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Dreaming is a multidisciplinary journal, the only professional journal devoted specifically to dreaming.

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Special issue of the APA journal Dreaming, Vol. 15, No. 3, September 2005. Includes articles about REM dreaming in the transition from late childhood to adolescence; influence of gender and age; trauma, dreaming, and psychological distress; children's interpretation of auditory messages in divine dreams; and earliest remembered dreams.

Special issue of the APA journal Dreaming, Vol. 14, No. 2/3, June/September 2004. Includes articles about dreaming in a number of different cultures around the world.

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• Published: 14 October 2019

Predicting the affective tone of everyday dreams: A prospective study of state and trait variables

• Eugénie Samson-Daoust 1 ,
• Sarah-Hélène Julien 1 ,
• Dominic Beaulieu-Prévost   ORCID: orcid.org/0000-0001-7926-5295 2 &
• Antonio Zadra   ORCID: orcid.org/0000-0003-3671-7081 1 , 3  

Scientific Reports volume  9 , Article number:  14780 ( 2019 ) Cite this article

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Although emotions are reported in a large majority of dreams, little is known about the factors that account for night-to-night and person-to-person variations in people’s experience of dream affect. We investigated the relationship between waking trait and state variables and dream affect by testing multilevel models intended to predict the affective valence of people’s everyday dreams. Participants from the general population completed measures of personality and trauma history followed by a three-week daily journal in which they noted dream recall, valence of dreamed emotions and level of perceived stress for the day as well as prior to sleep onset. Within-subject effects accounted for most of the explained variance in the reported valence of dream affect. Trait anxiety was the only variable that significantly predicted dream emotional valence at the between-subjects level. In addition to highlighting the need for more fine-grained measures in this area of research, our results point to methodological limitations and biases associated with retrospective estimates of general dream affect and bring into focus state variables that may best explain observed within-subject variance in emotions experienced in everyday dreams.

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Introduction.

Despite decades of advances in dream research, relatively little is known about how dreams are formed and what factors predict their content and emotional tone. One of the most widely studied models of dream content is the continuity hypothesis of dreaming 1 , 2 which posits that dreams are generally continuous with the dreamer’s current thoughts, concerns and salient experiences. In line with this conceptualization of dreams, a large proportion of dream research 1 , 3 , 4 , 5 , 6 , 7 has been dedicated to quantifying various dimensions of people’s dream reports and investigating their relationship to different aspects of people’s waking life. While much of this work has helped refine our understanding of which aspects of waking life (e.g., day-to-day actions, ongoing concerns, learning tasks, stressful experiences, psychological well-being) are most likely to be reflected or embodied in various facets of people’s dreams (e.g., settings, interpersonal interactions, activities, thematic contents), attempts to identify factors accounting for night-to-night or person-to-person variations in the intensity and valence of dream affect have yielded mixed results 7 , 8 , 9 , 10 , 11 , 12 , 13 .

Given that emotions are present in a vast majority of home and laboratory dream reports 7 , 14 , 15 , 16 , 17 and that some theorists 18 , 19 , 20 believe that affect plays a key role in structuring dream content, elucidating why people experience negatively toned dreams on some nights and positively toned dreams on others is of prime importance. Among the most studied factors hypothesised to influence dream valence are stress 21 , 22 , 23 , 24 , trait or personality characteristics 25 , 26 , 27 , history of traumatic experiences 28 , 29 , 30 , 31 , and psychological well-being 7 , 18 , 32 , 33 . Relatedly, one neurocognitive model 34 , 35 of dysphoric and everyday dream production suggests that variations in the frequency and intensity of negative dream emotions are partially determined by affect load , or day-to-day variations in emotional stress, and that the relation between dream content and stress varies as a function of affect distress , or the disposition to experience events with distressing, reactive emotions.

Many of the factors believed to predict the experience of negative dreams, including trauma history and psychopathology, have been associated with disturbed dreaming 28 , 36 , 37 , 38 and likely contribute to the development and heightening of affect distress 34 , 39 . Similarly, other dispositional traits related to the concept of affect distress, such as boundary thinness 40 (used to describe particularly sensitive and vulnerable individuals prone to mixing thoughts, images and feelings) and trait anxiety 41 (stable individual differences in the tendency to experience anxiety across situations) are also correlated with indices of negative dream content, including frequency of bad dreams and nightmares 27 , 33 , 42 , 43 , 44 , 45 . Thus, affect distress may be viewed as encompassing a range of factors known to impact dream affect, including trauma history, psychopathology, trait anxiety, and boundary thinness.

While several studies have investigated the differential impact of state and trait factors on dream content 7 , 11 , 12 , 32 , 42 , 46 , 47 , 48 , 49 , 50 , most have focused solely on nightmares, have been purely retrospective in nature, or did not weigh state-related findings against trait factors such as personality or psychopathology. Only two studies 42 , 48 have ever used a prospective design to assess the effect of trait and daily state measures on everyday dreams. The first one 42 assessed state anxiety and depression (what the authors termed “mood”) in relation to trait measures believed to underlie nightmare occurrence. They found statistically significant correlations between their state and trait variables and nightmare frequency, but only in individuals with thin psychological boundaries. The second study 48 obtained similar results in that daily stress was found to statistically predict general sleep-related experiences—a concept elaborated by Watson 51 to describe nocturnal phenomena such as nightmares, falling dreams, flying dreams and sleep paralysis—but only in young adults scoring high on a measure of trait dissociation (the tendency to experience psychological detachment from reality).

In sum, in addition to giving rise to inconsistent results, research on the determinants of dream affect has been limited by the often retrospective nature of the study design, single measurement points, focus on nightmare incidence or broad sleep-related experiences, and a failure to evaluate the interactive role of state and trait factors within a larger conceptual framework. We therefore used a prospective, multilevel design to investigate the interplay between daily fluctuations in perceived levels of stress and trait indices of affect distress as determinants of dream affect. Individuals from the general population first completed questionnaire measures of sleep and dream experiences, trait anxiety, boundary thinness, trauma history, and PTSD symptoms, followed by at least three consecutive weeks of daily assessments of perceived stress as well as dream recall, including the emotional valence associated with each remembered dream. Since daily measures ( N  = 2538) were nested within individuals ( N  = 128), multilevel hierarchical linear modelling (HLM) analyses were performed in order to examine the distinctive effect of state and trait variables.

Descriptive statistics and intercorrelations of tested variables

Table  1 presents the means, standard deviations and zero-order Pearson correlations between study variables. Daily measures were averaged per participant over the study’s duration to investigate their association to trait variables. All observed correlations were in the expected direction. The highest obtained correlation ( r  = 0.752) was between the mean daily level of maximum stress and the mean level of stress prior to bedtime. The fact that daily maximum stress was more strongly correlated with daily dream valence ( r  = 0.300) than was daily stress prior to bedtime ( r  = 0.185) suggests that the two variables tapped into different facets of perceived stress. As can be seen in the table, trait anxiety was statistically correlated with a majority of other studied variables, while sex did not show statistically significant correlations with any of the other measures.

Multilevel models predicting dream valence as outcome

A total of 1700 nights led to a dream recall in participants over the study’s three-week duration, of which 1653 (97.2%) contained ratings on the dream’s emotional valence. Of the 1700 nights, 773 (45.5%) yielded more than one recalled dream and participants reported an average of 6.9 dreams per week. Figure  1 presents the distribution of dream valence ratings for the 1653 dream reports. The mean dream valence score was 5.08 ( SD  = 2.27), or at the midpoint of the positive to negative rating scale. As can also be seen in the figure, highly positive dreams (scores of 1 or 2) were approximately twice as frequent as highly negative ones (scores of 9 or 10).

Distribution of dream emotional valence for 1653 dream reports.

Table  2 presents the intercepts-only model (i.e., unconditional model) for daily measures of dream valence. The intraclass correlation was 0.161, indicating that 16.1% of the variance in dream valence occurred between subjects, while 83.9% of the variance occurred within subjects (i.e., across days).

Table  3 presents the multilevel model predicting dream valence using trait (Level-2) and state (Level-1) predictors. At Level-2, when all predictors were entered in the model as fixed terms, trait anxiety (STAI-T) was the only variable to statistically predict dream valence. At Level-1, neither of the two daily measures of perceived stress statistically predicted the dream valence experienced on the subsequent night. Dream recall frequency per night was the only statistically significant Level-1 predictor. This measure was used as a control variable since dream valence was only provided for the best remembered dream on a given night when more than one dream was recalled (45.5% had multiple recalls) and thus the two variables were not entirely independent.

When standardized scores for trait anxiety (ZSTAI-T) were entered as a single predictor of dream valence in a separate model, it was found to be an even better predictor ( p  < 0.001) than when it was considered alongside other predictor variables, with each increase in standard deviation STAI-T scores explaining a 0.33 unit increase in dream valence ratings. This model reduced the unexplained between-subject variance by 11.6%, thus explaining a total of 1.9% of the variance in dream valence ratings obtained over the study’s 3-week duration.

Post Hoc multilevel models predicting dream valence as an outcome variable

Since interactions between predictors could potentially explain why neither of our perceived stress variables predicted dream valence 42 , 48 , we tested for possible interactions, particularly between trait variables (Level-2) and daily perceived stress (Level-1), but did not find a statistically significant interaction that could predict dream valence. The only statistically significant interaction predicting dream valence was between trait anxiety (STAI-T) scores and dream recall frequency ( p  = 0.007), which was positive and expected since the dream valence rating of the most vivid or best-remembered dream on a given night can increase when a greater number of dreams is recalled on that night.

Since daily perceived stress did not predict the dream valence experienced on the subsequent night, models testing for potential a dream-lag effect (i.e., increased incorporation in dreams of events having occurred 5–7 days prior to the dream) 52 , 53 were also computed post hoc. Separate datasets pairing daily perceived stress levels from previous days (i.e., two to seven days prior to recalled dreams) with reported valence of subsequently recalled dream were generated. No statistically significant effect of perceived stress from the past 2 to 7 days on dream valence was found in any of the datasets tested, thus refuting a possible delayed effect of perceived stress on subsequently experienced dream affect.

Additional multilevel models predicting perceived stress as outcome

Using a reversed model, we aimed to predict daily stress scores (both maximum and prior to bedtime) using dream valence and DRF from the preceding night, along with the other predictor variables. The models only yielded a statistically significant effect of trait anxiety as a predictor of both maximum ( p  = 0.031) and bedtime stress levels ( p  = 0.007) (see Supplementary Tables  S1 and S2 for more details).

We investigated the relationship between waking trait and state variables and dream affect by testing multilevel models aiming to predict the affective valence of people’s everyday dreams. Moreover, this was the first time a prospective day-by-day design was used to test predictors of dream valence at the between-subject as well as within-subject levels of variance. The results showed that daily measures of perceived stress collected from a non-clinical sample of adults do not, as suggested by some theorists, predict the emotional valence of dreams experienced later that night, nor on immediately subsequent nights. This study is also the first to identify trait anxiety as a key dispositional variable in predicting dream valence, even when trait measures are weighed against state variables.

Taken as a whole, these results run counter to previous findings indicating that state variables are better predictors of dysphoric dream frequency than are dispositional traits 46 , 47 , and that daily stress or mood interacts with trait variables to predict nightmares 42 , 48 . Previous positive results could be due to methodological considerations as these studies either lacked a multilevel, prospective design, focused on nightmare occurrence 42 , 46 , 47 or general sleep-related experiences 48 instead of everyday dreams, or focused on undergraduate (often psychology) students instead of recruiting participants from the general adult population 46 , 47 , 48 .

Our results are reminiscent of Cellucci and Lawrence’s study 49 of nightmare sufferers showing that daily ratings of general and maximum anxiety were statistically correlated with nightmare frequency and intensity in only a small minority of participants. Since trait variables were not assessed in their study, why nightmare occurrence was related to daily anxiety in some participants but not others remains to be determined. In line with this question, Soffer-Dudek and Shahar 48 found that daily stress predicted “general sleep-related experiences” only in individuals scoring high in trait dissociation (a trait strongly correlated with boundary thinness), while Blagrove and Fisher 42 found that correlations between state anxiety and nightly incidence of nightmares were only statistically significant in participants scoring high on boundary thinness. While the interplay between dispositional and state factors underlying nightmare occurrence may play a role in the emotional tone of everyday dreams, the current study showed no statistical interactions between various trait variables and daily levels of perceived stress in predicting dream valence.

With respect to the other dispositional traits investigated, it is noteworthy that although traumatic experiences, including aversive events during one’s childhood, are well-documented correlates of disturbed dreaming 21 , 34 , 54 , 55 , 56 , we found no statistically significant effect of trauma history on everyday dream affect. Most findings linking trauma and dream content, however, have come from work focused on trauma-related nightmares, typically in patients diagnosed with PTSD. By contrast, only 23 (18%) of our participants had a cut-off score of 3 or greater on the PC-PTSD (indicative of ongoing trauma-related difficulties) and only 16% reported more than one dream with an affect score of 9 or 10 (indicative of a nightmare) during the three weeks of the study. In fact, as shown in Fig.  1 , dreams with highly intense negative affect represented less than 8% of the over 1600 dream reports collected in the current study.

Similarly, while boundary thinness has been linked to dream content variables such as high dream recall, frequent nightmares and negatively-toned dreams 26 , 43 , 57 , 58 , 59 , it had no predictive value in our models of everyday dream valence. This trait variable may be better suited to the study of nightmare sufferers, a population specifically investigated by Hartmann et al . 59 when developing this personality construct, or to individuals prone to particularly vivid or bizarre dreams 26 .

Turning to the construct of affect load, the current study did not find evidence to support the idea that daily variations in perceived stress are temporally related night-to-night variations in dream affect. It should be noted that studies having reported an effect of affect load on the emotional content of dreams did so by measuring affect load retrospectively (e.g., for the past month) at a single point in time 7 , 46 , 47 rather than on a day-to-day basis. This underscores the importance of how state factors are assessed since correlates of retrospectively estimated state variables can be biased by dispositional factors (e.g., personality) and are not necessary correlates of prospective, day-to-day measurements of these constructs. In fact, this is not the first time in dream research that prospective study designs have yielded findings contradicting results obtained with retrospective measurements of dream-related variables, including correlates of dream recall and dream content 60 , 61 , 62 .

The concept of affect load may also need to be better defined to allow for more directly comparable study results. For example, in exploring the effects of stress on dreams, researchers have investigated acute stressors 63 , 64 , experimental stressors 22 , 65 , emotional stressors 66 , as well as cumulative stressors 21 . Additionally, in light of the recently proposed social simulation theory of dream function 67 in which dreaming is conceptualized as simulating social skills and bonds to strengthen waking social relationships, the study of social or interpersonal stressors 68 in relation to dream content may be particularly valuable, especially since a vast majority of dream reports feature social interactions 5 , 15 , 69 and that concerns of an interpersonal nature are frequent in everyday dreams 1 , 3 . Moreover, as suggested by some researchers 50 , dream content may be more reactive to the emotional nature of stressors than to the stressors per se . Finally, it is important to note that our participants were not particularly stressed—or at least did not perceive that they were—during the 3-week study as reflected by their mean score of 3.6 (out of 9) on our measure of daily maximum stress and 1.7 (out of 9) for daily bedtime stress. It is possible that direct or interaction effects of state and trait variables on dream affect become heightened, and thus more readily observable, during periods of acute or chronic stress.

When stress or affect load are studied in relation to dream content, they are usually assessed with self-report questionnaires. However, subjective levels of perceived stress can differ from variations or patterns in the biological markers of cortisol 70 , 71 . It is thus possible that physiological modulation of stress response, as opposed to subjective stress perception, plays a role in people’s nightly experience of dream affect. Of note, Nagy et al . 72 found a blunted cortisol awakening response in women reporting frequent nightmares, which was independent of lifestyle, psychiatric symptoms and demographic variables. This led the authors to hypothesize that low cortisol reactivity could be a trait-like feature of nightmare sufferers. Similarly, some researchers 73 have suggested that the gradual rise in people’s cortisol level from the middle of the night until its peak in the morning could account for observed increases in dream emotionality, bizarreness, vividness and length across the night 74 , independently of sleep stage. The use of biomarkers such as cortisol, which can be sampled in saliva 72 , could therefore be of particular interest in investigating the range and intensity of dream emotions reported both within and across nights.

Furthermore, since dream emotional valence was measured for the best-recalled dream upon awakening in the morning, the current study is limited to a narrow portion of participants’ sleep mentation. In addition, given the recency of morning dreams 75 and the aforementioned increase in dreamlike qualities of sleep mentation across the night, dream emotional valence was likely based on dreams occurring moments before morning awakenings. Affect load could thus have been processed through the emotional valence of dreams that were not collected in the present study (i.e., dreams from earlier periods of the night or other forms of unrecalled sleep mentation). Such a hypothesis could be tested with serial laboratory-based awakenings for dream collection across the sleep period, although the proportion of dreams containing emotions as well as their valence tend to differ when they are self-reported in the laboratory 13 , 14 , 17 , 76 as opposed to participants’ natural home enviornment 16 , 77 , 78 , 79 .

Finally, our sample of over 1600 dream reports revealed a roughly equal distribution of positive and negative emotions, as well as a higher proportion of intense positive emotions as opposed to negative emotions. This finding adds to the growing evidence showing that when the presence and valence of dreamed emptions are scored by the participants themselves as opposed to by external judges, as done in early studies of dream content 15 , a considerably higher proportion (70% to 100%) of dream reports are found to contain emotions 16 , 77 , 78 , 79 and that positive dream affect is particularly more frequent than when dream reports are assessed by external raters 17 , 79 . These findings also highlight the interest of investigating positive dimensions of waking states, such as mindfulness 27 and positive emotions 7 in relation to dream affect. In a related vein, the study of how self-regulation techniques such as relaxation and meditation may modulate the impact of state and trait factors on dream content also merits investigation.

In sum, results of the present study showed that trait anxiety, but not day-to-day levels of perceived stress, predicted the affective tone of home dream reports and revealed a potential bias in previous studies associated with the use of one-time retrospective assessments of state variables in predicting night-to-night variations in dream affect. The present results also underscore the need for additional research on factors underlying the valence of emotions experienced in everyday dreams as opposed to focusing solely on nightmares or trauma-related dreams. In particular, the study of different categories of stressors and the use of stress biomarkers could be particularly useful in elucidating the differential impact of state and trait factors on dream content.

Data were collected as part of a larger online study conducted on the Qualtrics Research Suite platform. After providing informed consent, participants were emailed a link giving them access to the study materials. Participants first completed a series of questionnaires on sleep, personality, trait anxiety and trauma history. They then received, over a maximum of four consecutive weeks, daily scheduled notifications to complete a questionnaire on dream recall in the morning as well as an evening questionnaire on the stress and emotions experienced that day. The project was approved by the Arts and Science Research Ethics Committee of the Université de Montréal, Canada (Project no. CERAS-2017-18-013-P) and all research was performed in accordance with their guidelines and regulations.

Participants

One hundred and twenty-eight non-paid participants (98 women, 30 men, M age  = 42.55, SD age  = 14.63, range = 19–76 years) were recruited from the general adult population between February and July 2018 via ads in free local newspapers (74.9% of sample), social networks (9.4%), email lists (8.6%) and community posters (7.1%). Study materials were available in both French and English to reflect the bilingual nature of Montreal, Canada. One hundred and twelve of the 128 volunteers (87.5%) completed the study in French. Eighty-eight participants (68.8% of sample) were working at the time of study, 20 (15.6%) were students, 12 (9.4%) were retired, 5 (3.9%) were unemployed, and 3 (2.3%) did not specify their occupation. Of the 285 people who initially expressed interest in the study, 151 provided written informed consent and completed the first set of questionnaires. Of these 151 participants, 23 (18 women, 5 men) were excluded for providing fewer than three consecutive days of matching stress and dream valence data. Participants’ morning dream data were paired with their stress ratings completed prior to bedtime the night before. Sixty-six of 128 participants (51.6%) completed one or more days of data collection beyond the 21 consecutive days required. These data were included in the analyses as they contained validly paired evening stress and morning dream valence scores.

Retrospective measures

Participants first completed a general Sleep and Dream Questionnaire 33 used to assess basic sleep, dream and demographic variables.

Boundary thinness

The short form of the Boundary Questionnaire (BQ18) 80 , which contains 18 items derived from the original Boundary Questionnaire 40 , was used to measure boundary thinness or thickness, a personality trait associated with various aspects of dreaming 57 , including high dream recall 43 and nightmare prevalence 58 . People with thin psychological boundaries are typically described as being creative, sensitive, vulnerable and easily mixing thoughts, images and feelings. The total score of the BQ18 consists of a sum of the ratings (ranging from 0 to 4) on the 18 items after inverting the ratings on 4 items. Scores on the BQ18 are positively correlated ( r  = 0.87, N  = 856) with total scores on the original Boundary Questionnaire 80 . Cronbach’s alpha (α) for the BQ18 in the present study was 0.70.

Trait anxiety

The Trait scale of the State-Trait Anxiety Inventory – Form Y (STAI-T) 81 measures anxiety as an enduring personality trait and consists of 20 statements that pertain to how participants “generally feel.” Each item is rated on a 4-point Likert scale. The total score is calculated as a sum of all the ratings (ranging from 0 to 80), with a higher score indicating higher trait anxiety. The STAI-T is widely used and has been translated in multiple languages, including in French Canadian 82 . The latter shows a correlation of r  = 0.82 with the original English version and a test-retest correlation of r  = 0.94. The original French-Canadian translation shows strong internal consistency (α = 0.91) and an identical reliability (α = 0.91) obtained in the present study.

Youth trauma

A shortened French version 83 of the Early Trauma Inventory Self Report (ETISR-SF) 84 was used to assess a range of physical, emotional, and sexual abuse experiences that may have occurred before the age of 18. The seven items, presented in “Yes-No” format, yield a total score ranging between 0 and 7. Cronbach’s alpha (α) for the ETISR-SF in the present study was 0.73.

Posttraumatic stress disorder

The Primary Care PTSD Screen (PC-PTSD) 85 measures four factors specific to posttraumatic stress disorder (PTSD): reexperiencing, avoidance, hyperarousal and numbing. A positive response to any of the yes/no items indicates that the responder may have PTSD or trauma-related problems, and a cut-off score of 3 is recommended to detect positive cases. Cronbach’s alpha (α) for the PC-PTSD in the present study was 0.75.

Prospective measures

Dream recall and content were assessed each morning via URL links emailed to each participant at 3:00 AM. To ensure that reported dream recall data was for the targeted day, daily links expired at 6:00 PM. This time range was sufficiently broad to accommodate participants’ occupations and schedules. Reminders were automatically sent out at 3:00 PM if the morning questionnaire had not been completed by that time. Waking perceived stress for the day was measured prior to bedtime with links sent out at 6:00 PM and expiring at 3:00 AM. A reminder was sent at 12:00 AM (i.e. midnight) if participants had not completed the evening questionnaire by that time.

Dream affect and content

Dream recall was assessed with a single item, “Did you dream last night?” and a “Yes-No” answer format. If “No” was selected, participants had the option of returning to the questionnaire if ever they remembered a dream later in the day. If participants answered “Yes,” they were required to indicate if they remembered one, two, or three or more dreams from that night. These values were used to calculate participants’ dream recall frequency. Participants then had to indicate (for the most vivid or best-remembered dream from the night if more than one dream was recalled), the dream’s emotional valence by answering the question, “What was the general emotion of your dream?” using a 10-point Likert scale ranging from positive (1) to negative (10).

Perceived stress

Two daily measures of perceived stress were completed prior to bedtime using a 10-point Likert scale ranging from not stressed at all (0) to extremely stressed (9). The first measure required participants to rate the maximum level of stress experienced that day while the second required participants to rate their stress level at the time of questionnaire completion (i.e., prior to bedtime). These scales, reviewed by Dr. Sonia J. Lupien, director of the Centre for Studies on Human Stress ( https://humanstress.ca/ ), were used instead of more exhaustive instruments such as the Daily Stress Inventory 86 due to the multi-week nature of the study and our desire to limit volunteers’ workload.

Statistical analyses

Data were analyzed using hierarchical linear modeling (HLM) with IBM SPSS Statistics (version 25), where affect load (level 1: affective dream content [outcome], perceived stress [predictor]) was underpinned by the participants’ dispositional measures (level 2 predictors: trait anxiety, boundary thinness, trauma history, PTSD, sex, age). The level of statistical significance for every analysis was set at p  = 0.05. This type of multilevel analysis is ideally suited to such a dataset as it a) allows for the analysis of multiple relationships while considering shared variance at both levels, b) takes into account dependency across measurement time points, c) doesn’t require balanced designs in which different individuals have a fixed number of prospective data points without any missing data, and d) has fewer assumptions and is less likely to underestimate error than other statistical methods 87 .

Although dream valence was the main outcome variable of interest, models predicting daily perceived stress were also tested to investigate possible effects of dreamed emotions on daytime stress. Dream valence had a normal distribution and enough anchor points (10) to approximate continuity. It was thus tested using linear mixed-effects modeling (MIXED command). Since both measures of daily perceived stress were positively skewed, they were tested under a Poisson distribution using a generalized estimating equation (GENLIN command) which, in both cases, presented a better model fit than with a normal distribution under a linear mixed-effects model.

When dream valence was the outcome variable, measures of daily stress from the preceding day were used as Level-1 predictors while trait, trauma and demographic variables were used as Level-2 predictors. Since dream recall frequency was measured daily, it was also used as a Level-1 predictor to assess its possible mediating effect on dream valence and other predictor variables, with values from 1 (one dream remembered on that night) to 3 (three or more dreams remembered). When daily stress was the outcome of interest, the dataset was shifted in order for a given night’s dream valence to be paired with levels of perceived stress of the following day. Considering that participants’ first daily measurement was for perceived stress, there was a smaller total of 2410 observations, not 2538, because the first stress values and last dream valence values were unpaired and thus excluded.

We first computed an intercepts-only model where time was not specified as a repeated measures variable and no predictors entered. This procedure is recommended to determine the amount of between-subject variance in the outcome variable, also known as the intraclass correlation 88 . The intraclass correlation was thus calculated by dividing the value of the intercept (between-group) variance by the sum of the residual (within-group) variance and intercept.

We then progressively added predictors to the unconditional model, beginning with individual Level-2 predictors. All Level-2 variables were grand mean centered. Level-1 stress predictor variables were centered to each participants’ mean for the duration of the study to account for dispositional biases in reported self-ratings.

Finally, post hoc analyses were performed to test alternate hypotheses. Interactions were tested between predictors to assess whether the model generalized to the whole sample or if some effects were moderated by other variables. We individually tested and reported the potential moderating effects of every level 2 predictor and of dream recall and valence (level 1) on each of the two level 1 stress predictors. The effect on dream valence of the stress variables from 2 to 7 days ago was also tested using lagged independent variables.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Domhoff, G. W. Finding meaning in dreams: a quantitative approach ., https://doi.org/10.1007/978-1-4899-0298-6 (Plenum Press, 1996).

Book   Google Scholar  

Hall, C. S. & Nordby, V. J. The individual and his dreams . (Signet, 1972).

Domhoff, G. W. The emergence of dreaming: mind-wandering, embodied simulation, and the default network . (Oxford University Press, 2018).

Han, H. J., Schweickert, R., Xi, Z. & Viau-Quesnel, C. The cognitive social network in dreams: transitivity, assortativity, and giant component proportion are monotonic. Cogn. Sci. 40 , 671–696 (2016).

Article   PubMed   Google Scholar  

Pesant, N. & Zadra, A. Dream content and psychological well-being: a longitudinal study of the continuity hypothesis. J. Clin. Psychol. 62 , 111–121 (2006).

Schredl, M. & Erlacher, D. Relation between waking sport activities, reading, and dream content in sport students and psychology students. J. Psychol. 142 , 267–275 (2008).

Sikka, P., Pesonen, H. & Revonsuo, A. Peace of mind and anxiety in the waking state are related to the affective content of dreams. Sci. Rep. 8 , 1–13 (2018).

Article   CAS   Google Scholar  

Davidson, J., Lee-Archer, S. & Sanders, G. Dream imagery and emotion. Dreaming 15 , 33–47 (2005).

Article   Google Scholar  

Gilchrist, S., Davidson, J. & Shakespeare-Finch, J. Dream emotions, waking emotions, personality characteristics and well-being — a positive psychology approach. Dreaming 17 , 172–185 (2007).

Nixon, A., Robidoux, R., Dale, A. L. & De Koninck, J. Pre-sleep and post-sleep mood as a complementary evaluation of emotionally impactful dreams. Int. J. Dream Res. 10 , 141–150 (2017).

Google Scholar  

Schredl, M. Factors affecting the continuity between waking and dreaming: emotional intensity and emotional tone of the waking-life event. Sleep Hypn. 8 , 1–5 (2006).

Schredl, M. & Reinhard, I. The continuity between waking mood and dream emotions: direct and second-order effects. Imagin. Cogn. Pers. 29 , 271–282 (2010).

St-Onge, M., Lortie-Lussier, M., Mercier, P., Grenier, J. & De Koninck, J. Emotions in the diary and REM dreams of young and late adulthood women and their relation to life satisfaction. Dreaming 15 , 116–128 (2005).

Fosse, R., Stickgold, R. & Hobson, J. A. The mind in REM sleep: reports of emotional experience. Sleep 24 , 1–9 (2001).

Hall, C. S. & Van de Castle, R. L. The content analysis of dreams. Central Psychology Series (Appleton-Century-Crofts, 1966).

Merritt, J. M., Stickgold, R., Pace-Schott, E. F., Williams, J. & Hobson, J. A. Emotion profiles in the dreams of men and women. Conscious. Cogn. 3 , 46–60 (1994).

Sikka, P., Valli, K., Virta, T. & Revonsuo, A. I know how you felt last night, or do I? Self- and external ratings of emotions in REM sleep dreams. Conscious. Cogn. 25 , 51–66 (2014).

Cartwright, R. D. The twenty-four hour mind: the role of sleep and dreaming in our emotional lives . (Oxford University Press, 2010).

Hartmann, E. The nature and functions of dreaming . (Oxford University Press, 2010).

Kramer, M. The selective mood regulatory function of dreaming: an update and revision. In The functions of dreaming . (eds Moffitt, A., Kramer, M. & Hoffmann, R.) 139–195 (State University of New York Press, 1993).

Cook, C. A. L., Caplan, R. D. & Wolowitz, H. Nonwaking responses to waking stressors: dreams and nightmares. J. Appl. Soc. Psychol. 20 , 199–226 (1990).

De Koninck, J. & Koulack, D. Dream content and adaptation to a stressful situation. J. Abnorm. Psychol. 84 , 250–260 (1975).

Delorme, M. A., Lortie-Lussier, M. & De Koninck, J. Stress and coping in the waking and dreaming states during an examination period. Dreaming 12 , 171–183 (2002).

Roberts, J., Lennings, C. J. & Heard, R. Nightmares, life stress, and anxiety: an examination of tension reduction. Dreaming 19 , 17–29 (2009).

Blagrove, M. & Pace-Schott, E. F. Trait and neurobiological correlates of individual differences in dream recall and dream content. In International Review of Neurobiology: Dreams and Dreaming . (eds Clow, A. & McNamara, P.) 92 , 155–180 (Elsevier, 2010).

Hartmann, E., Rosen, R. & Rand, W. Personality and dreaming: boundary structure and dream content. Dreaming 8 , 31–39 (1998).

Simor, P., Köteles, F., Sándor, P., Petke, Z. & Bódizs, R. Mindfulness and dream quality: the inverse relationship between mindfulness and negative dream affect. Scand. J. Psychol. 52 , 369–375 (2011).

Duval, M., McDuff, P. & Zadra, A. Nightmare frequency, nightmare distress, and psychopathology in female victims of childhood maltreatment. J. Nerv. Ment. Dis. 201 , 767–772 (2013).

Esposito, K., Benitez, A., Barza, L. & Mellman, T. A. Evaluation of dream content in combat-related PTSD. J. Trauma. Stress 12 , 681–687 (1999).

Article   CAS   PubMed   Google Scholar  

Helminen, E. & Punamäki, R.-L. Contextualized emotional images in children’s dreams: psychological adjustment in conditions of military trauma. Int. J. Behav. Dev. 32 , 177–187 (2008).

Valli, K. et al . The threat simulation theory of the evolutionary function of dreaming: evidence from dreams of traumatized children. Conscious. Cogn. 14 , 188–218 (2005).

Blagrove, M., Farmer, L. & Williams, E. The relationship of nightmare frequency and nightmare distress to well-being. J. Sleep Res. 13 , 129–136 (2004).

Zadra, A. & Donderi, D. C. Nightmares and bad dreams: their prevalence and relationship to well-being. J. Abnorm. Psychol. 109 , 273–281 (2000).

Levin, R. & Nielsen, T. A. Disturbed dreaming, posttraumatic stress disorder, and affect distress: a review and neurocognitive model. Psychol. Bull. 133 , 482–528 (2007).

Levin, R. & Nielsen, T. A. Nightmares, bad dreams and emotion dysregulation: a review and new neurocognitive model of dreaming. Curr. Dir. Psychol. Sci. 18 , 84–88 (2009).

Sandman, N. et al . Nightmares as predictors of suicide: an extension study including war veterans. Sci. Rep. 7 , 1–7 (2017).

Schredl, M. & Engelhardt, H. Dreaming and psychopathology: dream recall and dream content of psychiatric inpatients. Sleep Hypn. 3 , 44–54 (2001).

Soffer-Dudek, N. Arousal in nocturnal consciousness: how dream- and sleep-experiences may inform us of poor sleep quality, stress, and psychopathology. Front. Psychol. 8 , 1–10 (2017).

Nielsen, T. A. The stress acceleration hypothesis of nightmares. Front. Neurol. 8 , 1–23 (2017).

Hartmann, E. Boundaries in the mind: a new psychology of personality . (Basic Books, 1991).

Sylvers, P., Lilienfeld, S. O. & LaPrairie, J. L. Differences between trait fear and trait anxiety: implications for psychopathology. Clin. Psychol. Rev. 31 , 122–137 (2011).

Blagrove, M. & Fisher, S. Trait-state interactions in the etiology of nightmares. Dreaming 19 , 65–74 (2009).

Schredl, M., Schäfer, G., Hofmann, F. & Jacob, S. Dream content and personality: thick vs. thin boundaries. Dreaming 9 , 257–263 (1999).

Sándor, P., Horváth, K., Bódizs, R. & Konkolÿ Thege, B. Attachment and dream emotions: The mediating role of trait anxiety and depression. Curr. Psychol . 1–10, https://doi.org/10.1007/s12144-018-9890-y (2018).

Nielsen, T. A. et al . Development of disturbing dreams during adolescence and their relation to anxiety symptoms. Sleep 23 , 1–10 (2000).

