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The heart contracts to reward: Monetary incentives and
preejection period
MICHAEL RICHTER and GUIDO H. E. GENDOLLASection of Psychology, University of Geneva, Geneva, Switzerland
Abstract
Wright’s (1996) integration of motivational intensity theory (Brehm & Self, 1989) and Obrist’s (1981) active coping
approach predict that cardiovascular reactivity in active coping depends on the importance of success when task
difficulty is unclear. Despite the support for this perspective, one of the basic hypothesesFthe mediation of these
effects by beta-adrenergic activityFhas not been tested yet. To close this gap, participants worked on a delayed-
matching-to-sample task and could earn either 1, 15, or 30 Swiss Francs for a successful performance. Results showed
that preejection period reactivityFan indicator of beta-adrenergic impact on the heartFincreased with increasing
incentive value. Thus, this experiment closes a gap in the support of Wright’s model by demonstrating that beta-
adrenergic reactivity is associated with incentive value under conditions of unclear difficulty.
Descriptors: Cardiovascular reactivity, Incentive, Reward, Beta-adrenergic activity, Active coping
According tomotivational intensity theory (Brehm&Self, 1989),
task difficulty and the importance of success determine energy
mobilization in instrumental tasks (i.e., tasks that allow attaining
a personal goal). Wright (1996) adapted these predictions to
psychophysiology by integrating themwithObrist’s (1981) active
coping approach. The resulting integrative model deals with car-
diovascular adjustments in active coping tasks and is based on
the following two central predictions. (1) If task difficulty is
known, cardiovascular reactivity should be proportional to task
difficulty as long as success is possible and as long as the nec-
essary energy is justified by the importance of success. (2) If no
information is available about task difficulty (i.e., when task
difficulty is unclear), the importance of task success should di-
rectly determine cardiovascular reactivity until performers have
attained their maximum effort level. Once an individual’s max-
imum level has been reached, further increases in importance
should not make a difference. Numerous studies investigated
these hypotheses in the last 20 years and showed that adjustments
in blood pressure and heart rate (HR) follow the integrative
model’s predictions. Especially systolic blood pressure (SBP) re-
liably reacted to manipulations of task difficulty and success im-
portance (for reviews, seeGendolla&Brinkmann, 2005;Gendolla
& Wright, 2005; Richter, Gendolla, & Krusken, 2006; Wright,
1996, 1998; Wright & Kirby, 2001).
Despite this interest in the predictions of Wright’s model, re-
search that investigated the physiological mechanisms underly-
ing the observed cardiovascular adjustments is rare. According
to Wright (1996), energy mobilization in active coping should
be mediated by sympathetic (beta-adrenergic) impact on the
heart. Correspondingly, effects of task difficulty and success
importance on cardiovascular parameters should be due to
changes in beta-adrenergic activity. It follows that cardiovascular
measures that strongly depend on the force of myocardial con-
tractionFwhich is determined by beta-adrenergic dischargeFshould be more strongly influenced by task difficulty and success
importance than measures that are less strongly associated with
the force of contraction. This basic assumption of the integrative
model has often been used to explain the fact that SBP showed
the predicted effects more consistently than HR, diastolic blood
pressure (DBP), or mean arterial blood pressure (MAP; e.g.,
Gendolla &Wright, 2005, for a review). SBP strongly depends on
the force of myocardial contraction, whereas HR, DBP, and
MAP are less systematically associated with the force of myo-
cardial contraction (e.g., Berne & Levy, 1977; Levick, 2003).
However, increases in blood pressure are not an un-
ambiguous indicator of increases in myocardial contractility or
beta-adrenergic activity, respectively. Increases in peripheral re-
sistanceFsympathetically or parasympathetically mediatedFmay have similar effects on blood pressure as increases in
myocardial beta-adrenergic activity. Furthermore, the effects of
increased myocardial contractility on blood pressure can be coun-
teracted by accompanying dilative effects on the vasculature.
