Biological Psychology 111 (2015) 73–82
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Implicit fear and effort-related cardiac response Mathieu Chatelain, Guido H.E. Gendolla ∗ Section of Psychology, University of Geneva, Switzerland
a r t i c l e
i n f o
Article history: Received 30 April 2015 Received in revised form 25 August 2015 Accepted 25 August 2015 Available online 29 August 2015 Keywords: Effort Cardiovascular Implicit Affect Automaticity Fear
a b s t r a c t Based on the Implicit-Affect-Primes-Effort (IAPE) model (Gendolla, 2012, 2015), 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. © 2015 Elsevier B.V. All rights reserved.
1. 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 a systematic impact on behavior by activating emotion knowledge rather than emotional states. Referring to resource mobilization—the aspect of behavior that is traditionally of central interest for psychophysiologists—it has been found that implicitly processed motivational stimuli, like incentive cues, can influence related physiological reactions (e.g., Bijleveld, Custers, & Aarts, 2009; Capa, Cleeremans, Bustin, & Hansenne, 2011; Pessiglione et al., 2007; Silvia, 2012). Contributing to this accumulating evidence for automatic resource mobiliza-
∗ Corresponding author at: Geneva Motivation Lab, FPSE, Dept. of Psychology, University of Geneva, 40, Bd. du Pont-d’Arve, CH-1211 Geneva 4, Switzerland. Fax: +41 22 379 92 16. E-mail addresses:
[email protected] (M. Chatelain),
[email protected] (G.H.E. Gendolla). http://dx.doi.org/10.1016/j.biopsycho.2015.08.009 0301-0511/© 2015 Elsevier B.V. All rights reserved.
tion, our laboratory has found that implicitly processed affective stimuli that are processed during task performance systematically influence subjective task demand and effort-related cardiovascular response (e.g., Gendolla & Silvestrini, 2011; Lasauskaite, Gendolla, & Silvestrini, 2013; Silvestrini & Gendolla, 2011a). Other laboratories have recently reported corresponding effects of implicit affect on central nervous system (Chaillou, Giersch, Bonnefond, Custers, & Capa, 2015) and muscular force measures of effort (Blanchfield, Hardy, & Marcora, 2014). Our 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. Consequently, performance ease or difficulty become features of individuals’ mental representations of different affective states—their emotion concepts (see Niedenthal, 2008). The IAPE model posits that rendering this affect knowledge accessible during task performance leads 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 is mobilized proportionally to subjective demand as long as success is possible and the necessary effort is justified. This prediction has been well supported in numerous studies using cardiovascular indices of effort mobilization (see Gendolla, Wright, & Richter, 2012; Wright & Kirby, 2001 for reviews). In brief, the IAPE model posits that sadness and fear are associated with difficulty, while happiness and anger are associated with ease. The reason for this is that people should learn that performing
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tasks in a sad mood is subjectively more demanding than performing tasks 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 anger, in contrast to fear, is typically linked with experiences of high coping potential or ability (Lerner & Keltner, 2001), which reduces subjective difficulty (e.g., Wright & Dismukes, 1995). Activating the difficulty or ease concepts by exposing people to implicitly processed 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. So far, the predictions of the IAPE model have been tested and supported for implicit happiness, sadness, and anger. As expected, participants who were exposed to briefly flashed facial expressions of sadness during cognitive tasks rated subjective task difficulty as higher and showed stronger responses of cardiac pre-ejection period (PEP) and systolic blood pressure (SBP) than participants exposed to happiness or anger primes (e.g., Gendolla & Silvestrini, 2011; Lasauskaite et al., 2013). None of our studies found evidence that these effects occurred because the implicitly processed affect primes elicited emotional states. However, to date no study has tested the impact of implicit fear on effort-related physiological reactions. 1.1. Implicit fear
Several studies have also assessed responses of SBP, which is systematically influenced by cardiac contractility through its impact on cardiac output (see Gendolla & Richter, 2010; Wright & Gendolla, 2012; Wright & Kirby, 2001). However, both SBP and diastolic blood pressure (DBP) are also influenced by peripheral vascular resistance, which is not systematically affected by ß-adrenergic impact (Levick, 2003), and can mask contractility effects on SBP and DBP. Still other studies (e.g., Eubanks, Wright, & Williams, 2002) have quantified effort as responses in heart rate (HR). Though, HR is influenced by both sympathetic and parasympathetic impact and should only reflect resource mobilization if the sympathetic impact is stronger (Berntson, Cacioppo, & Quigley, 1993). Consequently, PEP is the most reliable and valid indicator of effort intensity among these parameters (Kelsey, 2012). Nevertheless, PEP should always be assessed together with blood pressure and HR to control for possible pre-load (ventricular filling) or after-load (arterial pressure) effects (Sherwood et al., 1990). 1.3. The present research Our goal was to provide the first experimental tests of the IAPE model (Gendolla, 2012, 2015) prediction that implicitly processed fear primes systematically influence effort-related cardiac response. That is, we tested the theory-based hypothesis that implicit fear leads, similarly as implicit sadness, to stronger performance-related PEP reactivity than both implicit happiness or anger. To test this, we conducted two experiments including two different types of task to facilitate generalization of our expected findings. Moreover, we compared the effects of implicit fear with those of implicit anger and happiness primes in Experiment 1, while we contrasted the effect of implicit fear with that of implicit anger and sadness in Experiment 2. That way we aimed at replicating the anticipated effect of implicit fear on performance-related cardiac PEP. Moreover, we did so to test the IAPE model idea that the effects of implicit affect are emotion-specific rather than valencespecific. Finding the predicted effect that fear and sadness primes lead to stronger PEP response than anger primes would support this idea—all three conditions exposed participants to affect primes of negative valence.