Levin, R., Fireman, G., Spendlove, S. & Pope, A. The relative contribution of affect load and affect distress as predictors of disturbed dreaming. Behav. Sleep Med. 9 , 173–183 (2011).

Schredl, M. Effects of state and trait factors on nightmare frequency. Eur. Arch. Psychiatry Clin. Neurosci. 253 , 241–247 (2003).

Soffer-Dudek, N. & Shahar, G. Daily stress interacts with trait dissociation to predict sleep-related experiences in young adults. J. Abnorm. Psychol. 120 , 719–729 (2011).

Cellucci, A. J. & Lawrence, P. S. Individual differences in self-reported sleep variable correlations among nightmare sufferers. J. Clin. Psychol. 34 , 721–725 (1978).

Malinowski, J. E. & Horton, C. L. Evidence for the preferential incorporation of emotional waking-life experiences into dreams. Dreaming 24 , 18–31 (2014).

Watson, D. Dissociations of the night: individual differences in sleep-related experiences and their relation to dissociation and schizotypy. J. Abnorm. Psychol. 110 , 526–535 (2001).

Nielsen, T. A., Kuiken, D., Alain, G., Stenstrom, P. & Powell, R. A. Immediate and delayed incorporations of events into dreams: Further replication and implications for dream function. J. Sleep Res. 13 , 327–336 (2004).

Eichenlaub, J.-B. et al . The nature of delayed dream incorporation (‘dream-lag effect’): personally significant events persist, but not major daily activities or concerns. J. Sleep Res. 28 , 1–8 (2019).

Agargun, M. Y. et al . Nightmares and dissociative experiences: the key role of childhood traumatic events. Psychiatry Clin. Neurosci. 57 , 139–145 (2003).

Wittmann, L., Schredl, M. & Kramer, M. Dreaming in posttraumatic stress disorder: a critical review of phenomenology, psychophysiology and treatment. Psychother. Psychosom. 76 , 25–39 (2007).

Duval, M. & Zadra, A. Frequency and content of dreams associated with trauma. Sleep Med. Clin. 5 , 249–260 (2010).

Aumann, C., Lahl, O. & Pietrowsky, R. Relationship between dream structure, boundary structure and the big five personality dimensions. Dreaming 22 , 124–135 (2012).

Zborowski, M., McNamara, P., Hartmann, E., Murphy, M. & Mattle, L. Boundary structure related to sleep measures and to dream content. Sleep 21S , 284 (1998).

Hartmann, E., Russ, D., Oldfield, M., Sivan, I. & Cooper, S. Who has nightmares? Arch. Gen. Psychiatry 44 , 49–56 (1987).

Wood, J. M. & Bootzin, R. R. The prevalence of nightmares and their independence from anxiety. J. Abnorm. Psychol. 99 , 64–68 (1990).

Robert, G. & Zadra, A. Measuring nightmare and bad dream frequency: impact of retrospective and prospective instruments. J. Sleep Res. 17 , 132–139 (2008).

Beaulieu-Prévost, D. & Zadra, A. Absorption, psychological boundaries and attitude towards dreams as correlates of dream recall: two decades of research seen through a meta-analysis. J. Sleep Res. 16 , 51–59 (2007).

Wood, J. M., Bootzin, R. R., Rosenhan, D., Nolen-Hoeksema, S. & Jourden, F. Effects of the 1989 San Francisco earthquake on frequency and content of nightmares. J. Abnorm. Psychol. 101 , 219–224 (1992).

Soffer-Dudek, N. & Shahar, G. Effect of exposure to terrorism on sleep-related experiences in Israeli young adults. Psychiatry Interpers. Biol. Process. 73 , 264–276 (2010).

Koulack, D., Prevost, F. & De Koninck, J. Sleep, dreaming, and adaptation to a stressful intellectual activity. Sleep 8 , 244–253 (1985).

Cartwright, R. D., Lloyd, S., Knight, S. & Trenholme, I. Broken dreams: a study of the effects of divorce and depression on dream content. Psychiatry 47 , 251–9 (1984).

Revonsuo, A., Tuominen, J. & Valli, K. Avatars in the machine: dreaming as a simulation of social reality. In Open Mind: Philosophy and the Mind Sciences in the 21st Century (eds Metzinger, T. & Windt, J. M.) 2 , 1295–1322 (MIND Group, 2016).

Takahashi, T. et al . Anxiety, reactivity, and social stress-induced cortisol elevation in humans. Neuroendocrinol. Lett. 26 , 351–354 (2005).

CAS   PubMed   Google Scholar  

Tuominen, J., Stenberg, T., Revonsuo, A. & Valli, K. Social contents in dreams: an empirical test of the Social Simulation Theory. Conscious. Cogn. 69 , 133–145 (2019).

van Eck, M. M., Berkhof, H., Nicolson, N. A. & Sulon, J. The effects of perceived stress, traits, mood states, and stressful daily events on salivary cortisol. Psychosom. Med. 58 , 447–458 (1996).

Hirvikoski, T., Lindholm, T., Nordenström, A., Nordström, A. L. & Lajic, S. High self-perceived stress and many stressors, but normal diurnal cortisol rhythm, in adults with ADHD (attention-deficit/hyperactivity disorder). Horm. Behav. 55 , 418–424 (2009).

Nagy, T. et al . Frequent nightmares are associated with blunted cortisol awakening response in women. Physiol. Behav. 147 , 233–237 (2015).

Payne, J. D. Memory consolidation, the diurnal rhythm of cortisol, and the nature of dreams: a new hypothesis. In International Review of Neurobiology: Dreams and Dreaming (eds Clow, A. & McNamara, P.) 92, 101–134 (Elsevier, 2010).

Carr, M. & Solomonova, E. Dream recall and content in different stages of sleep and time-of-night effect. In Dreams: Understanding Biology, Psychology, and Culture (eds Valli, K., Hoss, R. J. & Gongloff, R. P.) 188–194 (Greenwood Publishing Group, 2019).

Trinder, J. & Kramer, M. Dream recall. Am. J. Psychiatry 128 , 296–301 (1971).

Foulkes, D., Sullivan, B., Kerr, N. H. & Brown, L. Appropriateness of dream feelings to dreamed situations. Cogn. Emot. 2 , 29–39 (1988).

Nielsen, T. A., Deslauriers, D. & Baylor, G. W. Emotions in dream and waking event reports. Dreaming 1 , 287–300 (1991).

Schredl, M. & Doll, E. Emotions in diary dreams. Conscious. Cogn. 7 , 634–646 (1998).

Sikka, P., Feilhauer, D., Valli, K. & Revonsuo, A. How you measure is what you get: differences in self- and external ratings of emotional experiences in home dreams. Am. J. Psychol. 130 , 367–384 (2017).

Kunzendorf, R. G., Hartmann, E., Cohen, R. & Cutler, J. Bizarreness of the dreams and daydreams reported by individuals with thin and thick boundaries. Dreaming 7 , 265–271 (1997).

Spielberger, C. D., Gorsuch, R. L., Lushene, R. E., Vagg, P. R. & Jacobs, G. A. Manual for the State-Trait Anxiety Inventory (Form Y): Self-Evaluation Questionnaire. Consulting Psychologists Press , https://doi.org/10.5370/JEET.2014.9.2.478 (Consulting Psychologists Press, 1983).

Gauthier, J. & Bouchard, S. Adaptation canadienne-française de la forme revisée du State-Trait Anxiety Inventory de Spielberger. Rev. Can. des Sci. du Comport. 25 , 559–578 (1993).

Hébert, M., Cyr, M. & Zuk, S. Traduction et adaptation française du Early Trauma Inventory Self-Report - Short Form (ETISR-SF, 2007) de Bremner, Bolus et Mayer (2008).

Bremner, J. D., Bolus, R. & Mayer, E. A. Psychometric properties of the Early Trauma Inventory-Self Report. J. Nerv. Ment. Dis. 195 , 211–218 (2007).

Article   PubMed   PubMed Central   Google Scholar  

Prins, A. et al . The primary care PTSD screen (PC–PTSD): development and operating characteristics. Prim. Care Psychiatry 9 , 9–14 (2004).

Brantley, P. J., Waggoner, C. D., Jones, G. N. & Rappaport, N. B. A daily stress inventory: development, reliability, and validity. J. Behav. Med. 10 , 61–73 (1987).

Woltman, H., Feldstain, A., MacKay, C. & Rocchi, M. An introduction to hierarchical linear modeling. Tutor. Quant. Methods Psychol. 8 , 52–69 (2012).

Tabachnick, B. G. & Fidell, L. S. Using multivariate statistics , https://doi.org/10.1037/022267 (Pearson Education Inc., 2019).

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This research was funded by a grant from the Social Sciences and Humanities Research Council of Canada (SSHRC #435-2015-1181) and from the Canadian Institutes of Health Research (CIHR # MOP 97865) to A.Z. The authors would like to thank Pierre McDuff for his help with statistical analyses, the Interdisciplinary Research Centre on Intimate Relationship Problems and Sexual Abuse (CRIPCAS) and the Centre for Studies on Human Stress (CSHS) for their assistance in the early phases of the study.

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Researching Dreams

The Fundamentals

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• Delves into the intricacies of the history and future of dream research
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What can be gleaned from the study of our dreams? With research methods in mind—including the shortcomings and strengths of various strategies—the book presents a comprehensive introduction to the research obtained so far. Topics include the factors of dream recall; the continuity hypothesis of dreaming; the relationship between physiology and dream content; etiology and therapy of nightmares; and lucid dreaming. The book not only presents a comprehensive introduction to the research obtained so far but also provide the tools to carry our scientific dream studies—including the shortcomings and strengths of various approaches.

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A Vast Ocean of Neglected Dream Studies

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Do You Think That Scientific Psychology Has a Place for the Study of Dreaming? In Other Words, Do You Accept Introspection as Scientifically Useful?

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July 26, 2011

The Science Behind Dreaming

New research sheds light on how and why we remember dreams--and what purpose they are likely to serve

By Sander van der Linden

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For centuries people have pondered the meaning of dreams. Early civilizations thought of dreams as a medium between our earthly world and that of the gods. In fact, the Greeks and Romans were convinced that dreams had certain prophetic powers. While there has always been a great interest in the interpretation of human dreams, it wasn’t until the end of the nineteenth century that Sigmund Freud and Carl Jung put forth some of the most widely-known modern theories of dreaming. Freud’s theory centred around the notion of repressed longing -- the idea that dreaming allows us to sort through unresolved, repressed wishes. Carl Jung (who studied under Freud) also believed that dreams had psychological importance, but proposed different theories about their meaning.

Since then, technological advancements have allowed for the development of other theories. One prominent neurobiological theory of dreaming is the “activation-synthesis hypothesis,” which states that dreams don’t actually mean anything: they are merely electrical brain impulses that pull random thoughts and imagery from our memories. Humans, the theory goes, construct dream stories after they wake up, in a natural attempt to make sense of it all. Yet, given the vast documentation of realistic aspects to human dreaming as well as indirect experimental evidence that other mammals such as cats also dream, evolutionary psychologists have theorized that dreaming really does serve a purpose. In particular, the “threat simulation theory” suggests that dreaming should be seen as an ancient biological defence mechanism that provided an evolutionary advantage because of  its capacity to repeatedly simulate potential threatening events – enhancing the neuro-cognitive mechanisms required for efficient threat perception and avoidance.

So, over the years, numerous theories have been put forth in an attempt to illuminate the mystery behind human dreams, but, until recently, strong tangible evidence has remained largely elusive.

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Yet, new research published in the Journal of Neuroscience provides compelling insights into the mechanisms that underlie dreaming and the strong relationship our dreams have with our memories. Cristina Marzano and her colleagues at the University of Rome have succeeded, for the first time, in explaining how humans remember their dreams. The scientists predicted the likelihood of successful dream recall based on a signature pattern of brain waves. In order to do this, the Italian research team invited 65 students to spend two consecutive nights in their research laboratory.

During the first night, the students were left to sleep, allowing them to get used to the sound-proofed and temperature-controlled rooms. During the second night the researchers measured the student’s brain waves while they slept. Our brain experiences four types of electrical brain waves: “delta,” “theta,” “alpha,” and “beta.” Each represents a different speed of oscillating electrical voltages and together they form the electroencephalography (EEG). The Italian research team used this technology to measure the participant’s brain waves during various sleep-stages. (There are five stages of sleep; most dreaming and our most intense dreams occur during the REM stage.) The students were woken at various times and asked to fill out a diary detailing whether or not they dreamt, how often they dreamt and whether they could remember the content of their dreams.

While previous studies have already indicated that people are more likely to remember their dreams when woken directly after REM sleep, the current study explains why. Those participants who exhibited more low frequency theta waves in the frontal lobes were also more likely to remember their dreams.

This finding is interesting because the increased frontal theta activity the researchers observed looks just like the successful encoding and retrieval of autobiographical memories seen while we are awake. That is, it is the same electrical oscillations in the frontal cortex that make the recollection of episodic memories (e.g., things that happened to you) possible. Thus, these findings suggest that the neurophysiological mechanisms that we employ while dreaming (and recalling dreams) are the same as when we construct and retrieve memories while we are awake.

In another recent study conducted by the same research team, the authors used the latest MRI techniques to investigate the relation between dreaming and the role of deep-brain structures. In their study, the researchers found that vivid, bizarre and emotionally intense dreams (the dreams that people usually remember) are linked to parts of the amygdala and hippocampus. While the amygdala plays a primary role in the processing and memory of emotional reactions, the hippocampus has been implicated in important memory functions, such as the consolidation of information from short-term to long-term memory.

The proposed link between our dreams and emotions is also highlighted in another recent study published by Matthew Walker and colleagues at the Sleep and Neuroimaging Lab at UC Berkeley, who found that a reduction in REM sleep (or less “dreaming”) influences our ability to understand complex emotions in daily life – an essential feature of human social functioning.  Scientists have also recently identified where dreaming is likely to occur in the brain.  A very rare clinical condition known as “Charcot-Wilbrand Syndrome” has been known to cause (among other neurological symptoms) loss of the ability to dream.  However, it was not until a few years ago that a patient reported to have lost her ability to dream while having virtually no other permanent neurological symptoms. The patient suffered a lesion in a part of the brain known as the right inferior lingual gyrus (located in the visual cortex). Thus, we know that dreams are generated in, or transmitted through this particular area of the brain, which is associated with visual processing, emotion and visual memories.

Taken together, these recent findings tell an important story about the underlying mechanism and possible purpose of dreaming.

Dreams seem to help us process emotions by encoding and constructing memories of them. What we see and experience in our dreams might not necessarily be real, but the emotions attached to these experiences certainly are. Our dream stories essentially try to strip the emotion out of a certain experience by creating a memory of it. This way, the emotion itself is no longer active.  This mechanism fulfils an important role because when we don’t process our emotions, especially negative ones, this increases personal worry and anxiety. In fact, severe REM sleep-deprivation is increasingly correlated to the development of mental disorders. In short, dreams help regulate traffic on that fragile bridge which connects our experiences with our emotions and memories.

Are you a scientist who specializes in neuroscience, cognitive science, or psychology? And have you read a recent peer-reviewed paper that you would like to write about? Please send suggestions to Mind Matters editor Gareth Cook, a Pulitzer prize-winning journalist at the Boston Globe. He can be reached at garethideas AT gmail.com or Twitter @garethideas .

How scientists are studying dreams in the lab

Neuroimaging, sleepwalking, coin tosses.

By Angela Chen

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Once, studying dreams was the domain of mystics, prophets, and a certain sex-obsessed Austrian psychoanalyst. With neuroimaging techniques and better technology, dreams have become a focus of scientific research, from efforts to record dreams to studies investigating how lucid dreaming might be beneficial to mental health.

Journalist Alice Robb is the author of Why We Dream: The Transformative Power of Our Nightly Journey . The Verge spoke with Robb about theories of dreams, the most provocative studies, and the many questions that remain in the field.

This interview has been lightly edited for clarity.

Can you start by giving me a brief intellectual history of dreams? Before our modern scientific understanding, what were people’s theories of dreams?

If you look throughout history, you see people taking dreams really seriously. Dream diaries are some of the oldest examples of literature, and dreams in the Bible are often treated as prophetic. In the late 19th and early 20th century, Freud comes along and puts dreams at the center of psychoanalysis, arguing that they’re the royal road to the unconscious, and analysts should ask patients about them, and by unpacking them, you can get to the core of a patient’s issues. You see the idea taking off. On the flip side, Freud also said that dreams are all about sex — “a room represents a woman because it has an entrance” — which perhaps didn’t do dreams a favor.

Another part of the story is that the science of sleep is relatively new. Rapid-eye movement (REM) sleep was only discovered in the 1950s. And until then, most scientists thought that sleep was just a time when your brain turned off, and there wasn’t much to study. Or even if there was, they didn’t have a way to study it. So a big part of the story is also advances in technology and neuroimaging enabling us to study sleep and dreams. And now, you see people becoming much more aware of sleep as important for health, and so dreams and sleep are going to the lab.

From a very reductionist, neuroscientific point of view, what’s happening in the brain when we dream? What’s the difference between dreams at night and daydreaming and fantasy?

It’s time when the frontal lobe, the logic centers, are less activated. There’s less rational thinking. At the same time, dopamine is surging and people are often having intense emotional experiences.

Daydreaming, mind wandering, night dreaming — you can think of them as all on a spectrum. They are all involving the default mode network, the part of the brain that gets involved when everything else has quieted down, and you’re not actively engaged in something. Both mind-wandering and daydreams are involving the medial prefrontal cortex and medial temporal lobe. During REM dreams, you’re also the visual cortex so you’re having these more intensely visual experiences. Sight is the sense that’s more involved than, say, hearing or smell or touch.

Do people really smell things in dreams? I don’t believe I have, though I also generally have a weak sense of smell.

I do think smell is rare in dreams. I don’t have a stat off the top of my head, but dreams are predominantly visual, even for people who are blind, depending on what age they lost their sight. If they lost their sight after around the age of five, they can experience sight in dreams.

Nowadays, what are the main psychological theories for dreams? I’m assuming Freud is no longer in fashion?

Certain ideas of Freud’s have been borne out. One idea is that you are dreaming about things you are suppressing during the day, and there is actually research on something called the “ dream rebound effect .” The psychologist Daniel Wegner found that if people were told not to focus on something before going to bed, they’re more likely to dream about it. He told one group of students to focus on a target person before bed and told another group of students about this target person and found that the group that was trying to avoid those thoughts were actually reporting more dreams about the person.

There’s a theory from evolutionary psychology that’s pretty popular, and it argues that dreams have a survival function. They give us a chance to practice for things we’re stressed out about in real life. That would explain why dreams are predominantly negative. Dreams tend to be much more about anxiety than about pleasure and involve a lot of intense feelings and fear. The idea is that we wake up, and we’re more prepared to tackle the things we faced in our nightmares. That would also maybe explain why dreams tend to involve more primal settings. There are a lot of actions like running around and being chased, elaborate themes that don’t have much to do with our lives if we live in cities. We’re less likely to have dreams about reading and writing and activities that are more recent developments.

What tools are scientists using to study dreams? Do you have favorite studies?

There are a lot of indirect ways that scientists have found to study dreams, like studying the actions of sleepwalkers or putting recording devices in people’s rooms and catching the utterances that they make during sleep talking and analyzing the language of that.

Neuroimaging studies and studies of rats with electrodes have been important. Some of the first research on memory consolidation and dreams comes from rat studies. Matt Wilson, who’s at MIT, was trying to study memory in rats as they stepped into a maze. They went back to sleep and he noticed through the monitor that he had happened to leave on that their neurons were firing again, as if they were awake and running through the maze when they were in fact asleep. They’re replaying the path that they’ve taken through the day.

Building off that, other scientists ran an experiment where they released rats into a maze. The rats would run around randomly with no preference for any area. If the scientists gave them pleasurable stimulation while the rats were replaying a certain part of the maze during sleep, when the rat wakes up they tend to gravitate more toward that place.

Are there certain big questions that everyone in the field is trying to work on?

There’s definitely a lot of questions that are still unanswered. There’s no formula to determine why we have a certain dream on a certain night, why exactly we’re pulling different memories and mixing them up in the way that they appear.

There’s some really interesting new efforts to improve our ability to record dreams. One of the things that has held dream research back is that they’re so hard to study. Either you are asking people what they dreamed about, which obviously isn’t a perfect way to collect data, or you’re doing brain scans that you can only see, you can’t correlate perfectly to the actual dream content.

There was a Japanese study a few years ago where a group was actually able to create a very crude dream reading device . They scanned people’s brains while they were awake and thinking about certain objects and characters — like a man, a woman, computer, food — and then were able to look at those patterns and match them loosely to what they were thinking about when they were asleep. That correlated pretty well with the subject’s own dream reports.

There’s also a handful of researchers focusing on lucid dreaming. Scientists are looking at how we can induce lucid dreams more reliably, as well as clinical applications of lucid dreaming. I met one woman who used her lucid dreams to hypnotize herself and tell herself that she wouldn’t be anxious anymore. She said that had a positive effect on her waking state.

Another question is: if you rehearse for something in a lucid dream, how does that compare to practicing a task while you’re awake? There was one small study where students had a task tossing a coin in a cup and taking that and trying to have a lucid dream about that to see how effective that was .

That’s interesting, though I hate the idea that now I should be working in my dreams, too. What was the result of coin study?

Forty people tried to toss a coin into a cup about six feet away, and then, afterward, one group was allowed to practice in waking life, another tried to practice in a lucid dream and a control group did nothing. Practicing in real life helped the most, then the lucid dreaming group.

Dream research is typically considered a bit woo-woo. Do you feel like dream researcher is moving into the mainstream?

Dream researchers are definitely gaining more and more respectability, and it’s becoming a legitimate topic of study, as it deserves to be. But it’s still hard to get around the fact that dreams lend themselves to some theorizing that not all areas of study do. For example, I went to a conference in the Netherlands called the International Association for the Study of Dreams that has both people who are hard scientists and also people leading groups for dream analysis. It can be hard to disentangle the science from some of the more mystical ideas.

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MIT Dream Research Interacts Directly With an Individual’s Dreaming Brain and Manipulates the Content

By Sarah Beckmann, Massachusetts Institute of Technology July 27, 2020

“Dormio takes dream research to a new level, interacting directly with an individual’s dreaming brain and manipulating the actual content of their dreams,” says Robert Stickgold, director of the Center for Sleep and Cognition at Beth Israel Deaconess Medical Center. Credit: Helen Gao

Device not only helps record dream reports, but also guides dreams toward particular themes.

The study of dreams has entered the modern era in exciting ways, and researchers from MIT and other institutions have created a community dedicated to advancing the field, lending it legitimacy, and expanding further research opportunities.

In a new paper, researchers from the Media Lab’s Fluid Interfaces group introduce a novel method called “Targeted Dream Incubation” (TDI). This protocol, implemented through an app in conjunction with a wearable sleep-tracking sensor device, not only helps record dream reports, but also guides dreams toward particular themes by repeating targeted information at sleep onset, thereby enabling the incorporation of this information into dream content. The TDI method and accompanying technology serve as tools for controlled experimentation in dream study, widening avenues for research into how dreams impact emotion, creativity, memory, and beyond.

The paper, “Dormio: A Targeted Dream Incubation Device,” is co-authored by lead researcher Adam Haar Horowitz and professor of media arts and sciences Pattie Maes, who is also head of the Fluid Interfaces group. Additional authors on the paper are Tony J. Cunningham, postdoc at Beth Israel Deaconess Medical Center and Harvard Medical School, and Robert Stickgold, director of the Center for Sleep and Cognition at Beth Israel Deaconess Medical Center and professor of psychiatry at Harvard Medical School.

Previous neuroscience studies from researchers such as sleep and cognitive sciences expert Stickgold show that hypnagogia (the earliest sleep stage) is similar to the REM stage in terms of brainwaves and experience; however, unlike REM, individuals can still hear audio during hypnagogia while they dream.

“This state of mind is trippy, loose, flexible, and divergent,” explains Haar Horowitz. “It’s like turning the notch up high on mind-wandering and making it immersive — being pushed and pulled with new sensations like your body floating and falling, with your thoughts quickly snapping in and out of control.”

To facilitate the TDI protocol, an interdisciplinary team at the Media Lab designed and developed Dormio, a sleep-tracking device that can alter dreams by tracking hypnagogia and then delivering audio cues based on incoming physiological data, at precise times in the sleep cycle, to make dream direction possible. Upon awakening, a person’s guided dream content can be used to complete tasks such as creative story writing, and compared experimentally to waking thought content.

“Dormio takes dream research to a new level, interacting directly with an individual’s dreaming brain and manipulating the actual content of their dreams,” says Stickgold. “The potential value of Dormio for enhancing learning and creativity are literally mind-blowing.”

The Media Lab team’s first pilot study using Dormio demonstrated dream incubation and creativity augmentation in six people, and was presented alt.CHI in 2018. Multiple scientists began reaching out to the team expressing interest in replicating the dream-control research. These requests led to the first Dream Engineering workshop, which was held at the Media Lab in January 2019, organized by Maes, Haar Horowitz, and Judith Amores from the Fluid Interfaces group, and Michelle Carr, visiting researcher from the University of Rochester Sleep and Neurophysiology Laboratory. The workshop brought together many of the world’s leading dream researchers, including pioneers such as Deirdre Barrett, Bjorn Rasch, Ken Paller, and Stephen LaBerge, to brainstorm new technologies for studying, recording, and influencing dreams.

The talks and technologies presented at the workshop further led to a Special Issue on Dream Engineering for the journal Consciousness and Cognition , with Maes, Haar Horowitz, Amores, and Carr serving as guest editors.

“Most sleep and dream studies have so far been limited to university sleep labs and have been very expensive, as well as cumbersome, for both researchers and participants,” says Maes. “Our research group is excited to be pioneering new, compact, and cheap technologies for studying sleep and interfacing with dreams, thereby opening up opportunities for more studies to happen and for these experiments to take place in natural settings. Apart from benefiting scientists, this work has the potential to lead to new commercial technologies that go beyond sleep tracking to issue interventions that affect sleep onset, sleep quality, sleep-based memory consolidation, and learning.”

The research itself is central to Haar Horowitz’s thesis work in the Program of Media Arts and Sciences. This past year, he ran a larger dream study with 50 subjects, which replicated and extended the results of the previous study.

“We showed that dream incubation is tied to performance benefits on three tests of creativity, by both objective and subjective metrics,” Haar Horowitz states. “Dreaming about a specific theme seems to offer benefits post-sleep, such as on creativity tasks related to this theme. This is unsurprising in light of historical figures like Mary Shelley or Salvador Dalí, who were inspired creatively by their dreams. The difference here is that we induce these creatively beneficial dreams on purpose, in a targeted manner.”

An enhanced Dormio device has now also been built, as well as an analysis platform, a streaming platform, an iOS app for audio capture and streaming, and a web app for audio capture, storage, and streaming. These mobile and online platforms allow the TDI method to be shared through a variety of open-source technologies.

A number of other universities have likewise begun related Dormio studies; these include Duke University, Boston College, Harvard University, the University of Rochester, and the University of Chicago .

The Media Lab research team is also leading collaborations with artists, using dreams to create new artwork and augment artistic creativity. This work, which mixes sleep science and media art, has been shown at the Beijing Biennale and Ars Electronica festival, and a new collaboration with installation artist Carsten Holler looks to create an overnight experimental art piece.

Reference: “Dormio: A targeted dream incubation device” by Adam Haar Horowitz, Tony J. Cunninghamb, Pattie Maes and Robert Stickgold, 30 May 2020, Consciousness and Cognition . DOI: 10.1016/j.concog.2020.102938

The Dormio development team includes researchers Haar Horowitz, Tomás Vega, Ishaan Grover, Pedro Reynolds-Cuéllar, Oscar Rosello, Abhinandan Jain, and Eyal Perry, along with students in the MIT Undergraduate Research Opportunities Program Matthew Ha, Christina Chen, and Kathleen Esfahany.

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6 comments on "mit dream research interacts directly with an individual’s dreaming brain and manipulates the content".

I love the work on dream research being done here. I might have something for you , I painted a 7 foot by 5 foot painting of a lucid dream I had while I was attending NSCAD(Nova Scotia College of Art & Design) & think it would be a beautiful addition to your campus. Please send me an email & I will send over the image.

I’m not sure if anyone has come forward with this ability, but I’ve recently discovered I can trigger my mind to recall past dreams that I haven’t had for decades. The reason I felt this might be important to a dream study or research is, it might reveal what portion(s) of the brain are accessed during such a streaming recollection. I don’t hear any “mind’s audio” when recalling these dreams, and they somewhat just flow along in 1-2 second scenes, and by moving my eyes and pretending to look at those scenes, I can trigger the speed at which the scenes cycle to another dream. I’ve also determined over the course of about ten years that if I watch certain programs/movies while falling asleep (and keep those programs/movies playing all night) I have specific dreams. And, by replaying select portions of a program/movie, I can usually have that same (or a very similar) dream recur on demand.

Just in case this seems unique or something worth needing more info on. Maybe everyone can do this. It’s not that I’m simply able to rememeber a dream I had, it’s more like I can tap into an area of my mind that’s storing them all, even obscure ones that I’ve had only once. Over the course of my life, I’ve always had very colorful, detailed, real-seeming dreams, most of which I can remember as soon as I awaken. But it wasn’t until recently while trying to recall a particular dream that I stumbled onto “something” in my mind that allowed pieces of my dreams to emerge without having to actually try to recall them. It was like turning on a faucet and they just flowed.

Interested to read about the creative input link with your research. I’m a writer so use my dreams in my creative work, often recording them when I wake. I also remember dreams from many years ago (like the previous comment by Tom). At some points I have had lucid dreams but I’m not sure what conditions trigger this as I don’t consciously decide to have a lucid dream. On occasions when I have had lucid dreams, I’ve spent my waking day in nature, outside or with some form of relaxation/meditation beforehand.

My dream images do not seem to relate to waking experiences e.g. I have been male in some dreams with male anatomy and inhabited animal form. Plus I meet and talk in other languages which I cannot speak in waking life.

I would like to be a participant in a dream study. Where can I take part? I feel this is such a rich source of untapped creativity that needs to be scientifically explored.

Tom, I am so excited to read your comment because I think I know exactly what you are talking about. The 1-2 second scenes from previous dreams, and the faucet fits perfectly with what I have experienced. I can’t remember the plot, but I can remember the scenes of hundreds of dreams had over many years in a short window of time. And like you, my dreams are very detailed and lifelike. I have not experienced what you talk about with the movies – that is something I should try. And Polly, parts of your comment ring true to me, too. I have dreamt that I am male, and even that I am part alien. It isn’t so much that I am those beings, but that I am experiencing a story or part of their lives from the first person point of view. And the same with the lucid dreaming. I’m curious if your dreams fall into catergories. I have maybe 30 categories of dreams I’ve come up with, and they seem to cycle. For example, I may have dreams about tsunamis and airplanes and exotic birds in my backyard for a while, and then I’ll switch and have dreams about treehouses and rope swings and college courses.

I would just like to help in anyway possible if you ever need an accomplished lucid dreamer. I live two lives. My normal life and my dream life. And consequently I have two sets of memories. Im not sure if it’s a common thing to dream like I dream. But the complexity and in depth layering of my dreams along with the lucidity and what some people would probably call intriguing subject matter leads me to think otherwise. If I could be of any help to you or any colleagues in the area of dream research please let me know. Thanks. [email protected]

Iam not sure about my life but my dreams are come true.

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Frontiers for Young Minds

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The Science of Dreams

Dreams are a common experience. Some are scary, some are funny. Recent research into how the brain works helps us understand why we dream. Strange combinations of ideas in our dreams may make us more creative and give us ideas that help us to solve problems. Or, when memories from the day are repeated in the brain during sleep, memories may get stronger. Dreams may also improve our moods. Together, these studies show that dreams and sleep are important for performing well when we are awake.

When she was 8, my daughter told me about one of her dreams. She was in a spaceship with some animals. Although she knew she was in a spaceship in her dream, when telling me about the dream, she realized the spaceship was actually a washing machine. At times, she and the animals would be out in space, but they also came back to earth. She told me the dream with a laugh and then moved on with her day, ignoring the crazy animals and spaceships that entertained her in her sleep.

Since we remember our dreams and then often forget them, what is their purpose? Why do we dream about the things we do? New research tools, particularly those that can be used to investigate the brain, are being used to answer these questions.

What Are Dreams?

Although it is hard to define what a dream is, for the purpose of this article, we will define dreams as our thoughts during sleep that we recall when we wake up. So, sleeping dreams are not the same as “daydreaming.” Dreams are mostly visual (made up of scenes and faces; sound, taste, and smell are rare in dreams [ 1 ]). Dreams can range from truly strange to rather boring, snapshots from a recent event.

To study dreams, scientists need a measure of dreaming. Most studies use dream reports (a person writes out her dreams when she wakes up) or questionnaires (a person answers questions like “How many dreams have you recalled in the past month?” [ 2 ]). Dreams are more likely to be recalled when a person is woken up from REM sleep. REM sleep is a type of sleep that is named for the rapid eye movements that can be measured during this stage of sleep. We do not dream as much in non-REM sleep, the sleep stages that make up the rest of the night, and dream reports from non-REM sleep are often less strange.

Dream frequency (how often dreams happen) and content (what dreams are about) is very different for everyone, and there are many reasons why this may be true. For example, you will remember dreams more if you are woken up by someone or by an alarm clock. This might be because you can still recall that dream memory while it is fresh but, if you wake up on your own, you will transition through a few sleep stages and possibly lose that dream memory. Dream recall changes with age, too. Older people are less likely to report dreaming. This could also be related to memory: since older people have weaker memories, it could be that they dream but cannot remember their dreams by the time they wake up. A brain area called the medial prefrontal cortex is also related to dream recall. If this brain area is damaged, the person recalls few dreams, which may mean the person dreams less (or not at all). Also, how tightly packed the brain cells are in the medial prefrontal cortex can vary from person to person, which may cause some healthy people to dream more or less than other healthy people. There are also genes that affect how much REM sleep people get. People with less REM sleep may not have the strange dreams that tend to come in REM. So, how long you sleep, your age, and your genetics may all explain why you dream more or less than someone else.

Do dreams actually happen while we sleep, or are they ideas that come to us when we wake up and we just “feel” like it happened during sleep? A recent study using a type of brain imaging called magnetic resonance imaging or (MRI: Read more in the Young Minds article “How Is Magnetic Resonance Imaging Used to Learn About the Brain?” [ 3 ]) helped answer this question ( Figure 1A ). The scientists made maps of the brain activity that occurred when people looked at pictures of things—keys, beds, airplanes. Later, the people in the study slept in the MRI machine. The scientists matched the pattern of brain activity from the people as they slept to brain activity patterns for the pictures they viewed earlier, and then chose the best match ( Figures 1B,C ). This match predicted what the person said they dreamed about 60% of the time. Although 60% is not perfect, it is better than guessing! [ 4 ]. This means that dreams are created in the brain during sleep.

