Thesis
Reference
The role of implicit fear in the process of mental effort mobilization
CHATELAIN, Mathieu
Abstract
The purpose of the present thesis was to extend knowledge of the impact of implicit affect in the process of effort mobilization. The implicit-affect-primes-effort model (Gendolla, 2012, 2015) makes specific predictions and gives explanations for the influence of implicit affect on effort mobilization. Briefly, the IAPE model posits that implicit affect influences effort through its impact on subjective demand, which in turn influences effort according to the principles of motivational intensity theory (Brehm & Self, 1989). The present thesis focused on implicit fear, which is an implicit affect that was not studied to date. Based on the IAPE model (2012, 2015) implicit fear should activate the difficulty concept, which should increase experienced task demand and in turn determine effort mobilization according to the principles of motivational intensity theory (Brehm & Self, 1989). We conducted four studies to test these predictions.
CHATELAIN, Mathieu. The role of implicit fear in the process of mental effort mobilization. Thèse de doctorat : Univ. Genève, 2016, no. FPSE 643
URN : urn:nbn:ch:unige-879838
DOI : 10.13097/archive-ouverte/unige:87983
Available at:
http://archive-ouverte.unige.ch/unige:87983
Disclaimer: layout of this document may differ from the published version.
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Section de Psychologie
Sous la direction du Professeur Guido H. E. Gendolla
The role of implicit fear in the process of mental effort mobilization
THESE
Présentée à la Faculté de psychologie et des sciences de l’éducation de l’Université
de Genève pour l’obtention du titre de Docteur en Psychologie
Par
Mathieu Chatelain de
Genève, Suisse
Thèse No 643
Genève Juin 2016
Numéro d’étudiant : 05-324-967
Abstract
The purpose of the present thesis was to extend knowledge of the impact of implicit affect in the process of effort mobilization. The implicit-affect-primes-effort model (Gendolla, 2012, 2015) makes specific predictions and gives explanations for the influence of implicit affect–
i.e. automatic activation of mental representations of affective experiences–on effort mobilization. Briefly, the IAPE model posits that implicit affect influences effort through its impact on subjective demand, which in turn influences effort according to the principles of motivational intensity theory (Brehm & Self, 1989). Previous studies have tested and successfully demonstrated that implicit happiness, sadness, and anger influence effort–operationalized as cardiac reactivity–according to the IAPE’s predictions. The present thesis focused on a new type of implicit affect, which is implicit fear.
We conducted four different studies to test the effect of fear primes on mental effort in the context of a cognitive task. The first study aimed at showing for the first time the simple effect of implicit fear on effort mobilization as predicted by the IAPE model. Therefore, we contrasted implicit fear versus happiness and anger in a cognitive task, which revealed the predicted pattern: implicit fear led to stronger effort-related cardiac response than implicit happiness and anger. As it was the first time that such an effect was demonstrated, we conducted a second study to replicate it by contrasting implicit fear versus implicit sadness and anger. We observed a stronger effort-related cardiac response in the fear-prime and sadness-prime condition than in the anger-prime condition, successfully replicating the results of the previous study and adding evidence for the IAPE model’s predictions. The third study was designed to investigate if the fear prime effect can be moderated by objective task difficulty, which would suggest a prime effect through subjective demand. Accordingly, we contrasted implicit fear versus anger in a cognitive task with two difficulty levels expecting a crossover interaction pattern between affect primes and objective task difficulty on cardiac reactivity. The results revealed the predicted pattern: in the easy task condition implicit fear led to stronger cardiac reactivity than implicit anger, whereas the reverse pattern emerged in the difficult condition, reflecting disengagement when fear primes were flashed. The forth study was conducted to test the reason
of this disengagement observed in the difficult condition when participants were exposed to fear primes, which could be caused by effects on objective capability. Consequently, we manipulated incentive to test if the deficit of effort mobilization observed in the fear-prime/difficult task condition could be compensated by a high incentive. As expected, high incentive led to the strongest effort-related cardiovascular response and low incentive led to the weakest, whereas both anger-prime conditions fell in between. This provided some support for an impact of fear primes on effort mobilization, which cannot be explained by an effect on objective capacity.
In summary, we showed and replicated for the first time the simple effect of implicit fear on effort mobilization as predicted by the IAPE model. Moreover, this effect has been moderated by objective task difficulty in study 3 and by incentive in study 4, adding further support for the predictions of the IAPE model and its integration with the principles of motivational intensity theory.
Table of content
Abstract ... 2
1. Introduction ... 7
1.1. Context of the present thesis ... 7
1.2. Automaticity effects ... 8
1.3. Perception of affective information ...10
1.4. Perception of emotional expressions ...11
1.5. Implicit affective stimuli and behavior ...13
1.6. Motivational intensity theory ...14
1.7. Mental effort operationalized as cardiac reactivity ...16
1.8. Variables influencing task difficulty ...18
1.9. Implicit affect and mental effort ...19
1.10. The implicit-affect-primes-effort model ...19
1.10.1. Main effects ...20
1.10.2. First moderator: task difficulty ...23
1.10.3. Second moderator: potential motivation ...23
1.10.4. Discounting manipulation ...24
1.10.5. Prime frequency manipulation ...25
1.10.6. Prime visibility ...25
1.11. Emotions ...26
1.11.1. Fear ...27
1.11.2. Fear and anxiety ...28
1.11.3. Implicit fear stimuli ...29
1.12. The aim of the present research ...30
1.13. Hypotheses ...31
2. Empirical evidence ... 33
2.1. Study 1 and 2: Implicit Fear and Effort-Related Cardiac Response. ...33
2.2. Study 3: Task Difficulty Moderates Implicit Fear and Anger Effects on Effort Related Cardiac Response. ...60
2.3. Study 4: Monetary Incentive Moderates the Effect of Implicit Fear on Effort- Related Cardiovascular Response. ...79
3. General discussion... 101
3.1. PEP reactivity ... 101
3.2. Heart rate and blood pressure reactivity ... 103
3.3. Subjective demand ratings ... 103
3.4. Link between effort and performance ... 104
3.5. Conscious emotional feelings ... 105
3.6. Integrative discussion ... 105
3.7. Implicit fear and subjective difficulty ... 105
3.8. Affect primes and emotional response ... 106
3.9. Limitations ... 107
3.10. Conclusions ... 108
4. Résumé en français ... 110
4.1. But de la thèse ... 110
4.2. Les effets d’automaticité ... 110
4.3. La perception des stimuli affectifs implicites ... 111
4.4. L’affect implicite et le comportement ... 111
4.5. La théorie de l’intensité de la motivation ... 112
4.6. L’effort mental opérationnalisé avec la réactivité cardiaque ... 112
4.7. Le modèle “implicit-affect-primes-effort” ... 113
4.8. La peur explicite et implicite ... 116
4.9. Objectifs et hypothèses ... 117
4.10. Résultats principaux ... 118
4.11. Discussion ... 120
Acknowledgments ... 122
References ... 123
1. Introduction
1.1. Context of the present thesis
Numerous studies have shown that implicit activation of information can influence human behavior (Chartrand & Bargh, 1996; Custers & Aarts, 2007; Dijksterhuis & van Knippenberg, 1998; Wilson, 2002). More specifically, such influences have been shown for implicit affective stimuli’s effect on liking judgments (Murphy & Zajonc, 1993), overt behavior (Zemack-Rugar, Bettman, & Fitzsimons, 2007; Winkielman, Berridge, & Wilbarger, 2005), and physiological reactions (Öhman & Soares, 1994). Focusing on implicit affective stimuli, the present thesis will investigate their impact on a process that has received little attention outside our lab, namely effort mobilization.