Fear is associated with low control, low coping potential, and rather pessimistic judgments (Lerner & Keltner, 2001). Correspondingly, dispositionally anxious individuals have been found to rate the likelihood of negative events as higher than control participants, reflecting higher pessimism (Gasper & Clore, 1998). It has also been shown that anxiety has detrimental effects on creative performance (Byron & Khazanchi, 2010), arithmetic tasks (Ashcraft & Faust, 1994), and academic performance (Cassaday & Johnson, 2002), supporting the idea that fear is associated with obstacles and thus difficulty. Moreover, conscious feelings of fear and anxiety seem to tax working memory capacity, resulting in impaired performance (Eysenck & Calvo, 1992). That is, there is ample support for the idea that fear is associated with performance difficulties. However, all this evidence concerns effects of consciously experienced fear and anxiety on performance outcomes. Nothing seems to be known about the effect of implicit fear on physiological reactions related to resource mobilization. We conducted the present experiments to close this gap.
Participants worked on a “parity task” (Wolford & Morrison, 1980). During performance, facial expressions of fear, anger, or happiness were briefly flashed. Cardiovascular measures were recorded during a habituation period before the task and during task performance. As predicted by the IAPE model, we expected stronger PEP reactivity in the fear-prime condition than in both the happiness- and anger-prime conditions.
1.2. Effort-related cardiovascular response
2.1. Method
Wright (1996) has integrated motivational intensity theory (Brehm & Self, 1989) with Obrist’s (1981) active coping approach. This led to the prediction that beta-adrenergic sympathetic impact on the heart responds proportionally to the level of experienced task demand as long as success is possible and justified. Betaadrenergic impact on the heart is best assessed as cardiac PEP—a cardiac contractility index defined as the time interval between the beginning of ventricular excitation and the opening of the heart’s left ventricular valve in a cardiac cycle (Berntson, Lozano, Chen, & Cacioppo, 2004). In accordance with Wright’s integration, empirical evidence indicates that PEP sensitively responds to variations in experienced task demand (Richter, Friedrich, & Gendolla, 2008), incentive value (Richter & Gendolla, 2009), and combinations of both (Richter, 2010a, 2010b; Silvestrini & Gendolla, 2011b).
2.1.1. Participants and design Fifty-four university students with different majors (36 women, 18 men, mean age 28 years) were randomly assigned to a 3-cell between-persons design (Prime: fear vs. anger vs. happiness). Participation was remunerated with 10 Swiss Francs (approximately 11 USD). We had to remove 1 participant because of incomplete cardiac data due to measurement problems, 1 participant due to bad signal quality of the impedance measure, 1 participant because she took cardiac medication, and 1 participant because her PEP response exceeded the grand mean by 3.77 SDs and was thus considered as an outlier. Although we aimed at recruiting 20 participants for each cell as recommended (Simmons, Nelson, & Simonsohn, 2011) this left a final sample of 50 participants for the PEP and HR measures. Moreover, we lost 8 more participants in
2. Experiment 1
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the analysis of SBP and DBP because of technical problems with the blood pressure monitor. Bad contact between the sensor of the blood pressure monitor and participants’ wrists led to data loss, reducing the sample size for these measure to 42 participants. 2.1.2. Affect primes We used pictures with averaged neutral (FNES, MNES), fear (FAFS, MAFS), anger (FANS, MANS), and happiness (FHAS, MHAS) front perspective, low resolution, grey-scale facial expressions from the Averaged Karolinska Directed Emotional Faces (AKDEF) database (Lundqvist & Litton, 1998). Half the pictures showed averaged male faces; half showed averaged female faces. Examples of the stimulus material are depicted in Fig. 1. 2.1.3. Apparatus and physiological measures To assess cardiac PEP, impedance cardiogram (ICG) and electrocardiogram (ECG) signals were non-invasively measured with a Cardioscreen 1000 system (medis, Ilmenau, Germany). Four pairs of disposable spot electrodes (2 × 16-mm Ag/AgCl, Red Dot, 3 M) were placed on the right and left side of the base of the participant’s neck and on the left and right middle 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, 2010b). The first derivative of the change in thoracic impedance was calculated, and the resulting 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). HR was determined on the basis of IBIs assessed with the Cardioscreen system. Additionally, SBP and DBP (in mmHg) were assessed with a Vasotrac APM205A blood pressure monitor (MEDWAVE, St. Paul, MN), which uses applanation tonometry with a pressure sensor placed on the wrist (see Belani et al., 1999 for a validation study). Systolic and diastolic pressure values were recorded each 10–15 heart beats, i.e. 4–5 measures per minute, and directly stored on computer disk. Values were offline averaged over 1 min periods. 2.1.4. Procedure The study procedure had been approved by the local ethical committee. After having obtained signed consent, the experimenter attached the electrodes and the blood pressure sensor and went to a control room. The whole procedure was computerized (E-Prime, Psychology Software Tools, Pittsburgh, PA). Instructions were presented on the computer screen and responses were given with a numerical keyboard. The session started with assessment of biographical data (age, sex, etc.). Then participants rated their current mood with 2 items related to fear (frightened, anxious), 2 items related to anger (angry, irritated), 2 items related to happiness (cheerful, joyful) and 2 items related to sadness (sad, downcast) on scales ranging from 1 (not at all) to 7 (very much) to assess their affective state before exposure to the affect primes. This was followed by a habituation period (8 min) to assess participants’ cardiovascular baseline values. Participants watched a hedonically neutral documentary film about Portugal while physiological baseline values were taken. Next, the parity task (Wolford & Morrison, 1980) was administered. In each trial a neutral word flanked by 2 numbers was displayed on the screen (e.g., “3 garage 8”) and participants had to decide if the 2 numbers had the same parity (i.e., both even or both odd) or not (i.e., one even, one odd) by pressing a “yes” or a “no” key while ignoring the word. The numbers 2, 3, 5, and 8 were used to flank the neutral words. Trials started with a fixation cross
