Tilburg University
Respiratory sinus arrhythmia responses to cognitive tasks: Effects of task factors and
RSA indices
Overbeek, T.J.M.; van Boxtel, Ton; Westerink, J.H.D.M.
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Biological Psychology
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10.1016/j.biopsycho.2014.02.006
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Overbeek, T. J. M., van Boxtel, A., & Westerink, J. H. D. M. (2014). Respiratory sinus arrhythmia responses to
cognitive tasks: Effects of task factors and RSA indices. Biological Psychology, 99, 1-14. DOI:
10.1016/j.biopsycho.2014.02.006
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Biological Psychology 99 (2014) 1–14
Contents lists available at ScienceDirect
Biological Psychology
journal homepage: www.elsevier.com/locate/biopsycho
Respiratory sinus arrhythmia responses to cognitive tasks: Effects of
task factors and RSA indices
Thérèse J.M. Overbeek a , Anton van Boxtel b,∗ , Joyce H.D.M. Westerink a
a
b
Philips Group Innovation, Research, Brain, Body & Behavior Group, High Tech Campus 34, 5656AE Eindhoven, The Netherlands
Department of Psychology, Tilburg University, PO Box 90153, 5000LE Tilburg, The Netherlands
a r t i c l e
i n f o
Article history:
Received 3 May 2013
Received in revised form 27 January 2014
Accepted 4 February 2014
Available online 18 February 2014
Keywords:
Respiratory sinus arrhythmia
Cognitive control
Mental effort
Respiration
Heart rate
Facial EMG
a b s t r a c t
Many studies show that respiratory sinus arrhythmia (RSA) decreases while performing cognitive tasks.
However, there is uncertainty about the role of contaminating factors such as physical activity and stressinducing task variables. Different methods to quantify RSA may also contribute to variable results. In
83 healthy subjects, we studied RSA responses to a working memory task requiring varying levels of
cognitive control and a perceptual attention task not requiring strong cognitive control. RSA responses
were quantified in the time and frequency domain and were additionally corrected for differences in mean
interbeat interval and respiration rate, resulting in eight different RSA indices. The two tasks were clearly
differentiated by heart rate and facial EMG reference measures. Cognitive control induced inhibition
of RSA whereas perceptual attention generally did not. However, the results show several differences
between different RSA indices, emphasizing the importance of methodological variables. Age and sex did
not influence the results.
© 2014 Elsevier B.V. All rights reserved.
1. Introduction
Respiratory sinus arrhythmia (RSA) is the phasic modulation of
cardiac interbeat intervals (IBIs) which is caused by inspiratory gating of central tonic parasympathetic influences on the heart, the
degree of modulation being a function of respiratory period and
depth (for overviews see Berntson, Cacioppo, & Grossman, 2007;
Berntson, Cacioppo, & Quigley, 1993; Berntson et al., 1997). Under
conditions of controlled respiration, RSA amplitude shows a high
intraindividual correlation with variations in tonic vagal control of
the heart and may thus provide a valid autonomic index of such
variations.
Many studies have been devoted to the effects of cognitive
task performance on RSA. In the large majority of studies, RSA
appeared to be suppressed while performing more or less complex cognitive tasks such as intelligence tests (Houtveen, Rietveld,
& De Geus, 2002; Melis & van Boxtel, 2007), system monitoring and control tasks (De Rivecourt, Kuperus, Post, & Mulder,
2008; Fairclough, Venables, & Tattersall, 2005; Muth, Moss, Rosopa,
Salley, & Walker, 2012; Veltman & Gaillard, 1998), working memory/short term memory tasks (Althaus, Mulder, Mulder, van Roon,
& Minderaa, 1998; Bosch et al., 2001; de Geus, van Doornen, De
∗ Corresponding author. Tel.: +31 13 4662378; fax: +31 13 4662067.
E-mail address: [email protected] (A. van Boxtel).
http://dx.doi.org/10.1016/j.biopsycho.2014.02.006
0301-0511/© 2014 Elsevier B.V. All rights reserved.
Visser, & Orlebeke, 1990; Dywan, Mathewson, Choma, Rosenfeld,
& Segalowitz, 2008; Gianaros, van der Veen, & Jennings, 2004;
Grossman, van Beek, & Wientjes, 1990; Mulder et al., 1992), mental arithmetic (Allen and Crowell, 1989; Allen, Chambers, & Towers,
2007; Bernardi et al., 2000; Berntson, Cacioppo, & Fieldstone, 1996;
Berntson et al., 1994; Burleson et al., 2003; Dishman, Jackson,
& Nakamura, 2002; Heponiemi, Keltikangas-Järvinen, Kettunen,
Puttonen, & Ravaja, 2004; Herbert, Pollatos, Flor, Enck, & Schandry,
2010; Müller, Schandry, Montoya, & Gsellhofer, 1992; Sloan,
Korten, & Myers, 1991; Sloan, Shapiro, Bagiella, Gorman, & Bigger,
1995; Wetzel, Quigley, Morell, Eves, & Backs, 2006), Stroop interference tasks (Mathewson et al., 2010; Wright, O’Donnell, Brydon,
Wardle, & Steptoe, 2007), preparing or performing a speech task
(Burleson et al., 2003; Gianaros, Quigley, Mordkoff, & Stern, 2001;
Heponiemi et al., 2004; Wright et al., 2007), choice reaction time
tasks (Berntson et al., 1994; de Geus et al., 1990; Heponiemi et al.,
2004; Mulder et al., 1992; Sloan et al., 1995), or sensorimotor tasks
(Backs, Ryan, & Wilson, 1994; Duschek, Muckenthaler, Werner, &
Reyes del Paso, 2009). In several studies with varying task difficulty,
increasing difficulty led to stronger suppression of RSA (Althaus
et al., 1998; De Rivecourt et al., 2008; Fairclough et al., 2005;
Gianaros et al., 2004; Mulder, van Roon, Veldman, Elgersma, &
Mulder, 1995). Other studies found no significant effect of task difficulty (Backs et al., 1994; Veltman & Gaillard, 1998) or found such an
effect only for specific RSA response measures (Mukherjee, Yadav,
Yung, Zajdel, & Oken, 2011). Using a variety of cognitive tasks,
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T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
Walker, Muth, Odle-Dusseau, Moore, and Pilcher (2009) found that
RSA levels were lower during tasks characterized by high cognitive
demands (emphasizing executive functions and working memory
operations) and low controlled perceptual attention than during
tasks requiring high controlled perceptual attention and less intensive cognitive processing. Consistent with this result, other studies
found that RSA was not significantly diminished during situations
requiring attention to external events, such as a selective visual
attention task (Chang & Huang, 2012), auditory signal detection
(Müller et al., 1992), a nonstressing didactic television program
(Bosch et al., 2001), interpreting Rorschach cards (Kettunen, Ravaja,
Näätänen, & Keltikangas-Järvinen, 2000), or watching illusory or
nonillusory visual stimuli (Wetzel et al., 2006). Berntson et al.
(1996) found that visual illusions, attracting perceptual attention
but not demanding controlled cognitive processing, induced an
increase in RSA relative to baseline as well as an increase in IBI and
concluded that these effects could be interpreted as being caused
by selective vagal activation.
Although these results thus suggest that RSA inhibition is caused
by effortful cognitive behavior rather than by perceptual attention,
a complicating factor is that RSA responses to cognitive tasks may
be influenced by noncognitive task factors like physical responses
or stressors. In many tasks frequent verbal or nonverbal motor
responses were involved (e.g., Berntson et al., 1994, 1996; Burleson
et al., 2003; Duschek et al., 2009; Heponiemi et al., 2004; Sloan
et al., 1991, 1995). RSA may be influenced by low-to-moderate
physical activity levels as under such conditions heart rate (HR) is
predominantly under parasympathetic control (Grossman & Taylor,
2007; Grossman, Wilhelm, & Spoerle, 2004) and vagal withdrawal
is an immediate autonomic adjustment to physical activity (Kollai
& Mizsei, 1990; Ritz & Dahme, 2006). Backs et al. (1994) found that
cognitive and physical demands of a tracking task independently
influenced RSA. In several studies, time pressure was induced by
presenting task trials at a high rate or by verbal encouragement by
the experimenter to work faster (Hjortskov et al., 2004; Houtveen
et al., 2002; Sloan et al., 1995). In other studies, aversive sensory
feedback stimuli, negative financial incentives, or social stressors
were used to facilitate fast and correct responses (Allen & Crowell,
1989; Berntson et al., 1994; de Geus et al., 1990; Heponiemi et al.,
2004; Herbert et al., 2010; Hjortskov et al., 2004). As emphasized
by Gaillard and Wientjes (1994), effects of mental effort should
be distinguished from psychosocial stress responses when evaluating cognitive task effects. A specific problem when evaluating
RSA responses is the role of verbal responses required by the task.
Vocal or subvocal speech activity may induce changes in respiration rate causing spurious changes in RSA magnitude (Bernardi
et al., 2000; Sloan et al., 1991). Important for studies expressing
RSA as the high-frequency (0.15–0.4 Hz) component of the heart
rate variability (HRV) power spectrum, speech may cause a shift of
the dominant respiration rate from this frequency band to lower
frequencies, entraining high-frequency RSA power to lower frequencies, and thus causing a spurious decline in RSA (Bernardi et al.,
2000). Berntson et al. (2007) therefore recommend monitoring respiration to ensure that the respiratory power falls entirely within
the high-frequency band of the power spectrum.
The major purpose of this study was to evaluate the effects of
cognitive and attentional demands on RSA avoiding contaminating effects of physical activity or stressors. In a within-subjects
design, effects of cognitive and attentional demands were studied by using two different tasks: (a) a continuous working memory
task involving storage, processing, and updating of information and
thus requiring high cognitive control but not involving sustained
perceptual attention; (b) a visual stimulus detection task requiring
sustained perceptual attention but low cognitive control. Each task
was presented at three difficulty levels, varying working memory
load and visual stimulus intensity, respectively. Demanding motor
responses were avoided, the working memory task not requiring
any motor response and the stimulus detection task requiring infrequent weak finger flexions. During each task, potential stressors
such as time limits, feedback stimuli, or performance incentives
were also avoided. Based on earlier results, our main expectation
was that RSA would be diminished during high cognitive control
but not during high perceptual attention.
