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This study investigated variations in heart rate variability (HRV) as a function of cognitive demands. Participants completed an execution condition including the psychomotor vigilance task, a working memory task and a duration discrimination task. The control condition consisted of oddball versions (participants had to detect the rare event) of the tasks from the execution condition, designed to control for the effect of the task parameters (stimulus duration and stimulus rate) on HRV. The NASA-TLX questionnaire was used as a subjective measure of cognitive workload across tasks and conditions. Three major findings emerged from this study. First, HRV varied as a function of task demands (with the lowest values in the working memory task). Second, and crucially, we found similar HRV values when comparing each of the tasks with its oddball control equivalent, and a significant decrement in HRV as a function of time-on-task. Finally, the NASA-TLX results showed larger cognitive workload in the execution condition than in the oddball control condition, and scores variations as a function of task. Taken together, our results suggests that HRV is highly sensitive to overall demands of sustained attention over and above the influence of other cognitive processes suggested by previous literature. In addition, our study highlights a potential dissociation between objective and subjective measures of mental workload, with important implications in applied settings.
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Heart rate variability and cognitive processing: the autonomic response to task 1"
demands. 2"
Antonio Luque-Casado1,2,3, José C. Perales1,2, David Cárdenas3 & Daniel Sanabria1,2 3"
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1. Brain, Mind, & Behaviour Research Center. University of Granada, Spain 5"
2. Department of Experimental Psychology. University of Granada, Spain 6"
3. Department of Physical Education and Sport. University of Granada, Spain 7"
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Submitted to: Biological Psychology 11"
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Corresponding author: Daniel Sanabria 13"
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E-mail address: daniel@ugr.es 15"
Phone: +34 958247875 16"
Fax: +34 958246239 17"
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Abstract 1"
This study investigated variations in heart rate variability (HRV) as a function of 2"
cognitive demands. Participants completed an execution condition including the 3"
psychomotor vigilance task, a working memory task and a duration discrimination task. 4"
The control condition consisted of oddball versions (participants had to detect the rare 5"
event) of the tasks from the execution condition, designed to control for the effect of the 6"
task parameters (stimulus duration and stimulus rate) on HRV. The NASA-TLX 7"
questionnaire was used as a subjective measure of cognitive workload across tasks and 8"
conditions. Three major findings emerged from this study. First, HRV varied as a 9"
function of task demands (with the lowest values in the working memory task). Second, 10"
and crucially, we found similar HRV values when comparing each of the tasks with its 11"
oddball control equivalent, and a significant decrement in HRV as a function of time-12"
on-task. Finally, the NASA-TLX results showed larger cognitive workload in the 13"
execution condition than in the oddball control condition, and scores variations as a 14"
function of task. Taken together, our results suggests that HRV is highly sensitive to 15"
overall demands of sustained attention over and above the influence of other cognitive 16"
processes suggested by previous literature. In addition, our study highlights a potential 17"
dissociation between objective and subjective measures of mental workload, with 18"
important implications in applied settings. 19"
Key words: Sustained attention, psychomotor vigilance task, perception, working 20"
memory, executive processing, NASA-TLX, mental workload. 21"
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Introduction 1"
A large body of research has shown a direct link between cognitive processing 2"
and the cardiovascular system through autonomic vagal control (Thayer & Lane, 2009). 3"
A simple way of measuring that relationship is to look at heart rate variability (HRV), a 4"
non-invasive measurement of the interactions between the autonomic nervous system 5"
and the cardiovascular system, based on the study of oscillations of the interval between 6"
heartbeats (Malik et al., 1996; Pumprla, Howorka, Groves, Chester, & Nolan, 2002). 7"
Thayer et al. have recently proposed the Neurovisceral Integration Model to 8"
account for the link between cognitive processing and the functioning of the 9"
autonomous nervous system (Thayer, Hansen, Saus-Rose, & Johnsen, 2009; Thayer & 10"
Lane, 2009). They pointed out that HRV is a particularly sensitive index of the changes 11"
in a flexible neural network that is dynamically organized in response to situational 12"
requirements. The authors highlighted the role of the prefrontal cortex in the modulation 13"
of subcortical cardio-acceleratory circuits via an inhibitory pathway that is associated 14"
with vagal function and that can be indexed by HRV. The link of the frontal cortex to 15"
autonomic motor circuits responsible for both the sympathoexcitatory and 16"
parasympathoinhibitory effects on the heart seems to be controlled both by direct and 17"
indirect pathways. In this sense, one of the potential mediators underlying variations in 18"
HRV as a function of cognitive demands is the baroreceptor system, i.e., the negative 19"
feedback loop adjusting heart activity to blood pressure fluctuations. In fact, the 20"
baroreflex function appears to be influenced by specific behavioural manipulations of 21"
cognitive demands and mental workload (e.g., Duschek, Werner, & Reyes del Paso, 22"
2013; Reyes del Paso, González, & Hernández, 2004). Consequently, variations in the 23"
baroreflex function may therefore also mediate modulations in HRV during the specific 24"
task conditions. In any case, HRV is thought of as an overall index of central-peripheral 25"
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neural feedback and central nervous system-autonomic nervous system integration 1"
(Thayer & Lane, 2000, 2009). 2"
A cursory look to the literature on the relationship between HRV cognition 3"
shows that researchers have used a wide range of tasks, tapping different cognitive 4"
processes, which make it difficult to establish a finer-grained relationship between HRV 5"
and cognitive processing. In more specific terms, a number of studies have singled out a 6"
subset of mental workload components -executive demands- as key to understand the 7"
HRV-cognitive processing link, with lower HRV as executive demands increase (Backs 8"
& Seljos, 1994; Duschek, Muckenthaler, Werner, & del Paso, 2009; Hansen, Johnsen, 9"
& Thayer, 2003; Luft, Takase, & Darby, 2009; Mathewson et al., 2010; Mulder & 10"
Mulder, 1981). In this scenario, the above mentioned Neurovisceral Integration Model 11"
predicts an inverse relationship between executive task demands and levels of HRV, 12"
which seems to be confirmed by the studies cited above. However, the results of other 13"
studies appear to challenge this straightforward view of the relationship between HRV 14"
and cognitive processing. For instance, Fairclough & Houston (2004) failed to show 15"
differences between the congruent and incongruent conditions when participants had to 16"
name the colour of the ink in a Stroop task, a well-known executive task (e.g., Egner & 17"
Hirsch, 2005). On the contrary, they showed that HRV was sensitive to time-on-task, 18"
pointing to the role of overall attention demands on HRV. In this same line, Chang & 19"
Huang (2012) showed that HRV varied as a function of attentional demands in a visual 20"
search task, with lower HRV in a conjunction search task than in a feature search task 21"
and a control condition in which participants passively watched to the stimuli. 22"
Together with attention demands, perceptual difficulty seems to be another key 23"
factor modulating HRV. The results of two recent studies point in that direction. Chen, 24"
Tsai, Biltz, Stoffregen, and Wade (2015) reported lower HRV as a function of 25"
