Study 3: Task Difficulty Moderates Implicit Fear and Anger Effects on Effort

Chatelain, M., Silvestrini, N., & Gendolla, G.H.E. (2016). Task difficulty moderates implicit fear and anger effects on effort-related cardiac response. Biological Psychology, 115, 94–100. doi: 10.1016/j.biopsycho.2016.01.014

Abstract

Based on the implicit-affect-primes-effort (IAPE) model (Gendolla, 2012, 2015), the present experiment tested whether objective task difficulty moderates the previously found impact of fear and anger primes on effort-related cardiac response during an arithmetic task. We expected that fear primes would lead to stronger cardiac pre-ejection period (PEP) reactivity than anger primes in an easy task, but that anger primes would lead to a stronger PEP response than fear primes in a difficult task. Results corroborated these predictions. Moreover, there was no evidence that the affect primes induced conscious feelings that could explain the observed cardiac reactivity, suggesting that the primes had the intended implicit effect on effort mobilization. The findings contribute to the accumulating evidence in support of the IAPE model, showing that objective task difficulty is a moderator of implicit affect’s influence on effort-related cardiac response.

Introduction

Affective stimuli that are implicitly processed during cognitive tasks have been shown to have a systematic impact on effort-related responses of the cardiovascular system (e.g., Chatelain & Gendolla, 2015; Gendolla & Silvestrini, 2011; Lasauskaite, Gendolla, & Silvestrini, 2013). This suggests that the automatically activated cognitive representation of emotions is sufficient to influence behavior and that it is not necessary that emotions are experienced to have a motivational impact. These studies were guided by the Implicit-Affect-Primes-Effort (IAPE) model (Gendolla, 2012, 2015), which posits that people learn in everyday life that coping with challenges is easier in some affective states than in others. As a result, performance ease or

difficulty become features of individuals’ emotion concepts (see Niedenthal, 2008)—i.e., their mental representations of different affective states. The activation of this affect knowledge during task performance should lead to experiences of low or high task demand. This, in turn, determines the effort people mobilize according to the principles of motivational intensity theory (Brehm & Self, 1989): effort rises with subjective task demand as long as success is possible and the necessary effort is justified (see Gendolla, Wright, & Richter, 2012; Wright & Kirby, 2001 for reviews).

Cardiac Reactivity and Effort Mobilization

Wright (1996) has integrated the principles of motivational intensity theory (Brehm &

Self, 1989) with Obrist’s (1981) active coping approach. According to this analysis, beta-adrenergic sympathetic impact on the heart responds proportionally to the level of experienced task demand as long as success is possible and the necessary effort is justified. Non-invasively, beta-adrenergic impact on the heart is best reflected by increased cardiac contractility and thus shortened cardiac PEP—the time interval between ventricular excitation and the opening of the heart’s left ventricular valve (Berntson, Lozano, Chen, & Cacioppo, 2004). In support of Wright’s analysis, it has been found that PEP sensitively responds to variations in experienced task demand (Richter, Friedrich, & Gendolla, 2008), incentive value (Richter & Gendolla, 2009), and combinations of both (Silvestrini & Gendolla, 2011a). Consequently, we focused on PEP reactivity, which is the most reliable and valid cardiovascular indicator of effort mobilization among these measures (Kelsey, 2012). Nevertheless, PEP should always be assessed together with blood pressure and heart rate (HR) to control for possible pre-load (ventricular filling) or after-load (arterial pressure) effects (Sherwood et al., 1990).

Implicit Affect and Effort

In brief, the IAPE model posits that sadness and fear are associated with difficulty, while happiness and anger are associated with ease. This is because people should learn that performing tasks is more demanding in a sad mood than in a happy mood (see Gendolla &

Brinkmann, 2005; Gendolla, Brinkmann, & Silvestrini, 2012). That way, ease becomes a feature of the mental representation of happiness, whereas difficulty becomes a feature of the mental

representation of sadness. People should also learn to associate fear with difficulty and anger with ease. This occurs because fear, in contrast to anger, is typically linked with experiences of low coping potential or ability (Lerner & Keltner, 2001), which increases subjective demand (e.g., Wright & Dismukes, 1995). Indeed, research on appraisal theory of emotions posits that specific appraisal dimensions are the core variables that trigger emotions (e.g., Lazarus, 1991; Smith &

Ellsworth, 1985; Scherer, 1999). For instance, it has been empirically shown that even though fear and anger share the same negative valence, they differ in appraisal dimensions such as a low certainty and high situational control for fear and high certainty and low situational control for anger (Smith & Ellsworth, 1985), corroborating the idea that specific appraisal patterns are associated with different emotions. Activating the difficulty or ease concepts by exposing people to emotional stimuli should thus systematically influence physiological reactions related to resource mobilization. Importantly, the IAPE model posits that this process works implicitly, by automatic activation of people’s mental representations of emotions, rather than by eliciting emotional states. In support of the IAPE model, it was found that implicit sadness and fear lead to increased effort-related cardiac response, while implicit happiness and anger result in reduced effort (e.g., Chatelain & Gendolla, 2015; Gendolla & Silvestrini, 2011; Lasauskaite et al., 2013).