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(1000 ms) followed by a facial expression of the AKDEF database (27 ms; i.e. 2 frames on a 75 Hz monitor) and a backward mask (133 ms), which was a noise picture showing scattered black and white dots. Then the words flanked by 2 numbers appeared on the screen until a response was entered (maximal response window 4 s). After participants’ response the message “response entered” appeared. In case of no response, the message “please answer more quickly” was presented. The respective message appeared for 5 s minus participants’ reaction time so that every participant worked for the same time. The inter-trial interval randomly varied between 2 and 4 s Participants received the instruction to respond as quickly and accurately as possible. The task comprised 30 trials and lasted 5 min. In order to prevent fast adaptation to the affect primes, we flashed emotional faces (fear vs. anger vs. happiness) in 1/3 of the trials and neutral expressions in the other 2/3 of the trials. A previous study had revealed that this priming procedure was the most efficient in this paradigm (Silvestrini & Gendolla, 2011c). Before the task, participants performed 10 training trials in which we presented only neutral expressions and gave feedback about the correctness of participants’ responses. In order to prevent affective reactions to the feedback (e.g., Kreibig, Gendolla, & Scherer, 2012), which could interfere with the affective primes, we gave no feedback during the main task. After the task, participants retrospectively rated subjective task difficulty, their ability to succeed, the quantity of mobilized effort, the value of success, and the importance of success on 7-point scales (1— not at all, 7— very much). Then participants rated the same affect items again that had been assessed at the beginning of the procedure in order to control for possible affect prime effects on participants’ conscious feelings. Additionally, participants answered questions about medication, hypertension history in their family, and their smoking habits. Finally, the experimenter interviewed the 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, thanked, and received their remuneration.
2.1.5. Data Analyses We applied contrast analysis to test our theory-based predictions about the affect prime effects on effort-related cardiovascular response, which is the most powerful and thus appropriate statistical tool to test predictions about patterns of means (Rosenthal & Rosnow, 1985; Wilkinson and the Task Force for Statistical Inference, 1999). As outlined above, we expected stronger PEP responses in the fear-prime condition (contrast weight = +2) than in both the anger-prime and happiness-prime conditions (contrast weights = −1). In addition, we performed tests of the residuals to assess if the a priori contrast captured all significant variance or if it left significant variance that should be explained by orthogonal contrasts (see Abelson & Prentice, 1997).1 Other variables, for which we had no theory-based a priori predictions, were analyzed with conventional exploratory ANOVAs. The alpha-error level for all tests was 5%. For reasons of consistency and easier comparability of effect sizes, we transformed effect size coefficients r for tests with 1 degree of freedom (between participants) to coefficients Á2 .
1 In this test, we calculated the residual variance that was not captured by our theory-based a priori contrast by subtracting the sum of squares explained by our contrast from the total between groups sum of squares. Then, we tested if this residual variance attained significance.
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Fig. 1. Cell means and standard errors of cardiac pre-ejection period reactivity (in ms) during task performance.
2.2. Results and discussion 2.2.1. Cardiovascular baselines Repeated-measures ANOVAs of the eight 1-min scores of PEP, HR, SBP, and DBP activity assessed during the habituation period revealed significant Time main effects, Fs > 2.71, ps < .01, ŋ2 s > .04, due to higher cardiovascular activity at the beginning of the habituation period. Consequently, we calculated cardiovascular baselines by averaging the values of the last 3 min of the habituation period, which did not differ significantly according to Tukey tests (ps > .56) and proved high internal consistency (Cronbach’s ␣s > .97). Cell means and standard errors appear in Table 1. Exploratory oneway between-persons ANOVAs found no significant baseline differences between the later three affect-prime conditions for any cardiovascular index (ps > .38). Because of the relatively small number of men in the sample we did not include Gender as a between factor in the analysis. However, the results are basically the same if we restrict the data analysis to women. 2.2.2. Cardiovascular reactivity We averaged the 1-min scores of PEP, HR, SBP, and DBP assessed during task performance (Cronbach’s ␣s > .68) and subtracted the baseline values from these averaged task scores to create cardiovascular reactivity scores. Preliminary ANCOVAs did not find any significant associations between the cardiovascular values and reactivity scores (ps > .09). Table 1 Means and standard errors (in parentheses) of the cardiovascular baseline values. Primes
PEP SBP DBP HR
Fear
Anger
Happiness
100.02 (2.22) 114.26 (4.81) 66.24 (3.73) 75.18 (2.31)
105.27 (2.44) 114.35 (7.99) 65.65 (5.99) 73.65 (3.12)
103.65 (2.83) 110.49 (3.66) 60.35 (2.10) 73.54 (2.57)
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).
2.2.3. PEP reactivity Our theory-based a priori contrast was significant, F(1, 47) = 4.90, p = .03, ŋ2 = .09, while the test of the residual variance was not (F < 1), reflecting that the contrast left no significant variance unexplained. Cell means of the PEP responses, which are depicted in Fig. 1, displayed the expected pattern. Participants in the fear-prime condition showed stronger PEP reactivity (M = −2.46, SE = 0.66) than those in both the anger-prime (M = −0.87, SE = 0.94) and happiness-prime conditions (M = 0.13, SE = 0.75). Focused cell contrasts revealed that PEP reactivity in the fear-prime cell differed significantly from the happiness-prime condition, t(47) = 2.35, p = .01, ŋ2 = .10, and tended to differ from the anger-prime condition, t(47) = 1.44, p = .07, ŋ2 = .04. The difference between the angerand happiness-prime cells did not approach significance, t(47) = .88, p = .19. In summary, these results support our effort-related predictions: exposing participants to masked fear primes during task performance resulted in stronger PEP response than exposure to masked anger or happiness expressions—though the direct comparison between implicit fear and anger only tended towards significance.
