruber, Johnson, Oveis, & Keltner, 2008). Psychopathology can also arise on the negative affect side. In fact most of the research and literature on negative affect deals with psychopathology. Troubling moods became one of our species’ most widespread afflictions (Klinger, 1993). Therefore, moods have deserved an entire chapter in the Diagnostic and Statistic Manual (DSM-IV, American Psychiatric Association – APA, 1998) and also in the International Statistical Classification of Diseases and Related Health Problems (ICD-10; WHO, 2005), with special focus on depression and bipolar disorder. In addition, motivational effects of mood can influence the development of physical illness (Gendolla, Abele, Andrei, Spurk, & Richter, 2005; Gendolla & Brinkmann, 2005; Gendolla, Brinkmann & Richter, 2007), such as cardiovascular diseases (Gendolla & Richter, 2005a), which are the main reason for mortality in the world (G. Costa, 2006). Besides mood’s major influence on well-being, its strong impact
extends to other domains such as work and education, taking a special role on its motivational and performance aspects (Gendolla et al., 2007).
Individuals can actively regulate their behavior and experiences (see Baumeister & Vohs, 2004 for a review), and therefore self-regulation consumes cognitive resources (Muraven & Baumeister, 2000). There are many open questions about the implicated variables in this self-regulation and about their influence on resource mobilization. However, mood’s importance in self-regulation is unquestionable as it is supported by the numerous research that surrounds it and link it with diverse areas and aspects. For instance, there is ample evidence that moods influence two main aspects of self- regulation: individuals’ preferences for action goals and resource mobilization (Gendolla & Brinkmann, 2005). There are also recent models that emphasized the variability and context-dependency of mood effects on behavior (e.g., Martin, Ward, Achee, & Wyer, 1993), which means that one mood state can have different motivational and behavioral effects. Thus, in order to make a more comprehensive approach to the connection between mood and behavior, the Mood-Behavior Model (MBM) was introduced by Gendolla (2000). This theoretical framework is an integrative approach that elucidates how transient mood states influence aspects of behavior such as direction, intensity and persistence.
This model postulates that moods can influence behavior through two main processes: informational and directive impacts, which influence behavior-related judgments and action preferences respectively.
In the context of this thesis, our research program focused on the mood influences when they are used as information for behavior-related judgments (informational mood impact) on effort mobilization (that corresponds to motivational intensity). It has been shown that in the presence of a task demand (one that requires resource mobilization), moods can be used as information for experiences of task demand and effort regulation (Gendolla & Brinkmann, 2005). Nonetheless some questions did not yet found conclusive answers, such as: Are mood’s effects context-dependent? Is there a really informational mood impact? This thesis provides an experimental approach that concretely manipulates the judgment context itself to test the informational mood impact and therefore provides evidences for its existence. To test our predictions we will depart from the notion of effort mobilization as conceptualized by the motivational intensity theory (Brehm & Self, 1989), which was operationalized as cardiovascular reactivity (Wright, 1996).
In the following chapters we will present (1) a theoretical background about mood, (2) associations between mood and judgments, (3) measures associated with effort, (4) a description of the MBM, (5) the methodology and results of the conducted experiments, and finally (5) an integrative discussion about our findings.
“There are good and bad times, but our mood changes more often than our fortune.”
As always, the best way to start is from the beginning. Therefore, this first theoretical chapter will introduce some definitions, relations between concepts, and disambiguation. It intends to provide a foundation to better understand the present research program.
2.1.Definitions and presentation
Since this work focuses on mood effects on self-regulatory behavior, the following sections will disentangle and elucidate some basic concepts.
The term motivation has been used for over a century in the vocabulary of psychology. As other psychological concepts, its meaning has varied over time (e.g., R. C. Beck, 2003; Buck, 1985; Franken, 1993; Hull, 1943; Kleinginna & Kleinginna, 1981a; H. A. Murray, 1938; Rommanes, 1881) and only in 1953 motivation was established as an independent area of research (Chamorro-Premuzic, 2007).
Motivation is a psychological notion, i.e., a latent construct to explain behavior. Therefore we can only infer motivation through behavioral cues, there is no direct observation. According to Chamorro- Premuzic (2007), motivation is an internal state that: (a) drives people into action, which involves (b) goal setting; (c) energizes, directs and perpetuates behavior in direction of the (d) satisfaction of needs and drives, (e) can produce arousal, (f) general psychological force, and it is (g) dynamic, rather a process than a trait. The concept of human motivation can be described by a complex process which is reasoned by four basic dimensions: the initiation, the direction, the intensity, and the persistence of behavior (Geen, 1995; Vallerand & Thill, 1993). This means that human motivation can vary on those dimensions across individuals but also across situations: People may differ in the behavioral choices they make, the vigor or intensity of their actions, and the tenacity or persistence with which they pursue their goals.
Thus, motivation research can centre on one basic question about behavior: with what level of effort.
This is precisely one of the core issues of the present thesis. The empirical part of our research program
concentrates on the intensity aspect of motivation as enounced in motivational intensity theory (Brehm
& Self, 1989).
2.1.2. Affect, emotion, and mood
Affect, emotion and mood are concepts that have been interchangeably used in the literature.