• Figure 1 - (A) Magnetic resonance imaging (MRI) is a way to investigate the brain.
• The person lies on a bed inside a giant magnet. (B) MRI can measure the structure of the brain and the areas of the brain that are active. (C) MRI was used to measure dreaming. First, while the participant was awake, they viewed thousands of pictures in the MRI. This told scientists the specific brain responses to specific pictures. Later, when the participant slept in the MRI, scientists measured the brain activity patterns and matched this to the brain responses to the pictures the participant saw when they were awake. Scientists guessed that the best match would tell them what the participant was dreaming about. By asking the participant about their dreams in the MRI, scientists found that the dreams did tend to match the pictures predicted by the brain activity.

Dreams Support Memories

What is the purpose of our dreams? Researchers have found that sleep is important for memory (see this Frontiers for Young Minds article ; “Thanks for the Memories…” [ 5 ]). Memories move from temporary storage in the hippocampus , a brain structure that is very important for short-term memory, to permanent storage in other parts of the brain. This makes the memories easier to remember later. Memories improve with sleep because the memories are replayed during sleep [ 6 ]. If you want to learn all the words to your favorite scene in a movie, you might re-watch that scene over and over again. The brain works the same way: neurons (brain cells) that are active with learning are active again and replay the learned material during sleep. This helps store the memory more permanently.

Memory replay may show up in our dreams. Dreams in non-REM sleep, when most memory replay happens, often contain normal people and objects from recent events. However, sleep switches between non-REM and REM sleep (see Figure 2 ). So, bizarre dreams in REM sleep may come from a combination of many different recent memories, which were replayed in non-REM sleep, and get jumbled up during REM sleep. If dreams help with memory processing, does that mean your memories are not being processed if you do not dream? No. Memories are moving to storage even if we do not dream.

• Figure 2 - There are four types of sleep—REM sleep (purple) and three stages of non-REM sleep (blue).
• REM stands for rapid eye movements, which happen during this stage of sleep. During REM sleep, muscle and brain activity also differ from other sleep stages. Characteristics of dreams tend to be different for each of these sleep stages.

Dreams Improve Creativity and Problem Solving

My daughter’s dream of a spaceship made a great story that she recited to me, and later, to her classmates. The images were intense and interesting, inspiring her to draw scenes in a notebook and write about the dream for school. This is an example of how dreams can help make us more creative. Mary Shelley, the author of the book Frankenstein, got the idea for her book from a dream. Even scientists get ideas from dreams [ 7 ].

To measure creative problem solving, scientists used a remote associates task, in which three unrelated words are shown, and the person is to come up with a word they have in common. For instance, HEART, SIXTEEN, and COOKIES seem unrelated until you realize they all are related to SWEET (sweetheart, sweet sixteen, and cookies are sweet) ( Figure 3 ). The scientists wanted to see whether sleep helped people do better on this task. They found that people were better at thinking of the remote solution if they had a nap, particularly a nap with REM sleep. Given that REM is when most bizarre dreaming occurs, this supports the idea that these dreams might help us find creative solutions to problems [ 8 ].

• Figure 3 - REM sleep helps people find creative solutions.
• In the morning, participants did two tasks to test creativity and problem solving (A) . They did one task again in the afternoon. In between, they either stayed awake (“wake” group) or took a nap. Those that took naps either did not have REM sleep in their nap (“nREM” group) or had both nREM and REM sleep (“nREM + REM” group). (B) If subjects stayed awake between the morning and afternoon tests (yellow bar), they did not improve on the task. They also did not improve if they had a nap that was only nREM sleep (light blue bar). But, if they had a nap with both nREM and REM sleep, they did better in the afternoon compared with when they did the task in the morning (dark blue bar). So, REM sleep must help us find creative solutions (from Cai et al. [ 8 ]).

This study and research like it gives us reason to believe that REM dreams may help us be more creative and solve problems. Many different memories may be activated at the same time and when these memories are mixed together, the result when we wake up may be both the memory of a strange dream and a unique perspective on problems.

Dreams Regulate Our Moods and Emotions

Dreams are usually emotional. One study found that most dreams are scary, angry, or sad.

Dreams might seem to be emotional simply because we tend to remember emotional things better than non-emotional things. For example, in waking life, the day you got a puppy is more memorable than a normal school day. So, dreams about emotional events might be remembered more easily than boring, non-emotional dreams. It is also possible that dreams are emotional because one job of dreams is to help us process emotions from our day [ 9 ]. This may be why the amygdala , an area of the brain that responds to emotions when we are awake, is active during REM sleep. If you had a sad day, you are more likely to have sad dreams. But, sleep also improves mood–sleep after a disagreement or sad event will make you happier.

Dreams could also help prepare us for emotional events, through something called threat simulation theory [ 10 ]. For example, when I dreamt that my young daughter, who could not swim, fell into a swimming pool, recall of that dream convinced me to sign her up for swim lessons. By simulating this fearful situation, I could prevent it by being prepared.

These studies show us that sleep and dreams are important for our emotions. By processing emotions in sleep, we may be better prepared and in a better mood the next day.

Conclusions

There are different ways scientists measure dreams—from asking questions to using MRI. These studies show us that activity in the brain while we sleep gives us the interesting dreams we recall when we wake up. These dreams help us remember things, be more creative, and process our emotions.

We know most kids do not get enough sleep. Some diseases (like Alzheimer’s disease) also make people sleep less, while others (like REM sleep behavior disorder and mood disorders) affect dreams directly. It is important to study sleep and dreams to understand what happens when we do not get enough sleep and how we can treat people with these diseases.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Rapid Eye Movement (REM) : ↑ A stage of sleep in which the eyes move rapidly and there is no muscle activity.

Medial Prefrontal Cortex : ↑ A specific area in the front of the brain that is associated with dream recall but also has a role in memory and decision-making.

Magnetic Resonance Imaging (MRI) : ↑ A tool used to take pictures of internal body parts (including the brain). MRI can also be used to measure the activity in the brain.

Hippocampus : ↑ An area in the brain that is thought to be important for short-term memory.

Neuron : ↑ A cell in the nervous system (brain and spinal cord) that can transmit information to other cells.

Amygdala : ↑ An area of the brain involved in the experience of emotions.

Threat Simulation Theory : ↑ A theory of dreaming that says that threats (things that could be bad) are simulated or practiced in your dreams to prepare you for those situations when you are awake.

1. ↑ Zandra, A. L., Nielsen, T. A., and Donderi, D. C. 1998. Prevalence of auditory, olfactory, and gustatory experiences in home dreams. Percept. Mot. Skills 87:819–26.

2. ↑ Schredl, M. 2002. Questionnaires and diaries as research instruments in dream research: methodological issues. Dreaming 12:17–26. doi: 10.1023/A:1013890421674

3. ↑ Hoyos, P., Kim, N., and Kastner, S. 2019. How Is Magnetic Resonance Imaging Used to Learn About the Brain? Front. Young Minds . 7:86. doi: 10.3389/frym.2019.00086

4. ↑ Horikawa, T., Tamaki, M., Miyawaki, Y., and Kamitani, T. 2013. Neural decoding of visual imagery during sleep. Science 340:639–42. doi: 10.1126/science.1234330

5. ↑ Davachi, L., and Shohamy, D. 2014. Thanks for the Memories.… Front. Young Minds. 2:23. doi: 10.3389/frym.2014.00023

6. ↑ O’Neill, J., Senior, T. J., Allen, K., Huxter, J. R., and Csicsvari, J. 2008. Reactivation of experience-dependent cell assembly patterns in the hippocampus. Nat. Neurosci . 11:209–15. doi: 10.1038/nn2037

7. ↑ Barrett, D. 2001. The Committee of Sleep: How artists, scientists, and athletes use dreams for creative problem-solving–and How You Can Too . New York, NY: Crown.

8. ↑ Cai, D. J., Mednick, S. A., Harrison, E. M., Kanady, J. C., and Mednick, S. C. 2009. REM, not incubation, improves creativity by priming associative networks. Proc. Natl. Acad. Sci. U.S.A . 106:10130–4. doi: 10.1073/pnas.0900271106

9. ↑ Cremone, A., Kurdziel, L. B. F., Fraticelli, A., McDermott, J., and Spencer, R. M. C. 2017. Napping reduces emotional attention bias during early childhood. Dev. Sci . 20:e12411. doi: 10.1111/desc.12411

10. ↑ Revonsuo, A. 2000. The reinterpretation of dreams: an evolutionary hypothesis of the function of dreaming. Behav. Brain Sci . 23:877–901. doi: 10.1017/s0140525x00004015

177 Dream Research Topics & How to Write a Research Paper on Dreams

People have dreams every night. Dreams are different – sweet dreams and nightmares, colored and colorless. However, every psychologist knows that people need to sleep. Why? Well, let us give you the right to answer this question in your research paper on dreams.

A research paper on dreams is a serious research project. That is why you cannot simply write how dreams can be interpreted or describe your dreams in the research paper on dreams. Research papers on dreams require more serious topics and approach.

Below you will find several possible ideas for research papers on dreams.

• 🔎 Dreams Research Topics
• 💤 Dreams Definition
• ✍️ How to Write about Dreams

😴 Easy Research Topics on Dreams

🛌 essay about dreams topics, 😪 topics for a research paper on sleep and dreams, ✏️ importance of sleep essay topics, 👻 nightmare essay topics.

• 📝 My Dreams Essay – Example

✅ Interesting Facts about Dreams

🔎 dreams research topics – 2024.

• The link between our dreams and emotions.
• What is the role of dreaming in creativity development?
• The gender-based patterns in dreaming experience.
• Sigmund Freud and his theory of dreams.
• The key mechanisms that underlie dreaming.
• What knowledge can you gain from your dreams?
• The impact of eating patterns on the quality of dreams.
• How do different cultures perceive and interpret dreams?
• The advantages and disadvantages of dreaming.
• How can people control their dreams?
• The role of dreams in processing emotions.
• How do bizarre and emotionally intense dreams occur?

💤 What Are Dreams?

Psychologists are sure that dreams are the result of what we wish or think about when we are awake. For example, Freud, a famous psychologist, considered that if a man did not have sexual relations for a long time, he would dream about them. If you think about someone, you may also dream about him/her. This is what you may write about in the research paper on dreams if you want to consider this aspect.

Nightmares can also be a very interesting issue to discuss in research papers on dreams. Psychologists relate nightmares to the field of “unconscious”. Very often, people forget about the stressful situations they once had. However, those situations are reflected in their minds and they can appear in dreams. You may also find other points of view on nightmares and discuss them in your research paper on dreams.

✍️ How to Write a Research Paper about Dreams

A research paper about dreams generally includes an introduction, body paragraphs, and a conclusion. First, it is crucial to choose a relevant and exciting topic to write on and decide on the type of research paper (analytical, argumentative, etc.).

Choosing a Topic

Pick a topic that corresponds to your interests and expertise. It will help you stay more motivated throughout the research process. In addition, ensure that your topic is specific, relevant, and follows the assignment instructions.

If you need help choosing a good topic for your paper, try our free research title generator .

Finding Sources

After you have found a perfect topic on dreams, it is time to look for sources for your research. You can look up information in books, similar research papers, or online sources. Communicating with professionals related to dreams , like psychologists or neurologists, is also a good idea since it is an effective method to gain new knowledge or advice.

Writing a Research Paper

The format of your research paper on dreams should consist of the following elements:

• The relation between dreaming and the role of deep-brain structures.
• Dreaming capacity to repeatedly simulate potential threatening events.
• The role of amygdala and hippocampus in the dreaming process.
• The spiritual significance of dreams in different cultures.
• Dream interpretation and its value in self-understanding.
• How does dream recall reflect social relationships?
• The positive impact of dreams on our physical health.
• Dreams and their role in predicting the future.
• The peculiarities of dreams in pregnant women.
• Why does Charcot-Wilbrand syndrome cause the loss of the ability to dream?
• The role of dreaming in developing cognitive capabilities.
• How can dreams reflect the aging process?
• The repetitive character of some dreams and their meaning.
• Why are young people more likely to dream in color?
• The benefits and cautions of lucid dreaming.
• The influence of smartphones on the content of dreams.
• Why do people forget their dreams after waking up?
• The impact of suppressing intrusive thoughts on dream content.
• What is the role of dreams in developing long-term memory?
• The key causes and types of dreams.
• The peculiarities of dreaming during the COVID-19 pandemic.
• Everything you need to know about lucid dreams.
• The role of melatonin in determining the dream content.
• What can we learn from our dreams?
• The psychotomimetic nature of dreams.
• The terrors of sleep paralysis.
• Does screen time affect people’s dreams?
• Dreams and the future of sleep technology.
• Are AI technologies capable of generating dreams?
• The hidden cost of insufficient sleep.
• How can nap breaks improve your productivity at work?
• The main facts and myths about sleep and dreams.
• How can our understanding of dreams shape our worldview?
• The link between dreams and telepathy.
• The process of dreaming in animals.
• Why do some people wake up in the middle of the night?
• The impact of mental illnesses on dream content.
• The role of dreams in art as a source of inspiration.
• How do different societies interpret dreams?
• The power of dreaming in everyday life.
• How to become a morning person: the key strategies.
• The impact of sleep time on life length.
• Ways to decode the language of sleep.
• Using cannabis as a method to cope with nightmares.
• The impact of the daily schedule on improving the quality of sleep.
• How to get a good night’s sleep in a new place?
• Methods to combat morning grogginess.
• Taking care of your sleep as one of the pillars of health.
• The use of dreams in filmmaking and book writing.
• The phenomenon of dreaming during sleep.
• The main phases of sleep in a sleep cycle.
• How is alpha activity measured during sleep?
• The use of oneirology in uncovering the dreaming process.
• Dreaming in Christianity and Islam.
• What is the connection between race and sleep disorders?
• The theory of astral projection during sleep.
• The effect of sleep on pain thresholds and sensitivity.
• The consequences of chronic daytime sleepiness.
• Why is dreaming a key part of a sleep cycle?
• The natural patterns of sleeping in children and teenagers.
• REM and non-REM sleep : the difference.
• What is biphasic sleep, and how does it work?
• The influence of dreams on musical creativity.
• The cultural significance of dream symbols.
• How do moon phases affect your sleep?
• The nature and functions of dreaming.
• The use of dream content during expressive arts therapy.
• What are the possible functions of REM sleep and dreaming?
• The value of dreaming and sleep tracking.
• The analysis of mental activity of sleep and disturbing dreams.
• How do sleep disturbances impact skin health?
• The impact of age on our circadian rhythm.
• The phenomenon of conscious control in dreams.
• How do sleep patterns change across different life stages?
• The influence of sleep quality on academic performance.
• The psychological theories of dreaming purpose.
• The disadvantages of oversleeping for adults.
• How does your body use calories while you sleep?
• Factors influencing the memory of dreams.
• What impact does alcohol have on the sleep cycle and dreaming?
• How can dreams contribute to the healing process?
• The role of sleep in underlying psychological issues.
• The benefits of daytime napping for young people.
• Why does sleep deprivation increase the risk of substance abuse?
• The use of daytime naps to increase imagination.
• The value of bedtime routine for toddlers.
• The benefits of a good night’s sleep.
• What is the role of sleeping in achieving life goals?
• Lack of sleep as a key cause of hormonal imbalance.
• The damaging effect of shift work on sleep patterns and health.
• The link between sleep and the immune system.
• What impact does a change of clocks by an hour have on public health?
• The value of sleep for children’s physical, cognitive, and emotional development.
• What would happen if you did not sleep?
• The importance of sleep for children’s development and growth.
• The connection between good mood and quality sleep.
• Why does the lack of sleep increase aggression?
• The role of sleeping in cancer prevention and treatment.
• The value of sleep for the recovery process of athletes.
• How does the quality of sleep impact metabolism?

Essay about Sleep Deprivation

• The economic impact of sleep deprivation in the workplace.
• How can sleep deprivation lead to anxiety and depression?
• The role of sleep deprivation in worsening obesity and diabetes.
• The use of sleeping pills in sleep deprivation treatment.
• How is sleep deprivation diagnosed?
• The prevalence of sleep deprivation among shift workers.
• What is the difference between sleep deprivation and insomnia?
• The key stages of sleep deprivation.
• The role of DNA in the development of sleep deprivation.
• The unique challenges in diagnosing obstructive sleep deprivation.
• How does sleep deprivation affect the human body?
• The issue of sleep deprivation in teenagers due to exams.
• The role of medications in managing sleep deprivation.
• Ways of reducing the risk of developing sleep deprivation.
• What are the key symptoms of sleep deprivation?

Why Is Sleep Important? Essay Topics

• The efficiency of sleeping in losing weight.
• How can sleep improve concentration and productivity?
• Sleep as essential component of healthy aging.
• Why can a lack of sleep be dangerous?
• Sleep satisfaction and its impact on energy level.
• How is poor sleep linked to depression?
• The impact of sleep on emotional intelligence.
• How does sleep help to repair and restore tissues?
• The role of sleeping in removing toxins from the brain.
• Why can the lack of sleep be lethal?
• The link between sleep quality and mental resilience.
• Sleep loss and its impact on reducing the ability to regulate emotions.
• The role of sleep in the regulation of the central nervous system.
• How can the quality of sleep strengthen your heart?
• Sleeping as a method to maximize athletic performance.

Sleeping Disorders Essay Topics

• The connection between sleep disorders and dreaming.
• Do congenitally blind people have visual dreams?
• The effective ways of coping with insomnia.
• Sleep difficulties and their physical and emotional consequences.
• How does weight affect sleep apnea in adults?
• Breathing practices and their efficiency in overcoming sleep disorders.
• The key symptoms of sleep-related hypoventilation .
• What are the risk factors for sleep disorders?
• Minimizing stress as a method to cope with obstructive sleep apnea.
• The side effects of sleep disorder treatment.
• What are the major categories of sleep disorders, and how do they differ?
• Restless legs syndrome as one of the sleep disorder types.
• The effectiveness of light therapy in sleep disorder treatment.
• The peculiarities of sleep disorder diagnosis.
• How to deal with rapid eye movement sleep behavior disorder?
• Nightmare disorder and its impact on sleep quality.
• The role of negative thinking, stress, and anxiety in worsening nightmares.
• How may nightmares help to express unresolved emotions?
• The influence of nightmares on interpersonal relationships.
• The use of cognitive behavioral therapy in nightmare treatment.
• Are nightmares a possible consequence of drug abuse?
• The key symptoms of experiencing nightmares.
• The health effects of nightmares in adults.
• How are nightmares connected to waking activity?
• The possible consequences of nightmares.
• The efficiency of psychotherapy in nightmare treatment.
• The main causes of nightmares and methods to cope with them.
• How are nightmares different from sleep terrors?
• The role of sleep hygiene practices in preventing nightmares.
• How do nightmares affect the daily life of teenagers?
• Nightmares as a result of trauma-related experience.
• The link between nightmares and sleep paralysis.
• How does genetics impact the occurrence of nightmares?
• The neurobiological aspects of nightmares in children.
• The risk factors of having nightmare disorder.

📝 My Dreams Essay – Example

We have prepared a dream essay example to show you how everything works in practice!

How Do Different Societies Interpret Dreams?

Throughout history, dreams have been a mysterious experience for people worldwide, receiving various interpretations in many different countries and cultures. From ancient times to the present, people have believed that dreams provide crucial insights into our inner being and may even impact our perception of the universe.

For example, in ancient Egypt, snakes were often associated with danger, deceit, and the underworld. At the same time, seeing calm water in a dream was a good sign that meant peace and tranquility. If people were flying while asleep, it symbolized spiritual growth and escape from mortal concerns.

In ancient Mesopotamia, animals were frequently seen as symbols of the dreamer's personality traits. For instance, a lion might symbolize strength and power, while a sheep could represent humility and submission. Numbers also had a special meaning. People believed their appearance in dreams could be interpreted as messages from the gods.

Nowadays, people still interpret dreams in various ways based on their personal beliefs and traditions. However, it is crucial to understand that there is no correct or incorrect approach to interpreting dreams. The essential thing is to discover a method that resonates with you, allowing you to obtain insights into your subconscious mind. The use of the internet in academic contexts is on the rise, and its role in learning is hotly debated. For many teachers who did not grow up with this technology, its effects seem alarming and potentially harmful. The use of the internet in academic contexts is on the rise, and its role in learning is hotly debated. For many teachers who did not grow up with this technology, its effects seem alarming and potentially harmful.

Do you want to make your research paper on dreams interesting? Then, include a couple of facts into your research paper on dreams:

• Blind people dream;
• You forget 90% of your dreams;
• Dreams prevent psychosis;
• Not everyone sees colorful dreams;
• When you are snoring, you are not dreaming.

Who knows, maybe you will manage to interpret one of these facts from the psychological point of view in your research paper on dreams.

On our blog, useful information on how to write a good research paper and make a cover page for research papers can also be found.

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Why Do We Dream?

There's no single consensus about which dream theory best explains why we dream

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Dr. Sabrina Romanoff, PsyD, is a licensed clinical psychologist and a professor at Yeshiva University’s clinical psychology doctoral program.

Verywell / Madelyn Goodnight

What Is a Dream Theory?

• The Role of Dreams
• Reflect the Unconscious
• Process Information
• Aid In Memory
• Spur Creativity
• Reflect Your Life
• Prepare and Protect
• Process Emotions
• Other Theories

Lucid Dreaming

Stress dreams.

A dream theory is a proposed explanation for why people dream that is backed by scientific evidence. Despite scientific inquiry, we still don't have a solid answer for why people dream. Some of the most notable theories are that dreaming helps us process memories and better understand our emotions , also providing a way to express what we want or to practice facing our challenges.

At a Glance

There is no single dream theory that fully explains all of the aspects of why we dream. The most prominent theory is that dreams help us to process and consolidate information from the previous day. However, other theories have suggested that dreams are critical for emotional processing, creativity, and self-knowledge.

Some theories suggest that dreams also have symbolic meanings that offer a glimpse into the unconscious mind. Keep reading to learn more about some of the best-known theories about why we dream.

7 Theories on Why We Dream

A dream theory focuses on understanding the nature and purpose of dreams. Studying dreams can be challenging since they can vary greatly in how they are remembered and what they are about.

Dreams include the images, thoughts, and emotions that are experienced during sleep. They can range from extraordinarily intense or emotional to very vague, fleeting, confusing, or even boring.

Some dreams are joyful, while others are frightening or sad. Sometimes dreams seem to have a clear narrative, while many others appear to make no sense at all.

There are many unknowns about dreaming and sleep, but what scientists do know is that just about everyone dreams every time they sleep, for a total of around two hours per night, whether they remember it upon waking or not .

Beyond what's in a particular dream, there is the question of why we dream at all. Below, we detail the most prominent theories on the purpose of dreaming and how these explanations can be applied to specific dreams.

How Do Scientists Study Dreams?

The question of why we dream has fascinated philosophers and scientists for thousands of years. Traditionally, dream content is measured by the subjective recollections of the dreamer upon waking. However, observation is also accomplished through objective evaluation in a lab.

In one study, researchers even created a rudimentary dream content map that was able to track what people dreamed about in real time using magnetic resonance imaging (MRI) patterns. The map was then backed up by the dreamers' reports upon waking.

What Dream Theory Suggests About the Role of Dreams

Some of the more prominent dream theories suggest that the reason we dream is to:

• Consolidate memories
• Process emotions
• Express our deepest desires
• Gain practice confronting potential dangers

Many experts believe that we dream due to a combination of these reasons rather than any one particular theory. Additionally, while many researchers believe that dreaming is essential to mental, emotional, and physical well-being, some scientists suggest that dreams serve no real purpose at all.

The bottom line is that while many theories have been proposed, no single consensus has emerged about which dream theory best explains why we dream.

Dreaming during different phases of sleep may also serve unique purposes. The most vivid dreams happen during rapid eye movement (REM) sleep , and these are the dreams that we're most likely to recall. We also dream during non-rapid eye movement (non-REM) sleep, but those dreams are known to be remembered less often and have more mundane content.

Sigmund Freud's Dream Theory

Sigmund Freud’s theory of dreams suggests that dreams represent  unconscious desires, thoughts, wish fulfillment, and motivations. According to Freud, people are driven by repressed and unconscious longings, such as aggressive and sexual instincts .

While many of Freud's assertions have been debunked, research suggests there is a dream rebound effect, also known as dream rebound theory, in which suppression of a thought tends to result in dreaming about it.

What Causes Dreams to Happen?

In " The Interpretation of Dreams ," Freud wrote that dreams are "disguised fulfillments of repressed wishes." He also described two different components of dreams: manifest content (actual images) and latent content (hidden meaning).

Freud’s theory contributed to the rise and popularity of dream interpretation . While research has failed to demonstrate that the manifest content disguises the psychological significance of a dream, some experts believe that dreams play an important role in processing emotions and stressful experiences.

Activation-Synthesis Dream Theory

According to the activation-synthesis model of dreaming , which was first proposed by J. Allan Hobson and Robert McCarley, circuits in the brain become activated during REM sleep, which triggers the amygdala and hippocampus to create an array of electrical impulses. This results in a compilation of random thoughts, images, and memories that appear while dreaming.

When we wake, our active minds pull together the dream's various images and memory fragments to create a cohesive narrative.  

In the activation-synthesis hypothesis, dreams are a compilation of randomness that appear to the sleeping mind and are brought together in a meaningful way when we wake. In this sense, dreams may provoke the dreamer to make new connections, inspire useful ideas, or have creative epiphanies in their waking lives.

Self-Organization Dream Theory

According to the information-processing theory, sleep allows us to consolidate and process all of the information and memories that we have collected during the previous day. Some dream experts suggest that dreaming is a byproduct, or even an active part, of this experience processing.  

This model, known as the self-organization theory of dreaming , explains that dreaming is a side effect of brain neural activity as memories are consolidated during sleep.

During this process of unconscious information redistribution, it is suggested that memories are either strengthened or weakened. According to the self-organization theory of dreaming, while we dream, helpful memories are made stronger, while less useful ones fade away.

Research supports this theory, finding improvement in complex tasks when a person dreams about doing them. Studies also show that during REM sleep, low-frequency theta waves were more active in the frontal lobe, just like they are when people are learning, storing, and remembering information when awake.

Creativity and Problem-Solving Dream Theory

Another theory about dreams says that their purpose is to help us solve problems. In this creativity theory of dreaming, the unconstrained, unconscious mind is free to wander its limitless potential while unburdened by the often stifling realities of the conscious world. In fact, research has shown dreaming to be an effective promoter of creative thinking.

Scientific research and anecdotal evidence back up the fact that many people do successfully mine their dreams for inspiration and credit their dreams for their big "aha" moments.

The ability to make unexpected connections between memories and ideas that appear in your dreams often proves to be an especially fertile ground for creativity.

Continuity Hypothesis Dream Theory

Under the continuity hypothesis, dreams function as a reflection of a person's real life, incorporating conscious experiences into their dreams. Rather than a straightforward replay of waking life, dreams show up as a patchwork of memory fragments.

Still, studies show that non-REM sleep may be more involved with declarative memory (the more routine stuff), while REM dreams include more emotional and instructive memories.

In general, REM dreams tend to be easier to recall compared to non-REM dreams.

Under the continuity hypothesis, memories may be fragmented purposefully in our dreams as part of incorporating new learning and experiences into long-term memory . Still, there are many unanswered questions as to why some aspects of memories are featured more or less prominently in our dreams.

Rehearsal and Adaptation Dream Theory

The primitive instinct rehearsal and adaptive strategy theories of dreaming propose that we dream to better prepare ourselves to confront dangers in the real world. The dream as a social simulation function or threat simulation provides the dreamer a safe environment to practice important survival skills.

While dreaming, we hone our fight-or-flight instincts and build mental capability for handling threatening scenarios. Under the threat simulation theory, our sleeping brains focus on the fight-or-flight mechanism to prep us for life-threatening and/or emotionally intense scenarios including:

• Running away from a pursuer
• Falling over a cliff
• Showing up somewhere naked
• Going to the bathroom in public
• Forgetting to study for a final exam

This theory suggests that practicing or rehearsing these skills in our dreams gives us an evolutionary advantage in that we can better cope with or avoid threatening scenarios in the real world. This helps explain why so many dreams contain scary, dramatic, or intense content.

Emotional Regulation Dream Theory

The emotional regulation dream theory says that the function of dreams is to help us process and cope with our emotions or trauma in the safe space of slumber.

Research shows that the amygdala , which is involved in processing emotions, and the hippocampus , which plays a vital role in condensing information and moving it from short-term to long-term memory storage, are active during vivid, intense dreaming.

This illustrates a strong link between dreaming, memory storage, and emotional processing.

This theory suggests that REM sleep plays a vital role in emotional brain regulation. It also helps explain why so many dreams are emotionally vivid and why emotional or traumatic experiences tend to show up on repeat. Research has shown a connection between the ability to process emotions and the amount of REM sleep a person gets.

Sharing Dreams Promotes Connection

Talking about content similarities and common dreams with others may help promote belongingness and connection. Research notes heightened empathy among people who share their dreams with others, pointing to another way dreams can help us cope by promoting community and interpersonal support.

Other Theories About Why We Dream

Many other theories have been suggested to account for why we dream.

• One dream theory contends that dreams are the result of our brains trying to interpret external stimuli (such as a dog's bark, music, or a baby's cry) during sleep.
• Another theory uses a computer metaphor to account for dreams, noting that dreams serve to "clean up" clutter from the mind, refreshing the brain for the next day.
• The reverse-learning theory suggests that we dream to forget. Our brains have thousands of neural connections between memories—too many to remember them all—and that dreaming is part of "pruning" those connections.
• In the continual-activation theory, we dream to keep the brain active while we sleep, in order to keep it functioning properly.

Overfitted Dream Hypothesis

One recently introduced dream theory, known as the overfitted dream hypothesis, suggests that dreams are the brain's way of introducing random, disruptive data to help break up repetitive daily tasks and information. Researcher Erik Hoel suggests that such disruptions helps to keep the brain fit.

Lucid dreams are relatively rare dreams where the dreamer has awareness of being in their dream and often has some control over the dream content. Research indicates that around 50% of people recall having had at least one lucid dream in their lifetime and just over 10% report having them two or more times per month.

It is unknown why certain people experience lucid dreams more frequently than others. While experts are unclear as to why or how lucid dreaming occurs, preliminary research signals that the prefrontal and parietal regions of the brain play a significant role.

How to Lucid Dream

Many people covet lucid dreaming and seek to experience it more often. Lucid dreaming has been compared to virtual reality and hyper-realistic video games, giving lucid dreamers the ultimate self-directed dreamscape experience.

Potential training methods for inducing lucid dreaming include cognitive training, external stimulation during sleep, and medications. While these methods may show some promise, none have been rigorously tested or shown to be effective.

A strong link has been found between lucid dreaming and highly imaginative thinking and creative output. Research has shown that lucid dreamers perform better on creative tasks than those who do not experience lucid dreaming.

Stressful experiences tend to show up with great frequency in our dreams. Stress dreams may be described as sad, scary, and nightmarish .

Experts do not fully understand how or why specific stressful content ends up in our dreams, but many point to a variety of theories, including the continuity hypothesis, adaptive strategy, and emotional regulation dream theories to explain these occurrences. Stress dreams and mental health seem to go hand-in-hand.

• Daily stress shows up in dreams : Research has shown that those who experience greater levels of worry in their waking lives and people diagnosed with post-traumatic stress disorder (PTSD) report higher frequency and intensity of nightmares.
• Mental health disorders may contribute to stress dreams : Those with mental health disorders such as anxiety, bipolar disorder , and depression tend to have more distressing dreams, as well as more difficulty sleeping in general.
• Anxiety is linked to stress dreams : Research indicates a strong connection between anxiety and stressful dream content. These dreams may be the brain's attempt to help us cope with and make sense of these stressful experiences.

While many theories exist about why we dream, more research is needed to fully understand their purpose. Rather than assuming only one dream theory is correct, dreams likely serve various purposes. In reality, many of these dream theories may be useful for explaining different aspects of the dreaming process.

If you are concerned about your dreams and/or are having frequent nightmares , consider speaking to your doctor or consulting a sleep specialist.

National Institute of Neurological Disorders and Stroke. Brain basics: Understanding sleep .

Horikawa T, Tamaki M, Miyawaki Y, Kamitani Y. Neural decoding of visual imagery during sleep . Science . 2013;340(6132):639-42. doi:10.1126/science.1234330

De Gennaro L, Cipolli C, Cherubini A, et al. Amygdala and hippocampus volumetry and diffusivity in relation to dreaming . Hum Brain Mapp . 2011;32(9):1458-70. doi:10.1002/hbm.21120

Zhang W, Guo B. Freud's dream interpretation: A different perspective based on the self-organization theory of dreaming . Front Psychol . 2018;9:1553. doi:10.3389/fpsyg.2018.01553

Malinowski J, Carr M, Edwards C, Ingarfill A, Pinto A. The effects of dream rebound: evidence for emotion-processing theories of dreaming .  J Sleep Res . 2019;28(5):e12827. doi:10.1111/jsr.12827

Boag S. On dreams and motivation: Comparison of Freud's and Hobson's views .  Front Psychol . 2017;7:2001. doi:10.3389/fpsyg.2016.02001

Eichenlaub JB, Van Rijn E, Gaskell MG, et al. Incorporation of recent waking-life experiences in dreams correlates with frontal theta activity in REM sleep . Soc Cogn Affect Neurosci . 2018;13(6):637-647. doi:10.1093/scan/nsy041

Zhang W. A supplement to self-organization theory of dreaming .  Front Psychol . 2016;7. doi:10.3389/fpsyg.2016.00332

Luongo A, Lukowski A, Protho T, Van Vorce H, Pisani L, Edgin J. Sleep's role in memory consolidation: What can we learn from atypical development ?  Adv Child Dev Behav . 2021;60:229-260. doi:10.1016/bs.acdb.2020.08.001

Scarpelli S, D'Atri A, Gorgoni M, Ferrara M, De Gennaro L. EEG oscillations during sleep and dream recall: state- or trait-like individual differences ?  Front Psychol . 2015;6:605. doi:10.3389/fpsyg.2015.00605

Llewellyn S, Desseilles M. Editorial: Do both psychopathology and creativity result from a labile wake-sleep-dream cycle? . Front Psychol . 2017;8:1824. doi:10.3389/fpsyg.2017.01824

Revonsuo A. The reinterpretation of dreams: An evolutionary hypothesis of the function of dreaming .  Behav Brain Sci . 2000;23(6):877-901. doi:10.1017/s0140525x00004015

Ruby PM. Experimental research on dreaming: State of the art and neuropsychoanalytic perspectives . Front Psychol . 2011;2:286. doi:10.3389/fpsyg.2011.00286

Gujar N, McDonald SA, Nishida M, Walker MP. A role for REM sleep in recalibrating the sensitivity of the human brain to specific emotions . Cereb Cortex . 2011;21(1):115-23. doi:10.1093/cercor/bhq064

Blagrove M, Hale S, Lockheart J, Carr M, Jones A, Valli K. Testing the empathy theory of dreaming: The relationships between dream sharing and trait and state empathy . Front Psychol . 2019;10:1351. doi:10.3389/fpsyg.2019.01351

Poe GR. Sleep is for forgetting .  J Neurosci . 2017;37(3):464-473. doi:10.1523/JNEUROSCI.0820-16.2017

Zhang J. Continual-activation theory of dreaming .  Dynamical Psychology .