Although beta-adrenergic activity has been a topic of discus-
sion in the research onWright’s integrative approach, researchers
only recently started to directly address this issue by assessing
preejection period (PEP). PEPFthe time interval between the
We thank Kerstin Brinkmann for helpful comments on an early draft
of this article. The preparation of this article was facilitated by a research
grant of the Swiss National Science Foundation awarded to the first
author.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: Michael.Richter@unige.ch
Psychophysiology, 46 (2009), 451–457. Wiley Periodicals, Inc. Printed in the USA.Copyright r 2009 Society for Psychophysiological ResearchDOI: 10.1111/j.1469-8986.2009.00795.x
451
onset of ventricular depolarization and the opening of the aortic
valveFis a valid and reliable indicator of beta-adrenergic influ-
ences on the heart (e.g., Benschop et al., 1994; Harris,
Schoenfeld, &Weissler, 1967; Newlin & Levenson, 1979; Obrist,
Light, James, & Strogatz, 1987; Schachinger, Weinbacher, Kiss,
Ritz, & Langewitz, 2001). Annis, Wright, and Williams (2001)
manipulated subjective task difficulty by presenting different
difficulty levels of an active coping task together with a
bogus ability feedback. They observed that PEP reactivity
increased with increasing task difficulty as long as the task was
not too difficult. However, PEP reactivity scores were in
general slightly positive, indicating that participants invested
less energy during task performance than during habituation.
Thus, these data provide only weak support for Wright’s
hypotheses concerning the mobilization of energy during task
performance.
Using a more straightforward experimental design, Richter,
Friedrich, and Gendolla (2008) manipulated the objective diffi-
culty of a memory task and assessed PEP reactivity. Their results
were in accordance with the predictions of the integrative model.
When task success was possible, changes in PEP were propor-
tional to increases in task difficulty. When task difficulty was so
high that success was impossible, PEP reactivity was low. More-
over, there are several studies conducted outside the frame of
Wright’s integrative model that support the association between
PEP and task difficulty, as well (Kelsey, 1991; Light & Obrist,
1983; Sherwood, Davis, Dolan, & Light, 1992; Tomaka & Pala-
cios-Esquivel, 1997). However, other studies did not find any
association between task difficulty and PEP (Harrell & Clark,
1985; Kelsey et al., 1999; Sherwood, Royal, & Light, 1993).
Nevertheless, the studies that found a reliable difficulty–PEP as-
sociation consistently showed the pattern predicted by Wright’s
integrative model and, therefore, support its predictions about
the influence of task difficulty on cardiac reactivity when infor-
mation about task difficulty is available and clear.
However, evidence is still lacking for cardiovascular adjust-
ments under conditions of unclear task difficulty. Recently,
Richter and Gendolla (2006, 2007) demonstrated that the value
of rewardFone variable that determines success impor-
tanceFinfluences SBP reactivity when task difficulty is unclear.
They found SBP increases with reward under these conditions.
However, as in most of the preceding research, the authors only
assessed blood pressure and HR but not more valid indices of
beta-adrenergic influences on the heart. Therefore, these studies
cannot be considered as unambiguous support for the assump-
tion that beta-adrenergic activity underlies cardiovascular reac-
tivity in active coping with unclear task difficulty.
The present experiment aims to close this gap by investigating
the influence of incentive value on PEP reactivity. For this pur-
pose, participants performed a delayed-matching-to-sample task
with unclear task difficulty under one of threemonetary incentive
conditions (1 Swiss Franc vs. 15 Swiss Francs vs. 30 Swiss
Francs). Based on Wright’s integrative model, we expected that
PEP reactivity would proportionally increase with increasing in-
centive value: PEP reactivity should be low in the 1-Swiss-Franc
condition, moderate in the 15-Swiss-Francs condition, and high
in the 30-Swiss-Francs condition. Because preceding research
has reliably demonstrated that SBP reactivity follows the pre-
dictions of Wright’s approach (e.g., Gendolla & Wright, 2005,
for a review) we predicted the same pattern for systolic reactivity.