These concepts are frequently used in a lenient way, suffering from the absence of clear definitions for each (Forgas, 1991, 1999; Frijda, 1986). The term affect is broadly used as the higher-order term for subjective experiences, which can have a positive or negative valence. Therefore, this wider concept is often applied to designate emotions and moods (Bower & Forgas, 2000; Schwarz & Clore, 1996; Scott &
Ingram, 1998). However the distinction between mood and emotion seems to pose more problems, since their relation is not clear (e.g., Barret & Russel, 1998). There are perspectives that consider that each individual is at any time in a certain mood, which is interrupted by specific emotions (Morris, 1989). But the opposite view can also arise: emotion often drives a state of latent mood that persists even beyond the effect of emotions.
Emotions are normally triggered in response to a specific significant object or situation that is easily identified, and are also intense enough to disrupt cognitive processes (e.g., Frijda, 1993, Simon, 1982; for a review see Zajonc, 1998). They are defined as a short affective state, which is object-related and specific emotions involve a clear and stable motivational function (Arnold, 1969; Duffy, 1941; Izard, 1977; Leeper, 1948; Oatley & Johnson-Laird, 1996; Plutchik, 1980). Emotions define the organism's relationship with the environment and involve specific action tendencies to change or maintain it. As emotions are goal-orientated, they aim to provide the resources to a specific action tendency, like autonomic nervous system activity (Cacioppo, Klein, Berntson, & Hatfield, 1993; Cacioppo, Berntson, Larsen, Poehlmann & Ito, 2000; Frijda, 1986; Levenson, Ekman, & Friesen, 1990; Levenson, 1992;
Davidson & Cacioppo, 1992; Plutchik, 1980). Therefore, emotions prepare the body for action toward the emotion-eliciting events (e.g., Arnold, 1960; Batson, Shaw, & Oleson, 1992; Brehm, 1999; Frijda, 1986; Izard, 1993; Lang, 1979; Lazarus, 1991; McDougall, 1908; Plutchik, 1980; Young, 1961). Rather than being considered “states”, emotions are conceptualized as “processes”, because they involve different stages of conscious or unconscious information processing and appraisals (Scherer, 1982).
Specifically, emotions involve consideration of elaborated and conscious information regarding actions’
antecedents and consequents, resulting in different emotional reactions to them (see Clore, Schwarz, &
Although there are divergent opinions about the definition of mood, especially concerning its relationship with emotion (Morris, 1999), there are some unique aspects of moods. As Fridja (1993) claims, “moods are usually distinguished from emotions by one of three criteria: longer duration, lower intensity, and diffuseness or globality” (p. 381). Thus, contrary to emotions, moods are not typically identified with a particular stimulus. However emotions often lead to a state of latent mood that persists even beyond the effect of emotions. So moods can also be relatively specific "residuals of emotions" with eliciting incidents of which individuals are no longer aware (Bollnow, 1956). They are generalized feeling states that are pervasive, present in the background, and less accessible to consciousness (Forgas, 1995a). Furthermore, unlike emotions, moods are not sufficiently intense to interrupt ongoing cognitive processes (e.g., M. S. Clark & Isen 1982; Thayer 1989), and consequently they do not interrupt individuals’ behavior (Ellis & Moore, 1999; Hänze & Hesse, 1993). Moreover, whereas moods are often – but not always (e.g., Russell & Barrett 1999) – described in terms of their underlying dimensions (e.g., positive and negative) (e.g.,Watson 2000), emotions tend to be treated in their discrete forms (e.g., anger, fear, and joy) (e.g., Plutchik 1994). Moods are generally described on the basis of two-dimensional models (Russell 1978, Russell & Bullock, 1986, Wundt, 1897) being the most common dimensions valence – which represents moods’ pleasant (positive) or unpleasant (negative) aspect –, and intensity – representing the strength of the mood. In summary, moods are generally described as diffuse and long-lasting affective states which are not directly object-related and that are experienced without concurrent awareness of their origins (Averill, 1980; Bollonow, 1956; M. S.
Clark & Isen, 1982; Forgas, 1995a; Frijda, 1993; Gendolla, 2000; Schwarz, 1990; Schwarz & Clore, 1996;
Wyer, Clore, & Isbell, 1999; see Wilson, Laser, & Stone, 1982, for an empirical demonstration). Since moods are not object-related, recent research from our lab (e.g., de Burgo & Gendolla, in press – see Experimental part) points out that mood per se does not have specific and stable motivational implications.
Moods are naturally influenced by innumerous aspects, like the diurnal rhythm (Robbins & Tanck, 1987), the weather (Schwarz & Clore, 1983), changes in the endocrine system (Ashby, Isen, & Turken, 1999; Canter, 1972), odors (Ehrlichman & Halpern, 1988), light and illumination (Baron, Rea, & Daniels, 1992), the distribution of ions in the air (Baron, 1987), and the environmental pleasantness in general (Schwarz, Strack, Kommer, & Wagner, 1987).
Moods also seem to have an informational function: it has been demonstrated that moods can activate information in memory (see Bower, 1981; Blaney, 1986; Forgas, 1995a) and also serve as diagnostic information for evaluative judgments (see Clore et al., 1994; Schwarz, 1990; Gendolla, 2000).