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perceptual difficulty, but not as a function of working memory load (linked to executive 1"
functioning; e.g., Duncan & Owen, 2000). Particularly relevant here is the study by 2"
Luque-Casado et al. (Luque-Casado, Zabala, Morales, Mateo-March, & Sanabria, 3"
2013), who compared HRV during performance of three tasks, tapping three different 4"
cognitive functions: the psychomotor vigilance task (PVT; a vigilance task), an 5"
endogenous temporal orienting task (a cognitive control task), and a duration 6"
discrimination task (a perceptual task). The results showed lower levels of HRV in the 7"
perceptual task than in the other two tasks, with no significant differences in the main 8"
indexes of HRV between the PVT and the temporal orienting task. In addition, they 9"
showed that HRV decreased with time-on-task, a result that did not seem to depend on 10"
the particular task running at that moment. 11"
Overall, the outcome of the above-mentioned studies seem to nuance Thayer et 12"
al.’s Neurovisceral Integration Model, and point to some aspects of cognitive demand 13"
(i.e. perceptual difficulty and sustained attention) and not others (working memory i.e. 14"
workload, interference) as key task features modulating HRV. However, as Luque-15"
Casado et al. acknowledged in their article, brain structures typically associated with 16"
executive processing seem to be also involved in difficult perceptual discrimination 17"
(Duncan & Owen, 2000). Thus, the question remains of whether a task purposely 18"
developed to involve high executive demands would induce a larger reduction in HRV 19"
than the perceptual task used by Luque-Casado et al. (2013). 20"
The present study is aimed at further investigating the role of particular 21"
processing demands involved in task effects on HRV. We partially replicated Luque-22"
Casado et al.’s (2013) manipulation, using the PVT and the duration discrimination 23"
task, but replaced the temporal orienting task by a N-back task. The N-back task tackles 24"
working memory capacity, a core component of executive functioning, by asking 25"
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participant to tag and update short-term stored information on a trial-by-trial basis 1"
(Kirchner, 1958; Owen, McMillan, Laird, & Bullmore, 2005). Importantly, along with 2"
these three tasks, we included three parallel oddball tasks, with the same stimuli 3"
parameters for each of the three, but in which participants just had to detect a rare event 4"
within a sequence of frequent stimuli. 5"
The inclusion of the oddball condition allowed us to control for an important 6"
aspect that has been neglected in the majority of previous studies investigating the 7"
relationship between HRV and cognitive processing: the potential influence of stimulus 8"
parameters of the task on the relationship between autonomic response and cognitive 9"
performance. That is, whether stimulus setting features (e.g., stimulus duration, inter-10"
stimulus interval) may explain (at least partially) the influence of task performance on 11"
autonomic reactivity over and above any specific cognitive process (e.g., executive 12"
processing, memory, etc.) specifically tapped by the task. In this sense, to the best of 13"
our knowledge, the only task feature that has been investigated in relation to this issue is 14"
the motor activity during the cognitive task (Bush, Alkon, Obradović, Stamperdahl, & 15"
Boyce, 2011; Stephen W. Porges et al., 2007). While Porges et al. showed that only 16"
gross motor activity (e.g., bike pedaling) could modulate the relationship between 17"
autonomic response and cognitive processing, Bush et al. found changes on autonomic 18"
reactivity to various cognitive tasks that were related to the particular motor activity 19"
during each procedure. Here, by asking participants to perform an oddball version of the 20"
three main cognitive tasks we controlled for variations in HRV due to the particular 21"
stimulus features of the tasks (e.g., stimulus duration) regardless of the task demands, 22"
whilst largely reducing the motor activity. 23"
Here, as a cross-task and cross-condition manipulation check, subjective mental 24"
load was assessed with the National Aeronautics and Space Administration Task Load 25"
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Index (NASA-TLX) questionnaire (Hart & Staveland, 1988). The NASA-TLX 1"
sensitivity to mental workload has been demonstrated to be useful in a variety of 2"
cognitively demanding tasks such as aircraft piloting (Karavidas et al., 2010; Ma et al., 3"
2014), air traffic control (Brookings, Wilson, & Swain, 1996), surgery (Zheng et al., 4"
2012), or laboratory tasks context (Muth, Moss, Rosopa, Salley, & Walker, 2012). With 5"
the inclusion of the NASA-TLX we aimed at comparing objective (HRV) and 6"
subjective potential indices of mental load induced by the different task demands. This 7"
is not trivial since previous research has questioned the validity of subjective measures 8"
of mental load (see Annett, 2002, for discussion on this issue). 9"
On the basis of Luque-Casado et al.'s (2013) findings and the previous related 10"
research, we expected the N-back task to exert a stronger modulation over HRV than 11"
the PVT. The question of interest was to see whether the N-back task would also 12"
influence HRV to a greater extent than the duration discrimination task, a result that 13"
would add further support to the Neurovisceral Integration Model. Importantly, given 14"
that the three tasks in the oddball condition were essentially the same task (with 15"
variations only in stimuli parameters) with minimal response requirements, we did not 16"
expect significant differences in HRV across them. We predicted the NASA scores to 17"
parallel the HRV results, with larger perceived workload in the N-back task than in the 18"
other two tasks, and no differences across the three oddball tasks. 19"
Methods and design 20"
Participants 21"
Twenty-four males undergraduate students (age range: 18-28 years old; M= 21 22"
years old; SD= 2.6 years old) from the University of Granada (Spain) took part in the 23"
study in exchange of course credits. In order to take part in the experiment, participants 24"
8
were required to maintain a regular sleep–wake cycle for at least one day before the 1"
study and to abstain from stimulating beverages or any intense physical activity for the 2"
day of the experiment. Once in the laboratory, none of them reported having had any 3"
stimulating beverage or exercise session, and they all reported a regular sleep the night 4"
before (6-10 hours; M= 7.5; SD= 0.9). None of the participants smoked, and all of them 5"
reported normal hearing and normal or corrected-to-normal vision. 6"
The experiment was approved by the local ethics committee and complied with the 7"
ethical standards laid down in the 1964 Declaration of Helsinki. Participants read and 8"
signed an informed consent statement before the beginning of the experimental session. 9"
They were also informed about their right to leave the experiment at any time. All data 10"
were analyzed and reported anonymously. 11"
Apparatus and materials 12"
Participants were fitted with a Polar H3 heart rate sensor and a Polar RS800 CX 13"
monitor (Polar Electro Öy, Kempele, Finland) to record their HRV during the 14"
experimental session. We used a digital thermo-hygrometer Inovalley 207H01 15"
(Inovalley,Saint-Ouen-l'Aumône, France) to measure the temperature and humidity 16"
percentage in the laboratory during the experimental session. 17"
We used a PC Intel Quad Core i7-3770, a 24’’ LED monitor (BenQ XL2411T) and 18"
the E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) to control the 19"
stimulus presentation and response collection. The centre of the PC screen was situated 20"
at 60 cm (approx.) from the head of the participant and at his eye level. The PC 21"
keyboard was used to collect responses. !22"
Procedure 23"
9
The experimental protocol comprised two conditions (henceforth, execution and 1"
oddball). In the execution condition participants performed three different tasks: the 2"
PVT, a duration discrimination task and the N-back task. In the oddball condition, 3"
participants performed parallel ‘oddball’ versions of the three tasks mentioned above 4"
(see Experimental tasks section for more details). The order of presentation of the 5"
execution and oddball conditions, and the tasks within each condition, were 6"
