lucid dream research studies

ORIGINAL RESEARCH article

Findings from the international lucid dream induction study.

$\r\nDenholm Jay Adventure-Heart*$

School of Psychology, The University of Adelaide, North Terrace Campus, Adelaide, SA, Australia

The International Lucid Dream Induction Study (ILDIS) investigated and compared the effectiveness of five different combinations of lucid dream induction techniques including reality testing (RT), Wake Back to Bed (WBTB), the Mnemonic Induction of Lucid Dreams (MILD) technique, the Senses Initiated Lucid Dream (SSILD) technique, and a hybrid technique combining elements of both MILD and SSILD. Participants with an interest in lucid dreaming ( N = 355) completed a pre-test questionnaire and then a baseline sleep and dream recall logbook for 1 week before practicing the lucid dream induction techniques for another week. Results indicated that the MILD technique and the SSILD technique were similarly effective for inducing lucid dreams. The hybrid technique showed no advantage over MILD or SSILD. Predictors of successful lucid dream induction included superior general dream recall and the ability to fall asleep within 10 min of completing the lucid dream induction techniques. Successful lucid dream induction had no adverse effect on sleep quality. Findings indicated that the techniques were effective regardless of baseline lucid dreaming frequency or prior experience with lucid dreaming techniques. Recommendations for further research on lucid dream induction techniques are provided.

Introduction

In a lucid dream, the dreamer is aware that they are dreaming while the dream is still happening ( LaBerge, 1985 ). According to a recent meta-analysis by Saunders et al. (2016) , 55% of adults have experienced at least one lucid dream and 23% experience lucid dreams regularly (once per month or more). Recent research indicates that deliberate control is possible in approximately one third of lucid dreams ( Soffer-Dudek, 2020 ). Examples include changing location and deliberately waking up ( LaBerge and Rheingold, 1991 ; LaBerge and DeGracia, 2000 ; Love, 2013 ; Mota-Rolim et al., 2013 ). Lucid dreaming has many potential benefits and applications, such as treatment for nightmares ( Spoormaker and Van Den Bout, 2006 ; Lancee et al., 2010 ; Holzinger et al., 2015 ), improvement of physical skills and abilities through dream rehearsal ( Erlacher and Schredl, 2010 ; Stumbrys et al., 2016 ), creative problem solving ( Stumbrys and Daniels, 2010 ), and research opportunities for exploring mind-body relationships and consciousness (see Hobson, 2009 ). However, to date the effects reported in most studies have been weak and inconsistent, and more research is needed into the applications of lucid dreaming ( Baird et al., 2019 ; de Macêdo et al., 2019 ).

Many techniques exist for inducing lucid dreams (see Tholey, 1983 ; LaBerge and Rheingold, 1991 ; Stumbrys et al., 2012 ; Love, 2013 ). These techniques have been organized by Stumbrys et al. (2012) according to three broad categories. Cognitive techniques include mental exercises that increase the likelihood of lucid dreaming. The two most widely researched cognitive techniques are reality testing (RT; Tholey, 1983 ; LaBerge and Rheingold, 1991 ) and the Mnemonic Induction of Lucid Dreams (MILD) technique ( LaBerge, 1980 ; LaBerge and Rheingold, 1991 ). RT involves examining one’s environment and then performing a reliable test that differentiates between waking and dreaming, repeatedly throughout the day. The rationale is that if RT becomes habitual, it will eventually be performed while dreaming, triggering lucidity. The MILD technique involves creating a prospective memory intention to remember that one is dreaming by repeating the phrase “next time I’m dreaming, I will remember I’m dreaming” (or some variation). The MILD technique is performed during a brief awakening after 5 or so hours of sleep. Indeed, waking up after several hours of sleep for the purpose of lucid dream induction is itself a technique, known as Wake Back to Bed (WBTB; LaBerge and Rheingold, 1991 ). When successful, the MILD technique triggers lucidity during subsequent REM sleep. External stimulation techniques involve stimuli such as flashing lights presented during REM sleep that can be incorporated into dreams, serving as cues that trigger lucidity. Miscellaneous techniques include lucid dream inducing drugs and supplements (see LaBerge, 2004 ; see also Yuschak, 2006 ).

