AN ANALYSIS OF SLEEPINESS ON COGNITIVE IMPACT

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HOW SLEEP DEPRIVATION IMPACTS VIGILANCE RESPONSE TIMES: AN ANALYSIS OF
SLEEPINESS ON COGNITIVE IMPACT
Kaylah Crompton
Student Number: s5131054
Griffith University, Gold Coast
Course: 2007 PSY
Tutor: Bec Lawrence
Due: 21st
May 2022
Word Count: 1 997/2 000

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Abstract
The aim of this study was to identify any correlation between sleepiness and hours slept the
previous night with response times slower than 500 milliseconds on the Psychomotor Vigilance Test, a
leading experimental tool to assess vigilant attention. Vigilant attention has been researched and shown to
impact a variety of neural substrates, and is important for productivity and efficiency. A sample of 426
undergraduate students participated, self-reporting measures of the explanatory variables and participating
in the PVT online. Overall, a strong, positive correlation was found between sleepiness and lapses. In
addition, there was no correlation between hours slept and lapses occurred. These findings suggest that
those who experience more sleepiness may perform worse cognitively, as supported by previous
theoretical studies. However, hours slept is not an identifier of cognitive performance.

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HOW SLEEP DEPRIVATION IMPACTS VIGILANCE RESPONSE TIMES: AN ANALYSIS OF
SLEEPINESS ON COGNITIVE IMPACT
Introduction
Only 45% of Australians sleep to a level of satisfaction (Royal Phillips, 2020). Without
maintaining healthy sleep patterns and reducing sleepiness, individuals face a decrease in cognitive
performance (Baumann et al., 2014). Low cognitive performance is a common issue experienced by many
individuals due to sleepiness. Vigilant Attention (vigilance) is a cognitive function that represents the
continuous focus on one task (Douglas & Engleman, 2004). Studying the relationship between sleep and
sleepiness versus vigilant attention and other cognitive performance measures is beneficial to better
understand how sleep deprivation can affect neurobehavioral functions (Hinson et al., 2020).
Vigilance is responsible for good performance, including work performance, task completion,
driving, and any other tasks that require sustained attention, requiring both high executive function as
well as operational behaviours (Walker et al., 2011).
The recuperative benefits of getting the recommended amount of sleep, often overlooked. Many
studies support the influence proper REM sleep and scheduled, healthy sleep patterns to increase
productivity, attentiveness and almost all of the neural substrates of cognitive function (Balkin et al.,
2020)
The Psychomotor Vigilance Test is a ten-minute reaction time (RT) test, and the most common
assessment tool to measure sleep deprivation (Bansner & Dinges, 2011). It occurs by RT to a stimulus at
random interstimulus intervals (ISI), which varies between two and ten seconds. The widespread use of
this examination tool has identified that RT is negatively impacted due to sleep deprivation (Czeisler et
al., 2010). The PVT is a commonly used method to measure sustained attention and is often used in
assessments identifying the relationship between vigilance and sleepiness, like this current study. Studies
presented have used varieties of methods, sample groups and various approaches to sleep measurements.

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One study in 2010 presented participants with eight-hours of sleep followed by forty-hours of
sleep deprivation (SD), during which they would complete the PVT bi-hourly. This experiment found that
further into SD, the more lapses that would occur for a visual PVT test. However, when presented with an
audio version of the PVT, the decrease in performance was not as dramatic, allowing the authors to
conclude the increase in visual lapses was due to eye closure and visual distraction (Jung et al., 2010)
Dinges and Lim (2008) outlined that SD does decrease on participants speed to react to stimuli;
however, they also outlined prior research that identifies reaction time is only the surface of the other
neural substrates that vigilant attention impacts. This study supports that SD highly increases the
likelihood of lapses in participants.
Whilst these studies have focused on total SD on subjects, a 2008 study (Franzen et al.) recorded
self-reported sleepiness and mood from both total SD and non-SD control groups. Self-reported
sleepiness was found to be a major contributing factor to an increase in lapses between both groups.
Another study conducted on young adults identified that perhaps behavioural alertness performs
better and equally as good in the afternoon, regardless of hours slept. Yet, with total sleep deprivation
from twenty-five to twenty-nine hours, had a moderate correlation with an increase in lapses on the PVT
(Honn et al., 2020). However, this experiment conducted multiple decision-making and cognitive
function tests, conceptualising that there is more evidence for this theory than just the PVT.
The aim of the study was to replicate previous sleep and attention studies to an Australian
undergraduate sample, to identify if there was a correlational relationship between self-reported measures
of sleepiness and vigilant attention, and a correlational relationship between self-reported hours slept and
vigilant attention.
It was hypothesised that there would be a significantly strong, positive relationship between
sleepiness and the number of lapses in the PVT, aligned with three other sleep deprivation and sleepiness
studies that implemented the PVT (Franzen et al., 2008; Frey et al. 2004; Van Dongen et al. 2004). It was

