task difficulty effects on cardiac activity

Task difficulty effects on cardiac activity

MICHAEL RICHTER,a ANTONIA FRIEDRICH,b and GUIDO H. E. GENDOLLAa

aDepartment of Psychology, University of Geneva, Geneva, SwitzerlandbDepartment of Psychology, University of Leipzig, Leipzig, Germany

Abstract

An experiment with 64 participants manipulated task difficulty and assessed cardiac reactivity in active coping over

four levels of demand. Participants performed a memory task while preejection period, heart rate, and blood pressure

were assessed. In accordance with the theoretical predictions of R. A. Wright’s (1996) integration of motivational

intensity theory (J.W. Brehm&E. A. Self, 1989) with Obrist’s active coping approach (P. A. Obrist, 1981), preejection

period and systolic blood pressure reactivity increased with task difficulty across the first three difficulty levels. On the

fourth difficulty levelFwhere success was impossibleFreactivity of both preejection period and systolic blood pres-

sure were low. These findings provide the first clear evidence for the notion of Wright’s integrative model that energy

mobilization in active coping is mediated by beta-adrenergic impact on the heart.

Descriptors: Task difficulty, Beta-adrenergic response, Cardiovascular reactivity, Active coping

According tomotivational intensity theory (Brehm&Self, 1989),

human behavior is guided by a resource conservation principle.

Drawing on this basic assumption, the theory predicts that diffi-

culty and success importance are the two major determinants of

energy investment in instrumental behavior (i.e., behavior that

allows one to attain a certain goal). Energy expenditure should be

proportional to a behavior’s difficulty as long as success is pos-

sible and justified. If a behavior is either too difficult or the nec-

essary amount of energy for its execution exceeds the justified

level, energy investment should be low. Wright (1996) integrated

these predictions with Obrist’s (1981) active coping approach.

According to this perspective, energy mobilization in active

copingFwhen individuals have control over performance

outcomesFis reflected in cardiovascular reactivity. More spe-

cifically, based on Obrist’s demonstration that task engagement

is mediated by sympathetic (beta-adrenergic) discharge to the

myocardium (Obrist, 1976, 1981; see also Fredrikson, Klein, &

Oehman, 1990; Winzer et al., 1999), Wright postulated that

effects on cardiovascular reactivity should be mediated by beta-

adrenergic activity.

Past research supported this integrative perspective by dem-

onstrating that standard parameters of cardiovascular activ-

ityFheart rate and systolic, diastolic, and mean arterial blood

pressure follow the predicted interaction of task difficulty and

success importance. Especially, responses of systolic blood pres-

sure (SBP) reliably showed the expected pattern (see Gendolla &

Wright, 2005; Richter, Gendolla, & Krusken, 2006; Wright,

1996, 1998; Wright & Kirby, 2001, for reviews). However, these

previous findings are only of limited conclusiveness regarding the

postulated mediation of energy mobilization by beta-adrenergic

activity. Systolic, diastolic, and mean arterial blood pressure de-

pend more or less strongly on the force of myocardial contrac-

tion, which primarily reflects beta-adrenergic discharge to the

heart. However, increases in blood pressure due to changes in

myocardial contractility can be counteracted by other effects.

For instance, beta-adrenergic impact on the vasculature may

decrease peripheral resistance and, thus, negate myocardial beta-

adrenergic effects. Furthermore, blood pressuremeasures are not

very diagnostic for changes in beta-adrenergic activity because

increases in peripheral resistanceFeither sympathetically or pa-

rasympathetically mediatedFmay have the same effect on blood

pressure as increases in myocardial beta-adrenergic activity: an

increase in blood pressure (e.g., Levick, 2003). The same

logic applies to heart rate. Increases in beta-adrenergic activity

may increase heart rate. However, these effects may be counter-

acted by concurrent increases in parasympathetic activity.

Furthermore, increases in heart rate may be due to increases

in sympathetic beta-adrenergic activity or due to decreases in

parasympathetic activity (e.g., Levick, 2003).