Recent research from our lab (e.g., de Burgo & Gendolla, in press, 2009 – see Experimental part) points out that mood per se does not have specific and stable motivational implications, which derives from the notion that moods are not object-related. Therefore, contrary to emotions, we also consider that moods do not involve autonomic activation. Studies from our lab demonstrated that during mood induction there is no cardiovascular reactivity (e.g., de Burgo & Gendolla, in press, 2009; de Burgo, Gendolla & Richter, 2009; Gendolla & Küsken, 2002; Silvestrini & Gendolla, 2007).
The present research program explores mood effects on cardiovascular reactivity in self- regulation. The concept of regulation results from models describing the physiological processes related to homeostasis of organisms, namely the process by which the internal systems of the body maintain a balanced stable equilibrium. To cope with internal or external changes, organisms must have a homeostatic control system allowing them to minimize this variation and to maintain the balance of the body relatively constant (Vander, Sherman, & Luciano, 1994). In addition to physiological regulation, human beings have to regulate their behavior, their thoughts and their emotional states, which can be integrated into the concept of self-regulation (Mischel, 1973). Generally, the regulatory process is divided into three parts: the current state, the desired state, and a loop informing the imbalance between the two states (Carver & Scheier, 1990a; Powers, 1973). Following this idea, the goal of behavioral self-regulation is to reduce the perceived discrepancy between current and desired state.
Self-regulation comprehends several other aspects that are beyond the scope of this thesis.
2.2.Mood and psychopathology
Relatively stable and persistent moods can be tied to personality characteristics. For instance, neuroticism tends towards negative mood and extraversion towards positive mood (e.g., P. T. Costa &
McCrae, 1983). In addition, positive and negative affectivity may be even considered as traits of personality themselves (e.g., Watson & Tellegen, 1985). In particular, moods’ intensity and duration can determine psychopathological problems. If experienced too intensely and for too long period of time (either because of external events or due to personal predispositions), moods can lead individuals to develop certain types of disorders. Among the mood disorders, one distinguishes depressive “unipolar”
from “bipolar” disorders. These are, respectively, disorders characterized only by depressive episodes versus those also involving manic or mixed episodes (cf. DSM-IV; APA, 1994; and DSM-IV-TR; APA, 2000).
In terms of mood association, unipolar disorders, such as major depressive disorder (MDD) or dysphoria,
only involve negative mood, while bipolar disorders comprehend exaggerated positive moods alternating with negative moods. Depressive disorders may be of different severities depending on the intensity of moods, the duration of the disorder, presence of other symptoms (loss of appetite, loss of interest, sleep disturbance, etc..) or comorbidity – for example with anxiety (e.g., Aina & Susman, 2006;
Gorman, 1997), or with panic disorder (e.g., Ballenger, 1994; Breier, Charney, & Heninger, 1984), or even physical illness such as diabetes (e.g., R. Anderson, Freedland, Clouse, & Lustman, 2001; de Groot, Anderson, Freedland, Clouse, & Lustman, 2001).
In general, depression is regarded as a disorder that refers more to mood than to emotion (Scott
& Ingram, 1998). However, some researchers describe depression in emotion terms, for instance as a combination of sadness and disgust (see Power, 1999).
People suffering from depression or dysphoria are said not only to experience long lasting negative mood and to have emotional, functional, and cognitive deficits, but also to lack motivation (Heckhausen, 1991). This lack of motivation can be linked to behavioral approach and inhibition systems (Gray, 1982), self-regulation (e.g., Strauman, 2002), motivational influences on cognitive deficits (e.g., Hertel, 2000), and mood-congruent negative cognitions (e.g., Scott & Ingram, 1998).
Given the high prevalence of mood disorders in the population (e.g., Kessler 2002, Kessler et al., 2003), numerous studies have been conducted on the topic of depression and its influence on behavioral and cognitive processes. However, the the present thesis focuses on transient, non- pathological mood states.
2.3.Natural mood vs. Induced mood
The study of the interaction between affective and cognitive functioning based on natural occurring moods can be made by selecting individuals who have been diagnosed as depressed or anxious or who present (more or less stable) dysphoric or anxious traits. The clinical diagnosis of depression or anxiety is usually made as a result of psychiatric interviews and specific questionnaires – e.g., the Schedule for Affective Disorders and Schizophrenia (SADS; Spitzer & Endicott, 1978), or the Hamilton Rating Scale for Depression (HRS-D; Hamilton, 1967). The most common procedures propose to the subjects to complete questionnaires of self-evaluation, such as the Center for Epidemiologic Studies – Depression Scale (CES-D; Radloff, 1977) or the Beck Depression Inventory (BDI; A. T. Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) which assess mainly depressive tendencies, or the State Trait Anxiety Inventory (STAI, Spielberger, Gorsuch, & Lushene, 1983) for state anxiety. The CES-D and BDI allow the assessment of non-clinical depressive subjects. Their depressive state is diagnosed as
more or less severe depending on their score. Most of the research involving natural mood differences is related to individuals who are not clinical depressed, but who present elevated scores on depression scales (Kendall, Hollon, Beck, Hammen, & Ingram, 1987) – i.e., dysphoric individuals (e.g., Brinkmann &
Gendolla, 2007). To avoid difficulties inherent in choosing a “natural mood sample” (e.g., low number of depressive or dysphoric subjects in the general population, ethical issues), mood induction appears as the best alternative. Mood manipulation can induce both negative and positive affective states.