counterbalanced across participants. Immediately after each task, participants completed 7"
the NASA-TLX questionnaire (Hart & Staveland, 1988; Hart, 2006) for them to assess 8"
the subjective workload perceived for each task. 9"
At the beginning of each experimental condition, all the participants had a 10"
familiarization period. They received verbal and written instructions and, after that, they 11"
practiced each task for one minute. They also received the necessary instructions to 12"
complete the NASA-TLX questionnaire at the beginning of the first experimental 13"
condition. 14"
The timestamp of the start and end of each task was taken for further analysis of 15"
HRV. During the experiment, the participant was seated in front of the computer in a 16"
dimly illuminated room and isolated from external noise. Comfortable temperature 17"
(21.3±0.8 ºC) and relative humidity (43.4±3.1 %) values were maintained throughout 18"
the experimental session. 19"
Experimental tasks 20"
a) Execution condition 21"
PVT: We used a modified version of the task created by Wilkinson & Houghton 22"
(1982). On each trial, the number 3 in white colour (2.67° x 1.62°) appeared on the 23"
centre of the screen in a black background. Later, in a random time interval (from 2000 24"
10
to 10000 ms), this number changed its orientation from vertical to horizontal (1.62º x 1"
2.67º). The participants were instructed to respond with their dominant hand as fast as 2"
they could when the change in orientation occurred. Feedback of the response time was 3"
displayed on the screen on each trial during 300 ms. The next trial began after 1800 ms. 4"
Response anticipations were considered as errors. Participants were allowed 1500 ms to 5"
respond. If a response was not made during this time, the message "You did not answer" 6"
appeared on the screen. The task comprised a single block of 12 minutes and the total 7"
number of trials was 111±3.4 on average. 8"
Duration discrimination task: The duration discrimination task was a 9"
psychophysical task in which participants had to make temporal judgments regarding 10"
which of two visual stimuli were presented for a longer period of time (Paul et al., 11"
2011). The task started with the presentation of a fixation point at the centre of the 12"
screen for a random duration between 500-1000 ms. The fixation point was the “+” 13"
symbol (0.38° x 0.38°) that remained on and steady for the whole trial. Then, two 14"
consecutive visual stimuli were presented (the sample and the comparison stimuli) with 15"
a random time interval of 500-1000 ms between them. The sample stimulus was a white 16"
number 3 and the comparison stimulus a red number 3 (2.67° x 1.62°, both stimuli). The 17"
duration of the sample stimuli was 350 ms. Duration of the comparison stimulus was 18"
manipulated using the method of constant stimuli, lasting for either 160, 260, 300, 340, 19"
380, 420, 460 or 560 ms. Participants had 3000 ms to respond before the start of the 20"
next trial. Once the participant responded, a random inter-trial time of 500-1000 ms of 21"
duration was presented. 22"
Participants were instructed to discriminate whether the duration of the comparison 23"
stimulus was shorter or longer than the duration of the sample stimulus. If the duration 24"
of the comparison stimulus was longer than the duration of the sample stimulus, the 25"
11
participant should respond by pressing the up arrow. Otherwise, the participants should 1"
press the down arrow. The comparison stimuli of varying duration were randomly 2"
intermixed across trials. Each of the comparison stimuli was presented, on average, a 3"
12.5% of the total number of trials in the task. There was not feedback after each trial. 4"
The overall duration of the task was 12 minutes and the total number of trials was, on 5"
average, 177±9.3. In this case, accuracy was stressed over response speed. 6"
N-back task: One of four digits (1, 2, 3 or 4; 2.67° x 1.53°; 2.67° x 1.62°; 2.67° x 7"
1.62° and 2.67° x 1.81°, respectively) was presented for 500 ms, followed by a fixed 8"
delay of 2500 ms. Participants had to respond, at any time during the presentation of the 9"
stimulus or the delay period, whether the current stimulus displayed on the screen was 10"
the same as the stimulus presented two trials before. If the stimulus on the screen 11"
matched the stimulus presented two trials before, the participant had to press the letter 12"
“S” key on the PC Keyboard. Otherwise, the participants had to press the letter “N” key. 13"
A new stimulus was presented every 3000 ms (i.e., 500 ms of stimulus presentation and 14"
2500 ms of fixed delay). The digit appearing on each trial was randomly selected, which 15"
means that, on average, the current digit was the same as the one presented two trials 16"
earlier in 25% of the trials. There was not feedback after each trial. The overall duration 17"
of the task was 12 minutes and the total number of trials was 217±1.6 on average. 18"
Accuracy was stressed over response speed. 19"
b) Oddball condition: 20"
In this condition, instructions were essentially the same for the three tasks: to detect 21"
the presence of an infrequent stimulus presented amongst a series of frequent stimuli. 22"
Crucially, there were three versions of the oddball task, corresponding to each of the 23"
task procedures described for the execution condition. Hence, each oddball version 24"
shared all task parameters (i.e., the rate of stimulus appearance, physical characteristics 25"
12
of the stimuli and duration of each interval) with its corresponding “execution” task, 1"
with the sole difference being the tasks demands. In effect, participants were instructed 2"
to respond only when the oddball stimuli (i.e., the stimulus in a green colour) appeared 3"
on the screen (5% of the trials) in all three versions of the oddball task. The oddball 4"
stimuli could be displayed instead of the number 3 in landscape orientation in the 5"
oddball version of the PVT, instead of the comparison stimuli in the oddball version of 6"
the duration discrimination task and instead of any of the numbers in the oddball 7"
version of the N-back task. The oddball stimuli were randomly intermixed across trials. 8"
Response anticipations or no response to oddball stimuli were considered as errors. 9"
Feedback was provided only after incorrect answers. In these cases, the word 10"
"incorrect" in a red color was displayed on the screen during 300 ms. The overall 11"
duration of each task was 12 minutes and the total number of trials, on average, was 12"
94±2.5 for the oddball version of the PVT, 112±1.1 for the oddball version of the 13"
duration discrimination task and 216±0 for the oddball version of the N-back task. 14"
Accuracy was stressed over response speed in all cases. 15"
HRV measures 16"
The elastic electrode transmitter belt (Polar H3 heart rate sensor) was placed on the 17"
chest of the participant at the level of the lower third of the sternum (just below the 18"
chest muscles) with conductive gel being applied as described by the manufacturer. This 19"
transmitter belt contains two electrodes to detect the voltage differential on the skin 20"
during every heart beat and sends the signal continuously and wirelessly using an 21"
electromagnetic field to the Polar RS800 CX receiver unit. The data were collected with 22"
a sampling rate of 1000 Hz, providing a temporal resolution of 1ms for each RR 23"
interval. This Polar equipment has been shown to be a valid and highly reliable way to 24"
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measure short-term HRV at rest (Nunan et al., 2009; Radespiel-Tröger, Rauh, Mahlke, 1"
Gottschalk, & Mück-Weymann, 2003). 2"
All data sets were transferred to a password-protected PC under ASCII format via 3"
Polar-specific software (Polar® ProTrainer 5 software version 5.35.161). Subsequently, 4"
each RR interval file was analysed by means of the Kubios HRV Analysis Software 2.0 5"
(Tarvainen, Niskanen, Lipponen, Ranta-aho, & Karjalainen, 2009; The Biomedical 6"
Signal and Medical Imaging Analysis Group, Department of Applied Physics, 7"
University of Kuopio, Finland). 8"
The recordings were preprocessed to exclude artifacts by eliminating RR intervals 9"
which differed more than 25% from the previous and the subsequent RR intervals 10"
(Malik, Cripps, Farrell, & Camm, 1989). Removed RR intervals were replaced by 11"
conventional spline interpolation so that the length of the data did not change (i.e., 12"
resulting in the same number of beats). We used the smoothness prior method with a 13"