Since cardiovascular responses to cognitive tasks may be susceptible to practice effects, and may thus become smaller when a
task session is repeated within a period of several weeks (Kamarck
et al., 1992; Kasprowicz, Manuck, Malkoff, & Krantz, 1990), the
experimental session was replicated with an interval of four to six
weeks using the same tasks. Also, since mixed results are reported
regarding influences of sex and age on RSA responses during cognitive tasks (Dishman et al., 2002; Dywan et al., 2008; Heponiemi
et al., 2004; Mathewson et al., 2010; Mukherjee et al., 2011; Muth
et al., 2012; Wood, Maraj, Lee, & Reyes, 2002), we compared these
responses between males and females and between a younger and
middle-aged group.
Because several studies suggest that different RSA measures
may show different responses to cognitive tasks (e.g., Berntson,
Lozano, & Chen, 2005; Grossman et al., 1990; Mukherjee et al.,
2011), we calculated a time domain and a frequency domain measure of RSA which are frequently used in the literature. As a time
domain measure, we determined the root mean square of successive differences (RMSSD) in IBIs (Allen et al., 2007) which is
preferred over other time domain measures because it has better
statistical properties (Camm et al., 1996). As a frequency domain
measure, we determined the power in the high-frequency band
(0.15–0.4 Hz) of the power spectral density function (HF-PSD)
obtained by Fourier transformation of an IBI series (Berntson et al.,
1997). An important assumption of Fourier transformation is that
the IBI data series is at least weakly stationary. Although this
assumption may be violated, among others by phasic HR responses
coupled to the presentation of experimental stimuli or associated
motor responses (Berntson et al., 1997), the consequences of such
a violation are not clear. Nevertheless, during the performance
of different cognitive tasks and avoiding strong physical activity,
Houtveen and Molenaar (2001) found an almost perfect intraindividual correlation between the outcomes of spectral analysis using
Wavelet transformation (a method not requiring stationarity) and
HF-PSD, not only for stationary but also for nonstationary IBI data
segments.
A methodological issue regarding the quantification of RMSSD
and HF-PSD is whether these measures should be based on absolute
IBI values or on IBIs expressed as a proportion of the mean IBI during
the analysis interval. Both statistical and physiological reasons have
been proposed for such a normalization. From a statistical point of
view, RMSSD and HF-PSD are biased by the mean IBI level (Berntson
et al., 2005; Porges & Bohrer, 1990; van Dellen, Aasman, Mulder, &
Mulder, 1985), which makes it difficult to compare reactivity scores
if mean IBI varies between different experimental conditions. This
bias disappears by expressing IBIs as a proportion of mean IBI (van
Dellen et al., 1985). This also solves the problem that HF-PSD estimates may be influenced by the metric of the input signal. HF-PSD
may be calculated by using different heart beat time series as input
signal (see Camm et al., 1996): (a) IBIs as a function of time, (b)
instantaneous HR as a function of time, or (c) R-wave occurrence
times. Since these different time series are not always linearly interrelated (e.g., IBI is the reciprocal of instantaneous HR), the resulting
power spectra need not be linearly related and may thus be influenced by the metric of the heart beat time series (Janssen, Swenne,
De Bie, Rompelman, & van Bemmel, 1993; van Dellen et al., 1985).
Inconsistencies in RSA reactivity to specific cognitive tasks between
different studies may thus be related to differences in the metric. It
has been theoretically and empirically demonstrated that spectral
T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
estimates of HRV which are normalized by correcting them for differences in mean IBI (or mean HR, depending on the metric) are not
dependent on the specific metric (de Boer, Karemaker, & Strackee,
1984; Janssen et al., 1993; van Dellen et al., 1985).
The dependency of RSA indices on mean IBI may not only
be statistical but may also have a physiological background.
Although RSA is a generally assumed index of parasympathetic
effects on the heart, under conditions of moderate-to-large changes
in sympathetic outflow vagal effects may be modulated by
sympathetic-vagal interactions at the sinoatrial node (Berntson
et al., 1993, 1997, 2007; Hedman, Tahvanainen, Hartikainen, &
Hakumäki, 1995). Grossman and Taylor (2007) thus conclude
that RSA is a less reliable measure of intraindividual changes
in tonic cardiac vagal control if sympathetic background activity
changes. Based on earlier reports and renewed analysis of earlier
data, they suggest that normalizing RSA indices for mean IBI will
largely or completely remove any beta-adrenergic effects on RSA.
Considering the statistical and physiological arguments to apply
normalization, we calculated RMSSD and HF-PSD task responses
both as absolute magnitude scores and as normalized values.
Another methodological issue is the influence of respiratory
parameters. As various RSA indices showed positive intraindividual
correlations with respiratory period and depth under different experimental conditions (Grossman et al., 1990; Grossman,
Karemaker, & Wieling, 1991; Grossman & Kollai, 1993; Houtveen,
Groot, & de Geus, 2005; Kollai & Mizsei, 1990), an important question is to what extent RSA responses to cognitive tasks can be
explained by changes in respiratory parameters. Multiple studies
reporting a decrease in RSA during the performance of cognitive
tasks also observed a concomitant decrease in respiratory period
and/or respiratory depth (Allen & Crowell, 1989; Althaus et al.,
1998; Backs et al., 1994; Berntson et al., 1994, 1996; Burleson
et al., 2003; de Geus et al., 1990; Duschek et al., 2009; Fairclough
et al., 2005; Gianaros et al., 2001; Herbert et al., 2010; Houtveen
et al., 2002; Melis & van Boxtel, 2007; Mulder et al., 1995; Sloan
et al., 1991; Veltman & Gaillard, 1998; Wetzel et al., 2006). In
several studies investigating situations eliciting perceptual attention, significant changes in RSA were not observed and changes in
respiratory parameters were generally also absent (Berntson et al.,
1996; Chang & Huang, 2012; Wetzel et al., 2006). Different opinions
exist regarding the necessity to correct changes in RSA indices during cognitive performance for concurrent changes in respiratory
parameters (Allen et al., 2007; Berntson et al., 1997; Denver, Reed,
& Porges, 2007; Grossman & Taylor, 2007; Ritz, 2009). Although it
is generally recognized that such corrections are necessary to consider RSA changes as a valid index of changes in tonic vagal cardiac
control, it is also recognized that RSA changes may be underestimated or removed if RSA responses and respiratory responses are
correlated and thus both influenced by cognitive task demands.
Several authors therefore recommend to analyze RSA responses
both correcting and not correcting for respiratory effects in order
to evaluate whether experimental effects occur which are independent of respiratory changes (Allen et al., 2007; Houtveen et al.,
2002; Ritz & Dahme, 2006). We therefore corrected normalized and
nonnormalized RMSSD and HF-PSD response scores for changes in
respiration and compared them with uncorrected scores.
Besides RSA measures, HR and electromyographic (EMG) activity of corrugator supercilii, zygomaticus major, and orbicularis oculi
muscles were recorded as indices of effective manipulation of task
variables. In earlier studies, HR generally showed a stronger acceleration during complex cognitive tasks of increasing difficulty (e.g.,
Backs & Seljos, 1994; Carroll, Turner, & Hellawell, 1986; Mukherjee
et al., 2011; Veltman & Gaillard, 1998;) and a deceleration during conditions demanding perceptual attention to the environment
(e.g., Berntson et al., 1996; Jennings, van der Molen, Somsen,
& Brock, 1991). Corrugator and orbicularis oculi EMG activity
3
increased while performing cognitive tasks whereas zygomaticus
did not show significant changes (van Boxtel & Jessurun, 1993;
Waterink & van Boxtel, 1994). Corrugator activity also increased
when paying attention to an upcoming visual stimulus whereas
activity of orbicularis oculi and zygomaticus during such a state
of externally directed attention was inhibited relative to a resting
baseline condition (van Boxtel, Damen, & Brunia, 1996).
2. Methods
2.1. Participants
Eighty-three Dutch-speaking participants completed the experiment. Participants were solicited for two different age groups, resulting in a younger group
consisting of 42 participants (21 men; range 18–25 years; M = 19.81, SD = 1.89), and
a middle-aged group consisting of 41 participants (21 men; range 30–56 years;
M = 45.76, SD = 7.34).
The participants were recruited through advertisements in local newspapers,
the internet, and at universities. The majority of the participants in the younger
group were undergraduate students who received course credits or monetary compensation for their participation. The participants in the middle-aged group received
monetary compensation.
2.2. Experimental tasks and subjective rating scales
2.2.1. Continuous working memory task
The continuous working memory task was adapted from the numerical memory updating task used by Oberauer, Süß, and colleagues (Oberauer, Süß, Schulze,
Wilhelm, & Wittmann, 2000; Süß, Oberauer, Wittmann, Wilhelm, & Schulze, 2002;
see also Salthouse, Babcock, & Shaw, 1991). This task required simultaneous storage
of several items of numerical information in working memory, performing operations on this information, and updating the original information. Performance on this
task and on other working memory capacity tasks which not only require storage
but also transformation of information is strongly related to individual differences in
Spearman’s (Spearman, 1904) general intelligence factor g (Colom, Rebollo, Palacios,
Juan-Espinosa, & Kyllonen, 2004; Conway, Kane, & Engle, 2003; Süß et al., 2002). A
matrix of 3 × 3 cells appeared on a computer monitor. A subset of cells in the matrix
were used which was indicated by the color of the cells (light gray for active cells,
dark gray for inactive cells). First, a digit appeared successively in each active cell, the
presentation time for each cell being 5 s and the number of active cells being either
2, 3, or 4. The task of the participant was to remember which number belonged to
which cell. After this round of initial numbers, either the number +1 or −1 successively appeared in each active cell, the presentation time also being 5 s for each cell.
Participants had to apply the required addition or subtraction operation to the initial number and to remember the outcome (always being a digit) as the new value
for the cell. Several rounds of computations were completed until a total time of
150 s had passed. Then, a question mark appeared successively in each cell and the
participant had to report the current value of the cell using the keypad. The varying
numbers of active cells (being either two, three, or four) represented the three task
difficulty levels.
Performance on the working memory task was scored as follows. For each
response given by the participant (i.e., two responses in the easy, three in the intermediate, and four in the difficult task condition) the absolute difference with the
correct response was calculated after which difference scores were averaged. The
mean absolute difference score indicated to what extent responses on average deviated from the correct solution, a higher mean score indicating a larger response
error.