Additionally, it is not necessary to induce intense mood states; usually mood states of medium intensity are sufficient to produce effects in many experimental settings.
The present research program uses induced mood for manipulating mood states. Therefore, procedures and measures of mood induction will be presented, with a stronger focus on the procedures and measures used in the experiments of our research program.
2.3.1. Mood induction: Procedure 22.214.171.124. Simple procedures
A review of the literature reveals a large number of experimental procedures to induce mood changes in a controlled manner. These induction procedures are extremely diverse (Blaney, 1986; M.
Martin, 1990; Gerrards-Hesse, Spies & Hesse, 1994; Westermann, Spies, Stahl & Hesse, 1996) and can be defined as strategies aimed at causing in an individual an ephemeral, artificial, and controlled affective state. Mood induction intends to be the experimental and artificial reflex of naturally occurring moods.
The meta-analysis of Abele (1995), M. Martin (1990), Gerrards-Hesse et al. (1994), Thayer (1989), and Westermann et al. (1996) list a large number of procedures used to induce positive or negative moods.
In addition, some procedures have been created or adapted to try to induce other affective states such as anxiety, anger, disgust or fear (Bodenhausen, Sheppard & Kramer, 1994; Mayer, Allen & Beauregard, 1995). However, as noted by Corson (2002), due to the reduced amount of studies regarding these other affective states, a coherent image cannot yet be drawn. Among the main techniques used to induce positive and negative moods we encounter:
- film excerpts presentation (more detailed in the paragraph below);
- music, reported for the first time by Sutherland, Newman and Rachman (1982);
- autobiographic recall, developed by Brewer, Doughtie, and Lubin (1980), from the work Mosak and Dreikurs (1973); a modified version (not only recall, but having 10 min to write in detail a
happy or sad event, according to the mood condition) was introduced by Ric and Krauth-Gruber (2000);
- the «Velten Mood Induction Procedure» (Velten, 1968);
- hypnosis (Bower, 1981 ; Friswell & McConkey, 1989);
- guided imagery (Miller et al., 1987);
- experiences of success or failure or little rewards (Isen, Daubman, & Nowicki, 1987;
Nummenmaa & Niemi, 2004);
- changes in forehead blood temperature (Zajonc, Murphy, & Inglehart, 1989);
- exposure to specific stimuli: photographs (Fox, 1996) or odors (Ehrlichman & Halpern, 1988).
The present thesis uses a single simple procedure of mood induction: movie clips presentation.
The screening of film clips is a widespread technique, because it has the advantage of being simple to use and it poses fewer ethical problems than other procedures, since participants are daily exposed to emotionally loaded images via television. Moreover, many studies have highlighted the effectiveness of this method on the induction of positive and negative moods (e.g., Gendolla & Krüsken, 2002a, 2002b, 2002c; Silvestrini & Gendolla, 2007), but also other affective states (Dalle & Niedenthal, 2003; Gouaux, 1971; Gross & Levenson, 1995 ; Hänze & Hesse, 1993; McHugot, Smith & Lanzetta, 1982; Philippot, 1993; Rottenberg, Ray & Gross; 2004, S. M. Smith & Shaffer, 1991).
126.96.36.199. Composed procedures
In an attempt to increase the efficiency of the mood induction, some researchers have combined several types of existing induction procedures (Gerrards-Hesse et al., 1994). The success of such procedures is to bind a first induction, which monopolizes the attention, to a second manipulation that helps to create a congruent atmosphere in the background. For example, the procedure of Velten (1968) was successively associated with hypnosis (Natale & Hantas, 1982), with music (A. Mathews & Bradley, 1983; Mayer, Gayle, Meehan & Haarman, 1990) and with an imagination proceeding (Richardson &
Taylor, 1982). Other researchers have developed an induction process involving a task of guided imagery and music (Mayer et al., 1995). As stated before, the present research program does not apply a composed procedure of mood induction. It uses the presentation of movie excerpts as a simple procedure of mood manipulation.
2.3.2. Mood induction: Measures
Mood induction efficiency can be evaluated according to three distinct indicators: a) behavioral and cognitive indices, b) physiological indices, and c) self-report measures. To assess mood we applied verbal manipulation checks, via self-report questions. The physiological measures used in the present research program serve as indices of effort and not as indicators of mood inductions.
A brief presentation of each mood manipulation assessment indicator is presented below, with a special focus on the self-report measures.
188.8.131.52. Behavioral and cognitive indices
In this case, the evaluation of induced affective states is based on the detection of cognitive and behavioral patterns usually associated with those induced affective states. The most frequently used behavioral index remains the psychomotor delay which is operationalized by the writing speed (D. M.
Clark, 1983) – slowness is usually indicative of negative affect. Similarly, the presence of facial expressions can indicate a particular affective state (Ekman, Friesen & Ancoli, 1980).
184.108.40.206. Physiological indices
The physiological element of affective states can be objectively measured, which is not possible with subjective and behavioral components (Doron & Parot, 2007). Heart rate, blood pressure, respiratory rate, body temperature, changes in eye movements, pupillary dilation, electrodermal response, and muscle contractions (especially facial electromyography) are all indicators that can be used to denote the presence of an affective state (Cacioppo & Petty, 1983; Wagner & Manstead, 1989).