The effects of implicit affective stimuli on mental effort are explained by the implicit- affect-primes-effort model (Gendolla, 2012, 2015). Basically, this model posits that implicit affective stimuli take their effect on subjective demand, which in turn influences effort mobilization according to the principles of motivational intensity theory (Brehm & Self, 1989).
Existing data have shown the predicted effects by the IAPE model integrated with motivational intensity theory for implicit happiness, sadness, and anger (see Gendolla, 2012, 2015). The purpose of the present thesis was to generalize these effects to implicit fear, which is an implicit affect that was not studied to date.
I will start to give an overview of research investigating automaticity. Then I will focus on processing of implicit affective information and its impact on behavior. I will outline the principles of motivational intensity theory, which explains how effort is mobilized and how we operationalized its measure. This will lead me to explain the mechanism posited by the IAPE model and the supporting evidence. Finally, I will discuss fear, which is the affect of interest, and its related concept anxiety.
1.2. Automaticity effects
The present research investigates the effect of the automatic activation of affective knowledge on behavior, more specifically on the intensity of behavior. Therefore, below is a general overview of the different studies demonstrating an impact of this knowledge activation on behavior.
Our environment includes a huge amount of information that we are continuously exposed to. This information is selected and processed according to our needs, values, and goals (Dijksterhuis & Aarts, 2010). The perception of these stimuli can be operated at different levels of consciousness, raising the question if our behavior can be influenced by stimuli we are not fully aware of. There is a growing automaticity literature supporting such a possibility (Hassin, Uleman, & Bargh, 2005).
Priming refers to the impact of activated knowledge structures on subsequent reactions (Fiedler, 2003). Basically, we can classify primed content in three categories, which are goal priming, semantic priming, and procedural priming (Förster, Lieberman, & Friedman, 2007).
There is an abundant literature supporting priming effects within each of these categories, namely activation of goals (e.g. Custers & Aarts, 2007, 2010), of semantic content (Neely, 1977;
Dijksterhuis & van Knippenberg, 1998), and of procedures (Schooler, 2002). Moreover, there is growing empirical evidence showing that activation of these constructs can have an impact on a great variety of behaviors.
Chartrand and Bargh (1996) conducted an experiment in which the implicit activation of the goals of impression formation versus memorization revealed a better memory performance in the latter condition, as suggested by the literature on goals and memory. It has also been shown that implicit activation of the elderly concept led participants to walk slower and activation of a rudeness concept made participants interrupting the experimenter’s conversation earlier than if they were primed with politeness (Bargh, Chen, & Burrows, 1996). They also conducted a third experiment which showed that individuals behaved more aggressively when primed with African American faces. Priming of an elderly stereotype has shown detrimental
effects on memory performance (Dijksterhuis, Aarts, Bargh, & van Knippenberg, 1998). The priming of World War 2 or Vietnam War extracts had an influence on participants’ opinion of whether the United States should help an ally in war or not (Gilovich, 1981). Finally, an activation of the professor (vs. hooligan) stereotype led to better performance in a general knowledge task (Dijksterhuis & van Knippenberg, 1998). Bargh and Chartrand (1999) have explained these effects by an automatic link between perception and behavior. This idea takes its origin from James (1890), who proposed the notion of ideomotor action, which describes how thinking about doing something increases the probability of performing the action. The mechanism to account for this idea is based on the “common coding hypothesis” (Prinz, 1990). It posits the existence of a shared representational system for perception and action, implying that activation of features in the perception area can have an impact on our actions. Therefore, priming constructs stored in memory such as stereotypes, can produce corresponding effects on behavior, as in previous studies.
More relevant to the present thesis, priming of semantic constructs has also revealed effects on our measure of mental effort operationalized as cardiac reactivity (Gendolla &
Silvestrini, 2010). In this experiment, brief exposition to action-related words led to higher effort mobilization and performance in a cognitive task than exposure to inaction-related words.
Priming of procedures has been shown in a study which involved analytical reasoning in a first phase, activating certain procedures which, had then a facilitating effect on solving problems (Schooler, 2002). Another kind of procedure priming called response priming is illustrated by the Simon effect (Simon, 1969). In this effect, a motor response is primed when there is a correspondence between the location of a stimulus and the required response key, which is reflected by faster reaction times when both share the same location, even if this information is not relevant to perform the task. The priming effects demonstrated in these studies can be explained by an assimilative mechanism, which means that participants behave in line with the primed constructs.
However, this picture is more complex, because the literature on priming has also shown that increasing accessibility of concepts can have opposite effects or no effect at all on behavior,
rather than those expected according to the studies presented above. Indeed, a correction or even overcorrection process can occur when awareness of a prime is high, as suggested by an experiment that showed that if individuals knew that the construct of “kindness” had been made activated, they rated a target person as less kind (Lombardi, Higgins, & Bargh, 1987). Another example comes from a study using an exemplar (Einstein) instead of a category (Professor) as a prime, which led participants to evaluate their own intelligence as lower in the former case than in the latter, explained by the fact that exemplars lead to comparisons, making contrast effects likely (Dijksterhuis et al., 1998). Finally, Loersch and Payne (2012) have demonstrated that the implicit presentation of words had an assimilative effect, but no effect when the accessibility of the primed concept was attributed to an external source.
In summary, these studies add evidence to the basic idea that our judgments and behaviors are, at least partly, determined by the constructs in memory, which are available and accessible during the judgment at hand. However, priming effects do not always occur in line with accessible constructs, but can be eliminated or even inverted depending on the context.
1.3. Perception of affective information
In the present thesis, we primed participants with affective stimuli. Therefore, in the following section we will summarize the efficiency and automaticity in the perception of affective information present in the surroundings. We will also discuss the neurological processes corroborating this affective processing.
We are all the time surrounded by a huge amount of affective stimuli in our environment. Indeed, the mere presence of other individuals implies affective information processing to categorize perceived emotions and more generally to regulate social interactions.