Each condition started with a practice trial consisting of the initial numbers as
indicated above followed by two arithmetic operations on each active cell. Then, a
120-s fragment from an aquatic video (Coral Sea Dreaming, Small World Music Inc.)
was presented to determine physiological baseline levels after which the actual task
started. Watching an excerpt from this relaxing video appeared to be more effective in lowering cardiovascular activation levels than a period of inactive rest (Piferi,
Kline, Younger, & Lawler, 2000). Different video fragments were presented preceding each of the three task conditions to avoid habituation. The order of the three task
conditions was counterbalanced across participants, resulting in six different orders.
To exclude systematic effects of baseline stimuli on task conditions, presentation
order of baseline film fragments was kept invariable across participants. Thus, different baseline-task condition combinations were created for different participants.
The entire working memory task lasted about 20 min.
2.2.2. Sustained perceptual attention task
The sustained perceptual attention task was a visual vigilance task adapted
from Lieberman, Coffey, and Kobrick (1998). Participants had to give an unspeeded
response to a signal, a small light gray rectangle (4 × 8 pixels on a screen with a
resolution of 640 × 480 pixels) that appeared on random locations within a black
circle against a dark gray background on the screen by pressing the Enter key on the
keypad. In an initialization phase, 70 signals of seven different intensity levels (10
signals per level) were presented on random locations within the circle with time
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T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
intervals quasi-randomly varying between 2.2 and 5.2 s. Presentation time of signals
was 1 s. A response within this interval was considered a hit and no response was
considered a missed signal. Based on the percentage of hits for each signal intensity level, the method of constant stimuli was used to determine signal intensity for
three task difficulty levels: low difficulty (an intensity level high enough to result in
100% hits), intermediate difficulty (75% hits), and high difficulty (50% hits). Signal
intensity levels during the subsequent measurement phase were thus adjusted to
the performance level of each individual participant in each separate session. Perceptual sensitivity (i.e., proportion of detected stimuli) may be used as an index
of the total task demand (See, Howe, Warm, & Dember, 1995). Each task difficulty
condition lasted 150 s and during this time 10 signals were presented on random
locations within the circle with time intervals quasi-randomly varying between 5
and 23 s. Signals were presented at a relatively low rate to prevent a rapid decrement
of sustained perceptual attention over time (See et al., 1995). Task performance was
scored as the percentage of hits in each condition.
Each task condition started with a 120-s fragment from the aquatic video (Piferi
et al., 2000) to determine physiological baseline levels after which the actual task
started. The video fragments shown before each task condition were again different
from each other and were also different from those used in the working memory task. The order of the three task conditions was again counterbalanced across
participants but baseline film fragments were presented in a fixed order for all participants, resulting in different baseline-task condition combinations for different
participants. The entire sustained attention task lasted about 25 min.
The sessions took place in a dimly lit room without windows. The participant
was seated in a comfortable chair in front of a 20-inch computer monitor. During the
entire session, the experimenter was in the same room as the participant, available
for questions and monitoring the acquisition of the physiological signals. A screen
was placed in between participant and experimenter.
During the first session, the participant started with reading and signing an
informed consent form and was given the opportunity to ask questions. Then the
electrodes were attached and the quality of the signals was checked. The participants
first participated in a study of the effects of emotional pictures and film fragments on
RSA, the results of which are reported elsewhere (Overbeek, van Boxtel, & Westerink,
2012). After this part of the experiment and a five-minute break during which the
participant was offered a caffeine-free drink, circumaural headphones (Sennheiser,
type HD 565) were put on the participant’s head by the experimenter for binaural
presentation of the video sounds and participants performed the cognitive tasks, followed by the perceptual attention tasks. At the end of each separate task condition,
the participant provided ratings of invested mental effort and affective experiences.
After completion of all the cognitive tasks, the electrodes were removed and the
session was completed.
During the second session, the participants were asked whether they had any
questions regarding the study procedures and/or the first session before the experimenter attached the electrodes. The experiment then proceeded as in the first
session, including the initialization phase of the perceptual attention task to determine signal intensity levels (see Section 2.2.2).
2.2.3. Subjective rating scales
Immediately after completing each condition of the working memory task and
the sustained perceptual attention task, participants rated the amount of mental
effort they experienced while performing the task condition using the Rating Scale
Mental Effort (RSME; Zijlstra, 1993). The RSME is a 150-point visual analog scale
with nine verbally labeled anchor points along its axis, ranging from “not at all
effortful” (corresponding to a score of 3) to “awfully effortful” (corresponding to a
score of 114), which were determined by means of magnitude estimation. RSME is
a sensitive measure of the experienced workload of different types of mental tasks,
including short-lasting tasks (Verwey & Veltman, 1996).
In addition, participants were asked to what extent the task had caused them
to experience pleasure, displeasure, and arousal. Each of these items were rated
on a computerized five-point Likert scale (range 1–5, labeled “not at all”, “a little”,
“to some extent”, “strongly”, and “very strongly”). Pleasure and displeasure were
measured on separate unipolar scales since positive and negative affect may be
considered independent aspects of a task rather than opposite ends of a bipolar
valence continuum, and can thus be experienced at the same time (Cacioppo &
Berntson, 1994; Larsen, McGraw, & Cacioppo, 2001; Schimmack, 2001).
2.5. Analysis of physiological signals
2.3. Recording of physiological signals
The physiological signals recorded in this study were the electrocardiogram
(ECG), finger pulse wave signal, skin conductance level, thoracic and abdominal respiration, and the electromyogram (EMG) of corrugator supercilii, orbicularis oculi,
and zygomaticus major muscles. In the current analyses, only the ECG, thoracic respiration (in the remainder of this paper referred to as “respiration”), and facial EMG
data were used. The other measures are outside the scope of this study and will
therefore not be presented here.
ECG, respiration, and facial EMG were recorded with a VitaPort 3 system (Temec
Instruments B.V., The Netherlands). ECG and EMG signals were recorded relative to a
reference electrode (Ag/AgCl, 19 mm diameter contact area) that was placed below
the left collarbone. The ECG was recorded with two electrodes (Ag/AgCl, 19 mm
diameter contact area) that were placed on the sternum and the precordial position
V6. The facial EMG electrodes (Ag/AgCl, 4 mm diameter contact area; 14 mm distance
between electrode centers) were attached to the corrugator supercilii, orbicularis
oculi, and zygomaticus major muscles on the left side of the face (for details of
electrode placement, see van Boxtel et al., 1996). Respiration was monitored by
means of a piezo-electric strain gauge (Pro-Tech, USA) attached to Velcro® straps
around the participant’s thorax.
The signals were analog filtered before they were digitized. The ECG signal was
bandpass filtered (2.3–19.8 Hz) and sampled at a rate of 1024 Hz. The respiration signal was bandpass filtered (0.03–8 Hz) and sampled at a rate of 16 Hz. The EMG signals
were bandpass filtered (10.6–512 Hz) and sampled at a rate of 1024 Hz. The digitized
signals underwent further digital filtering and processing. The ECG signal was 5-Hz
high-pass filtered, resulting in a definitive bandwidth of 5–19.8 Hz. The respiration
signal was 0.5-Hz low-pass filtered, resulting in a bandwidth of 0.03–0.5 Hz. The
EMG signals were 20-Hz high-pass filtered, resulting in a bandwidth of 20–512 Hz,
and rectified.
2.4. Procedure
Each participant participated in two identical experimental sessions, each
session lasting between 3 and 4 h. The second session generally took place four to
six weeks after the first session (range 28–131 days; M = 33.59, SD = 13.68). It started
at the same time as the first session, and occurred in most cases also at the same
day of the week as the first session.
2.5.1. ECG signal
A computer-assisted procedure was executed to detect ECG R-waves and to
make corrections for (1) prolonged IBIs due to missing R-waves; and (2) short IBIs
due to false R-wave detections. Missed or falsely detected R-waves were corrected
because they can substantially distort time and frequency domain measures of RSA
(Berntson & Stowell, 1998; Mulder, 1992). A prolonged IBI was defined as being
either longer than 1400 ms or longer than 150% of the mean value of the preceding
10 IBIs. A short IBI was defined as being either shorter than 400 ms or shorter than
50% of the mean value of the preceding 10 IBIs.
If a prolonged or short IBI was detected, the respective ECG signal segment was
visually displayed and missing or false R-wave detections could be manually corrected. Also ectopic beats (which in a few participants sporadically occurred during
the experimental sessions and which did not alter the ongoing cardiac rhythm) were
corrected using this routine. A correction was applied if the IBI before the ectopic
beat was too short according to the above-mentioned criteria or if the IBI following
the ectopic beat was too long. In these cases the average length of the IBIs immediately preceding and following the ectopic beat was taken to determine the moment
of the R-wave replacing the ectopic beat. These correction procedures proved to be
effective so that no data of individual tasks or entire subjects had to be removed.
For each 120-s baseline and 150-s task period, mean HR (in beats per minute;
BPM) was determined. For these periods, RSA was quantified in the time domain
(RMSSD) and in the frequency domain (HF-PSD). Power spectra were determined
using the computer program CARSPAN V1.34 (Mulder, Hofstetter, & van Roon, 2007).
Only IBIs that fell entirely within the length of a baseline or task period were included
in the analysis. For calculating HF-PSD, a data segment consisting of a series of Dirac
delta functions corresponding with the R-wave events was cross-multiplied with a
cosine window tapering 5% of each end of the segment to reduce spectral leakage
(Bingham, Godfrey, & Tukey, 1967). Next, it was subjected to power spectral analysis using a Fourier transform algorithm directly operating on the arrival times of
the R-waves without interpolation of the non-equidistant time series (Rompelman,
Snijders, & van Spronsen, 1982). Using this input signal metric, this method produces
a power spectrum of variations in instantaneous HR as a function of time, that is,
an HRV spectrum (as opposed to a spectrum of IBI variations as a function of time,
i.e., an IBI variability spectrum). From the obtained HRV spectra, RSA values were
calculated as the power within the high frequency band (i.e., 0.15–0.40 Hz).
Normalized versions of time and frequency domain RSA indices were determined by correcting them for mean IBI (in case of RMSSD) or mean HR (in case
of HF-PSD) during the analysis interval. For this purpose, RMSSD was converted
into the coefficient of variation of successive differences (CVSD; van Dellen et al.,
1985): CVSD = (RMSSD/mean IBI) × 100. Power spectral estimates were normalized
in a similar way by expressing them as squared coefficients of variation (SCV) and
summating the obtained values within the high-frequency band (HF-SCV; Mulder
et al., 2007).
