Some Theoretical Models

As introduced above, some authors have posited that emotions have an adaptive impact on behavior (Darwin, 1872; Izard, 1991; Izard & Ackerman, 2000). Emotions activate approach and avoidance behavior. From this perspective, numerous theories explain the role of emotions in motivation. Most of them explore the influence of affects on the directional aspect of behavior (see Aue, 2009).

Among them, Plutchik (1970, 1980, 1994) has suggested, in a psycho-evolutionary model, that emotions are indicators of expressions and physiological changes. Based on Darwin’s works, Plutchik posits that eight basic emotions (joy, anticipation, anger, disgust, sadness, surprise, fear and acceptation) are associated with specific adaptive behaviors in specific contexts. An event leads to a cognitive evaluation of the situation, triggering the associated emotion followed by a physiological reaction and then behavior. The function of this sequence is protection and survival.

A different approach is taken by Lang and colleagues (Lang 1994; Lang, Bradley,

& Cuthbert, 1990) who has suggested that behavior is organized on an appetitive-aversive dimension. The authors have distinguished two motivational systems: one for appetitive behavior and the other one for aversion. The motivational system responsible for the appetitive dimension leads to behavioral approach. By contrast, the motivational system responsible for the aversive dimension leads to a behavioral avoidance. This model posits

21 that one of the systems can inhibit the other come involving a moderating effect of positive and negative events.

Among other theoretical models considering motivational-affective systems underlying adaptive behavior, Gray (1994) has proposed three motivational systems: a system managing fight/flight motivation (FFS), a system managing behavioral inhibition (BIS) and a system referring to approach motivation (BAS). Based on this model, numerous studies suggest that positive affect is associated with the BAS, leading to approach, and that negative affect is associated with the BIS, leading to avoidance (see Carver & White, 1994).

These discussed theoretical models focus essentially on the directional aspect of motivation. However, motivation consists of a second aspect: intensity. The Mood Behavior Model (MBM, Gendolla, 2000) suggests predictions about how mood influences effort mobilization during cognitive tasks, investigating the influence of affect on the intensity aspect of motivation. This model posits that moods influence effort mobilization by their informational impact on task demand appraisals via mood-congruency effects (Brinkmann & Gendolla, 2007, 2008; Gendolla, Brinkmann, &

Richter, 2007; Gendolla & Krüsken, 2001; Silvestrini & Gendolla, 2007, 2009a, 2009b).

This results in lower task demand in a happy mood than in a sad mood, leading, in accordance with motivational intensity theory (Brehm & Self, 1989), to lower effort intensity in a happy mood than in a sad mood as long as success is possible and justified (Gendolla, Abele, & Krüsken, 2001; Richter, Gendolla, & Krüsken, 2006).

To summarize, numerous approaches have considered the influence of affective states on motivation. Although the majority of theoretical models focuses on the directional aspect of motivation, the MBM (Gendolla, 2000) explains how moods influence the intensity aspect of motivation. Before discussing the impact of implicit affect on effort mobilization, the second part of this introduction will now focus on the intensity aspect of motivation and discuss variables influencing effort mobilization.

22 I.2. Effort Mobilization: The Intensity of Motivation

I.2.1. Definitions

As already mentioned earlier, the term “motivation” refers to the process determining the direction and the intensity of behavior (Elliot, 2006; Gendolla, Wright, &

Richter, 2012). In this work, we will focus on the second aspect of motivation: effort intensity during cognitive tasks.

Effort is defined as the mobilization of resource to execute instrumental behavior (Gendolla & Wright, 2009). Effort is systematically involved in achievement contexts and goal pursuit (Gollwitzer, 1990, 1993). The factors influencing effort intensity have been described in motivational intensity theory (Brehm & Self, 1989).

I.2.2. Motivational Intensity Theory

Motivational intensity theory (Brehm & Self, 1989) explains resource mobilization during goal pursuit. This model is established on one important postulate.