Lambda value of 500 to remove disturbing low frequency baseline trend components 14"
(Tarvainen, Ranta-aho, & Karjalainen, 2002). 15"
NASA-TLX questionnaire 16"
The NASA-TLX provides an overall workload score (from 0 to 100 points) 17"
based on a weighted average of ratings on six dimensions: Mental Demands, Physical 18"
Demands, Temporal Demands, Own Performance, Effort, and Frustration. 19"
Participants were instructed to rate each dimension on a visual analog scale 20"
(from 0 to 100 points). After that, participants were presented with 15 paired 21"
comparisons of each dimension and asked to choose which of them had a greater impact 22"
on their performance. A specific weight to each dimension from 0 to 5 was applied from 23"
these comparisons. The rating of each dimension was then multiplied by its respective 24"
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weight and the total score (the sum of the scores of each dimension) was divided by 15 1"
(the total number of paired comparisons) to obtain the final workload score. 2"
Design and data reduction 3"
Behavioural: 4"
We performed descriptive analysis on the behavioural data with the sole purpose 5"
of checking the proper performance during the experimental session. With this aim, we 6"
obtained the overall mean reaction times (RTs), the Just noticeable difference (JND) 7"
and the overall accuracy percentage, for the PVT, duration discrimination task and N-8"
back task, respectively. For oddball tasks, the percentage of accuracy of response to 9"
oddball stimuli was calculated. Only the experimental blocks were included in the 10"
analysis in all tasks. 11"
For the PVT trials, with RTs below 100 ms (0.9%) and anticipations (i.e., 12"
responses prior to the target presentation; 1.7%) were discarded from the analysis 13"
(Basner & Dinges, 2011). JNDs were calculated for each participant by subtracting the 14"
stimulus onset asynchrony (SOA) at which the best fitting line crossed the 0.75 point 15"
from the SOA at which the same line crossed the 0.25 point and dividing by two. Thus 16"
JND = SOA (75%) SOA (25%)/2 and SOA(75%) = (z-score(75%) b/m) and 17"
SOA(25%) = (z-score(25%) b/m) with b = intercept and m = slope. The result of the 18"
equation is JND = .675/m (Coren, Ward, & Enns, 1999). 19"
HRV and NASA-TLX: 20"
The analysis of the HRV focused on both the time and frequency domains. The 21"
root-mean-square difference of successive normal R-R intervals (rMSSD) and the 22"
proportion of NN50 (i.e., the number of pairs of successive NNs that differ by more 23"
15
than 50 ms) were used as indexes of vagal control within the time domain (Allen, 1"
Chambers, & Towers, 2007). The High Frequency (HF; 0.15 to 0.40 Hz) was used as 2"
the index of vagal tone in the frequency domain (Berntson et al., 1997; Reyes del Paso, 3"
Langewitz, Mulder, van Roon, & Duschek, 2013). The denotations and definitions for 4"
the HRV parameters in this paper follow the guidelines given in Task force of the 5"
European society of cardiology and the North American society of pacing and 6"
electrophysiology (Malik et al., 1996). 7"
In order to investigate the time course of HRV during task performance, 3 intervals 8"
of 4’ each were considered for the analysis of the HRV. The ln-transformed HRV data 9"
were analyzed through repeated measures analysis of variance (ANOVA) with the 10"
within-participants factors of condition (execution and oddball), task (PVT, duration 11"
discrimination task and N-back task) and time-on-task (1, 2, 3). The NASA-TLX data 12"
were analysed through repeated measures ANOVA with the within-participants factors 13"
of condition (execution and oddball) and task (PVT, duration discrimination task and N-14"
back task). The effect sizes were reported by partial eta-squared (ηpartial2). Sphericity 15"
was tested by means of the Mauchley Sphericity test and the Green-House Geisser 16"
correction was applied when violation of this assumption occurred (corrected values are 17"
reported). 18"
Results 19"
Behavioural results: 20"
The descriptive analyses conducted on behavioural data showed a normal and 21"
typical execution for all tasks. On the one hand, the overall mean RT for the PVT was 22"
210.27 ± 26.89 ms, the JND for the duration discrimination task was 77.19 ± 10.90 ms 23"
and the overall accuracy percentage for the N-back task was 91.27 ± 6.07. On the other 24"
16
hand, the percentage of accuracy of response to oddball stimuli was 98.61± 6.80 and 1"
98.44 ± 7.65 for the version oddball of the PVT and N-back task, respectively. The 2"
percentage of accuracy for the oddball version of the duration discrimination task could 3"
not be calculated due to faulty response recording. However, there was no problem in 4"
carrying out the task by the participants. 5"
HRV:!6"
The ANOVAs showed that the main effect of condition (execution vs oddball) was 7"
not significant for any of the parameters, all Fs<1 (see Table 1). On the contrary, the 8"
main effect of task reached statistical significance in all indexes, rMSSD, 9"
F(2,46)=10.52, p<.001, ηpartial2=.31 (see Figure1), pNN50, F(2,46)=5.98, p<.01, 10"
ηpartial2=.20, HF, F(2,46)=9.21, p<.001, ηpartial2=28. All indexes showed the lowest 11"
values in the N-back task, all ps.01. The difference between the PVT and the duration 12"
discrimination task did not reach statistical significance in any of the parameters (all 13"
ps>.05). HRV values in the duration discrimination task and the NBack tasks were 14"
significantly different for the rMSSD, p<.01, and HF, p<.01, but not for pNN50, 15"
p=.053. The main effect of time-on-task was significant for rMSSD, F(2,46)=6.51, 16"
p<.01, ηpartial2= .22 (see Figure 2), and HF, F(2,46)=6.12, p<.01, ηpartial2=.21, and did 17"
not reach statistical significant for pNN50, F(2,46)=2.42, p=.10. There were differences 18"
between block 1 and block 2 (rMSSD, p<.001, and HF, p<.001) and between block 1 19"
and block 3 (rMSSD, p=.01 and HF, p=.04). The difference between block 2 and block 20"
3 was not significant for any of the indexes, all ps>.42. 21"
Crucially, the interaction between task and condition did not reach statistical 22"
significance for any of the parameters, F(2,46)=2.71, p=.08, F(2,46)=1.66, p=.20, F<1, 23"
for rMSSD, pNN50 and HF, respectively. The interactions between task and time-on-24"
17
task, and between task, time-on-task and condition were not significant for any of the 1"
HRV indexes [all Fs<1, except for Task x Time-on-task for HF, F(2,46)=1.63, p=.18]. 2"
Table 1. Mean (± standard deviation) for the HRV parameters as a function of 3"
condition, task and time-on-task. 4"
HRV parameters
Task
rMSSD (ms)
pNN50 (%)
HF (ms2)
Block1
Block2
Block3
Block1
Block2
Block3
Block3
Execution condition
PVT
82.4
(28.3)
76.9
(27.1)
79.2
(29.7)
46.2
(15.2)
43.9
(16.6)
43.0
(17.6)
2130.6
(1536.8)
DDT
79.6
(32.2)
73.3
(31.0)
70.7
(27.0)
43.5
(18.7)
39.7
(18.1)
39.4
(15.4)
2062.9
(1568.9)
NBT
69.4
(29.0)
66.5
(26.3)
67.1
(23.2)
38.8
(18.5)
36.7
(17.5)
37.2
(17.0)
1749.0
(1221.5)
Oddball condition
PVT
77.2
(26.4)
73.1
(24.3)
74.0
(24.5)
41.7
(15.3)
40.4
(15.7)
41.1
(14.2)
1935.1
(1102.5)
DDT
78.1
(33.1)
76.2
(33.5)
73.2
(28.9)
41.0
(16.1)
40.7
(16.7)
38.5
(15.3)
2086.6
(1843.2)
NBT
73.1
69.2
72.5
40.0
38.6
39.5
1900.5
18
(27.9)
(25.7)
(27.3)
(16.6)
(14.7)
(15.6)
(1645.1)
PVT: psychomotor vigilance task; DDT: duration discrimination task; NBT: n-back 1"
task; O-PVT; O-DDT and O-NBT: the oddball version of each task respectively. 2"
Figure 1. HRV (rMSSD index) as a function of the task. The root-mean-square 3"
difference of successive normal R-R intervals (rMSSD) in miliseconds (ms) for each of 4"
the cognitive tasks (PVT= psychomotor vigilance task; DDT= duration discrimination 5"
task; NBT= n-back task). Bars represent standard errors of the mean. 6"
7"
Figure 2. HRV (rMSSD index) as a function of the time-on-task. The root-mean-8"
square difference of successive normal R-R intervals (rMSSD) in miliseconds (ms) for 9"
each of the blocks of the three cognitive tasks (Block1= between 0 and 4 minutes of 10"
each task; Block 2= between 4 and 8 minutes of each task; Block3= between 8 and 12 11"
minutes of each task). Bars represent standard errors of the mean. 12"
19
1"
NASA-TLX scores: 2"
The repeated-measures ANOVA with the within-participants factors of condition 3"
(execution and oddball) and task (PVT, duration discrimination task and N-back task) 4"
revealed a significant main effect of condition, F(1,23)=76.36, p<.01, ηpartial2=.77, with 5"
