Consciousness and Cognition 87 (2021) 103051
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Consciousness and Cognition
journal homepage: www.elsevier.com/locate/concog
Does COVID-19 impact the frequency of threatening events in
dreams? An exploration of pandemic dreaming in light of
contemporary dream theories
Jiaxi Wang a, Steve Eliezer Zemmelman b, Danping Hong a, Xiaoling Feng a,
Heyong Shen a, c, *
a
School of Psychology, South China Normal University, Guangzhou, Guangdong, China
C.G. Jung Institute of San Francisco, San Francisco, CA, USA
c
Institute of Analytical Psychology, City University of Macao, China
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Continuity hypothesis
COVID-19
Dreaming
Hyperassociation
Social simulation theory
Threat simulation theory
Ninety-one dreams collected during the Covid-19 pandemic (the epidemic-situation sample) were
compared with ninety-one dreams collected before the start of the epidemic (the non-epidemicsituation sample). The dreams were classified according to their content, using methods based
on previous studies. The frequency of themes was compared to predictions that would be
anticipated by three contemporary theories of dreaming: 1) threat simulation theory (TST); 2)
incorporation continuity hypothesis (ICH); and 3) social simulation theory (SST). The epidemicsituation sample dreamed more of threatening events than the non-epidemic-situation sample
(supporting the TST) and more of non-aggression threatening events, possibly due to the
hyperassociation during sleep. However, the epidemic-situation sample did not show a greater
prevalence of illness events in dreams (not supporting the ICH). Additionally, there was no sig­
nificant difference in social neutral and positive events in dreams between the two samples as
would have been predicted by the SST.
1. Introduction
Dreaming consists of narratives experienced during sleep, the contents of which we may only learn through subjective reports.
Foulkes (1985) describes dreams as credible world analogues, an organized form of consciousness that simulates what waking life is
like: dreaming as a simulation of the waking world (for a review, see Revonsuo, Tuominen, & Valli, 2015a).
Two variations of simulation theories suggest that dreaming has an evolutionary adaptive function. One of these is threat simu­
lation theory - TST (Revonsuo & Valli, 2000; Revonsuo, 2000). The core idea of TST is that threat simulation during dreaming re­
hearses the cognitive mechanisms required for efficient threat perception and threat avoidance. TST applies a Darwinian perspective in
that greater efficiency in assessing and avoiding threatening situations in waking life is associated with an increased probability of
reproductive success in the service of human evolution. The idea here is that the situations encountered in dreams help us learn how to
recognize and respond to threatening circumstances, and that the better we get at applying this knowledge to real threats in our waking
life, the more likely we are to survive and reproduce.
* Corresponding author at: School of Psychology, South China Normal University, NO.55, West of Zhongshan Avenue, Guangzhou 510631, China.
E-mail address: shenheyong@hotmail.com (H. Shen).
https://doi.org/10.1016/j.concog.2020.103051
Received 17 March 2020; Received in revised form 2 November 2020; Accepted 11 November 2020
Available online 13 November 2020
1053-8100/© 2020 Elsevier Inc. All rights reserved.
Consciousness and Cognition 87 (2021) 103051
J. Wang et al.
A variation on TST which also integrates an evolutionary perspective is social simulation theory - SST (Revonsuo et al., 2015a).
With an emphasis on the value inherent in being able to effectively use social networks in the service of adaptation, SST states that
dreams simulate social connections and networks of the dreamer to give an additional selective advantage and enhance the survival of
the dreamer in waking life.
In contrast to TST and SST simulation theories of dreaming are continuity hypotheses. Continuity hypotheses do not emphasize the
adaptive evolutionary function of dreaming but instead understand dreaming as a continuation of mental functioning from the waking
state. There are currently two versions of the continuity hypothesis. One version suggests that waking events and activities are
incorporated into dreams so that they act as mirrors for waking experience (Schredl & Hofmann, 2003; Schredl, 2012). Another version
of a continuity hypothesis suggests that dreams express personal concerns and preoccupations in waking life (e.g., Domhoff, 2003,
2011). Tuominen, Stenberg, Revonsuo, and Valli (2019) termed the former version of the continuity hypothesis the incorporation
continuity hypothesis (ICH) and the latter version the cognitive continuity hypothesis (CCH).
Revonsuo and his colleagues propose that the future progress of dream science will benefit from different dream theories, and these
theories should be empirically testable (Revonsuo, Tuominen, & Valli, 2015b). Following this idea, they tested a hypothesis based on
the ICH with a competing hypothesis based on the SST (Tuominen et al., 2019). They found that dreams overrepresented social events
when compared to reports from waking states. This result partly supported the SST but did not support the ICH. In the opinion of the
authors of this study, and consistent with Revonsuo’s observation regarding the need for empirical testing of the range of dream
theories, we attempted in the present research to compare dream reports from the pandemic and pre-pandemic situations to see if they
could shed further light on the validity of different dream theories.