2.2.4. SBP, DBP, and HR reactivity Cell means and standard errors appear in Table 2. Neither the a priori contrasts for SBP, DBP, or HR reactivity, Fs < 2.24, ps > .13, nor the tests of residuals (Fs < 1) were significant.
Table 2 Means and standard errors (in parentheses) of systolic blood pressure, diastolic blood pressure and heart rate reactivity during task performance. Primes
SBP DBP HR
Fear
Anger
Happiness
−0.62 (2.63) −0.81 (1.51) 3.37 (0.68)
3.78 (2.68) 2.57 (2.01) 2.07 (0.73)
0.52 (3.03) 1.18 (1.92) 2.23 (0.66)
Note: SBP = systolic blood pressure (in mmHg), DBP = diastolic blood pressure (in mmHg), HR = heart rate (in beats/min).
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2.2.5. Task performance An oneway ANOVA of the committed errors did not reveal a significant prime effect (p = .36; average M = 2.93%, SE = 0.43). Likewise, an ANOVA of the reaction times in milliseconds for correct responses did not find any significant effect (p = .70; average M = 1187.38, SE = 41.42). 2.2.6. Affect ratings We created affect scores by averaging the two items related to fear (rs > .64), happiness (rs > .84), sadness (rs > .30), and anger (rs > .35), for both times of measure and subjected those scores to 3 (Prime) × 2 (Time) mixed model ANOVAs.2 The analysis of the happiness scores revealed a significant main effect of Time, F(1, 47) = 12.79, p = .001, ŋ2 = .21. Happiness increased from the pre-task measure (M = 3.75, SE = 0.14) to the post-task measure (M = 4.46, SE = 0.21). The Prime main effect and the interaction were not significant (ps > .18). The ANOVA of the sadness scores did not reveal any significant effects (ps > .13; average M = 1.51, SE = 0.13). Concerning the fear scores, the ANOVA yielded a significant main effect of Time, F(1, 47) = 7.56, p = .008, ŋ2 = .14, due to higher scores before the task (M = 1.97, SE = 0.15) than after the task (M = 1.60, SE = 0.15). No other effects were significant (ps > .51). Finally, the analysis of the anger scores revealed a significant Prime effect, F(2, 47) = 3.75, p = .03, ŋ2 = .14. According to post-hoc comparisons performed with Tukey tests, participants in the anger-prime condition (M = 1.61, SE = 0.21) had significantly higher scores than those in the happiness-prime condition (M = 1.09, SE = 0.07; p = .03). The fear prime condition (M = 1.28, SE = 0.08) did not differ from the happiness-prime and the anger-prime conditions (ps > .17). Neither the Time main effect (p = .80), nor the Prime x Time interaction (p = .84) were significant, indicating that the differences between the prime conditions existed already before the task and were not moderated by the affect primes. Consequently, these data do not provide evidence for the possibility that the affect primes had an impact on conscious affect. Finally, we tested with ANCOVAs if the post-task affect scores were significantly associated with PEP reactivity. The only significant effect was an association with the anger scores, F(1, 46) = 4.44, p = .04, ŋ2 = .09 (other ps > .11). However, the above reported a priori contrast effect on PEP reactivity remained significant when controlling for anger, F(1, 46) = 4.29, p = .04, ŋ2 = .08. This makes it implausible that the manipulation effect on PEP response actually occurred due to conscious anger. 2.2.7. Task ratings Given that the ratings of task difficulty and reverse-coded ability were highly correlated (r = .60) we created a subjective demand index by averaging them. An ANOVA of this index was not significant (p = .36; average M = 1.83, SE = 0.12). We also averaged the ratings of success value and success importance (r = .70) to create a success importance index. However, also an oneway ANOVA of
2
Given that the correlations between the two items of the anger and sadness scales were modest, we also analyzed the items separately. A 3 (Prime) × 2 (Time) mixed model ANOVA of the “angry” item did not reveal any significant effects (ps > .58). The ANOVA of the item “irritated” found a Prime main effect, F (2, 47) = 5.33, p = .001, 2 = .19. According to a Tukey tests it reflected significantly higher general anger scores in the anger-prime than in the happiness-prime condition (p = .006; M = 2.03, SE = 0.38 vs. M = 1.06, SE = 0.63), whereas neither the difference between the fear-prime (M = 1.47, SE = 0.22) and happiness-prime conditions nor the difference between anger-prime and fear-prime conditions were significant (ps > .13). Given that there was no prime x time interaction on the item “irritated”, this does not speak for higher irritation ratings because of the anger primes. Rather, the irritation ratings were already higher before exposure to the primes (pre-task measure: M = 2.06, SE = 0.40, post-task measure: M = 2.00, SE = 0.36). Mixed model ANOVAs of items “sad” (ps > .09; average M = 1.44, SE = 0.14) and “downcast” (ps > .18; M = 1.68, SE = 0.14) did not reveal any significant effects.