Thus, there is only limited evidence for Wright’s (1996) pre-

diction that energy mobilization is accompanied by increases in

myocardial beta-adrenergic activity. Given the basic role of beta-

adrenergic activity in Wright’s adaptation of motivational inten-

sity theory to psychophysiology, it is astonishing that research on

the integrative perspective has nearly exclusively been limited to

cardiovascular measures that are, at best, mediocre indicators of

myocardial beta-adrenergic activity. A more valid indicator of

beta-adrenergic impact on the myocardium is preejection pe-

riodFthe time interval between the onset of ventricular depo-

larization and the opening of the aortic valve. Preejection period

(PEP) reflects the force of myocardial contraction and is mainly

influenced by beta-adrenergic activity (e.g., Benschop et al.,

We are grateful to Kerstin Brinkmann, Judith Dirk, and Nicolas

Silvestrini for helpful comments on an early draft of this article.Address reprint requests to: Michael Richter, University of Geneva,

FPSE, Department of Psychology, 40, Bd. du Pont-d’Arve, CH-1211Geneva 4, Switzerland. E-mail: [email protected]

Psychophysiology, 45 (2008), 869–875. Wiley Periodicals, Inc. Printed in the USA.Copyright r 2008 Society for Psychophysiological ResearchDOI: 10.1111/j.1469-8986.2008.00688.x

869

1994; Harris, Schoenfeld, &Weissler, 1967; Newlin & Levenson,

1979; Obrist, Light, James, & Strogatz, 1987; Schachinger, We-

inbacher, Kiss, Ritz, & Langewitz, 2001). So far, only one study

examined Wright’s predictions using PEP. Annis, Wright, and

Williams (2001) demonstrated that subjective task diffi-

cultyFmanipulated by ability feedbackFdetermines PEP reac-

tivity during task performance as long as the necessary energy is

justified. However, even if the overall pattern of PEP reactivity

corresponded to Wright’s predictions, only in one of six condi-

tions was PEP reactivity negative (i.e., mean PEP values were

lower during task performance than during habituation). In all

other conditions PEP reactivity was positive, indicating that

participants mobilized less energy during task performance than

during the habituation period. Therefore, an interpretation of

these results in terms of energy mobilization seems at least ques-

tionable. Thus, Wright’s integrative model still lacks clear sup-

port for the hypothesis that changes in beta-adrenergic activity

underlie energy mobilization in active coping situations.

The present experiment aimed to complement the preceding

research on Wright’s integrative perspective. We assessed PEP

reactivity to examine the changes in beta-adrenergic activity that

should underlie energy mobilization in active coping. In contrast

to Annis et al. (2001)Fwho were primarily concerned with the

role of ability perception in energy mobilizationFwe focused on

the influence of manipulated task difficulty on energy mobiliza-

tion. Consequently, we opted for a less complex and more direct

experimental design. In addition to three difficulty levels on

which success was possible, we also included a difficulty level on

which success was impossible. This allowed us to test the full

range of Wright’s (1996) predictions concerning the influence of

task difficulty on energy mobilization. In theory, beta-adrenergic

activity should increase proportionally with task difficulty as

long as success is possible and should be low when success is

impossible.

To test this hypothesis, we presented participants one of four

memory tasks, ranging in difficulty from low (easy) to extreme

(impossible). We measured PEP, SBP, diastolic blood pressure

(DBP), mean arterial bloodpressure (MAP), and heart rate (HR)

during a habituation period and during performance. Central

predictions were twofold. First, we expected a steady increase in

cardiovascular (especially PEP) reactivity as difficulty rose from

low tomoderate to high. Second, we expected low cardiovascular

reactivity where difficulty was extreme.

Methods

Participants and Design

Participants were 64 university students (mean age 24 years) with

variousmajors. They were randomly assigned to one of four task

difficulty conditions (low, moderate, high, impossible). The dis-

tributions of women and men were balanced (10 women and

6 men in the low difficulty condition, 12 women and 4 men in the

moderate difficulty and in the high difficulty condition, 13

women and 3 men in the impossible difficulty condition). All

participants received course credit for their anonymous and

voluntary participation.