However, variations of these psychophysiological indices do not accurately indicate the presence of a particular affective state (Gerrards-Hesse et al. 1994; Schachter & Singer, 1962).
220.127.116.11. Self-report measures
Self-evaluation measures with assistance of scales remain the most common method for evaluating the effectiveness of mood induction. Literature outlines a wide set of self-reports and Lickert- type scales including the Nowlis Mood Adjective Checklist (Nowlis & Green, 1957; Nowlis, 1965), Profile of Mood State (POMS; McNair, Lorr, & Droppelman, 1981), Multiple Affective Adjective Checklist- Revisited (MAACL; Zuckerman & Lubin, 1985), Visual Analogue Mood Scale (Vamsi; Teasdale & Dent, 1987), Brief Mood Introspection Scale (BMIS; Mayer & Gaschke, 1988), Positive and Negative Affect Scale (PANAS; Watson, Clark & Tellegen, 1988), Differential Emotions Scale (DES; Izard, Dougherty, Bloxom &
Kotsch, 1974; Philippot, 1993), Implicit Positive and Negative Affect Test (IPANAT; Quirin, Kazén, Rohrmann, & Kuhl, 2009) or the University of Wales Institute of Science and Technology (UWIST) Mood Adjective Checklist (MACL) developed by Matthews, Jones and Chamberlain (1990).
A reduced version of this latter scale (from now on referred simply as UWIST) is the self-report measure used in the present research program. The UWIST is a survey instrument consisting of 42 adjectives that a subject must rate on seven-point scales (ranging from 1 – ”not at all true" to 7 –
”completely true") accordingly to how well each item describes his or her mood at that moment. The UWIST measures the affective state based on the two-dimensional affect representation – Russell’s (1980) affect circumplex model. In this model, each specific affect is characterized along two dimensions: “valence", which indicates how pleasant or unpleasant the emotional state is, and
“arousal", which characterizes how activated or deactivated the person feels. For example, feeling bored would imply a low-activation, unpleasant, affective state, whereas feeling excited would imply a highly activated, pleasant, affective state. Despite the intuitive nature of the survey, the accuracy of the valence/arousal representation of affect is not universally accepted in the psychological literature (Ekman & Davidson, 1994). The scores for eight categories that correspond to different sectors in the affect circumplex are calculated based on UWIST responses: (1) Pleasant (happy, pleased, content), (2) Unpleasant (miserable, troubled, unhappy), (3) Activated (aroused, alert, hyperactivated), (4) Deactivated (sleepy, still, quiet), (5) Pleasant-Activated (interested, excited, strong, enthusiastic, proud, inspired, determined, attentive, active), (6) Pleasant-Deactivated (relaxed, at rest, serene, calm, at ease), (7) Unpleasant-Activated (distressed, upset, guilty, scared, hostile, irritable, ashamed, nervous, jittery, afraid), and (8) Unpleasant-Deactivated (tired, sluggish, droopy, dull, drowsy, bored). Due to some limitations associated with research on affective states – such as the specific process for eliciting affect, insufficient mood intensity in a laboratory setting, or the purity of emotional experience (i.e., experiencing only one emotion at a time) – the UWIST often serves as a useful first-order approximation.
Previous studies from our laboratory have successfully applied a reduced version of the UWIST hedonic tone scales, focusing on eight adjectives: four of the positive hedonic tone scale (happy, joyful, contented, cheerful) and four of the negative hedonic tone (sad, frustrated, depressed, dissatisfied). The long list of 42 adjectives does not meet the purposes of our experiments, since this extensive list would draw too much attention to the mood manipulation and thus uncover our intentions of a masked mood manipulation. The present research work departs – as well as other more recent research from the Geneva Motivation Lab (e.g., Silvestrini & Gendolla, in press-a) – from this eight adjectives and uses a
simplified version of four adjectives: two positive (joyful, cheerful) and two negative (sad and depressed) – the ones that evidenced higher internal validity.
Despite the broad use of self-report measures – due to their savings in terms of time and money – these instruments also have some constraints. These issues will be addressed later, in section 4.9. of this Theoretical part.
2.4. Research on Mood
Vincent Nowlis and his wife Helen Howard Nowlis were pioneers in mood research, considering mood as a source of information of an organism’s functional status. They also suggested that mood could act consciously, changing the likelihood of actions, or unconsciously, by acting as indices influencing self-regulatory behavior. In addition, they introduced a measure of mood, the Nowlis Mood Adjective Check List (Nowlis & Nowlis, 1956). As its name suggests, this instrument is composed of a list of adjectives to describe individuals’ mood states. There are two versions: one with 12 items and another with 36. The longer version has 12 factors: aggression, anxiety, surgency, elation, concentration, fatigue, social affection, sadness, skepticism, egotism, vigor, nonchalance. These promising ideas about mood were followed by other psychologists of the time (e.g., Jacobsen, 1957; Pribram, 1970), but started to fade away with the advent of general cognitive psychology. Research on mood regained vitality during the 1970s with the study of affects’ influence on pro-social behavior and memory. The majority of mood research has been associated with mood influence on cognitive or behavioral processes – such as memory, pro-social behavior, judgments, self-focus, health perception, and even welfare. Mood and judgment approaches will be described further and in more detail, since they constitute the core issues of this thesis.