The coronavirus disease (COVID-19) is a pandemic (for an introduction, see Chen et al., 2020). The virus has spread all over the
world, causing serious illness and over a million deaths so far, as well as economic repercussions effecting every aspect of people’s
lives. Clearly, COVID-19 is a threatening event. According to the TST, real threatening experiences trigger the activation of the threat
simulation system (for an introduction, see Valli & Revonsuo, 2009). This system will activate threatening memories in dreams to
provide opportunities for threat simulation. The TST predicts that the more people encounter threatening experiences in waking life,
the more they are likely to report dreams about threatening events. Since COVID-19 is a threat to people, the TST would suggest that
the frequency of dreams including threatening events will increase concurrently with the increased prevalence of the virus in the
population.
As noted above, the essential feature of both the ICH and the CCH is that there is a continuity in thematic development between
waking and dreaming. Both the ICH and the CCH predict that the frequency with which an element (e.g., characters, objects, actions,
places) appears in dreams is functionally related to the intensity of a waking element: greater frequency of the occurrence of a dream
element is positively correlated with increased valence of that element in the psyche of the dreamer. In this light, statistically sig­
nificant differences between the appearance of dream elements in a population may be related to the importance of that element in the
waking lives of the dreamers in each community. Further, there is a slight but important difference between the ICH and the CCH (e.g.,
Domhoff, 2017; Schredl, 2017). The ICH focuses on waking, lived experience (e.g., see Hobson & Schredl, 2011), while the CCH
focuses on cognitive aspects of those experiences (personal conceptions and concerns - see Domhoff, 2017). In general, the ICH
contains not only cognitive aspects (as described by the CCH) but also activities (e.g., talking, speaking et al.).
Applying the ICH to our study of the impact of COVID-19 on dreaming, we would expect that the more people have experiences
related to the virus in their waking lives, the more likely their dreams will reflect themes and elements related to illness and death from
the virus. In contrast, when we turn to CCH, which holds that people tend to dream about their waking cognitive concerns, we found it
impossible to formulate a testable definitive hypothesis of dreaming related to COVID-19. From the perspective of cognition, COVID19 can be a threat to people, a disease to people, or something else: the conceptualizations of CCH is simply too general in its
formulation. Therefore, we did not test any hypothesis derived from the CCH in this research.
In addition, the SST points out that a simulation of social connections and networks may provide an advantage to the dreamer
during waking life. This theory mainly focuses on socially neutral and socially positive events in dreams. COVID-19 is an epidemic that
is a threatening event. The socially neutral and socially positive events hypothesis of dreams may not be adequate in such extreme
situations: socially neutral and socially positive events in waking life may not have any effect on the socially neutral and socially
positive events in dreams.
To test these hypotheses in relation to pandemic dreaming we collected dreams that happened during the onset of the COVID-19
pandemic in China. These dreams were reported by people whose living areas were extraordinarily impacted by illness and death
related to the virus (epidemic-situation sample). We compared these dream reports with those taken from a sample of dreamers prior to
the onset of the epidemic (non-epidemic-situation sample). We hypothesized that: (1) the epidemic-situation sample would dream
more of threatening events than the non-epidemic-situation sample (as predicted by the TST); (2) the epidemic-situation sample would
dream more of disease and illness events than the non-epidemic-situation sample (as predicted by the ICH); (3) there would be no
significant difference in frequency of socially neutral and socially positive events, and no significant difference in the perception of
social events in dreams between the epidemic-situation sample and the non-epidemic-situation sample (as predicted by the SST).
2. Method
2.1. Participants
The local research ethics committee approved this study, and all subjects gave written informed consent before the start of the
study. Participants, all of whom were either undergraduates or postgraduates in colleges in China, were assigned to either of two
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J. Wang et al.
groups. The epidemic-situation sample group consisted of individuals who had experienced the spread of COVID-19. This group
consisted of 127 participants (age range of 18–39 (M = 25.6, SD = 3.98)) who reported their most recent dreams; of these, 91 (72
female, 19 male) submitted dreams they had had within the prior week. The data from the non-epidemic-situation sample group was
drawn from a study completed prior to the virus’ appearance in China. This group included 140 participants (age range of 19–37 (M =
22.94, SD = 3.14)) who reported their most recent dreams at the time they were collected; of these, 91 (73 females, 18 males) sub­
mitted dreams they had had within the week prior to the dreams being collected.