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this score was not significant (p = .63; average M = 5.54, SE = 0.20). Finally, the analysis of self-reported effort was significant, F(2, 47) = 5.11, p = .01, ŋ2 = .18. Post-hoc comparisons performed with Tukey tests found that the difference between the fear-prime (M = 2.78, SE = 0.21) and anger-prime (M = 4.12, SE = 0.35) conditions was significant (p = .007), whereas no other significant differences emerged (ps > .25). Subjective effort in the happiness-prime condition fell in between the anger- and fear-prime cells (M = 3.44, SE = 0.34). 2.2.8. Funnel debriefing In the funnel debriefing, no participant could guess the purpose of the experiment. When asked to describe a trial of the parity task, 64% of the participants reported to have seen faces during the task. However, only 37.5% of these participants were able to differentiate the faces’ gender and only 19% of them (i.e., 6 participants in total) reported to have seen an emotional expression. This suggests that 88% of the participants had processed the affect primes implicitly. 2.3. Conclusion These present results provide the first evidence for the effect of implicit fear on PEP reactivity during a cognitive task. In accordance with the IAPE model predictions (Gendolla, 2012, 2015), participants who were exposed to briefly flashed fear expressions during task performance showed stronger PEP reactivity than participants who were exposed to anger- or happiness-primes—though we have to acknowledge that the direct comparison between the implicit fear and anger conditions only tended towards significance. Moreover, participants’ ratings of conscious affect provided no evidence that the affect primes elicited corresponding emotional feelings and the majority of the participants had processed the affect primes without awareness of their emotional content, suggesting implicit prime processing. However, given that the present study is the first that revealed evidence for the systematic impact of implicit fear on effort-related cardiac response we run a second experiment. This replication sought to facilitate generalization of the fear-prime effect on cardiac PEP by administering a different cognitive task and including a sadness-prime condition. 3. Experiment 2 Participants worked on a mental concentration task during which they were exposed to briefly flashed facial expressions of fear, sadness, or anger. This allowed for a more stringent test of the IAPE model idea that implicit affect has emotion-specific rather than valence-specific effects on effort-related cardiac response. As posited by the IAPE model, we expected stronger PEP response in both the fear-prime and sadness-prime conditions than in the anger-prime cell. 3.1. Method 3.1.1. Participants and design We wanted to increase the number of participants to ensure higher statistical power than in Experiment 1, which is recommended for replication studies (e.g., McShane & Böckenholt, 2014). Therefore, we randomly assigned 93 participants (68 woman, 25 men, mean age 26 years) to a 3-cell between-persons design (Prime: fear vs. anger vs. sadness). However, we had to exclude 3 participants from the analysis, because of incomplete cardiovascular data, 3 participants because they took cardiac medication, and 1 participant because of extreme PEP responses, exceeding the grand mean for 4.24 SDs. This left a final sample of 86 participants.
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Table 3 Means and standard errors (in parentheses) of the cardiovascular baseline values. Primes
PEP SBP DBP HR
Fear
Sadness
Anger
99.31 (1.76) 116.70 (4.78) 65.68 (2.88) 71.29 (3.40)
100.78 (2.26) 118.34 (3.73) 66.57 (2.37) 71.02 (3.15)
100.33 (1.75) 128.94 (3.74) 73.97 (2.74) 77.55 (1.64)
Table 4 Means and standard errors (in parentheses) of systolic blood pressure, diastolic blood pressure and heart rate reactivity during task performance. Primes
SBP DBP HR
Fear
Sadness
Anger
9.27 (2.70) 5.15 (1.32) 0.64 (1.25)
1.67 (4.44) 0.42 (2.37) 1.25 (2.40)
6.78 (1.18) 3.50 (0.78) 1.78 (4.14)
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).
Note: SBP = systolic blood pressure (in mmHg), DBP = diastolic blood pressure (in mmHg), HR = heart rate (in beats/min).
3.1.2. Procedure The basic experimental procedure and the physiological apparatus and measurement techniques were identical with Experiment 1—excepted for the blood pressure monitor, which had made some measurement problems in the previous study. Therefore, we assessed SBP and DBP (in millimeters of mercury [mmHg]) 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 the participants’ nondominant arm was automatically inflated in 1-min intervals. Values were stored on hard disk. After assessment of biographical data, cardiovascular baselines, and experienced affect with the same items as in the previous study, participants worked on a modified “d2” mental concentration task (Brickenkamp & Zillmer, 1998). In this task, participants indicated if the letter “d” was presented with exactly two apostrophes on the screen by pressing “yes” or “no” keys. Distraction stimuli were the letter “p” with 2 apostrophes and the letters “d” and “p” with 1, 3, or 4 apostrophes. The 44 trials started with a fixation cross (1000 ms), followed by a facial expression from the AKDEF database (27 ms), and a backward mask (133 ms). Then the d2-task letter appeared until the participant responded (maximal response window 2 s). After participants’ response, “response entered” was displayed for 3 s minus participants’ reaction time. Before the main task, participants worked on 10 training trials with correctness feedback. As in Experiment 1, no correctness feedback was given in the main task in order to avoid affective reactions that could interfere with the anticipated affect-prime effect. After the task, participants completed the same ratings and funnel debriefing procedure as in Experiment 1. Data were analyzed as in Experiment 1. As predicted by the IAPE model (Gendolla, 2012, 2015), we expected stronger effortrelated cardiovascular response, especially PEP, in both the fearand sadness-prime conditions (contrast weights −1) than in the anger-prime condition (contrast weight +2).
ity scores by subtracting the baseline scores from these task scores. Preliminary ANCOVAs found significant associations between baseline values and reactivity scores of SBP, DBP, and HR Fs (1, 82) > 7.66, ps < .008, ŋ2 s > .08, but not for PEP (p = .25).