Task Difficulty as a Moderator

There is also evidence that objective task difficulty moderates these simple effects of happiness, sadness, and anger primes. The logic behind this moderator effect refers to the principles of motivational intensity theory (Brehm & Self, 1989).

To avoid wasting resources, people should seek and consider all available information about task demand. Consequently, accessible subjective ideas about difficulty or ease, activated by affect primes, and information about objective difficulty should have an additive effect on subjective demand. Thus, as depicted in Figure 1, sadness and fear primes should increase the subjective difficulty of an easy task, resulting in relatively high effort (high subjective demand), but lead to low effort for a difficult task (disengagement because of too high demand). By contrast, the effect of objective task difficulty should be inverted by happiness or anger primes.

Here, an objectively easy task should lead to low effort (low subjective demand), whereas effort should be high for an objectively difficult task (high but feasible demand).

Supporting this idea, Silvestrini and Gendolla (2011c) found that briefly flashed happiness-primes led indeed to weaker cardiac pre-ejection period (PEP) and systolic blood pressure (SBP) reactivity than sadness-primes in an easy condition of an attention task, whereas the opposite pattern emerged in a difficult condition. Here, the happiness-primes led to stronger reactivity than the sadness-primes (see also Lasauskaite Schüpbach, Gendolla, & Silvestrini, 2014). Moreover, objective task difficulty and the affect primes had an additive effect on ratings of subjective demand. The effects in the difficult condition were recently replicated for muscular effort in a highly demanding physical endurance task (Blanchfield, Hardy, & Marcora, 2014).

Another study from our lab tested the effect of primes of two negative emotions—anger vs.

sadness—in a short-term memory task (Freydefont, Gendolla, & Silvestrini, 2012). As predicted, PEP—and also heart rate (HR)—responses were stronger in the sadness-prime condition than in the anger-prime condition when the task was easy. But when the task was difficult, anger primes led to stronger cardiac reactivity than sadness primes.

Thus, so far, these findings support the idea that objective task difficulty moderates the effect of implicit happiness, sadness, and anger on effort-related responses in the cardiovascular system. What is, however, still unclear is if the same effect also applies to implicit fear, which leads to stronger PEP response in moderately difficult tasks (Chatelain & Gendolla, 2015). The present study aimed at closing this gap and tested the idea that objective task difficulty moderates the effect of implicit fear on effort-related cardiac reactivity.

The Present Experiment

Based on the IAPE model (Gendolla, 2012, 2015) and our previous research (Freydefont et al., 2012; Lasauskaite Schüpbach et al., 2014; Silvestrini & Gendolla, 2011c), we tested the hypothesis that objective task difficulty moderates the effect of implicit fear on cardiac PEP during a mental arithmetic task. We contrasted the effect of implicit fear with that of implicit anger, another negative emotion, but one that should be associated with ease. In that way, the affect prime effect cannot merely be explained by the valence of the primes. As depicted in Figure 1, we predicted that implicit fear should lead to stronger effort-related cardiac response, especially PEP, if the task was easy than if the task was difficult. This is because fear is associated with difficulty and should thus increase subjective demand. By contrast, anger primes should lead

to higher effort than sadness primes in a difficult task. This is because implicit anger should set subjective demand on a high but still feasible level, while fear primes should lead to the experience of excessive demand, resulting in disengagement. Thus, in summary, we predicted a crossover interaction effect between the affect primes and objective task difficulty on effort-related cardiovascular response, especially PEP.

Figure 1. Theoretical predictions for the combined effect of fear versus anger primes and objective task difficulty on effort-related cardiovascular response.

Method Participants and Design

Eighty-seven psychology students (64 women, 23 men, mean age 21 years) participated in the experiment for course credit. They were randomly assigned to a 2 (Prime: fear vs. anger) x 2 (Difficulty: easy vs. difficult) between-persons design. We had to remove 3 participants because of incomplete cardiovascular data, 2 participants because they indicated taking medication and 2 participants due to bad signal quality of the impedance cardiogram. This left a final sample of 80 participants (21 in the anger-prime/easy condition, 20 in the anger-prime/difficult condition, 19 in the fear-prime/easy condition, and 20 in the fear-prime/difficult condition).