2.2. Materials
2.2.1. Dreams
Participants filled out a questionnaire via an online marketing research platform, WenJuanXing, that allows researchers to
distribute and collect survey questionnaires. The questionnaire used in this study asked participants to report a most recent dream, the
same as Malinowski (2015). “Please write down the last dream you remember having, i.e. your most recent dream. This could be as
recent as last night or from as far back as childhood but should be the most recent one you can remember having, no matter how long or
short it is. Please describe this dream exactly and as fully as you remember. Your report should contain, whenever possible: a
description of the setting of the dream, whether it was familiar to you or not; a description of the people, their age, sex, and relationship
to you; any animals that appeared in the dream. If possible, describe your feelings during the dream and whether it was pleasant or
unpleasant. Be sure to tell exactly what happened to you and the other characters in the dream”.
2.2.2. Dream coding
To code participants’ dreams, the present study used parts of two existing coding systems, the Social Content Scale (SCS) used in
Tuominen et al. (2019) and the rating scale used in Valli et al. (2005). We used four dimensions of the SCS: social perception, social
neutral interaction, social negative interaction, and social positive interaction (for details see Tuominen et al., 2019). The rating scale
used in Valli et al. (2005) coded for both non-aggressive threatening events in dreams and aggressive threatening events in dreams. The
latter overlapped with the dimension identified as “social negative interaction” in the SCS. The results were then sorted into five
categories for purposes of this study as shown in Table 1.
2.3. Procedure
Participants finished an online questionnaire via a Chinese online questionnaire resource Wenjuanxing (which is similar to the
Qualtrics) where they recorded their most recent dream. All the dreams were reported to have occurred within the week prior to
collection, a check on false or inaccurate reporting. Approximately half the questionnaires were collected in June 2019 for use as part
of a prior study. There was no threat from COVID-19 at that time. The other half of the questionnaires was collected in February 2020,
during which COVID-19 was spreading dangerously and lethally throughout China, presenting a serious threat to health and life for
millions of people.
Table 1
Operational definitions for coding dreams.
Category
Dream Content
Non-aggressive threatening eventsa
1 Escape and pursuits
2 Accidents and misfortunes
3 Failures
4 Catastrophes
5 Disease and illness
Social interactions that are neither negative social interaction events nor positive social interaction events
1 physical violence
2 verbal aggression
3 forcing
4 non-consensual sexual interaction
5 avoidance behavior
6 abandonment
Neutral social interaction eventsb
Negative social interaction eventsb
Positive social interaction eventsb
Social perception eventsb
1 physical affection
2 verbal affection
3 consensual sexual interaction
4 altruistic behavior
5 approach cues
6 request for support
7 mediating behavior
No interaction, perception only
a
Detail see Valli et al. (2005). Note that in the present study, we coded the threatening events that were caused by people as the negative social
interaction events.
b
Detail see Tuominen et al. (2019).
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J. Wang et al.
2.4. Data analysis
The collected dreams were randomized and scored by two independent raters (both psychological postgraduates) blind to the
subject variables. The raters worked independently of each other, and using operationalized definitions, classified the dreams as one of
five types, each of which identified the level or kind of threat or social interaction variables manifest in the dream (see Table 1). A
dream could be rated under more than one heading, such as in cases where it included both a social perception event and a non-social
interaction threatening event. In case of a disagreement between raters about the appropriate category, agreement on the final code
was reached by discussion. Inter-rater agreement rates were calculated after the independent coding stage and before the discussion.
The range of their Cronbach’s consistency coefficient (α) was between 0.76 and 0.87.
After all the dreams were categorized, an independent judge scored numbers of each kind of event in dreams. The judge indicated
with a ’yes’ or ’no’ if a dream contained mention of category 1 = non-aggression threatening events; category 2 = neutral social
interaction events; category 3 = negative social interaction events; category 4 = positive social interaction events; category 5 = social
perception events.
To test hypothesis 1 – that the epidemic-situation sample would dream more of threatening events in dreams than the nonepidemic-situation sample (as predicted by the TST) - we combined the non-aggressive interaction threatening events and the
negative social interaction events, thus creating an overriding category we called “threatening events.” To test our second hypothesis that the epidemic-situation sample would dream more of disease and illness events than the non-epidemic-situation sample (as pre­
dicted by the ICH) - a second overriding category was created: “disease and illness events.” A score of “0” for no presence of disease or
illness or “1” for presence of disease or illness was used for analysis. The third hypothesis - that there would be no significant difference
in either the frequency of socially neutral and socially positive events, and no significant difference in the perception of social events in
dreams between the epidemic-situation sample and the non-epidemic-situation sample (as predicted by the SST) - was also evaluated.