Hoel E. The Overfitted Brain: Dreams evolved to assist generalization . Published online 2020.doi:10.48550/arXiv.2007.09560

Vallat R, Ruby PM. Is it a good idea to cultivate lucid dreaming? Front Psychol . 2019;10:2585. doi:10.3389/fpsyg.2019.02585

Baird B, Mota-Rolim SA, Dresler M. The cognitive neuroscience of lucid dreaming . Neurosci Biobehav Rev . 2019;100:305-323. doi:10.1016/j.neubiorev.2019.03.008

Stumbrys T, Daunytė V. Visiting the land of dream muses: The relationship between lucid dreaming and creativity . 2018;11(2). doi:10.11588/ijodr.2018.2.48667

Rek S, Sheaves B, Freeman D. Nightmares in the general population: Identifying potential causal factors . Soc Psychiatry Psychiatr Epidemiol . 2017;52(9):1123-1133. doi:10.1007/s00127-017-1408-7

Sikka P, Pesonen H, Revonsuo A. Peace of mind and anxiety in the waking state are related to the affective content of dreams . Sci Rep . 2018;8(1):12762. doi:10.1038/s41598-018-30721-1

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

7 Major Questions (and Answers) About Dreaming

Dreaming is a mysterious process — one that scientists are still figuring out..

Posted July 28, 2017 | Reviewed by Ekua Hagan

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As a sleep specialist, not a day goes by that I don't talk to someone about their dreams. My patients, my kids, the guy who sells me my morning coffee — everyone wants to know, “What do my dreams mean?” We’re all fascinated by dreams, and understandably so. Dreaming is a strange and mysterious process — one that we still don’t fully understand. Let’s take a closer look at the stuff of which dreams are made:

1. Why Do We Dream?

The why of dreaming is one of the great mysteries of sleep. There are many theories about why dreaming happens. Some think that dreaming has no specific underlying purpose, that our dreams might be a byproduct of other things going on in the brain during sleep. But many researchers studying sleep and dreams believe there is a primary purpose to our dreams. Some theories suggest that dreams are:

• A way to process memory and learning, moving memories from short-term to long-term storage and giving the brain a clean slate before the next waking day.
• A way to maintain emotional balance, by working through difficult, complicated, unsettling thoughts, emotions, and experiences.
• A different state of consciousness that unites past, present, and future — to process information from the first two, and prepare for the third.
• A kind of dress rehearsal for the brain, to prepare itself to face threats, dangers, and challenges in waking life.
• The brain responding to biochemical changes and electrical impulses that occur during sleep.

There may not be a single answer to why we dream. Our dreams might serve several purposes at once.

2. What Is a Dream? Do We All Dream?

At its most basic level, a dream is a collection of images, impressions, events, and emotions that we experience during sleep. Sometimes dreams have real storylines, with plots and characters that could be plucked from a movie screen. Other times dreams are more impressionistic, filled with emotions and visual imagery.

Typically, a person will spend two hours or more a night dreaming, experiencing somewhere between 3 to 6 dreams over the course of a night’s rest. Most dreams appear to last from 5 to 20 minutes.

I often hear people say, “I don’t dream.” You may not remember your dreams, but that doesn’t mean you’re not having them. Dreaming is a universal human experience. The truth is, the vast majority of dreams we experience will — for most of us — never be remembered. Memories of dreams usually fade very quickly after we awaken.

3. Why Can’t I Remember My Dreams?

The ability to recall dreams varies greatly from one individual to another. Some people regularly remember their dreams, while others may have only hazy recollections of themes or subjects — or no recollection at all.

There are a number of possible explanations for this. Studies suggest dream recall may be linked to patterns of activity in the brain. Our ability to recall our dreams may be influenced by interpersonal attachment styles — the way we tend to form bonds with other people in our lives.

Changing hormone levels throughout the night might also have a role in our ability to recall our dreams. During REM sleep — a time of active dreaming — levels of the hormone cortisol are high and may interfere with communication between areas of the brain that are involved in memory consolidation.

Our most active dreaming occurs during REM sleep. Adults spend roughly 25 percent of their sleep time in REM sleep, with longer periods of REM sleep occurring later in the night and in the early morning.

REM sleep is one part of the normal sleep cycle . In addition to REM sleep, sleep cycles contain three other stages. Dreaming can occur in every stage of sleep. Dreams during REM sleep appear to be more visually vivid, bizarre, and narratively driven than dreams during other sleep stages.

Have you ever woken and not been able to move or speak? This scary sleep phenomenon is indirectly related to dreaming. During REM sleep, the body goes into a state of temporary paralysis, a condition known as REM atonia. This appears to be the body’s way of protecting itself during dreaming. REM atonia keeps us from acting out physically in response to dreams. Think of some of the scary or exciting dreams you’ve experienced. Maybe you’ve been flying over a mountain range, or been chased by a masked intruder. Imagine if you could respond physically to these dream experiences? You might fly yourself right out of bed onto the floor.

It’s possible to awaken and still be in a state of sleep paralysis. This can be a really frightening experience, particularly the first time it happens. Waking in sleep paralysis is a sign that your body may not be making smooth transitions between the stages of sleep. This can be the result of stress, sleep deprivation, and other sleep disorders including narcolepsy, as well as a side effect of medications or over-consumption of drugs or alcohol .

4. Are There Different Kinds of Dreams?

Not all dreaming is the same. Dreaming runs the gamut of human experience. Our dreams encompass a dizzying range of emotions and events — and sometimes they’re just downright bizarre. Dreams can be funny, frightening, sad, and strange. Flying dreams can be euphoric, chasing dreams can be terrifying, forgot-to-study-for-my-exam dreams can be stressful .

There are several different types of dreams, including recurring dreams, wet dreams, and lucid dreams. (Nightmares are their own special kind of dream, which I’ll talk about in a separate article.) Let’s look at some distinct forms of dreaming.

Recurring dreams may contain more threatening and disturbing content than regular dreams. Research suggests there are links between recurring dreams and psychological distress in both adults and children.

Wet dreams are also called nocturnal emissions. These dreams involve ejaculation during sleep, usually accompanied by a sexual dream. Wet dreams may happen to boys during puberty , when testosterone starts to be produced in the body, and they are a normal part of healthy development.

Lucid dreams are an especially fascinating form of dream. In lucid dreams, the dreamer is aware of the fact that he or she is dreaming. Lucid dreamers often can even manipulate or control their dream as it unfolds. It seems that lucid dreaming is related to unusually elevated levels of brain activity. Lucid dreamers have shown significantly higher brain wave frequencies than non-lucid dreamers, as well as increased activity in parts of the frontal lobe of the brain. This area of the brain is deeply involved with our conscious awareness, a sense of self, as well as language and memory. Research into lucid dreams is not only shedding light on the mechanics of dreaming but also teaching us about the brain and about consciousness itself.

5. What Are the Most Common Dreams?

Examining and interpreting the content of dreams has fascinated people since ancient times. In ancient cultures, dream interpreters were sought-after and revered experts. Most of what we know today about dream content has been gathered using dream reports and questionnaires. Dream experiences vary widely, but some well-established themes occur among many dreamers across ages and cultures, including:

• School dreams (studying, taking tests)
• Being chased
• Sexual dreams
• Being attacked physically
• Dreaming of someone dead being alive, or someone alive being dead

New brain-imaging technology is allowing scientists to peek into dreaming minds like never before. Scientists are now analyzing brain activity during sleep to decode the content of dreams. A group of scientists in Japan has been able to predict dream content using MRI imaging with 70 percent accuracy. Scientists at the University of Wisconsin-Madison recently found that the areas of the brain used to perform tasks in our waking lives are also used for those tasks in dreams. One example: If a dream involves movement, the area of the brain used for movement perception becomes more active.

6. How Much of Dreaming Comes From My Daily Life?

Our waking lives seem to have an enormous influence over our dreams. A significant percentage of the people who appear in dreams are known to the dreamer. One study found more than 48 percent of dream characters were recognizable by name to dreamers. Another 35 percent of characters were identifiable to dreamers by their generic social role or relationship — as a friend, or a doctor or police officer, for example. Fewer than one-fifth of dream characters — 16 percent — were unrecognizable to dreamers.

A lot of our dreams contain content that’s related to autobiographical memories — memories about the self. Pregnant women dream more about pregnancy and childbirth. Hospice workers who act as caregivers to others dream about the experiences of caregiving and the people for whom they care. Musicians dream twice as often about music as non-musicians do.

There’s also some fascinating research that shows our capacity to dream beyond our waking experiences. Dream reports of people born paralyzed reveal that they walk, swim, and run in their dreams as often as people without paralysis. Dream reports of people born deaf indicate they often hear in their dreams.

Daily life experiences don’t always present themselves in dreams immediately. Sometimes an experience from life will filter through to a dream after several days or even a week. This delay is what’s known as dream lag. Scientists studying the relationship of memory to dreams have identified different types of memory that can be incorporated into dreams. Both very short-term memories (known as day-residue) and slightly longer-term memories (from a period of about a week) often present themselves in dreams. Dreaming of these events may actually be an important part of the memory consolidation process. The incorporation of memories into dreams isn’t necessarily seamless or even realistic. Rather, memories from waking life often appear in dreams in incomplete pieces, like shards of glass from a broken mirror.

As much as dreams may contain aspects of everyday, routine life, dreaming is also a state in which we can contend with extraordinary and difficult experiences. Another possible function of dreaming is processing and coming to terms with traumatic events. Grief , fear , loss, abandonment, even physical pain, are all emotions and experiences that often replay themselves in dreams. Studies of people who’ve experienced the loss of loved ones indicate that most of them dream about the deceased. Grieving people report several similar themes to these dreams, including:

• Recalling past experiences when loved ones were alive.
• Seeing loved ones happy and at peace.
• Receiving messages from loved ones.

The same study found that 60 percent of bereaved dreamers said their dreams exerted influence over their grieving process.

7. Can Dreaming Give Me a Performance Boost?

Dreams may help us solve problems and be creative. One study of musicians’ dreams found that not only did they dream frequently of music, but nearly half of the music they recalled from their dreams was unfamiliar and novel to them, suggesting that composing is possible in dreams. Paul McCartney famously credited the composition of "Yesterday” to a dream. Other artists, from the poet William Blake to the filmmaker Ingmar Bergman, have claimed to rely on dreams for creative inspiration and guidance. The golfer Jack Nicklaus said he sorted out a nagging problem with his golf swing in a dream.

Dreaming can help with at least some types of problem-solving. Lucid dreamers can use their dreams effectively to solve creative problems, according to research. Dreams seem to be fertile territory for influencing and enhancing our waking frame of mind.

Dreams can provide us with insight into what is preoccupying our minds and our hearts. Often healing, often mysterious, always fascinating, dreams can both shape us and show us who we are.

Michael J. Breus, Ph.D. , is a clinical psychologist and a diplomate of the American Board of Sleep Medicine. He is the author of Beauty Sleep.

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How to Interpret Your Dreams

Staff Writer

Jay Summer is a health content writer and editor. She holds a B.S. in psychology and master's degrees in writing and public policy.

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Internal Medicine Physician

Dr. Rehman, M.D., is a board-certified physician in Internal Medicine as well as Endocrinology, Diabetes, and Metabolism.

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Table of Contents

Theories of Dream Interpretation

Common dreams and possible meanings, what do nightmares mean, tips for analyzing and interpreting your dreams.

• Scientific research supporting dream interpretation is still relatively new.
• Current theories about dreams suggest that they help with emotional processing, memory consolidation, performance, and creativity.
• Common dream topics include teeth falling out, sex, and falling through the air.
• You can interpret your dreams by remembering common themes, keeping a dream diary, and considering influences in your personal life.

Dreams are a normal part of healthy sleep, with the average person spending around two hours Trusted Source National Institute of Neurological Disorders and Stroke (NINDS) NINDS aims to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease. View Source dreaming every night. Despite the amount of time people spend dreaming, there is a great deal researchers still do not understand about the phenomenon. It is still unclear whether individual dreams carry deeper meaning.

Dreams can weave a complex narrative that feels deeply personal. Many people are eager to share the content of their dreams and work to understand their underlying meaning.

We’ll cover the science of dream interpretation, from the psychoanalysts of the early twentieth century to the most recent science-based theories examining the underlying meaning of dreams. We’ll also take a look at the most common dream topics and tips to help with dream interpretation.

People have tried to decipher the meaning of dreams since the dawn of civilization, though scientific research on dreams is relatively new. The most prominent theories of dream interpretation include pioneers Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source from the early twentieth century to modern neuroscientists Trusted Source SpringerLink SpringerLink provides researchers with access to millions of scientific documents from journals, books, series, protocols, reference works, and proceedings. View Source .

Freud, the most cited psychologist of the 20th century, published The Interpretation of Dreams in 1900. This book represented a significant milestone Trusted Source American Psychological Association (APA) APA is the leading scientific and professional organization representing psychology in the United States, with more than 121,000 researchers, educators, clinicians, consultants and students as its members. View Source in the field of dream interpretation.

According to Freud, dreams represent a form of wish fulfillment Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source and hold the key to a person’s desires. He indicated that the subject of a person’s dreams stems from reality, but dreams are not identical to waking life and cannot be taken at face value. Instead, the underlying meaning of a dream is hidden in a person’s unconscious mind, the thoughts and feelings that lay outside of their conscious awareness.

Carl Jung was a contemporary of Freud and was greatly influenced Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source by Freud’s Interpretation of Dreams . But as Jung matured, his thoughts on dream interpretation began to diverge from Freud’s.

Jung believed people experience different types of dreams that can be viewed through the lens of “compensation.” According to his theory of compensation, dreams are a mechanism that allow the unconscious mind to fully develop or balance parts that are in conflict with one another.

Psychologist Calvin S. Hall theorized in the 1950’s that dreams were images that represent a person’s thoughts or ideas. Hall proposed that dreams are akin to plays or enactments based on the ideas a person has about themself, other people, conflicts, impulses and urges, and their external environment.

Hall suggested that dream interpretation could help a person better understand themselves and inform their behavior in daily life.

William Domhoff’s career spanned from the 1960s to the publication of The Emergence of Dreaming in 2018 and combined the analysis of dream content with brain imaging techniques Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source and electroencephalogram (EEG) .

According to Domhoff’s research, dreaming is similar to the daydreams most people experience in waking life. His work suggests that dreams do not serve a specific function and are likely a byproduct of the way the brain works.

Modern Theories

The complex theories developed by early pioneers of dream interpretation have largely been replaced by a neuroscience-focused approach.

• Emotional processing: One prominent theory suggests that the contents of a dream may help the dreamer process Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source difficult life experiences. In particular, the vivid dreams of REM sleep may help the brain process waking experiences and regulate emotions.
• Memory consolidation: Dreaming may also play an important role in forming Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source new memories. This theory asserts that dreams are a key part of the nervous system process that converts short-term memories formed during the day into long-term memories.
• Performance and creativity: According to the overfitted brain hypothesis, the typical experiences of daily life do not prepare the brain for unexpected events. Dreams offer people a hallucinatory narrative that serves to improve cognitive performance and boost creativity during waking hours.

Individual personality and interests can influence Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source the narratives of a person’s dreams. However, certain consistent themes are known to arise in dreams for many different people.

Teeth Falling Out

Dreams about teeth falling out are one of the most common Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source dream narratives. Numerous theories have been proposed to explain the deeper significance of dreams involving teeth.

The ancient Greek Artemidorus believed these dreams could be interpreted based on which specific tooth or teeth a dreamer loses. The early twentieth-century psychoanalyst Sigmund Freud theorized that dreams about teeth had a sexual basis. Other experts have proposed that this type of dream represents anxiety around aging. However, recent research suggests that dental irritation or tension in the jaw while sleeping may contribute to dreams about teeth.

Sex and Cheating

Dream narratives that include sexual content are frequently reported, with more than 70% of people Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source experiencing dreams about sex . These types of dreams may mirror a person’s feelings about sex or reflect unsatisfied desires. Similarly, one study found that dreaming about a cheating partner may be linked to low levels of intimacy or feelings of jealousy in a relationship.

Natural Disasters

Dreams about natural disasters may involve a flood , fire, earthquake, or apocalyptic narrative. Natural disaster dreams may be related to the traumatic events Trusted Source Medline Plus MedlinePlus is an online health information resource for patients and their families and friends. View Source and stressors of a person’s lived experience. People affected by natural disasters such as hurricanes Trusted Source The Substance Abuse and Mental Health Services Administration (SAMHSA) SAMHSA is the agency within the U.S. Department of Health and Human Services that leads public health efforts to advance the behavioral health of the nation. View Source and tornadoes Trusted Source The Substance Abuse and Mental Health Services Administration (SAMHSA) SAMHSA is the agency within the U.S. Department of Health and Human Services that leads public health efforts to advance the behavioral health of the nation. View Source may experience feelings of depression, anxiety, or fear about the storm in the form of nightmares.

The feeling of falling through the air while dreaming is a frequent theme that has emerged from dream research. Falling is a common sensation people experience before a hypnic jerk. Hypnic jerks are involuntary lurches that can involve a part of the body or the body as a whole.

Nightmares may signify that a person is struggling with stress , trauma , or a sleep disorder . Nightmares are vivid dream sequences that involve distressing events and often wake a person from sleep. They can invoke feelings of fear and anxiety, but nightmares can also cause embarrassment, anger, and disgust.

Nightmares occur in people of all ages from time to time, though they are more common in children. Occasional nightmares can be disturbing but many people who experience nightmares do not require treatment. They may be a sign that a person is experiencing a stressful life event like a move, starting a new school or job, or having troubles at home.

Other reasons a person may experience nightmares include starting or stopping a prescription medication, the use of illegal drugs, drinking too much alcohol or sudden alcohol withdrawal, using non-prescription sleep aids, or having an illness accompanied by a fever.

People who experience repeated nightmares may want to speak with a health care provider. Frequent, distressing nightmares can be an indication of sleep apnea or another sleep disorder. Persistent nightmares may also be a sign of a mental health condition such as an anxiety disorder , depression, or post-traumatic stress disorder (PTSD) .

Interpreting dreams is far from an exact science. But a few tips can help people better understand their dreams.

• Keep a dream journal: Using a journal Trusted Source American Psychological Association (APA) APA is the leading scientific and professional organization representing psychology in the United States, with more than 121,000 researchers, educators, clinicians, consultants and students as its members. View Source or smart-phone app to record your dreams shortly after waking up can help you document the details of your dream. Researchers have used dream journals and dream diaries to help study participants recall their dreams with more accuracy Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source .
• Consider your biases: Research suggests that your interpretation of a dream may be influenced by religious beliefs and interpersonal relationships. When reflecting on a dream, consider whether you are looking for information that confirms Trusted Source SAGE Publishing SAGE produces high quality educational resources that support instructors to prepare the citizens, policy makers, educators and researchers of the future. View Source your current beliefs.
• Talk with a professional: Consider speaking with a health care professional if you have recurring dreams or nightmares that negatively affect your well-being. These may be a warning sign of another medical condition such as anxiety, depression, or a sleep disorder such as sleep apnea or nightmare disorder. A health care provider can help make a diagnosis and discuss treatment, if needed. If you feel that your particular situation does not warrant the help of a medical professional, you can consider speaking with a sleep consultant. Sleep consultants are trained to handle a number of sleep issues and can guide you in the right direction toward a better night’s sleep.
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References 20 sources.

National Institute of Neurological Disorders and Stroke. (2022, April, 1). Brain basics: Understanding sleep.

Hughes, J. D. (2000). Dream interpretation in ancient civilizations. Dreaming, 10(1):7-18.

Yeung, A. (2021). Is the influence of Freud declining in psychology and psychiatry? A bibliometric analysis. Frontiers in Psychology, 12, 631516.

Shaw, B. (2016) Developments in the neuroscience of dreams. Activitas Nervosa Superior, 58, 45–50.

Webb, W. B. (1994). Retrospective review: Sigmund Freud’s The Interpretation of Dreams. Dreaming, 4(1), 54–58.

Zhang, W., & Guo, B. (2018). Freud’s dream interpretation: A different perspective based on the self-organization theory of dreaming. Frontiers in Psychology, 9, 1553.

Zhu, C. (2013). Jung on the nature and interpretation of dreams: A developmental delineation with cognitive neuroscientific responses. Behavioral Sciences, 3(4), 662–675.

Domhoff, G. W., & Fox, K. C. (2015). Dreaming and the default network: A review, synthesis, and counterintuitive research proposal. Consciousness and Cognition, 33, 342–353.

Scarpelli, S., Bartolacci, C., D’Atri, A., Gorgoni, M., & De Gennaro, L. (2019). The functional role of dreaming in emotional processes. Frontiers in Psychology, 10, 459.

Wamsley, E. J. (2014). Dreaming and offline memory consolidation. Current Neurology and Neuroscience Reports, 14(3), 433.

Nir, Y., & Tononi, G. (2010). Dreaming and the brain: from phenomenology to neurophysiology. Trends in cognitive sciences, 14(2), 88–100.

Rozen, N., & Soffer-Dudek, N. (2018). Dreams of teeth falling out: An empirical investigation of physiological and psychological correlates. Frontiers in Psychology, 9, 1812.

Shao, X., Wang, C., Jia, Y., & Wang, W. (2020). Sexual dream and family relationships in frequent sexual dreamers and healthy volunteers. Medicine, 99(36), e21981.

Clarke, J., DeCicco, T. L., & Navara, G. (2010). An investigation among dreams with sexual imagery, romantic jealousy and relationship satisfaction. International Journal of Dream Research, 3(1), 54–59.

A.D.A.M. Medical Encyclopedia. (2020, May 10). Nightmares. MedlinePlus.

U.S. Department of Health and Human Services. (2022, April 14). Hurricanes and Tropical Storms. Substance Abuse and Mental Health Services Administration.

U.S. Department of Health and Human Services. (2022, April 14). Tornadoes and Severe Storms. Substance Abuse and Mental Health Services Administration.

Barrett, D., & Luna, K. (2018, December). Speaking of psychology: The science of dreaming. American Psychological Association.

Mangiaruga, A., Scarpelli, S., Bartolacci, C., & De Gennaro, L. (2018). Spotlight on dream recall: the ages of dreams. Nature and science of sleep, 10, 1–12.

Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220.

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74 Dreaming Essay Topic Ideas & Examples

🏆 best dreaming topic ideas & essay examples, ✍️ interesting topics to write about dreaming, 🔖 good essay topics on dreaming, ❓ research questions about dreams.

• The Importance of Sleeping and Dreaming Finally, I would not take this pill since I love seeing dreams and realize that this “miracle medicine” will cause too many negative consequences.
• Dreaming, Consciousness and Cognition For instance, the behaviorist supposition that the brain is always awakened and only from the external by sense organ procedures cannot define daydreams; likewise, for the statement that consciousness is the straight or restricted product […]
• Dreams and the Process of Dreaming Analysis Dreams are said to be like opening a door to the rest of the mind, all of one’s friends, fears, phobias, hopes, wishes, good times, and bad times are there.
• Lucid Dreaming in Science Fiction and Technology The author provides an interesting and intriguing article about the phenomenon of lucid dreaming and its representation in culture and media.
• Impoverished and Excessive Dreaming Many patients saw a dog in their dreams that tried to bite them; they began to defend themselves or hit the dog, and, in reality, they hit their spouses or walls/beds.
• Nature and Functions of Dreaming Still, other researchers argue that one of the key functions of dreams is to maintain our bodily and psychological health. To conclude, it is obvious that many suggestions have been put forward by researchers about […]
• Kertha Gosa Ceiling vs. “Dreaming” paintings by Aborigines of Australia Over a long period, Aborigine’s paintings have advanced to the point of intertwining with the public dissertation, with a great recognition in Australia and the rest of the world.
• Concept of Dreaming Theories in Psychology One of the theories that are common is the belief that dreams occur as a result of the human mind trying to incorporate external stimuli while one is sleeping.
• The Use of Illusion Argument, Dreaming Argument, and Evil Genius Argument by Descartes
• The Centrality of the Dreaming and Its Importance for Aboriginal Spirituality
• An Overview of the Dream State and the Concept of Human Dreaming
• Animal Dreaming And Substantiation A Connection To Humanity
• Understanding the Unconcious Dreaming
• How Is the Power of Dreams and Dreaming in the Novel of Mice and Men
• Dreams, Dreaming and Phases of Sleep
• Phenomenology of Dreaming
• The Beauty Of Dreaming: How Dreams Drive The Individual
• The Dreaming and Traditional Aboriginal Spirituality
• Freud’s Theory of Dreaming and Repression
• Sleeping and Dreaming and Theories of Sleep
• Gender And Dreaming In Mapuche Shamanistic Practices
• The Benefits Of Lucid Dreaming
• An Overview of the Controversy of Dreaming, a Cognitive Activity During Sleep
• The Importance of Dreaming and Sleeping
• Procrastination and Day Dreaming
• The Psychological Theories Of The Function Of Dreaming
• Difference Between Astral Projection And Lucid Dreaming
• Dreaming as Significant Process in Human Life Experience
• Exploring Causes of Sleep Difficulty and Dreaming Problems
• Dreams and Dreaming Nightmares in Children
• Dreaming Can Bring Misery in the Great Gatsby By F. Scott
• Varieties of Lucid Dreaming Experience, by Stephen Laberge
• The Significance of Land to the Dreaming for Aboriginal People and the Impact of the Land Rights Movement
• Dreaming And Post Traumatic Stress Disorder
• Understanding the Science of Dreaming Through Oneirology
• The Importance of Dreaming and the Sub-Conscious
• Descartes’ Meditations: Dreaming and Evil Demon Arguments
• Dreaming Various Amount Of People Experiences Different Effects
• Comparing and Contrasting Psychological Theories of Dreaming
• The Skeptical Dreaming Argument of Rene Descartes, and the Priori and the Posteriori
• Dreaming Is Known As The Journey Your Mind
• Day Dreaming in the Middle of the Summer Heat
• Synchroncities in the History of Paranormal Dreaming
• What Dreams May Come True?
• What Every Athlete Dreams, of but Few Achieve?
• What Makes Your Friend’s Dreams Come True?
• What Does the Bible Say About Dreams?
• When Dreams and Reality Collide?
• Why Do We Forget Dreams?
• Why Are Dreams Interesting for Philosophers?
• What Makes a Nightmare a Nightmare?
• What’s the Most Common Nightmare?
• What Are the Most Typical Nightmare Themes and What Do They Mean?
• Why Are Dreams Important to Duddy Kravitz?
• Why Do People Dream and What the Dreams Mean?
• What Are Dreams, and Do They Affect Us in a Good Way or a Bad Way?
• What Are the Key Similarities and Differences Between Freud and Jung’s Theories of Dreams?
• What Are You Doing to Achieve Your Dreams?
• How Dreams Affect Our Personalities?
• How Dreams and Omens Support the Theme of Interconnection?
• How Can Dreams Sustain People Through Life, or Can Break Them Down When It Doesn’t Come True?
• How Do Dreams Have Symbolic Meaning?
• How Women Follow Their Dreams Without Embarrassment?
• How Do Different People Use Different Things to Escape Life Problems or Find Motivation to Dreams?
• Can Dreams Tell the Future?
• Are Dreams Messages From Our Subconscious Mind or Insignificant Manifest?
• Are Dreams the Reason for Mythology?
• Can Blind Person See Dreams?
• What Are the Most Rare Dreams?
• How Long Do Dreams Last?
• Can You Learn From Your Dreams?
• Do We Dream Differently Across the World?
• Do We Know When We Are Dreaming?
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The cognitive neuroscience of lucid dreaming

Benjamin baird.

a Wisconsin Institute for Sleep and Consciousness, University of Wisconsin-Madison, Madison, Wisconsin, USA

Sergio A. Mota-Rolim

b Brain Institute, Physiology Department and Onofre Lopes University Hospital - Federal University of Rio Grande do Norte, Natal, Brazil

Martin Dresler

c Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands

Lucid dreaming refers to the phenomenon of becoming aware of the fact that one is dreaming during ongoing sleep. Despite having been physiologically validated for decades, the neurobiology of lucid dreaming is still incompletely characterized. Here we review the neuroscientific literature on lucid dreaming, including electroencephalographic, neuroimaging, brain lesion, pharmacological and brain stimulation studies. Electroencephalographic studies of lucid dreaming are mostly underpowered and show mixed results. Neuroimaging data is scant but preliminary results suggest that prefrontal and parietal regions are involved in lucid dreaming. A focus of research is also to develop methods to induce lucid dreams. Combining training in mental set with cholinergic stimulation has shown promising results, while it remains unclear whether electrical brain stimulation could be used to induce lucid dreams. Finally, we discuss strategies to measure lucid dreaming, including best-practice procedures for the sleep laboratory. Lucid dreaming has clinical and scientific applications, and shows emerging potential as a methodology in the cognitive neuroscience of consciousness. Further research with larger sample sizes and refined methodology is needed.

1. Introduction

Becoming aware that one is dreaming while dreaming, what is today referred to as lucid dreaming , has been known about since antiquity. In Western literature, it may have first been mentioned by Aristotle in the fourth century BCE in the treatise On dreams of his Parva Naturali , in which he states: “often when one is asleep, there is something in consciousness which declares that what then presents itself is but a dream” ( Aristotle, 1941 , p. 624). Likewise, in Eastern cultures, particularly of the south Asian subcontinent, reports of individuals engaging in practices to cultivate awareness of dream and sleep states date back millennia ( LaBerge, 2003 ; Norbu and Katz, 1992 ; Wallace and Hodel, 2012 ). These include meditative practices specifically designed to “apprehend the dream state” ( Padmasambhava, 1998 , p. 156).

Although numerous references to lucid dreaming can be found throughout world literature (see LaBerge, 1988a for an overview), the modem nomenclature of lucid dream was not introduced until 1913 by the Dutch psychiatrist Frederik Van Eeden. In a detailed and engaging account of his personal experiences with dreams, Van Eeden (1913) referred to lucid dreams as dreams in which “…the reintegration of the psychic functions is so complete that the sleeper remembers day-life and his own condition, reaches a state of perfect awareness, and is able to direct his attention, and to attempt different acts of free volition” (pp. 149-150). Research over the last four decades has largely confirmed Van Eeden’s accounts: as we review below, evidence suggests that during lucid dreams individuals can be physiologically asleep while at the same time aware that they are dreaming, able to intentionally perform diverse actions, and in some cases remember their waking life ( Dresler et al., 2011 ; LaBerge, 1985 , 1990 ; LaBerge, 2015 ; LaBerge, Nagel, Dement and Zarcone, 1981c ; Windt, 2015 ).

Despite the fact that such personal accounts of lucid dreams have been described for centuries, the topic faced skepticism from some scientists and philosophers (e.g., Malcolm, 1959 ), in part due to the lack of objective evidence for the phenomenon. This began to change in the late 1970s and early 1980s, however, with the first validation of lucid dreaming as an objectively verifiable phenomenon occurring during rapid eye movement (REM) sleep. Building on prior research that showed that shifts in the direction of gaze within a dream can be accompanied by corresponding movements of the sleeper’s eyes ( Dement and Wolpert, 1958 ), lucid dreamers were asked to move their eyes in a distinct pre-agreed upon sequence (full-scale up-down or left-right movements) as soon as they became lucid ( Hearne, 1978 ; LaBerge et al., 1981c ).

Through this technique, which has since become the gold standard, reports of lucid dreams could be objectively verified by the presence of distinct volitional eye movement patterns as recorded in the electrooculogram (EOG) during polysomnography-verified sleep ( Figure 1 ). The most common version of the eye signaling technique asks participants to signal when they realize they are dreaming by rapidly looking all the way to the left then all the way to the right two times consecutively then back to center in the dream without pausing (referred to as left-right-left-right eye signals, abbreviated as LRLR). As can be seen in Figure 1 , the LRLR signal is readily discernable in the horizontal EOG, which exhibits a distinctive shape containing four consecutive full-scale eye movements that have larger amplitude compared to typical REMs. As we describe in detail below, lucid dreams can be validated by this method through the convergence between reports obtained after awakening of becoming lucid and making the eye movement signals during the dream, accompanied by the objective eye movement signals recorded in the EOG with concurrent polysomonographic evidence of REM sleep.

Exemplary left-right-left-right-center (LRLR) eye movement signal during polysomnographcally-verified REM sleep. Participants signal when they realize they are dreaming by rapidly looking all the way to the left (as if looking at their ear) then all the way to the right two times consecutively then back to center without pausing. The LRLR signal is readily discernable in the HEOG, which exhibits a distinctive shape of four consecutive full-scale eye movements of higher amplitude compared to typical REMs. Note high-frequency electroencephalogram (EEG) with theta rhythm (~5 Hz) and lack of alpha at OZ as well as minimal electromyogram (EMG) amplitude due to muscle atonia characteristic of REM sleep (left) compared to wakefulness (right).

The eye signaling technique also offers a way of objectively contrasting lucid REM sleep to baseline non-lucid REM sleep, providing a method to investigate the changes in brain activity associated with lucid dreaming. Furthermore, lucid dreamers can not only signal to indicate that they are aware that they are dreaming, but they can also make the eye movement signals to time-stamp the start and end of experimental tasks performed during lucid dreams ( LaBerge, 1990 ). By providing objective temporal markers, this technique has opened up a new method for studying the psychophysiology of REM sleep, allowing, for example, investigations into the neural correlates of dreamed behaviors (e.g., Dresler et al., 2011 ; Erlacher, Schredl and LaBerge, 2003 ; LaBerge, 1990 ; Oudiette et al., 2018 ). Lucid dreaming thus provides a way to establish precise psychophysiological correlations between the contents of consciousness during sleep and physiological measures, as well as enables experimental control over the content of dreams, and therefore provides a potentially highly useful experimental methodology.

While neuroscientific studies on lucid dreaming have been performed since the late 1970s, the topic has received increasing attention in recent years due to its relevance to the emerging neuroscience of consciousness. In this article, we review the existing literature on the neuroscience of lucid dreaming, including electrophysiological, neuroimaging, brain lesion, pharmacological and brain stimulation studies. Additionally, we review recent studies that illustrate how lucid dreaming can be used as a methodology in the cognitive neuroscience of consciousness. Finally, we present strategies to measure lucid dreaming both physiologically and with questionnaires, and discuss procedures to investigate lucid dreaming in the sleep laboratory.