We made no specific predictions for the reactivity of HR, DBP,
and MAP because these parameters are only loosely associated
with beta-adrenergic influences on the myocardium (e.g., Berne
& Levy, 1977; Levick, 2003).
Method
Participants and Design
Thirty-one psychology students (mean age 28 years) participated
for course credit. They were randomly assigned to one of three
experimental conditions (incentive: 1 Swiss Franc vs. 15 Swiss
Francs vs. 30 Swiss Francs). The distributions of women and
men were balanced (9 women and 2 men in the 1-Swiss-Franc
cell, 8 women and 2 men in both the 15-Swiss-Francs and the
30-Swiss-Francs cells). Participation was anonymous and
voluntary.
Apparatus and Physiological Measurement
Cardiovascular measures were assessed during two periods: ha-
bituation and task performance. A Vasotrac APM205A system
(Medwave, Arden Hills, MN) measured SBP, DBP, and MAP
(all measures in millimeters of mercury [mmHg]). The system’s
cuff was placed around the wrist of the participant’s nondom-
inant arm and one blood pressuremeasure was obtained every 12
to 15 heart beats. Electrocardiogram (ECG) and thoracic im-
pedance (impedance cardiogram, ICG) signals were sampled
with 800 Hz using a Cardioscreen 1000 system (medis, Ilmenau,
Germany; see Scherhag, Kaden, Kentschke, Sueselbeck, &
Borggrefe, 2005, for a validation of the system). Four pairs of
disposable spot electrodes were placed on the right and left sides
of the base of the participant’s neck and on the left and right
middle axillary lines at the height of the xiphoid. All obtained
measures and signals were directly stored on a computer disk so
that participants and experimenter were ignorant of all values
obtained during the experiment. Experiment generation software
(INQUISIT by Millisecond Software, Seattle, WA) controlled
the presentation of stimuli and instructions as well as the collec-
tion of participants’ responses.
Procedure
The experiment was run in individual sessions. The experimenter
assessed participants’ weight and height and applied the blood
pressure cuff and the spot electrodes. Then, participants an-
swered some biographical questions and were instructed to relax
and to sit as quietly as possible during the next minutes of ha-
bituation. Blood pressure measures were taken in intervals of 12
to 15 heart beats during the following 10 min of habituation.
ECG and ICG were continuously measured. After the habitu-
ation period participants received the task instructions.
We used a slightly modified version of the delayed-matching-
to-sample task employed in an experiment by Thorne, Dawson,
and Schell (2006). The task consisted of 28 trials each lasting for a
total time of 11.5 s. At the beginning of each trial, a mask con-
sisting of 45 points painted in dark gray was presented. After 400
ms some of the points changed their color to light gray. This
patternFthe sample patternFlasted for 2400 ms. Then, the
mask was displayed again for a duration of 800 ms. It was fol-
lowed by a pattern consisting of either 2, 4, 6, 8, 10, or 12 points
painted in light gray. This target pattern was masked after 1600
ms by a slide asking participants to decide if the pattern of light
gray points differed between sample and target pattern by press-
ing either one of two keys. In half of the trials the target pattern
showed the same pattern of light gray points as the sample pat-
452 M. Richter and G.H.E. Gendolla
tern. In the other half of the trials the position of one point
differed between both. The number of light gray points did al-
ways match. Different patterns of 2, 4, 10, and 12 light gray
points were presented four times during each task; patterns con-
sisting of 6 and 8 points were presented three times. The sequence
of the trials was randomized. After having pressed a key, par-
ticipants received feedback that their response had been re-
corded. Independent of response time, the software always
waited for the total of 11.5 s before advancing to the next trial. If
participants did not respond within this time frame the software
automatically advanced to the next trial.