In addition, each of these four RSA indices was also determined while correcting
for changes in respiration. It has been shown that the influence of respiratory period
and tidal volume on RSA is typically strong within individuals but not between
individuals (Grossman et al., 1991; Grossman & Kollai, 1993; Houtveen et al., 2005;
Ritz & Dahme, 2006; Ritz, Thöns, & Dahme, 2001). Ritz and Dahme (2006) warn
that correction of RSA for respiratory parameters should be made for each separate
individual since relationships between respiratory parameters and RSA may considerably differ between individuals. The contribution of respiratory period and tidal
volume to RSA can be removed using within-subjects covariance analysis or multiple regression analysis. Tidal volume can be estimated by calibrating thoracic and
abdominal respiratory recordings during paced breathing at different rates in the
T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
baseline condition (Ritz & Dahme, 2006; Ritz et al., 2001). However, such a procedure
is quite cumbersome since the calibrated output of a spirometer or pneumotachograph is related to the combined outputs of thoracic and abdominal recordings using
multiple regression analysis. Moreover, such a calibration should be repeated every
time body posture is even slightly changed (Ritz et al., 2002). We have avoided these
complications by correcting RSA only for changes in respiratory period given that
this parameter is highly correlated with tidal volume during spontaneous breathing
in a quiet situation without environmental challenges requiring metabolic adjustments (Ritz & Dahme, 2006). Under such circumstances, adjustment for changes in
respiratory period (or respiration rate) may thus be expected to largely compensate
for changes in tidal volume. In addition, cognitive tasks typically had stronger effects
on respiratory period than on tidal volume (Carroll et al., 1986; Houtveen et al., 2002;
Karavidas et al., 2010; Mulder et al., 1995; Wientjes, Grossman, & Gaillard, 1998).
The influence of respiration rate (RR; which was determined as described in
Section 2.5.2) on RMSSD, CVSD, HF-PSD, and HF-SCV was removed by means of
removing the linear regression of the particular RSA measure on RR for each participant and each session separately, including the mean values during all baseline
and cognitive task periods within a session in the regression equation. The unstandardized residuals were saved as the RR-corrected values for that RSA measure. As
recommended by Riniolo and Porges (2000), RR-uncorrected and RR-corrected RSA
values were subjected to natural logarithmic transformation since interindividual
distributions of all different RSA measures were strongly positively skewed.
2.5.2. Respiratory signal
A computer-assisted procedure, similar to the procedure used for detecting ECG
R-waves, was applied to the respiration signal to find the moments of maximal
inspiration and to determine respiratory periods. Automated detection of respiratory maxima was followed by visual inspection of the signal if the respiratory period
was (1) either longer than 6 s or longer than 130% of the mean value of the preceding
10 respiratory periods; or if it was (2) either shorter than 2 s or shorter than 50% of
the mean value of the preceding 10 respiratory periods. These criteria were based
on a pilot study performed on 8 of the 83 participants. Manual correction could be
used to add missed inspiratory maxima or remove false maxima which were caused
by discontinuous inspirations or expirations, or movement artifacts. Average RR (in
cycles per minute) was calculated for each baseline period and task period.
2.5.3. Facial EMG signals
Visual inspection of facial EMG signals was performed to remove any artifacts
due to signal clipping (caused by strong movements) or inadequate electrode contact. In case of such a correction, the facial EMG response was determined on the
basis of the artifact-free segment of a baseline or task period, which was considered
representative of the entire recording period. Corrections had to be performed for
only few recording periods of a small number of participants and the length of the
remaining data segments was generally at least 50% of the total recording period.
For each baseline and task period, the mean rectified EMG amplitude (in microvolts)
was calculated.
2.5.4. Baseline correction
Physiological responses were expressed as a difference score (the average
activity during the task period minus the average activity during the preceding
120-s baseline period) for the RSA measures, HR, and RR since these measures are
expressed on an interval scale. For the facial EMG measures, reactivity was expressed
as a percentage score (the average activity during the task period divided by the
average activity during the preceding baseline period multiplied by 100) because
EMG amplitude is measured on a ratio scale.
2.6. Statistical analyses
All statistical analyses were performed using SPSS 17.0 (SPSS Inc., 2008). An
alpha level of .05 (two-tailed) was used for all tests, and partial eta squared (2P )
values are reported as estimates of effect size. Significant results may be classified
as having either no substantial (smaller than .02), a small (equal to or larger than .02
and smaller than .13), a medium (equal to or larger than .13 and smaller than .26),
or a large (equal to or larger than .26) effect size (cf. Cohen, 1988, pp. 413–414).
For each experimental task, performance scores, subjective ratings (mental
effort, pleasure, displeasure, arousal), and physiological responses were analyzed
using MANOVA for repeated measures with Task Difficulty (three levels: low, intermediate, high) and Session (two levels: session 1, session 2) as within-subjects
factors, and Age Group (two levels: younger, middle-aged) and Sex (two levels:
female, male) as between-subjects factors. Regarding Task Difficulty, linear and
quadratic trend effects across the levels of this factor were also tested. For physiological response measures, it was also tested whether the task elicited a significant
overall response, that is, whether the mean activity level across the three task difficulty conditions and the two sessions significantly deviated from baseline level. Only
significant main effects or subeffects (i.e., linear and quadratic trend contrast scores)
and first order interactions (including associated simple effects) are reported since
these are primarily relevant with regard to the research questions. Nonsignificant
effects are only reported insofar as they are necessary for a systematic comparison
of different physiological response parameters.
5
3. Results
3.1. Continuous working memory task
3.1.1. Task performance scores
Fig. 1 shows the mean performance scores for the working memory task during both experimental sessions. Response error showed
a significant linear increase with task difficulty, F(1,79) = 70.42,
p < .001, 2P = .471. Performance was subjective to a learning effect
as shown by a significantly smaller response error during the second than during the first session, F(1,79) = 5.63, p < .05, 2P = .067.
3.1.2. Subjective ratings
Fig. 1 shows the mean values of subjective ratings during the
working memory task in both experimental sessions. The degree
of mental effort experienced while performing the task curvilinearly increased with task difficulty as apparent from significant
positive linear, F(1,79) = 325.39, p < .001, 2P = .805, and quadratic
trend effects, F(1,79) = 14.83, p < .001, 2P = .158. Consistent with an
improvement of performance scores across sessions, mental effort
was rated as being significantly lower during the second than during the first session, F(1,79) = 23.19, p < .001, 2P = .227.
When task difficulty was increased, rated pleasantness of the
task linearly decreased, F(1,79) = 3.11, p < .01, 2P = .112, whereas
ratings of displeasure linearly increased, F(1,79) = 30.60, p < .001,
2P = .280. Displeasure in fact showed a curvilinear increase since
also a significant positive quadratic trend effect was found,
F(1,79) = 6.58, p < .05, 2P = .077. Ratings of displeasure did not significantly vary between experimental sessions, F(1,79) = 0.86, ns,
2P = .011. The effect of experimental sessions on pleasure ratings
differed between both age groups as apparent from a significant
Session x Age Group interaction, F(1,79) = 7.01, p < .05, 2P = .082.
The younger group rated the task as being significantly less pleasant during the second than during the first session, F(1,81) = 28.39,
p < .001, 2P = .260, whereas ratings did not significantly differ
between sessions in the middle-aged group, F(1,81) = 2.27, ns, 2P =
.027.
Arousal scores linearly increased with task difficulty,
F(1,79) = 54.30, p < .001, 2P = .407. Arousal also differed between
sessions but this effect varied between age groups as revealed by a
significant Session × Age Group interaction, F(1,79) = 6.53, p < .05,
2P = .076. In the younger group, the task elicited significantly
lower feelings of arousal during the second than during the first
session, F(1,81) = 19.09, p < .001, 2P = .190, whereas in the middleaged group, arousal did not significantly differ between sessions,
F(1,81) = 0.53, ns, 2P = .006.
3.1.3. Physiological responses
3.1.3.1. RSA responses. Fig. 2 shows the mean RSA responses during both experimental sessions. During the task, all RSA response
measures, either being corrected or not corrected for changes
in RR, showed a significant overall decrease relative to baseline
level, effect sizes being large for RMSSD, F’s(1,79) ≥ 34.17, p’s < .001,
2P ranging from .302 to .383, medium for CVSD and HF-SCV,
F’s(1,79) ≥ 15.13, p’s < .001, 2P ranging from .161 to .258, and small
for HF-PSD, F’s(1,79) ≥ 4.82, p’s < .05, 2P ranging from .057 to .101.
RSA inhibition was systematically, but not always significantly,
stronger during the first than during the second session. A significant difference was observed for RMSSD and HF-SCV, either
being corrected or not corrected for changes in RR, F’s(1,79) ≥ 4.81,
p’s < .05, 2P ranging from .057 to .082. Corrected and uncorrected
CVSD and HF-PSD responses did not significantly differ between
sessions, F’s (1,79) ≤ 2.76, ns, 2P ≤ .034.
Effects of task difficulty differed between the various RSA
response measures but did not depend on whether a measure
was corrected for changes in RR or not corrected. Regarding
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T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
Perceptual aenon task
Working memory task
100
1.0
Detected signals
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80
0.6
60
%
Response error
Task performance
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Mental effort
Mental effort
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Arousal
Subjecve rang
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Intermediate
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Intermediate
Task difficulty
Low
High
Session 1
Session 2
Task difficulty
Fig. 1. Mean (±SEM) values of task performance scores and subjective ratings of mental effort, pleasure, displeasure, and arousal for each cognitive task during both
experimental sessions.
time domain measures, an increase in task difficulty produced
a significant linear increase in inhibition of RR-uncorrected and
RR-corrected RMSSD, F(1,79) = 6.94 and 6.00, p’s < .05, 2P = .081
and .071, respectively. RR-uncorrected and RR-corrected CVSD
showed a tendency toward a significant linear increase in inhibition, F(1,79) = 3.61 and 2.88, p = .061 and .094, 2P = .044 and .035,
respectively. RR-uncorrected and RR-corrected RMSSD and CVSD
inhibition also showed a significant negative quadratic trend across
task difficulty levels, F’s(1,79) ≥ 5.07, p’s < .05, 2P ranging from .060
to .066. On the other hand, inhibition of frequency domain measures (HF-PSD, HF-SCV), either being corrected or not corrected for
changes in RR, did not significantly vary with task difficulty, F’s
(2,78) ≤ 2.36, ns, 2P ≤ .057.