Stumbrys et al. (2012) identified 35 empirical studies on lucid dream induction techniques in a systematic review. Most (24) were field studies, with the others conducted in sleep laboratories (11). Stumbrys et al. (2012) evaluated these studies using a methodological quality checklist developed by Downs and Black (1998) and found that most (60%) were of poor methodological quality. The others were classified as moderate quality. More than half of the studies were unpublished Ph.D. dissertations or otherwise not published in peer-reviewed journals. All studies showed poor external validity. Participants were mostly university students or self-selected and highly experienced lucid dreamers. Most lucid dreaming studies are also limited by small sample sizes, lack of random allocation, failure to investigate variables that operationalize the way in which techniques were practiced (e.g., number of technique repetitions), and inconsistent operationalization of lucid dreaming rates (see Aspy et al., 2017 for a more detailed discussion). These widespread limitations are a major impediment to lucid dream research and make it difficult to compare the effectiveness of techniques across studies.

Several additional lucid dream induction studies have been published since the publication of Stumbrys et al. (2012) . Taitz (2011) found that daily RT for 2 weeks was ineffective. Poor success rates were reported in laboratory studies of external stimulation (flashing lights and vibration; Franc et al., 2014 ) and transcranial direct current stimulation (tDCS) to the dorsolateral prefrontal cortex (DLPFC) during REM sleep ( Stumbrys et al., 2013 ). Dyck et al. (2017) found that keeping a dream diary, RT, and a combined WBTB and affirmation technique were ineffective. In a study by Konkoly and Burke (2019) , 19 participants performed RT, MILD, and the Wake-Induced Lucid Dream technique (WILD). However, the authors did not provide statistics to indicate how effective this training program was except that 39 lucid dreams were reported. Saunders et al. (2017) found that a greater proportion of participants who practiced several techniques over a 12-week period (including RT, MILD and WBTB) experienced lucid dreaming compared to a control group (45 vs. 6%). However, the frequency of lucid dreaming is unclear. Kumar et al. (2018) reported a low success rate (at most 6% of days had lucid dreams) for Tholey’s combined technique, which involves regular reality tests combined with autosuggestion and intention to have a lucid dream ( Tholey, 1983 ). Sparrow et al. (2018) found that the drug Galantamine was effective for inducing lucid dreams. However, results do not permit calculation of lucid dreaming rates. LaBerge et al. (2018) found that lucid dreaming occurred on 42% of nights when participants ingested 8 mg of Galantamine in addition to practicing the MILD technique, and in most cases, using an external stimulation device (flashing light). A success rate of 14% was reported for a control condition involving the same techniques but with placebo pills.

The National Australian Lucid Dream Induction Study (NALDIS; Aspy et al., 2017 ) provided a thorough investigation into RT, MILD and WBTB using a highly diverse sample of Australian participants ( N = 169). During Week 1, participants recorded baseline dream recall rates and were then randomly allocated to one of three experimental groups for Week 2. Because RT, WBTB and MILD are often used in combination, and in the interests of identifying a maximally effective approach to lucid dream induction, an additive approach in which groups involving RT only ( RT only group), RT and WBTB ( RT + WBTB group) and RT, WBTB, and MILD ( RT + WBTB + MILD group) were compared. A significant increase in lucid dreaming was observed in the RT + WBTB + MILD group, with lucid dreaming reported on 17.4% of nights in Week 2 compared to 9.4% of nights in Week 1. No significant changes in lucid dreaming frequency were observed in the other two groups. However, although RT was ineffective when practiced in isolation, it remained uncertain whether RT contributed to the significant increase in lucid dreaming rates observed in the RT + WBTB + MILD group. This is important because RT is a burdensome practice, and if ineffective, it would be better to simply practice WBTB and MILD. Higher general dream recall was a significant predictor of lucid dreaming following practice of the MILD technique. However, the strongest predictor of lucid dreaming was the amount of time taken to fall back asleep after completing the MILD technique. Lucid dreaming was experienced on 45.8% of occasions when participants completed the MILD technique and then fell asleep within 5 min. A likely explanation is that returning to sleep quickly makes it more likely that the MILD intention will persist into REM sleep and trigger lucidity.