3.2. Results and discussion 3.2.1. Cardiovascular baselines Consistent with the previous study, we created cardiovascular baseline scores by averaging assessed cardiovascular activity values of the last 3 min of the habituation period, which were highly consistent (Cronbach’s ␣s > .91) and did not differ significantly from one another (ps > .78). Cell means and standard errors appear in Table 3. Oneway ANOVAs did not reveal significant a priori differences between the experimental conditions (ps > .05). As in Experiment 1, we did not consider Gender as between-persons factor because of the limited number of men in our sample. However, the effects reported below were similar when restricted to women. 3.2.2. Cardiovascular reactivity We averaged again the 1-min scores of cardiovascular activity assessed during task performance (␣s > .85) and created reactiv-
3.2.3. PEP reactivity Our theory-based a priori contrast revealed the expected significant effect on PEP, F(1, 83) = 6.13, p = .015, ŋ2 = .07, while the test of residual variance was not significant (F < 1). The pattern of PEP reactivity corresponded to the prediction and is depicted in Fig. 2: PEP reactivity in the both the fear-prime condition (M = −1.23, SE = 0.76) and in the sadness-prime condition (M = −1.69, SE = 0.60) was stronger than in the anger-prime condition (M = 0.50, SE = 0.54), reflecting the effort-related pattern predicted by the IAPE model. Moreover, this effect supports the idea of emotion-specific instead of valence-specific effects of affect primes on effort-related cardiac PEP. Additional cell contrasts revealed a significant difference between the anger-prime and the sadness-prime conditions, t (83) = 2.40, p = .009, ŋ2 = .06. The difference between the fear-prime and anger-prime conditions was also significant, t (83) = 1.90, p = .03, ŋ2 = .04, while the difference between the fear-prime and sadness-prime cells was not, t (83) = .51, p = .30. In summary, these results support our predictions that were based in the IAPE model. 3.2.4. SBP, DBP, and HR reactivity Cell means and standard errors appear in Table 4. A priori contrasts for baseline-adjusted responses of SBP, DBP, and HR did not reveal any significant effects, Fs (1, 82) < 1.46, ps > .22 (residual Fs < 1), although the pattern of the blood pressure responses at least largely described the effort-related pattern. 3.2.5. Task performance Oneway ANOVAs did not reveal significant effects on the number of committed errors (average M = 6.69%, SE = 1.81; p = .94) or the reaction times for correct responses in milliseconds (average M = 700.88, SE = 17.18; p = .39) in the mental concentration task. 3.2.6. Affect ratings Given that the single items of the anger (rs > .62), fear (rs > .64), happiness (rs > .79), and sadness (rs > .39) scales were positively correlated, we calculated again affect scores by averaging the two pre-task and post-task measures of each emotion and subjected those scores to 3 (Prime) × 2 (Time) mixed model ANOVAs.3 The analysis of the happiness ratings yielded a significant Time effect, F (1, 82) = 7.64, p = .007, ŋ2 = .08, indicating higher happiness before the task (M = 4.53, SE = 0.13) than after the task (M = 4.26, SE = 0.15). No other effects were significant (ps > .32). Similarly, there was also
3 Given the relatively low correlation between the two items assessing sadness, we additionally analyzed the single items separately with 3 (Prime) × 2 (Time) mixed model ANOVAs. Both analyses revealed only effects falling short of significance (ps > .06; “sad” average M = 1.41, SE = 0.09; “downcast” average M = 1.53, SE = 0.11).
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Fig. 2. Cell means and standard errors of cardiac pre-ejection period reactivity (in ms) during task performance.
a Time main effect on the sadness ratings, F (1, 82) = 4.36, p = .04, ŋ2 = .05, which were higher before the task (M = 1.53, SE = 0.09) than after the task (M = 1.40, SE = 0.08). Additionally, there was a significant Prime × Time interaction, F (2, 81) = 3.27, p = .04, ŋ2 = .07. Tukey HSD tests found only that participants in the fear-prime condition had significantly (p = .02) higher sadness scores before the task (M = 1.71, SE = 0.18) than after the task (M = 1.35, SE = 0.11). There were no other significant differences (ps > .98). Finally, also the analysis of the fear scores revealed a significant Time effect, F (1, 82) = 19.32, p < .001, ŋ2 = .19, reflecting higher fear ratings before the task (M = 1.98, SE = 0.12) than after the task (M = 1.60, SE = 0.10). No other effect was significant (ps > .06). The analysis of the anger ratings did not reveal any significant effects (ps > .19; average M = 1.43, SE = 0.08). In summary, these affect measures did not provide any evidence that the affect primes had elicited conscious feelings. We tested again with ANCOVAs for possible associations between the post-task affect scores and PEP reactivity. The only significant association existed for the anger scores, F (1, 81) = 8.96, p = .004, ŋ2 = .09 (others ps > .18). Nevertheless, the above reported a priori contrast effect on PEP reactivity remained significant after controlling for anger, F (1, 81) = 4.76, p = .03, ŋ2 = .05. This makes it implausible anger had caused the PEP responses. 3.2.7. Task ratings We created subjective demand (r = .54; average M = 2.16, SE = 0.13) and success importance indices (r = .79; average M = 5.28, SE = 0.14) by averaging the same items as in Experiment 1. Oneway ANOVAs of these indices did not reveal significant manipulation effects (ps > .65). Also a oneway ANOVA of the subjective effort ratings (average M = 3.40, SE = 0.15) was not significant, F (2, 82) = 2.41, p = .10. However, as the pattern of cell means of subjective effort corresponded to that of PEP reactivity, we applied the same contrast as for the PEP responses. The contrast was significant, F (1, 82) = 4.54, p = .04, 2 = .05. Focused comparisons revealed that subjective effort in the fear-prime condition (M = 3.71, SE = 0.26) was significantly higher than in the anger-prime condition (M = 2.96, SE = 0.23), t (82) = 2.12, p = .018, 2 = .05. Moreover, effort ratings in the sadness-prime condition (M = 3.52, SE = 0.26) tended to be