3.1.3.2. Facial EMG responses. Fig. 3 shows the mean EMG
responses during both experimental sessions. Overall, corrugator
T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
RSA reactivity (ln ms)
RR-uncorrected
RR-corrected
0.00
0.00
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RMSSD
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Low
RSA reactivity (ln ms)
RMSSD
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Intermediate
Low
High
0.00
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7
Intermediate
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HF-SCV
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Intermediate
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High
Task difficulty
Task difficulty
Session 1
Session 2
Fig. 2. Mean (±SEM) values of RSA responses to continuous working memory task during both experimental sessions. RR-uncorrected: RSA response measures uncorrected
for respiration rate; RR-corrected: RSA response measures corrected for respiration rate. HF-SCV reactivity is expressed as mCV2 , which is a dimensionless measure equal to
the squared coefficient of variation multiplied by 106 (Mulder et al., 2007).
8
T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
EMG reactivity (% of baseline level)
Working memory task
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EMG reactivity (% of baseline level)
Perceptual attention task
Respiration rate
Respiration rate
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Session 1
Session 2
Fig. 3. Mean (±SEM) values of EMG (corrugator, zygomaticus, orbicularis oculi), heart rate, and respiration rate responses to each cognitive task during both experimental
sessions.
T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
EMG activity was significantly increased during the task relative
to baseline level, F(1,79) = 1844.85, p < .001, 2P = .959, but the size
of this increase was not significantly influenced by task difficulty,
F(2,78) = 0.43, ns, 2P = .011. The increase was smaller in the second
than in the first session, F(1,79) = 4.20, p < .05, 2P = .050.
Overall, orbicularis oculi EMG activity showed a small but significant increase during the task, F(1,79) = 3208.53, p < .001, 2P =
.976. This EMG response linearly increased with task difficulty,
F(1,79) = 9.98, p < .01, 2P = .112, but did not significantly differ
between sessions, F(1,79) = 2.13, ns, 2P = .028.
Zygomaticus EMG activity showed a small but significant overall inhibition during the task, F(1,79) = 2001.18, p < .001, 2P =
.962. Task difficulty, F(2,78) = 1.16, ns, 2P = .029, and session,
F(1,79) = 0.65, ns, 2P = .008, did not significantly influence this
inhibitory response.
3.1.3.3. HR responses. Fig. 3 shows the mean HR responses in
both experimental sessions. HR showed a marked acceleration
during the task, F(1,79) = 91.20, p < .001, 2P = .536, which linearly
increased across task difficulty levels, F(1,79) = 22.43, p < .001, 2P =
.221. The acceleration was significantly smaller during the second
than during the first session, F(1,79) = 36.09, p < .001, 2P = .314.
3.1.3.4. RR responses. Fig. 3 shows the mean RR responses in both
experimental sessions. RR showed a significant increase during
the task, F(1,79) = 21.70, p < .001, 2P = .215, but this increase did
not systematically vary with task difficulty, F(2,78) = 1.02, ns, 2P =
.026, nor did it differ between sessions, F(1,79) = 2.11, ns, 2P =
.026.
3.2. Sustained perceptual attention task
3.2.1. Task performance scores
Fig. 1 shows the mean percentage of detected signals in both
experimental sessions. Percentage of detected signals linearly
decreased when the task became more difficult, F(1,79) = 17.35,
p < .001, 2P = .180. In addition, there was a significant negative
quadratic effect of difficulty, F(1,79) = 5.60, p < .05, 2P = .066. Percentage of detected signals did not significantly differ between
sessions, F(1,79) = 0.09, ns, 2P = .001.
3.2.2. Subjective ratings
Fig. 1 shows the mean values of subjective ratings of the
perceptual attention task in both experimental sessions. Mental
effort was experienced as linearly increasing with task difficulty,
F(1,79) = 55.73, p < .001, 2P = .414. The strength of this increase
differed between the two age groups, as indicated by a significant Task Difficulty x Age Group interaction for this linear
trend effect, F(1,79) = 10.27, p < .01, 2P = .115. The linear trend
was significant for both groups but was stronger for younger,
F(1,81) = 55.04, p < .001, 2P = .405, than for middle-aged participants, F(1,81) = 8.91, p < .01, 2P = .099. The degree of mental
effort spent to this task was rated as being slightly, but significantly, higher during the second than during the first session,
F(1,79) = 6.28, p < .05, 2P = .074.
The task was not experienced as becoming less pleasant,
F(2,78) = 0.25, ns, 2P = .006, or more unpleasant, F(2,78) = 1.82, ns,
2P = .045, when signal detection became more difficult. Pleasure
ratings were only slightly, but significantly, smaller during the second than during the first session, F(1,79) = 6.01, p < .05, 2P = .071,
whereas ratings of displeasure did not significantly differ between
sessions, F(1,79) = 0.03, ns, 2P = .000. Arousal experienced during
the task did not differ between different task difficulty levels,
F(2,78) = 1.19, ns, 2P = .030, or sessions, F(1,79) = 0.73, ns, 2P = .009.
9
3.2.3. Physiological responses
3.2.3.1. RSA responses. Fig. 4 shows the mean RSA responses during both experimental sessions. Regarding overall changes in RSA
from baseline level, the various RSA measures showed heterogeneous results. RMSSD, either RR-uncorrected or RR-corrected, did
on the average not show a significant change from baseline, F’s
(1,79) ≤ 1.04, ns, 2P ≤ .013. RR-uncorrected CVSD and HF-SCV were
significantly inhibited relative to baseline, F’s (1,79) ≥ 5.88, p’s < .05,
2P ranging from .069 to .079, whereas RR-corrected CVSD and HFSCV did not significantly change from baseline, F’s (1,79) ≤ 1.41,
ns, 2P ranging from .001 to .017. Finally, RR-uncorrected and
RR-corrected HF-PSD were significantly inhibited relative to baseline, although the effect was stronger for RR-uncorrected HF-PSD,
F (1,79) = 17.16, p < .001, 2P = .178, than for RR-corrected HFPSD, F (1,79) = 8.20, p < .01, 2P = .094. None of the RSA measures,
either being corrected or not corrected for changes in RR, showed
significantly different responses between the two sessions, F’s
(1,79) ≤ 2.49, ns, 2P ≤ .031. Neither was for any measure a significant effect of task difficulty found, F’s (2,78) ≤ 1.64, ns, 2P ≤ .040.
3.2.3.2. Facial EMG responses. Fig. 3 shows the mean EMG
responses during both experimental sessions. Overall, corrugator
EMG activity was significantly increased relative to baseline during
the task, F(1,79) = 1337.36, p < .001, 2P = .944, but this increase was
not significantly influenced by task difficulty, F(2,78) = 0.50, ns, 2P =
.013, and did not differ between the two sessions, F(1,79) = 0.51, ns,
2P = .006.
Orbicularis oculi EMG activity was significantly inhibited during
the task, F(1,79) = 2795.71, p < .001, 2P = .973, but the inhibition
was not significantly influenced by task difficulty, F(2,78) = 1.46, ns,
2P = .036, or session, F(1,79) = 0.49, ns, 2P = .006.
The same response pattern was observed for zygomaticus EMG
activity, that is, a significant overall inhibition during the task,
F(1,79) = 3052.07, p < .001, 2P = .975, but no significant effects of
task difficulty, F(2,78) = 0.30, ns, 2P = .008, or session, F(1,79) = 1.85,
ns, 2P = .023, on the degree of inhibition.
3.2.3.3. HR responses. Fig. 3 shows the mean HR responses during
both experimental sessions. During the task, HR showed a significant deceleration relative to baseline level, F(1,79) = 55.83, p < .001,
2P = .414. This deceleration showed a tendency toward a significant linear deepening with increasing task difficulty, F(1,79) = 3.53,
p = .064, 2P = .043, but also showed a significant negative quadratic
relationship with task difficulty, F(1,79) = 9.90, p < .01, 2P = .111. HR
deceleration did not significantly differ between the two sessions,
F(1,79) = 0.17, ns, 2P = .002.
3.2.3.4. RR responses. Fig. 3 shows the mean RR responses in both
experimental sessions. RR was significantly increased during the
task, F(1,79) = 24.82, p < .001, 2P = .239, but this increase did not
significantly vary between task difficulty levels, F(2,78) = 1.62, ns,
2P = .040, nor between sessions, F(1,79) = 0.28, ns, 2P = .004.
4. Discussion and conclusions
The primary purpose of this study was to evaluate RSA responses
to two different cognitive tasks, requiring high cognitive control
in combination with low perceptual attention or vice versa, while
avoiding interfering physical activities or stress factors during performance of the tasks. Thayer, Hansen, Saus-Rose, and Johnsen
(2009) present a model of RSA as an index of the functional capacity of prefrontal cortical structures which are critical for cognitive
control involved in working memory and executive functions. In
conformity with this model, and based on earlier evidence, we
expected RSA to be inhibited during the continuous working memory task. Performance on this task and on other frontal cognitive
10
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RR-uncorrected
RR-corrected
0.10
0.10
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RSA reactivity (ln ms)
RMSSD
0.05
0.05
0.00
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-0.10
Low
Intermediate
High
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0.10
RSA reactivity (ln ms)
CVSD
0.05
0.05
0.00
0.00
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-0.15
Low
0.10
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High
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0.10
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0.05
RSA reactivity (ln mHz 2 )
High
0.10
CVSD
Intermediate
High
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0.05
0.00
0.00
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-0.30
Low
Intermediate
High
Low
0.15
Intermediate
High
0.15
HF-SCV
HF-SCV
0.10
RSA reactivity (ln mCV 2)
Intermediate
0.10
0.05
0.05
0.00
0.00
-0.05
-0.05
-0.10
-0.10
-0.15
-0.15
-0.20
-0.20
-0.25
-0.25
Low
Intermediate
High
Low
Intermediate
High
Task difficulty
Task difficulty
Session 1
Session 2
Fig. 4. Mean (±SEM) values of RSA responses to sustained perceptual attention task during both experimental sessions. RR-uncorrected: RSA response measures uncorrected
for respiration rate; RR-corrected: RSA response measures corrected for respiration rate. HF-SCV reactivity is expressed as mCV2 , which is a dimensionless measure equal to
the squared coefficient of variation multiplied by 106 (Mulder et al., 2007).
T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
control tasks strongly depends on the investment of mental effort.