Corresponding to the manipulations that have been previ-
ously employed to create tasks with unclear difficulty (Richter &
Gendolla, 2006, 2007), task instructions explained only the gen-
eral procedure of the task. Task instructions did not inform par-
ticipants about the number of light gray points, presentations
times, or total performance time. Furthermore, participants
learned that they could win a prize for successful task perfor-
mance. They were also informed that the computer would ran-
domly assign to them a performance standard at the end of the
task. If their performance matched this standard, they would
receive a monetary reward. Participants could win either 1 Swiss
Franc (about USD 0.85), 15 Swiss Francs (about USD 12.50), or
30 Swiss Francs (about USD 25.00). It was emphasized that the
standard could range anywhere between 1 and 28 correct trials.
Thus, the performance standard or task difficulty, respectively,
was unclear to participants.
Next, participants rated the importance of task success
(‘‘How important does task success appear to you?’’) on a scale
ranging from 1 (unimportant) to 9 (very important) and incentive
attractiveness (‘‘How attractive does winning the reward appear
to you?’’) on a scale ranging from 1 (unattractive) to 9 (very
attractive). Participants then performed the 28 task trials. Blood
pressure measures were collected in intervals of 12 to 15 heart
beats; ECG and ICG were continuously assessed.1After task
performance participants received their reward if applicable.
They were debriefed, probed for suspicion, and received their
course credit.
Data Scoring, Reduction, and Analysis
ECG and ICG signals were automatically down-sampled to 200
Hz by the Cardioscreen 1000 system and were processed using a
software developed for our laboratory (Richter, 2006). A thresh-
old peak-detection algorithm automatically identified R-peaks,
which were visually confirmed afterward (ectopic beats were re-
placed using a nonlinear predictive interpolation; see Lippman,
Stein, & Lerman, 1994). HR (in beats per minutes) was calcu-
lated based on the detected R-peaks. The first derivative of the
change in thoracic impedance was calculated and the resulting
dZ/dt signal was ensemble averaged over periods of 20 s using the
detectedR-peaks (Kelsey&Guethlein, 1990; Kelsey et al., 1998).
Only artifact-free cycles were used to construct the ensemble
averages. R-onset and B-point were scored for each ensemble
average, visually inspected, and, if necessary, corrected as sug-
gested by Sherwood et al. (1990).2 PEP (in milliseconds) was
determined as the interval between R-onset and B-point (Bernt-
son, Lozano, Chen, & Cacioppo, 2004).
The arithmeticmeans of themeasures obtained during the last
4 min of habituation constituted our SBP, DBP, MAP, HR, and
PEP baseline scores (all Cronbach’s as4.98).3 The arithmetic
means of the measures assessed during the first 4 min of task
performance were used as SBP, DBP, MAP, HR, and PEP task
scores (all Cronbach’s as4.99). Cardiovascular reactivity
(change) scores were computed for each participant and each
cardiovascular parameter by subtracting baseline scores from
their respective task scores (Llabre, Spitzer, Saab, Ironson, &
Schneiderman, 1991). Reactivity scores were not significantly
correlated with their respective baseline scores (� .14ors
o� .04, ps4.48). Therefore, reactivity scores were not adjusted
with regard to baseline values (see Benjamin, 1967; Llabre et al.,
1991). Correlations between cardiovascular baseline and reac-
tivity measures can be found in Table 1. All cardiovascular re-
activity scores were analyzed using single factor ANOVAs.
To examine our theory-driven predictions about the impact of
incentive value on beta-adrenergic reactivity in more detail, we
compared the reactivity scores of PEP and SBP between the
1-Swiss-Franc group and the 15-Swiss-Franc group as well as
between the 15-Swiss-Franc group and the 30-Swiss-Franc
group using t tests. Given that our predictions were clearly di-
rectional, all t tests were tested one-tailed. Because beta-
adrenergic activity is not a major determinant of HR, DBP,
and MAP, we made no specific predictions for these measures
and did not apply the t tests to these measures.