Apparatus and Physiological Measurement

Cardiovascular measures were collected during a habituation

period and during task performance. SBP, DBP, and MAP (all

measures inmillimeters ofmercury [mmHg]) were assessedwith a

computer-aided multichannel monitor (Physioport III-S, Par

Electronics, Berlin, Germany) using oscillometry. For that, a

blood pressure cuff was placed over the brachial artery above the

elbow of the participant’s left arm and automatically inflated in

1-min intervals. HR (in beats per minute [bpm]) and PEP (in

milliseconds [ms]) were continuously measured using a Cardio-

screen 1000 system (medis, Ilmenau, Germany) that sampled

electrocardiogram (ECG) and thoracic impedance (impedance

cardiogram, ICG) signals using four pairs of disposable spot

electrodes (sampling rate was 800 Hz; see Scherhag, Kaden,

Kentschke, Sueselbeck, &Borggrefe, 2005, for a validation of the

system). The electrodes 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. All obtained measures

and signals were directly stored on a computer disk so that both

participant and experimenter were ignorant of any values ob-

tained during the experiment. Experiment generation software

(INQUISIT by Millisecond Software, Seattle, WA) controlled

the presentation of stimuli and instructions. The software also

collected and stored participants’ responses.

Procedure

The experiment was run in individual sessions. After participants

had taken a seat in front of a personal computer, the experi-

menter applied the blood pressure cuff and the spot electrodes.

Participants then answered some biographical questions and

read the instructions for the following habituation period, which

lasted 10min. During this time, participants read an old issue of a

magazine while eight blood pressure measures were taken in 1-

min intervals. The interval between the first and second measure

in each measurement period was 2 min due to calibration of the

system. ECG and ICG were continuously assessed during the

10 min of habituation. After habituation, participants received

the task instructions.

Memory Task and Difficulty Manipulation

We used a modified version of the Sternberg task (Sternberg,

1966), which has been successfully employed in other studies on

cardiovascular reactivity (e.g., de Geus, van Doornen, de Visser,

&Orlebeke, 1990; Eubanks,Wright, &Williams, 2002). The task

consisted of a total of 72 trials, each including the presentation of

a fixation cross, a nonsense letter series, and a single target letter

printed in blue. At the beginning of each trial, a fixation crosswas

displayed in the center of the screen for 750 ms. It was followed

by a nonsense letter series consisting of four capital letters (e.g.,

FKDR). Depending on the experimental condition of a given

participant, each letter series was presented either for 1000 ms

(low difficulty), 550 ms (moderate difficulty), 100 ms (high diffi-

culty), or 15 ms (impossible difficulty). The letter series was then

masked and a single blue letter appeared above the masked letter

series and lasted on the screen for the rest of the trial. During the

presentation of the blue letter, participants had to decide whether

this letter was included in the preceding letter series or not by

pressing either one of two keyboard keys. Total trial time was

fixed at 4000 ms. Even if participants reacted very quickly, the

software waited for the total 4000 ms before advancing to the

next trial. If participants did not respond within this time win-

dow, the software continued with the next trial.

Task instructions informed participants about the task pro-

cedure, the stimulus presentation times, and the total task du-

ration. Furthermore, participants learned that they should try to

870 M. Richter, A. Friedrich, and G.H.E. Gendolla

correctly solve at least 90% of all trials. After participants had

read task instructions, they performed 24 practice trials. The

participants then rated task difficulty (‘‘How difficult does task

success appear to you?’’) on a scale ranging from 1 (very easy) to

9 (very difficult), and the importance of task success (‘‘How im-

portant does task success appear to you?’’) on a scale ranging

from 1 (unimportant) to 9 (very important). Participants per-

formed 72 task trials. Four blood pressure measures were taken

in 1-min intervals during task performance. ECG and ICG were

continuously measured during task performance. After task per-

formance, we assessed participants’ weight and height. Once we

had made these assessments, we debriefed the participants,

probed for suspicion, and gave the participants course credit for

their participation.