higher than in the anger-prime condition, t (82) = 1.58, p = .059, 2 = .03. The difference between the fear-prime and sadness-prime conditions was far from significance (p = .29). In summary, the ratings of subjective effort paralleled those of PEP reactivity. 3.2.8. Funnel debriefing During the funnel debriefing, no participant correctly guessed the aim of the study. When asked to describe a trial of the d2 task, 63% of the participants reported to the experimenter to have seen faces. When asked further about what kind of faces they had seen, 37% of these participants reported to have seen pictures of men and women. Only 8% (i.e. 5 participants in total) reported to have seen emotional expressions. This suggests again that 94% of the participants had processed the affect primes without awareness of their content. 3.3. Conclusions This study conceptually replicated the fear-prime effect on cardiac PEP response we had found in Experiment 1 and extended it to a new cognitive task. Once more we found the pattern of effort-related cardiac response as predicted by the IAPE model (Gendolla, 2012, 2015). Participants presented with masked fearful or sad faces online during a mental concentration task showed stronger PEP reactivity than those exposed to briefly flashed angry faces. Moreover, there was again no evidence that the affect primes, which were processed without awareness of their content by nearly all participants, induced corresponding emotional feelings. This suggests again that the prime effects on PEP response occurred due to implicit rather than explicit affect. 4. General discussion The present two experiments provide the first evidence for the systematic impact of implicit fear on effort-related cardiac response during the performance of cognitive tasks. As predicted by the IAPE model (Gendolla, 2012, 2015), Experiment 1 revealed stronger cardiac PEP reactivity when fearful faces were briefly flashed during a
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parity task (Wolford & Morrison, 1980) than when masked angry or happy faces appeared. Experiment 2 conceptually replicated and generalized these findings. In further support of the IAPE model, PEP reactivity was stronger when masked fearful or sad facial expressions appeared during a mental concentration task (Brickenkamp & Zillmer, 1998) than when angry expressions were flashed. The IAPE model posits that affective stimuli (e.g., facial expressions, emotion words, etc.) that are implicitly processed during task performance can influence subjective demand and thus effort-related cardiac response by rendering information about performance ease or difficulty accessible. The reason is that people learn that coping with challenges is more difficult when they are sad or fearful than when they are happy or angry. Consequently, performance ease and difficulty become features of peoples’ mental representations of these affective states—their emotion concepts (see Niedenthal, 2008). Making this knowledge accessible during a task leads to experiences of low or high task demand. This, in turn, determines the effort people mobilize according to the principles of motivational intensity theory: Effort rises with subjective demand as long as success is possible and the necessary effort is justified (Brehm & Self, 1989). According to Wright’s integration of motivational intensity theory with the active coping approach (Obrist, 1981), beta-adrenergic sympathetic impact on the heart, and thus cardiac PEP—its most reliable noninvasive measure—should mirror effort mobilization. Therefore, we tested the IAPE model’s general prediction that PEP response should be stronger when people process fear or sadness primes during a cognitive task than when happiness or anger primes appear. The present two experiments supported this hypothesis and add to the accumulating evidence for the IAPE model. Our previous studies have provided replicated evidence for the systematic impact of implicit sadness, anger, and happiness on effort-related cardiovascular response (e.g., Gendolla & Silvestrini, 2011; Lasauskaite et al., 2013; Silvestrini & Gendolla, 2011a). The present two experiments are the first demonstrations of implicit fear’s systematic impact on PEP response during cognitive performance. Both of the present experiments found the predicted effects of implicit affect on PEP reactivity but not on other indices of cardiovascular activity. This discrepancy is not surprising, because PEP is the most sensitive noninvasive measure of beta-adrenergic sympathetic impact on the heart (Kelsey, 2012; Wright, 1996). Some of our previous studies revealed also significant affect prime effects on SBP (e.g., Gendolla & Silvestrini, 2011; Silvestrini & Gendolla, 2011a, 2011c), but this was not always the case (e.g., Freydefont, Gendolla, & Silvestrini, 2012; Freydefont & Gendolla, 2012; Lasauskaite et al., 2013). SBP has been widely used as index of effort (see Gendolla, Wright et al., 2012; Wright & Kirby, 2001; Gendolla, Tops, Koole, 2015 for reviews). The reason for this has been that cardiac contractility can have a systematic impact on SBP by influencing cardiac output. However, it is of note that blood pressure depends also on total peripheral resistance and can be masked by it (see Levick, 2003). Therefore, it is not surprising if effort-related manipulations result in effects on PEP but not on blood pressure. Some of our previous studies also revealed affect prime effects on HR (e.g., Freydefont et al., 2012; Freydefont & Gendolla, 2012). However, HR is determined by both sympathetic and parasympathetic nervous system and thus should respond to effort mobilization only when the sympathetic impact is stronger, which is not always the case. Moreover, cognitive tasks usually result in rather modest HR increases, which are likely to reflect parasympathetic withdrawal rather than sympathetic discharge (see Berntson et al., 1993). Based on Wright’s (1996) integration of motivational intensity theory with the active coping approach (Obrist, 1981), we have predicted systematic affect prime effects on PEP—and those occurred in support of the IAPE model (Gendolla, 2012, 2015). However, regarding
the other here-assessed indices of cardiovascular activity, it is important that the predicted effects on PEP were not accompanied by decreases in HR or blood pressure. This makes it implausible that the present PEP effects could have occurred due to preload (ventricular filling) or afterload (arterial pressure) effects rather than beta-adrenergic impact on the heart (cf. Sherwood et al., 1990). Both of the present studies have revealed the expected effects of affect primes on PEP responses during the cognitive tasks participants worked on, but not on performance indices like reaction times or errors. Although some of our priming studies have found correspondences between cardiac effort measures and performance (e.g., Gendolla & Silvestrini, 2010; Lasauskaite et al., 2013) most others have not. However, as discussed in detail elsewhere (Gendolla & Richter, 2010), this is not problematic. Effort refers to the mobilization of resources to carry out instrumental behavior, whereas performance describes the outcome of instrumental behavior. Consequently, these constructs are not conceptually interchangeable. Moreover, beside effort, performance depends also (or even more) on ability and strategy use (Locke & Latham, 1990). Thus, one cannot expect that variations in effort automatically result in variations in performance. For the present tasks, the very low number of errors