Opposite to such resource-limited tasks, the perceptual attention
task can be considered as a data-limited process (Navon & Gopher,
1979; Norman & Bobrow, 1975). Performance on data-limited tasks
improves as a function of mental effort up to a certain effort level
but does not further improve when additional effort is invested,
implying that cognitive control is of limited importance for execution of the task.
4.1. Effects of continuous working memory task
Absolute values of mental effort ratings indicate that the continuous working memory task was experienced as cognitively
demanding (Zijlstra, 1993). Increasing task difficulty was associated with lower task performance scores and higher mental
effort ratings. Although pleasure and displeasure ratings also
changed with increasing task difficulty (i.e., less pleasure and
stronger displeasure), absolute values of these ratings suggest
that affective responses were relatively weak when compared
with ratings obtained from the same subject sample when
exposed to emotion-inducing pictures or film fragments (Overbeek
et al., 2012). In addition, in the current study pleasure ratings were on average higher than displeasure ratings suggesting
that the task was not experienced as a stressor (Fig. 1). The
observed arousal scores, which increased with task difficulty,
suggest that the task elicited moderate to strong subjective
arousal. Summarizing, we conclude that the task was experienced
as cognitively demanding but did not elicit negative affective
responses.
Corresponding to our expectations (see introduction), HR as well
as corrugator and orbicularis oculi EMG activity increased during the task. Consistent with the observed pattern of performance
scores and mental effort ratings, increases in HR and orbicularis
oculi EMG activity became stronger if task difficulty increased.
Orbicularis oculi responses were in agreement with earlier observations that rate of spontaneous eyeblinks increases with increasing
working memory load (Bauer, Goldstein, & Stern, 1987; Goldstein,
Bauer, & Stern, 1992), explaining the increase in EMG activity
with task difficulty in our study. Zygomaticus EMG activity did
not confirm to our expectation that it would not be influenced
by the task but showed an inhibition which was not dependent
on task difficulty. Repeated presentation of the task led to smaller
HR and corrugator EMG responses, just as it also led to improved
performance scores and lower ratings of mental effort due to
practice and habituation effects. Considering the pattern of performance scores and subjective ratings of mental effort, HR and
EMG responses thus seem to reliably reflect mental effort associated with variations in cognitive control during the different task
conditions.
As expected on the basis of the literature presented in the
introduction, the continuous working memory task induced an
inhibition of RSA. This inhibitory effect was observed for all RSA
measures. It was largest for RMSSD, intermediate for CVSD and HFSCV, and smallest for HF-PSD. Increasing task difficulty induced a
linearly increasing inhibition of RMSSD, and a trend toward such an
effect for CVSD, whereas the frequency domain measures (HF-PSD,
HF-SCV) did not significantly vary with task difficulty. Repeated
presentation of the task led to smaller inhibition of RMSSD and
HF-SCV. Although the experimental effects were not entirely consistent between the different RSA measures, they suggest that the
time domain measure RMSSD is more sensitive to manipulations
of cognitive control than the frequency domain measure HF-PSD.
Time and frequency domain RSA measures normalized for mean
IBI (i.e., CVSD, HF-SCV) did not reveal overall inhibitory effects of
the task, or effects of task difficulty, which were systematically different from those obtained for nonnormalized measures (RMSSD,
11
HF-PSD). This leads us to the provisional conclusion that normalization does not lead to basically different results when studying
effects of cognitive control on RSA. The same conclusion can be
drawn regarding the effect of correction of RSA measures for taskinduced changes in RR. This correction did not influence overall RSA
inhibition during the task or effects of task difficulty or task repetition, neither for time domain nor for frequency domain measures.
However, this could be explained by the fact that although the task
induced a significant increase in RR with a medium effect size, absolute increases in RR were relatively small and were not influenced
by task difficulty or task repetition.
4.2. Effects of sustained perceptual attention task
Compared with the continuous working memory task, the
perceptual attention task elicited a different response pattern. Lowering visual signal intensity caused a smaller proportion of detected
signals and higher mental effort ratings but did not significantly
influence ratings of pleasure, displeasure, or arousal. Consistent
with the properties of a data-limited process, repeated presentation of the task did not influence percentage of detected signals but
was associated with higher ratings of mental effort and slightly, but
significantly, lower ratings of pleasure. It did not affect displeasure
or arousal ratings. The low absolute values of pleasure, displeasure,
and arousal ratings suggest that this task did not elicit clear affective
or arousal responses.
As expected, HR showed a deceleration and corrugator EMG
activity an increase during this task. We also observed the expected
inhibition in orbicularis oculi and zygomaticus EMG activity. Inhibition of orbicularis oculi and zygomaticus activity is typical of
attention to visual signals (van Boxtel et al., 1996). Inhibition of
orbicularis oculi can be explained by a reduced eyeblink rate (Stern,
Walrath, & Goldstein, 1984), which helps to prevent that signals are
missed. HR and EMG responses were not significantly influenced
by task difficulty or repeated task presentation. This suggests that
lower signal intensity levels were thus not attended with increased
cognitive control and mental effort, being in accordance with a
data-limited process.
The perceptual attention task induced small or medium but significant inhibitory effects on CVSD, HF-PSD, and HF-SCV if these
measures were not corrected for changes in RR. Similar to the
continuous working memory task, the perceptual attention task
elicited a small but significant increase in RR (cf. Boiten, 1993) not
differing between task difficulty levels or sessions. After correction
for changes in RR, only the decrease in HF-PSD remained significant. Insofar as inhibitory effects on uncorrected and corrected
RSA indices were observed, these effects were not dependent on
task difficulty and did not change across sessions. Our results are
in agreement which those of Chang and Huang (2012) who neither
found an effect of visual attention level on RR and HF-PSD. Similarly to the continuous working memory task, normalization of
RSA measures did not lead to results systematically differing from
those obtained for nonnormalized measures.
4.3. Differential effects of both tasks
We conclude that the two tasks had dissimilar effects on RSA
reactivity. The continuous working memory task, requiring high
cognitive control, had a substantial inhibitory effect on all time and
frequency domain measures, either being corrected or uncorrected
for respiratory changes. The perceptual attention task, involving
low cognitive control, had a limited inhibitory effect on several RSA
measures (CVSD, HF-PSD, HF-SCV) but these effects largely disappeared after correction for changes in RR. The intensity of cognitive
control thus seems an important determinant of differential effects
of cognitive tasks on RSA.
12
T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
An important question of this study was whether cognitive task
effects were significantly affected by correcting RSA responses for
changes in RR. Similar to other studies of cognitive task effects in
which physical responses or stressful task factors were avoided
(Gianaros et al., 2001; Herbert et al., 2010; Melis & van Boxtel,
2007), we found that task-induced increases in RR were quite small.
This might explain why significant effects of the continuous working memory task on time and frequency domain measures of RSA
remained unaltered after correcting for changes in RR although
effect sizes showed a limited but systematic decline. Also in other
studies, correction for changes in respiratory variables did not abolish effects of cognitive tasks on RSA (Berntson et al., 1994; Gianaros
et al., 2001; Houtveen et al., 2002; Wetzel et al., 2006). Regarding
the perceptual attention task, correction had as an effect that the
relatively small inhibitory effects on several RSA measures were no
longer significant.
We also investigated whether RSA responses during the tasks
were significantly influenced by sex and age. During both tasks, RSA
reactivity was not significantly influenced by sex or age, confirming
earlier negative results (Mukherjee et al., 2011; Muth et al., 2012).
4.4. Conclusions
Summarizing, we conclude that (1) tasks demanding cognitive
control but avoiding concomitant physical and affective responses
elicit inhibitory RSA responses indicating cardiac vagal withdrawal,
(2) RMSSD shows a larger sensitivity to manipulations of cognitive control than HF-PSD, (3) inhibitory responses observed when
calculating normalized and nonnormalized RSA measures do not
systematically differ, and (4) changes in respiration rate do not
significantly affect RSA responses.
Nevertheless, the results of this study should be interpreted
in the light of several limitations. First, the tasks were presented
during a prolonged experimental session and were preceded by a
study of the effects of emotional stimuli on RSA with only a short
break between both studies. Although the emotional tasks involved
only passive exposure to pictures and film fragments and did not
require effortful cognitive control, participants had to produce subjective ratings of the stimuli which, combined with the length of the
session, may have contributed to a certain state of mental fatigue
during the cognitive tasks.
Another limitation may be that RSA measures were only corrected for changes in respiration rate, and not for changes in tidal
volume. Although these two parameters are usually negatively correlated during sedentary conditions not requiring physical exercise
(Ritz & Dahme, 2006), effects of large and sudden changes in tidal
volume on RSA responses, particularly those occurring during sighing, cannot be ruled out since performing effortful cognitive tasks
may be associated with enhanced frequency of sighing (Vlemincx,
Taelman, De Peuter, Van Diest, & Van den Bergh, 2011).
Acknowledgements
This research was supported in part by a grant from the Casimir
Program of the Netherlands Organization for Scientific Research.
This funding source had no involvement in the design of this study,
data collection, or reporting the results.
We are grateful to Ton Aalbers (Tilburg University) for developing the computer programs controlling the experiment and
presentation of the cognitive tasks.
References
Allen, J. J. B., Chambers, A. S., & Towers, D. N. (2007). The many metrics of cardiac
chronotropy: A pragmatic primer and a brief comparison of metrics. Biological
Psychology, 74, 243–262.
Allen, M. T., & Crowell, M. D. (1989). Patterns of autonomic response during laboratory stressors. Psychophysiology, 26, 603–614.
Althaus, M., Mulder, L. J. M., Mulder, G., van Roon, A. M., & Minderaa, R. B. (1998).
Influence of respiratory activity on the cardiac response pattern to mental effort.
Psychophysiology, 35, 420–430.
Backs, R. W., Ryan, A. M., & Wilson, G. F. (1994). Psychophysiological measures of
workload during continuous manual performance. Human Factors, 36, 514–531.
Backs, R. W., & Seljos, K. A. (1994). Metabolic and cardiorespiratory measures of mental effort: The effects of level of difficulty in a working memory task. International
Journal of Psychophysiology, 16, 57–68.
Bernardi, L., Wdowczyk-Szulc, J., Valenti, C., Castoldi, S., Passino, C., Spadacini, G.,
et al. (2000). Effects of controlled breathing, mental activity and mental stress
with or without verbalization on heart rate variability. Journal of the American
College of Cardiology, 35, 1462–1469.