Data Analysis

ECG and ICG signals were automatically downsampled to 200

Hz by the Cardioscreen system, and the resultant signals were

processed using software developed for our laboratory. R-peaks

were identified using a threshold peak-detection algorithm and

visually confirmed (ectopic beats were replaced using a nonlinear

predictive interpolation; see Lippman, Stein, & Lerman, 1994).

HR was calculated based on the detected R-peaks. The first de-

rivative of the change in thoracic impedance was computed and

the resulting dZ/dt signal was ensemble averaged over periods of

20 s using the detected R-peaks (Kelsey & Guethlein, 1990;

Kelsey et al., 1998). Only artifact-free cycles were included in the

ensemble averages. R-onset and B-point were automatically

scored for each ensemble averageFB-point location was esti-

mated based on the RZ interval as proposed by Lozano et al.

(2007)Fvisually inspected, and, if necessary, corrected as rec-

ommended by Sherwood et al. (1990). PEP was determined as

the interval between R-onset and B-point (Berntson, Lozano,

Chen, & Cacioppo, 2004).

The arithmetic mean of the last four measures during habit-

uation constituted our SPB, DBP, and MAP baseline scores

(Cronbach’s a of the last four measures was .95 for SBP, .93 for

DBP, and .95 for MAP). The arithmetic mean of the first four

blood pressure measures during task performance served as task

scores of SBP, DBP, and MAP (Cronbach’s a of the first four

measures was .94 for SBP, .80 for DBP, and .90 for MAP).

Correspondingly, HR and PEP baseline scores were based on the

arithmetic mean of all measures obtained during the last four

minutes of habituation (Cronbach’s a was .99 for both mea-

sures). HR and PEP task scores were based on the measures

obtained during the first 4 min of task performance (Cronbach’s

a was .99 for HR and .98 for PEP).1 Cardiovascular change

(delta–) scores were computed for each participant and each

cardiovascular measure by subtracting the baseline scores from

the respective task scores (Llabre, Spitzer, Saab, Ironson, &

Schneiderman, 1991).

Based on our theory-driven predictions about the impact of

task difficulty on beta-adrenergic reactivity, we analyzed cardio-

vascular reactivity scoreswith a specific planned contrast (Furr&

Rosenthal, 2003; Rosenthal & Rosnow, 1985). Contrast weights

were � 3 in the low difficulty condition,11 in the moderate

difficulty condition, 15 in the high difficulty condition, and � 3

in the impossible difficulty condition. Task ratings and perfor-

mance measures were analyzed with classical orthogonal poly-

nomial contrasts because we made no specific predictions for

these measures.

Results

Preliminary Analyses

All statistical analyses are based on 63 participants. One partic-

ipant in the high difficulty condition showed a mean PEP reac-

tivity that exceeded the cell mean by more than three standard

deviations and was therefore excluded from all analyses. Possible

gender effects were analyzed with 2 (gender) � 4 (task difficulty)

between-persons ANOVAs. There was a significant gender main

effect on SBP baselines, F(1,55)5 5.17, po.03, Z2p 5 .09, and

were significant interactions of gender and task difficulty onDBP

and MAP baselines, Fs(3,55)42.89, pso.05, .12 oZ2po.14 (all

other ps4.13). The gendermain effect on SBP baselines reflected

higher values for men (M5 120.91, SE5 3.17) than for women

(M5 112.32, SE5 1.54)Fa common physiological finding

(Wolf et al. 1997). The significant interactions of gender and

task difficulty were unexpected. However, there were no signifi-

cant gender main or interaction effects on reactivity measures

(ps4.18) and gender did not significantly moderate the predicted

a priori contrast (ps4.11). Thus, gender was not considered in

the further analyses.