greater scores in the execution condition than in the oddball condition (see Table 2). 6"
The main effect of task was also significant, F(2,46)=9.91, p<.01, ηpartial2=.30. 7"
Importantly, these main effects were better qualified by the significant interaction 8"
between condition and task, F(2,46)=6.29, p<.01, ηpartial2=.21 (see Figure 3). In the 9"
execution condition, there were significant differences between the PVT and N-back 10"
task, p<.01, between the PVT and duration discrimination task, p=.04, and between the 11"
N-back and the duration discrimination task, p=.03. In all cases, the PVT and N-back 12"
task elicited the lowest and the highest scores respectively. Instead, comparisons 13"
between tasks for the oddball condition did not reveal significant differences (all 14"
ps>.21). 15"
16"
Figure 3. NASA-TLX scores as a function of task for the execution and oddball 17"
condition. Mean of the NASA-TLX overall scores for the execution and oddball 18"
20
condition in each of the cognitive tasks (PVT= psychomotor vigilance task; DDT= 1"
duration discrimination task; NBT= n-back task). Bars represent standard errors of the 2"
mean. 3"
4"
5"
Table 2. Mean (± standard deviation) for the NASA-TLX overall scores as a function of 6"
condition and task. 7"
NASA-TLX overall scores
Execution Condition
PVT
DDT
NBT
51.0 (23.8)
60.5 (16.0)
68.1 (14.4)
Oddball Condition
O-PVT
O-DDT
O-NBT
28.2 (18.6)
31.0 (21.8)
29.6 (20.3)
21
PVT: psychomotor vigilance task; DDT: duration discrimination task; NBT: n-back 1"
task; O-PVT; O-DDT and O-NBT: the oddball version of each task respectively. 2"
Discussion 3"
HRV is sensitive to cognitive processing with significant variations as a function 4"
of changes in task demands. Previous accounts have pointed to executive demands of 5"
the task at the key parameter to explain those variations in HRV in the variety of tasks 6"
that have been tested in the laboratory (Thayer & Lane, 2009). On the contrary, other 7"
recent studies suggest that rather than broadly ranging executive demand, other, more 8"
molecular factors such as perceptual difficulty influence HRV. Here, we investigated 9"
this issue by comparing HRV values during performance of a vigilance task, a working 10"
memory task, and a duration discrimination task. Crucially, we also added a condition 11"
to control for the effect of the particular stimulus parameters of each task, minimizing 12"
the motor activity by using an oddball procedure. The NASA-TLX was used as a 13"
subjective measure of cognitive workload. 14"
HRV indeed varied as a function of task demands, with lower values in the N-15"
Back task than in the other two tasks, and no differences between the PVT and the 16"
duration discrimination task. At this point, these results would confirm the hypothesis 17"
stated by Luque-Casado et al. (2013) in their conclusions, whereby a task with greater 18"
executive demands than the temporal orienting task used in their study would induce 19"
lower HRV than the perceptual task. 20"
However, the oddball control condition revealed intriguing results. The 21"
interaction between condition and task was not significant, apparently meaning that the 22"
reliable task effect was not (only) due to the particular demands of the tasks, since 23"
participants performed exactly the same task in the three version of the oddball 24"
22
procedure. It would however suggest that the particular stimulus features of the N-Back 1"
task, in comparison to the PVT and the duration discrimination task, were responsible 2"
for HRV decrements. The three tasks (in both the execution and oddball control 3"
conditions) were only differentiated in terms of stimuli duration, interval between 4"
stimuli, and, above all, presentation rate. The N-Back task, both in the execution and the 5"
oddball procedure, had twice the number of trials (in 12’) as compared to the duration 6"
discrimination task and even more compared to the PVT, while the difference in the 7"
number of trials was much less between the PVT and the duration discrimination task 8"
(with no significant differences in HRV either). This larger number of trials in the N-9"
Back task resulted in a larger number of motor responses, even in the oddball task. 10"
However, based on the scarce previous research (Bush et al., 2011; Porges et al., 2007) 11"
and given that the number of targets was very low in the oddball condition, one would 12"
argue that the motor demands of the task cannot explain the reliable task effect shown 13"
here. Note that if motor activity were responsible of the changes in HRV, significant 14"
differences would have emerged between the execution condition and the oddball 15"
condition for every task. On the contrary, it is more plausible that the higher 16"
presentation rate resulted, both in the execution and oddball conditions, in a significant 17"
increase in the demands of sustained attention (Lanzetta, Dember, Warm, & Berch, 18"
1987; Parasuraman & Giambra, 1991; Sarter, Givens, & Bruno, 2001) with respect to 19"
the other two tasks, which in turn resulted in lower HRV. Crucially, two results from 20"
our study appear to support the hypothesis of the HRV sensitivity to sustained attention 21"
demands: the non-significant main effect of condition and the significant main effect of 22"
time-on-task. 23"
The non-significant main effect of condition was driven by similar HRV values 24"
when comparing each of the tasks in the execution condition with their oddball 25"
23
equivalent. An oddball task like the one used here requires participants to maintain a 1"
high level of vigilance in order to detect the infrequent targets (Eason & Dudley, 1970; 2"
Fruhstorfer & Bergström, 1969) and it has been used as a paradigm to assess sustained 3"
attention (Czisch et al., 2012; Weber, Van Der Molen, & Molenaar, 1994). In addition, 4"
the robust time-on-task effect shown in our experiment is consistent with previous 5"
accounts suggesting the sensitivity of HRV to vigilance decrements or mental fatigue 6"
(e.g., Middleton, Sharma, Agouzoul, Sahakian, & Robbins, 1999; Porges & Raskin, 7"
1969). For example, Luque-Casado et al. (2013), using a similar procedure to that of our 8"
execution condition, showed a gradual decrement in participants’ HRV as a function of 9"
the time on task. Fairclough & Houston (2004) also showed that the 0.1 Hz component 10"
of HRV was sensitive to time on task although it did not seem sensitive to a 11"
manipulation of workload within the same task. Additionally, Chua et al. (2012) have 12"
recently shown that HRV provides information about a person’s vigilance state, and that 13"
this measure could potentially be used to predict when an individual is at increased risk 14"
of attentional failure. 15"
Taken together, our results therefore point to sustained attention demands of the 16"
tasks in general and of the NBack task in particular, as the major factor influencing 17"
HRV in our study, over and above any other of task-related cognitive components (e.g., 18"
working memory, cognitive control, perceptual processing) or task parameters. 19"
Sustained attention has been considered one of the executive functions linked to 20"
the prefrontal cortex (Alvarez & Emory, 2006; Stuss, Shallice, Alexander, & Picton, 21"
1995). In that sense, our results do not plainly contradict, but nuance the Neurovisceral 22"
Integration Model. Moreover, it would explain the outcome of previous research that 23"
failed to show differences between experimental conditions varying in terms of 24"
cognitive control demands (e.g., Fairclough & Houston, 2004; Hansen et al., 2003; 25"
24
Luque-Casado et al., 2013), and those reports showing that overall attentional load is 1"
crucial in order to explain HRV variations (e.g., Chang & Huang, 2012)."Importantly, 2"
on the basis of our results, it would be interesting for future research to incorporate 3"
experimental designs including specific manipulations of the sustained attention load"4"
(e.g., by manipulating the likelihood of the target appearance) for a more thorough 5"
investigation of the specific effect of the sustained attention demands on HRV."In fact, 6"
this could be considered as a potential limitation of our study since the level of 7"
sustained attention load was not systematically manipulated in addition to task-related 8"
cognitive components or task parameters. 9"
The results of the NASA-TLX replicated previous studies (e.g., Karavidas et al., 10"
