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J Camosun Psyc Res. (2021). Vol. 3(2), pp. 59-71.
Submitted Winter 2021; accepted July 2021.

What Are the Biological Mechanisms of Dream
Abnormalities?
Authors: Marcos Blanco Guillen* and Talia Doll
Supervising Instructor: Michael Pollock, Psyc 215 (“Biological Psychology”)
Department of Psychology, Camosun College, 3100 Foul Bay Road, Victoria, BC, Canada V8P 5J2
*Corresponding author email: marcosblanco80@gmail.com

ABSTRACT
In this paper, we sought to understand both the biological and psychological mechanisms
responsible for dream abnormalities, in hopes to use the information to improve our relationships
with sleep and dreams and to understand these concepts on a deeper level. Previous research has
predicted dream abnormalities by variables such as sleep fragmentation, altered sleep
architecture, and the involvement of the prefrontal cortex. In our correlational study, we tested
the strength of these relationships by examining naturalistic daily changes in their variables
longitudinally over a two-week period. We used Fitbit devices to measure both sleep
fragmentation (by the number of sleep interruptions) and REM sleep amounts, and used
subjective scales to measure prefrontal cortex activity during REM sleep (by the level of risktaking and impulsive behaviour in dreams), the lucidity of dreams, and the nightmarish nature of
dreams. Though it varied across participants, data pooled in our correlation study showed that
there was a significant correlation of nightmares and lucid dreaming with sleep fragmentation,
altered sleep architecture, and prefrontal cortex activity during REM sleep. This correlational
study supports the role of these biological mechanisms in producing dream abnormalities.
1. Introduction
1.1 Research Problem
In this paper we hope to build a clear
relationship between dream abnormalities
and both the biological and psychological
mechanisms that cause them. A rationale for
researching this topic is its usefulness in
understanding the basics of sleep and the
many myths that exist about dreams and
their biological and psychological
implications. This topic of research also
serves to build upon how we have come to
understand REM sleep in the past and how
that has adapted over time. To begin to

understand dream abnormalities one must
understand what is happening while we
sleep and why we dream in the first place.
Another rationale for wanting to study the
biological and psychological mechanisms of
dream abnormalities is because it is
fascinating that even when the body is not in
motion, the mind is still able to function and
create images or scenarios. Obtaining the
answers to why these types of dream
abnormalities occur will be beneficial to an
individual and the collective. This type of
study can be valuable in many ways,
especially for students, as a first step to
improve quality of sleep and actualize the
impacts of dream abnormalities on day-to59

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day living while also navigating a way to
understand them and how they work.
1.2 Literature Review
Some of the factors that past research has
discovered to be mechanisms of dream
abnormalities and lucid dreaming are sleep
fragmentation and awakenings. by Gott et al.
(2020) outlined how self-reported
awakenings result in higher accounts of
lucid dreaming and left room for speculation
that disrupted sleep cycles have a connection
to dream lucidity. In their study, participants
were asked to fill out sleep questionnaires on
a scale about how disrupted they assessed
their own sleep to be, how many times they
awoke during the night, and at which level
of sleep continuity they believed produced
the most lucid dreams. The results of this
study lead to a connection between wakeful
brain activity and how that may transfer to
vivid dreams during REM sleep.
Fragmented wake-REM sleep cycles often
led to full arousal as a result of attempting to
predict lucid dreams, proving to be harmful
for the posed hypothesis. Experiences of
lucid dreams can be connected to
metacognition but more importantly poses
the question of what level of arousal elicits
vivid lucid dreams without completely
disrupting REM sleep. This leads to the
necessity for further research that sleep
fragmentation can be associated with lucid
dreaming and at what level of arousal it is
the most effective.
Another mechanism previously found to
be a cause of dream abnormalities is altered
sleep architecture, which is the structure of
the sleep cycle and the five stages of sleep.
In an experiment conducted by Simor et al.
(2012), participants complete an online
questionnaire assessing dream quality as
well as other personality factors. The
Nightmare Subjects (NMs) were selected