Entering a restaurant, interacting with the waiter, following him to the booked table and sitting down involves exposition to a great amount of affective stimuli which are more or less consciously attended. For instance we might look at the waiter’s facial expression, categorizing his emotional expression and interact with an adaptive response. Therefore, we may consciously process part of these affective information on one hand, but on the other hand it has been
suggested that affective stimuli can also be processed automatically with relatively little attention (Murphy & Zajonc, 1993; Öhman, 1999).
To test the idea that we process much more affective information than we are aware of, research in which affective primes were briefly presented and masked to render them hardly visible was conducted. The results supported the idea of an automatic activation of an evaluation associated with the primes (Greenwald, Draine, & Abrams, 1996; Greenwald, Klinger, & Liu, 1989). Since these original affective priming studies, there has been growing empirical evidence supporting the idea that affective stimuli, especially emotional faces, can be perceived quickly and efficiently (Junghöfer, Bradley, Elbert, & Lang, 2001; Zajonc, 1980). The underlying reason for this high ability in perceiving affective information may come from the fact that it is particularly relevant for well-being and survival to detect which aspects of our environment are
“good” or “bad” (Clore, Schwarz, & Conway, 1994) and should correspondingly be “approached”
or “avoided” (Gray, 1987). Therefore, it is well established that people can perceive and automatically process affective information, even when stimuli are not clearly visible.
1.4. Perception of emotional expressions
The present thesis will use emotional facial expressions to active knowledge about emotions which leads us to briefly present data in this part regarding the process of face perceptions. For our ancestors it was of critical importance to recognize and memorize information included in other individuals’ faces. The reason for this is that individuals living in a group assured better life protection from predators and permitted better organization in hunting (Lundqvist & Öhman, 2005). Moreover, being part of a group implied social interaction, which demands the ability to understand and predict behaviors of other members of the group (Humphrey, 1983). In sum, it was extremely important for survival to be able to decode emotion and motivational states conveyed by facial expressions.
Many studies have used emotional faces to activate implicit affect, which is the reason why we will discuss both, conscious and nonconscious processing of faces in the following part.
The high efficiency in processing faces should be corroborated by specific neural circuits in the brain, responsible for identifying the identity of faces and their emotional expressions.
Facial identity processing involves a structure called fusiform face area (FFA) located in the inferior temporal lobe (Adolphs, 2002). Dynamic features of face recognition, such as gaze direction and emotional expressions, are mainly processed by the superior temporal sulcus (STS) (Haxby, Hoffman, & Gobbini, 2000), which is assumed to form a system for social cognition in conjunction with the amygdala and the orbitofrontal cortex (Allison, Puce, & McCarthy, 2000).
Research including patients with brain damages has also given some support for specific neural structures involved in face processing. For instance, lesion of the FFA has been shown to cause an inability to recognize faces (Marotta, Genovese, & Behrmann, 2001), while recognition of emotional faces remained unimpaired (Tranel, Damasio, & Damasio, 1988). This supports the fact that certain brain areas are specialized in processing specific information contained in faces.
There is evidence for nonconscious face recognition reflecting the efficiency of face processing, which has particularly been demonstrated in priming studies. Backward masking studies permit to make sure that face presentations are not consciously perceived and it blocks the normal processing in the visual cortex. Therefore, it allows to study early processing of facial stimuli. For instance, such priming paradigms have shown that backwardly masked angry faces elicited reliable skin conductance response in the observer (Esteves, Dimberg, & Öhman, 1994).
Perception of masked fear faces activates the amygdala differently if faces are perceived consciously or not. It has been shown that the right amygdala was activated during perception of masked fearful faces, whereas the left amygdala was activated during conscious processing (Morris, Öhman, & Dolan, 1998).
Other studies investigated masked facial processing by recording brain activity of patients with lesions in the primary visual cortex, which implies that it is not possible to receive any input. However, these patients have intact subcortical structures, which permits studying their visual abilities while processing in the visual cortex is impaired. Experiments conducted with these patients have demonstrated that they could still discriminate between facial expressions, even though they could not recognize faces (de Gelder, Vroomen, Pourtois, & Weiskrantz, 1999).
Another experiment has shown that in a classical conditioning paradigm, in which visual cues had
been paired with an electric shock, these patients still responded to the conditioned stimuli (Hamm et al., 2003). This corroborates the idea that masked facial expressions are processed in a pathway that does not involve the striate cortex but involves the right amygdala, pulvinar, and superior colliculus.
1.5. Implicit affective stimuli and behavior
Numerous studies have investigated the impact of flashed affective stimuli on behavior.
In Murphy and Zajonc’s (1993) famous experiment, participants rated neutral Chinese ideographs more positively when these were preceded by flashed smiling faces compared to scowling faces.
Generalizing the impact of implicit affective stimuli on overt behavior, researchers conducted an experiment activating sadness and guilt concepts and observed the expected effects on indulging behavior (Zemack-Rugar et al., 2007). Another experiment tested the impact of the suboptimal presentation of happy and angry faces on consumption behaviors and showed that this led to a higher consumption of an unknown beverage in the former case than in the latter (Winkielman et al., 2005).
More relevant for the present thesis, implicit affective stimuli can also have an impact on physiological reactions. Referring to emotional reactions, it has been shown that phobic people manifested a strong skin conductance response when they were exposed to masked related phobia stimuli such as spiders and snakes (Öhman & Soares, 1994). It has also been demonstrated that subliminal affective pictures have a different effect on electrophysiological activity in patients with schizophrenia and healthy controls (Chaillou, Giersch, Bonnefond, Custers, & Capa, 2015). Moreover, the impact of implicit affect has been generalized to muscular force measures and perception of effort in a physical task(Blanchfield, Hardy, & Marcora, 2014).
The purpose of the present research is to test the impact of implicit affective stimuli on effort mobilization as operationalized by cardiac reactivity. To give an explanation about how effort is invested in instrumental behavior, the principles of the motivational intensity theory (Brehm &
Self, 1989) will be introduced in the next section.
1.6. Motivational intensity theory
Elliot (2006) defined motivation as the process determining the direction and the intensity of behavior. The first aspect refers to what people do, whereas the second aspect deals with the amount of effort people mobilize in instrumental behavior, which will be our present focus. Effort is defined as the mobilization of resources to carry out instrumental behavior (Gendolla & Wright, 2009). In the process of goal pursuit, effort is necessary to overcome obstacles and deterrents during goal pursuit.
The motivational intensity theory (Brehm & Self, 1989) postulates that resources are limited, which leads to the fact that we should avoid wasting them. To mobilize the right amount of resources in instrumental behavior, the theory posits that one key variable determining effort is subjective demand. Therefore, effort increases with subjective demand. However, there is a limit to this difficulty–effort relationship. The first limit is set by the person’s ability to perform the task, which means that if task difficulty is so high that success is impossible, people should withdraw effort and disengage. The second limit is set by the second key variable of the theory, which is potential motivation. Potential motivation can be called success importance and refers to the maximal amount of effort people would be willing to mobilize for accomplishing goals, that is the justified effort (Wright, 2008). Therefore, if the effort needed to successfully perform a task is not justified by the success’ benefits, individuals should withdraw effort. The reason for this disengagement is that because resources are limited, people are willing to mobilize required effort only under the condition that success is worthwhile. In other terms, if the accomplishment of a task is not justified by a worthwhile incentive, individuals do not mobilize any effort to avoid wasting resources.