2. Electrophysiology of lucid dreaming

As noted in the introduction, the first physiological studies of lucid dreaming began in the late 1970s and early 1980s. These pioneer works established that lucid dreams occur in REM sleep, characterized by all of the EEG features of REM sleep according to the Rechtschaffen and Kales (1968) sleep scoring criteria ( Hearne, 1978 ; LaBerge et al., 1981c ). LaBerge (1980b) also observed that lucid dreams are associated with increased physiological activation, as measured by increased phasic activity (e.g., increased REM density). Autonomic nervous system arousal (e.g., heart rate, respiration rate, and skin potential) were also found to be elevated during lucid REM sleep compared to non-lucid REM sleep ( LaBerge, Levitan and Dement, 1986 ). Additionally, lucid dreams were found to occur in REM sleep periods later in the night ( LaBerge et al., 1986 ). These findings suggest that lucid dreaming is associated with increased cortical activation ( LaBerge, Nagel, Taylor, Dement and Zarcone, 1981a ), which reaches its peak during phasic REM sleep. In addition to physiological markers of phasic activity, lucid REM sleep was found to be associated with h-reflex suppression ( Brylowski, Levitan and LaBerge, 1989 ), a spinal reflex that is reliably suppressed during REM sleep ( Hodes and Dement, 1964 ). Together these results indicate that lucid dreams occur in activated periods of REM sleep, as opposed to, for example, a state that is intermediate between waking and REM sleep.

These findings also raise the further question of whether lucid REM sleep is associated with localized activation of specific brain regions or changes in specific frequencies of neural oscillations compared to non-lucid REM sleep. In this section, we will review EEG studies that have attempted to address this question. As will be discussed below, while these studies represent important first steps toward measuring the electrophysiological changes associated with lucid dreaming, all of them have interpretive issues and most suffer from low statistical power. As a result, there is considerable discrepancy among findings. Below we group and discuss results based on the regional EEG band power changes reported to be associated with lucid REM sleep dreaming.

2.1. EEG studies of lucid REM sleep dreaming

2.1.1. central and posterior alpha.

Ogilvie and colleagues conducted some of the first studies to examine EEG spectral changes during lucid REM sleep. In an early case study, Ogilvie, Hunt, Sawicki and McGowan (1978) compared two lucid REM sleep epochs to six non-lucid REM sleep epochs and found an increase in the percentage of alpha band (8-12 Hz) power in a single central EEG channel. A follow-up study examined a larger group of ten participants for two nights in the sleep laboratory ( Ogilvie, Hunt, Tyson, Lucescu and Jeakins, 1982 ). Participants were awakened during the two periods of highest alpha power and two periods of lowest alpha power from a central EEG electrode during REM sleep, after which they were asked several questions about their lucidity, “prelucidity” (i.e., thoughts pertaining to dreaming without becoming lucid), and degree of dream control. Unfortunately, statistics for the number of lucid dreams reported by participants were not documented in this study, nor whether there was a significant difference between conditions in the number of lucid dreams. Instead, a composite measure was constructed by collapsing across pre-lucidity, lucidity and control, which was significantly elevated in the high alpha trials. Therefore, the number (if any) of lucid dreams that were captured by this procedure is unknown (see LaBerge (1988b) for further discussion).

Subsequent research has not supported the hypothesis that lucid REM sleep is associated with increased alpha activity. For example, in a follow-up study Tyson, Ogilvie and Hunt (1984) found that only pre-lucid but not lucid dreams significantly differed in alpha activity compared to non-lucid REM sleep. In another study of eight frequent lucid dreamers using a similar experimental design, Ogilvie, Hunt, Kushniruk and Newman (1983) observed no difference in the number of lucid dreams following awakenings from periods of high or low alpha activity. Furthermore, in a replication attempt of the original case report that observed an increase in alpha during lucid REM sleep ( Ogilvie et al., 1978 ), LaBerge and colleagues found no significant differences in alpha power at the same central channel (C3) from a single subject ( LaBerge, 1988b ). Finally, a later follow-up case study analyzed EEG spectral power in five lucid REM sleep epochs compared to non-lucid REM sleep periods, and observed no differences in alpha power ( Ogilvie, Vieira and Small, 1991 ). Together this evidence does not support a reliable association between alpha power and lucid dreaming. However, the limited spatial coverage of EEG montages in these studies (in several cases consisting of only one EEG channel) makes it unclear to what extent these results can be generalized to other brain areas.

2.1.2. Parietal beta

Holzinger, LaBerge and Levitan (2006) examined EEG spectral changes during lucid REM sleep in a group of eleven participants who reported prior experience with lucid dreaming. Six out of the eleven participants succeeded in becoming lucid in the sleep laboratory, and some during multiple REM periods, for a total of 16 signal-verified lucid dreams. The authors found increased power in the low beta frequency range (13-19 Hz) in parietal electrodes for lucid compared to non-lucid REM sleep. This study has the advantage of analyzing a larger number of lucid REM sleep periods. However, a limitation is that EEG signals were only evaluated at four electrodes (F3, F4, P3, P4). Consequently, localized changes in EEG spectra may have been missed by the low spatial resolution. Furthermore, due to technical limitations, the online low-pass filter for the EEG recordings in this study was set at 35 Hz, and therefore changes in higher-frequency activity were unable to be evaluated.

The potential functional significance of increased low beta band power in parietal areas to lucid dreaming is not understood. Holzinger et al. (2006) speculated that the increased beta power in the parietal EEG could reflect the understanding of the meaning of the words “This is a dream.” As discussed below, recent neuroimaging studies have linked parietal regions to several other cognitive functions associated with lucid dreaming, including self-reflection ( Kjaer, Nowak and Lou, 2002 ; Lou et al., 2004 ), episodic memory ( Berryhill, Phuong, Picasso, Cabeza and Olson, 2007 ; Wagner, Shannon, Kahn and Buckner, 2005 ) and agency ( Cavanna and Trimble, 2006 ), suggesting several other possible interpretations of the results. Neural oscillations in this frequency range were originally thought to reflect sensorimotor behavior; however, evidence now suggests that oscillations in this range could play a role in cognitive processing as well as facilitate large-scale neural integration (e.g., Donner and Siegel, 2011 ; Engel and Fries, 2010 ). If these results can be replicated in future studies, one hypothesis is that the increased oscillatory activity in the beta band could reflect a mechanism of integration between parietal regions and other areas, which, in some way still to be understood, helps facilitate lucid dreaming. However, it is also possible that differences in sensorimotor behavior between lucid and non-lucid REM sleep could boost brain rhythms that overlap with this frequency range ( Koshino and Niedermeyer, 1975 ; Pfurtscheller, Stancak and Neuper, 1996 ; Vanni, Portin, Virsu and Hari, 1999 ). Additional research is needed to clarify these findings.

2.1.3. Frontolateral gamma

Mota-Rolim et al. (2008) and Voss, Holzmann, Tuin and Hobson (2009) reported increased gamma band (40 Hz) power in frontolateral scalp electrodes during lucid compared to non-lucid REM sleep in three participants each. However, besides an unsuccessful replication in patients with narcolepsy ( Dodet et al., 2014 ), interpretation of these results are restricted by several experimental limitations, including the small sample size ( LaBerge, 2010 ). Importantly, caution is warranted in interpreting these findings given that, as briefly discussed by Voss et al. (2009) , scalp measurement of cortical gamma, particularly when selectively localized in the frontal and periorbital regions, may be confounded with mircrosaccades. Ocular myogenic artifacts, which occur during both saccades and microsaccades and are distinct from the artifacts associated with corneo-retinal dipole offsets, may confound scalp measurement of gamma activity. One type of such artifacts is referred to as the saccadic spike potential (SP), which occurs due to contraction of the ocular muscles during both saccades and microsaccades ( Yuval-Greenberg and Deouell, 2009 ; Yuval-Greenberg, Tomer, Keren, Nelken and Deouell, 2008 ). The influence of the SP artifact on gamma power was overlooked for a number of years; however, the need to account for this artifact on scalp measurement of induced gamma activity has now been thoroughly documented (e.g., Hipp and Siegel, 2013 ; Keren, Yuval-Greenberg and Deouell, 2010 ).

Voss et al. (2009) corrected for eye movement artifacts using regression of the EOG signal ( Gratton, Coles and Donchin, 1983 ) and by computing current source densities (CSD) in addition to scalp potentials. However, regression-based correction procedures are insufficient to remove the SP artifact, and while the CSD derivation attenuates the SP artifact at posterior channels, it is not sufficient to remove it at anterior scalp locations ( Keren et al., 2010 ; Yuval-Greenberg and Deouell, 2009 ). As Keren et al. (2010) state: “In conclusion, SCD [CSD] seems to be effective in attenuating the SP effect at posterior sites. However at sites anterior to Cz and closer to the orbits efficacy gradually decreases, preserving the temporal and spectral signature of the SP and its amplitude relative to baseline.” (p. 2258). The influence of the SP artifact on gamma band power in the comparison of lucid to non-lucid REM sleep is particularly relevant given that, as noted above, lucid REM sleep has been associated with increased phasic activation and higher eye movement density (e.g., LaBerge, 1990 ).

It is important to note that the need to account for the SP artifact does not preclude a potential association between increased frontal gamma power and lucid REM sleep. Indeed, given the link between gamma band power, local field potentials (LFPs) and the blood-oxygen-level dependent (BOLD) signal ( Lachaux et al., 2007 ; Nir et al., 2007 ), if the transition from non-lucid to lucid REM sleep involves activation or recruitment of additional frontal brain regions (a plausible hypothesis, also in light of the findings of Dresler et al. (2012) ; see Neuroimaging of lucid dreaming below), regional increases in gamma power might be predicted. However, the spatial topography and frequency localization of any such effects are likely to be strongly influenced by the correction and removal of the SP artifact.

With the aforementioned considerations in mind, more research is needed to clarify whether the increase in frontolateral gamma power during lucid REM sleep observed by Mota-Rolim et al. (2008) and Voss et al. (2009) reflects ocular myogenic or neural activity. Furthermore, future studies evaluating the relationship between lucid REM sleep and gamma activity from sensor-level EEG, particularly at anterior electrodes, need to control for the SP artifact in order for the results to be interpretable. Several methods have been shown to be suitable for removal this artifact, including direct identification and removal of contaminated data by rejecting overlapping windows of time-frequency transformed data or data correction using independent component analysis ( Hipp and Siegel, 2013 ). A study that evaluates the effect of direct removal and/or correction of the SP artifact on gamma power during lucid contrasted with baseline REM sleep would be an important addition to the literature. In summary, studies that rigorously control for myogenic artifacts, and in particular the SP artifact, are needed before conclusions can be drawn regarding the relationship between lucid REM sleep and frontal gamma activity.

2.1.4. Fronto-central delta

Dodet, Chavez, Leu-Semenescu, Golmard and Arnulf (2014) evaluated changes in EEG band power during lucid REM sleep in a group of narcoleptic patients. Given that narcoleptic patients often report a high rate of lucid dreams ( Rak, Beitinger, Steiger, Schredl and Dresler, 2015 ), they are a potentially useful population for cognitive neuroscience studies of lucid dreaming. In the experiment, while both control and narcoleptic patients reported achieving lucidity and performing the LRLR signals during overnight and afternoon nap recordings, only the eye signals of the patients during the nap recordings could be unambiguously identified. This is likely at least partially attributable to the specific instructions used for making the eye movement signal (see Section 9 below for discussion of this issue). Despite this, the study succeeded in recording 14 signal-verified lucid dreams during naps from seven narcoleptic patients. The main finding was that EEG power was reduced in the delta band during lucid REM sleep at frontal and central electrodes. The study also reported that the coherence between several electrodes was reduced in lucid compared to non-lucid REM sleep in delta, theta, beta and gamma bands, but these differences are difficult to interpret since no statistics were reported for this analysis and no corrections for multiple comparisons were made.

A limitation of the Dodet et al. (2014) study is that EEG signals were only evaluated at six electrodes, and only in frontal and central scalp regions (Fp1, Fp2, F7, F8, C3, C4). Parietal electrodes were not included in the EEG montage, and occipital channels reportedly could not be evaluated due to noise. Thus, it is possible that local changes in EEG spectra in posterior regions, such as parietal or occipital areas, were missed due to the limited electrode montage.

The finding of lower delta activity during lucid REM sleep is in line with previous observations that lucid dreams tend to occur during periods of increased cortical activation ( LaBerge, 1990 ). Specifically, slow waves, reflected by delta (~0.5-4 Hz) power in the EEG, are associated with neuronal down states (“off’ periods) in which neurons are hyperpolarized ( Steriade, Timofeev and Grenier, 2001 ). Decreased delta power (EEG activation) therefore reflects recovery of neural activity. While the bi-stability between “on” and “ off’ periods is a central feature of non-REM sleep, slow wave activity has also been observed in REM sleep ( Baird et al., 2018b ; Funk, Honjoh, Rodriguez, Cirelli and Tononi, 2016 ). Neuronal down states have also been linked to the loss of consciousness during both anesthesia and sleep ( Purdon et al., 2013 ; Tononi and Massimini, 2008 ), which is hypothesized to be related to the breakdown of causal interactions between bi-stable neurons ( Pigorini et al., 2015 ; Tononi, Boly, Massimini and Koch, 2016 ). Therefore, one potential explanation is that this finding reflects reduced bi-stability and increased causal interactions between cortical neurons in these areas during lucid REM sleep. Notably, reduced delta power in posterior cortex has been found to be associated with dreaming as opposed to dreamless sleep in both REM and NREM sleep ( Siclari et al., 2017 ). An intriguing speculation based on these results is therefore that this reduction in delta power also extends to frontal regions during lucid REM sleep dreaming. However, it remains to be seen whether these findings can be replicated and whether the results generalize to non-clinical populations.

2.2. General discussion of EEG studies of lucid REM sleep

In summary, EEG studies show substantial disagreement regarding the spatial and spectral changes associated with lucid dreaming. As reviewed above, different studies have observed an increase in central or posterior alpha, parietal beta, frontolateral gamma or a reduction in frontocentral delta during lucid compared to baseline REM sleep. Aside from the general uncertainty in the results of some studies due to low statistical power, these discrepant results might be partially explained by the use of limited electrode montages and evaluation of different EEG frequency bands. In the spatial domain in particular, many studies have used less than six scalp electrodes, in several cases only covering some scalp regions but not others, precluding analysis of EEG activity in regions in which significant effects were found in other studies. For instance, the study by Dodet et al. (2014) did not include electrodes in parietal or occipital regions, precluding the possibility to replicate the findings of increased parietal beta by Holzinger et al. (2006) . Studies by Ogilvie et al. (1978 , 1982 ) evaluated only a single central EEG channel (C3). These non-overlapping spatial montages limit the comparison of results across some of these studies.

Another factor that might contribute to the discrepant findings is the fact that lucid dreaming can be achieved and executed in different ways. For example, the observed changes in the EEG during lucid REM sleep might depend in part on the degree of vividness, working memory, emotional tone, self-consciousness, attention and insight, which could vary across individuals as well as specific dreams. Relatedly, different subjective experiences and contents during lucid dreams plausibly have their own neurobiological substrates ( Mota-Rolim, Erlacher, Tort, Araujo and Ribeiro, 2010 ), just as in non-lucid dreams ( Siclari et al., 2017 ). Changes in brain activity during lucidity may also partly depend on how experienced the lucid dreamer is. For instance, lucid dreams of less experienced individuals may often be more ephemeral and involve less control over dream content, while more experienced lucid dreamers may be more likely to have longer and more stable lucid dreams, as well as the capacity to exert greater amounts of control. This might lead to a more distinct signal in the EEG for experienced lucid dreamers on the one hand, but also presumably less neural activity related to the effort needed to maintain the state (neural efficiency). In line with this, Dodet et al. (2014) suggested that the mental effort needed to achieve and sustain lucidity might be reduced in narcoleptic patients, who may access the lucid REM sleep state with less effort.

These comments should not be taken to indicate that there is not a consistent neurobiology of lucid dreaming. However, it does suggest that analysis of the EEG spectral changes associated with lucid dreaming used in previous studies may need to be optimized for detecting more subtle and localized effects. All studies reviewed above have measured the average power over a given spectral band and region over comparably long time intervals. However, it is possible that lucid dreaming is associated with spectral changes that can only be detected by a better time resolved analysis, such as time-frequency analysis, that may be overlooked by averaging over large windows in time or frequency space.

Furthermore, these considerations emphasize the need for more careful assessment of the phenomenology of lucid dreams. In this regard, we would like to note that it is plausible that there are at least two different neural signatures associated with lucid dreaming. The first captures what might be termed the “moment of lucidity”—that is, the transient moment of meta-awareness in which one has the metacognitive insight that one is currently dreaming ( Schooler, 2002 ). The second captures potential sustained differences in brain activity between lucid and non-lucid REM sleep dreaming. This second neural signature is unlikely to be a signature of meta-awareness per se, as during lucid dreams individuals do not continuously engage in metacognitive reflection on their state of consciousness. Rather, this second signature captures the “state-shift” in consciousness that occurs from non-lucid to lucid dreaming, with enhanced volition, episodic memory and accessibility of metacognition ( Dresler et al., 2014 ; Spoormaker, Czisch and Dresler, 2010 ). Changes in these aspects of cognition in the shift to lucidity have been hypothesized to reflect an overall change in the conscious experience of being a cognitive subject ( Windt and Metzinger, 2007 ). Both the physiological correlates of the moment of lucidity as well as overall differences in brain activity between lucid and non-lucid dreams are interesting research targets. However, these research targets are at least conceptually distinct, a point that has, in our view, not received adequate attention in the research literature. To our knowledge, no studies have yet evaluated differences in brain activity with EEG or functional magnetic resonance imaging (fMRI) specifically associated with the moment of lucidity, and this remains an interesting question for future work.

Larger sample sizes are needed in future studies to achieve adequate statistical power. One way to approach this would be to undertake more extensive population screening for high-frequency lucid dreamers. For example, through mass surveys, thousands of potential participants could be screened and the top few percent reporting the highest lucid dream frequency could be selected for training before undergoing sleep laboratory recordings. As we discuss below, new techniques for lucid dream induction also have the potential to enable efficient collection of larger datasets.

Overall, studies with higher statistical power, better assessment of phenomenological content, higher spatial resolution EEG montages, and more sophisticated analysis of the EEG signal will be needed to address these issues and shed light on the conflicting findings of EEG studies of lucid dreams conducted to date. Studies using high-density EEG would also be valuable, enabling both higher temporal as well as higher spatial resolution analysis of neural oscillatory activity. Source modeling of such data could also potentially be informative for localizing changes in neural oscillations associated with lucid dreaming to specific cortical areas, though it remains unclear whether methods for source localization will be able to produce a valid specification of the generators relevant for lucid dreaming. In particular, the generators might be distributed widely in the brain and active concurrently.

Another interesting question with respect to the electrophysiology of lucid dreaming is whether there are possible sleep pattern traits that are associated with lucid dreaming. It would be informative to investigate whether there are common sleep patterns seen in frequent lucid dreamers compared to non-frequent lucid dreamers. For example, do frequent lucid dreamers tend to have more phasic REM sleep, or more fragmented REM sleep with a higher number of transitions (especially gradual transitions) between REM sleep and waking? As far as we know, no study has investigated lucid dreaming with this approach; therefore, studies addressing these questions would be valuable.

2.3. EEG studies of lucid dreaming during non-REM sleep

The activated EEG of REM sleep was originally thought to be exclusively associated with dreaming ( Antrobus and Antrobus, 1967 ; Dement and Wolpert, 1958 ), while the low-frequency activity of non-REM (NREM) sleep was thought to be associated with the absence of dreaming. Subsequent research has shown, however, that participants report dreams or related forms of sleep mentation in up to 70% of awakenings from NREM sleep ( Siclari et al., 2017 ; Siclari, LaRocque, Postle and Tononi, 2013 ; Stickgold, Malia, Fosse and Hobson, 2001 ). NREM dreams tend to be less emotional and visually vivid, as well as more thought-like ( Cavallero, Cicogna, Natale, Occhionero and Zito, 1992 ; Hobson, Pace-Schott and Stickgold, 2000 ). Research suggests that lucid dreams, on the other hand, are predominantly a REM sleep phenomenon ( LaBerge et al., 1986 ; LaBerge et al., 1981c ). However, this does not imply that lucid dreams cannot occur during NREM sleep. There have been several reports of lucid dreams during NREM sleep stages N1 (transition from wake state to sleep) and N2 (consolidated light sleep), although in many of the published cases it is uncertain whether the lucid episode occurred in an unambiguous stage of NREM sleep ( Dane and Van de Caslte, 1984 ; LaBerge, 1980b , 1990 ; LaBerge et al., 1981c ; Stumbrys and Erlacher, 2012 ).

In one study, LaBerge (1980b) recorded polysomnography from a single subject who reported frequently experiencing lucid dreams at sleep onset. The participant rested quietly while drifting to sleep and upon falling asleep was awakened and asked for a dream report. Forty-two dream reports were collected over three nights, 25 of which the participant reported as a lucid dream, and all of which reportedly occurred during stage N1. However, the dream reports were mostly short “dreamlets”, thus it is plausible that these N1 lucid dreams could differ phenomenologically from REM sleep lucid dreams. Furthermore, none of these lucid dreams were verified with eye movement signals. In another study, two participants reported lucid dreams upon spontaneous awakening from N1 and N2 ( LaBerge et al., 1981c ). In the N2 instance, the participant reported only a brief moment of lucidity just before waking up. Furthermore, the participant did not make eye movement signals to time-stamp the moment of lucidity, and it is therefore difficult to ascertain whether the moment of lucidity occurred in the process of awakening. The N1 case was also ambiguous: while in this case the participant reported making eye movements to signal lucidity, the signals could not be verified on the polysomnogram.

In a study on the effects of posthypnotic suggestion on lucid dreaming, Dane and Van de Caslte (1984) tested hypnotically susceptible females with no prior experience in lucid dreaming. Importantly for the present discussion, lucid dreams were reported following awakening from both REM and NREM sleep. Five lucid dreams were reported in total from stage N2, but in all cases the LRLR eye signal occurred after arousal/awakening, and thus none of these could be objectively confirmed. However, several N1 lucid dreams were confirmed by LRLR signaling. This study was thus the first to provide objective evidence for lucid dreaming during stage 1 NREM sleep. However, as noted above, how these N1 dreams compare phenomenologically to REM sleep lucid dreams remains unclear.

Stumbrys and Erlacher (2012) reported two potential cases of lucid dreams during NREM with eye signaling. However, due to the study protocol, the experimenters could only collect dream reports the following morning. In the first case, the participant reported the next morning making an eye movement signal in a lucid dream early in the night, but given the long amount of time between the report and the signal there is uncertainty whether the report corresponds to the observed signal during NREM sleep. Furthermore, while the two 30-second polysomnography epochs for sleep scoring preceding the eye signal appear to be unambiguous N2 sleep, the EEG dynamic shifts during the 30-second epoch containing the eye-signal to lower amplitude activity without apparent spindles or K-complexes. Without knowing the stage of the epochs following the eye signal, which was not reported in the study, it is possible that the eye signal occurred in a transitional sleep stage. In the second case reported by Stumbrys and Erlacher (2012) , the eye signals occurred with some signs of arousal and the participant had no memory of executing the eye signals the following morning. These data therefore provide ambiguous evidence for signal-verified lucid dreams during NREM sleep.

In three further case reports of eye movement signaled NREM lucid dreams, one case was reported to occur in N1 and two cases in N2 visually scored sleep stages ( Mota-Rolim et al., 2015 ). The first case was scored as N1, since an increase in theta (4-7 Hz) and a decrease in alpha (7-14 Hz) power was observed in more than half of the 30 second scoring epoch, meeting the AASM criteria for classification of stage N1 sleep. The other two cases were scored as occurring during N2 episodes since they had spindles and K-complexes in the 30 second scoring epoch with the eye signals. These data thus replicate signal-verification of N1 lucid dreams and provide preliminary evidence for signal-verified N2 lucid dreams.

Together, these results suggest that although most lucid dreams occur during REM sleep, they can also occur during NREM sleep. However, additional studies providing objective evidence of NREM lucid dreams confirmed by eye-signaling, particularly in N2 sleep, are needed. Currently there are no reports of lucid dreams recorded during NREM stage 3 (N3), also known as deep sleep, or slow wave sleep. While there are intriguing reports of practitioners of both Transcendental Meditation and Tibetan Dream Yoga claiming to have developed the ability to maintain a type of lucid awareness throughout the entire sleep cycle, including also states of “lucid dreamless sleep” ( Gackenbach, Cranson and Alexander, 1986 ; Mason and Orme-Johnson, 2010 ; Wallace, 2013 ; Windt, Nielsen and Thompson, 2016 ), these claims have not been corroborated with physiological measures. However, several studies have found a relationship between meditation practice and lucid dreaming (e.g., Baird, Riedner, Boly, Davidson and Tononi, 2018c ; Gackenbach et al., 1986 ; Mota-Rolim et al., 2013 ; Stumbrys, Erlacher and Malinowski, 2015 ), which could be due to changes in REM sleep patterns induced by meditation practice and/or to neurocognitive changes associated with meditation practice (e.g., increased mental control or meta-awareness).

3. Neuroimaging of lucid dreaming

As noted, dream-like mental activity can be observed during all sleep stages. However, REM sleep dreams tend to be more vivid, emotional, bizarre, and more often include a narrative structure ( Cavallero et al., 1992 ; Hobson et al., 2000 ). These phenomenological characteristics have been suggested to be associated with the neural activation and deactivation patterns observed during REM sleep (e.g., Nir and Tononi, 2010 ). For example, higher visual areas show increased regional cerebral blood flow during REM sleep compared to both wakefulness and slow wave sleep ( Braun et al., 1998 ), which is in line with the visuospatial experiences that are common during REM sleep dreaming (e.g., Windt, 2010 ). Additionally, the amygdala, medial prefrontal cortex and anterior cingulate cortex show increased regional cerebral blood flow during REM sleep ( Braun et al., 1997 ; Maquet et al., 1996 ). All of these brain areas have been implicated in emotional processing, mirroring the intense emotions that can be experienced in REM sleep dreams. In contrast, the anterior prefrontal cortex (aPFC) and parietal cortex, including the inferior parietal lobule and precuneus, show low regional cerebral blood flow during normal REM sleep ( Braun et al., 1997 ; Maquet et al., 1996 ). Deactivation of these regions has been postulated to underlie the diminished insight into the global state of consciousness and restricted volitional control typical of non-lucid dreaming (e.g., Hobson and Pace-Schott, 2002 ; Nir and Tononi, 2010 ).

3.1. Neuroimaging lucid REM sleep dreaming

At the current time, there is only one fMRI study of lucid REM sleep, and it is a case study ( Dresler et al., 2012 ). Two separate signal-verified lucid dreams were recorded from a single subject during EEG-verified REM sleep inside the MRI scanner. Compared to non-lucid REM sleep, lucid REM sleep showed increased fMRI BOLD signal in a number of cortical regions, including the superior frontal gyrus, aPFC, medial and lateral parietal cortex, inferior/middle temporal gyri and occipital cortex ( Figure 2a ). Interestingly, several of these regions, particularly the parietal regions and frontal pole, are areas that, as noted above, consistently show reduced regional cerebral blood flow during non-lucid REM sleep compared to wakefulness ( Nir and Tononi, 2010 ).

a) Blood-oxygen-level dependent (BOLD) activation in fMRI case study of lucid dreaming ( Dresler et al., 2012 ). Clusters show regions with significantly increased BOLD signal during lucid REM sleep ( p FDR < 0.005) in the left lateral hemisphere view (left) and right lateral hemisphere view (right). Increased activity was observed in anterior prefrontal cortex (aPFC), medial and lateral parietal cortex, including the supramarginal and angular gyrus and inferior/middle temporal gyrus during lucid REM sleep contrasted with non-lucid REM sleep. b) Seed-based resting-state functional connectivity differences between frequent lucid dreamers and controls ( Baird et al., 2018a ). To estimate connectivity, spherical regions-of-interest were defined in aPFC based on the peak voxel reported in Dresler et al. (2012) (red circle). Frequent lucid dreamers had increased resting-state functional connectivity between left aPFC and bilateral angular gyrus, bilateral middle temportal gyrus and right inferior frontal gyrus. All clusters are significant at p <0.05, corrected for multiple comparisons at the cluster level.

Evidence linking frontopolar cortex to lucid dreaming is consistent with a role of this region in metacognition and self-reflection. For example, research has found that aPFC shows increased activation during self-reflection on internal states, such as the evaluation of one’s own thoughts and feelings ( Christoff, Ream, Geddes and Gabrieli, 2003 ; McCaig, Dixon, Keramatian, Liu and Christoff, 2011 ). Individuals can also learn to voluntarily modulate activity in aPFC through a metacognitive awareness strategy ( McCaig et al., 2011 ). Furthermore, inter-individual variance in metacognitive ability has also been linked to aPFC gray matter volume ( Fleming, Weil, Nagy, Dolan and Rees, 2010 ) and aPFC resting-state functional connectivity ( Baird, Smallwood, Gorgolewski and Margulies, 2013 ). Finally, patients with damage to this region frequently display metacognitive deficits such as inability to monitor disease symptoms or accurately appraise their cognitive functioning ( Joseph, 1999 ; Schmitz, Rowley, Kawahara and Johnson, 2006 ), which might be compared to the lack of metacognitive insight into the state of consciousness characteristic of non-lucid REM sleep dreams ( Dresler et al., 2015 ).

Dresler et al. (2012) additionally observed increased BOLD signal during lucid dreaming in the bilateral precuneus and inferior parietal lobules (angular and supramarginal gyri). As noted above, parietal cortex and the precuneus in particular has been implicated in self-referential processing, episodic memory, and the experience of agency ( Cavanna and Trimble, 2006 ), mirroring the increase of these cognitive capabilities during lucid dreaming. Finally, activation increases during lucid dreaming were also found in some occipital and inferior temporal regions, which are part of the ventral stream of visual processing involved in conscious visual perception ( Rees et al., 2002 ). While these activations may seem puzzling at first sight, as non-lucid dreams are also characterized by vivid dream imagery, they are in line with reports that lucid dreams can be associated with increased visual vividness and clarity of the dream scene (e.g., Green, 1968 ).

There are several limitations to the study by Dresler et al. (2012) that are important to note. First, as mentioned above, the findings are based on observations from a single subject and caution is therefore warranted in generalizing from the results. Currently no group-level fMRI study of lucid dreaming has been conducted, and such a study, along with systematic replications, will be needed before firm conclusions can be drawn. Another limitation is that, as described below (see Section 7.1 ), the participant was performing a task during the lucid REM sleep segment (repeated eye signaling and hand clenching). While Dresler and colleagues accounted for task activation via nuisance regression of the left and right fist clenching task, it is possible that some of the activations observed still partially reflect task execution and maintenance of attention/task-set rather than lucidity per se. One way to address this in future studies would be to contrast periods of lucid REM sleep when the participant is not performing an explicit task to non-lucid REM sleep, i.e., a “no-task, within-state paradigm” ( Siclari et al., 2013 ).

3.2. Neuroimaging individual differences in lucid dreaming

Several studies have taken an individual differences approach to neuroimaging of lucid dreaming. While most people spontaneously experience lucid dreams infrequently, there is substantial variation in lucid dream frequency, ranging, by current estimates, from never (approximately 40-50%) to monthly (approximately 20%) to a small percentage of people that report lucid dreams several times per week or in some cases every night ( Mota-Rolim et al., 2013 ; Saunders, Roe, Smith and Clegg, 2016 ; Snyder and Gackenbach, 1988 ). This variation invites the question of whether individuals who experience frequent lucid dreams show differences in anatomical or functional properties of the brain. Studies addressing this question can provide complementary evidence on the neurobiology of lucid dreaming.

In the first neuroimaging study to evaluate the relationship between lucid dream frequency and brain anatomy, Filevich, Dresler, Brick and Kuhn (2015) measured whole-brain gray matter volume using voxel-based morphometry in individuals with higher (“high lucidity”) vs. lower (“low lucidity”) scores on a scale assessing the frequency of lucid dreams and/or dream content hypothesized to be related to lucidity. Consistent with the hypothesized connection between the metacognitive functions of aPFC and lucid dreaming discussed above, the study found increased gray matter volume in two regions of the frontal pole (BA9/10), as well as the right anterior cingulate cortex, left supplementary motor area and bilateral hippocampus in the high lucidity group. Additionally, the two identified frontopolar regions showed higher BOLD signal in the monitoring component of a metacognitive thought-monitoring task performed while awake.

A limitation of the study by Filevich et al. (2015) is that participants were not frequent lucid dreamers per se, but rather subjects from a database who scored higher relative to other participants on the scale. Lucid dream frequency for the two groups was not reported in the study, thus it remains unclear to what extent the “high lucidity” group experienced frequent lucid dreams in absolute terms. Furthermore, the composite measure of dreaming used to distinguish the two groups measured not only frequency of lucid dreams but also different dimensions of dream content. While several of these content dimensions have been found to be higher in lucid dreams ( Voss, Schermelleh-Engel, Windt, Frenzel and Hobson, 2013 ), it is likely that several of these dimensions also co-vary more generally with dream recall and/or cognitive content in dreams unrelated to lucidity. As a consequence, as the authors note, some of the results could have been partly influenced by differences in dreaming “styles”, content or dream recall.

More recently, Baird, Castelnovo, Gosseries and Tononi (2018a) evaluated a sample of high-frequency lucid dreamers who reported lucid dreams in the range of three to four times per week to multiple times per night compared to a control group who reported lucid dreams once per year or less. The frequent lucid dream group and control group were case-control matched on age, gender, and dream recall frequency. Based on the previous research reviewed above, the primary aim of the study was to investigate whether individuals who have frequent lucid dreams would show increased gray matter density and/or resting-state functional connectivity of aPFC. Consistent with this, compared to the control group, individuals who reported frequent lucid dreams showed increased resting-state functional connectivity between the left aPFC and the bilateral angular gyrus, bilateral middle temporal gyrus and right inferior frontal gyrus ( Figure 2b ). The frequent lucid dream group also showed decreased functional connectivity between left aPFC and bilateral insula. Whole-brain graph-theoretic analysis revealed that left aPFC had increased node degree and strength in the frequent lucid dream group compared to the control group. However, in contrast to the findings of Filevich et al. (2015) , no significant differences in gray matter density were observed between groups in either a whole-brain analysis or an aPFC region-of-interest analysis.