Berntson, G. G., Bigger, J. T., Jr., Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik, M.,
et al. (1997). Heart rate variability: Origins, methods, and interpretive caveats.
Psychophysiology, 34, 623–648.
Berntson, G. G., Cacioppo, J. T., Binkley, P. F., Uchino, B. N., Quigley, K. S., & Fieldstone, A. (1994). Autonomic cardiac control. III. Psychological stress and cardiac
response in autonomic space as revealed by pharmacological blockades. Psychophysiology, 31, 599–608.
Berntson, G. G., Cacioppo, J. T., & Fieldstone, A. (1996). Illusions, arithmetic, and the
bidirectional modulation of vagal control of the heart. Biological Psychology, 44,
1–17.
Berntson, G. G., Cacioppo, J. T., & Grossman, P. (2007). Whither vagal tone. Biological
Psychology, 74, 295–300.
Berntson, G. G., Cacioppo, J. T., & Quigley, K. S. (1993). Respiratory sinus arrhythmia: Autonomic origins, physiological mechanisms, and psychophysiological
implications. Psychophysiology, 30, 183–196.
Berntson, G. G., Lozano, D. L., & Chen, Y.-J. (2005). Filter properties of root mean
square successive difference (RMSSD) for heart rate. Psychophysiology, 42,
246–252.
Berntson, G. G., & Stowell, J. R. (1998). ECG artifacts and heart period variability:
Don’t miss a beat!. Psychophysiology, 35, 127–132.
Bingham, C., Godfrey, M. D., & Tukey, J. W. (1967). Modern techniques of power
spectrum estimation. IEEE Transactions on Audio and Electroacoustics, AU-15,
56–66.
Bauer, L. O., Goldstein, R., & Stern, J. A. (1987). Effects of information-processing
demands on physiological response patterns. Human Factors, 29, 213–234.
Boiten, F. (1993). Component analysis of task-related respiratory patterns. International Journal of Psychophysiology, 15, 91–104.
Bosch, J. A., de Geus, E. J. C., Kelder, A., Veerman, E. C. I., Hoogstraten, J., & Nieuw
Amerongen, A. V. (2001). Differential effects of active versus passive coping on
secretory immunity. Psychophysiology, 38, 836–846.
Burleson, M. H., Poehlmann, K. M., Hawkley, L. C., Ernst, J. M., Berntson, G. G.,
Malarkey, W. B., et al. (2003). Neuroendocrine and cardiovascular reactivity
to stress in mid-aged and older women: Long-term temporal consistency of
individual differences. Psychophysiology, 40, 358–369.
Cacioppo, J. T., & Berntson, G. G. (1994). Relationship between attitudes and evaluative space: A critical review, with emphasis on the separability of positive and
negative substrates. Psychological Bulletin, 115, 401–423.
Camm, A. J., Malik, M., Bigger, J. T., Breithardt, G., Cerutt, I. S., Cohen, R. J., et al.
(1996). Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology
and the North American Society of Pacing and Electrophysiology. Circulation, 93,
1043–1065.
Carroll, D., Turner, J. R., & Hellawell, J. C. (1986). Heart rate and oxygen consumption during active psychological challenge: The effects of level of difficulty.
Psychophysiology, 23, 174–181.
Chang, Y.-C., & Huang, S. L. (2012). The influence of attention levels on psychophysiological responses. International Journal of Psychophysiology, 86, 39–47.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ:
Erlbaum.
Colom, R., Rebollo, I., Palacios, A., Juan-Espinosa, M., & Kyllonen, P. C. (2004). Working
memory is (almost) perfectly predicted by g. Intelligence, 32, 277–296.
Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and
its relation to general intelligence. Trends in Cognitive Sciences, 7, 547–552.
de Boer, R. W., Karemaker, J. M., & Strackee, J. (1984). Comparing spectra of a series
of point events particularly for heart rate variability data. IEEE Transactions on
Biomedical Engineering, BME-31, 384–387.
de Geus, E. J. C., van Doornen, L. J. P., De Visser, D. C., & Orlebeke, J. F. (1990). Existing
and training induced differences in aerobic fitness: Their relationship to physiological response patterns during different types of stress. Psychophysiology, 27,
457–478.
Denver, J. W., Reed, S. F., & Porges, S. W. (2007). Methodological issues in
the quantification of respiratory sinus arrhythmia. Biological Psychology, 74,
286–294.
De Rivecourt, M., Kuperus, M. N., Post, W. J., & Mulder, L. J. M. (2008). Cardiovascular
and eye activity measures as indices for momentary changes in mental effort
during simulated flight. Ergonomics, 51, 1295–1319.
Dishman, R. K., Jackson, E. M., & Nakamura, Y. (2002). Influence of fitness and gender
on blood pressure responses during active or passive stress. Psychophysiology,
39, 568–576.
Duschek, S., Muckenthaler, M., Werner, N., & Reyes del Paso, G. A. (2009). Relationships between features of autonomic cardiovascular control and cognitive
performance. Biological Psychology, 81, 110–117.
T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
Dywan, J., Mathewson, K. J., Choma, B. L., Rosenfeld, B., & Segalowitz, S. J. (2008).
Autonomic and electrophysiological correlates of emotional intensity in older
and younger adults. Psychophysiology, 45, 389–397.
Fairclough, S. H., Venables, L., & Tattersall, A. (2005). The influence of task demand
and learning on the psychophysiological response. International Journal of Psychophysiology, 56, 171–184.
Gaillard, A. W. K., & Wientjes, C. J. E. (1994). Mental load and work stress as two
types of energy mobilization. Work and Stress, 8, 141–152.
Gianaros, P. J., Quigley, K. S., Mordkoff, J. T., & Stern, R. M. (2001). Gastric myoelectrical and autonomic cardiac reactivity to laboratory stressors. Psychophysiology,
38, 642–652.
Gianaros, P. J., van der Veen, F. M., & Jennings, J. R. (2004). Regional cerebral blood
flow correlates with heart period and high-frequency heart period variability
during working-memory tasks: Implications for the cortical and subcortical
regulation of cardiac autonomic activity. Psychophysiology, 41, 521–530.
Goldstein, R., Bauer, L. O., & Stern, J. A. (1992). Effect of task difficulty and interstimulus interval on blink parameters. International Journal of Psychophysiology, 13,
111–118.
Grossman, P., Karemaker, J., & Wieling, W. (1991). Prediction of tonic parasympathetic cardiac control using respiratory sinus arrhythmia: The need for
respiratory control. Psychophysiology, 28, 201–216.
Grossman, P., & Kollai, M. (1993). Respiratory sinus arrhythmia, cardiac vagal tone,
and respiration: Within- and between-individual relations. Psychophysiology,
30, 486–495.
Grossman, P., van Beek, J., & Wientjes, C. (1990). A comparison of three quantification
methods for estimation of respiratory sinus arrhythmia. Psychophysiology, 27,
702–714.
Grossman, P., & Taylor, E. W. (2007). Toward understanding respiratory sinus
arrhythmia: Relations to cardiac vagal tone, evolution and biobehavioral functions. Biological Psychology, 74, 263–285.
Grossman, P., Wilhelm, F. H., & Spoerle, M. (2004). Respiratory sinus arrhythmia,
cardiac vagal control, and daily activity. American Journal of Physiology: Heart
and Circulation Physiology, 287, H728–H734.
Hedman, A. E., Tahvanainen, K. U. O., Hartikainen, J. E. K., & Hakumäki, M. O. K.
(1995). Effect of sympathetic modulation and sympatho-vagal interaction on
heart rate variability in anaesthetized dogs. Acta Physiologica Scandinavica, 155,
205–214.
Heponiemi, T., Keltikangas-Järvinen, L., Kettunen, J., Puttonen, S., & Ravaja, N. (2004).
BIS-BAS sensitivity and cardiac autonomic stress profiles. Psychophysiology, 41,
37–45.
Herbert, B. M., Pollatos, O., Flor, H., Enck, P., & Schandry, R. (2010). Cardiac awareness
and autonomic cardiac reactivity during emotional picture viewing and mental
stress. Psychophysiology, 47, 342–354.
Hjortskov, N., Rissén, D., Blangsted, A. K., Fallentin, N., Lundberg, U., & Søgaard,
K. (2004). The effect of mental stress on heart rate variability and blood
pressure during computer work. European Journal of Applied Physiology, 92,
84–89.
Houtveen, J. H., Groot, P. F. C., & de Geus, E. J. C. (2005). Effects of variation in posture
and respiration on RSA and pre-ejection period. Psychophysiology, 42, 713–719.
Houtveen, J. H., & Molenaar, P. C. M. (2001). Comparison between the Fourier and
Wavelet methods of spectral analysis applied to stationary and nonstationary
heart period data. Psychophysiology, 38, 729–735.
Houtveen, J. H., Rietveld, S., & De Geus, E. J. C. (2002). Contribution of tonic vagal modulation of heart rate, central respiratory drive, respiratory depth, and respiratory
frequency to respiratory sinus arrhythmia during mental stress and physical
exercise. Psychophysiology, 39, 427–436.
Janssen, M. J. A., Swenne, C. A., De Bie, J., Rompelman, O., & van Bemmel, J. H. (1993).
Methods in heart rate variability analysis: Which tachogram should we use.
Computer Methods and Programs in Biomedicine, 41, 1–8.
Jennings, J. R., van der Molen, M. W., Somsen, R. J. M., & Brock, K. (1991). Weak sensory
stimuli induce a phase sensitive bradycardia. Psychophysiology, 28, 1–10.
Kamarck, T. W., Jennings, J. R., Debski, T. T., Glickman-Weiss, Johnson, P. S., Eddy, M. J.,
et al. (1992). Reliable measures of behaviorally-evoked cardiovascular reactivity
from a PC-based test battery: Results from student and community samples.
Psychophysiology, 29, 17–28.
Karavidas, M. K., Lehrer, P. M., Lu, S.-E., Vaschillo, E., Vaschillo, B., & Cheng, A. (2010).
The effects of workload on respiratory variables in simulated flight: A preliminary study. Biological Psychology, 84, 157–160.
Kasprowicz, A. L., Manuck, S. B., Malkoff, S. B., & Krantz, D. S. (1990). Individual
differences in behaviorally evoked cardiovascular response: Temporal stability
and hemodynamic patterning. Psychophysiology, 27, 605–619.