The biggest impediment to research into the potential benefits and applications of lucid dreaming is the lack of effective and reliable lucid dream induction techniques. Despite a reduction of research interest in lucid dream induction over the past few decades ( Stumbrys et al., 2012 ), many promising avenues for research remain. Numerous lucid dream induction techniques have been developed by lucid dreaming enthusiasts but have not been investigated scientifically. One promising example is the cognitive technique known as the Senses Initiated Lucid Dream (SSILD) technique (the double “S” in the acronym is intentional; Gary Zhang, 2013 ). The SSILD technique involves waking up after approximately 5 h of sleep (as with MILD) and then repeatedly shifting one’s attention between visual, auditory, and physical sensations before returning to sleep. The International Lucid Dream Induction Study (ILDIS) aimed to investigate the effectiveness of the SSILD technique and address unanswered questions from the NALDIS about the effectiveness of the MILD technique when practiced alone compared to when practiced in combination with RT. The ILDIS also aimed to compare two different types of RT and examine the effectiveness of a hybrid technique combining elements of both MILD and SSILD. Recruitment took place during a media release and subsequent media coverage that occurred when the NALDIS was published. The following hypotheses were tested:

• It was hypothesized that general dream recall rates would be positively correlated with lucid dreaming frequency at both pre-test and during Week 2.

• It was hypothesized that Week 2 lucid dreaming rates would be significantly higher than Week 1 lucid dreaming rates.

• It was hypothesized that lucid dreaming rates would be significantly higher when participants took 5 min or less to fall asleep after practicing lucid dreaming techniques compared to when they took more than 5 min to fall asleep.

Materials and Methods

Participants.

An initial sample of 1618 participants completed the pre-test questionnaire. A total of 843 participants continued to complete Week 1 of the study and 355 participants completed Week 2. In the final sample there were 190 (53.5%) females, 162 (45.6%) males and 3 (0.9%) “other.” Mean age was 35.3 ( SD = 12.4, range: 18–84). Most participants ( n = 255) were employed non-students (71.8%), with 69 (19.4%) students and 31 (8.7%) unemployed or retired. Just over half of participants (54.9%) reported prior experience with lucid dream induction techniques. Only six participants (1.7%) had participated in prior lucid dreaming research. Participants reported M = 1.1 lucid dreams in the month prior to commencing the study ( SD = 2.4, range: 0–28). Participants heard about the study from a wide range of sources that directed them to the present author’s website, where they could sign up to participate. Sources included: 183 (51.6%) from Facebook; 83 (23.4%) from other internet sources (e.g., email lists and social media); 40 (11.3%) from newspaper articles; 28 (7.9%) from a friend; 18 (5.1%) from radio interviews; and 3 (0.9%) from a television interview with the author. Country of residence was: 111 in United States (31.3%); 76 in Australia (21.4%); 26 in United Kingdom (7.3%); 25 in Canada (7.0%); 14 in Germany (3.9%); 9 in Mexico (2.5%); and 94 in a wide variety of other countries (26.5%). Participants were excluded from the study if they had been diagnosed with any kind of mental health disorder, sleep disorder, or neurological disorder; suspected they might have one of these disorders; were experiencing a traumatic or highly stressful life event that was interfering with their sleep; suffered from persistent insomnia or were unable to keep a regular sleep schedule; had experienced sleep paralysis more than once in the past 6 months; found it unpleasant to think about their dreams; or were under 18 years of age. No material incentive was offered. This study was granted ethics approval by the School of Psychology Human Research Ethics Subcommittee at the University of Adelaide. Participants were given an information sheet and then gave informed consent prior to participating.