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also hypothesised that there would be a significantly moderate, negative relationship between hours slept
and number of lapses in the PVT, aligned with the aforementioned studies.
Method (553/300 words)
Participants
After some data removal, including duplicate and incomplete data sets, there was a total of 426
participants (319 Female, 103 Male). 68.5% of the participants were aged between 17 to 24; 21.4% were
aged 25-34; 6.1% were 35-44, 3.1% were 45-54 years old and 0.9% represented 55 to 64-year old’s.
Participants were recruited from undergraduate students studying 2007 PSY during trimester 1, 2022. The
test was voluntary, and the participants signed an electronic informed consent sheet.
Apparatus and Materials
Self-report measures were used to obtain both demographic information, such as age and gender,
as well as measures of sleepiness on a scale of one to seven and hours slept the previous night, from ‘less
than one’ to ’13 or more’ in index numbers. Participants also had the option of ‘I prefer not to answer this
question’. The study required participants to complete the Psychomotor Vigilance Test (PVT) via the
INQUISIT software. The PVT is a performance measurement test that presents a red millisecond timer on
a white screen at random intervals between two and ten seconds (Baumann, Landolt & Werth, 2014). This
randomisation of intervals prevents habitual and predictive behaviour of pressing the ‘Space’ bar,
requiring the participant to remain vigilant and attentive. Participants reactions to the stimuli are timed
from when the stopwatch appears to when they react by pressing the ‘Space’ button. These reaction times
were presented to participants after each response. The measurement recorded, were the number of times
a participant took more than 500 milliseconds to respond to the stimuli. Therefore, the fewer lapses, the
better the performance of the individual. The PVT lasts ten minutes, which can allow the attention to be
sustained and measured for a sleep-deprivation sensitive length of time (Basner and Dinges, 2011). For
example, one who is sleepy may be able to focus their attention for one minute, but over ten minutes,

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performance may drop off. IBM SPSS Version 28 was used to analyse the data and create an output of
statistical and frequency analysis.
Procedure
Firstly, students were required to download the Inquisit program via the link supplied. They were
then prompted to accept the informed consent form, and instructions on how to complete the test. The
program then required students to complete the demographic questions, such as gender and age, as well as
self-report their tiredness on a scale of one to ten, and the number of hours they slept the previous night.
Each student received a 9-digit identifying number which they used to access the Psychomotor Vigilance
Test (PVT). This test required students to respond using the ‘Space’ button as rapidly as they could to a
red millisecond stopwatch that appeared on the screen. . The test lasted ten-minutes. Each occurrence had
randomised intervals.
Statistical Analysis
The current study was a survey study. To test the hypotheses, a correlation analysis was
conducted. The explanatory variable in the current study was sleepiness, and hours slept, whilst the
response variable was the lapses (responses longer than 500 milliseconds) during the test. Therefore, the
sleep and sleepiness, can be reported with explanation of the vigilance of an individual.
Results
The software used to analyse and export the data was IBM SPSS Version 28.
Descriptive Statistics
The Descriptive Statistics for all three variables can be found in Table 1, below.
Table 1
Descriptive Statistics