There was an unexpected difference between the task diffi-

culty conditions regarding participants’ body-mass index (BMI),

F(1,57)5 3.14, po.04, Z2p 5 .11.2 BMI values were low in the

impossible difficulty cell (M5 19.65,SE5 0.35) compared to the

other cells (M5 21.83 and SE5 0.77 in the low difficulty cell,

M5 21.49 and SE5 0.42 in the moderate difficulty cell,

M5 21.13 and SE5 0.54 in the high difficulty cell). Including

BMI as covariate in the analysis of cardiovascular measures

showed a significant covariate effect on SBP baselines,

F(1,56)5 5.54, po.03, Z2p 5 .09, and on SBP reactivity,

F(1,56)5 4.56, po.04, Z2p 5 .08 (all other ps4.09). Therefore,

BMI was considered as covariate only in the analyses of SBP

baseline and SBP reactivity. All other cardiovascular measures

were not corrected for BMI.

Cardiovascular Baselines

Single factor between-persons ANOVAs of the baseline scores

did not reveal any significant differences between the difficulty

conditions (ps4.24). Means and standard errors of baseline

scores appear in Table 1.3

Cardiovascular Reactivity

Reactivity of preejection period and systolic blood pres-

sure. The a priori contrast was significant for PEP reactivity,

F(1,59)5 57.70, po.001, MSE5 7.43, Z2p 5 .49, and captured

all significant variance (residual F[2,59]5 1.39). Cell means and

Task difficulty and cardiac activity 871

1Cronbach’s as of the HR scores are based on 1-min averages of HRvalues.

2The analysis of the BMI data is based on 61 participants because welost the data of 2 participants of the moderate difficulty group due toequipment error. Correspondingly, analyses of BMI corrected SBP base-line and reactivity are also based on 61 participants.

3Please note thatHR baseline scoreswere significantly correlatedwiththeir respective reactivity scores, r5 � .31, po.02. However, includingHR baseline as covariate in the analysis of HR reactivity did not changethe results reported in the following. The effect of the covariate wassignificant, F(1,58)5 6.94, po.02, whereas the planned contrast did notapproach significance (p4.11). SBP, DBP, MAP, and PEP baselinescores were not significantly associated with their reactivity scores(ps4.12).

standard errors of mean were as follows:M5 � 0.27, SE5 0.52

in the low difficulty cell,M5 � 1.97, SE5 0.59 in the moderate

difficulty cell, M5 � 6.14, SE5 0.94 in the high difficulty cell,

and M5 1.04, SE5 0.65 in the impossible difficulty cell. Figure

1 further shows that the pattern appeared as predicted. The a

priori contrast was also significant for BMI-corrected SBP re-

activity, F(1,56)5 8.36, po.01,MSE5 40.76, Z2p 5 .13. The test

of the residual was not significant (F[2,56]5 1.90) indicating that

the contrast captured all significant variance. Cell means and

standard errors of mean were M5 5.12, SE5 1.63 in the low

difficulty cell,M5 5.89,SE5 1.72 in themoderate difficulty cell,

M5 8.27, SE5 1.65 in the high difficulty cell, and M5 0.17,

SE5 1.68 in the impossible difficulty cell. Figure 2 shows the

pattern of SBP reactivity.

Reactivity of diastolic pressure, mean arterial blood pressure,

and heart rate. The a priori contrast was not significant for DBP

(p4.17) and did only approach significance for MAP,

F(1,59)5 2.90, po.10, MSE5 31.03, Z2p 5 .05, and HR,

F(1,59)5 3.37, po.08,MSE5 20.51, Z2p 5 .05.Means and stan-

dard errors ofHR reactivity, DBP reactivity, andMAP reactivity

can be found in Table 2. Reactivity scores of PEP, SBP, DBP,

and MAP were correlated with one another, � .48orso.84,

pso.001. HR reactivity correlated only with SBP reactivity,

r5 .28, po.04 (all other ps4.07).

Task Ratings

Task difficulty ratings showed a significant effect of task diffi-

culty, F(3,59)5 16.17, po.001, MSE5 2.52, Z2p 5 .45. Further-

more, the linear contrast,F(1,59)5 42.59, po.001,Z2p 5 .42, and

the cubic contrast, F(3,57)5 5.83, po.02, Z2p 5 .09, were both

significant. The quadratic trend was not significant (p4.90). The

strong effect for the linear trend indicates that we were successful

in manipulating task difficulty. Task difficulty had no significant

effects on success importance ratings (ps4.12). Means and stan-

dard errors of task ratings appear in Table 3.