Materials included a pre-test questionnaire, logbooks for Week 1 and Week 2, and technique instructions documents. All pre-test, Week 1 logbook and Week 2 logbook measures were hosted online using the survey management website Survey Monkey . Instructions were sent via email. In the present paper, pre-test variables are identified by a capital “P” and logbook variables by a capital “L.”

Pre-test Questionnaire

Participants indicated their gender, age, occupation, how they heard about the study, their country of residence, and if they had ever participated in a scientific study on lucid dreaming techniques. Retrospective general dream recall was operationalized as Dream Recall Frequency (DRF; the percentage of days on which there was dream recall) and measured by asking “How many days during the last week did you remember your dreams from the previous night?” ( P DRF ). Response options ranged from “0 days” to “7 days.” Retrospective lucid dreaming rates were operationalized as Dream Count ( L DC Lucid per month ; the number of dreams recalled over the past month) and assessed using a question adapted from Brown and Donderi (1986) Sleep and Dream Questionnaire (SDQ): “Lucid dreams are those in which a person becomes aware of the fact that he or she is dreaming while the dream is still ongoing. For example: ‘I was in England talking to my grandfather when I remembered that (in real life) he had died several years ago and that I had never been to England. I concluded that I was dreaming and decided to fly to get a bird’s eye view of the countryside…’ Please estimate the number of lucid dreams you have had in the past month.” Response options ranged from 0 to 30 or “more than 30” (scale unit = 1, range: 0–20). Participants were asked “Have you ever tried to have lucid dreams by learning and then practicing a lucid dreaming technique?” ( P Lucid tech prior ; “yes” or “no”). Participants were asked, “How often have you practiced a lucid dreaming technique recently (in the past several months)?” ( P Lucid tech freq ). Response options from Schredl (2004) widely used dream recall measure were used (0 = never; 1 = less than once a month; 2 = about once a month; 3 = two or three times a month; 4 = about once a week; 5 = several times a week; and 6 = almost every morning). Responses were converted to the approximate number of days per week using the following class means: 0 = 0; 1 = 0.125; 2 = 0.25; 3 = 0.625; 4 = 1.0; 5 = 3.5; 6 = 6.5.

Participants wrote the date for each logbook entry. This information was used to calculate the number of days taken to complete all seven logbook entries ( L Days to complete log ). The total number of logbook entries was also counted ( L Total log entries ). Participants reported whether they could recall anything specific about their dreams from the preceding night and provided brief titles for each dream they could recall. Using this information, general dream recall was operationalized as both Dream Recall Frequency ( L DRF ; the percentage of days on which there was dream recall) and Dream Count ( L DC per day ; the number of dreams recalled each day). Participants also rated how much content they could recall from each dream according to four categories, operationalizing dream recall as Dream Quantity ( L DQ ). The measure was developed by Aspy (2016) and is based on an earlier measure developed by Reed (1973) . Category ratings are converted to numerical values (“Fragmentary” = 1, “Partial” = 2, “Majority” = 4, “Whole” = 8) and summed (higher scores indicate superior dream recall). The number values 1, 2, 4, and 8 reflect the proportionate increase in dream content associated with the category labels and descriptions, based on qualitative data collected by Reed (1973) . Lucid dreaming was operationalized as DRF ( L DRF Lucid ; the percentage of mornings on which lucid dreaming was reported) using the following question: “Did you have any lucid dreams last night? (Lucid dreams are those in which a person becomes aware of the fact that he or she is dreaming while the dream is still ongoing)” (“yes” or “no”). DRF was used instead of DC because participants were unsure of how many lucid dreams they had in some cases, and in other cases lost and regained lucidity within the same dream.

Participants estimated their total time asleep ( L Time asleep ): “How much time in total do you think you spent sleeping last night? hours, minutes.” Participants rated their subjective sleep quality ( L Sleep quality ): “On a scale of 1–5, what was the overall quality of your sleep last night?” (1 = “terrible,” 2 = “poor,” 3 = “okay,” 4 = “good,” 5 = “excellent”). Participants indicated how tired they felt on waking when they were finished sleeping ( L Tiredness on waking ): “On a scale of 1–5, how tired do you feel this morning?” (1 = “not at all tired,” 2 = “slightly tired,” 3 = “somewhat tired,” 4 = “quite tired,” 5 = “very tired”). Participants indicated their level of sleep deprivation from the previous day ( L Sleep dep yesterday ): “On a scale of 1–5, how sleep deprived were you yesterday?” (1 = “not at all,” 2 = “slightly,” 3 = “somewhat,” 4 = “quite,” 5 = “very”). This measure was included to assess any potential effect of sleep deprivation on lucid dream induction, e.g., due to a REM rebound effect.