3. Moods and judgments
“A theory which is not refutable by any conceivable event is non-scientific.“
Mood’s impact(s) on behavior is a pertinent issue, because people are never moodless (Morris, 1989) and minor changes in our moods can have enormous influences on our perception of the world (Schwarz & Clore, 1988). Mood influences on judgments about objects, events, other people or even ourselves (see Gendolla, 2000, and Morris, 1999, for reviews) will systematically influence behavior.
Those influences on behavior-related judgments shape our world or, as suggested by Emerson (1993, p.
85), color it with different lenses. Specifically, people tend to adjust their judgments and behavior in a mood congruent way. For example, people in a positive mood make more optimistic judgments than people in a negative mood.
Since the 1970’s, mood manipulations have become standard tools in psychology (Schwarz &
Clore, 2003). For about twenty years it was defended that the behavioral consequences of being in a certain mood would rely mainly on the valence of the current mood state (Richter, Gendolla & Krüsken, 2006). However, the stability and specificity of mood effects started to be questioned and different theoretical models emerged. One point of convergence between the different views is that affect can serve informational functions, an idea introduced by Wyer and Carlston (1979).
Mood effects on judgments is of central interest to this thesis, and in particular, the informational mood impact on behavior-related judgments. Therefore some of the major theoretical contributions in this domain will be presented. The models that convey best to a richer understanding of the Mood-Behavior Model – from which our predictions are drawn – are the mood as priming, the mood as information, the affect infusion model, the mood as input, and the mood and information integration approach. Nevertheless, other models are briefly presented since they give a wider perspective of the theoretical antecedents and are mainly useful as counterpoint in the Discussion part.
3.1. Mood as priming
The associative-network models of memory (e.g., Wyer & Carlston, 1979; Bower, 1981; M. S. Clark
& Isen, 1982; Isen, Shalker, Clark & Karp, 1978) launched the study of mood effects on cognitive
processes. The model developed by Bower (1981; 1991) was certainly one of the most influential accounts of the interaction between affect and cognition. Thus, the influence of mood on cognitive processes was first interpreted as a consequence of semantic priming in mood according to the network theory of memory (Bower, 1991). Initially, Bower’s model (1981) applied to specific emotions rather than moods. It referred to the nodes of memory involving specific emotions that initiated the activation of other units linked to this node. Accordingly, emotions represent information units or nodes in individuals' semantic memory network, where they are connected with associative pointers (Bower, 1981; M.S. Clark & Isen, 1982). Every emotion (joy, anger, sadness, ...) then corresponds to a specific node in memory, each connected to other nodes which contain data to define the various aspects of that emotion. The model was based on the classical properties of a network model and more precisely on the idea of spreading activation (introduced by Wyer & Carlston, 1979). This model with its extensions (Bower, 1987, 1992, Bower & Cohen, 1982) was used as a starting point for most research on the effects of affect on memory. Later (Bower, 1991, M. S. Clark & Isen, 1982), Bower’s model was extended by postulating the existence of nodes in memory associated with mood. Activation of mood states would influence memory for specific events (Bower, 1991). According to this idea, people in a positive mood make more optimistic judgments concerning evaluations/expectations/situations than people in a negative mood, because they activate mood congruent concepts in memory. By other words, once activated an elated mood influences information processing by the spreading of activation through the associative network, so that situations are more likely to be interpreted or appraised positively.
These mood congruent effects depend on the extent of activated information and on its mood congruency (e.g., Forgas & Bower, 1987). Therefore, the main implication of the mood-as-priming view is that moods influence judgments (e.g., demand appraisals) by making mood-congruent information highly accessible. It is assumed that people in a positive mood make more optimistic judgments concerning evaluations/expectations/situations than people in a negative mood, because they activate more positive mood-congruent concepts in memory.
Studies testing these assumptions have achieved mixed results, with some supporting the model (e.g., Bower, 1981, 1991; Forgas & Bower, 1987) and others refuting it (see Blaney, 1986; Isen, 1987;
Morris, 1989, for reviews). Mood-congruent recall is a fragile phenomenon that is difficult to replicate and it is most likely to be obtained for self-referenced material (Schwarz & Clore, 1988). An explanation for the several failures to demonstrate mood-congruent recall was advance by Niedenthal and colleagues (e.g., Niedenthal, Halberstadt, & Setterlund, 1997; Niedenthal & Settedund, 1994). They posited that mood refers to a valence dimension (positive-negative affect), while affect-related
information is stored in categories (specific emotions). Consequently, unspecific affect cannot increase the accessibility of specific knowledge. Niedenthal and colleagues demonstrated that inducing sadness facilitated retrieving sadness-related information, while impairing access to anger-related information.
Since both sadness and anger share the same (negative) valence, this contradicts the assumption that unspecific affect can increase the accessibility of affective information. These researchers claimed a specific emotion priming idea. However, it is also possible that moods are more specific than simply positive or negative, as Ekman (1984) argues. Due to methodological aspects of the studies from Niedenthal and collaborators, their results can also be interpreted as supporting mood-as-priming effects. Wyer, Clore, and Isbell (1999) proceeded with a more blunt argument: affective states cannot prime any information. Affective priming is indirect and depends on the extent of thinking about the affective state, rather than on affect per se. Results from Niedenthal et al. (1997) demonstrate that this view is incorrect and that it does not work as an alternative explanation.