Based on the basic principle of resource conservation, this theory posits that individuals avoid wasting energy and thus mobilize resources proportionally to subjective task demand. However, to comply further with this principle of resource conservation, effort is mobilized as long as success is possible and justified. When task demand is considered as too difficult or the necessary effort to succeed is not justified, individuals disengage.

Moreover, this model defines “potential motivation” as the maximally justified effort to attain a goal (Wright, 1996). It is determined by needs, outcome expectations, abilities, and performance-contingent incentives. According to motivational intensity theory (Brehm & Self, 1989), potential motivation influences effort mobilization indirectly via its interaction with task difficulty (Brinkmann, 2008).

I.2.3. Difficulty and Effort Mobilization

Based on the principle of resource conservation, motivational intensity theory suggests that effort increase proportionally to task demand as long as success is possible and justified. When potential motivation is low, as described in Panel A of Figure 1, the maximal effort mobilized in the task will be also low. According to the theory, there is no

23 justification to mobilize the high effort that is necessary for challenging the task. By contrast, as described in the Panel B, Figure 1, when potential motivation is high, the model predicts that individual engage in higher effort intensity. As shown in panel B (Figure 1), when task difficulty increases, effort intensity increases up to the task as long as success is possible and justified. When task difficulty is too high or success not justified, we observe disengagement.

Figure 1: Predictions of motivational intensity theory (Brehm & Self, 1989)

According to motivational intensity theory (Brehm & Self, 1989), task difficulty determines effort as long as success is possible and justified. However, if task difficulty is unclear, effort mobilization directly depends on factors influencing potential motivation as success importance. If task difficulty is unclear, individuals do not have task difficulty information and should rely on success importance in accordance with the resources conservation principle to avoid wasting resources (see Richter, 2012; Richter & Gendolla, 2006, 2007, 2009a, 2009b).

24 To summarize, effort mobilization during cognitive tasks relies on one main principle. According to motivational intensity theory (Brehm & Self, 1989), individuals, avoiding wasting energy, mobilize resources proportionately to subjective task demand as long as success is important and justified.

In order to quantify effort intensity during cognitive tasks, Obrist (1981) has demonstrated that cardiovascular activity sensitively responds to task demand in active coping, permitting to quantify effort mobilization during cognitive tasks. The next part will discuss relevant cardiovascular measures used to assess effort mobilization.

25 I.3. Cardiovascular Measures of Mental Effort

Mental effort mobilized during a cognitive task cannot be measured directly.

Numerous types of measures, such as self-reports, are available for conducting studies on effort mobilization during a cognitive task. However, Obrist (1981) has demonstrated that cardiovascular activity sensitively responds to task demand in active coping (when individuals can control outcomes). This part will discuss the functioning of the cardiovascular system and why cardiovascular measures provide relevant indicators of effort intensity.

I.3.1. The Cardiovascular System

The cardiovascular system involves complex cyclic mechanisms to maintain homeostasis. This vital system is a closed system composed of the heart (a pump) and the vasculature (peripheral system of veins and arteries). Through the flow of blood, the cardiovascular system provides oxygen and nutrients required for the functioning of the body’s organs. This transportation system responds to the demands of the body, which can vary extremely. More relevant for our work, Obrist (1981) demonstrated that the cardiovascular system also responds to mental demand.

I.3.1.a. The Heart

“The crucial pump of the cardiovascular system” (Berntson, Quigley, & Lozano, 2007) is a muscle composed of two atriums (right and left) and two ventricles (right and left). The electric activation of the sinoatrial node in the right atrium and of the atrioventricular node in the right ventricular provides the electrical trigger for the contraction of the cardiac muscle. This contraction permits the circulation of the blood through the lungs and then through the organs.

Blood, poor in oxygen, enters into the right atrium by the superior and inferior veins cava. After the filling of the atrium and the opening of the tricuspid valve, the blood fills the right ventricle. The ventricular contraction ejects the blood through the pulmonary valve and sends it into the lung for oxygenation (through the pulmonary artery). During the same contraction, the blood, rich in oxygen goes back into the heart

26 through the pulmonary vein, first into the left atrium and then into the left ventricle, with the opening of the mitral valve. With the ventricular contraction, the blood is ejected into the aorta through the aortic valve. Then the blood, rich in oxygen is transported through the body. During each cardiac contraction, blood is simultaneously ejected into the pulmonary artery and the aorta.