Figure 1. Predictions of motivational intensity theory (Brehm & Self, 1989) when potential motivation is low (panel A) or high (panel B).
As illustrated by panel A, when potential motivation is low, the proportional relationship between effort and task demand is only present for lower difficulty levels. This is explained by the fact that the effort required to succeed exceeds the justified effort because potential motivation is low, leading to disengagement. However, when potential motivation is high (panel B), the justified effort is also high, leading to increased effort at a higher level of task difficulty.
The level of potential motivation is determined by variables such as the need for the available incentive, the value of the incentive per se and the instrumentality of success (Wright, 1996). In sum, predictions of motivational intensity theory (Brehm & Self, 1989) can be summarized as following: effort increases with subjective task difficulty as long as success is possible and worthwhile.
The difficulty–effort relationship presented so far assumes that individuals have a clear idea about the task difficulty level. However, there are situations in which task difficulty is unspecified, for instance when no clear performance standard is provided. For this kind of situation, motivational intensity theory predicts that value of success, namely potential motivation, will directly determine effort mobilization to point the best performance possible.
Motivational intensity theory (Brehm & Self, 1989) gives an explanation regarding resources mobilization and its key determinants. The next section will discuss which indicator will be used as a measure of mental effort.
1.7. Mental effort operationalized as cardiac reactivity
Studies that investigated effort have used several measures, such as performance or self-report. Performance seems an attractive measure to study the intensity of motivation (e.g., Aarts, Custers, & Marien, 2008; Atkinson & Raynor, 1974). However, effort and performance are not conceptually interchangeable because effort refers to the mobilization of resources to carry out instrumental behavior whereas performance refers to the outcome of instrumental behavior.
Moreover, performance measures do not systematically reflect effort. For instance, people with a high ability to perform a task will mobilize little effort but perform well, whereas people with low ability will mobilize high effort, without the guarantee of a good outcome. Even though self- report measures of effort have been previously used (e.g., Efklides, Kourkoulou, Mitsiou, &
Ziliaskopoulou, 2006), they are influenced by self-presentation. Indeed, research on self- handicapping has revealed that individuals report withdrawing effort when they are in a situation in which failure is possible (Rhodewalt & Fairfield, 1991), which makes it difficult to study effort in difficult tasks. On the contrary, the present thesis will operationalize effort as cardiovascular
reactivity, which is a more valid and reliable indicator of motivational intensity. This idea takes its origin with works of the psychophysiologist Obrist (1981) whose work has shown that in an active coping setting, namely a situation in which individuals have control over their behavior’s outcomes, cardiovascular reactivity was observed. More specifically, Obrist conducted a series of experiments in which he manipulated coping dimensions while measuring cardiovascular reactivity (Obrist et al., 1978). The results showed that when participants were administered different types of stressors without the possibility to actively cope with them, a situation called passive coping, little sympathetic discharge was observed to the myocardium. However, the opportunity to have control over the stressors’ administration was characterized by stronger sympathetic impact to heart. Manipulating coping dimension through objective task difficulty, further studies showed an increase in cardiac contractility from easy to difficult conditions. When a task was manipulated to be impossible, a situation corresponding to passive coping, cardiac contractility response was weak. In sum, the active coping approach (Obrist, 1981) suggests that beta-adrenergic activity on the myocardial muscle increases with objective task difficulty when people have control over their behavior’s outcomes. It is of note that Obrist’s approach applies to cognitive challenges, suggesting beta-adrenergic activity as an indicator of mental effort.
Wright has integrated motivational intensity theory (Brehm & Self, 1989) with the active coping approach (Obrist, 1981), leading to the idea that beta-adrenergic impact on the heart reflects effort intensity as long as success is possible and the necessary effort is justified.
The most sensitive indicator of beta-adrenergic activity in the left ventricle is pre- ejection period (Kelsey, 2012). The pre-ejection period (PEP) refers to the time interval between the onset of the left ventricular depolarization to the opening of the aortic valve (Berntson, Lozano, Chen, & Cacioppo, 2004). This indicator is measured in milliseconds (ms), reflecting an increase of cardiac contractility and effort mobilization when a shortening of the PEP is observed.
In sum, PEP is the most reliable assessment of the beta-adrenergic impact on the heart which, makes it our primary measure of mental effort.
Myocardial contractility has a systematic influence on systolic blood pressure (SBP) through its impact on cardiac output. SBP is defined as the maximum arterial pressure following a heartbeat and is measured in millimeters of mercury (mmHg). Numerous studies have used SBP
as an index of effort mobilization (see Gendolla & Richter, 2010; Wright & Kirby, 2001). But though SBP is systematically influenced by beta-adrenergic activity, peripheral vascular resistance has also an impact on it (Levick, 2003). Diastolic blood pressure (DBP), which is the minimal arterial pressure following a heartbeat, is even more strongly related to vascular resistance. Therefore, both blood pressure indices are worse than PEP in the assessment of mental effort. It is of note that blood pressure measures are also used to control for an afterload effect (arterial pressure), which can have a potential impact on PEP (Sherwood et al., 1990).
Finally, heart rate (HR) has also been used to assess mental effort (e.g., Eubanks, Wright,
& Williams, 2002). HR is defined as the pace at which the heart beats and is operationalized as the number of beats per minute (beat/min). However, HR depends on the sympathetic and the parasympathetic nervous system, leading to the fact that HR is a less pure index of beta- adrenergic activity and effort mobilization. However, HR is used to control for a preload effect (ventricular filling), which can influence PEP (Sherwood et al., 1990).
In summary, effort mobilization is operationalized as beta-adrenergic impact on the heart, which is reflected by different cardiovascular indices. Nevertheless, the most sensitive and reliable among them is PEP (Kelsey, 2012), leading the present thesis to make specific predictions mainly on this physiological indicator.