Results
Preliminary Analyses
Statistical analyses of blood pressure values are based on 30
participants. Due to equipment malfunction 1 participant in the
1-Swiss-Franc condition had less than four blood pressure mea-
sures during baseline and task performance and was, therefore,
excluded from the analyses. Two (gender) � 3 (incentive value)
between-persons ANOVAs found significant effects of gender
on PEP baseline, F(1,25)5 18.43, po.001, Z2p ¼ :42, and on
DBP reactivity scores, F(1,24)5 4.28, po.05, Z2p ¼ :15. Fur-
thermore, the gender � incentive value interaction on HR base-
line scores was significant, F(2,25)5 5.13, po.02, Z2p ¼ :29.However, given the low number of male participants, these re-
sults should be regarded with caution. Instead of using gender as
a covariate in our analyses of cardiovascular measures, we re-
peated all analyses with a sample restricted to female partic-
ipants. Because the results of these analyses did not differ from
those conducted with the whole sample, we report in the follow-
ing only the results for the whole sample.
Based on the assessed weights and heights of the participants
we calculated participants’ body mass indexes (BMI). BMI val-
ues were significantly correlated with DBP (r5 � .39) andMAP
(r5 � .37) reactivity (pso.05). All other cardiovascular mea-
sures were not significantly associated with BMI values,
� .36orso.33, ps4.06. Because including BMI values in the
analyses of DBP and MAP reactivity resulted in a significant
Incentives and preejection period 453
1Due to the Vasotrac’s samplingmode the number of samples assesseddiffered with HR. Single factor analyses of variance (ANOVAs) of thevariance within baseline and task samples did not showa significant effectof incentive value on any of the three cardiovascular measures. Further-more, HR did not significantly correlate with the variance of SBP, MAP,or DBP samples. Thus, the different numbers of samples for each par-ticipant and period did not lead to differences in the stability of our bloodpressure measures.
2The software used the R-Z interval to estimate B-point location asdescribed in Lozano et al. (2007).
3Cronbach’s as for HR, SBP, DBP, andMAP scores are based on 1-min averages.
covariate effect, Fs(1,25)44.95, pso.04, we analyzed BMI-cor-
rected DBP and MAP reactivity.
Cardiovascular Baselines
Single factor between-persons ANOVAs did not show significant
baseline differences between the experimental conditions (all
ps4.28). Means and standard errors of all cardiovascular base-
line measures are displayed in Table 2.
Cardiovascular Reactivity
Preejection period reactivity. The single factor ANOVA
showed a significant effect of incentive value, F(2,28)5 8.91,
po.01,MSE5 14.35, Z2p ¼ :39. Both t tests were significant: The1-Swiss-Franc cell (M5 0.97, SE5 1.16) differed from the 15-
Swiss-Francs cell (M5 � 2.40, SE5 1.26), t(19)5 1.96, po.04,
and the 15-Swiss-Francs cell differed from the 30-Swiss-Francs
cell (M5 � 6.01, SE5 1.10), t(18)5 2.16, po.03. Figure 1 re-
flects that the pattern appeared as predicted.
Blood pressure reactivity. The single factor ANOVA resulted
in a significant effect for incentive value on SBP reactivity,
F(2,27)5 3.46, po.05, MSE5 72.97, Z2p ¼ :20. As predicted,
the 1-Swiss-Franc cell (M5 4.12, SE5 2.60) reliably differed
from the 15-Swiss-Franc cell (M5 12.55, SE5 3.28),
t(18)5 2.01, po.02. However, the difference between the 15-
Swiss-Francs cell and the 30-Swiss-Francs cell (M5 13.08,
SE5 2.09) was not significant, t(18)5 0.14, p4.45. Figure 2
shows this pattern of SBP reactivity. Single factor analyses of
covariance (ANCOVAs) showed an effect of incentive value nei-
ther on BMI-corrected DBP reactivity, F(2,25)5 2.19, p4.13,
MSE5 54.39, Z2p ¼ :15, nor on BMI-corrected MAP reactivity,
F(2,25)5 2.90, p4.07, MSE5 58.54, Z2p ¼ :19.4 Means and
standard errors of BMI-corrected DBP and MAP reactivity
scores appear in Table 3.