I.3.1.b. The Cardiac Cycle

One cardiac cycle occurs from one beat to the next beat. The cardiac cycle is composed of two main periods: the diastole (filling with blood) and the systole (ejection of the blood from the ventricles).

The cardiac cycle starts with the depolarization of the sinoatrial node during the diastole. This depolarization wave is followed by the atrial contraction. This contraction results in the opening of the tricuspid valve and the passage of the blood from the right atrium to the right ventricle. When the ventricular contraction is sufficiently strong (or pressure in ventricles sufficiently high), the pulmonary and the aortic valves open and the blood is ejected from the ventricles to the body. When the pressure in the ventricles falls at the end of the systole, the valves permitting blood ejection from ventricles close, and the valves linking the atriums and ventricles open. At the end of the systole, the ventricles are re-polarized. The ejection of the blood from the ventricles and the fall of the ventricles’ pressure announce the beginning of the diastole period and the beginning of a new cycle.

The heart can perform autonomously with an activity around 105-110 beats/minute. To respond to body demands, there exist two branches of the autonomic nervous system permitting an increase or a decrease of cardiovascular activity. These branches are the sympathetic and parasympathetic nervous systems.

27 I.3.2. Sympathetic and Parasympathetic Systems

The cardiovascular system is influenced by two branches of the autonomic nervous system: the sympathetic and parasympathetic nervous systems, which influence cardiac activity in two opposite ways.

The sympathetic nervous system is an important actor for the vascular cardiac muscle (Berntson et al. 2007). The sympathetic nervous system is usually identified as the system responsible of the increase of cardiac activity by an increase of the activity of ß-adrenergic receptors. The ß-ß-adrenergic impact directly influences the contractility force of the cardiac muscle. The sympathetic nervous system also influences the cardiovascular system by affecting the contraction of the veins and arteries by increased α-adrenergic impact (Brownley et al., 2000).

By contrast, the parasympathetic nervous system is identified as supporting energy storage. It influences the cardiac system as an inhibitor system.

Consequently, the sympathetic nervous system is involved in action and increase of cardiac activity. More specifically, Obrist (1981) showed that active coping involves stronger β-adrenergic impact. Consequently, indicators of β-adrenergic impact should refer to effort mobilization.

I.3.3. Main Cardiovascular Measures

Numerous cardiovascular activity measures permit an evaluation of the cardiac activity at rest and during activity (physical or mental). Among these different indices, some are influenced by the sympathetic branch of the autonomic nervous system, some by the parasympathetic system, and some by both. The following measures were considered as quantitative measures of effort mobilization in numerous studies (for a review, see Wright & Gendolla, 2012).

28 I.3.3.a. Pre-ejection Period

The cardiac pre-ejection period (PEP) is defined as the time interval between the onset of the heart’s left ventricular excitation and the opening of the aortic valve (Berntson, et al., 2004). This cardiac index is measured in milliseconds (ms) and refers directly to the contractility of the left ventricle before blood ejection into the vasculature.

PEP is directly influenced by ß-adrenergic impact on the heart (Kelsey, 2012). The shorter the PEP is, the stronger is contractility. As PEP is directly influenced by ß-adrenergic impact, this indicator is the most reliable index of effort.

I.3.3.b. Systolic, and Diastolic Blood Pressure, and Heart Rate

Blood pressure can be measured by two indices: systolic blood pressure (SBP) and diastolic blood pressure (DBP). In each cardiac cycle, blood pressure varies between a maximal (systolic) and a minimal (diastolic) pressure. By definition, SBP is the maximal arterial pressure after a heart beat (during the systole). DBP is the minimal pressure during the diastole period of the cardiac cycle, i.e., the minimal arterial pressure between two heart beats. Both SBP and DBP are measured in millimeters of mercury (mmHg), with typical rest values around 120 mmHg for SBP and around 70 mmHg for DBP in young healthy adults.