2010; Ma et al., 2014; Muth et al., 2012), showing larger cognitive workload in the 11"
execution condition than in the oddball condition, and scores variations as a function of 12"
task, with larger values for the N-Back task, followed by the duration discrimination 13"
task and the PVT. Interestingly enough, NASA scores appear clearly differentiated from 14"
HRV measures, suggesting a dissociation between objective and subjective cognitive 15"
workload, and contributing to the discussion regarding the validity of subjective 16"
measures of cognitive workload (Annett, 2002). The present results show that two 17"
conditions that contribute differently to subjective load (which would suggest that one 18"
of them is much less loading, and, supposedly, less interfering or potentially dangerous 19"
than the other one), can actually be equally loading in psychophysiological terms. In 20"
other words, some aspects of mental workload can remain hidden to subjective insight. 21"
In sum, the outcome of the present study suggests that HRV is highly sensitive 22"
to sustained attention over and above the influence of other cognitive processes, a 23"
finding that needs to be considered by any research looking at the link between 24"
autonomic control and cognitive processing. In addition, our study highlights a potential 25"
25
dissociation between objective and subjective measures of mental workload, which has 1"
important implications in applied settings. 2"
3"
26
Acknowledgments 1"
This research was supported by a predoctoral grant FPU-AP2010-3630 2"
(Ministerio de Educación, Cultura y Deporte, Spain) to Antonio Luque-Casado, a 3"
research grant DEP21013-48211-R (Ministerio de Economía y Competitividad, Spain) 4"
to David Cárdenas, and research grants SEJ-6414 (Proyecto de Excelencia, Junta de 5"
Andalucía, Spain) and PSI2013-46385-P (Ministerio de Economía y Competitividad, 6"
Spain) to Daniel Sanabria. We thank Guillermo Sánchez-Delgado for his assistance 7"
during the data collection. 8"
9"
27
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... (Hill et al., 1992), mais comme toutes mesures subjectives elle présente des limites (voir paragraphe 4.2.3.1). De par la multi-dimensionnalité de la charge mentale, de nombreux auteurs ont choisi de la mesurer avec un ensemble de mesures mesure unique (Bednarik et al., 2018;Jafari et al., 2020;Luque-Casado et al., 2016;Wanyan et al., 2014) Les résultats de certaines études suggèrent que les trois types de mesures ne vont pas nécessairement dans le même sens, notamment la mesure subjective du NASA-TLX. Dans une récente étude (Jafari et al., 2020), les mesures comportementales et physiologiques ont montré des données cohérentes avec le niveau de charge mentale et seulement avec la dimension mentale du NASA-TLX. ...
... Jafari et al., (2020) ont mis en évidence que selon les études, la ou les dimensions du NASA-TLX liées à la variation de la charge mentale ne sont pas toujours les mêmes. Une différence importante a également été retrouvée entre la mesure de la fréquence cardiaque et celle du NASA-TLX (Luque-Casado et al., 2016). ...
... ien que les mesures comportementales et physiologiques indiquent des résultats allant dans le même sens, la mesure subjective du NASA-e plus grande charge mentale perçue en RA par les participants. Le fait que les données physiologiques et comportementales pas dans le même sens que les données subjectives a déjà été observé (Luque-Casado et al., 2016;Miyake, 2001) NASA-TLX nous indique que les participants perçoivent et rapportent uniquement une différence sur la dimension physique entre les postes simples et complexes. vraisemblablement pas en lien avec le poids du casque qui était porté, peu importe la condition, mais davantage avec le fait que le poste complexe nécessite plus de force due ...
Thesis
La réalité augmentée (RA) s’intègre de plus en plus au sein du milieu professionnel, notamment dans le domaine de la formation industrielle. Afin d’assurer la sécurité et la santé des opérateurs, il est central d’évaluer les risques sanitaires potentiellement liés à une utilisation régulière et prolongée de la RA. Le but de cette thèse est d’évaluer les potentiels effets physiologiques et cognitifs de la RA. Nous avons réalisé deux études évaluant les effets de la RA sur la vision binoculaire. Ces études montrent que l’utilisation de la RA ne présente pas de risque de fatigue visuelle ou d’inconfort visuel, pour des utilisateurs présentant ou non des troubles de la vision binoculaire préexistants. Nous avons également réalisé deux études sur l’attention visuelle en RA. Ces dernières suggèrent quel es mécanismes attentionnels sont influencés par le traitement simultané d’informations virtuelles et réelles. Ainsi, lors de l’utilisation de la RA, le fait dépasser entre des informations virtuelles et réelles peut dégrader les performances visuelles et la capacité à détecter des évènements extérieurs inattendus peut être altérée. Enfin nous avons également évalué l’intégration effective de la RA directement en milieu industriel, afin d’évaluer les effets de la présentation d’instructions en RA sur l’efficacité des performances d’assemblage et sur la charge mentale. L’utilisation de la RA ne présenterait pas nécessairement de bénéfice en termes d’efficacité sur les performances (temps et erreurs d’assemblage) et peut engendrer une augmentation de la charge mentale lorsque le poste d’assemblage est simple, mais engendrerait une charge équivalente à celle d’un ordinateur quand le poste est complexe. Dans le cadre d’une utilisation industrielle, la RA devrait donc être utilisée avec une certaine précaution. Toute intégration de la RA devrait donc être associée à un protocole d’évaluation afin de quantifier les potentiels impacts sur les performances et de s’assurer que la solution RA proposée apporte plus de bénéfices que la solution déjà existante.
... In this review, Cegarra and Chevalier (2008) show the advantages of combining physiological, behavioral and subjective measures of mental workload to compensate the different limitations of each measurement. Due to the multi-dimensionality of mental workload, many authors have chosen to measure it with a set of variables rather than with a single variable (Bednarik et al., 2018;Jafari et al., 2020;Luque-Casado et al., 2016;Wanyan et al., 2014). While having important advantages for the study of mental workload, using the NASA TLX in combination with other measures would provide more evidence on the impact of AR. ...
... Although the behavioral and physiological measures indicate results in the same direction, the subjective measurement of NASA TLX does not indicate a greater perceived mental workload in AR. The fact that physiological and behavioral data show different directions has already been observed (Luque-Casado et al., 2016;Miyake, 2001). The study of the different NASA TLX dimensions indicates that participants perceive and report a difference only on the physical dimension between simple and complex workstation. ...
Article
Studies examining the potential of augmented reality (AR) to improve assembly tasks are often unrepresentative of real assembly line conditions and assess mental workload only through subjective measurements and leads to conflicting results. We proposed a study directly carried out in industrial settings, to compare the impact of AR-based instructions to computerized instructions, on assembly effectiveness (completion time and errors) and mental workload using objective (eye tracking), subjective (NASA-TLX) and behavioral measurements (dual task paradigm). According to our results, AR did not improve effectiveness (increased assembly times and no decrease in assembly errors). Two out of three measurements indicated that AR led to more mental workload for simple assembly workstation, but equated computer instructions for complex workstation. Our data also suggest that, AR users were less able to detect external events (danger, alert), which may play an important role in the occurrence of work accidents.
... Traumatic events coupled with greater SDNN reflected a similar pattern. While this effect may initially seem somewhat counterintuitive as greater HRV is not typically associated with negative outcomes, it is important to note that greater parent HRV may suggest that the child's distress is not engaging the parent [51]. Such a lack of engagement paired with the negative effects of parent stress may be sufficient to encourage this type of "acting out" behavior in the child. ...