Given the link to metacognition, it has also been suggested that lucid dreaming may be linked to large-scale networks that regulate executive control processes, in particular the frontoparietal control network ( Dresler et al., 2015 ; Spoormaker et al., 2010 ). To address this question, Baird et al. (2018a) additionally evaluated the association between frequent lucid dreaming and connectivity within established large-scale brain networks, including the frontoparietal control network. Consistent with a link between the frontoparietal control network and lucid dreaming, Baird and colleagues found that frequent lucid dreamers had increased functional connectivity between aPFC and a network of regions that showed the greatest overlap with a frontoparietal control sub-network ( Dixon et al., 2018 ; Yeo et al., 2011 ). However, neither connectivity within the frontoparietal control network broadly defined through meta-analysis (or within or between any other large-scale networks), nor connectivity within frontoparietal control sub-networks, as defined through parcellation of resting-state networks, showed significant differences between the lucid dream group and the control group. The authors speculate that this could be attributed to both the partial overlap of the regions that showed increased aPFC connectivity in lucid dreamers with the frontoparietal control network. However, it is important to keep in mind that while this study did not find a significant difference in resting-state connectivity within the frontoparietal network in frequent lucid dreamers, it remains an open question whether lucid REM sleep dreams show increased connectivity within the frontoparietal control network compared to non-lucid REM sleep dreams.

Overall, the resting-state connectivity results of Baird et al. (2018a) converge with the fMRI case study of lucid REM sleep dreaming described above ( Dresler et al., 2012 ), which found that an overlapping network of brain areas increased fMRI BOLD signal during lucid compared to baseline REM sleep, including bilateral aPFC, bilateral inferior parietal lobule (including the angular gyrus), and bilateral middle temporal gyrus ( Figure 2 ). Together, these results suggest that increased intrinsic functional connectivity between aPFC and the angular gyrus/middle temporal gyrus—regions that, as reviewed above, show reduced activity in REM sleep ( Nir and Tononi, 2010 ) and increased activity during lucid REM sleep ( Dresler et al., 2012 )—is associated with the tendency to have frequent lucid dreams. However, while the convergence between these two preliminary studies is encouraging, the paucity of neuroimaging data on this question limits strong conclusions at the current time.

3.3. Future directions for neuroimaging of lucid dreaming

Neuroimaging studies of lucid REM sleep using larger samples sizes are needed. In particular, a group-level fMRI study of lucid REM sleep dreaming using a no-task, within-state paradigm is perhaps the most important next step in this line of research. In addition to evaluating activation and deactivation as revealed by changes in BOLD signal, it would also be informative to evaluate differences in functional connectivity in such a study. For example, as noted, one question that arises from the above neuroimaging findings is whether there could be increased connectivity within the network of regions identified in Dresler et al. (2012) and Baird et al. (2018a) during lucid contrasted with non-lucid REM sleep dreaming. Furthermore, building on the individual differences results, in future work it would also be interesting to evaluate whether high frequency lucid dreamers show increased functional connectivity and/or higher BOLD signal in these brain areas during baseline REM sleep. If so, this could suggest that it may be possible to bias the brain toward increased metacognitive awareness of dreaming during REM sleep, for example, as discussed in the next sections on induction of lucid dreams, through techniques to increase activation of these regions.

In line with these remarks, it has been suggested that not only regional activation of frontoparietal brain areas but also connectivity between these regions could be important for lucidity to emerge during REM sleep ( Spoormaker et al., 2010 ). Indeed, while activation in these frontal and parietal regions has been linked to key functions associated with lucid dreaming, including metacognition, as discussed above, regional activations and metabolic increases in these regions have also been observed during states of global unconsciousness and subliminal information processing. For instance, subliminally presented no-go stimuli during a response inhibition task activate frontoparietal cortices in the absence of awareness ( van Gaal, Ridderinkhof, Scholte and Lamme, 2010 ). Moreover, loss of consciousness during the tonic phase of generalized tonic-clonic seizures is associated with a transient increase rather than decrease in metabolism in frontoparietal cortex ( Blumenfeld, 2008 ; Engel, Kuhl and Phelps, 1982 ).

Recent findings have suggested that a potentially more sensitive marker of unconscious states may be reduced connectivity between frontoparietal areas, particularly from frontal to parietal regions. For example, several neuroimaging studies of patients in a persistent vegetative state or under different categories of general anesthetics have shown a specific impairment of the backward connectivity from frontal to parietal regions ( Boly et al., 2011 ; Boly et al., 2012 ; Lee et al., 2013 ). These findings converge with theoretical work and computational modeling ( Tononi, 2011 ), which has suggested a link between consciousness and effective connectivity within a neural architecture, or the capacity of a set of neural elements to exert causal influence over other neural groups in a system. At present, it is unclear whether this reduction of top-down frontoparietal connectivity is linked to changes in global brain activity, alterations in primary consciousness (i.e., subjective, phenomenal states of seeing, hearing or feeling), or whether it could relate to self-awareness (i.e., explicit conscious awareness of oneself and one’s state). A common interpretation of these results is that top-down frontoparietal connectivity is a marker of global loss of consciousness, including primary consciousness (e.g., Mashour, 2014 ). However, given that the reduction in top-down connectivity has also been observed under ketamine ( Lee et al., 2013 ), during which patients often report vivid dream-like experiences, it is plausible that the reduction of frontoparietal connectivity could instead indicate loss of self-awareness.

Cognitive neuroscience studies of lucid dreaming are uniquely placed to contribute to this question because the comparison of lucid REM sleep to non-lucid REM sleep is perhaps the only contrast that allows for a direct comparison between the global loss and recovery of reflective consciousness independently of global shifts in primary consciousness, arousal or vigilance state. Thus, the question of whether lucid REM sleep is associated with altered connectivity between frontal and parietal cortices has implications for several broad questions in the cognitive neuroscience of consciousness. Future studies evaluating changes in effective connectivity during lucid REM sleep dreaming, and in particular changes in top-down frontoparietal connectivity, would be valuable.

4. Brain lesions and lucid dreaming

In the neurological literature, to our knowledge only one paper has reported changes in lucid dreaming as a result of neurological insult or brain lesions ( Sagnier et al., 2015 ). The paper describes two case reports of young patients who reported lucid dreams following unilateral ischemic stroke to the left mediodorsal thalamus. The first patient was a 26-year-old female with a left anterior and mediodorsal thalamic stroke in an area supplied by the premamillary artery, as revealed by MRI scans. She reported frequent lucid dreams in the early morning hours, along with increased nightmares and nocturnal awakenings. She also reported that her lucid dreams mostly involved the hospital and medical staff that she encountered during her hospitalization, and included catastrophic events such as helicopter crashes and hyper-aggressive behaviors. The second patient was a 36-year-old male with a left mediodorsal thalamic stroke in the area supplied by the paramedian artery as revealed by MRI scans. He reported frequent lucid dreams following the stroke, which also tended to occur in the early morning hours, along with increased nocturnal awakenings, but without an increase in nightmares. Lucid dreams subsided after one month for both patients.

Lucid dreams have not previously been reported following either unilateral or bilateral thalamic stroke. However, loss of neurons in the anterior and dorsomedian thalamic nuclei that occurs in familial fatal insomnia is associated with loss of nocturnal sleep as well as oneiric ‘intrusions’ during wakefulness ( Montagna, 2005 ; Raggi, Cosentino, Lanuzza and Ferri, 2010 ). Furthermore, hypersomnia and irregular sleep are frequently reported following paramedian thalamic stroke ( Hermann et al., 2008 ; Luigetti et al., 2011 ). These clinical features could be manifestations of disruption of the intralaminar and midline thalamic nuclei located in the mediodorsal thalamus, which are part of the brain’s arousal network ( Van der Werf, Witter and Groenewegen, 2002 ). Thus, one possibility is that lucid dreams in these patients could have partly resulted from increased or abnormal functioning of the brain’s arousal systems during sleep, which is consistent with the reported increase in nocturnal awakenings. Both patients also reported that their dreams contained highly anxious and emotional content, which could reflect abnormal connectivity between these thalamic nuclei and limbic structures with which they are densely connected ( Van der Werf et al., 2002 ). The highly emotional or disturbing content may have also contributed to lucid dreaming, as these types of experiences could induce individuals to question whether the explanation for such surprising or frightening experiences is that they are dreaming ( LaBerge and Rheingold, 1990 ).

A systematic study of the incidence of lucid dreams following thalamic strokes is lacking. Particularly given that the lucid dreams subsided after a relatively short duration (one month) for both patients, it is possible that lucid dreaming as a consequence of thalamic strokes has been under-reported and/or overlooked. More generally, a large-scale study of the occurrence of lucid dreaming in neuropsychological cases has not been undertaken and would be a welcome addition to the literature. The converse case, of a brain lesion causing loss of lucid dreaming in an individual who regularly experiences lucid dreams has also to our knowledge not been reported. Such a case would likely be very unusual, particularly considering the small percentage of individuals who experience lucid dreams spontaneously with high frequency. However, if such a case were identified it could be informative to the neurobiology of lucid dreaming.

5. Pharmacological induction of lucid dreaming

A main target of research is to develop methods to make the lucid dream state more accessible. Indeed, reliable techniques to induce lucid dreams are needed for it to be effectively used in both clinical and scientific applications. Evidence suggests that lucid dreaming is a learnable skill ( LaBerge, 1980a ) that can be developed by training with various induction strategies ( LaBerge and Rheingold, 1990 ; Price and Cohen, 1988 ; Stumbrys, Erlacher, Schadlich and Schredl, 2012 ). These include training in prospective memory techniques ( LaBerge and Rheingold, 1990 ), which can be further aided by application of external sensory cues ( LaBerge and Levitan, 1995 ; LaBerge, Levitan, Rich and Dement, 1988 ; LaBerge, Owens, Nagel and Dement, 1981b ) and/or interrupting sleep with short periods of wakefulness ( Aspy, Delfabbro, Proeve and Mohr, 2017 ; LaBerge, 1980a ; LaBerge, Phillips and Levitan, 1994 ; Stumbrys et al., 2012 ). Within cognitive neuroscience, studies have evaluated pharmacological as well as non-invasive brain stimulation approaches to lucid dream induction. In this section, we review studies that have taken a pharmacological approach to lucid dream induction and in the next section we review electrical brain stimulation studies.

5.1. Effects of Acetylcholinesterase inhibitors (AChEIs) on lucid dreaming

As discussed above, lucid dreaming is associated with increased cortical activation ( LaBerge et al., 1981a ), which reaches its peak during phasic REM sleep. Given the relationship between phasic REM sleep and lucid dreaming, as well as the role of Acetylcholine (ACh) in REM sleep regulation (e.g., Amatruda, Black, McKenna, McCarley and Hobson, 1975 ; Velazquez-Moctezuma, Shalauta, Gillin and Shiromani, 1991 ), agents acting on the cholinergic system have received particular interest. In an initial pilot study, LaBerge (2001) evaluated the effect of donepezil (Aricept), an Acetylcholinesterase inhibitor (AChEI), on lucid dreaming in a small group of participants (N=10) who reported prior experience with lucid dreaming. On each night, participants received either 0 mg (placebo), 5 mg, or 10 mg of donepezil, with the dose order counterbalanced across the three nights of the experiment. Nine of the ten participants (90%) reported at least one lucid dream on donepezil, while only one participant reported a lucid dream on the placebo dose.

Following on these results and additional pilot research, LaBerge, LaMarca and Baird (2018b) conducted a double blind, placebo-controlled study in a large group of participants (N=121) with high dream recall and an interest in lucid dreaming. The first goal of the study was to quantify the size and reliability of the effect of AChEI on lucid dreaming. The second goal was to test the effectiveness of an integrated lucid dream induction protocol that combined cholinergic stimulation with other methods, including sleep interruption and the Mnemonic Induction of Lucid Dreams (MILD) technique, which trains participants to use prospective memory to induce lucid dreams ( LaBerge, 1980a ; LaBerge and Rheingold, 1990 ). Participants were randomly assigned counterbalanced orders of three doses of galantamine (0 mg=placebo, 4 mg, and 8 mg), an AChEI that is readily accessible, fast acting and has a mild side effect profile. On three consecutive nights, participants awoke approximately 4.5 hours after lights out (after approximately the 3 rd REM cycle), recalled a dream, ingested the capsules and stayed awake for at least 30 minutes. Participants then returned to sleep practicing the MILD technique. After each subsequent awakening, participants rated their dreams on a range of variables including lucidity, recall, vividness, bizarreness, complexity, affect, cognitive clarity, metacognition and control. Full reports of lucid dreams were also collected.

Galantamine was found to significantly increase the frequency of lucid dreaming in a dose-related manner. Increased incidence of lucid dreaming was observed for both 4 mg (27% of participants) and 8 mg (42% of participants) doses compared to 14% of participants for the active placebo procedure (which included sleep interruption and MILD). Galantamine was also found to be associated with significantly increased sensory vividness and environmental complexity, which might be expected given the general intensification of REM sleep, and associated dreaming, triggered by cholinergic stimulation ( Riemann, Gann, Dressing, Muller and Aldenhoff, 1994 ).

Another recent double blind, placebo-controlled study conducted by Sparrow, Hurd, Carlson and Molina, 2018 ) also tested the effect of galantamine on lucid dreaming. 35 participants completed an eight-night study that tested the effect of 8 mg of galantamine paired with 40 minutes of sleep interruption (termed “Wake-Back-to-Bed” (WBTB) in the study). The study additionally tested combining galantamine with middle-of-the-night meditation and the imaginary reliving of a distressing dream (termed meditation and dream reliving or MDR; Sparrow, Thurston and Carlson, 2013 ). The study included pre- and post-baseline nights and six conditions: 1) WBTB; 2) WBTB + placebo; 3) WBTB + galantamine; 4) MDR; 5) MDR + placebo; and 6) MDR + galantamine. MDR conditions matched the 40 minutes of sleep interruption in the WBTB conditions. Lucid dreams were measured with self-reports on the dream lucidity scale (DLS; Sparrow et al., 2013 ), which in this study included three categories: 0=non-lucid, l=pre-lucid (which included either “questioning things in the dream without actually concluding that you were dreaming” or “doing things that are ordinarily impossible to do”), or 2=lucid.

Both galantamine conditions (WBTB + galantamine; MDR + galantamine) significantly increased self-ratings of lucidity on the DLS compared to the other conditions. However, no significant difference was observed between WBTB + galantamine and MDR + galantamine. The number of participants who had a lucid dream in each condition was not reported, only the conditional means for the DLS, which reflects the effect collapsed across both pre-lucid and lucid dreams. We therefore contacted the authors for this information in order to compare the results of this study to the study by LaBerge et al. (2018b) . 9% of participants reported a lucid dream in the WBTB + placebo condition and 11% in the MDR + placebo, whereas 40% of participants reported a lucid dream in the WBTB + galantamine and 34% in the MDR + galanatamine (S. Sparrow, personal communication, December 17, 2018). Overall, therefore, these results are comparable to the effect of 8 mg galantamine observed by LaBerge and colleagues.

5.2. Effects of L-alpha glycerylphosphorylcholine (α-GPC) lucid dreaming

In contrast to the positive findings for AChEIs, a double blind randomized placebo-controlled study found no significant effect of 1200 mg of the ACh precursor L-alpha glycerylphosphorylcholine (α-GPC) on the frequency of lucid dreams in 33 participants with varying degrees of lucid dreaming experience ( Kern, Appel, Schredl and Pipa, 2017 ). One interpretation of this result is that, in contrast to AChEIs, α-GPC is not effective for inducing lucid dreams, perhaps due to differences in the neurobiological effects of choline—an ACh precursor—and AChEIs. However, participants in this study appear to have received no training in mental set, such as recalling and attending to dreams in an effort to become lucid. Thus, an alternative, not mutually exclusive, interpretation is that training in at least the minimal mental set for lucid dream induction is needed for pharmacological (and other) interventions to effectively increase the frequency of lucid dreams.

5.3. General discussion of pharmacological induction of lucid dreaming

Overall, these data provide strong initial evidence that cholinergic enhancement with AChEls, and galantamine in particular, facilitates a state of the brain favorable to lucid dreams. However, a limitation of all pharmacological studies on lucid dreaming performed to date is that they were not conducted in a sleep laboratory and there was therefore no validation of lucid dreams with eye-signaling methods. It will be important to follow up these results with sleep laboratory studies to objectively validate lucid dreams with polysomnographic recording. Such studies would also be valuable to investigate the physiological effects of galantamine on the brain during REM sleep, and which effects are predictive of lucidity. Given the high success rate of lucid dreams in the combined induction protocol with cholinergic stimulation reported by LaBerge, LaMarca and Baird (2018) and Sparrow et al. (2018) , studies of galantamine have the potential to enable efficient collection of large sample sizes in electrophysiological and neuroimaging studies of lucid dreaming.

The mechanism by which AChEls facilitate lucid dreams remains unclear. There are several, not mutually exclusive, possibilities, including increasing REM sleep intensity/phasic activation, influencing the brain regions/networks associated with lucid dreaming, and influencing cognitive processes associated with becoming lucid. In general, it is known that AChEls inhibit the metabolic inactivation of ACh by inhibiting the enzyme acetylcholinesterase (AChE), leading to accumulation of ACh at synapses. Furthermore, ACh and its agonists as well as AChE and its antagonists are involved in the generation of REM sleep ( Amatruda et al., 1975 ; Gillin et al., 1985 ; Velazquez-Moctezuma et al., 1991 ). For example, evidence suggests that REM sleep is controlled by cholinergic neurons in the brainstem ( Baghdoyan, 1997 ; Hernandez-Peon, Chavez-Ibarra, Morgane and Timo-Iaria, 1963 ), and studies have observed that microinjection of the ACh agonist carbachol in the pontine area of the brainstem results in REM sleep in both humans and animals ( Amatruda et al., 1975 ; Baghdoyan, 1997 ). Administration of galantamine has been associated with increased phasic activity and shortened REM latency ( Riemann et al., 1994 ). The increased frequency of lucid dreams associated with AChEls could therefore plausibly be related to its effects on cholinergic receptors during REM sleep, leading to longer, intensified REM periods with increased phasic activity, which, as noted above, has been found to be associated with lucid dreams ( LaBerge et al., 1981a ).

Beyond intensification of REM sleep/phasic activation, AChEls might also directly act on cognitive processes associated with lucidity and their neural underpinnings. One key question is whether AChEIs could facilitate lucid dreaming through increasing activation within the network of frontopolar-temporo-parietal areas observed in the neuroimaging studies of Dresler et al. (2012) and Baird et al. (2018a) . The relationship between cholinergic modulation and frontoparietal activation is complex and depends on the task context and population under study (see Bentley, Driver and Dolan, 2011 for a review). However, pro-cholinergic drugs in general tend to increase frontoparietal activity in conditions in which these areas show low baseline activation, which is thought to reflect increased attentional-executive functions ( Bentley et al., 2011 ). Given that frontoparietal activity is typically suppressed during REM sleep ( Braun et al., 1997 ; Maquet et al., 1996 ), it is plausible that AChEIs could increase activation within this network during REM sleep dreaming.

Notably, AChEls are also prescribed to manage the cognitive symptoms of Alzheimer’s disease ( Koontz and Baskys, 2005 ). Theoretically, another way that AChEls could facilitate lucid dreams is therefore through their effect on memory. For example, AChEIs could enhance the ability to remember to recognize that one is dreaming (a form of prospective memory), which is the core of the MILD technique for lucid dream induction that participants engaged in during the study by LaBerge, LaMarca and Baird (2018b) . However, evidence for cognition enhancing effects of AChEls in healthy subjects, particularly for single doses, is sparse ( Dresler et al., 2018 ), rendering the theoretical possibility of a memory-mediated effect on dream lucidity unlikely. Overall, it remains unclear whether galantamine also exerts a direct influence on cognitive processes associated with lucidity, and the MILD technique in particular, or whether it merely optimizes the physiological conditions for such techniques.

One last possibility is that AChEIs could influence lucidity indirectly by affecting other neuromodulators. For example, evidence suggests that AChEls also increase systemic norepinephrine and dopamine ( Cuadra, Summers and Giacobini, 1994 ; Giacobini, Zhu, Williams and Sherman, 1996 ). It is therefore possible that the increase in lucid dreams associated with AChEI might instead be directly linked to aminergic modulation that occurs as a result of the increases in ACh. Additional research will be needed to understand how the neuromodulatory changes caused by AChEIs stimulate lucid REM sleep dreaming. Studies using Positron Tomography (PET) or pharmaco-fMRI would be valuable to address this issue.

Several other pharmacological substances have been suggested to increase the frequency of lucid dreams, including various types of supplements and drugs (e.g., Yuschak, 2006 ). For instance, recreational drugs such as alcohol, cocaine and cannabis have been reported to be associated with lucid dreaming. These substances suppress REM sleep ( Roehrs and Roth, 2001 ; Schierenbeck, Riemann, Berger and Hornyak, 2008 ), which leads to a phenomenon referred to as REM rebound, in which longer REM sleep periods occur following REM sleep suppression ( Vogel, 1975 ). Intensified, prolonged REM sleep as a result of REM rebound could potentially increase lucid dream frequency, particularly in individuals predisposed to have lucid dreams or with prior experience in lucid dreaming. Another example is LSD, since there is some evidence that it can prolong REM sleep periods at some doses ( Muzio, Roffwarg and Kaufman, 1966 ), which could potentially be favorable to lucid dreaming. To our knowledge, however, no studies have systematically evaluated whether these substances, or other substances not reviewed here, increase the incidence of lucid dreams. Furthermore, it is prudent to remain cautious about such claims given the placebo effect. Placebo-controlled studies will be essential to substantiate any purported effects of pharmacological substances on lucid dreaming.

6. Induction of lucid dreams with transcranial electrical brain stimulation

Two studies have attempted to induce lucid dreams through transcranial electrical stimulation of the frontal cortex during REM sleep. Stumbrys, Erlacher and Schredl (2013b) tested direct current stimulation and Voss et al. (2014) tested alternating current stimulation. We review each of these studies in turn.

6.1. Effects of frontal transcranial direct current stimulation on lucid dreaming

In an investigation of the effect of transcranial direct current stimulation (tDCS) on lucid dreaming, Stumbrys et al. (2013b) applied either tDCS or sham stimulation (counterbalanced across nights) over a frontolateral scalp region in 19 participants. Stimulation was delivered through two pairs of electrodes with anodes at positions F3 and F4, respectively, and cathodes at the supraclavicular area of the same side. Stimulation was applied during all REM sleep periods except the first for two consecutive nights in the sleep laboratory. In total, 109 stimulations were performed, after which participants were awakened to complete a dream report.

Compared to sham stimulation, tDCS resulted in a small numerical increase in self-ratings of the unreality of dream objects as assessed by the Dream Lucidity Questionnaire (DLQ; Stumbrys et al., 2013b ). Post-hoc analyses revealed that this effect was seen only in subjects with a high baseline frequency of lucid dreams, but not in participants with little or no lucid dreaming experience. However, tDCS did not significantly increase the number of dreams rated by judges to have a clear indication of lucidity: seven dreams in total, three from sham stimulation and four from tDCS stimulation. Furthermore, only one lucid dream in total was confirmed by eye signaling, which was in the stimulation condition. Given a difference of only one lucid dream between stimulation and sham conditions for either of these two assessment criteria, overall frontal stimulation with tDCS as tested in this study does not appear to be a reliable method for inducing lucid dreams.

6.2. Effects of frontal transcranial alternating current stimulation on lucid dreaming

A study by Voss et al. (2014) reported a more pronounced increase in lucid dreaming by applying transcranial alternating current stimulation (tACS) in the gamma frequency band over the frontal cortex. The study tested 27 participants for up to four nights in the sleep laboratory. tACS was applied for 30 seconds after two minutes of uninterrupted REM sleep to frontolateral scalp regions at several different frequencies (2, 6, 12, 25, 40, 70 or 100 Hz or sham stimulation). Stimulation was delivered through two pairs electrodes with anodes at positions F3 and F4, respectively, and cathodes over the mastoids close to TP9 and TP10, respectively. Participants were then awakened and completed a dream report and the Lucidity and Consciousness in Dreams (LuCiD) scale ( Voss et al., 2013 ). The LuCiD scale measures dream content on several different factor-analytically derived dimensions, including insight, control, thought, realism, memory, dissociation and affect.

The authors reported they were able to induce lucid dreams with a success rate of 58% with 25 Hz stimulation and 77% with 40 Hz stimulation. However, it is important to note how lucid dreams were classified in this study: instead of assessment of lucid dreams with eye signaling, self-report, or through statistical analysis of judges’ ratings of dream reports, as in Stumbrys et al. (2013b) , dreams were assumed to be lucid if subjects reported “elevated ratings (>mean + 2 s.e.) on either or both of the LuCiD scale factors insight and dissociation”. While dissociation scores (i.e., “seeing oneself from the outside” or a “3 rd person perspective”) have been found to be increased in lucid dreams before ( Voss et al., 2013 ), dissociation in the sense of adopting a 3 rd person perspective has never been considered a defining feature of lucid dreams per se (e.g., DeGracia and LaBerge, 2000 ; Gackenbach and LaBerge, 1988 ; Green, 1968 ; LaBerge, 1985 , 1990 ). It is therefore controversial to classify dreams as lucid based solely on higher ratings of dissociation.

The insight subscale corresponds more closely to the standard definition of lucid dreams adopted by other researchers as well as the general public. Mean ratings in the insight subscale increased from approximately 0.1-0.2 in the sham stimulation to 0.5-0.6 in the 25 Hz or 40 Hz stimulation conditions, similar to the effects in the dissociation subscale, which was approximately 0.9-1.2. However, the scale anchors ranged from 0 (strongly disagree) to 5 (strongly agree), indicating that, on average, in both the 25 Hz and 40 Hz stimulation conditions, participants disagreed that their dreams had increased insight. Moreover, in the original validation of the LuCiD scale, non-lucid dreams scored on average at 0.3 or 0.8 on the insight scale (depending on whether the assessment was laboratory or survey based), whereas lucid dreams scored at 3.2 or 3.5 ( Voss et al., 2013 ). Thus, though significantly stronger in relation to sham simulation, mean responses of 0.5-0.6 in the 25 and 40 Hz stimulation conditions were much lower than the values reported for lucid dreams in the validation study, and still on the non-lucid (disagree that the dream contains insight) end of the scale spectrum in absolute values. Overall, therefore, the results of this study appear to be comparable to the tDCS finding of Stumbrys et al. (2013b) in that prefrontal tACS gamma band stimulation induced numerical increases in some measures of dream content, but does not appear to have lead to increases in the number of lucid dreams when defined in the traditional sense of being aware of dreaming while dreaming.

6.3. General discussion of induction of lucid dreaming with electrical brain stimulation

In summary, studies examining induction of lucid dreams with electrical brain stimulation (tDCS and tACS) have thus far observed intriguing effects on dream cognition, but a method for reliably inducing lucid dreams by electrical stimulation of the brain is still yet to be identified. We think this is a particularly interesting direction for upcoming work. Future studies might consider stimulating a wider number of brain areas and different types of stimulation. For example, transcranial magnetic stimulation (TMS) is another method of noninvasive brain stimulation that could be used to attempt to induce lucid dreams that has not yet been examined ( Noreika, Windt, Lenggenhager and Karim, 2010 ; Mota-Rolim et al., 2010 ). In contrast to tACS or tDCS, high-frequency repetitive TMS (rTMS) stimulation sequences can be used to increase neuronal excitability in focal areas of the cortex (e.g., Hallett, 2007 ). Several practical challenges with application of TMS during sleep include the auditory artifacts produced by the TMS coil as well as tactile sensations on the scalp during stimulation, which may lead to awakenings. However, noise masking has been used to prevent subjects from hearing the clicks associated with TMS pulses (e.g., Nieminen et al., 2016 ), and scalp sensations could be decreased, for example, by reducing stimulation intensity or potentially by application of a topical anesthetic cream.

Studies of electrical brain stimulation have also thus far only tested a small part of the parameter space and there are many other stimulation protocols that appear to be worth evaluating. For example, given the neuroimaging results reviewed above, synchronous frontoparietal tACS—in which synchrony is increased between frontal and parietal regions by simultaneous stimulation of these regions (e.g., Violante et al., 2017 )—is another stimulation method that would be interesting to examine. Finally, as noted above, these methods are likely to maximize their effect when participants are trained in at least the minimal mental set for lucid dream induction, which was not done in either the study by Voss et al. (2014) or Stumbrys et al. (2013b) .

7. Lucid dreaming as a methodology for the cognitive neuroscience of consciousness

Psychophysiological studies of non-lucid dreams have been used to study neural correlates of conscious experiences during sleep ( Perogamvros et al., 2017 ; Siclari et al., 2017 ). However, there are significant limitations to this approach. Specifically, while methods such as “serial awakenings” are useful for contrasting the global presence vs. absence of dream experience during sleep ( Siclari et al., 2017 ), it is an inefficient method to collect data on specific conscious contents during sleep. For instance, given that non-lucid dreamers usually have no control over their dream content, these studies typically employ a shot-in-the-dark approach, in which large numbers of sleep recordings are made and small subsets of data in which the content of interest appears by chance are extracted. Additionally, and perhaps most importantly, establishing temporally precise correlations between retrospective dream reports and physiological measurements is a substantial challenge. Lucid dreaming provides ways to overcome several of these challenges. For example, as mentioned above, lucid dreamers can conduct specific tasks within REM sleep dreams, enabling experimental control over dream content. Furthermore, as noted, participants can be trained to time-stamp the onset and offset of particular content or actions with eye movement signals, which provides a way to obtain more precise correlations between conscious experiences and physiological measurements during sleep ( Dresler et al., 2011 ; LaBerge, 1990 ; LaBerge, Greenleaf and Kedzierski, 1983 ). In this section we briefly summarize several recent studies that illustrate how lucid dreaming can be used as a methodology in the cognitive neuroscience of consciousness.

7.1. Activation of sensorimotor cortex during dreamed movement in REM sleep

Dresler et al. (2011) used lucid dreaming to test whether a motor task performed during dreaming elicits neuronal activation in the sensorimotor cortex. Participants made a series of dreamed left and right hand clenches in their lucid dreams and marked the start and end of the sequences of clenches with LRLR eye movements. Specifically, participants clenched their left hand in the dream ten times, then signaled LRLR, then clenched their right hand in the dream ten times, then signaled LRLR, and continued repeating this sequence for as long as possible until awakening. Additionally, participants performed an executed hand-clenching task as well as imagined hand-clenching task during wakefulness for comparison. Six participants with prior experience in lucid dreaming participated in the study. Of these, two participants succeeded in performing the task during lucid REM sleep: one under simultaneous EEG-fMRI (in two different signal-verified lucid dreams) and one under combined EEG-near-infrared spectroscopy (NIRS) recording (again in two different signal-verified lucid dreams).

Contrasting left vs. right fist clenches, increased BOLD signal in the contralateral sensorimotor cortex was observed in both lucid dreams of the fMRI experiment, as well as in waking and imagination conditions. Compared to executed hand clenches during wakefulness, however, BOLD signal increases during dreaming in contralateral sensorimotor cortex were more localized, which could indicate either weaker activation or more focal activation of hand areas only. BOLD signal fluctuations during dreaming were approximately 50% of those observed for executed hand clenches during wakefulness. Correspondingly, the NIRS data showed increased oxygenated hemoglobin and decreased deoxygenated hemoglobin in the contralateral sensorimotor cortex during dreamed hand clenching. This hemodynamic response was also observed in the supplementary motor area, which is involved in the timing, monitoring and preparation of movements ( Goldberg, 1985 ). This differed with the fMRI data in which no significant differences in the supplementary motor area were found. Interestingly, during dreamed hand clenching, hemodynamic responses were smaller in the sensorimotor cortex but of similar amplitude in the supplementary motor area when compared to overt motor performance during wakefulness.

Overall, these data suggest that the pattern of brain activation observed during dreamed motor execution overlaps with motor execution during wakefulness. However, given the different patterns of activation, the data may also suggest that the interactions between the supplementary motor area, somatosensory and sensorimotor cortex differs between REM sleep dreaming and waking. The authors suggest that the reduced activation in sensorimotor cortex could be due in part to the lack of sensory feedback as a result of REM sleep atonia. However, given that this study consisted of two case reports—one for each imaging modality—the data should be interpreted cautiously. Clarification of the neural correlates of dreamed motor activity, as well as clarification of any differences in this network compared to overt motor performance, awaits larger-scale group-level studies. Nevertheless, this experiment provides a proof-of-concept that neuroimaging of specific dream content can be accomplished using lucid dreaming as a methodology.

7.2. Voluntary control of central apneas during REM sleep dreaming

A similar type of experiment was recently undertaken by Oudiette et al. (2018) to examine respiratory behavior during REM sleep dreaming. Specifically, the researchers used lucid dreaming to investigate whether irregular breathing during REM sleep has a cortical origin and whether such breathing patterns can reflect the mental content of dreams. This research follows up on a study from LaBerge and Dement (1982) which observed tachypnea during volitional rapid breathing and apnea during voluntary breath holding during lucid REM sleep dreaming. Polysomnography and respiration were recorded during early morning naps from 21 patients with narcolepsy who reported frequent lucid dreams. Participants were instructed to modify their dream scenario so that it involved vocalizations or an apnea (e.g., diving under water)—two respiratory behaviors that purportedly require a cortical control of respiration ( McKay, Adams, Frackowiak and Corfield, 2008 ). Participants signaled the onset of lucidity as well as the start and end of the respiration tasks with LRLR eye movement signals. Participants also performed the task during waking by actually executing the respiratory task as well as during waking imagination. In total, 32 lucid REM sleep naps were included in the analysis. Physiological data was scored for the presence of central apneas, which were defined as “cessation of nasal flow for more than 10 s in the absence of cyclic thoracic and abdominal movements” as well as preparatory breaths preceding the central apnea.

In 16 out of the 32 signal-verified lucid dreams, the physiological data showed the expected respiratory behavior (e.g., a central apnea, in some cases including preparatory breaths), which was appropriately marked by LRLR signals and confirmed by dream reports of becoming lucid and executing the task. In the remainder of cases, participants either failed to control the dream (N=2) or appropriately execute the LRLR signals (N=2), or there was incongruence between the report and the data (participants either did not recall performing the task (N=8) or there was no physiological evidence of the task despite a report of completing the task (N=4)). As the authors discuss, the incongruences between reports and physiological data may in part have been attributable to the fact that reports were obtained at the end of the 30-minute nap sessions rather than awakening subjects directly after completing the task. Thus, in some cases this resulted in a long delay between the dream task and the report. Despite the presence of these ambiguous cases, overall the data show that voluntarily control of central apneas during REM sleep occurred in a majority of participants.