Affect Primes

We used pictures with averaged neutral (FNES, MNES), fear (FAFS, MAFS), and anger (FANS, MANS) front perspective, low resolution, grey-scale facial expressions from the Averaged Karolinska Directed Emotional Faces (AKDEF) database (Lundqvist & Litton, 1998) as affect primes. Half the pictures showed averaged male faces; half showed averaged female faces. To create the pictures Lundqvist and Litton (1998) averaged 35 faces of male and female volunteers.

Apparatus and Physiological Measures

PEP was assessed noninvasively with measures of impedance cardiography (ICG) and electrocardiography (ECG) using a Cardioscreen 1000 system (Medis, Ilmenau, Germany). Four pairs of disposable spot electrodes (Ag/AgCl, Medis, Ilmenau, Germany) were placed on the right and left side of participants’ neck and on the right and left side of the axillary line at the height of the xiphoid. The signals were amplified and digitalized with a sampling rate of 1000 Hz, and analyzed offline with BlueBox 2.V1.22 software (Richter, 2010) applying a 50 Hz low pass filter.

R-peaks were identified using a threshold peak-detection algorithm and visually confirmed. The ICG’s first derivative (ICG dZ/dt signal) was ensemble averaged over periods of 1 min using the detected R-peaks (Kelsey & Guethlein, 1990). B-point location was estimated based on the RZ interval of valid heart beat cycles (Lozano et al., 2007), visually inspected, and if necessary corrected as recommended (Sherwood et al., 1990). PEP (in milliseconds) was determined as the

interval between R-onset and B-point (Berntson et al., 2004). Heart rate (HR) was determined on the basis of IBIs assessed with the detected R-peaks.

In addition to PEP and HR, systolic (SBP) and diastolic (DBP) blood pressure (in mmHg) were assessed with a Dinamap ProCare monitor (GE Medical Systems, Information Technologies Inc., Milwaukee, WI) that uses oscillometry. A blood pressure cuff placed over the brachial artery above the elbow of participants’ nondominant arm was automatically inflated in 1-min intervals.

Values were stored on hard disk.

Procedure

The study procedure had been approved by the local ethical committee. After having signed consent, participants were seated in a comfortable chair. The experimenter attached the electrodes and the blood pressure cuff. The whole procedure was computerized (E-Prime, Psychology Software Tools, Pittsburgh, PA) and data were collected in individual sessions. First, we assessed biographical data (age, sex, etc.) and collected information about medication, family hypertension history, and participants’ smoking habits. Then, participants rated their current affective state with two items related to fear (anxious, frightened) and two items related to anger (angry, irritated) on a 7-point scales (1- not at all, 7 - very much) to assess their affective state before exposure to the affect primes.

Next, participants watched a hedonically neutral documentary about Portugal (8 min) while cardiovascular baseline values were collected. Then, a mental arithmetic task was administrated in which participants had to decide if arithmetic equations appearing on the computer screen were correct or not (cf. Bijleveld, Custers, & Aarts, 2010). Trials started with a fixation cross (1000 ms), followed by a picture of a facial expression of the AKDEF database (27 ms; i.e. 2 frames on a 75 Hz monitor), which was immediately masked with a random dot pattern mask (133 ms). We flashed emotional faces (fear vs. anger) in 1/3 of the trials and emotionally neutral faces in the other 2/3 in sets of 6 (2 emotional, 4 neutral). The same expression never appeared consecutively. It has been shown that this was the most effective priming manipulation in the present paradigm (Silvestrini & Gendolla, 2011b). After the mask, an equation composed of three single digits adding up to a two digits’ sum was presented for 5000 ms in the easy condition vs. 3000 ms in the difficult condition (at maximum). We chose these presentation times

according to pre-tests and a previous experiment (Lasauskaite Schüpbach et al., 2014).

Participants had to decide whether the presented equation was correct or incorrect by pressing a “yes” or “no” key with the fingers of their choice of their dominant hand while the equations were displayed. Participants’ responses were followed by the feedback message “response entered” displayed for 6500 ms minus participants’ reaction time. If participants did not respond while the equation was displayed, the message “please respond faster” appeared according to the same timing principle. That way, the task had the same length for all participants, which was 5 minutes for 36 trials. The intertrial intervals varied randomly between 1 and 3 sec. Before task onset, participants performed 10 practice trials with neutral facial expressions and correctness feedback. During the task, we gave no feedback to prevent affective reactions that could interfere with the anticipated effect of the affect primes (e.g., Kreibig, Gendolla, & Scherer, 2010).