SBP and DBP are both influenced by cardiac output but also by total peripheral resistance (the level of resistance to the blood flow in veins and arteries composing the systemic circulation). An increase in cardiac activity leads to an increase in blood pressure, especially SBP. Because DBP is much more influenced by total peripheral resistance, it does not represent an estimate of ß-adrenergic impact.

Heart Rate (HR) is defined as the number of beats emitted by the heart per minute.

One beat happens each cardiac cycle. A “normal” HR is around 60-80 beats/minute at rest. HR has largely been used to assess arousal. However as we will discuss in the following part, this index is influenced by both sympathetic and parasympathetic impact, and thus not very suitable for monitoring effort.

29 I.3.4. Cardiovascular Activity and Effort Mobilization

Obrist (1981) has suggested that cardiovascular activity can be a relevant indicator of task engagement (i.e., effort) in cognitive tasks. Obrist (1976) has posited that cardiac activity increases proportionally with task demand only when individuals have control over performance outcomes (active coping). More relevant, the author found that β-adrenergic impact on cardiovascular activity is proportional to task demand. Wright (1996) has integrated the active coping approach (Obrist, 1981) with motivational intensity theory (Brehm & Self, 1989). Accordingly, effort intensity during cognitive tasks can be quantified as adrenergic impact on the heart. As discussed above, ß-adrenergic impact directly influences cardiac contractility. PEP is the most reliable noninvasive index to assess ß-adrenergic impact. Consequently, PEP is the most reliable cardiovascular index of effort mobilization (Gendolla & Silvestrini, 2011; Kelsey, 2012;

Silvestrini & Gendolla, 2011c; Richter, Baeriswyl, & Roets, 2012).

However, numerous authors have also considered SBP as an index of effort (e.g., Silvestrini & Gendolla, 2007, 2009a, 2009b; Wright, 1998; Wright & Dismukes, 1995;

Wright & Kirby, 2001). SBP is systematically influenced by cardiac contractility but also by peripheral resistance. Thus, SBP cannot be considered as the better indicator of effort-related cardiac activity than PEP. DBP is even more strongly influenced by total peripheral resistance. Thus, DBP is a less suitable effort measure than SBP. HR is influenced by sympathetic and parasympathetic nervous system. Consequently, we consider PEP as the best indicator of effort among these measures (Kelsey, 2012).

However, one should record SBP, DBP and HR together with PEP to control for possible pre-load and after-load effect on PEP, as will be explained below.

Besides reflecting ß-adrenergic impact, some authors suggest that cardiac pre-load and after-load can influence PEP reactivity (Sherwood, Dolan, & Light, 1990). The pre-load effect refers to the amount of ventricular filling during the diastole (Newlin &

Levenson, 1979). This means that an increase in ventricular filling increases the force of myocardial contractility, and thus shortens PEP. In this case, decreases in PEP do not reflect increase in ß-adrenergic activity. If PEP is influenced by pre-load, HR will decrease. Observing of an increases or stability of HR coupled with a decrease in PEP indicates the absence of a pre-load effect.

30 The after-load effect refers to the load against which the ventricle contracts (Newlin, 1979). The left ventricle exerts pressure until the aortic valve opens. This means that if PEP is influenced by peripheral resistance (after-load effect) and not by ß-adrenergic activity, we should observe a decrease in PEP together with a decrease in DBP. Observing increased or stable DBP coupled with a decrease in PEP can be interpreted as indicating an absence of after-load effects. Moreover, in studies investigating effort intensity in cognitive tasks, in a stable position of the body, pre-load effects are very unlikely (Bernston et al., 1993; Cacioppo et al., 2000; Kelsey, 1991).

To summarize, based on Obrist’ work (1976, 1981) and Wright’s (1996) elaboration, effort in active coping can be quantified as ß-adrenergic impact on the heart (Gendolla, 2004). Among cardiovascular indices, PEP constitutes the purest non-invasive indicator of ß-adrenergic impact (Kelsey, 2012; Sherwood et al., 1990), and thus the most reliable index of effort.