As the authors discuss, the pons is hypothesized to regulate cessation of breathing, which assimilates input from supra-brainstem structures and inhibits medullary respiratory neurons ( McKay et al., 2008 ). Thus, they conclude that the fact that voluntary control of central apneas is possible during REM sleep suggests that this cortico-pontine drive is preserved during REM sleep. However, it remains unclear from this study to what extent non-lucid dreams containing this type of mental content (for example, diving under water), would result in central apneas. Indeed, as the authors note, some participants reported that they voluntarily held their breath when diving under water in the dream even though they did not feel that they had to. In general, it remains unclear whether central apneas as observed in the study are linked to the voluntary control of respiration enabled during lucid dreams in particular or whether they systematically occur in dreams with this type of content/scenario. In either case, however, the data support the conclusion that voluntarily control of central apneas is possible during REM sleep.

7.3. Smooth pursuit eye movements during visual tracking in REM sleep dreaming

One last recent example of the use of lucid dreaming as methodology in cognitive neuroscience is provided by LaBerge, Baird and Zimbardo (2018a) , who addressed the question of whether smooth pursuit eye movements (SPEMs) occur during tracking of a slowly moving visual target during lucid REM sleep dreaming. Seven participants with high reported dream recall and frequency of lucid dreams participated in the study. Participants marked the moment of lucidity as well as the initiation and completion of the tracking tasks with LRLR eye movement signals. After making the LRLR signal, participants performed one of two eye-tracking tasks: circle tracking or one-dimensional movement tracking on the horizontal meridian. For each tracking task, participants followed the tip of their thumb with their gaze as they traced the pattern. Participants performed the tracking tasks in three conditions: 1) lucid REM sleep dreaming (“dreaming”), 2) awake with eyes open (“perception”) and 3) tracking the imagined movement while awake with eyes closed (“imagination”). Eye movements were recorded with direct current EOG and subjected to a validated algorithm that classifies saccades, fixations and smooth pursuit eye movements ( Komogortsev and Karpov, 2013 ).

The results revealed that intentional slow tracking of visual motion (of both circles and lines) during lucid REM sleep dreams results in SPEMs. Pursuit eye movements in REM sleep dreams did not significantly differ from pursuit during waking perception, and both were characterized by high pursuit ratios and low saccade rates. In contrast, tracking in imagination was characterized by low pursuit with frequent saccadic intrusions. A Bayesian classification model that included pursuit ratio and saccade rate discriminated both REM sleep dreaming and perception from imagination with greater than 98% accuracy.

Together, these findings help to address several broad questions within cognitive neuroscience and sleep research. First, the data provide empirical evidence for a difficult to test question that has been asked at least since Aristotle: “Are dreams more like perception or imagination?” ( Nir and Tononi, 2010 ). Based on the smooth tracking behavior, the findings of this study suggest that, at least in this respect, the visual quality of REM sleep dream imagery is more similar to waking perception than imagination. Second, the findings help to address a longstanding question in the psychophysiology of sleep since the discovery of REM in the 1950’s: whether the eye movements of REM sleep track the gaze of the dreamer—the so-called “scanning hypothesis” (for a review, see Arnulf, 2011 ). By demonstrating that individuals can trace circles and lines with their gaze while in EEG-verified REM sleep, which can be recorded with EOG, the data provide unique evidence that shifts in the perceived gaze direction in dreams give rise to the appropriate corresponding eye movements. This is consistent with the view that a subset of eye movements during REM sleep are linked to the direction of subjective gaze during dreams. Lastly, the results also provide a novel source of data for a central research question on the topic of smooth pursuit in humans and non-human primates that dates back at least forty years. Specifically, an enduring question has been whether a physical stimulus and/or retinal image motion is necessary to drive the neural circuitry of smooth pursuit ( Spering and Montagnini, 2011 ). By demonstrating that sustained SPEMs can be elicited in the absence of visual input to the cortex, as is the case during REM sleep, the findings provide strong evidence that neither a physical motion stimulus nor readout of retinal image motion are necessary for SPEMs.

7.4. General discussion of lucid dreaming as experimental methodology

Altogether, these studies illustrate the potential of lucid dreaming as an experimental methodology for the study of consciousness in general and REM sleep dreaming in particular. The fact that lucid dreamers can exercise volitional control over their actions while dreaming and conduct experiments from within EEG-verified REM sleep dreams opens up the ability to perform experiments that would otherwise be difficult or impossible to conduct. This methodology also has the potential to more efficiently target specific research questions. As discussed above, lucid dreaming enables both experimental control over the content of dreams as well as a way to establish precise psychophysiological correlations between the contents of consciousness during sleep and physiological measures in a way that is not possible in studies of non-lucid dreaming. In sum, lucid dreaming provides a methodology that allows for new ways of studying the relationship between consciousness and neurophysiological processes, and, as the above studies highlight, shows emerging potential for research on consciousness in sleep science.

8. Clinical cognitive neuroscience of lucid dreaming

Research on lucid dreaming is not only relevant to the neuroscience of consciousness but could also have clinical implications. While a detailed treatment of the clinical applications of lucid dreaming is beyond the scope of this review (see e.g., Garfield, Fellows, Halliday and Malamud, 1988 ), here we briefly note several interesting applications and future directions with particular relevance for cognitive neuroscience.

8.1. Lucid dreaming in the treatment of persistent nightmares

The most researched application of lucid dreaming is to treat recurring nightmares ( Abramovitch, 1995 ; Holzinger, Klösch and Saletu, 2015 ; Spoormaker and Van Den Bout, 2006 ; Tanner, 2004 ; Zadra and Pihl, 1997 ). Lucid dreaming was included by the American Academy of Sleep Medicine in their therapy suggestions for nightmare disorder ( Morgenthaler et al., 2018 ). Conceptually, the idea is that becoming lucid during a nightmare should allow the dreamer to realize the content of the nightmare is not real and thus has no reason to be afraid, and to be able to exert control over the dream and/or work with the psychological content of the nightmare ( LaBerge and Rheingold, 1990 ; Mota-Rolim and Araujo, 2013 ). Neurocognitive models of nightmare generation suggest a hyper-responsivity of the amygdala coupled with a failure of prefrontal regions to dampen this activation ( Levin and Nielsen, 2007 ). Studying the influence of lucidity on nightmare resolution could therefore provide an interesting opportunity to study top-down regulation of amygdala activation, perhaps through reactivation of prefrontal regions ( Dresler et al., 2012 ).

A case study ( Zadra and Pihl, 1997 ), and one small, controlled pilot-study ( Spoormaker and Van Den Bout, 2006 ) have found that lucid dreaming therapy was effective in reducing nightmare frequency. Also a study of 32 patients who suffer from frequent nightmares found a slight advantage of lucid dreaming as add-on to Gestalt therapy compared to the latter alone ( Holzinger et al., 2015 ). In contrast, a larger online study did not find any additional effect of lucid dreaming therapy as an add-on to other cognitive-behavioral techniques, such as imagery rehearsal therapy ( Lancee, Van Den Bout and Spoormaker, 2010 ), although low power and high dropout rates (>70%) limited the scope of the conclusions. Controlled experiments with larger sample sizes are needed to further evaluate the potential usefulness of lucid dreaming for the treatment of recurring nightmares and related disorders, as well as to examine the neural mechanisms of these interactions.

8.2. Lucid dreaming and narcolepsy

Consistent with a potential beneficial role of lucid dreaming in recurring nightmares, patients with narcolepsy report that becoming lucid provides psychological relief ( Rak et al., 2015 ). Generally, narcolepsy patients experience longer, more complex, and more vivid dreams and more frequent nightmares than healthy subjects ( Mazzetti et al., 2010 ). Moreover, patients with narcolepsy report higher reflective consciousness within dreams ( Fosse, 2000 ) and accordingly a considerably increased lucid dreaming frequency ( Dodet et al., 2014 ; Rak et al., 2015 ). The mechanisms for this increased lucid dreaming frequency are not entirely understood yet: nightmares often lead to the insight into the current dream state, however it is currently unclear if an increased nightmare frequency causes an increased lucid dreaming frequency in narcolepsy, or if the strongly increased frequency of REM sleep in narcolepsy affects nightmares and lucid dreaming independently. In particular sleep onset REM (SOREM) episodes appear to prompt lucid dreams ( Dodet et al., 2014 ), which is in line with the dream lucidity-enhancing effects of the early morning “Wake-Back-to-Bed” procedure in healthy subjects. Of note, patients with narcolepsy make a clear distinction between their experiences of lucid dreaming and sleep paralysis ( Dodet et al., 2014 ), and the increased lucid dreaming frequency in this patient group does not appear to be associated with medication use (Rak et al., 2014). It has been speculated whether, potentially as a result of a considerably increased lucid dreaming experience, narcoleptic patients might exhibit different neurophysiological correlates of lucid dreaming ( Dodet et al., 2014 ).

8.3. Lucid dreaming as a model of insight in psychiatric conditions

Another area in which research on lucid dreaming might be applied is in psychiatric disorders in which patients suffer from lack of insight into their state. Indeed, there is considerable overlap between preliminary findings of brain regions related to insight into the dream state and brain regions impaired in psychotic patients with disturbed insight into their pathological state ( Dresler et al., 2015 ; Mota, Resende, Mota-Rolim, Copelli and Ribeiro, 2016 ). The concept of insight is becoming an increasingly important area in schizophrenia research (e.g., Baier, 2010 ), for example, where between 50 and 80% of patients diagnosed with schizophrenia have poor insight into the presence of their disorder ( Lincoln, Lullmann and Rief, 2007 ). Evidence suggests that poor insight in turn leads to more relapses, re-hospitalizations and poorer therapy success ( Mintz, Dobson and Romney, 2003 ). With regard to dreaming, lack of insight into the current state characterizes almost all dream experiences, with the exception of lucid dreaming. Thus, lucid dreaming could potentially be used as a model for at least some cognitive dimensions of insight ( David, 1990 ). In this context it is interesting to note that historical approaches to psychosis used the term lucid to denote the awareness of the patient into his or her illness ( Berrios and Markova, 1998 ). At the current time, these ideas remain highly speculative, but this appears to us to be an area worthy of additional research.

8.4. Using lucid dreaming to establish brain activity markers of self-awareness

Another potential clinical application of research on lucid dreaming is in the development of neuroimaging-based diagnostic markers of awareness. Such measures have the potential to improve the diagnosis and monitoring of patients who are unresponsive due to traumatic injury, aphasia, motor impairment, or other physical limitations. Neurobiological studies of lucid dreaming could provide information relevant to development of these brain activity markers since, in addition to assessing a patient’s capacity for primary consciousness (i.e., if a patient can see, hear or experience pain), an important clinical goal is to assess whether patients, who may nevertheless be unresponsive via behavioral assessment, are aware of themselves and their state ( Laureys, Perrin and Bredart, 2007 ). This information is critical to making appropriate therapeutic choices and determining prognosis ( Laureys et al., 2007 ). Research indicates that the bedside behavioral assessment of such patients is challenging and has a high rate of misdiagnosis (over 40% according to some studies) ( Laureys, Owen and Schiff, 2004 ; Schnakers et al., 2009 ). Accordingly, identification of a reliable brain activity marker of a patients’ capacity for self-awareness—which could be informed by studying the changes in brain activity between lucid and non-lucid REM sleep dreaming—is an important goal. Research on lucid dreaming has the potential to contribute a valuable source of data to this question since, as noted above, the contrast between lucid and non-lucid REM sleep is perhaps the only one currently known in which a global loss and recovery of self-awareness can be contrasted within the same vigilance state in healthy individuals.

9. The measurement of lucid dreaming in cognitive neuroscience research

In this last section, we review methodological issues and strategies in the measurement of lucid dreaming in cognitive neuroscience research. We review the validation of lucid dreaming both physiologically and with questionnaires, and discuss best-practice procedures to investigate lucid dreaming in the sleep laboratory.

9.1. Eye signaling methodology

The eye signaling methodology is the gold standard in lucid dreaming research, as it allows for objective confirmation of lucid dreams through the execution of pre-agreed upon sequences of eye movements recorded with the EOG during EEG-verified REM sleep ( Figure 1 ). While there are some types of research studies (e.g., field studies) where eye signaling is not possible or not applicable, we recommend considering this method as the standard practice for laboratory-based studies of lucid dreaming. In general, most recent studies have adhered to this convention; however, there is some confusion about how to properly instruct participants in executing the eye movements, which has resulted in studies implementing several different variations of the method. For instance, while some experimenters instruct participants to move their eyes left and right (e.g., Dresler et al., 2012 ), in other cases participants have been instructed to “scan the horizon” from left to right ( Dodet et al., 2014 ).

Differences in eye signaling instructions could partly account for the varying degrees of effectiveness in objectively identifying the eye signals across studies. For example, Dodet et al. (2014) reported that while three control participants and twelve narcoleptic patients reported making the eye signal in overnight EEG recordings, none of these could be objectively identified, and only about half of reported signals could be unambiguously identified from nap recordings. While it is possible that some of these instances represent cases where participants misremembered or misreported a lucid dream, it is likely that this high rate of ambiguous eye movement signals is partly attributable to the specific instructions that were given. Similarly, while the exact instructions provided to participants was not described, Voss et al. (2009) stated that they were unable to obtain reliable eye signals using the standard signaling method and therefore attempted to develop a novel signaling method employing two sets of eye-signals separated by a pause. Again, suboptimal results could plausibly be due to the instructions provided and the way the eye signals were executed by participants in the study.

One reason for the confusion and diverse instructions given for the eye signaling methodology is that a standardized set of instructions for making the signals has never been published. Here we report a simple set of instructions adapted from LaBerge et al. (2018a) that has been reported to yield nearly 100% correspondence between subjective reports of eye signals and objective EOG recordings. The instruction is as follows:

When making an eye movement signal, we would like you to look all the way to the left then all the way to the right two times consecutively, as if you are looking at each of your ears. Specifically, we would like you to look at your left ear, then your right ear, then your left ear, then your right ear, and then finally back to center. Make the eye movements without moving your head, and make the full left-right-left-right-center motion as one rapid continuous movement without pausing.

Looking at the ears is one of the critical aspects of the instruction, as it encourages full-scale eye movements without corresponding head movements. These extreme and rapid consecutive eye movements make the signals easy to discern in the horizontal EOG time series. The signals are of higher amplitude than typical REMs, and have a distinctive shape ( Figure 1 ) which can be identified with template matching ( LaBerge et al., 2018a ). To optimize execution of the eye signals, participants are given the opportunity to practice the signals in the laboratory while awake and connected to the EOG, with an opportunity to view the signals and receive feedback from the experimenter. Additionally, participants practice making the eye signals in any lucid dreams they have at home in the weeks leading up to the sleep laboratory visit, in order to gain experience with executing the signals.

9.2. Questionnaire assessment of lucid dreaming

A related issue concerns how the phenomenology of lucid dreaming should optimally be assessed in cognitive neuroscience research. Several questionnaires have been developed to assess changes in conscious experience during dreaming. For instance, the Metacognitive, Affective, Cognitive Experience questionnaire (MACE; Kahan, LaBerge, Levitan and Zimbardo, 1997 ; Kahan and Sullivan, 2012 ) was designed to assesses the monitoring and regulation of both cognitive and affective experience during both wakefulness and sleep. In its latest form, it consists of ten items, including four self-monitoring questions, three questions on self-reflective consciousness and three questions that target self-regulatory behaviors. Another measure, the Dream Lucidity Questionnaire (DLQ; Stumbrys et al., 2013b ), is designed to assess different aspects of lucidity within dreams. It consists of ten items, scored on a 5-point scale: 0 – not at all, 1 – just a little, 2 – moderately, 3 – pretty much, 4 – very much. The DLQ evaluates different types of awareness (awareness of dreaming, awareness that the physical body is asleep, awareness that dream characters and objects are not real, awareness of different possibilities) and control (deliberately choosing an action, changing dream events, dream characters, dream scenes, breaking physical laws). In a similar manner, the Lucidity and Consciousness in Dreams scale (LuCiD, Voss et al., 2013 ) includes eight subscales, derived from factor analysis of a sample of lucid and non-lucid dream reports, which assess various dimensions of dreaming experience and cognition: 1) Insight, 2) Control, 3) Thought, 4) Realism, 5) Memory, 6) Dissociation, 7) Negative emotion, and 8) Positive emotion.

All three questionnaires provide measures for evaluating how some content dimensions might differ across lucid and non-lucid dreams. However, as mentioned, questionnaires such as these are not sufficient to objectively establish whether a participant had a lucid dream. It is worth noting that only the DLQ in its first question (“I was aware that I was dreaming”) directly queries whether the dreamer was lucid, and the MACE was never intended for this purpose. Most studies using the DLQ and LuCiD questionnaires have collected a dream report from participants in addition to the questionnaire responses. We would like to emphasize the importance of this point: Instead of relying on the inference of lucidity solely from questionnaire measures of dream content, accurate assessment of lucidity can be facilitated by, in addition to using the eye signaling method, collecting a full dream report from participants. In making the dream report, participants are typically asked to describe in as much detail as possible the narrative of the dream, including the sequence of events and any thoughts, feelings or sensations that they experienced. In dream reports for lucid dreams, the participant is also asked to include a specific description of how they became lucid in the dream (for example, by noticing an oddity of an event, action or person in the dream, which is also known as “dream sign”) and also to explicitly note any eye signals made at the appropriate instances in the dream narrative.

Following the collection of a full open-ended dream report, further confirmation of lucidity and eye signaling can be accomplished with simple follow-up questions, including, for example: 1) “Were you aware of the fact that you were dreaming while you were dreaming?” (YES/NO), If YES: 2) “How confident are you that you became lucid?” (0-4 scale), 3) “Did you have a wake-initiated lucid dream (WILD) or a dream initiated lucid dream (DILD)?” (DILD/WILD), 4) “Did you make the eye movement signal to indicate that you became lucid?” (YES/NO), 5) “Please briefly describe how you became lucid.” These example questions are not meant to be exhaustive, but they illustrate the types of questions that in our view are useful to obtaining an accurate and thorough assessment of lucid dreams following collection of a full dream report.

9.3. Measurement of individual differences in lucid dreaming

Another related question facing researchers is how to measure individual differences in lucid dream frequency, which has been done in inconsistent ways, and could be improved in future research. A method used in several studies consists of an 8-point scale that asks participants to self-rate the frequency with which they experience lucid dreams, ranging from “never” to “several times per week” ( Schredl and Erlacher, 2004 ; Mota-Rolim et al., 2013 ). While this method provides a straightforward coarse assessment of an individual’s estimated frequency of lucid dreams that has shown high test-retest reliability ( Stumbrys, Erlacher and Schredl, 2013a ), a limitation is that it does not measure lucid dream frequency greater than several times per week. The scale could be improved by including additional categories on the higher end of the measure, including, for example, “Every night” and “Multiple times per night.” While individuals who experience lucid dreams on a nightly basis represent a very small percentage of respondents (according to Baird et al. (2018a) approximately one in one thousand), optimally this kind of instrument would enable a researcher to distinguish respondents that have lucid dreams once or several times a week from those that have them every night or multiple times per night. Indeed, these “virtuoso” lucid dreamers represent perhaps one of the most interesting populations for cognitive neuroscience studies of individual differences in lucid dreaming ( Baird et al., 2018a ).

A related method is to query the number of lucid dreams reported in a given period of time (i.e., the last 6 months), which has the advantage of asking participants to report a specific number rather than a coarse estimate. In principle, this method has the potential to be more accurate and to capture increased variance. However, some participants, particularly those with high lucid dream rates, may not be able to recall all instances of their lucid dreams within the requested time interval, and may therefore resort to heuristics when answering the question, making it akin to simply asking participants to report frequency using a multiple-choice scale. Furthermore, this method may not accurately assess lucid dream frequency over longer periods of time – i.e., an individual may normally experience lucid dreams frequently, but has not experienced them as frequently during the last queried interval of time. If using this approach, it is therefore advisable to also collect additional estimates of frequency, including the most lucid dream episodes experienced in any 6-month period (e.g., LaBerge et al., 2018b ).

A limitation of the above methods is that they do not measure variation in the length or degree of lucid dreams. Indeed, lucid dreams can range from a mere fleeting thought about the fact that one is dreaming followed by an immediate loss of lucidity or awakening, to extended lucid dreams in which an individual is able to engage in sequences of actions (e.g., LaBerge and Rheingold, 1990 ). Distinguishing between these different “levels” or subtypes of lucid dreams will likely be valuable to understanding observed differences (or lack of differences) in brain structural or functional measures associated with lucid dream frequency. Along these lines, several recent questionnaires have taken first steps to measure individual differences in specific characteristics of lucid dreaming. For example, the Lucid Dreaming Skills Questionnaire (LUSK) measures participants’ frequency of several different but inter-related aspects of awareness and control in lucid dreams ( Schredl, Rieger and Göritz, 2018 ). Another questionnaire, the Frequency and Intensity Lucid Dreaming Questionnaire (FILD) queries participants regarding the duration of their lucid dreams and various aspects of dream control, as well as the frequency with which they deliberately attempt to induce lucid dreams ( Aviram and Soffer-Dudek, 2018 ). In addition to providing data for individual differences studies, such questionnaires could also potentially be useful in selecting participants for sleep laboratory experiments of lucid dreaming, for example by selecting participants who report high levels of dream control in addition to frequent lucid dreams (in order to select participants who are more likely to be able to effectively make the eye signals or engage in experimental tasks during lucid dreams).

For all the above methods, important steps need to be taken to minimize measurement error, particularly to ensure that participants have a clear understanding of the meaning of lucid dreaming ( Snyder and Gackenbach, 1988 ). These include providing participants with a written definition of lucid dreaming, asking participants to provide written examples of their own lucid dreams to ensure clear understanding, as well as additional vetting through participant interviews ( Baird et al., 2018a ). Ultimately, however, basing the measurement of individual differences in lucid dreaming solely on self-report is not optimal. One way to further validate participant questionnaire responses would be to attempt to physiologically validate at least one lucid dream in the sleep laboratory for each participant. While additional validations such as this could potentially be valuable to incorporate in future studies, it is important to note that the estimated frequency of lucid dreaming would still depend on questionnaire assessment. Thus, approaches such as this do not obviate the reliance on questionnaire assessment.

An intriguing, though ambitious, method for deriving a measure of lucid dream frequency not dependent on questionnaire assessment would be to utilize home-based EEG recording systems to collect longitudinal sleep polysomnography data, from which estimates of lucid dreaming frequency could be derived from the frequency of signal-verified lucid dreams collected over many nights. However, this approach would only measure the frequency of signal-verified lucid dreams, and instances in which participants achieved lucidity but did not make the eye signal due to factors such as awakening, forgetting the intention, or lack of dream control would be missed by this procedure. There are many more points that could be addressed on the topic of questionnaire assessment of lucid dreaming frequency, but an extended analysis of this issue is beyond the scope of the present review.

9.6. General discussion of the measurement of lucid dreaming in cognitive neuroscience

In summary, cognitive neuroscience studies of lucid dreaming have at their disposal a unique set of rigorous methodological tools, including in particular the eye movement signaling method, which allows for the objective validation of lucid dreaming as well as precise time-stamping of physiological data. However, refinement of the instructions given to participants, as described above, could help further increase the reliability of the technique. Accurate phenomenological assessment of lucidity has been more mixed. In sleep laboratory studies using the eye signaling method, phenomenological reports are important but less critical due to the presence of an objective marker of lucidity. In contrast, in studies without eye signaling, accurate phenomenological assessment of whether an individual was lucid becomes essential, and inadequate or ambiguous measurement of lucidity can undermine the interpretability of the results. Questionnaire measures such as the DLQ ( Stumbrys et al., 2013b ) or LuCiD scale ( Voss et al., 2013 ) by themselves do not provide an unambiguous assessment of whether an individual had a lucid dream. Cognitive neuroscience research on lucid dreaming would greatly benefit from the further development of improved questionnaire measures for the validation of lucidity and the cognitive and experiential changes that accompany it, as well as further development of standardized measures for quantifying both the frequency and degree of lucidity. Overall, this brief discussion highlights the need for a set of standard operating procedures for both the phenomenological and objective sleep laboratory assessment of lucid dreaming.

10. Conclusion

Despite having been physiologically validated for approximately four decades, the neurobiology of lucid dreaming is still incompletely characterized. Most studies conducted to date have relied on small sample sizes, which limits the generalizability of the findings. Not surprisingly, the results of such underpowered studies are not consistent: almost every EEG study reports changes in spectral power in a different frequency band or brain area. Neuroimaging data on lucid dreaming is even sparser. Currently, there is only one fMRI study contrasting lucid and non-lucid REM sleep and it is a case study. Nevertheless, the results of this study converge with MRI studies that have evaluated individual differences in lucid dreaming frequency. Together, this preliminary evidence suggests that regions of anterior prefrontal, parietal and temporal cortex are involved in lucid dreaming. The involvement of these brain regions in metacognitive processes during the waking state is also in line with these findings.

A primary goal is to develop reliable strategies for making lucid dreaming more accessible. As reviewed above, several studies have explored methods for non-invasive electrical stimulation of the brain as well as pharmacological approaches to lucid dream induction. Electrical stimulation of prefrontal brain areas has resulted in statistically significant but weak increases of dream “insight” ratings, but so far it has not resulted in significant increases in the frequency of lucid dreams. Currently it remains too early to tell how effective (if at all) electrical stimulation of the frontal cortex, or other brain areas, could be for lucid dream induction. Pharmacological induction with agents acting on the cholinergic system, in particular the AChEI galantamine, has shown promising results; however, these findings need to be replicated systematically, and lucid dreams objectively confirmed with polysomnography. Other approaches to lucid dream induction not discussed in the current review, and that do not directly target neural mechanisms, such as cognitive/psychological approaches, also appear promising ( Stumbrys et al., 2012 ). For example, advances in the research of targeted memory reactivation via olfactory or acoustic stimuli during sleep (e.g., Oudiette, Antony, Creery and Paller, 2013 ) might lead to new strategies for lucid dream induction, together with continued research on stimulating lucid dreams with visual or auditory cues ( LaBerge and Levitan, 1995 ; LaBerge et al., 1988 ; LaBerge et al., 1981b ).

In conclusion, additional studies with larger sample sizes, for example large-scale group-level high-density EEG, MEG, and concurrent EEG/fMRI studies, will be important next steps toward characterizing the neural functional changes associated with lucid dreaming. For now, a more detailed understanding of the neurobiological basis of lucid dreaming remains an open question for ongoing research. Lucid dreaming shows promise as a useful methodology for psychophysiological studies of REM sleep, with potential applications in both clinical and basic research domains. Perhaps the largest potential of research on lucid dreaming is that it provides a unique method to investigate the neurobiology of consciousness, which remains one of the largest lacunas in scientific knowledge.

• EEG studies of lucid dreaming are mostly underpowered and show mixed results
• Preliminary neuroimaging data implicates frontoparietal cortices in lucid dreaming
• Cholinergic stimulation with mental set shows promise for inducing lucid dreams
• We present best-practice procedures to investigate lucid dreaming in the laboratory

Acknowledgements

We thank Stephen LaBerge for helpful discussion.

BB was supported by the National Institutes of Health (NIH) under Ruth L. Kirschstein National Research Service Award F32NS089348 from the NINDS. SAM-R was supported by the Coordena9ao de Aperfei9oamento de Pessoal de Nivel Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Financiadora de Estudos e Projetos do Ministério da Ciência e Tecnologia (FINEP), and Fundação de Apoio à Pesquisa do Estado do Rio Grande do Norte (FAPERN). MD was supported by the Netherlands Organisation for Scientific Research (NWO) through a Vidi fellowship (016.Vidi.185.142). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, CAPES, CNPq, FINEP, FAPERN or NWO.

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Competing interests

The authors declare no competing interests.