shows that participants’ capacity was largely sufficient to succeed on the task. This suggests that performance was mainly capacity-driven and that there was little room for effort effects on performance—which are more likely for objectively difficult and thus more taxing tasks in which effort can compensate performance decrements (see Hockey, 1997; Silvestrini & Gendolla, 2013). A possible point of critique on the present research could be that we did not find evidence for the IAPE model idea that affect primes influence effort-related cardiac response by influencing subjective task demand. Neither of the current studies found affect prime effects on the post task ratings of task difficulty or capability to succeed. This is in contrast to some of our previous studies that have found affect prime effects on subjective demand and effortrelated cardiovascular responses (Gendolla & Silvestrini, 2011; Lasauskaite et al., 2013; Silvestrini & Gendolla, 2011a). However, it is important to keep in mind that the IAPE model predicts an impact of affect primes on subjective demand during task performance whereas the present assessments of subjective demand were made after performance. These measures were retrospective judgments, which are subject to several biases (Robinson & Clore, 2002), which may explain the absence of affect prime effects on the demand ratings. However, we might also assume that task demand is evaluated automatically, i.e. without awareness (De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009). This way, the discrepancy between effort-related cardiac response and the demand ratings would not be surprising because self-reports can only measure what can be consciously perceived and reflected. Accordingly, subjective demand during the task and self-reports of perceived demand measured after the task assess different issues. In addition, regarding the signal to noise ratio in the PEP and self-report measures, it is of note that the PEP scores for the task performance period were based on about 400 cardiac cycles, while we have assessed experienced demand with only one single item. This bears reliability problems for this self-report measure with the consequence that prime effects must be rather strong to be detected. This reliability issue may also explain the equivocal effects on subjective effort. In Experiment 2, participants’ ratings of experienced effort corresponded to the pattern of PEP responses, while they did not in Experiment 1. However, it is most relevant that the present studies have found replicated evidence for the predicted systematic affect prime effects on our dependent variable of interest—betaadrenergic sympathetic impact on the heart, which is a reliable index of effort mobilization in cognitive tasks (Kelsey, 2012; Obrist, 1981; Wright, 1996).
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Finally, it is also of note that none of the present studies found evidence that the affect primes induced conscious emotional feelings. Also none of our previous studies found that the here-applied affect priming procedure elicited conscious affect. Although zeroeffects are of very limited conclusiveness, this lack of evidence for affect prime effects on conscious affects corresponds to the IAPE model idea that masked affective stimuli that are processed during cognitive tasks have implicit effects on effort mobilization. That is, affect primes are not expected to elicit emotional states. Rather, they are supposed to activate mental representations of those states and thus the concepts of performance ease or difficulty, which are features of these representations. The idea that this process functions implicitly is further supported by the finding that the funnel debriefing procedures of the present experiments revealed that the majority of participants processed the affect primes without awareness of their emotional content. Taken together, we regard these findings as further support for the idea that implicit affect has systematic effects on resource mobilization, as conceptualized in the IAPE model (Gendolla, 2012, 2015). Moreover, also the physiological responses elicited by the affect primes speak against the idea that conscious rather than implicit affect was responsible for the here reported effects. Both experiments involved an anger-prime condition and both studies found the expected effects on PEP responses. However, neither study found effects on blood pressure—which are typical for anger (see Kreibig, 2010). This speaks against the possibility that the anger primes made participants angry. Moreover, Bongard, Pfeiffer, Al’Absi, Hodapp, and Linnenkemper (1997) investigated the cardiovascular effects of anger during effortful performance of a cognitive task and found that a provocation led to intensified feelings of anger and additional responses of HR and DBP. This pattern was not evident in the present anger-prime condition. Most relevant, in both experiments the significant affect prime effects on PEP remained intact after controlling for conscious affect. To conclude, we interpret the present findings as first evidence for implicit fear’s impact on effort-related cardiac response in the context of cognitive tasks—implicit fear can increase resource mobilization. This replicated finding adds to the already available evidence for systematic effects of implicitly processed affective stimuli on effort-related cardiac response as conceptualized in the IAPE model. Acknowledgments This research was supported by a grant from the Swiss National Science Foundation (SNF 100014-140251) awarded to the second author. We thank Marjorie Texier for her help as hired experimenter. References Abelson, R. P., & Prentice, D. A. (1997). Contrast tests of interaction hypotheses. Psychological Methods, 2, 315–328. http://dx.doi.org/10.1037//1082-989X.2.4. 315 Ashcraft, M. H., & Faust, M. W. (1994). Mathematics anxiety and mental arithmetic performance: an exploratory investigation. Cognition and Emotion, 8, 97–125. http://dx.doi.org/10.1080/02699939408408931 Belani, K., Ozaki, M., Hynson, J., Hartman, T., Reyford, H., Martino, J. M., et al. (1999). A new noninvasive method to measure blood pressure. Anesthesiology, 91, 686–692. http://dx.doi.org/10.1097/00000542-199909000-00021 Berntson, G. G., Cacioppo, J. T., & Quigley, K. S. (1993). Cardiac psychophysiology and autonomic space in humans: empirical perspectives and conceptual implications. Psychological Bulletin, 114, 296–322. http://dx.doi.org/10.1037/ 0033-2909.114.2.296 Berntson, G. G., Lozano, D. L., Chen, Y. J., & Cacioppo, J. T. (2004). Where to Q in PEP. Psychophysiology, 41, 333–337. http://dx.doi.org/10.1111/j.1469-8986.2004. 00156.x Bijleveld, E., Custers, R., & Aarts, H. (2009). The unconscious eye opener: pupil dilation reveals strategic recruitment of resources upon presentation of
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