As discussed above, this thesis focuses on the intensity aspect of motivation and the impact of implicit affect primes on effort mobilization. Thus, the next chapter of this introduction will introduce the influence of implicit affect on effort mobilization.

31 I.4. Implicit Affect and Effort Mobilization

A large body of recent work has shown that mechanisms of motivation are generated by cognitive, affective, and action-related systems to execute behavior (Aarts, Custers, & Marien, 1998; Bargh, 1990). Among them, research investigating the relationship between affective states and goal striving suggests that positive emotional states, compared to negative emotional states, facilitate goal striving (Kazén & Kuhl, 2005). In this study, the authors found a Stroop interference (Stroop, 1935) reduction after positive achievement primes. By contrast, they observed increased Stroop interference after primes related to negative achievement episodes.

However, numerous studies investigating the influence of affect on motivation use experimental paradigms which permit implicit (i.e., suboptimally) stimuli presentation.

After having defined the concepts of “priming” and “implicit”, we will present the Implicit-Affect-Prime-Effort model (Gendolla, 2012), which makes predictions about the influence of implicit affect on effort mobilization.

I.4.1. Definitions

Implicit priming is a process permitting to activate mental representations by presenting stimuli suboptimally (outside of awareness). Usually, priming increases the level of accessibility of concepts in memory. The literature on motivation reports numerous studies showing an impact of implicit priming on the activation of goal concepts (see Dijksterhuis & Aarts, 2010, Kruglanski et al., 2002). Moreover, implicit priming is also used to activate implicit affect.

Implicit affect priming refers to the automatic activation of affect-related knowledge in memory (Quirin, Kazen, & Kuhl, 2009). Accordingly, it has been posited that affective stimuli activate knowledge about affective states and systematically influence behavior without eliciting explicit affect intensity of conscious feeling (Gendolla, 2012).

32 I.4.2. Implicit Affect Primes and Behavior

Numerous studies have found that exposure to suboptimally presented affective stimuli systematically influences evaluative judgments and human behavior (e.g., Murphy

& Zajonc, 1993; Öhman, Flykt, & Lundquist, 2000; Winkielman, Berridge, & Wilbarger, 2005). For example, Winkielman and collaborators (2005) reported that implicit affect priming influences consumption behavior. The authors demonstrated that participants primed with happiness stimuli drink higher quantities of an unknown beverage than participants primed with anger stimuli. Correspondingly, Isen and Reeves (2005) have demonstrated that induced explicit positive affect increases the degree to which an enjoyable task is liked.

As masked words or objects can activate semantic knowledge in long-term memory (Förster & Liberman, 2007; Higgins, 1996), masked affective stimuli can influence evaluative judgments and behavior by activating emotion concepts (see Niedenthal, 2008). As concepts are mental representations of experiences, situations, and actions in memory, Niedenthal defines emotion concepts as knowledge about affective states. Activated, this knowledge provides information for judgments and evaluations.

Recent studies have also revealed that implicit affect primes, activating emotion concepts, influence the subjective evaluation of task difficulty and consequently effort mobilization in task performance contexts (Gendolla & Silvestrini, 2011; Lasauskaite, Gendolla, &

Silvestrini, 2012; Silvestrini & Gendolla, 2011b, 2011c).

I.4.3. The Implicit-Affect-Primes-Effort Model

As demonstrated in several studies, affective states influence effort mobilization (e.g., Brinkmann & Gendolla, 2007, 2008; Silvestrini & Gendolla, 2009a, 2009b). Based on the Mood-Behavior-Model (Gendolla, 2000), moods influenced the subjective evaluation of task difficulty. Gendolla, and Krüsken (2001) demonstrated that after music

As demonstrated in several studies, affective states influence effort mobilization (e.g., Brinkmann & Gendolla, 2007, 2008; Silvestrini & Gendolla, 2009a, 2009b). Based on the Mood-Behavior-Model (Gendolla, 2000), moods influenced the subjective evaluation of task difficulty. Gendolla, and Krüsken (2001) demonstrated that after music