After the task, participants rated subjective task difficulty and the importance of success on 7-point scales (1—not at all, 7—very much). Then, the same affect items as assessed at the beginning of the experiment were rated again to control for possible prime effects on participants’ conscious feeling.

After this, the experimenter interviewed participants in a standardized funnel debriefing procedure about the study’s purpose and what they had seen during the trials. Participants who mentioned “flashes” were asked about their content. Finally, participants were debriefed and thanked.

Results Cardiovascular Baselines

We tested for each cardiovascular index with repeated measures ANOVAs for differences between the eight 1-min scores of cardiovascular activity assessed during the habituation period. Because the stability of the different cardiovascular measures differed between the indices, we created baseline scores using different periods. The ANOVAs revealed Time effects on SBP, DBP, and HR, Fs(7, 553) > 4.22, ps < .001, ɳ²s > .05, reflecting stronger cardiovascular activity at the beginning of the habituation period than at its end. Tukey tests revealed that the last seven minutes for SBP (ps > .08) and the last two minutes for DBP (p = .12) did not significantly differ. Consequently, we constituted SBP and DBP baselines on the basis of

these periods. For HR, the last minute differed significantly from the others (ps < .001).

Consequently, we used only the last minute’s score as HR baseline. However, for PEP, our main dependent variable, the ANOVA did not reveal a significant Time effect, F(7, 553) = 1.31, p = .241.

Consequently, PEP baselines were constituted on the basis of the entire habituation period. Cell means and standard errors of the baseline values, which showed sufficient internal consistency (Cronbach’s αs > .75), are presented in Table 1.

We conducted 2 (Prime) x 2 (Difficulty) between-persons ANOVAs to test for a priori differences in baseline values between the experimental conditions. These revealed a significant Prime main effect on HR, F(1, 76) = 4.22, p = .043, ɳ² = .05, reflecting higher HR baselines in the fear-prime than in the anger-prime condition (M = 79.64, SE = 1.61 vs. M = 75.10, SE = 1.49).

Below we will deal with this finding with an ANCOVA. No other effects were significant (ps > .19).

We did not analyze Gender effects because of the limited number of men in the sample.

However, excluding men from the analysis revealed very similar results as the here reported analysis of the entire final sample.

Table 1

Means and Standard Errors (in Parentheses) of the Cardiovascular Baseline Values.

Fear Primes Anger Primes

Easy task Difficult task Easy task Difficult task

PEP

Note: PEP = pre-ejection period (in ms), SBP = systolic blood pressure (in mmHg), DBP = diastolic blood pressure (in mmHg), HR= heart rate (in beats/min).

Cardiovascular Reactivity

We created cardiovascular reactivity scores by averaging the 1-min scores of PEP, HR, SBP, and DBP assessed during task performance (Cronbach’s αs > .96) by subtracting

cardiovascular baseline values from the task scores. Preliminary analyses of covariance (ANCOVA) of the reactivity scores with their respective baseline values as covariate revealed significant associations between baseline and reactivity scores for DBP, F(1, 75) = 18.09, p <

.001, ɳ² = .19, and SBP, F(1, 75) = 9.81, p = .002, ɳ² = .12, but no other cardiovascular index (ps >

.08). Consequently, we analyzed DBP and SBP reactivity with baseline adjustment.

PEP Reactivity

A 2 (Prime) x 2 (Difficulty) between persons ANOVA revealed a significant Difficulty main effect, F(1, 76) = 4.05, p = .048, ɳ² = .05, reflecting stronger PEP response in the difficult condition (M = -2.50, SE = 0.60) than in the easy condition (M = -0.75, SE = 0.65), and a non-significant Prime main effect (p = .778). Most relevant, the expected cross-over interaction was also significant, F(1, 76) = 12.88, p = .001, ɳ² = .14. As depicted in Figure 2, the reactivity pattern corresponded to the predictions: in the easy condition participants exposed to fear-primes showed stronger PEP reactivity (M = -2.43, SE = 0.89) than participants who processed anger-primes (M = 0.77, SE = 0.83), t(76) = 2.74, p = .004, η² = .09. This pattern was reversed in the difficult condition. Here, the anger-primes led to stronger PEP reactivity (M = -3.86, SE = 0.82) than the fear-primes (M = -1.13, SE = 0.77), t(76) = 2.34, p = .011, η² = .07. That is, as expected, objective task difficulty moderated the effect of the affect primes on our primary measure of effort-related cardiac response.