More globally, Isen (1987) proposed that the posited memory nodes were more related to the dimension of emotional valence states: There was information related to both positive affects, and to negative affects. In addition, it has been suggested that positive affect is linked to more information and thus to more elaborated information, due to their pleasantness. This has been shown, for example, in creative tasks (e.g., Isen et al., 1987, Isen, Johnson, Mertz, & Robinson, 1985). Isen (1984) also considered the distinction between controlled and automatic processes concerning mood effects. These processes suggest that if mood can have an automatic influence on cognition and on behavior, mood can also influence intentional behaviors, which need effort (e.g., attempts to regulate mood – Silvestrini
& Gendolla, 2007).
Another criticism to Bower’s model comes from Mecklenbrauker and Hager (1984). They argued that this model is too general to allow accurate predictions about the effects of mood on memory, and, moreover, it does not take into account the individuals’ conscious or controlled strategies. According to Teasdale and Barnard (1993), Bower’s associative network theory cannot explain the cognitive- emotional activation underlying depression, due to formal reasons. Indeed, if several nodes are activated simultaneously, the activation is then divided. This reduced activation of a node makes the representation congruent with the mood less accessible, resulting in an increase in the activation threshold. This may call in to question the principle of accessibility of congruent representations defined by Bower’s model (1981). Other criticisms concern the inability of the model to explain the effects of
“mood-incongruency” (Parrott & Sabini, 1990; Smith & Petty, 1995) or the asymmetry highlighted by numerous studies (see Blaney, 1986, for a review).
3.1.1. Other network models
Although most of the research on mood and judgments concerns negative mood and its clinical implications (e.g., MacLeod, 1999; Mineka & Gilboa, 1998; Scott & Ingram, 1998; Williams, Watts, MacLeod & Mathews, 1997, for reviews), there is also some research on positive mood influences on cognitive processing.
Focusing on positive mood, research by Isen (1993, 1999, 2000) generated a large number of results showing positive mood effects on a wide variety of social behaviors and activities or even on complex cognitive processing (learning, memory, resolution problems, categorization, decision making, risk assessment, ...). It should be noted that in most situations, positive mood has a facilitator and not a disruptive influence (Isen, 1993, 1999, 2000).
Isen (1984, 1987) suggests that positive mood causes a change in cognitive organization and an increased perception of relations between concepts. Positive mood is expected to lead to broader, richer cognitive organization; therefore more qualitatively different ideas would be perceived as potentially related or similar. Isen (1987) suggested that the creation of a more complex cognitive context in a positive mood is the result of a preferential activation of positive information, more and better integrated in memory (following a mood-as-priming perspective). However, in a more complex cognitive environment, the positive mood leads to a parallel decrease in cognitive resources available and thus to the use of heuristic processing (which is easier). The processes invoked are mainly strategic, although an automatic component remains. The influence of positive mood on the organization of knowledge has been demonstrated in many experimental situations (Bless, Schwarz, & Wieland, 1996;
Isen, Niedenthal, & Cantor, 1992; Isen, Daubman, & Gorgoglione 1987; Kahn & Isen, 1993; Lee &
Sternthal, 1999; N. Murray, Sujan, Hirt, & Sujan, 1990). N. Murray et al. (1990) have also shown that positive mood facilitates not only the organization of items into broader categories but also greater cognitive flexibility.
Hänze and Hesse (1993) proposed other processes involved in the positive mood influences on cognitive reasoning. According to these authors, positive mood has a direct influence on cognitive processes. They assumed that positive mood facilitates the spread of activation within the semantic network, and thus positive mood has a direct impact by changing the permeability of the network. Isen’s conceptualization of positive mood influences on cognitive processes is mostly strategic: positive mood would cause a decrease in the amount of available resources. Hänze and Meyer (1998) have confirmed and extended the assumptions made by Hänze and Hesse (1993). Accordingly, positive mood fosters the
spread of automatic activation, and additionally the implementation of automatic processes. People in a positive mood would be more likely to choose automatic data processing at the expense of a more controlled, strategic analysis. More specifically, it is predicted that positive mood promotes the recovery of direct and simple solutions in memory while inhibiting the execution of complex and relatively slow processing. According to this approach, positive mood generates the implementation of automatic processes and inhibits the spontaneous use of controlled strategies, which are privileged by neutral or negative moods (Hesse & Spies, 1996).
3.2.Mood and cognitive capacity
3.2.1. Resource allocation model and cognitive interference
The model developed by Ellis and Ashbrook (1988), adopts the concept of capacity or resource allocation originally defined by general models of attention (Kahneman, 1973). These models suggest that there is a limited amount of attentional resources that can be allocated for the execution of a cognitive task. Also, the tasks vary in the amount of attentional resources they require: some tasks are very demanding, while others require little or even no attentional resources. Following this perspective, the model of Ellis and Ashbrook (1988), claims that affective states can influence and regulate the amount of attentional resources that can be allocated to a cognitive or motor task. The model is formed on five postulates:
a) affective states regulate the allocation of attentional capacity (negative affect is associated with the reduction of attentional resources – Ellis, 1985 ; Ellis, Thomas & Rodriguez, 1984);
b) allocation of cognitive ability or effort (information encoding requires cognitive effort that can deteriorate other cognitive activities);
c) positive correlation between cognitive effort and memory (performance is improved by the engagement of significant cognitive effort);
d) negative mood and inappropriate strategies (that will diverge resources, which will not be allocated to properly process the demanded task); and
e) negative mood and extra-task processing (negative mood is associated with an increase in activation and with extra-task information processing, which consumes considerable resources – Leight & Ellis, 1991).