1.8. Variables influencing task difficulty
There is ample evidence supporting the predictions of motivational intensity theory (Brehm & Self, 1989). Indeed, task difficulty and importance of success have shown an interactional impact on physiological measures of mental effort (Wright & Kirby, 2001). Several experiments have revealed that subjective demand is influenced by task demand (Richter, Friedrich, & Gendolla, 2008), fixed performance standards (Wright, Contrada, & Patane, 1986), ability beliefs (Wright & Dismukes, 1995), fatigue (Wright, Martin, & Bland, 2003), depression (Brinkmann & Gendolla, 2008), mania (Harmon-Jones et al., 2007) and extraversion (Kemper, Leue, Wacker, Chavanon, Henninghausen, & Stemmler, 2008). More relevant, it has been shown that mood states have an informational impact, which determines judgments of task difficulty
(Gendolla, Abele, & Krüsken, 2001) as predicted by the mood–behavior model (MBM; Gendolla, 2000). The next question raised the point regarding the possibility of influencing judgments of task difficulty, and effort accordingly, by the mere activation of affective states’ representations rather than consciously experienced feelings. The implicit-affect-primes-effort model (Gendolla, 2012, 2015) posits this possibility and evidence supporting these predictions will be discussed below.
1.9. Implicit affect and mental effort
As previously mentioned, ample evidence exists supporting an impact of implicit affective stimuli on judgment, behavior (Murphy & Zajonc, 1993; Winkielman et al., 2005;
Zemack-Rugar et al., 2007), and physiological reactions (Blanchfield et al., 2014; Chaillou et al., 2015; Öhman & Soares, 1994). However, the present thesis will focus on a realm that has not received so much attention so far, namely mental effort. The implicit-affect-primes-effort model (Gendolla, 2012, 2015) makes specific predictions and postulates a mechanism to explain how implicit affect takes its effect on effort mobilization.
Similarly to semantic information, Niedenthal (2008) has suggested that we have knowledge about affective states stored in semantic memory. As demonstrated for semantic knowledge (Förster & Liberman, 2007), this affective knowledge can be activated providing available information for judgments and behaviors. Following this idea, implicit affect refers to the automatic activation of affective states’ knowledge representation in long term memory (Quirin, Kazen, & Kuhl, 2009). The IAPE model posits that activation of this affective information in the context of a cognitive task influences experiences of subjective demand and effort mobilization, as will be discussed in the next part.
1.10. The implicit-affect-primes-effort model
The paradigm used to test the IAPE model is basically the same for all of the studies, which will be presented next. First, biographical questions were administered followed by items assessing current mood. Then, participants’ cardiovascular activity at rest was assessed while they were watching a hedonically neutral documentary. Next, they read task instructions and
performed practice trials before achieving the task. During the task, participants were primed with emotional faces, which were briefly flashed. Each trial started with a fixation cross (1000 ms), followed by an emotional expression (27 ms) backwardly masked (133 ms). To prevent habituation to the emotional expressions, they were only flashed in 1/3 of the trials while neutral expressions were presented in the other 2/3 (Silvestrini & Gendolla, 2011a). All of the tasks lasted 5 minutes and participants could press a key on a numerical keyboard to give a response. After the task, the same mood items as at the beginning were administered to control for possible prime impact on conscious feelings. In addition, ratings of task difficulty and success importance were collected. Finally, a debriefing procedure was conducted in order to control if participants could discriminate the content of the affect primes and for their suspicion regarding the aim of the study.
1.10.1. Main effects
It is well-established that consciously experienced affective states have an impact on judgments and behavior (Schwarz & Clore, 2007). As predicted by the mood-behavior-model (MBM; Gendolla, 2000), in the context of a task performance it has been shown that positive mood led to evaluate task difficulty as lower, whereas negative mood led to higher experienced task difficulty with corresponding effects on effort mobilization (Gendolla & Brinkman, 2005).
The IAPE model (See Gendolla, 2012, 2015) postulates that implicit affect influences effort mobilization through its impact on subjective demand similarly to conscious feelings, even though the posited mechanism is assumed to be different.
The IAPE model predicts that the mere activation of emotion concepts is sufficient to influence subjective demand and effort in a cognitive task. The reason underlying this effect relies on the fact that different affective states have been associated with ease or difficulty. We have learnt through our personal history that it was easier to perform a task when we are in a happy mood than in a sad mood (De Burgo & Gendolla, 2009) leading to create associations between the experience of happiness and sadness with ease and difficulty, respectively. Anger is commonly accompanied by experiences of high control and high coping potential (Lerner &
Keltner, 2001) leading to associate anger with performance ease. On the contrary, fear should be
associated with difficulty. Indeed, anxious individuals rate the probability of negative events higher than controls, indicating more pessimistic evaluations (Gasper & Clore, 1998). Moreover, it has been shown that anxiety impairs performance in creative tasks (Byron & Khazanchi, 2010), arithmetic tasks (Ashcraft & Faust, 1994) and the academic field (Cassady & Johnson, 2002).
Finally, anxiety has an impact on the functioning of working memory, causing detrimental effects on performance (Eysenck & Calvo, 1992). All these studies support the IAPE model idea about a link regarding the concepts of fear and performance difficulty.
The IAPE model posits that the activation of emotion concepts (Niedenthal, 2008) will correspondingly increase the accessibility of the knowledge associated with its respective emotions (Förster & Liberman, 2007), providing information for the judgment at hand. Part of this knowledge are difficulty and ease concepts. Based on the resource conservation principle of motivational intensity theory (Brehm & Self, 1989), individuals evaluate task difficulty to save resources. Therefore, activation of emotions’ representations will increase the accessibility of their features, namely ease and difficulty, which will influence judgments of task difficulty and effort mobilization according to motivational intensity theory (Brehm & Self, 1989). As depicted in Figure 1, the upper part of the IAPE model posits a link between sadness and fear with difficulty. Thus, activation of sadness and fear will increase the accessibility of the difficulty concept and in turn, increase subjective demand and effort mobilization as long as success if possible and worthwhile. On the other hand, activation of happiness and anger concepts will render the ease concept more accessible, which will reduce the level of subjective demand and effort.
Figure 1. The implicit-affect-primes-effort model representing the basic process regarding the impact of implicit affect on effort mobilization through subjective demand. (Figure from Gendolla, 2012, and affect primes from the AKDEF database by Lundqvist, & Litton, 1998).
There is ample evidence supporting the impact of implicit affective stimuli on effort mobilization. First experiments have shown that brief exposition to sad faces led to stronger cardiovascular reactivity than exposure to happy or angry faces, supporting the predictions of the IAPE model (Gendolla & Silvestrini, 2011). The reason for this is that exposition to sadness prime should increase the accessibility of the difficulty concept and in turn increase subjective demand and effort mobilization. On the contrary, happiness and anger primes should increase the ease concept and lower subjective demand and effort. Moreover, it is of note that these effects on effort mobilization went along with effects on ratings of task demand, providing evidence for the impact of implicit affect on effort through its influence on subjective demand.
Finally, participants were not able to report the emotional content of the affect primes, indicating that they processed their content implicitly.
1.10.2. First moderator: task difficulty
Further examination of the IAPE model implicated the moderation of these affect prime main effect by task context variables. The first key variable moderating affect primes’ effect on effort is objective task difficulty. Indeed, the IAPE model (Gendolla, 2012, 2015) and its integration with the principles of the motivational intensity theory (Brehm & Self, 1989) implies that affect primes’ impact on effort mobilization should be moderated by objective task difficulty.