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Hours Slept Sleepiness Rating Lapses
Mean 7.03 3.62 3.91
Standard Deviation 1.494 1.372 5.881
Inferential Statistics
To analyse the correlation between variables, the current study used the Spearman’s Rho (r),
allocating significant findings a score of p=-.05 or less. The relationship between hours slept and lapses
presented a negative, weak correlation of very low significance (r=-.075, p=.120); therefore, there is
insufficient evidence to conclude there is a linear relationship between hours slept and lapses in the PVT.
This disproves the hypothesised outcome, as the number of hours slept does not correlate with the lapses
on the PVT for this experiment. The relationship between sleepiness rated and lapses presented a positive,
weak correlation of strong significance (r=.132, p=.006); therefore, there is sufficient evidence to reject
the null hypothesis, concluding that there is a relationship within the population between sleepiness rating
and lapses in the PVT. This is aligned with the hypothesis, as there was an evident increase in lapses with
higher reports of sleepiness measures.
Discussion (650/700)
The aim of this current study was to identify any existing relationships between sleepiness and
vigilant attention or hours slept the previous night and vigilant attention. The study found a strong,
positive correlation between self-reported measures of sleepiness and increase in lapses (poorer
performance) on the PVT test. It also found that there was no significant correlation between the number
of hours slept the previous night and the number of lapses.
The hypothesis that there would be a strong, positive correlation between sleepiness levels and
lapses was strongly supported. This implies that when people feel sleepier, they are more likely to face
lower sustained attention. This poses a problem, from an academic and productivity point of view,

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considering over half the population within Australia face a lack of sleep satisfaction (Royal Phillips,
2020). This finding is aligned with the majority of literature available on sleep and vigilance, especially
Baumann et al. (2014), which found that those with sleep apnoea and sleep-wake disorders, which
commonly results in sleepiness in the morning, perform worse than the control-group in regard to
vigilance.
Opposingly, the hypothesis that hours slept and vigilant attention would present a strong, negative
correlation was incorrect, as there was no correlation. This contradicted most of the research found, as
total sleep deprivation (TSD) experiments, such as that by Buysse et al. (2008) displayed a strong
negative correlation between hours slept and lapses. It may be possible that the individuals with less
hours slept might not even feel sleepy, some individuals only need four hours sleep to feel satisfied,
whilst some may underperform if they get less than ten. This concept is supported by Fu et al. (2016) who
identified that there are substantial differences in the hours of sleep required for an individual to reach
maximum cognitive function, as well as the fact that there may be a predisposition, or vulnerability to
sleepiness in individuals, whether they are sleep-deprived or well rested.
These findings have implications for various groups, such as shift workers, young adults,
individuals with sleep disorders to help them understand why their sustained attention is failing.
Specifically, the fact that hours slept does not correlate to lapses in sustained attention, but sleepiness
does, should encourage people who lack sleep satisfaction to engage a healthy sleep routine, possibly
implemented through mobile apps, less blue light and more self-awareness of deep REM sleep.
Limitations and Recommendations
This experiment faced a few challenging limitations. Most importantly, the self-reported measures
expose the data to biases, particularly introspective ability and individual’s inability to assess themselves
accurately (Cecile and Serge, 2020). This was rectified in the comprehensive research of Buysse et al.
(2008) as they used a total of nine various subjective and objective measures, including self-reports,

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Multiple Sleep Latency Test, pupil diameter measurements and affective reactivity. Baumann et al.
(2014) also utilised Steer clear test to measure sustained vigilance for thirty-minutes in addition to the
PVT. Any further study should implement more measurements of sleepiness and sleep deprivation.
Furthermore, this test was conducted during various times of day during tutorial classes. This is a
big fault, as identified by Balkin et al (2020), there is a desired period of alertness for both sleep deprived
and not sleepy people, and those who are highly sleep deprived may be able to perform in the afternoon to
the capacity of a morning, not sleep deprived individual in the morning on the PVT. Therefore, it is
recommended that every participant completes the experiment at the same time of the day in further
studies.
Conclusion
Overall, the findings suggest that vigilant attention is highly impacted by the amount of sleepiness
an individual perceives that they have. Additionally, the hours slept the previous night had no significant
impact on their vigilant attention performance. These findings suggest that sleepiness and sleep
deprivation can highly impact the neurobehavioral function of vigilant and sustained attention across a
ten-minute task.

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