The Week 2 logbooks included additional measures related to lucid dreaming technique practice. All participants were asked “Did you turn on the light when the alarm woke you up to do the lucid dreaming technique?” ( L Light on when awoke ; “yes” or “no”); “Did you get out of bed (including if you went to the toilet) when the alarm woke you up to do the lucid dreaming technique?” ( L Out of bed when awoke ; “yes” or “no”); “How long (approximately) did you spend on doing the technique? minutes.” ( L Technique min ); “Did you fall asleep while you were still trying to do the technique?” (“yes” or “no”) ( L Asleep during technique ); and “If you answered “no” to the above question, how long (approximately) did it take for you to get to sleep after you stopped doing the technique? minutes.” ( L Min back to sleep ). Participants who practiced RT (Groups 2 and 3) were asked “How many reality tests did you perform yesterday?” (blank space provided) ( L Reality tests ). Participants in Groups 1, 2, 3, and 4 that all involved the MILD technique were asked “How many times (approx.) did you repeat “next time I’m dreaming, I will remember I’m dreaming” after the alarm woke you up?” ( L MILD phrase repetitions ). Participants in Group 5 who practiced the SSILD technique were asked “How many fast and slow cycles did you do? Fast, Slow.” ( L Fast cycles and L Slow cycles ). Participants in Group 6, which involved the hybrid MILD and SSILD technique, were asked “How many cycles did you do after the alarm woke you up?” ( L Hybrid technique cycles ).

Lucid Dream Induction Technique Documents

All participants were advised to print their lucid dream induction technique instructions, keep them beside the bed, spend a full hour familiarizing themselves with them before commencing the study, practice their techniques at least once during the day to ensure understanding, and to revise the instructions directly before bed each night. All participants were instructed to set an alarm 5 h after going to bed, to place the alarm somewhere that would require getting out of bed to turn it off, and to then practice their assigned “Nighttime Technique” when the alarm went off. Based on findings from the NALDIS, the importance of falling asleep quickly after practicing the techniques was emphasized. Participants were advised that if they were falling asleep too quickly, they could try turning the lights on for a few minutes and reading over the technique instructions to increase wakefulness. They were advised to keep the lights off, put the alarm next to their bed, and use a quieter alarm tone if they had trouble returning to sleep. All participants were given instructions on how to perform an RT if they suspected they were dreaming but were not sure. Participants were told not to practice RT during the day except for participants in Group 2 and Group 3 (see section “Group 2: MILD + WBTB + RT Breath” and section “Group 3: MILD + WBTB + RT Hands”). Participants were also given information and advice about sleep paralysis (see LaBerge and Rheingold, 1991 ; Sleep Paralysis Information Service, 2013 ; University of Waterloo, 2013 ). Instructions that were specific to each group are provided below.

Group 1: MILD + WBTB (No RT)

Participants in this group were given a “Nighttime Lucid Dreaming Technique” document that contained instructions for the MILD technique. This involved recalling a dream from directly prior to waking up (or alternatively, any other recent dream), laying down comfortably, and then repeating the phrase “next time I’m dreaming, I will remember I’m dreaming.” The importance of strong intention was emphasized. Participants were told to simultaneously visualize being back in the dream they had recalled and noticing something unusual that causes them to realize they are dreaming. They were advised to continue until they felt their intention was set.

Group 2: MILD + WBTB + RT Breath

These participants were given the same MILD instructions as Group 1. They were also provided with instructions for performing a minimum of 10 inhalation RT per day. This involves closing one’s lips and then attempting to inhale through the mouth, which is possible in dreams but not while awake (see Aspy et al., 2017 ).