Silvestrini and Gendolla (2011b) tested this prediction by contrasting the effect of sadness and happiness primes with an easy and a difficult version of an attention task (Brickenkamp & Zillmer, 1998). As predicted, when the task was easy, participants exposed to sadness primes showed stronger effort-related cardiovascular response than when they were exposed to happiness primes. In the difficult condition the reverse pattern was observed, namely, the strongest cardiac reactivity was observed when participants were exposed to happiness primes whereas exposition to sadness primes led to disengagement. The explanation given by the IAPE model was supported by ratings of subjective demand. Indeed, in the happiness-prime condition, participants rated task difficulty as lower than in the sadness-prime condition supporting the impact of affect primes on subjective demand. This moderated effect of affect primes on effort mobilization has been extended to anger primes in a study by Freydefont, Gendolla, and Silvestrini (2012), which manipulated anger vs. sadness primes in a short-term memory task including two difficulty levels.
The results replicated the previously found effect of sadness primes (Silvestrini & Gendolla, 2012b) and showed that anger primes led to weaker cardiac reactivity in the easy condition than in the difficult condition. Corroborating the idea of the IAPE model, the effects found in this study were emotion category specific, which suggests that processing of emotional faces was not restricted to their valence.
1.10.3. Second moderator: potential motivation
The second key variable moderating affect primes’ effect on effort mobilization is potential motivation. A study investigated the impact of anger vs. sadness primes in a difficult short-term memory task, while manipulating potential motivation with monetary incentive
(Freydefont & Gendolla, 2012). According to the IAPE model (Gendolla, 2012, 2015) and its integration with the principles of the motivational intensity theory (Brehm & Self, 1989), it was expected that the weakest reactivity should occur in the sadness-prime/low-incentive condition, whereas the strongest reactivity was expected in the sadness-prime/high-incentive condition.
Cardiac reactivity in both anger cells should not be influenced by incentive manipulation and fall in between. This pattern was expected because sadness primes should increase subjective demand, making effort unjustified at higher difficulty levels, leading to disengagement in the low incentive condition. On the contrary, when incentive was high we expected high effort mobilization, because the high required effort was justified by the high monetary incentive. In the anger-prime cells subjective demand should be lower, leading to lower required effort and leaving the increase of justified effort unnecessary. Therefore, this study showed that the effort mobilization deficit observed in the difficult condition could be compensated by a high incentive.
This makes the possibility unlikely that affect primes take their effect on objective capacity, strengthening the idea that affect primes take their effect on effort mobilization through subjective demand.
1.10.4. Discounting manipulation
To test the idea that affect primes influence effort mobilization through subjective demand, Lasauskaite Schüpbach (2013, chapter 2) conducted an experiment based on an idea suggested by Schwarz et al. (1991). In their study they found that giving participants a cue that their experience of task difficulty could have been influenced during the task should weaken its impact. Similarly, if affect primes influence effort mobilization through their impact on subjective demand, warning individuals that their experienced task difficulty might be manipulated during the task should weaken the affect primes’ effect on effort. Participants performed a short-term memory task while happiness and sadness primes were briefly flashed. Half the participants received the information that their subjective experience of difficulty could have been manipulated while performing the task, whereas the other half did not receive this information.
The results showed that when participants were not warned about a possible influence on their experienced subjective demand, they significantly mobilized more effort when primed with
sadness than with happiness, replicating previous results (Gendolla & Silvestrini, 2011; Silvestrini
& Gendolla, 2011a). However, in the warning condition, the difference between the happiness- and sadness-prime conditions was not significant, which supports the idea that attributing the subjective experience of task difficulty to an external source reduces primes’ impact on effort mobilization. In sum, this study supports the idea of a prime effect on effort mobilization, because of its influence on subjective demand.
1.10.5. Prime frequency manipulation
Other variables have shown that affect primes’ effect on effort is not fixed, but depends on moderators, such as prime frequency. The studies presented so far presented the affect primes in 1/3 of the trials to prevent habituation to them. To test if higher proportion of affect primes would weaken their effect on effort mobilization due to a habituation effect, Silvestrini and Gendolla (2011a) contrasted happiness versus sadness primes in 1/3, 2/3, and 3/3 of the trials in an attention task (Brickenkamp & Zillmer, 1998). They found that the expected priming effect was stronger in the 1/3 condition than in 2/3 and 3/3 conditions, supporting the idea that too much exposition to these affective stimuli reduces their effect on effort mobilization. This attenuated priming effect due to repeated exposure to primes is also supported by studies recording cerebral activity (Breiter et al., 1996). Therefore, this is a demonstration that the assimilative effect of affective priming depends on contextual variables, in the present case prime frequency.
1.10.6. Prime visibility
Another moderator of affect priming effects on judgment and behavior is suggested by Murphy and Zajonc’s (1993) experiment. They found that participants liked Chinese ideographs more when they were preceded by flashed smiling faces than angry faces. However, when the emotional faces were clearly perceived, the ratings were not dependent on the emotional faces, suggesting a correction effect due to controlled processing of the affect primes. Another study investigated if control processing can have such an impact on effort mobilization by manipulating the presentation time of the affect primes (Lasauskaite Schüpbach, Gendolla, & Silvestrini 2014).
The predicted effect of the IAPE model is based on the fact that individuals are unaware of the influence of the affective information processed during the task. Therefore, awareness of this available affective information provided by an external source should exclude its influence and result in a weakened effect or a correction process. To test this idea, participants were given an arithmetic task in which they had to decide if a calculation was correct or incorrect (Bijleveld, Custers, & Aarts, 2010), while happiness versus sadness primes were either briefly presented (suboptimally) or clearly visible (optimally). In addition, the administered task was easy or difficult. The results in the suboptimal condition replicated the crossover interaction pattern obtained previously (Silvestrini & Gendolla, 2012b) namely, stronger effort-related cardiovascular response when participants were primed with sadness in the easy task and the reverse pattern in the difficult task. However, when primes were clearly visible, the pattern was inverted suggesting a contrast effect. Consequently, full processing of affect primes moderates their effect on effort mobilization.
In sum, it is well established that implicit affective stimuli have an impact on effort mobilization as predicted by the IAPE model (Gendolla, 2012, 2015). So far, it has been demonstrated that implicit happiness, sadness, and anger have effects on effort mobilization predicted by the IAPE model. However, nothing is known about the impact of implicit fear on effort-related cardiovascular response. The purpose of the present thesis is to close this gap by investigating if implicit fear can also influence effort mobilization as postulated by the IAPE model.