based on the International Classification of
Sleep Disorders. Subjects with one or more
nightmares and/or bad dreams per week
were placed in the NMs, whereas subjects
with less than two nightmares and/or bad
dreams during the last year were inducted
into the Control Subjects (CTLs). A
developed program was used to determine
output sleep architecture variables such as
the following: wake time after sleep onset
(WASO), sleep efficiency (sleep time/ time
in bed), sleep latency (the period between
lights off and the first epoch scored as Stage
2), the absolute and relative duration of
Non-REM sleep, Stage 1, Stage 2, slowwave sleep (Stage 3 and 4), REM sleep, and
REM latency. NMs had significantly
different sleep variables than the CTLs, such
as WASO, sleep efficiency, and slow-wave
sleep duration. The NMs revealed a worse
sleep quality than the CTLs with less sleep
efficiency, increased wakefulness, and less
slow-wave sleep. However, the NMs had a
longer sleep latency, an increase of Stage 1
sleep, and a higher number of nocturnal
awakenings in Stage 2 sleep. Based on the
results, Simor et al. (2012) suggest that
nightmares (a dream abnormality) are
associated with altered sleep architecture.
In addition to sleep fragmentation and
altered sleep architecture, another
mechanism found to be responsible for
dream abnormalities is activity of the
prefrontal cortex. In the Stumbrys et al.
(2013) study), the prefrontal cortex was
stimulated in hopes of finding a connection
between lucid dreams and the activation of
parts of the brain that are considered not as
active during REM sleep. In this study, 23
participants were screened for sleep
disorders and surveyed on the quality of
their sleep and then tested for their
sensitivity to transcranial direct current
stimulation (tDCS) to ensure significant
results. After these first tests, the
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experimental period began where the
participants were subjected to tDCS
stimulation at random, on both the second
and third night. Many of the participants
awoke after the stimulation to the prefrontal
cortex, and lucid dreams were mostly
reported by those who already assessed
themselves as frequent lucid dreamers. This
study, although valuable in affirming the
connection between brain activity in specific
regions and dream abnormalities, more
importantly, demands from future research a
way in which dreams can come to be
understood beyond subjectivity.
1.3 Hypotheses
Based on the literature review, we
predicted the following hypotheses:
● Hypothesis #1: If sleep
fragmentation increases during the night
then lucid dreaming will increase.
● Hypothesis #2: If REM sleep
increases then nightmares will increase.
● Hypothesis #3: If activity in the
prefrontal cortex increases during REM
sleep then lucid dreaming will increase.
2. Methods
2.1 Participants
The two authors of this paper served as
the participants in its studies. The
participants ranged in age from 20 to 21
years old, with an average age of 20.5 years,
and included one male and one female. The
participants were all undergraduate students
at Camosun College who completed the
current studies as an assignment for Psyc
215 (“Biological Psychology”) and were
grouped together due to their mutual interest
in dream abnormalities. All participants had
experiences or associations with dream
abnormalities and the amount of sleep that

the participants received during these studies
fell within their normal ranges.
2.2 Materials and Procedure
We first performed a correlational study
to test concurrently all of our hypotheses by
examining naturalistic daily changes in their
variables longitudinally. Each participant
kept a study journal with them at all times
over this study’s two-week period in order to
record self-observations of the following
five variables: (1) sleep fragmentation, (2)
altered sleep architecture, (3) prefrontal
cortex activity during REM sleep, (4) lucid
dreaming, and (5) nightmares.
Sleep fragmentation and altered sleep
architecture were measured using a FitBit,
which automatically tracks sleep patterns,
and an iOS application on an apple watch
called Pillow Automatic Sleep Tracker
(created by Neybox Digital Ltd.). Pillow
Automatic Sleep Tracker uses smartwatch
sensors to detect and analyze sleep
automatically, through bodily movements
and heart rates. To measure sleep
fragmentation participants recorded how
many times their sleep was interrupted as
indicated by these devices. After the
participants awoke they recorded how many
times their device indicated a level of
activity that signified some form of
awakening during the night. To measure
sleep architecture alteration, each participant
used their device to track each stage of sleep
that they went through. Immediately when
the participants awoke in the morning, they
wrote down the number of REM sleep
episodes they had throughout the night.
In order to measure activity in the
prefrontal cortex during sleep, each
participant monitored their own dreams for
experiences of risk-taking behaviours and
impulsivity. Participants were required to
rate how risky and impulsive their behaviour
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was in their dreams on a scale of 0-10. The
prefrontal cortex is responsible for many
functions and is known to play a major role
in action planning and behaviour
management, and its inactivity has been
explored in relevant research as a cause for
bizarre imagery typically found in dreams
(Hobson et al., 1999).
To measure lucid dreaming, participants
rated the lucidity of their dreams on a scale
from 0-10, 0 being not lucid at all with no
control, and 10 being highly lucid with
complete control. Participants were also
asked to mark down every time they became
conscious of the fact that they were
dreaming. All answers were recorded in
study journals each morning.
To be able to measure nightmares,
participants recorded on a scale 0-10 and
marked down any indications of nightmares
they experienced throughout the night. The
scale was rated from 0 being no experience
of disturbed dreams and feelings, 5 being a
moderate experience of disturbed dreams
and feelings, and 10 being an experience of
highly disturbed dreams and feelings.
Participants' answers were documented in a
dream journal each morning they awoke.
To assess the strength and statistical
significance of associations between
variables predicted by our three hypotheses,
we performed Pearson product moment
correlations of their predictor variables
(sleep fragmentation, sleep architecture, and
prefrontal cortex activity) with their
outcome variables (lucid dreaming and
nightmares). For hypothesis #1, we
correlated higher sleep fragmentation and
states of arousal with a higher chance of
lucid dreaming. For testing Hypothesis #2,
we correlated altered sleep architecture of
each participant for each night in which the
participant had nightmares. For Hypothesis
#3, we correlated activity in the prefrontal
cortex during REM sleep of each participant