All statistical analyses were performed in IBM SPSS 18.0 software.
3. Results
Dream length was measured by the number of words used to tell the narrative of the dream. For the epidemic-situation sample, the
minimum number of words used to describe a dream was 11, the maximum 1094. The mean word length was 164.04 (SD = 180.99).
For the non-epidemic-situation sample, the minimum number of words used to describe a dream was 10, the maximum 1434. The
mean word length was 143.14 (SD = 176.54). There was no significant difference of dream length between the epidemic-situation
sample and the non-epidemic-situation sample, t = 0.79, p = .43.
A higher frequency of threatening events was found in dreams of the epidemic-situation sample compared to dreams of the nonepidemic-situation sample, χ2 = 5.94, p = .015, phi = 0.18. However, there was no significant difference between the two samples in
the frequency of disease and illness events in dreams (χ2 = 0.42, p = .52). Similarly, there were no significant differences between the
two samples in the frequency of neutral social interaction events, positive social interaction events and social perception events, χ2 =
0.35, p = .55, χ2 = 0.76, p = .38, χ2 = 0.03, p = .87, respectively (see Table 2).
As threatening events can be caused by either negative social interaction that has an aggressive cast as well as negative social
interaction that is not aggressive in nature (e.g., illness), we made further comparisons. The epidemic-situation sample dreamed more
of non-aggressive threatening events in dreams than the non-epidemic-situation sample, χ2 = 9.51, p = .002, phi = 0.23. However,
there was no significant difference between the two samples on the frequency of negative social interaction events, χ2 = 0.61, p = .44.
4. Discussion
Our results indicated a higher frequency of threatening events in dreams of the epidemic-situation sample than dreams of the nonepidemic-situation sample. This result supported our hypothesis 1, which was derived from the TST. As stated in the introduction, the
epidemic is a threat to people that may activate the threat simulation system during sleep. Similarly, Lafrenière, Lortie-Lussier, Dale,
Robidoux, and De Koninck (2018) also found that the experience of threats the day before dreaming was associated with the presence
of oneiric threats. In this light the appearance of an epidemic would increase the frequency of threatening events in dreams.
Our results showed that there was no significant difference in the frequency of disease and illness in dreams between the epidemicTable 2
Frequency of different kinds of events in dreams of the epidemic situation sample and dreams of the non-epidemic situation sample.
Category
Epidemic-situation samplea
Non-epidemic-situation sampleb
Threatening events
Non-aggression threatening events
Neutral social interaction events
Negative social interaction events
Positive social interaction events
Social perception events
Disease and illness events
70.3%
47.3%
45.1%
31.9%
20.9%
30.8%
15.4%
52.7%
25.3%
49.5%
37.4%
26.4%
31.9%
12.1%
a
b
The total number of dreams of the epidemic-situation sample was 91.
The total number of dreams of the non-epidemic-situation sample was 91.
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J. Wang et al.
situation sample and the non-epidemic-situation sample. It showed that the active threat of COVID-19 did not cause more dreams
concerning disease and illness than usual (in the absence of the epidemic). This result did not support our hypothesis 2, and thus may
not support the ICH. This is consistent with the suggestion by some researchers who have argued that a limitation of the ICH is that it
cannot explain the discontinuity between waking and dreaming (e.g., Horton, 2017). For example, it cannot explain why most dream
content only contains fragments of waking sources (e.g., similar characters, objects, places, actions, emotions). To explain the
discontinuity between waking and dreaming, Horton proposes an autobiographical memory (AM) model to describe the construction
of dreams (Horton & Malinowski, 2015). According to Horton, in the AM model “Experiences, information, and perceptions from
waking are broken down into unitary elements and activated offline during subsequent sleep. Each element activates associations,
which may be somewhat distantly related to the original element via hyperassociativity. Dreams reflect the activation of these ele­
ments and their associations.” (Horton & Malinowski, 2015, p9). A most important part of this model is the hyperassociativity. This
phenomenon refers to the increased activation of semantically related concepts and networks that are only weakly connected with the
activation of a specific concept or memory (Horton et al., 2015). Stickgold, Scott, Rittenhouse, and Hobson (1999) found that weak
priming (e.g., ’crime’ with ’gun’) exceeds strong priming (e.g., ’hot’ with ’cold’) after REM sleep, suggesting that hyperassociative
thinking is occurring during REM sleep. Similarly, Hartmann (1996) proposes that dreaming makes connections more broadly than
waking in the nets of the mind. These findings imply that during sleep, waking resources are more likely to associate weak semantic
memories than strong ones. COVID-19 is a kind of epidemic that can be semantically associated in a strong way with disease and
illness, while it may be semantically associated in a weak way with non-aggressive threatening events. According to the AM model, the
COVID-19 pandemic could activate weak semantic memories in dreams (as opposed to strong semantic memories) via hyper­
association. Compared with the non-epidemic-situation sample, the epidemic-situation sample dreamed more of non-aggressive
threatening events rather than disease and illness events. The results of the present study lend support to the AM model. In addi­
tion, it is important to note that these results did not contradict the TST, which suggests that threatening events in waking life will
activate the threat simulation system, and thus dreams that occur during times of threat will contain more threatening content. Any
threatening events in dreams that occur following a threatening encounter in waking life can be seen as supporting the TST.