Group 3: MILD + WBTB + RT Hands

This group was given a different kind of RT from Group 2, which involves attempting to push the fingers of one hand through the palm of the other. This was chosen because it is one of the most widely practiced RT. The ability to push the fingers through the palm indicates that one is dreaming. Participants were advised to also inspect their hands for anomalies during each test.

Group 4: MILD + WBTB (No RT)

Instructions for this group were the same as the instructions for Group 1, with no modifications. The decision to include a second MILD + WBTB (no RT) group in Cohort 2 was based on the fact that some participant sample characteristics changed over time during the recruitment process (see section “Preliminary Analyses”). The inclusion of a second MILD + WBTB (no RT) group in Cohort 2 permitted valid comparison of the MILD and SSILD techniques.

Group 5: SSILD + WBTB (No RT)

Instructions for the SSILD technique were designed with consultation from the creator of the technique. It was explained that the technique works by conditioning the mind and body into a subtle state that is optimized for lucid dreams to occur, and that it involves performing several “cycles” that each involve the following three steps:

Step 1. Focus on Vision : Close your eyes and focus all your attention on the darkness behind your closed eyelids. Keep your eyes completely still and totally relaxed. You might see colored dots, complex patterns, images, or maybe nothing at all. It doesn’t matter what you can or cannot see – just pay attention in a passive and relaxed manner and don’t “try” to see anything.

Step 2. Focus on Hearing : Shift all of your attention to your ears. You might be able to hear the faint sounds of traffic or the wind from outside. You might also be able to hear sounds from within you, such as your own heartbeat or a faint ringing in your ears. It doesn’t matter what, if anything, you can hear – just focus all of your attention on your hearing.

Step 3. Focus on Bodily Sensations : Shift all of your attention to sensations from your body. Feel the weight of the blanket, your heartbeat, the temperature of the air, etc. You might also notice some unusual sensations such as tingling, heaviness, lightness, spinning sensations, and so on. If this happens simply relax, observe them passively and try not to get excited.

Participants were instructed to first perform four fast cycles (2 or 3 s on each step) and then four to six slow cycles (approximately 20 s on each step). They were told not to count the number of seconds, and that it is important to complete at least four slow cycles. Participants were instructed to fall asleep as normal after completing six slow cycles.

Group 6: SSILD/MILD Hybrid + WBTB

Participants were asked to do only four to six slow cycles (no fast cycles) and to repeat the MILD phrase “next time I’m dreaming, I will remember I’m dreaming” every time they switched to a new sensory modality. The importance of strong intention was emphasized. Participants were not asked to recall dreams or do any visualization.

The ILDIS was conducted entirely via the internet, allowing people from around the world to complete the study at home. Participants were directed to a web page about the ILDIS using a URL included in a range of media items (see section “Participants”), where they read the information sheet and completed the pre-test questionnaire. Participants were sent emails with instructions and web URLs for accessing the Week 1 logbooks hosted on Survey Monkey . Participants were instructed to complete each logbook entry immediately upon waking, and to not practice any lucid dreaming techniques during Week 1. Participants were given instructions on how to improve their dream recall during both Week 1 and Week 2. Upon completing Day 7 of the Week 1 logbook, participants were sent further instructions, lucid dream induction technique documents, and additional web URLs to access the Week 2 logbooks. Participants were asked to practice the techniques and make logbook entries on consecutive days if possible, but not to practice the techniques if they were sleep deprived. They were instructed to make up for any skipped days at the end. Once sufficient sample sizes had been achieved for the three groups in Cohort 1 (permitting comparison of MILD practiced with and without two kinds of RT), the author began randomly allocating new participants to the three groups in Cohort 2 (permitting comparison of MILD with SSILD and the SSILD/MILD hybrid technique, all without RT). NALDIS group sizes were used as a guide in determining adequate group sizes in the ILDIS.