Figure 2. Cell means and ± 1 standard errors of cardiac pre-ejection period reactivity (in ms) during task performance.

SBP, DBP, and HR Reactivity

Cell means and standard errors appear in Table 2. Except for the above reported significant covariate effects, 2 (Prime) x 2 (Difficulty) between persons ANCOVAs of SBP and DBP reactivity with the respective baselines as covariates revealed no significant effects (ps > .52).

The same applied to a 2 (Prime) x 2 (Difficulty) between persons ANOVA of HR reactivity (ps >

.25).

Table 2

Means and Standard Errors (in Parentheses) of Systolic Blood Pressure, Diastolic Blood Pressure and Heart Rate Reactivity During Task Performance.

Note: PEP = pre-ejection period (in ms), SBP = systolic blood pressure (in mmHg), DBP = diastolic blood pressure (in mmHg), HR = heart rate (in beats/min).

a Baseline-adjusted.

Task Performance

A 2 (Prime) x 2 (Difficulty) between persons ANOVA of response accuracy revealed only a significant main effect of Difficulty, F(1, 76) = 27.56, p < .001, ɳ² = .27. This difference reflected a higher rate of correct responses in the easy (M = 77%, SE = 2.10) than in the difficult condition (M = 59%, SE = 2.73). No other effects attained significance (ps > .54). Also an ANOVA of the reaction times (in ms) for correct responses yielded only a Difficulty main effect, F(1, 76) = 77.10,

Fear Primes Anger Primes

Easy task Difficult task Easy task Difficult task

SBP 3.68^a

p < .001, ɳ² = .50, because of faster responses in the difficult condition (M = 2159.70, SE = 37.52) than in the easy condition (M = 3090.82, SE = 98.67). Taken together these results indicated a successful task difficulty manipulation. Participants were more accurate in the easy condition and responded faster in the difficult condition where they had higher time pressure, because the response window was shorter.

Task Ratings

A 2 (Prime) x 2 (Difficulty) ANOVA of participants’ subjective difficulty ratings found a significant Difficulty main effect, F(1, 76) = 10.98, p = .001, ɳ² = .13, indicating that subjective demand was higher in the difficult condition (M = 5.37, SE = 0.24) than in the easy condition (M

= 4.20, SE = 0.26). No other effect attained significance (ps > .30). A 2 (Prime) x 2 (Difficulty) ANOVA of the success importance ratings did not reveal any significant effects (ps > .10; average M = 4.72, SE = 0.14). In sum, this suggests that the task difficulty manipulation was effective and that there was no evidence that the priming manipulation influenced success importance.

Affect Ratings

Since internal consistency was high (rs > .71), we calculated pre-task and post-task affect scores by averaging the two items assessing fear and anger at both times of measure. A 2 (Prime) x 2 (Difficulty) x 2 (Time) mixed model ANOVA of the fear scores yielded a trend regarding a Prime main effect, F(1, 76) = 3.89, p = .052, ɳ² = .05,reflecting that participants in the fear-prime condition (M = 2.44, SE = 0.22) tended to have in general higher fear scores than participants in the anger-prime condition (M = 1.88, SE = 0.17). However, the Prime x Time interaction was far from significance (p = .444), meaning that the fear scores were not moderated by time, i.e. the priming manipulation (also the Difficulty main effect was not significant, p = .240). In order to test directly if the priming manipulation influenced the fear scores, we compared the scores assessed in the fear-prime and anger-prime conditions across time. These comparisons revealed

Since internal consistency was high (rs > .71), we calculated pre-task and post-task affect scores by averaging the two items assessing fear and anger at both times of measure. A 2 (Prime) x 2 (Difficulty) x 2 (Time) mixed model ANOVA of the fear scores yielded a trend regarding a Prime main effect, F(1, 76) = 3.89, p = .052, ɳ² = .05,reflecting that participants in the fear-prime condition (M = 2.44, SE = 0.22) tended to have in general higher fear scores than participants in the anger-prime condition (M = 1.88, SE = 0.17). However, the Prime x Time interaction was far from significance (p = .444), meaning that the fear scores were not moderated by time, i.e. the priming manipulation (also the Difficulty main effect was not significant, p = .240). In order to test directly if the priming manipulation influenced the fear scores, we compared the scores assessed in the fear-prime and anger-prime conditions across time. These comparisons revealed