Heart rate reactivity. The single factorANOVAdid not show
a significant effect of incentive value, F(2,28)5 0.98, p4.38,
MSE5 7.67, Z2p ¼ :07. Means and standard errors of HR reac-
tivity scores appear in Table 3.
Task Ratings
Ratings of reward attractiveness and success importance were
significantly correlated, r5 .39, po.04, and a multivariate anal-
ysis of both measures found a significant incentive effect,
F(4,54)5 6.35, po.001, Z2p ¼ :32. Because both ratings should
indicate the effectiveness of our manipulation of incentive value,
we further analyzed them with the same t tests as PEP and SBP
reactivity. Attractiveness ratings significantly differed between
the 1-Swiss-Franc cell (M5 2.27, SE5 0.27) and the 15-Swiss-
Francs cell (M5 3.70,SE5 0.45), t(19)5 2.78, po.02, aswell as
between the 15-Swiss-Francs cell and the 30-Swiss-Francs cell
(M5 5.00, SE5 0.56), t(18)5 1.82, po.05. Importance ratings
only differed between the 1-Swiss-Franc cell (M5 3.00
SE5 0.45) and the 15-Swiss-Francs cell (M5 4.90, SE5 0.31),
t(19)5 3.41, po.01. The difference between the 15-Swiss-Franc
cell and the 30-Swiss-Francs cell (M5 4.50, SE5 0.43) was not
reliable, t(18)5 0.75, po.24. Reward attractiveness significantly
correlated with PEP reactivity, r5 � .52, po.01; all other cor-
relations between task ratings and reactivity scores were not sig-
nificant,� .19orso.33, ps4.07.
Task Performance
Single factor ANOVAs demonstrated that participants did not
differ in the percentage of correctly solved trials, F(2,28)5 0.79,
p4.46, MSE5 95.70, Z2p ¼ :05, or in their reaction times,
F(2,28)5 0.80, p4.46, MSE5 58,877, Z2p ¼ :05. Means and
standard errors of the performance measures appear in Table 4.
Mediation Analysis
We further examined the relationships between our incentive
manipulation, task ratings, and cardiovascular reactivity using
Sobel tests (Preacher & Hayes, 2004). None of the tests was
454 M. Richter and G.H.E. Gendolla
Table 1. Correlations between Cadiovascular Baselines and Reactivity Scores
Measures 1 2 3 4 5 6 7 8 9 10
1. PEP baseline F .22 .25 .32 .27 .09 .24 .22 .20 .262. SBP baseline F .99n .97n .15 .32 .12 .03 .03 .143. MAP baseline F .99n .18 .29 .13 .05 .04 .144. DBP baseline F .22 .25 .12 .05 .05 .145. HR baseline F .11 .27 .24 .26 .136. PEP reactivity F .35 .39n .41n .307. SBP reactivity F .98n .95n .088. MAP reactivity F .98n .079. DBP reactivity F .0310. HR reactivity F
n5 30 for all correlations including blood pressure measures, n5 31 for all other correlations. PEP: preejection period,SBP: systolic blood pressure, MAP: mean arterial blood pressure, DBP: diastolic blood pressure, HR: heart rate.npo.05.
Table 2. Cell Means and Standard Errors of Cardiovascular
Baseline Scores
Mean Standard error
1 SwissFranc
15 SwissFrancs
30 SwissFrancs
1 SwissFranc
15 SwissFrancs
30 SwissFrancs
PEP baseline 102.99 107.96 105.91 4.15 3.33 2.53HR baseline 75.99 76.83 75.04 2.79 3.60 3.52SBP baseline 125.01 116.74 129.35 5.83 2.80 7.26DBP baseline 67.31 62.59 69.50 4.68 1.92 5.50MAP baseline 88.74 82.34 92.55 5.32 2.44 6.44
n5 11 in the 1-Swiss-Franc cell of HR and PEP baseline, n5 10 in allother cells. PEP: preejection period, HR: heart rate, SBP: systolic bloodpressure, DBP: diastolic blood pressure, MAP: mean arterial bloodpressure. Preejection period is in milliseconds, heart rate is in beats perminute, and systolic blood pressure, diastolic blood pressure, and meanarterial blood pressure are in millimeters of mercury.