More recently, Ellis and Moore (1999) introduced some changes to the previous model. First, the first three propositions, seen as central to the model of 1988, became the consequences of the last two assertions. Moreover, the assumptions regarding the negative states are extended to positive mood, which is supposed to cause the same memory deficits. Mood itself is not responsible for the observed memory deficits; mood’s positive or negative valence has no specific influence on memory. Memory dysfunctions are the result of irrelevant thoughts, distractions and a lack of attention to the task.
Therefore, it is not the affective states that have an impact, but the cognitive consequences of these states (Oaksford, Morris, Grainger, & Williams, 1996; Riskind, 1989). This model of cognitive interference was supported by research with clinical (depressed individuals – Weingartner, Cohen, Murphy, Martello
& Gerdt, 1981; R. M. Cohen, Weingartner, Smallberg, Pickar & Murphy, 1982) and non-clinical populations (induced mood – Ellis et al., 1984; Potts, Camp, & Coyne, 1989). In a negative mood state, the intrusion of irrelevant thoughts seems inevitable (Gunther, Ferraro & Kirchner, 1996; Sutherland et al., 1982), which may lead to significant memory deficits (Hertel, 1998). Despite research in favor of the cognitive interference perspective, other studies casted doubts on the hypothesis of an attentional resources deficit associated with positive or negative mood (Bless et al., 1996; Hesse & Spies, 1996).
Mood effects on self-focus attention have been analyzed by several investigations. In general, findings show that people in a negative mood tend to have more thoughts and feelings related to themselves (see Mor & Winquist, 2002, for a review). These results were found with experimentally manipulated mood (Greenberg & Pyszczynski, 1986; Salovey, 1992; Sedikides, 1992; Wood, Saltzberg, &
Goldsamt, 1990), in correlational studies with a non-clinical populations (Csikszentmihalyi & Figurski, 1982; Larsen & Cowan, 1988; Wood, Saltzberg, Neale, Stone, & Rachmiel, 1990), and also with a clinical (depressed) population (see Ingram, 1990, for a review).
Positive mood effects on self-focused attention evidence is less clear. Salovey (1992) proposed and confirmed that both types of mood – positive and negative – had a similar effect on self-focus, but this was not a congruent finding. For example, other studies suggested that positive moods could reduce self-focus attention (Sedikides, 1992; Sedikides & Green, 2000, Wood et al., 1990). However, recent research has suggested that positive mood might be dependent on context, having thus different effects on self-focus attention. Abele, Silvia, and Zöller-Utz (2005) manipulated participants’ mood and the context: in one condition it was announced that a difficult task would follow, whereas in another condition there was no announcement concerning the task. Then, self-focus attention was measured
with a sentence completion task consisting of a choice of different personal pronouns (e.g., they, we, I).
A frequent choice of first person pronouns (I or we) indicated higher self-focus. The results illustrated that when participants expect a difficult task, positive mood decreased self-focus attention; by contrast, if they are not expecting the task, positive mood lead to increased self-focus. These mood effects on self-focused attention may influence effort-related judgments and will therefore be considered in the Discussion part (section 2.2.2.)
Carver and Scheier’s (1981) model of self-regulation can account for the asymmetrical effects of negative and positive mood in self-focus attention. This model postulates the existence of a feedback loop that allows the organism to reduce the distance between its current state and a desired state.
Thus, the purpose of affective states is to inform people about the gap between the current state and the desired one, or according to a more recent development of the model (Carver & Scheier, 1990a), to inform about progress towards the desired state: Negative affect emerges when there is a big distance between current and desired state or when progress towards that desired state is too slow, while close proximity to and progress toward the aspired state gives rise to positive affect. This would imply that people in a negative mood are more self-focused in order to highlight problems that need to be solved to attain a goal (desired state). Even though this theory supports implications for self-focused attention, it mainly proposes that moods have a precise origin and function: namely, to inform the individual about the distance between a current and a desired state. However, we can conceive that this informational function does not directly imply an exact cause and that it does not necessarily lead to specific behaviors.
Some authors defend the idea that self-regulation mechanisms – such as stress management, negative mood regulation, or resisting temptations – consume resources (Muraven & Baumeister, 2000). This view suggests that when resources are used for self-control, fewer resources will be available for a subsequent self-regulation. According to Muraven and Baumeister (2000), people in a negative mood will have fewer resources, because they exert a greater control that depletes them. Evidence for this assertion comes from studies on stress-induced eating (see Greeno & Wing, 1994, for a review), or on resistance to temptation/delay of reward (Fry, 1975; Mischel, Ebbesen, & Zeiss, 1972). Another study showed that watching a funny movie does not help to suppress certain thoughts’ contents (Muraven, 1998). Thus, self-regulation and self-control seems to consume resources, although not much is known