Article
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Cumulative stress and trauma in parents may alter autonomic function. Both may negatively impact child behaviors, however these links have not been well established. We tested hypotheses that parent stress and trauma are associated with and interact with altered autonomic function during the toy wait task, an acute parent–child interaction challenge, to predict greater negative child behaviors. Sixty-eight parents and their 2–5 year old children were enrolled. More parent major and traumatic life events, and more parent recent life events coupled with increased heart rate and decreased heart rate variability (HRV), each related to more child disruptive/aggressive behavior. More major life and traumatic life events coupled with greater HRV predicted more child attention seeking behavior. Our novel approach to assessing parental life stress offers a unique perspective. Interventions mitigating parent stress and regulating physiological coping during parent–child interactions may both promote better parent health and improve child behavioral outcomes.
... Finally, it is important to note that while HRV is considered a well-validated biomarker of ER capacity and flexibility [20][21][22][23][24][25] and demonstrates inverse relationships with self-reported difficulties in ER [26][27][28] it remains relatively non-specific. Indeed, lower HRV is associated not only with difficulties in ER and poor mental health but cognitive deficits and poor physical health more broadly [49][50][51][52]79]. Thus, while we have used HRV here as a proxy for physiological ER, an alternative proposal could be as a general biomarker of wellness. ...
Article
Full-text available
Background Emotion regulation (ER) is a key process underlying posttraumatic stress disorder (PTSD), yet, little is known about how ER changes with PTSD treatment. Understanding these effects may shed light on treatment processes. Methods We recently completed a non-inferiority design randomised controlled trial demonstrating that a breathing-based yoga practice (Sudarshan kriya yoga; SKY) was not clinically inferior to cognitive processing therapy (CPT) across symptoms of PTSD, depression, or negative affect. Here, in secondary exploratory analyses (intent-to-treat N = 85; per protocol N = 59), we examined whether self-reported ER (Difficulties in Emotion Regulation Scale; DERS) and physiological ER (heart rate variability; HRV) improved with treatment for clinically significant PTSD symptoms among US Veterans. Results DERS-Total and all six subscales improved with small-to-moderate effect sizes ( d = .24–.66) following CPT or SKY, with no differences between treatment groups. Following SKY (but not CPT), HR max–min (average difference between maximum and minimum beats per minute), LF/HF (low-to-high frequency) ratio, and normalised HF-HRV (high frequency power) improved (moved towards a healthier profile; d = .42–.55). Conclusions To our knowledge, this is the first study to demonstrate that a breathing-based yoga (SKY) improved both voluntary/intentional and automatic/physiological ER. In contrast, trauma-focused therapy (CPT) only reliably improved self-reported ER. Findings have implications for PTSD treatment and interventions for emotional disorders more broadly. Trial registration Secondary analyses of ClinicalTrials.gov NCT02366403 .
... A scientifically valid explanation was provided in the system descriptions to the participants to convince them of the efficacy of the physiologically-based approach. The participants were told that changes in heart rate [67,81] and perceived emotions [84] are correlated to cognitive demand and stress-levels that can be captured using a webcam, and that an AI (i.e., neural network) aggregates those two metrics to calculate the currently perceived cognitive workload and stress level. The difficulty of the word puzzles was then supposedly adapted according to these measures to optimize user performance. ...
Preprint
Full-text available
In medicine, patients can obtain real benefits from a sham treatment. These benefits are known as the placebo effect. We report two experiments (Experiment I: N=369; Experiment II: N=100) demonstrating a placebo effect in adaptive interfaces. Participants were asked to solve word puzzles while being supported by no system or an adaptive AI interface. All participants experienced the same word puzzle difficulty and had no support from an AI throughout the experiments. Our results showed that the belief of receiving adaptive AI support increases expectations regarding the participant's own task performance, sustained after interaction. These expectations were positively correlated to performance, as indicated by the number of solved word puzzles. We integrate our findings into technological acceptance theories and discuss implications for the future assessment of AI-based user interfaces and novel technologies. We argue that system descriptions can elicit placebo effects through user expectations biasing the results of user-centered studies.
... The autonomic nervous system is responsible for the regulation of the heart rate variability through parasympathetic and sympathetic modulation, the balance of which is disrupted after training [66]. Heart rate variability was shown to be very sensitive to task-related cognitive demands [67]. ...
Article
Full-text available
Appropriate training burden monitoring is still a challenge for the support staff, athletes, and coaches. Extensive research has been done in recent years that proposes several external and internal indicators. Among all measurements, the importance of cognitive factors has been indicated but has never been really considered in the training monitoring process. While there is strong evidence supporting the use of cognitive demand indicators in cognitive neuroscience, their importance in training monitoring for multiple sports settings must be better emphasized. The aims of this scoping review are to (1) provide an overview of the cognitive demand concept beside the physical demand in training; (2) highlight the current methods for assessing cognitive demand in an applied setting to sports in part through a neuroergonomics approach; (3) show how cognitive demand metrics can be exploited and applied to our better understanding of fatigue, sport injury, overtraining and individual performance capabilities. This review highlights also the potential new ways of brain imaging approaches for monitoring in situ. While assessment of cognitive demand is still in its infancy in sport, it may represent a very fruitful approach if applied with rigorous protocols and deep knowledge of both the neurobehavioral and cognitive aspects. It is time now to consider the cognitive demand to avoid underestimating the total training burden and its management.
... Finally, it is important to note that while HRV is considered a well-validated biomarker of ER capacity and exibility (20-25) and demonstrates inverse relationships with self-reported di culties in ER (26-28) it remains relatively non-speci c. Indeed, lower HRV is associated not only with di culties in ER and poor mental health but cognitive de cits and poor physical health more broadly (49)(50)(51)(52)75). Thus, while we have used HRV here as a proxy for physiological ER, an alternative proposal could be as a general biomarker of wellness. ...
Preprint
Full-text available
Background Emotion regulation (ER) is a key process underlying posttraumatic stress disorder (PTSD), yet, little is known about how ER changes with PTSD treatment. Understanding these effects may shed light on treatment processes. Methods We recently completed a randomised controlled trial demonstrating that a breathing-based yoga practice (Sudarshan kriya yoga; SKY) was not clinically inferior to cognitive processing therapy (CPT) across symptoms of PTSD, depression, or negative affect. Here, in secondary exploratory analyses (intent-to-treat N=85; per protocol N=59), we examined whether self-reported ER (Difficulties in Emotion Regulation Scale; DERS) and physiological ER (heart rate variability; HRV) improved with treatment for clinically significant PTSD symptoms among US Veterans. Results DERS-Total and all six subscales improved with small-to-moderate effect sizes (d = .24-.66) following CPT or SKY, with no differences between treatment groups. Following SKY (but not CPT), HR max–min (average difference between maximum and minimum beats per minute), normalised HF-HRV (high frequency power), and LF/HF (low-to-high frequency) ratio improved (moved towards a healthier profile; d = .32-.55). Conclusions To our knowledge, this is the first study to demonstrate that a breathing-based yoga (SKY) improved both voluntary/intentional and automatic/physiological ER. In contrast, trauma-focused therapy (CPT) only reliably improved self-reported ER. Findings have implications for PTSD treatment and interventions for emotional disorders more broadly. Trial registration Secondary analyses of ClinicalTrials.gov NCT02366403.
... As mentioned, apart from the stress-related origin, changes in cognitive functioning can also be reflected by the physiological arousal. Considering that GSR and HRV have been widely used as measures of cognitive load or mental effort in a variety of behavioural studies (e.g., Luque-Casado et al. 2016;Nourbakhsh et al. 2017), we suggest that these two measures might have mirrored more of the intensity of cognitive processing in the present study. In order to confirm this, we further examined the participants' responses to another question of the NASA TLX questionnaire, on effort: "How hard did you have to work to accomplish your level of performance?" ...