4Analyzing uncorrected DBP and MAP reactivity resulted in thesame pattern of results: Neither MAP nor DBP reactivity showed a sig-nificant influence of incentive value (ps4.10).
significant (ps4.14). However, examining the indirect effect by
bootstrapping (number of bootstrap resamples was 5,000)Fwhich is less conservative than the Sobel testFresulted in a
confidence interval that did not include zero for the indirect effect
of incentive value, task importance, and PEP reactivity,
M5 0.73, CI.95 5 0.02, 1.84. All other confidence intervals in-
cluded zero. In sum, these results suggest that the effect of in-
centive value on PEP reactivity was mediated by subjective task
importance.
Discussion
Wright’s (1996) integration of motivational intensity theory
(Brehm& Self, 1989) and Obrist’s (1981) active coping approach
posits that success importance determines cardiac activity during
active copingwith unclear task difficulty. PEP and SBP reactivity
data of our experiment corroborate this hypothesis. PEP reac-
tivity depended on the level of monetary incentiveFour manip-
ulation of success importanceFoffered for successful task
performance: PEP reactivity was low in the 1-Swiss-Franc con-
dition, moderate in the 15-Swiss-Francs condition, and high in
the 30-Swiss-Francs condition. SBP reactivity showed a similar
pattern. However, the difference between the 15-Swiss-Francs
and the 30-Swiss-Francs groups was not significant. DBP,MAP,
and HR reactivity were not reliably influenced by the manipu-
lation of incentive value.
The pattern of PEP reactivity clearly supports Wright’s no-
tion that incentive effects on energy mobilization under condi-
tions of unclear task difficulty are mediated by beta-adrenergic
impact on the heart. Because changes in PEP reliably reflect beta-
adrenergic activity on the heart under most conditions (e.g.,
Harris et al., 1967; Lewis, Rittogers, Forester, & Boudoulas,
1977; Sherwood et al., 1990), we interpret our findings as re-
flecting the expected association between incentive value and
beta-adrenergic activity. However, changes in ventricular filling
or aortic diastolic pressure can also result in changes of PEP
(e.g., Lewis, Leighton, Forester, & Weissler, 1974). For a valid
interpretation of PEP it is, therefore, recommended to evaluate
PEP reactivity only in the light of the reactivity of HR and DBP
(Obrist et al., 1987; Sherwood et al., 1990). If changes in PEP are
caused by changes in aortic diastolic pressure, one would expect
that increases in PEP reactivity would go along with decreases in
DBP. Increases in ventricular filling that cause increases in PEP
should be accompanied by decreases in HR (Obrist et al., 1987).
Thus, if changes in PEP are caused by changes in aortic diastolic
pressure and/or ventricular filling, DBP and/or HR should
decrease with increasing PEP. This was clearly not the case in our
study. Therefore, it seems unlikely that ventricular filling or aor-
tic diastolic pressure can account for the observed effects on PEP.
Our results are also congruent with preceding research on
cardiovascular reactivity in active coping with unclear difficulty
(e.g., Richter & Gendolla, 2006, 2007). Corresponding to the
results of these experiments, SBP reactivity showed an influence
of incentive value, as well. However, these effects were small
compared to incentive effects on PEP reactivity. The match be-
Incentives and preejection period 455
Figure 1. Cell means and standard errors of preejection period (PEP)
reactivity during task performance.
Figure 2.Cell means and standard errors of systolic blood pressure (SBP)
reactivity during task performance.