in which the participant had lucid dreaming.
We performed all of the above correlations
separately for each participant as well as
using data pooled across all of the
participants. For the correlations using
pooled data, in addition to using the raw
data, we also performed correlations after
we had first transformed the data from each
participant into z-scores in order to
standardize differences in averages and
variability seen between the participants in
their data and thus make them more
comparable. A correlation coefficient was
considered statistically significant if the
probability of its random occurrence (p) was
< .05 (i.e., less than 5% of the time expected
by chance alone).
3. Results
As shown in Table 1, all three variables
(sleep fragmentation, altered sleep
architecture, and prefrontal cortex activity)
were significantly correlated with
nightmares and lucid dreaming. Although
sleep fragmentation was not significantly
correlated with lucid dreaming for one of the
participants and when using pooled raw data
(r = .21, p = .028; see Figure 1A), sleep
fragmentation was significantly correlated
with lucid dreaming using pooled
standardized data (r = .42, p = .019; see
Figure 1B). Similarly, although altered sleep
architecture was not significantly correlated
with nightmares for both participants or
when using pooled raw data (r = .24, p =
.21; see Figure 2A), altered sleep
architecture was significantly correlated
with nightmares using pooled standardized
data (r = .42, p = .018; see Figure 2B). In
comparison, while prefrontal cortex activity
and lucid dreaming was not significantly
correlated for both participants, prefrontal
cortex activity was significantly correlated
with lucid dreaming using both pooled raw
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data (r =.41, p = .022; see Figure 3A) and
pooled standardized data (r = .49, p = .005;
see Figure 3B). Based on a comparison of
the correlation coefficients using either the
pooled raw data or the pooled standardized
data, the strongest correlation seen was
between prefrontal cortex activity and lucid
dreaming.
4. Discussion
4.1 Summary of Results
Based on previous research we
hypothesised that three variables would
predict dream abnormalities (lucid
dreaming/nightmares): sleep fragmentation
(Hypothesis #1), altered sleep architecture
(Hypothesis #2), and activity in the
prefrontal cortex (Hypothesis #3). Data
pooled across participants in our
correlational study showed statistically
significant results for all three hypotheses.
4.2 Relation of Results to Past Research
The ability of sleep fragmentation to
predict dream abnormalities based on our
correlational study aligned well with
previous research. While Gott et al. (2020)
stated that self-reported awakenings resulted
in higher dream lucidity, the devices used in
our correlational study made it easier to
record sleep fragmentation and numbers of
awakenings in a more objective manner. The
similarity of both of our conclusions despite
using different research designs suggests a
generalized relationship exists between sleep
fragmentation and lucid dreaming.
The strong relationship found between
altered sleep architecture and nightmares in
our correlational study is consistent with
past research. Simor et al. (2012) found that
nightmares are caused by altered sleep
architecture. While the Simor et al. (2012)