Task Performance

Means and standards errors of performance measures appear in

Table 3. The percentage of correctly solved trials and the reaction

times of the 72 task trials were analyzed as measures of task

performance. The percentage of correct trials reflected a signifi-

cant difficulty effect, F(3,59)5 64.35, po.001, MSE5 100.7,

Z2p 5 .77, as well as significant effects for the linear trend,

F(1,59)5 182.56, po.001, Z2p 5 .76, the quadratic trend,

F(1,59)5 5.02, po.03, Z2p 5 .08, and the cubic trend,

F(1,59)5 5.39, po.03, Z2p 5 .08.4 Furthermore, performance in

the low difficulty, the moderate difficulty, and the high difficulty

cells was better than chance as indicated by significant t tests that

compared the percentage of correctly solved trials with 50%,

ts(15)47.27, pso.001.5 Performance in the impossible difficulty

cell did not significantly differ from 50% (p4.87). Thus, as ex-

pected, success in this condition was beyond participants’ con-

trol. Participants’ reaction times did not vary with task difficulty

(ps4.30).

When we collapsed across all four conditions, measures of

task performance and cardiovascular reactivity were not corre-

lated, � .24orso.13, ps4.06. However, when we excluded the

impossible difficulty conditionFwhere cardiovascular reactivity

and performance should be dissociatedFsome correlations


Table 1. Cell Means and Standard Errors of Cardiovascular Baseline Scores

Mean Standard error

Low difficultyModeratedifficulty High difficulty

Impossibledifficulty Lowdifficulty

Moderatedifficulty High difficulty

Impossibledifficulty

PEP baseline 96.59 98.57 98.81 99.56 2.69 2.96 3.20 3.05HR baseline 81.04 73.32 77.90 79.17 2.12 2.77 3.28 2.92SBP baseline 115.17 110.96 114.62 115.88 2.75 2.90 2.78 2.84DBP baseline 79.41 76.31 78.57 78.17 1.42 1.66 1.40 2.49MAPbaseline 95.74 91.75 93.92 92.66 2.13 1.91 1.71 2.98

Note: n5 15 in the high difficulty cell, n5 16 in all other cells. PEP: preejection period, HR: heart rate, SBP: systolic blood pressure, DBP: diastolicblood pressure, MAP: mean arterial blood pressure. Preejection period is in milliseconds. Heart rate is in beats per minute. Systolic blood pressure,diastolic blood pressure, andmean arterial blood pressure are inmillimeters ofmercury. Systolic blood pressure values are corrected for body-mass indexand n5 14 in the moderate difficulty cell (see Footnote 2).

Figure 1. Cell means and standard errors of preejection period (PEP)

reactivity during task performance. ms: milliseconds.

4Analyses of the number of correctly solved trials during the practicetrials also revealed a significant linear contrast, F(1,59)5 100.89,po.001, MSE5 338.82, Z2

p 5 .63, and a significant quadratic contrast,F(1,59)5 4.72, po.04, Z2

p 5 .07. The cubic trend was not reliable(p4.24). Cell means showed the same pattern as the number of cor-rectly solved trials during task performance.

5Please note that the t test including the high difficulty cell is based ononly 14 degrees of freedom.

emerged. Specifically, there were correlations between the per-

centage of correct trials, on the one hand, and PEP (r5 .63,

po.001), DBP (r5 � .31, po.04), and MAP reactivity

(r5 � .32, po.03), on the other.6 No other correlations were

significant, � .28orso.12, ps4.05.

Discussion

PEP reactivity supported our predictions about task difficulty

effects derived from Wright’s (1996) integrative model: PEP

changes increased with increasing task difficulty over the first

three difficulty levels and were low when the task was too diffi-

cult. SBP reactivity showed a similar pattern even though task

difficulty effects were less pronounced and mainly carried by the

low reactivity in the impossible difficulty condition. In contrast

to PEP and SBP reactivity, task difficulty effects on DBP, MAP,

and HR reactivity were weak and not significant.