Kettunen, J., Ravaja, N., Näätänen, P., & Keltikangas-Järvinen, L. (2000). The relationship of respiratory sinus arrhythmia to the co-activation of autonomic and facial
responses during the Rorschach test. Psychophysiology, 37, 242–250.
Kollai, M., & Mizsei, G. (1990). Respiratory sinus arrhythmia is a limited measure
of cardiac parasympathetic control in man. Journal of Physiology (London), 424,
329–342.
Larsen, J. T., McGraw, A. P., & Cacioppo, J. T. (2001). Can people feel happy and sad
at the same time? Journal of Personality and Social Psychology, 81, 684–696.
Lieberman, H. R., Coffey, B., & Kobrick, J. (1998). A vigilance task sensitive to
the effects of stimulants, hypnotics, and environmental stress: The Scanning
Visual Vigilance Test. Behavior Research Methods, Instruments, and Computers,
30, 416–422.
Mathewson, K. J., Jetha, M. K., Drmic, I. E., Bryson, S. E., Goldberg, J. O., Hall, G. B., et al.
(2010). Autonomic predictors of Stroop performance in young and middle-aged
adults. International Journal of Psychophysiology, 76, 123–129.
13
Melis, C., & van Boxtel, A. (2007). Autonomic physiological response patterns related
to intelligence. Intelligence, 35, 471–487.
Mukherjee, S., Yadav, R., Yung, I., Zajdel, D. P., & Oken, B. S. (2011). Sensitivity to mental effort and test-retest reliability of heart rate variability measures in healthy
seniors. Clinical Neurophysiology, 122, 2059–2066.
Mulder, L. J. M. (1992). Measurement and analysis methods of heart rate and respiration for use in applied environments. Biological Psychology, 34, 205–236.
Mulder, L. J. M., Hofstetter, H., & van Roon, A. (2007). CARSPAN for Windows: User’s
manual. Groningen: University of Groningen.
Mulder, L. J. M., van Roon, A. M., Veldman, J. B. P., Elgersma, A. F., & Mulder, G. (1995).
Respiratory pattern, invested effort, and variability in heart rate and blood pressure during the performance of mental tasks. In M. Di Rienzo, G. Mancia, G. Parati,
A. Pedotti, & A. Zanchetti (Eds.), Computer analysis of cardiovascular signals (pp.
219–234). Amsterdam: IOS Press.
Mulder, L. J. M., Veldman, J. B. P., van der Veen, F. M., van Roon, A. M., Rüddel, H.,
Schächinger, H., et al. (1992). On the effects of mental task performance on heart
rate, blood pressure and its variability measures. In M. Di Rienzo, G. Mancia, G.
Parati, A. Pedotti, & A. Zanchetti (Eds.), Blood pressure and heart rate variability:
Computer analysis, modelling and clinical applications (pp. 153–166). Amsterdam:
IOS Press.
Müller, A., Schandry, R., Montoya, P., & Gsellhofer, B. (1992). Differential effects of
two stressors on heart rate, respiratory sinus arrhythmia, and T-wave amplitude.
Journal of Psychophysiology, 6, 252–259.
Muth, E. R., Moss, J. D., Rosopa, P. J., Salley, J. N., & Walker, A. D. (2012). Respiratory
sinus arrhythmia as a measure of cognitive workload. International Journal of
Psychophysiology, 83, 96–101.
Navon, D., & Gopher, D. (1979). On the economy of the human-processing system.
Psychological Review, 86, 214–255.
Norman, D. A., & Bobrow, D. G. (1975). On data-limited and resource-limited processes. Cognitive Psychology, 7, 44–64.
Oberauer, K., Süß, H.-M., Schulze, R., Wilhelm, O., & Wittmann, W. W. (2000). Working memory capacity – Facets of a cognitive ability construct. Personality and
Individual Differences, 29, 1017–1045.
Overbeek, T. J. M., van Boxtel, A., & Westerink, J. H. D. M. (2012). Respiratory sinus arrhythmia responses to induced emotional states: Effects of RSA
indices, emotion induction method, age, and sex. Biological Psychology, 91,
128–141.
Piferi, R. L., Kline, K. A., Younger, J., & Lawler, K. A. (2000). An alternative approach
for achieving cardiovascular baseline: Viewing an aquatic video. International
Journal of Psychophysiology, 37, 207–217.
Porges, S. W., & Bohrer, R. E. (1990). The analysis of periodic processes in psychophysiological research. In J. T. Cacioppo, & L. G. Tassinary (Eds.), Principles
of psychophysiology. Physical, social, and inferential elements (pp. 708–753).
Cambridge: Cambridge University Press.
Riniolo, T. C., & Porges, S. W. (2000). Evaluating group distributional characteristics:
Why psychophysiologists should be interested in qualitative departures from
the normal distribution. Psychophysiology, 37, 21–28.
Ritz, T. (2009). Studying noninvasive indices of vagal control: The need for respiratory control and the problem of target specificity. Biological Psychology, 80,
158–168.
Ritz, T., & Dahme, B. (2006). Implementation and interpretation of respiratory sinus
arrhythmia measures in psychosomatic medicine: Practice against better evidence? Psychosomatic Medicine, 68, 617–627.
Ritz, T., Dahme, B., Dubois, A. R., Folgering, H., Fritz, G. K., Harver, A., et al. (2002).
Guidelines for mechanical lung function measurements in psychophysiology.
Psychophysiology, 39, 546–567.
Ritz, T., Thöns, M., & Dahme, B. (2001). Modulation of respiratory sinus arrhythmia
by respiration rate and volume: Stability across posture and volume variations.
Psychophysiology, 38, 858–862.
Rompelman, O., Snijders, J. B. I. M., & van Spronsen, C. J. (1982). The measurement of heart rate variability spectra with the help of a personal computer.
IEEE Transactions on Biomedical Engineering BME, 29, 503–510.
Salthouse, T. A., Babcock, R. L., & Shaw, R. J. (1991). Effects of adult age on structural and operational capacities in working memory. Psychology and Aging, 6,
118–127.
Schimmack, U. (2001). Pleasure, displeasure, and mixed feelings: Are semantic opposites mutually exclusive? Cognition and Emotion, 15, 81–97.
See, J. E., Howe, S. R., Warm, J. S., & Dember, W. N. (1995). Meta-analysis of the
sensitivity decrement in vigilance. Psychological Bulletin, 117, 230–249.
Sloan, R. P., Korten, J. B., & Myers, M. M. (1991). Components of heart rate reactivity
during mental arithmetic with and without speaking. Physiology and Behavior,
50, 1039–1045.
Sloan, R. P., Shapiro, B. A., Bagiella, E., Gorman, J. M., & Bigger, J. T. (1995). Temporal
stability of heart period variability during a resting baseline and in response to
psychological challenge. Psychophysiology, 32, 191–196.
Spearman, C. (1904). “General Intelligence,” objectively determined and measured.
American Journal of Psychology, 15, 201–293.
Stern, J. A., Walrath, L. C., & Goldstein, R. (1984). The endogenous eyeblink. Psychophysiology, 21, 22–33.
Süß, H.-M., Oberauer, K., Wittmann, W. W., Wilhelm, O., & Schulze, R. (2002).
Working-memory capacity explains reasoning ability – And a little bit more.
Intelligence, 30, 261–288.
Thayer, J. F., Hansen, A. L., Saus-Rose, E., & Johnsen, B. H. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: The neurovisceral
integration perspective on self-regulation, adaptation, and health. Annals of
Behavioral Medicine, 37, 141–153.
14
T.J.M. Overbeek et al. / Biological Psychology 99 (2014) 1–14
van Boxtel, A., Damen, E. J. P., & Brunia, C. H. M. (1996). Anticipatory EMG responses
of pericranial muscles in relation to heart rate during a warned simple reaction
time task. Psychophysiology, 33, 576–583.
van Boxtel, A., & Jessurun, M. (1993). Amplitude and bilateral coherency of facial
and jaw-elevator EMG activity as an index of effort during a two-choice serial
reaction task. Psychophysiology, 30, 589–604.
van Dellen, H. J., Aasman, J., Mulder, L. J. M., & Mulder, G. (1985). Time domain versus
frequency domain measures of heart-rate variability. In J. F. Orlebeke, G. Mulder,
& L. J. P. van Doornen (Eds.), The psychophysiology of cardiovascular control (pp.
353–374). New York, NY: Plenum Press.
Veltman, J. A., & Gaillard, A. W. K. (1998). Physiological workload reactions to increasing levels of task difficulty. Ergonomics, 41, 656–669.
Verwey, W. B., & Veltman, H. A. (1996). Detecting short periods of elevated workload:
A comparison of nine workload assessment techniques. Journal of Experimental
Psychology: Applied, 2, 270–285.
Vlemincx, E., Taelman, J., De Peuter, S., Van Diest, I., & Van den Bergh, O. (2011).
Sigh rate and respiratory variability during mental load and sustained attention.
Psychophysiology, 48, 117–120.
Walker, A. D., Muth, E. R., Odle-Dusseau, H. N., Moore, D. W., & Pilcher, J. J. (2009). The
effects of 28 hours of sleep deprivation on respiratory sinus arrhythmia during
tasks with low and high controlled attention demands. Psychophysiology, 46,
217–224.
Waterink, W., & van Boxtel, A. (1994). Facial and jaw-elevator EMG activity in relation to changes in performance level during a sustained information processing
task. Biological Psychology, 37, 183–198.
Wetzel, J. M., Quigley, K. S., Morell, J., Eves, E., & Backs, R. W. (2006). Cardiovascular measures of attention to illusory and nonillusory visual stimuli. Journal of
Psychophysiology, 20, 276–285.
Wientjes, C. J. E., Grossman, P., & Gaillard, A. W. K. (1998). Influence of drive and
timing mechanisms on breathing pattern and ventilation during mental task
performance. Biological Psychology, 49, 53–70.
Wood, R., Maraj, B., Lee, C. M., & Reyes, R. (2002). Short-term heart rate variability during a cognitive challenge in young and older adults. Age and Ageing, 31,
131–135.
Wright, C. E., O’Donnell, K., Brydon, L., Wardle, J., & Steptoe, A. (2007). Family history
of cardiovascular disease is associated with cardiovascular responses to stress
in healthy young men and women. International Journal of Psychophysiology, 63,
275–282.
Zijlstra, F. H. R. (1993). Efficiency in work behavior: A design approach for modern tools.
Delft: Delft University Press.