study a large number of sleep variables in
laboratory conditions (WASO, sleep
efficiency, sleep latency, slow-wave sleep,
and REM sleep), our correlational study
recorded participants' number of REM sleep
episodes that occurred in a natural setting.
The fact that we found the same relationship
between altered sleep architecture and dream
abnormalities (nightmares) despite these
differences in methodology speaks to the
universality of its relationship.
In addition, the relationship between
prefrontal cortex activity and lucid dreaming
was strong in our correlational study and
was in line with previous research. While
Stumbreys et al. (2013) conducted a tDCS
study to stimulate the prefrontal cortex and
the effects of activation of this part of the
brain that is usually inactive during sleep,
we took an approach more similar to Hobson
et al. (2000), who looked at dream themes
and dream behaviour to predict brain
activity. Despite these different methods of
research, our findings of similar conclusions
reveal a universal relationship between
prefrontal cortex activity and lucid
dreaming.
4.3 Implications of Results
Throughout this correlational study, there
was a clear relationship between biological
and psychological factors and dream
abnormalities. The practical application of
this correlational study further extends the
knowledge of how biological mechanisms of
sleep related to lucid dreaming and
nightmares, which can provide insight into
our personal sleep habits. Both lucid
dreaming and nightmares were significantly
associated with sleep cycles and patterns,
leaving room for further research that can
blur the divide between our objective
understanding of sleep and our subjective
experiences of it. The findings of this study
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shed a positive light on the research question
as sleep fragmentation, sleep architecture,
and prefrontal cortex activity are biological
mechanisms that showed positive
correlations with dream abnormalities.

References
Gott, J., Rak, M., Bovy, L., Peters, E., van
Hooijdonk, C. F. M., Mangiaruga, A.,
Varatheeswaran, R., Chaabou, M.,
Gorman, L., Wilson, S., Weber, F.,
Talamini, L., Steiger, A., & Dresler, M.
(2020). Sleep fragmentation and lucid
dreaming. Consciousness and Cognition,
84, 102988.
https://doi.org/10.1016/j.concog.2020.10
2988
Hobson, J. A., Pace-Schott, E. F., &
Stickgold, R. (2000). Dreaming and the

brain: Toward a cognitive neuroscience
of conscious states. Behavioral and Brain
Sciences, 23(6), 793–842.
https://doi.org/10.1017/s0140525x000039
76
Simor, P., Horváth, K., Gombos, F., Takács,
K. P., & Bódizs, R. (2012). Disturbed
dreaming and sleep quality: Altered sleep
architecture in subjects with frequent
nightmares. European Archives of
Psychiatry and Clinical Neuroscience,
262(8), 687–696. https://doiorg.libsecure.camosun.bc.ca:2443/10.100
7/s00406-012-0318-7
Stumbrys, T., Erlacher, D., & Schredl, M.
(2013). Testing the involvement of the
prefrontal cortex in lucid dreaming: A
tDCS study. Consciousness & Cognition,
22(4), 1214–1222. https://doiorg.libsecure.camosun.bc.ca:2443/10.101
6/j.concog.2013.08.005

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Table 1
Correlation coefficient (r) values, with number of daily trials (n) per correlation in brackets.

Participant
#1

Participant
#2

Pooled raw
data

Pooled
standardized data

Sleep Fragmentation
& Lucid Dreaming

.52(15) *

.32(15)

.21(30)

.42(30) *

Altered Sleep
Architecture
& Nightmares

.55(15) *

.30(15)

.24(30)

.42(30) *

Prefrontal Cortex
Activity
& Lucid Dreaming

.62(15) *

.36(15)

.41(30) *

.49(30) *

Variables correlated

* p < .05.

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Figure 1
Scatterplot of sleep fragmentation and lucid dreaming using pooled (A) raw and (B) standardized
data across participants.
A.

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B.

Marker color indicates which participant data is from: red = participant #1 and orange = participant
#2. Some data might not be visible in the figure due to overlapping markers.

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Figure 2
Scatterplot of altered sleep architecture and nightmares using pooled (A) raw and (B)
standardized data across participants.
A.

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B.

Marker color indicates which participant data is from: red = participant #1 and orange = participant
#2. Some data might not be visible in the figure due to overlapping markers.

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Figure 3
Scatterplot of prefrontal cortex activity and lucid dreaming using pooled (A) raw and (B)
standardized data across participants.
A.

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B.

Marker color indicates which participant data is from: red = participant #1 and orange = participant
#2. Some data might not be visible in the figure due to overlapping markers.

71