Because PEP reactivity reflects beta-adrenergic influence on

the heart (e.g., Harris et al., 1967; Lewis, Rittogers, Forester, &

Boudoulas, 1977; Sherwood et al., 1990), our results support

Wright’s notion that the influence of task difficulty on energy

mobilization in active coping tasks is mediated by changes in

beta-adrenergic activity. However, it is important to note that

under certain conditions changes in cardiac preload and after-

load may also influence PEP reactivity (e.g., Lewis, Leighton,

Forester, & Weissler, 1974). This suggests that our results could

also stem from changes in these parameters. Increases in cardiac

preload (ventricular filling) increase the force of myocardial

contraction via the Frank–Starling mechanism and, thus,

shorten PEP. Increases in cardiac afterload (aortic diastolic

pressure) due to increases in peripheral resistance lengthen PEP

because it takes longer to build up the necessary force to open the

aortic valves. Changes in preload and afterload may sometimes

occur due to changes in body posture, for instance (e.g., Ho-

utveen, Groot, & de Geus, 2005; Lewis et al., 1977). However,

because there were no systematic differences in participants’

body position between the conditions it is unlikely that posture-

related changes in preload or afterload account for the observed

PEP pattern.

According to Obrist et al. (1987) and others (Sherwood et al.,

1990), decreases in PEP should only be interpreted as reflecting

increases in beta-adrenergic activity if they are accompanied by

stable or increased HR and DBP. Our DBP reactivity

dataFDBP changes can be used as a rough estimation of

changes in peripheral resistanceFdo not suggest that changes in

cardiac afterload could explain PEP reactivity in our study. If

PEP changes were due to changes in peripheral resistance, one

would expect that increases inDBP are accompanied by increases

in PEP. Our data even show the opposite: Increases in DBP were

accompanied by decreases in PEP. The same applies to the HR

reactivity data. HR should decrease if decreases in PEP were due

to increased preload (Obrist et al., 1987). However, we found

that decreases in PEP were accompanied by increases in HR.

Therefore, preload effects offer no plausible explanation for the

observed PEP reactivity pattern. Furthermore, to our knowledge

there are no theoretical or empirical considerations suggesting

that cardiac preload or afterload are influenced by task difficulty

in the predicted way. Thus, it is unlikely that PEP reactivity was

due to effects of cardiac preload or afterload. Consequently, the

observed PEP effects should be interpreted as reflecting changes

in beta-adrenergic activity. Therefore, the PEP reactivity data

support the notion of Wright’s integrative model that changes in

beta-adrenergic influences on the heart underlie energy mobili-

zation in active coping.

Congruent with previous research (e.g. Bongard & Hodapp,

1996; Wright, Williams, & Dill, 1992), we found difficulty effects

on SBP reactivity. Effects on SBP reactivity were not as pro-

nounced as on PEP reactivity but showed the same pattern. Re-

sponses of DBP,MAP, and HR tended to show the same pattern

but were not significantly related to task difficulty. Altogether,

these findings replicate preceding research on the integrative

model that has reliably found effects on SBP reactivity but only

rarely on other cardiovascular parameters (for reviews, see, e.g.,

Brinkmann & Gendolla, 2008; Gendolla & Wright, 2005; Rich-

ter et al., 2006; Silvestrini & Gendolla, 2007; Wright, 1996,

Wright & Kirby, 2001).

Previous studies on difficulty effects on PEP reactivity showed

inconsistent results. Some of them found an association between

the difficulty of a task and PEP (Annis et al., 2001; Kelsey, 1991;

Light & Obrist, 1983; Sherwood, Davis, Dolan, & Light, 1992;

Tomaka & Palacios-Esquivel, 1997), others did not (Harrell &

Clark, 1985; Kelsey et al., 1999; Sherwood, Royal, & Light,

1993). The interpretation of the results of these studies is further

complicated by the fact that the manipulation of task difficulty

was often crossed with the manipulation of other variables (e.g.,

prior task exposure; see Kelsey et al., 1999). Furthermore, stud-

ies that demonstrated a relationship between task difficulty and

PEP reactivity often manipulated task difficulty only over two

levels and were therefore not suitable to examine the postulated

proportional relationship between task difficulty and beta-ad-

renergic activity (for exceptions, see Annis et al., 2001; Light &

Obrist, 1983; see also Richter & Gendolla, 2006).