Table 3. Cell Means and Standard Errors of HR, DBP, and MAP
Reactivity Scores
Mean Standard error
1 SwissFranc
15 SwissFrancs
30 SwissFrancs
1 SwissFranc
15 SwissFrancs
30 SwissFrancs
HR reactivity 0.77 1.11 2.39 0.62 1.04 0.91DBP reactivity 2.74 7.64 9.68 2.46 2.33 2.33MAP reactivity 3.14 9.66 11.17 2.55 2.42 2.42
n5 11 in the 1-Swiss-Franc cell of HR reactivity, n5 10 in all other cells.HR: heart rate, DBP: diastolic blood pressure,MAP:mean arterial bloodpressure. Heart rate is in beats per minute and diastolic blood pressureand mean arterial blood pressure are in millimeters of mercury and cor-rected for the influences of BMI.
Table 4. Cell Means and Standard Errors of Performance Scores
Mean Standard Error
1 SwissFranc
15 SwissFrancs
30 SwissFrancs
1 SwissFranc
15 SwissFrancs
30 SwissFrancs
% correct 64.29 67.14 69.64 2.59 2.43 3.96Reaction time 860.02 946.74 991.01 65.76 96.37 61.20
n5 11 in the 1-Swiss-Franc cell, n5 10 in both other cells. % correct:percentage of correctly solved trials.
tween both cardiovascular measures suggests that SBP reactivity
effects were mainly driven by changes in the force of myocardial
contraction. Because incentive value should affect beta-ad-
renergic impact on the heart it is not surprising that DBP,
MAP, and HR only roughly resembled PEP reactivity and that
they were not reliably associated with incentive value. Moreover,
this pattern of results is congruent with preceding research on the
integrative model that has consistently found effects on SBP re-
activity but only rarely on other cardiovascular parameters (for
reviews, see, e.g., Gendolla & Wright, 2005; Richter et al., 2006;
Wright, 1996, 1998). Our results also replicate previous studies
by Wright and colleagues that investigated the influence of in-
centive value on cardiovascular reactivity (Wright, Killebrew, &
Pimpalapure, 2002; Wright, Tunstall, Williams, Goodwin, &
Harmon-Jones, 1995). They showed that systolic reactivity in-
creased with increasing incentive valueFmanipulated either by
social evaluation or by monetary rewardFwhen performers
could choose their own performance standard. DBP, MAP, and
HR reactivity were not systematically influenced by incentive
value.
Our results also relate to preceding research that has studied
the relationship between reward and cardiovascular responding.
Fowles and other researchers postulated a positive relationship
between reward value and heart rate and supported this hypoth-
esis with empirical data (Belanger & Feldman, 1962; Elliot, 1969;
Fowles, 1988; Fowles, Fisher, & Tranel, 1982; Tranel, 1983;
Tranel, Fisher, & Fowles, 1982). Our data replicate these studies
insofar as heart rate changes were the most pronounced at the
highest level of incentive value. However, the differences in heart
rate reactivity between the incentive groups were not so pro-
nounced that they would support the notion of a linear or a
monotonic relationship between incentive value and changes in
heart rate. Based on Fowles’s work and Gray’s motivation the-
ory (e.g., Gray, 1982), researchers have recently started to use
cardiac activity as an indicator of approach motivation or an
activation of the motivational approach system. For instance,
Brenner, Beauchaine, and Sylvers (2005) found that preejection
period and heart rate were sensitive to interindividual differences
in reward-related approach motivation. Our data suggest that
there is also a situational influence of reward value on cardiac
activity that should be considered when cardiac measures are
used as indicator of approach motivation.
In sum, our results clearly support the hypothesis of Wright’s
integrative approach that energy mobilization under conditions
of unclear task difficulty is related to incentive value. Moreover,
the observed association between incentive value and preejection
period reactivity suggests that incentive effects in active coping
with unclear difficulty are based on the effects of incentives on
beta-adrenergic activity on the heart. Thereby, our research clar-
ifies preceding studies that have relied on less direct measures of
beta-adrenergic activity and closes a gap in the empirical support
for Wright’s integrative model.
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(Received March 26, 2008; Accepted July 28, 2008)
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