• Abramovitch H, 1995. The nightmare of returning home: A case of acute onset nightmare disorder treated by lucid dreaming . Israel journal of psychiatry and related sciences 32 , 140–145. [ PubMed ] [ Google Scholar ]
• Amatruda TT, Black DA, McKenna TM, McCarley RW, Hobson JA, 1975. Sleep cycle control and cholinergic mechanisms: Differential effects of carbachol injections at pontine brain stem sites . Brain Res . 98 , 501–515. [ PubMed ] [ Google Scholar ]
• Antrobus JS, Antrobus JS, 1967. Discrimination of two sleep stages by human subjects . Psychophysiology 4 , 48–55. [ PubMed ] [ Google Scholar ]
• Aristotle, 1941. On Dreams, in: McKeon R (Ed.), The basic works of Aristotle . Random House, New York, NY, pp. 618–625. [ Google Scholar ]
• Arnulf I, 2011. The’scanning hypothesis’ of rapid eye movements during REM sleep: A review of the evidence . Arch. Ital. Biol 149 , 367–382. [ PubMed ] [ Google Scholar ]
• Aspy DJ, Delfabbro P, Proeve M, Mohr P, 2017. Reality testing and the Mnemonic Induction of Lucid Dreams: Findings from the national Australian lucid dream induction study . Dreaming 27 , 206–231. [ Google Scholar ]
• Aviram L, Soffer-Dudek N, 2018. Lucid dreaming: Intensity, but not frequency, is inversely related to psychopathology . Front. Psychol 9 , 384. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Baghdoyan HA, 1997. Cholinergic mechanisms regulating REM sleep, in: Schwartz W (Ed.), Sleep science: Integrating basic research and clinical practice. Karger , Basel, pp. 88–116. [ Google Scholar ]
• Baier M, 2010. Insight in schizophrenia: A review . Curr. Psychiatry Rep 12 , 356–361. [ PubMed ] [ Google Scholar ]
• Baird B, Castelnovo A, Gosseries O, Tononi G, 2018a. Frequent lucid dreaming associated with increased functional connectivity between frontopolar cortex and temporoparietal association areas . Sci. Rep [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Baird B, Castelnovo A, Riedner BA, Lutz A, Ferrarelli F, Boly M, Davidson RJ, Tononi G, 2018b. Human rapid eye movement sleep shows local increases in low-frequency oscillations and global decreases in high-frequency oscillations compared to resting wakefulness . eNeuro 5. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Baird B, Riedner BA, Boly M, Davidson RJ, Tononi G, 2018c. Increased lucid dream frequency in long-term meditators but not following mindfulness-based stress reduction training. Psychology of Consciousness: Theory , Research and Practice . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Baird B, Smallwood J, Gorgolewski KJ, Margulies DS, 2013. Medial and lateral networks in anterior prefrontal cortex support metacognitive ability for memory and perception . J. Neurosci 33 , 16657–16665. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bentley P, Driver J, Dolan RJ, 2011. Cholinergic modulation of cognition: Insights from human pharmacological functional neuroimaging . Prog. Neurobiol 94 , 360–388. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Berrios GE, Markova IS, 1998. Insight in the psychoses: A conceptual history, in: Amador X, David A (Eds.), Insight and psychosis . Oxford University Press, New York, NY, pp. 33–46. [ Google Scholar ]
• Berryhill ME, Phuong L, Picasso L, Cabeza R, Olson IR, 2007. Parietal lobe and episodic memory: Bilateral damage causes impaired free recall of autobiographical memory . J. Neurosci 27 , 14415–14423. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Blumenfeld H, 2008. Epilepsy and consciousness, in: Laureys S (Ed.), The neurology of consciousness . Elsevier Academic Press, Oxford, pp. 247–260. [ Google Scholar ]
• Boly M, Garrido MI, Gosseries O, Bruno M-A, Boveroux P, Schnakers C, Massimini M, Litvak V, Laureys S, Friston K, 2011. Preserved feedforward but impaired top-down processes in the vegetative state . Science 332 , 858–862. [ PubMed ] [ Google Scholar ]
• Boly M, Moran R, Murphy M, Boveroux P, Bruno M-A, Noirhomme Q, Ledoux D, Bonhomme V, Brichant J-F, Tononi G, 2012. Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness . J. Neurosci 32 , 7082–7090. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Braun A, Balkin T, Wesenten N, Carson R, Varga M, Baldwin P, Selbie S, Belenky G, Herscovitch P, 1997. Regional cerebral blood flow throughout the sleep-wake cycle. An H2 (15) O PET study . Brain 120 , 1173–1197. [ PubMed ] [ Google Scholar ]
• Braun AR, Balkin TJ, Wesensten NJ, Gwadry F, Carson RE, Varga M, Baldwin P, Belenky G, Herscovitch P, 1998. Dissociated pattern of activity in visual cortices and their projections during human rapid eye movement sleep . Science 279 , 91–95. [ PubMed ] [ Google Scholar ]
• Brylowski A, Levitan L, LaBerge S, 1989. H-reflex suppression and autonomic activation during lucid REM sleep: A case study . Sleep 12 , 374–378. [ PubMed ] [ Google Scholar ]
• Cavallero C, Cicogna P, Natale V, Occhionero M, Zito A, 1992. Slow wave sleep dreaming . Sleep 15 , 562–566. [ PubMed ] [ Google Scholar ]
• Cavanna AE, Trimble MR, 2006. The precuneus: A review of its functional anatomy and behavioural correlates . Brain 129 , 564–583. [ PubMed ] [ Google Scholar ]
• Christoff K, Ream JM, Geddes L, Gabrieli JD, 2003. Evaluating self-generated information: Anterior prefrontal contributions to human cognition . Behav. Neurosci 117 , 1161–1168. [ PubMed ] [ Google Scholar ]
• Cuadra G, Summers K, Giacobini E, 1994. Cholinesterase inhibitor effects on neurotransmitters in rat cortex in vivo . J. Pharmacol. Exp. Ther 270 , 277–284. [ PubMed ] [ Google Scholar ]
• Dane J, Van de Caslte R, 1984. A comparison of waking instruction and posthypnotic suggestion for lucid dream induction . Lucidity Letter 3. [ Google Scholar ]
• David AS, 1990. Insight and psychosis . The British Journal of Psychiatry 156 , 798–808. [ PubMed ] [ Google Scholar ]
• DeGracia DJ, LaBerge S, 2000. Varieties of lucid dreaming experience, in: Kunzendorf RG, Wallace B (Eds.), Individual differences in conscious experience . John Benjamins, Amsterdam, pp. 269–307. [ Google Scholar ]
• Dement W, Wolpert EA, 1958. The relation of eye movements, body motility, and external stimuli to dream content . J. Exp. Psychol 55 , 543–552. [ PubMed ] [ Google Scholar ]
• Dixon ML, De La Vega A, Mills C, Andrews-Hanna J, Spreng RN, Cole MW, Christoff K, 2018. Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks . Proc. Natl. Acad. Sci , E1598–E1607. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dodet P, Chavez M, Leu-Semenescu S, Golmard J, Arnulf I, 2014. Lucid dreaming in narcolepsy . Sleep 38 , 487–497. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Donner TH, Siegel M, 2011. A framework for local cortical oscillation patterns . Trends Cog. Sci 15 , 191–199. [ PubMed ] [ Google Scholar ]
• Dresler M, Eibl L, Fischer CF, Wehrle R, Spoormaker VI, Steiger A, Czisch M, Pawlowski M, 2014. Volitional components of consciousness vary across wakefulness, dreaming and lucid dreaming . Front. Psychol 4 , 987. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dresler M, Koch SP, Wehrle R, Spoormaker VI, Holsboer F, Steiger A, Sämann PG, Obrig H, Czisch M, 2011. Dreamed movement elicits activation in the sensorimotor cortex . Curr. Biol 21 , 1833–1837. [ PubMed ] [ Google Scholar ]
• Dresler M, Sandberg A, Bublitz C, Ohla K, Trenado C, Mroczko-Wqsowicz A, Kühn S, Repantis D, 2018. Hacking the brain: Dimensions of cognitive enhancement . ACS Chemical Neuroscience . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dresler M, Wehrle R, Spoormaker VI, Koch SP, Holsboer F, Steiger A, Obrig H, Sämann PG, Czisch M, 2012. Neural correlates of dream lucidity obtained from contrasting lucid versus non-lucid REM sleep: A combined EEG/fMRI case study . Sleep 35 , 1017–1020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dresler M, Wehrle R, Spoormaker VI, Steiger A, Holsboer F, Czisch M, Hobson JA, 2015. Neural correlates of insight in dreaming and psychosis . Sleep Med. Rev 20 , 92–99. [ PubMed ] [ Google Scholar ]
• Engel AK, Fries P, 2010. Beta-band oscillations—signalling the status quo? Curr. Opin. Neurobiol 20 , 156–165. [ PubMed ] [ Google Scholar ]
• Engel J, Kuhl DE, Phelps ME, 1982. Patterns of human local cerebral glucose metabolism during epileptic seizures . Science 218 , 64–66. [ PubMed ] [ Google Scholar ]
• Erlacher D, Schredl M, LaBerge S, 2003. Motor area activation during dreamed hand clenching: A pilot study on EEG alpha band . Sleep Hypnosis 5 , 182–187. [ Google Scholar ]
• Filevich E, Dresler M, Brick TR, Kühn S, 2015. Metacognitive mechanisms underlying lucid dreaming . J. Neurosci 35 , 1082–1088. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fleming SM, Weil RS, Nagy Z, Dolan RJ, Rees G, 2010. Relating introspective accuracy to individual differences in brain structure . Science 329 , 1541–1543. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fosse R, 2000. REM mentation in narcoleptics and normals: An empirical test of two neurocognitive theories . Conscious. Cogn 9 , 488–509. [ PubMed ] [ Google Scholar ]
• Funk CM, Honjoh S, Rodriguez AV, Cirelli C, Tononi G, 2016. Local slow waves in superficial layers of primary cortical areas during REM sleep . Curr. Biol 26 , 396–403. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gackenbach J, Cranson R, Alexander C, 1986. Lucid dreaming, witnessing dreaming, and the transcendental meditation technique: A developmental relationship . Lucidity Letter 5 , 34–40. [ Google Scholar ]
• Gackenbach J, LaBerge S, 1988. Conscious mind, sleeping brain: Perspectives on lucid dreaming . Plenum Press, New York, NY. [ Google Scholar ]
• Garfield P, Fellows P, Halliday G, Malamud JR, 1988. Clinical applications of lucid dreaming, in: Gackenbach J, LaBerge S (Eds.), Conscious Mind, Sleeping Brain . Plenum Press, New York, NY, pp. 289–319. [ Google Scholar ]
• Giacobini E, Zhu X-D, Williams E, Sherman K, 1996. The effect of the selective reversible acetylcholinesterase inhibitor E2020 on extracellular acetylcholine and biogenic amine levels in rat cortex . Neuropharmacology 35 , 205–211. [ PubMed ] [ Google Scholar ]
• Gillin JC, Sitaram N, Janowsky D, Risch C, Huey L, Storch FI, 1985. Cholinergic mechanisms in REM sleep, in: Wauquier A, Gail lard J, Monti J, Radulovacki M (Eds.), Sleep: Neurotransmitters and neuromodulators . Raven Press, New York, NY, pp. 29–42. [ Google Scholar ]
• Goldberg G, 1985. Supplementary motor area structure and function: Review and hypotheses. Behav . Brain. Sci 8 , 567–588. [ Google Scholar ]
• Gratton G, Coles MG, Donchin E, 1983. A new method for off-line removal of ocular artifact . Electroencephalogr. Clin. Neurophysiol 55 , 468–484. [ PubMed ] [ Google Scholar ]
• Green CE, 1968. Lucid dreams. Hamilton , London. [ Google Scholar ]
• Hallett M, 2007. Transcranial magnetic stimulation: A primer . Neuron 55 , 187–199. [ PubMed ] [ Google Scholar ]
• Hearne KM, 1978. Lucid dreams: An elecro-physiological and psychological study (Unpublished doctoral dissertation) . Liverpool University, Liverpool, UK. [ Google Scholar ]
• Hermann DM, Siccoli M, Brugger P, Wachter K, Mathis J, Achermann P, Bassetti CL, 2008. Evolution of neurological, neuropsychological and sleep-wake disturbances after paramedian thalamic stroke . Stroke 39 , 62–68. [ PubMed ] [ Google Scholar ]
• Hernandez-Peon R, Chavez-lbarra G, Morgane P, Timo-Iaria C, 1963. Limbic cholinergic pathways involved in sleep and emotional behavior . Exp. Neurol 8 , 93–111. [ Google Scholar ]
• Hipp JF, Siegel M, 2013. Dissociating neuronal gamma-band activity from cranial and ocular muscle activity in EEG . Front. Hum. Neurosci 7 , 338. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hobson JA, Pace-Schott EF, 2002. The cognitive neuroscience of sleep: Neuronal systems, consciousness and learning . Nat. Rev. Neurosci 3 , 679–693. [ PubMed ] [ Google Scholar ]
• Hobson JA, Pace-Schott EF, Stickgold R, 2000. Dreaming and the brain: Toward a cognitive neuroscience of conscious states . Behav. Brain. Sci 23 , 793–842. [ PubMed ] [ Google Scholar ]
• Hodes R, Dement WC, 1964. Depression of electrically induced reflexes (“H-reflexes”) in man during low voltage EEG “sleep” . Electroencephalogr. Clin. Neurophysiol 17 , 617–629. [ PubMed ] [ Google Scholar ]
• Holzinger B, Klösch G, Saletu B, 2015. Studies with lucid dreaming as add-on therapy to Gestalt therapy . Acta Neurol. Scand 131 , 355–363. [ PubMed ] [ Google Scholar ]
• Holzinger B, LaBerge S, Levitan L, 2006. Psychophysiological correlates of lucid dreaming . Dreaming 16 , 88–95. [ Google Scholar ]
• Joseph R, 1999. Frontal lobe psychopathology: Mania, depression, confabulation, catatonia, perseveration, obsessive compulsions, and schizophrenia . Psychiatry 62 , 138–172. [ PubMed ] [ Google Scholar ]
• Kahan TL, LaBerge S, Levitan L, Zimbardo P, 1997. Similarities and differences between dreaming and waking cognition: An exploratory study . Conscious. Cogn 6 , 132–147. [ PubMed ] [ Google Scholar ]
• Kahan TL, Sullivan KT, 2012. Assessing metacognitive skills in waking and sleep: A psychometric analysis of the Metacognitive, Affective, Cognitive Experience (MACE) questionnaire . Conscious. Cogn 21 , 340–352. [ PubMed ] [ Google Scholar ]
• Keren AS, Yuval-Greenberg S, Deouell LY, 2010. Saccadic spike potentials in gamma-band EEG: Characterization, detection and suppression . Neuroimage 49 , 2248–2263. [ PubMed ] [ Google Scholar ]
• Kern S, Appel K, Schredl M, Pipa G, 2017. No effect of α-GPC on lucid dream induction or dream content . Somnologie 21 , 180–186. [ Google Scholar ]
• Kjaer TW, Nowak M, Lou HC, 2002. Reflective self-awareness and conscious states: PET evidence for a common midline parietofrontal core . Neuroimage 17 , 1080–1086. [ PubMed ] [ Google Scholar ]
• Komogortsev OV, Karpov A, 2013. Automated classification and scoring of smooth pursuit eye movements in the presence of fixations and saccades . Behav. Res. Methods 45 , 203–215. [ PubMed ] [ Google Scholar ]
• Koontz J, Baskys A, 2005. Effects of galantamine on working memory and global functioning in patients with mild cognitive impairment: A double-blind placebo-controlled study . Am. J. Alzheimer’s Dis. Other Dementias 20 , 295–302. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Koshino Y, Niedermeyer E, 1975. Enhancement of rolandic mu-rhythm by pattern vision . Electroencephalogr. Clin. Neurophysiol 38 , 535–538. [ PubMed ] [ Google Scholar ]
• LaBerge S, 1980a. Lucid dreaming as a learnable skill: A case study . Percept. Motor Skills 51 , 1039–1042. [ Google Scholar ]
• LaBerge S, 1980b. Lucid dreaming: An exploratory study of consciousness during sleep (Unpublished doctoral dissertation) . Stanford University, Stanford, CA. [ Google Scholar ]
• LaBerge S, 1985. Lucid dreaming: The power of being awake and aware in your dreams . Jeremy P. Tarcher, Los Angeles. [ Google Scholar ]
• LaBerge S, 1988a. Lucid dreaming in western literature, in: LaBerge JGS (Ed.), Conscious Mind, Sleeping Brain . Plenum Press, New York, NY, pp. 11–26. [ Google Scholar ]
• LaBerge S, 1988b. The psychophysiology of lucid dreaming, in: Gackenbach J, LaBerge S (Eds.), Conscious mind, sleeping brain: Perspectives on lucid dreaming . Plenum Press, New York, NY, pp. 135–153. [ Google Scholar ]
• LaBerge S, 1990. Lucid dreaming: Psychophysiological studies of consciousness during REM sleep, in: Bootzin RR, Kihlstrom JF, Schacter DL (Eds.), Sleep and Cognition . American Psychological Association, Washington DC, pp. 109–126. [ Google Scholar ]
• LaBerge S, 2001. The paradox and promise of lucid dreaming Research update: Cholinergic stimulation of lucid dreaming; voluntary control of auditory perception during REM lucid dreams, International Association for the Study of Dreams , Berkeley. [ Google Scholar ]
• LaBerge S, 2003. Lucid dreaming and the yoga of the dream state: A psychophysiological perspective, in: Wallace BA (Ed.), Buddhism & science: Breaking new ground . Columbia University Press, New York, NY, pp. 233–258. [ Google Scholar ]
• LaBerge S, 2010. Signal-verfied lucid dreaming proves that REM sleep can support reflective consciousness . Int. J. Dream Res 3 , 26–27. [ Google Scholar ]
• LaBerge S, 2015. Lucid dreaming: Metaconsciousness during paradoxical sleep, in: Glucksman MKM (Ed.), Dream research: Contributions to clinical practice . Routledge, New York, NY, pp. 198–214. [ Google Scholar ]
• LaBerge S, Baird B, Zimbardo PG, 2018a. Smooth tracking of visual targets distinguishes lucid REM sleep dreaming and waking perception from imagination . Nat. Comm 9 , 3298. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• LaBerge S, Dement WC, 1982. Voluntary control of respiration during REM sleep . Sleep Res . 11 , 107. [ Google Scholar ]
• LaBerge S, Greenleaf W, Kedzierski B, 1983. Physiological responses to dreamed sexual activity during lucid REM sleep . Psychophysiology , 454–455. [ Google Scholar ]
• LaBerge S, LaMarca K, Baird B, 2018b. Pre-sleep treatment with galantamine stimulates lucid dreaming: A double-blind, placebo-controlled, crossover study . PLoS ONE 13 , e0201246. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• LaBerge S, Levitan L, 1995. Validity established of DreamLight cues for eliciting lucid dreaming . Dreaming 5 , 159–168. [ Google Scholar ]
• LaBerge S, Levitan L, Dement WC, 1986. Lucid dreaming: Physiological correlates of consciousness during REM sleep . J. Mind Behav 7 , 251–258. [ Google Scholar ]
• LaBerge S, Levitan L, Rich R, Dement WC, 1988. Induction of lucid dreaming by light stimulation during REM sleep . Sleep Res . 17 , 104. [ Google Scholar ]
• LaBerge S, Nagel L, Taylor W, Dement W, Zarcone V, 1981a. Psychophysiological correlates of the initiation of lucid dreaming . Sleep Res . 10 , 149. [ Google Scholar ]
• LaBerge S, Owens J, Nagel L, Dement WC, 1981b. “This is a dream”: Induction of lucid dreams by verbal suggestion during REM sleep . Sleep Res . 10 , 150. [ Google Scholar ]
• LaBerge S, Phillips L, Levitan L, 1994. An hour of wakefulness before morning naps makes lucidity more likely . NightLight 6 , 1–4. [ Google Scholar ]
• LaBerge S, Rheingold H, 1990. Exploring the world of lucid dreaming . Ballantine Books; New York, NY. [ Google Scholar ]
• LaBerge SP, Nagel LE, Dement WC, Zarcone VP, 1981c. Lucid dreaming verified by volitional communication during REM sleep . Percept. Motor Skills 52 , 727–732. [ PubMed ] [ Google Scholar ]
• Lachaux JP, Fonlupt P, Kahane P, Minotti L, Hoffmann D, Bertrand O, Baciu M, 2007. Relationship between task-related gamma oscillations and BOLD signal: New insights from combined fMRI and intracranial EEG . Hum. Brain Mapp 28 , 1368–1375. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lancee J, Van Den Bout J, Spoormaker VI, 2010. Expanding self-help imagery rehearsal therapy for nightmares with sleep hygiene and lucid dreaming: A waiting-list controlled trial . Int. J. Dream Res 3 , 111–120. [ Google Scholar ]
• Laureys S, Owen AM, Schiff ND, 2004. Brain function in coma, vegetative state, and related disorders . Lancet Neurol . 3 , 537–546. [ PubMed ] [ Google Scholar ]
• Laureys S, Perrin F, Brédart S, 2007. Self-consciousness in non-communicative patients . Conscious. Cogn 16 , 722–741. [ PubMed ] [ Google Scholar ]
• Lee U, Ku S, Noh G, Baek S, Choi B, Mashour GA, 2013. Disruption of frontal-parietal communication by ketamine, propofol, and sevoflurane . Anesthesiology 118 , 1264–1275. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Levin R, Nielsen TA, 2007. Disturbed dreaming, posttraumatic stress disorder, and affect distress: A review and neurocognitive model . Psychol. Bull 133 , 482. [ PubMed ] [ Google Scholar ]
• Lincoln TM, Lüllmann E, Rief W, 2007. Correlates and long-term consequences of poor insight in patients with schizophrenia. A systematic review . Schizophrenia Bulletin 33 , 1324–1342. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lou HC, Luber B, Crupain M, Keenan JP, Nowak M, Kjaer TW, Sackeim HA, Lisanby SH, 2004. Parietal cortex and representation of the mental self . Proc. Natl. Acad. Sci 101 , 6827–6832. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Luigetti M, Di Lazzaro V, Broccolini A, Vollono C, Dittoni S, Frisullo G, Pilato F, Profice P, Losurdo A, Morosetti R, 2011. Bilateral thalamic stroke transiently reduces arousals and NREM sleep instability . J. Neurol. Sci 300 , 151–154. [ PubMed ] [ Google Scholar ]
• Malcolm N, 1959. Dreaming . Routledge, London, England. [ Google Scholar ]
• Maquet P, Péters J-M, Aerts J, Delfiore G, Degueldre C, Luxen A, Franck G, 1996. Functional neuroanatomy of human rapid-eye-movement sleep and dreaming . Nature 383 , 163–166. [ PubMed ] [ Google Scholar ]
• Mashour GA, 2014. Top-down mechanisms of anesthetic-induced unconsciousness . Front. Sys. Neurosci 8 , 115. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Mason LI, Orme-Johnson DW, 2010. Transcendental consciousness wakes up in dreaming and deep sleep . Int. J. Dream Res 3 , 28–32. [ Google Scholar ]
• Mazzetti M, Bellucci C, Mattarozzi K, Plazzi G, Tuozzi G, Cipolli C, 2010. REM-dreams recall in patients with narcolepsy-cataplexy . Brain Res. Bull 81 , 133–140. [ PubMed ] [ Google Scholar ]
• McCaig RG, Dixon M, Keramatian K, Liu I, Christoff K, 2011. Improved modulation of rostrolateral prefrontal cortex using real-time fMRI training and meta-cognitive awareness . NeuroImage 55 , 1298–1305. [ PubMed ] [ Google Scholar ]
• McKay LC, Adams L, Frackowiak RS, Corfield DR, 2008. A bilateral cortico-bulbar network associated with breath holding in humans, determined by functional magnetic resonance imaging . NeuroImage 40 , 1824–1832. [ PubMed ] [ Google Scholar ]
• Mintz AR, Dobson KS, Romney DM, 2003. Insight in schizophrenia: A meta-analysis . Schizophrenia Research 61 , 75–88. [ PubMed ] [ Google Scholar ]
• Montagna P, 2005. Fatal familial insomnia: A model disease in sleep physiopathology . Sleep Med. Rev 9 , 339–353. [ PubMed ] [ Google Scholar ]
• Morgenthaler TI, Auerbach S, Casey KR, Kristo D, Maganti R, Ramar K, Zak R, Kartje R, 2018. Position paper for the treatment of nightmare disorder in adults: An American Academy of Sleep Medicine position paper . Journal of Clinical Sleep Medicine 14 , 1041–1055. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Mota NB, Resende A, Mota-Rolim SA, Copelli M, Ribeiro S, 2016. Psychosis and the control of lucid dreaming . Front. Psychol 7 , 294. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Mota-Rolim S, Pantoja A, Pinheiro R, Camilo A, Barbosa T, Hazboun I, Araujo J, Ribeiro S, 2008. Lucid dream: Sleep electroencephalographic features and behavioral induction methods, First Congress IBRO/LARC of Neurosciences for Latin America, Caribbean and Iberian Peninsula . Bdzios, Brazil. [ Google Scholar ]
• Mota-Rolim SA, Araujo JF, 2013. Neurobiology and clinical implications of lucid dreaming . Medical hypotheses 81 , 751–756. [ PubMed ] [ Google Scholar ]
• Mota-Rolim SA, Brandão DS, Andrade KC, de Queiroz CMT, Araujo JF, de Araujo DB, Ribeiro S, 2015. Neurophysiological features of lucid dreaming during N1 and N2 sleep stages: Two case reports . Sleep Sci . 4 , 215. [ Google Scholar ]
• Mota-Rolim SA, Erlacher D, Tort AB, Araujo JF, Ribeiro S, 2010. Different kinds of subjective experience during lucid dreaming may have different neural sub-strates . Int. J. Dream Res 25 , 550–557. [ Google Scholar ]
• Mota-Rolim SA, Targino ZH, Souza BC, Blanco W, Araujo JF, Ribeiro S, 2013. Dream characteristics in a Brazilian sample: An online survey focusing on lucid dreaming . Front. Hum. Neurosci 7 , 836. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Muzio JN, Roffwarg HP, Kaufman E, 1966. Alterations in the nocturnal sleep cycle resulting from LSD . Clin. Neurophysiol 21 , 313–324. [ PubMed ] [ Google Scholar ]
• Nieminen JO, Gosseries O, Massimini M, Saad E, Sheldon AD, Boly M, Siclari F, Postle BR, Tononi G, 2016. Consciousness and cortical responsiveness: A within-state study during non-rapid eye movement sleep . Sci. Rep 6 , 30932. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Nir Y, Fisch L, Mukamel R, Gelbard-Sagiv H, Arieli A, Fried I, Malach R, 2007. Coupling between neuronal firing rate, gamma LFP, and BOLD fMRI is related to interneuronal correlations . Curr. Biol 17 , 1275–1285. [ PubMed ] [ Google Scholar ]
• Nir Y, Tononi G, 2010. Dreaming and the brain: From phenomenology to neurophysiology . Trends Cog. Sci 14 , 88–100. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Norbu N, Katz M, 1992. Dream yoga and the practice of natural light . Snow Lion Publications, Ithaca, NY. [ Google Scholar ]
• Noreika V, Windt JM, Lenggenhager B, Karim AA, 2010. New perspectives for the study of lucid dreaming: From brain stimulation to philosophical theories of self-consciousness . Int. J. Dream Res 3 , 36–45. [ Google Scholar ]
• Ogilvie R, Hunt H, Kushniruk A, Newman J, 1983. Lucid dreams and the arousal continuum . Lucidity Letter 2. [ Google Scholar ]
• Ogilvie R, Hunt H, Sawicki C, McGowan K, 1978. Searching for lucid dreams . Sleep Res . 7 , 165. [ Google Scholar ]
• Ogilvie R, Vieira K, Small R, 1991. EEG activity during lucid dreaming . Lucidity Letter 10. [ Google Scholar ]
• Ogilvie RD, Hunt HT, Tyson PD, Lucescu ML, Jeakins DB, 1982. Lucid dreaming and alpha activity: A preliminary report . Percept. Motor Skills 55 , 795–808. [ PubMed ] [ Google Scholar ]
• Oudiette D, Antony JW, Creery JD, Paller KA, 2013. The role of memory reactivation during wakefulness and sleep in determining which memories endure . J. Neurosci 33 , 6672–6678. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Oudiette D, Dodet P, Ledard N, Artru E, Rachidi I, Similowski T, Arnulf I, 2018. REM sleep respiratory behaviours mental content in narcoleptic lucid dreamers . Sci. Rep 8 , 2636. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Padmasambhava, 1998. Natural liberation: Padmasambhava’s teachings on the six bardos . Wisdom, Boston, MA. [ Google Scholar ]
• Perogamvros L, Baird B, Seibold M, Riedner B, Boly M, Tononi G, 2017. The phenomenal contents and neural correlates of spontaneous thoughts across wakefulness, NREM sleep, and REM sleep . J. Cogn. Neurosci 29 , 1766–1777. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pfurtscheller G, Stancak A, Neuper C, 1996. Post-movement beta synchronization. A correlate of an idling motor area? Electroencephalogr. Clin. Neurophysiol 98 , 281–293. [ PubMed ] [ Google Scholar ]
• Pigorini A, Sarasso S, Proserpio P, Szymanski C, Arnulfo G, Casarotto S, Fecchio M, Rosanova M, Mariotti M, Russo GL, 2015. Bistability breaks-off deterministic responses to intracortical stimulation during non-REM sleep . Neuroimage 112 , 105–113. [ PubMed ] [ Google Scholar ]
• Price RF, Cohen DB, 1988. Lucid dream induction: An empirical evaluation . Conscious mind, sleeping brain: Perspectives on lucid dreaming , 105–134. [ Google Scholar ]
• Purdon PL, Pierce ET, Mukamel EA, Prerau MJ, Walsh JL, Wong KFK, Salazar-Gomez AF, Harrell PG, Sampson AL, Cimenser A, 2013. Electroencephalogram signatures of loss and recovery of consciousness from propofol . Proc. Natl. Acad. Sci 110 , E1142–E1151. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Raggi A, Cosentino FI, Lanuzza B, Ferri R, 2010. Behavioural and neurophysiologic features of state dissociation: A brief review of the literature and three descriptive case studies . Behavioural neurology 22 , 91–99. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rak M, Beitinger P, Steiger A, Schredl M, Dresler M, 2015. Increased lucid dreaming frequency in narcolepsy . Sleep 38 , 787–792. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rechtschaffen A, Kales A, 1968. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects . Government Printing Office National Institutes of Health, Washington, DC. [ Google Scholar ]
• Rees G, Wojciulik E, Clarke K, Husain M, Frith C, Driver J, 2002. Neural correlates of conscious and unconscious vision in parietal extinction . Neurocase 8 , 387–393. [ PubMed ] [ Google Scholar ]
• Riemann D, Gann H, Dressing H, Müller WE, Aldenhoff JB, 1994. Influence of the cholinesterase inhibitor galanthamine hydrobromide on normal sleep . Psychiatry Res . 51 , 253–267. [ PubMed ] [ Google Scholar ]
• Roehrs T, Roth T, 2001. Sleep, sleepiness, sleep disorders and alcohol use and abuse . Sleep Med. Rev 5 , 287–297. [ PubMed ] [ Google Scholar ]
• Sagnier S, Coulon P, Chaufton C, Poll M, Debruxelles S, Renou P, Rouanet F, Olindo S, Sibon I, 2015. Lucid dreams, an atypical sleep disturbance in anterior and mediodorsal thalamic strokes . Revue neurologique 171 , 768–772. [ PubMed ] [ Google Scholar ]
• Saunders DT, Roe CA, Smith G, Clegg H, 2016. Lucid dreaming incidence: A quality effects meta-analysis of 50 years of research . Conscious. Cogn 43 , 197–215. [ PubMed ] [ Google Scholar ]
• Schierenbeck T, Riemann D, Berger M, Hornyak M, 2008. Effect of illicit recreational drugs upon sleep: Cocaine, ecstasy and marijuana . Sleep Med. Rev 12 , 381–389. [ PubMed ] [ Google Scholar ]
• Schmitz TW, Rowley HA, Kawahara TN, Johnson SC, 2006. Neural correlates of self-evaluative accuracy after traumatic brain injury . Neuropsychologia 44 , 762–773. [ PubMed ] [ Google Scholar ]
• Schnakers C, Vanhaudenhuyse A, Giacino J, Ventura M, Boly M, Majerus S, Moonen G, Laureys S, 2009. Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment . BMC Neurol . 9 , 35. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schooler JW, 2002. Re-representing consciousness: Dissociations between experience and meta-consciousness . Trends Cog. Sci 6 , 339–344. [ PubMed ] [ Google Scholar ]
• Schredl M, Erlacher D, 2004. Lucid dreaming frequency and personality . Pers. Individ. Dif 37 , 1463–1473. [ Google Scholar ]
• Schredl M, Rieger J, Göritz AS, 2018. Measuring lucid dreaming skills: A new questionnaire (LUSK) . Int. J. Dream Res [ Google Scholar ]
• Siclari F, Baird B, Perogamvros L, Bernardi G, LaRocque JJ, Riedner B, Boly M, Postle BR, Tononi G, 2017. The neural correlates of dreaming . Nat. Neurosci 20 , 872–878. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Siclari F, LaRocque JJ, Postle BR, Tononi G, 2013. Assessing sleep consciousness within subjects using a serial awakening paradigm . Front. Psychol 4. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Snyder TJ, Gackenbach J, 1988. Individual differences associated with lucid dreaming, in: LaBerge S, Gackenbach J (Eds.), Conscious mind, sleeping brain . Plenum, New York, NY, pp. 221–259. [ Google Scholar ]
• Sparrow G, Hurd R, Carlson R, Molina A, 2018. Exploring the effects of galantamine paired with meditation and dream reliving on recalled dreams: Toward an integrated protocol for lucid dream induction and nightmare resolution . Conscious. Cogn 63 , 74–88. [ PubMed ] [ Google Scholar ]
• Sparrow GS, Thurston M, Carlson R, 2013. Dream reliving and meditation as a way to enhance reflectiveness and constructive engagement in dreams . Int. J. Dream Res 6 , 84–93. [ Google Scholar ]
• Spering M, Montagnini A, 2011. Do we track what we see? Common versus independent processing for motion perception and smooth pursuit eye movements: A review . Vision Res . 51 , 836–852. [ PubMed ] [ Google Scholar ]
• Spoormaker VI, Czisch M, Dresler M, 2010. Lucid and non-lucid dreaming: Thinking in networks . Int. J. Dream Res 3 , 49–51. [ Google Scholar ]
• Spoormaker VI, Van Den Bout J, 2006. Lucid dreaming treatment for nightmares: A pilot study . Psychother. Psychosom 75 , 389–394. [ PubMed ] [ Google Scholar ]
• Steriade M, Timofeev I, Grenier F, 2001. Natural waking and sleep states: A view from inside neocortical neurons . J. Neurophysiol 85 , 1969–1985. [ PubMed ] [ Google Scholar ]
• Stickgold R, Malia A, Fosse R, Hobson JA, 2001. Brain-mind states: I. Longitudinal field study of sleep/wake factors influencing mentation report length . Sleep 24 , 171–179. [ PubMed ] [ Google Scholar ]
• Stumbrys T, Erlacher D, 2012. Lucid dreaming during NREM sleep: Two case reports . Int. J. Dream Res 5 , 151–155. [ Google Scholar ]
• Stumbrys T, Erlacher D, Malinowski P, 2015. Meta-awareness during day and night: The relationship between mindfulness and lucid dreaming . Imagin. Cogn. Pers 34 , 415–433. [ Google Scholar ]
• Stumbrys T, Erlacher D, Schadlich M, Schredl M, 2012. Induction of lucid dreams: A systematic review of evidence . Conscious. Cogn 21 , 1456–1475. [ PubMed ] [ Google Scholar ]
• Stumbrys T, Erlacher D, Schredl M, 2013a. Reliability and stability of lucid dream and nightmare frequency scales . Int. J. Dream Res 6 , 123–126. [ Google Scholar ]
• Stumbrys T, Erlacher D, Schredl M, 2013b. Testing the involvement of the prefrontal cortex in lucid dreaming: A tDCS study . Conscious. Cogn 22 , 1214–1222. [ PubMed ] [ Google Scholar ]
• Tanner BA, 2004. Multimodal behavioral treatment of nonrepetitive, treatment-resistant nightmares: A case report . Percept. Motor Skills 99 , 1139–1146. [ PubMed ] [ Google Scholar ]
• Tononi G, 2011. Integrated information theory of consciousness: An updated account . Arch. Ital. Biol 150 , 56–90. [ PubMed ] [ Google Scholar ]
• Tononi G, Boly M, Massimini M, Koch C, 2016. Integrated information theory: From consciousness to its physical substrate . Nat. Rev. Neurosci 17 , 450–461. [ PubMed ] [ Google Scholar ]
• Tononi G, Massimini M, 2008. Why does consciousness fade in early sleep? Ann. N. Y. Acad. Sci 1129 , 330–334. [ PubMed ] [ Google Scholar ]
• Tyson PD, Ogilvie RD, Hunt HT, 1984. Lucid, prelucid, and nonlucid dreams related to the amount of EEG alpha activity during REM sleep . Psychophysiology 21 , 442–451. [ PubMed ] [ Google Scholar ]
• Van der Werf YD, Witter MP, Groenewegen HJ, 2002. The intralaminar and midline nuclei of the thalamus. Anatomical and functional evidence for participation in processes of arousal and awareness . Brain Res. Rev 39 , 107–140. [ PubMed ] [ Google Scholar ]
• Van Eeden F, 1913. A study of dreams , Proceedings of the Society for Psychical Research , pp. 431–461. [ Google Scholar ]
• van Gaal S, Ridderinkhof KR, Scholte HS, Lamme VA, 2010. Unconscious activation of the prefrontal no-go network . J. Neurosci 30 , 4143–4150. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Vanni S, Portin K, Virsu V, Hari R, 1999. Mu rhythm modulation during changes of visual percepts . Neuroscience 91 , 21–31. [ PubMed ] [ Google Scholar ]
• Velazquez-Moctezuma J, Shalauta M, Gillin JC, Shiromani PJ, 1991. Cholinergic antagonists and REM sleep generation . Brain Res . 543 , 175–179. [ PubMed ] [ Google Scholar ]
• Violante IR, Li LM, Carmichael DW, Lorenz R, Leech R, Hampshire A, Rothwell JC, Sharp DJ, 2017. Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance . Elife 6 , e22001. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Vogel GW, 1975. A review of REM sleep deprivation . Arch. Gen. Psychiatry 32 , 749–761. [ PubMed ] [ Google Scholar ]
• Voss U, Holzmann R, Hobson A, Paulus W, Koppehele-Gossel J, Klimke A, Nitsche MA, 2014. Induction of self awareness in dreams through frontal low current stimulation of gamma activity . Nat. Neurosci 17 , 810–812. [ PubMed ] [ Google Scholar ]
• Voss U, Holzmann R, Tuin I, Hobson JA, 2009. Lucid dreaming: A state of consciousness with features of both waking and non-lucid dreaming . Sleep 32 , 1191–1200. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Voss U, Schermelleh-Engel K, Windt J, Frenzel C, Hobson A, 2013. Measuring consciousness in dreams: The lucidity and consciousness in dreams scale . Conscious. Cogn 22 , 8–21. [ PubMed ] [ Google Scholar ]
• Wagner AD, Shannon BJ, Kahn I, Buckner RL, 2005. Parietal lobe contributions to episodic memory retrieval . Trends Cog. Sci 9 , 445–453. [ PubMed ] [ Google Scholar ]
• Wallace BA, 2013. Buddhism and science: Breaking new ground . Columbia University Press. [ Google Scholar ]
• Wallace BA, Hodel B, 2012. Dreaming yourself awake: Lucid dreaming and Tibetan dream yoga for insight and transformation . Shambhala Publications, Boston, MA. [ Google Scholar ]
• Windt JM, 2010. The immersive spatiotemporal hallucination model of dreaming . Phenomenol. Cogn. Sci 9 , 295–316. [ Google Scholar ]
• Windt JM, 2015. Dreaming: A conceptual framework for philosophy of mind and empirical research . MIT Press, Cambridge, MA. [ Google Scholar ]
• Windt JM, Metzinger T, 2007. The philosophy of dreaming and self-consciousness: What happens to the experiential subject during the dream state?, in: Barrett D, McNamara P (Eds.), The new science of dreaming: Cultural and theoretical perspectives . Praeger; , Westport, CT, pp. 193–247. [ Google Scholar ]
• Windt JM, Nielsen T, Thompson E, 2016. Does consciousness disappear in dreamless sleep ? Trends Cog. Sci 20 , 871–882. [ PubMed ] [ Google Scholar ]
• Yeo B, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zöllei L, Polimeni JR, 2011. The organization of the human cerebral cortex estimated by intrinsic functional connectivity . J. Neurophysiol 106 , 1125–1165. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Yuschak T, 2006. Advanced lucid dreaming: The power of supplements . Lulu Enterprises, Raleigh, NC. [ Google Scholar ]
• Yuval-Greenberg S, Deouell LY, 2009. The broadband-transient induced gamma-band response in scalp EEG reflects the execution of saccades . Brain Topogr . 22 , 3–6. [ PubMed ] [ Google Scholar ]
• Yuval-Greenberg S, Tomer O, Keren AS, Nelken I, Deouell LY, 2008. Transient induced gamma-band response in EEG as a manifestation of miniature saccades . Neuron 58 , 429–441. [ PubMed ] [ Google Scholar ]
• Zadra AL, Pihl RO, 1997. Lucid dreaming as a treatment for recurrent nightmares . Psychother. Psychosom 66 , 50–55. [ PubMed ] [ Google Scholar ]