We know of only one other study that examined PEP reac-

tivity during the performance of an impossible task. Light and

Obrist (1983) found longer PEP values when the task was im-

possible than when it was easy or moderately difficult. Unfor-


Figure 2. Cell means and standard errors of BMI-adjusted systolic blood

pressure (SBP) reactivity during task performance. mmHg: millimeters of

mercury.

6Please note that these correlations indicate that cardiovascular re-activity and performance were negatively related: the higher the cardio-vascular reactivity, the worse the performance. Participants investedmore energy for higher task levels but this energy did not compensate forthe high difficulty. Correspondingly, performance decreased with in-creasing difficulty.

tunately, this study had several shortcomings. Equipment mal-

function reduced the amount of baseline data so that reactivity

scores could not be calculated and only absolute PEP values were

analyzed. PEPwas not continuously assessed and PEP values did

not show task difficulty effects until the fifth minute of task per-

formance. Furthermore, PEP values in the easy and the mod-

erately difficult tasks did not differ. Thus, none of the preceding

studies investigated the full range of difficulty levels that are of

relevance for the predictions of Wright’s integrative model.

Investigating task difficulty influences over three levels of

possible difficulty and one level of impossible difficulty allowed

us to entirely testWright’s predictions about the influence of task

difficulty on beta-adrenergic activity in active coping. The results

for PEP reactivity support the predictions and extend existing

research on the integrative model that has mainly focused on

blood pressure reactivity. These studies could not adequately

address the postulatedmediation of energymobilization by beta-

adrenergic activity. Only Annis and colleagues have addressed

this question so far in the frame of Wright’s model. However, as

mentioned above, it is questionable if PEP changes in this study

did indeed reflect energy mobilization because PEP changes were

positive (i.e., participants had higher absolute PEP values during

the task than during habituation). Therefore, our study provides

the first conclusive evidence for the hypothesis that increases in

task difficulty raise beta-adrenergic influences on the heartFbut

only as long as task success is possible.

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Table 2. Cell Means and Standard Errors of HR, DBP, and MAP Reactivity Scores

Mean Standard error

Lowdifficulty

Moderatedifficulty

Highdifficulty


Lowdifficulty

Moderatedifficulty

Highdifficulty


HR reactivity 0.82 3.54 4.12 2.55 0.97 1.12 1.09 1.35DBP reactivity 2.07 2.64 4.93 3.21 1.00 0.98 1.85 1.08MAP reactivity 1.92 2.85 5.79 3.34 0.83 1.23 2.07 1.30

Note: n5 15 in the high difficulty cell, n5 16 in all other cells. HR: heart rate, DBP: diastolic blood pressure, MAP: mean arterial blood pressure. Heartrate is in beats per minute, diastolic blood pressure and mean arterial blood pressure are in millimeters of mercury.

Table 3. Cell Means and Standard Errors of Task Ratings and Performance Scores

Mean Standard error

Lowdifficulty

Moderatedifficulty

Highdifficulty


Lowdifficulty

Moderatedifficulty

Highdifficulty


Task difficulty 3.81 4.06 6.53 6.88 0.48 0.35 0.29 0.45Success importance 5.31 5.19 4.40 4.31 0.51 0.59 0.50 0.59% correct 92.25 88.02 64.91 49.34 1.20 1.79 2.05 4.06Reaction time 996.28 1045.02 1076.69 1098.26 54.75 61.16 56.93 106.83

Note: n5 15 in the high difficulty cell, n5 16 in all other cells. % correct: percentage of correctly solved trials.

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(Received October 9, 2007; Accepted February 7, 2008)


task difficulty effects on cardiac activity

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