Article
Translators may experience significant psychological and physiological responses to time pressure. This study examines such responses with the aim of identifying valid indicators of time pressure in written translation. Forty-five postgraduates participated in the study, translating three comparable English texts into Chinese under three time conditions ( Short, Standard , and Free ). A positive relation between time stringency and the arousal level detected by a set of self-reporting and biomarker measures was hypothesised. The hypothesis was corroborated by results derived from participants’ self-reporting on stress and anxiety, and the biomarkers of heart rate, blood pressure, and pupil dilation, but not by skin temperature, galvanic skin response (GSR), and heart rate variability (HRV). Thus, the measures that confirm the hypothesis are considered successful indicators of time pressure in translation. In addition, an inverted ‘U-shaped’ pattern was observed in the relation between time stringency and the arousal level indexed by GSR and HRV. These findings may facilitate research and training in translation and other cognitively demanding language-processing activities.
Article
In medicine, patients can obtain real benefits from a sham treatment. These benefits are known as the placebo effect. We report two experiments (Experiment I: N=369; Experiment II: N=100) demonstrating a placebo effect in adaptive interfaces. Participants were asked to solve word puzzles while being supported by no system or an adaptive AI interface. All participants experienced the same word puzzle difficulty and had no support from an AI throughout the experiments. Our results showed that the belief of receiving adaptive AI support increases expectations regarding the participant’s own task performance, sustained after interaction. These expectations were positively correlated to performance, as indicated by the number of solved word puzzles. We integrate our findings into technological acceptance theories and discuss implications for the future assessment of AI-based user interfaces and novel technologies. We argue that system descriptions can elicit placebo effects through user expectations biasing the results of user-centered studies.
Article
Objective: We examine the spatiotemporal dynamics of neural activity and its correlates in heart rate and its variability (HR/HRV) during a fatiguing visuospatial working memory task. Background: The neural and physiological drivers of fatigue are complex, coupled, and poorly understood. Investigations that combine the fidelity of neural indices and the field-readiness of physiological measures can facilitate measurements of fatigue states in operational settings. Method: Sixteen healthy adults, balanced by sex, completed a 60-minute fatiguing visuospatial working memory task. Changes in task performance, subjective measures of effort and fatigue, cerebral hemodynamics, and HR/HRV were analyzed. Peak brain activation, functional and effective connections within relevant brain networks were contrasted against spectral and temporal features of HR/HRV. Results: Task performance elicited increased neural activation in regions responsible for maintaining working memory capacity. With the onset of time-on-task effects, resource utilization was seen to increase beyond task-relevant networks. Over time, functional connections in the prefrontal cortex were seen to weaken, with changes in the causal relationships between key regions known to drive working memory. HR/HRV indices were seen to closely follow activity in the prefrontal cortex. Conclusion: This investigation provided a window into the neurophysiological underpinnings of working memory under the time-on-task effect. HR/HRV was largely shown to mirror changes in cortical networks responsible for working memory, therefore supporting the possibility of unobtrusive state recognition under ecologically valid conditions. Applications: Findings here can inform the development of a fieldable index for cognitive fatigue.
Article
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We evaluated a variety of non-invasive physiological technologies and a series of test approaches for examination of aviator performances under conditions of mental workload in order to provide a standard real-time test for physiological and psychological pilot fatigue assessments. Twenty-one male aviators were selected for a simulated flight in a hypobaric cabin with artificial altitude conditions of 2400 meter above sea level. The simulated flight lasted for 1.5 h, and was repeated for two times with an intervening 0.5 h rest period outside the hypobaric cabin. Subjective criteria (a fatigue assessment instrument [FAI]) and objective criteria (a standing-position balance test as well as a critical flicker fusion frequency (CFF) test) were used for fatigue evaluations. No significant change was observed in the FAI scores before and after the simulated flight, indicating that there was no subjective fatigue feeling among the participants. However, significant differences were observed in the standing-position balance and CFF tests among the subjects, suggesting that psychophysiological indexes can reflect mental changes caused by workload to a certain extent. The CFF test was the simplest and clearly indicated the occurrence of workload influences on pilot performances after a simulated flight. Results showed that the CFF test was the easiest way to detect workload caused mental changes after a simulated flight in a hypobaric cabin and reflected the psychophysiological state of aviators. We suggest that this test might be used as an effective routine method for evaluating the workload influences on mental conditions of aviators.
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In the present study, we investigated the relation between cognitive performance and heart rate variability as a function of fitness level. We measured the effect of three cognitive tasks (the psychomotor vigilance task, a temporal orienting task, and a duration discrimination task) on the heart rate variability of two groups of participants: a high-fit group and a low-fit group. Two major novel findings emerged from this study. First, the lowest values of heart rate variability were found during performance of the duration discrimination task, compared to the other two tasks. Second, the results showed a decrement in heart rate variability as a function of the time on task, although only in the low-fit group. Moreover, the high-fit group showed overall faster reaction times than the low-fit group in the psychomotor vigilance task, while there were not significant differences in performance between the two groups of participants in the other two cognitive tasks. In sum, our results highlighted the influence of cognitive processing on heart rate variability. Importantly, both behavioral and physiological results suggested that the main benefit obtained as a result of fitness level appeared to be associated with processes involving sustained attention.
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Positive-drive cam mechanisms are used in a great variety of applications and are of complexity and speciality in synthesis theory and methods. Based on the concepts "floating axis", "instantaneous and whole nested interval" and introducing the new concepts "go and return travel", "radius-marking line", It deeply and systematically researches the second mechanisms task of conjugate-cam, groove-cam and yoke radial cam with roller follower. Research results is as follow: not being simple combination and summation of force-drive cam mechanisms, the synthesis of positive-drive cam mechanisms is of complicated, deep, special and original research cultivation. Regarding the yoke radial cam with roller follower as sample, a series of research thought, research method and research conclusions are obtained. A mechanism synthesis example is displayed.
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The baroreflex consists of a negative feedback loop adjusting heart activity to blood pressure fluctuations. This review is concerned with interactions between baroreflex function and behavior. In addition to changes in baroreflex cardiac control subject to behavioral manipulations, interindividual differences in reflex function predicted psychological and central nervous features. The sensitivity of the reflex was inversely related to cognitive performance, evoked potential amplitudes, experimental pain sensitivity, and the severity of clinical pain. Possible variables moderating the strength of the associations are tonic blood pressure, gender, and psychiatric disease. It is suggested that these observations reflect inhibition of higher brain function by baroreceptor afferents. While in many cases increased baroreflex function implies stronger inhibition, individual and situational factors modulate the behavioral impact of cardiac regulation.
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This article evaluates the suitability of low frequency (LF) heart rate variability (HRV) as an index of sympathetic cardiac control and the LF/high frequency (HF) ratio as an index of autonomic balance. It includes a comprehensive literature review and a reanalysis of some previous studies on autonomic cardiovascular regulation. The following sources of evidence are addressed: effects of manipulations affecting sympathetic and vagal activity on HRV, predictions of group differences in cardiac autonomic regulation from HRV, relationships between HRV and other cardiac parameters, and the theoretical and mathematical bases of the concept of autonomic balance. Available data challenge the interpretation of the LF and LF/HF ratio as indices of sympathetic cardiac control and autonomic balance, respectively, and suggest that the HRV power spectrum, including its LF component, is mainly determined by the parasympathetic system.
Conference Paper
NASA-TLX is a multi-dimensional scale designed to obtain workload estimates from one or more operators while they are performing a task or immediately afterwards. The years of research that preceded subscale selection and the weighted averaging approach resulted in a tool that has proven to be reasonably easy to use and reliably sensitive to experimentally important manipulations over the past 20 years. Its use has spread far beyond its original application (aviation), focus (crew complement), and language (English). This survey of 550 studies in which NASA-TLX was used or reviewed was undertaken to provide a resource for a new generation of users. The goal was to summarize the environments in which it has been applied, the types of activities the raters performed, other variables that were measured that did (or did not) covary, methodological issues, and lessons learned