1.11. Emotions
Emotions are defined as “an episode of interrelated, synchronized changes in the states of all or most of the five organismic subsystems in response to the evaluation of an external or internal stimulus event, as relevant to major concerns of the organism” (Scherer, 2001). This definition implies that emotions are triggered by stimulus events which are relevant for our well- being. According to this view, stimuli are evaluated according to several appraisal dimensions resulting in a response synchronization (Scherer, 2001), which permits organism to cope adaptively with environmental events by the mobilization of resources. The sequence of
appraisals changes rapidly due to new information and re-evaluation, allowing fast readjustments to changing situations. Emotional response patterns are assumed to be of high intensity and short-lived, so that resources remain available to behave flexibly when facing future environmental stimuli.
So that emotional responses are useful to face environmental demands, organisms must have an efficient perceptual system to detect emotional stimuli. Focusing on fear, it is very important to efficiently locate threat-related stimuli so that defensive behaviors can be quickly triggered. It is of note that from an evolutionary point of view, failing to detect a potentially harmful stimulus and its appropriate response is more costly than eliciting a defensive reaction to a harmless stimulus. In other term, it is preferable to be too cautious, which makes it probable that our perceptual system is biased toward identifying threat in the surroundings.
1.11.1. Fear
Fear is an emotional state which motivates avoidance behavior when facing danger (Gallagher & Holland, 1994). It has been described as being highly relevant for one’s current goals and associated with very low coping potential (Scherer, 2001), leading to behavior characterized by inhibition or avoidance (Frijda, Kuipers, & Ter Schure, 1989). This idea is based on the emotion system theory by Gray (1994). This model posits that individuals have a behavioral approach system (BAS) and a behavioral inhibition system (BIS) which can be more or less dominant depending on the kind of behavior involved by the emotion. Corroborating this idea, Davidson and Hugdahl (1995) demonstrated that anxiety/fear is linked with dominance of the inhibition system (BIS). Moreover, fear has been classified as one of the “basic emotions”, because of its biological and social functions (Plutchik, 1980). Öhman and Mineka (2001) have suggested an evolved fear module, which is a neural system highly sensitive to evolutionary relevant threat stimuli, allowing their fast detection and adaptive behavior. This idea suggests that we are more likely to fear events and stimuli, which were threatening for the survival of our ancestors such as spiders or snakes rather than dead objects, such as weapons or motorcycles (Marks, 1962). Data have shown that fear can be elicited by these threatening stimuli, even when they were rendered
less visible by a masking procedure, supporting the fear module hypothesis (Öhman & Soares, 1994).
At a neurological level, LeDoux (1996) posited a system to explain the high ability of organisms to detect and show adaptive response to threatening stimuli. In his model, he proposed a direct neural link between the thalamus and the “significance evaluator” and “fear effector system” located in the amygdala. This monosynaptic link bypasses the traditional thalamo-cortical sensory pathway, to allow a faster detection of emotional stimuli. LeDoux (1996) qualified this route as “quick and dirty”. The first point to test LeDoux’ model (1996) is that fear stimuli should activate the amygdala, even if they are not consciously identified by the perceiver. Presentations of pictures specific to participants’ phobia showed bilateral activation of the amygdala when phobic pictures, namely snakes for snake-phobics and spiders for spider- phobics, but not for fear-relevant but non phobic pictures (Carlsson et al., 2004). Moreover, it is of note that amygdala activation has been found even when fear stimuli were rendered less visible by a backward masking procedure. In order to find more conclusive evidence regarding the monosynaptic link between thalamus and amygdala, Morris, Öhman, and Dolan (1999) examined the connectivity between these regions. They found that amygdala activation by masked stimuli could be predicted by the activation of the superior colliculus and the right pulvinar nucleus located in the thalamus, supporting LeDoux’s hypothesis (1996).
1.11.2. Fear and anxiety
Anxiety and fear are both aversive affective states focused on a threat. They both imply intense negative feelings and somatic changes. Despite this similarity, the question regarding the overlap between fear and anxiety is still debated. Some authors suggest that they form a single personality dimension in the population (Eysenck, 1967), whereas in the clinical population they can be dissociated (e.g., Wolpe & Lang, 1977). Authors tried to decipher this discrepancy between anxiety and fear conceptualization. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR; American Psychiatric Association [APA], 2000) anxiety is defined as an
”apprehensive anticipation of future danger or misfortune accompanied by a feeling of dysphoria or somatic symptoms of tension” (p. 820). According to this definition, we can distinct both
concepts regarding the fact that fear must be triggered by an identifiable stimulus. From that point of view, anxiety is an anticipatory response to more or less threatening stimuli, whereas fear is a response to identifiable fear stimuli. Another distinction comes from Epstein (1972), who argued that fear is associated with coping behavior, more precisely flight and avoidance responses, whereas fear turns into anxiety when no control over the situation is possible. In sum, Epstein views fear as a “motivation to escape” and anxiety as a “a state of undirected arousal following the perception of threat”. He also mentioned that in the case of fear, the eliciting stimulus is quite clear, which is not the case for anxiety, whose threatening stimulus is assumed to be more fuzzy, making action to cope with defensive behavior more difficult. Finally, Gray and McNaughton (2000) suggested that anxiety is elicited when a threatening stimulus must be approached whereas fear, which is characterize by a flight response if such behavior is available in the situation, is elicited by threatening stimuli which can be avoided. Therefore, the two draw upon distinction different emotional and motivational states according to the possible behaviors used to cope with the threat.
At the neurological level, anxiety and fear share common neural mechanisms but at the same time activate distinct specific brain areas. On the one hand, it has been suggested that the central nucleus of the amygdala is activated during fear responses to clear eliciting stimuli whereas the bed nucleus of the stria terminalis is activated during anxiety, which is a longer state, without a distinct eliciting stimulus (Davis, 1998; Davis & Shi, 1999). On the other hand, these structures are related to the same parts of the hypothalamus and the brainstem that influence fear and anxiety, illustrating the large overlap between fear and anxiety responses.
1.11.3. Implicit fear stimuli
Very little is known about the impact of implicit fear on judgments and behavior. Öhman and Soares (1994) used the term “unconscious anxiety” in their paradigm. However, the term
"unconscious anxiety” had nothing to do with an eventual “unconscious” emotional reaction, when facing the stimulus, but was directly related to the eliciting stimulus himself. In other terms, it referred to the fact that a masked threatening stimulus elicited a conscious fear reaction.
Therefore, these experiments are not directly linked with our conception of implicit fear.
Brosschot, Verkuil, and Thayer (2010) conceptualized “perseverative cognition” as
“repetitive or sustained activation of cognitive representations of past stressful events or feared events in the future” which can be either conscious or unconscious. Their point relies on the fact that it is not stressful events per se, which are responsible for sustained physiological activation, but rather perseverative cognition such as worry and rumination. They even argue that unconscious perseverative cognition is the main determinant of stress-related physiological arousal. This has important implications, because stress has traditionally been studied in paradigms that contained a real stressor. On the contrary, these authors suggested that the mere cognitive representation of stress, which is assumed to be personally relevant, is powerful enough to evoke long-lasting arousal effects.