There were no significant differences between the epidemic-situation sample and the non-epidemic-situation sample on the fre­
quency of social interaction events (neutral or positive) or social perception events. These results supported our third hypothesis - that
between the two groups there would be no significant difference in either the frequency of socially neutral and socially positive events,
and no significant difference in the perception of social events in dreams, which was derived from the SST. Revonsuo and his colleagues
(Revonsuo et al., 2015a) claim that the TST covers threatening events in dreams, whether social in nature or not, and the SST explains
dreams that TST does not cover and thus focus on social simulations that are largely independent of the threat-simulation function.
Their argument suggests that dreaming may happen in relation to two stimuli: social simulation and threat simulation. Our results
showed that the epidemic may have affected threatening events in dreams but did not appear to have a significant impact on social
interaction events in dreams and social perception events in dreams. Our results lend further support to the above argument. The threat
simulation and social simulation theories may predict dream content more or less accurately in different circumstances. Additional
studies could be designed to test this idea.
4.1. Methodological limitations
There were several limitations in the present work. Firstly, we used the Most Recent Dream method to collect participants’ dreams.
This method may bring out potential errors caused by memory flaws. Further this method cannot differentiate whether the most recent
dream happened in the REM period or in the non-REM period. If possible, future studies should use better controls to address these
issues.
Another limitation was the limited age range of participants. Would we have seen similar patterns of dreaming in an older pop­
ulation as we did with these college-age participants?
The study was also limited by the fact that dream reports were collected via an anonymous online questionnaire where there was no
opportunity to speak with participants and explore the nature of their dreams in greater detail. In this light we were unable to probe
further into the possible meaning of the dreams by asking participants to associate to the different themes and actions they experienced
while dreaming.
Finally, we were not able to establish with certainty that the threatening situation influencing the dreamers in the epidemic sit­
uation sample was, in fact, the pandemic. While a major threat to health and life, there could easily have been any number of other
threats impacting the dream life of participants in the study.
4.2. Conclusion
The work reported here follows the suggestions by Revonsuo et al. (2015b, p1), “we propose that instead of a general multi­
functional theory of sleep and dreaming, where no hypothesis is excluded, the future progress of dream science will benefit more from
opposing, competing and mutually exclusive theories about the specific functions of dreaming. This, however, demands that the
opposing theories and their predictions must be risky, clearly formulated, and empirically testable.”
Based on the TST, the ICH, and the SST, the present work explored the possible influence of COVID-19 on people’s dreams. We
compared dreams of the epidemic-situation sample and dreams of the non-epidemic-situation sample. Results suggested that (1)
COVID-19 increased the frequency of threatening events in dreams (supporting the TST); (2) COVID-19 did not increase the frequency
of disease and illness events in dreams (did not support the ICH), but increased the frequency of non-aggression threatening events in
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J. Wang et al.
dreams (supporting the AM model); and (3) COVID-19 did not affect the frequency of social neutral and social positive events in
dreams (supporting the SST). In our opinion, these results suggest there may be two simulation systems in dreams, just as the argument
proposed by Revonsuo and his colleagues. Future study can explore this idea.
CRediT authorship contribution statement
Jiaxi Wang: Conceptualization, Formal analysis, Writing - original draft. Steve Eliezer Zemmelman: Writing - review & editing.
Danping Hong: Formal analysis. Xiaoling Feng: Formal analysis. Heyong Shen: Writing - review & editing.
Acknowledgement
Our deepest gratitude goes to the anonymous reviewers for their careful work and thoughtful suggestions that have helped improve
this manuscript substantially.
Funding
Supported by the Innovation Project of Graduate School of South China Normal University [2019WKXM005]; Institute of
Analytical Psychology, City University of Macao.
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