Other researchers used the term “unconscious fear” in a paradigm in which they investigated the impact of masked emotional faces on an attention response (van Honk, Peper,
& Schutter, 2005). They showed an attentional biased reflecting slower responses when masked fearful faces were displayed in comparison to neutral faces. However, most of the affective priming studies have not used fearful faces in their manipulations. Murphy and Zajonc (1993), Winkielman, Zajonc, and Schwarz (1997) or Winkielman et al. (2005) for instance have used happy versus angry faces to investigate their effects on liking judgment of ideographs. Niedenthal (1990) conducted an experiment in which cartoons were evaluated after being preceded by flashed happy or disgust faces. Zemack-Rugar et al. (2007) used words related to sadness and guilt to study the effect of their activation on behavior. This lack of data regarding the influence of implicit fear on human behavior is surprisingly. We will contribute to close this gab by providing evidence for an impact of implicit fear on effort mobilization.
1.12. The aim of the present research
The purpose of the present thesis is to test the impact of implicit fear on effort mobilization as predicted by the implicit-affect-primes-effort model (Gendolla, 2012, 2015). First evidence supporting the IAPE model had shown the main effects of implicit happiness, sadness, and anger on effort mobilization (Gendolla & Silvestrini, 2011). Corroborating the postulated mechanism, this latter study indicated that sadness primes led to higher subjective demand than
happiness and anger primes with a corresponding effect on mental effort. Further studies integrated the manipulation of task context variables to show the moderated prime effect on effort. The manipulation of objective task difficulty has shown a moderated effect on implicit happiness and sadness’ impact on effort mobilization (Silvestrini & Gendolla, 2011) illustrating the crossover interaction pattern predicted by the IAPE model. Indeed, participants showed higher effort mobilization when exposed to sadness- than happiness-primes when a task was easy and the pattern was inverted when a task was difficult. Freydefont, Silvestrini, and Gendolla (2012) generalized this moderator effect to a new implicit emotion, which is implicit anger. They showed similar effects for anger than for happiness, which corroborated the idea that affect primes’ effect are not merely due to valence. Another study investigated the role of incentive in the resources mobilization process, showing an effort mobilization deficit due to exposition to sadness primes can be compensated by high incentive. This suggests that the affect prime effect cannot be explained by an impact on objective capacity. Following this research, the present thesis aims at investigating the effect of a new implicit emotion, namely implicit fear. More specifically, we will first test and replicate the main effect of implicit fear on effort mobilization in two different studies. Secondly, we will attempt to demonstrate that this main effect can be moderated by objective task difficulty. Finally, we will investigate the role of incentive in the effort mobilization process during exposition to fear primes.
1.13. Hypotheses
Based on the implicit-affect-primes-effort (IAPE) model (Gendolla, 2012, 2015) and its integration with motivational intensity theory (Brehm & Self, 1989), we predicted that fear primes should activate the difficulty concept, which will increase experienced task demand and in turn, mental effort as long as success is possible and worthwhile. More specifically, we expected fear primes to increase cardiac reactivity, especially the pre-ejection period (Kelsey, 2012), which is our main dependent measure of effort. In study 1, we expected fear primes to increase subjective demand, which should lead to stronger PEP reactivity than happiness- and anger primes in a parity task. In order to facilitate a generalization of our findings, we conducted study 2 in which participants performed an attention task while being exposed to fear primes
versus sadness- and anger primes. We expected the fear- and sadness-prime conditions to show stronger PEP reactivity than the anger-prime condition.
The fear prime effect should be moderated by objective task difficulty. Indeed, when a task is objectively easy, fear primes should increase subjective demand and mental effort, provided these do not exceed the level of justified effort, resulting in higher effort mobilization.
However, when the task is objectively difficult, the additive effect of fear primes and task difficulty also lead to higher subjective demand, but with the effect of lower effort. The reason is that the additive effect should result in very high demand, resulting in disengagement. To test this hypothesis, we conducted study 3 in which an easy versus a difficult an arithmetic task was administered during which we briefly presented fear versus anger primes. We anticipated stronger PEP reactivity in the fear-prime condition when the task was easy, and stronger PEP reactivity in the anger-prime condition when the task was difficult.
According to the IAPE model and its integration with the principles of motivational intensity theory, the level of incentive should moderate the implicit fear effect for a difficult task.
We predicted that the motivational deficit observed in the fear prime/difficult condition could be compensated by a high incentive, leading to high effort. The reason for this is that the additive effect of fear primes and an objectively difficult task should set subjective demand and required effort at a high level, which is justified by the high incentive. However, when incentive was low, we expected required effort to exceed justified effort, which should cause participants to disengage. To test these predictions, we conducted study 4 in which fear- versus anger primes were briefly presented in a difficult version of a short-term memory task while a low versus high monetary reward was promised contingent upon success. We predicted the fear prime/high incentive condition to show the highest PEP reactivity, whereas the weakest PEP reactivity was expected in the fear prime/low incentive condition. Both anger-prime conditions should fall in between.
2. Empirical evidence
2.1. Study 1 and 2: Implicit Fear and Effort-Related Cardiac Response.
Chatelain, M., & Gendolla, G.H.E. (2015). Implicit fear and effort-related cardiac response. Biological Psychology, 111, 73–82. doi: 10.1016/j.biopsycho.2015.08.009
Abstract
Based on the Implicit-Affect-Primes-Effort (IAPE) model (Gendolla, 2012), two experiments tested the impact of fear primes on effort-related cardiac response. The main dependent variable was reactivity of cardiac pre-ejection period (PEP) during the performance of cognitive tasks. The IAPE model predicts that activation of implicit fear and sadness results in stronger PEP responses during task performance than activation of implicit happiness or anger.
To test this, Experiment 1 exposed participants to masked facial expressions of fear, anger, or happiness while they performed a cognitive “parity task”. As expected, PEP responses in the implicit fear condition were stronger than in both the implicit anger and happiness conditions.
Experiment 2 conceptually replicated the implicit fear effect and revealed, as expected, stronger PEP responses for implicit fear and sadness than implicit anger during a “mental concentration”
task. The findings provide the first evidence for the systematic impact of implicit fear on effort-
related cardiac response and complete the existing evidence for the IAPE model.
INTRODUCTION
Experienced emotions are strong motivators (see Lench, Bench, Darbor, & Moore, 2015). They give behavior an approach or avoidance direction and mobilize the necessary bodily resources to execute it—which is probably the main reason for physiological changes involved in emotional experiences (see Kreibig, 2010). However, a provocative question is if it is really necessary that emotions are experienced to influence behavior. Maybe the implicit activation of peoples’ knowledge about emotions is sufficient for this. The present research is part of a series of studies that has tested this idea by investigating if implicitly processed emotional stimuli have