Preliminary Analyses

Analyses were conducted using IBM SPSS 26 for Windows. Non-parametric tests were used in all cases because most variables were non-normally distributed. There was no significant difference in the proportions of participants who were employed non-students, students, and unemployed or retired who did and did not complete the full study: χ 2 (2, N = 1615) = 3.43, p = 0.180, V = 0.05. The proportion of participants who reported prior experience with lucid dreaming techniques at pre-test was significantly higher for participants who completed the full study (54.9%) compared to those who did not (43.5%): χ 2 (1, N = 1615) = 14.59, p = 0.001, V = 0.10. Mann-Whitney tests indicated that participants who completed the full study had significantly higher general dream recall rates and P Lucid tech freq at pre-test. These findings and descriptive statistics for pre-test variables are presented in Table 1 .

Table 1. Descriptive statistics for pre-test variables with Mann-Whitney tests for differences between participants who did and did not complete the full study.

There were no significant differences between Cohort 1 and Cohort 2 on any pre-test, Week 1 or Week 2 variables except for: P Age (Cohort 1 M = 32.4, SD = 10.2; Cohort 2 M = 37.2, SD = 13.4; Z = 3.28, p = 0.001, r = 0.17); Week 1 L Sleep quality (Cohort 1 M = 3.6, SD = 0.5; Cohort 2 M = 3.4, SD = 0.5; Z = 2.10, p = 0.036, r = 0.11); and Week 1 Days to complete log (Cohort 1 M = 7.8, SD = 1.5; Cohort 2 M = 7.9, SD = 6.8; Z = 3.95, p = 0.001, r = 0.21). There were no significant differences between the three groups within Cohort 1 or within Cohort 2 on these variables. Non-significant test results are not reported for the sake of brevity. Descriptive statistics and Wilcoxon tests of differences between Week 1 and Week 2 logbook variables are presented in Table 2 . Results showed that participants reported significantly higher L Time asleep and significantly lower general dream recall rates, L Tiredness on waking and L Total log entries in Week 2 of the study compared to in Week 1.

Table 2. Descriptive statistics and Wilcoxon tests for differences between week 1 and week 2 logbook variables for participants who completed the full study.

Relationships With Lucid Dreaming

It was hypothesized that general dream recall rates would be positively correlated with lucid dreaming frequency at both pre-test and during Week 2. Spearman rho non-parametric correlations supported the hypothesis and are presented in Table 3 . All pre-test general dream recall variables were related to P DC Lucid per month . Correlations between pre-test general dream recall variables and Week 2 L DRF Lucid were weaker but still significant in all cases. All Week 2 general dream recall variables were significantly correlated with both P DC Lucid per month and Week 2 L DRF Lucid , with the relationships being stronger with Week 2 L DRF Lucid in all cases. This pattern of findings highlights the imperative to not treat retrospective and logbook variables of dream recall as equivalent (see Aspy et al., 2017 ; see also Aspy, 2016 ). A weak correlation was observed between P Lucid tech freq and P DC Lucid per month but not with Week 2 L DRF Lucid . Pre-test and Week 2 lucid dreaming rates were positively correlated. P Age was weakly correlated with P DC Lucid per month but not with L DRF Lucid.

Table 3. Spearman rho non-parametric correlations between pre-test and week 2 lucid dreaming rates and other pre-test and week 2 variables.

Lucid Dream Induction

It was hypothesized that Week 2 lucid dreaming rates would be significantly higher than Week 1 lucid dreaming rates. This hypothesis was supported. Dependent samples Wilcoxon tests showed that Week 2 L DRF Lucid was significantly higher for all participants combined and for each of the six Week 2 groups, with medium to large effect sizes in all cases. These results are presented in Table 4 . Logbook day was significantly related to L DRF Lucid in both Week 1 [χ 2 (6) = 13.21, N = 2448, p = 0.040, V = 0.07] and Week 2 [χ 2 (6) = 28.51, N = 1647, p = 0.001, V = 0.13], with the tendency for L DRF Lucid to decrease slightly over time. Because of the significant difference in L Total Log entries between Week 1 ( M = 6.9) and Week 2 ( M = 4.6) noted in section “Preliminary Analyses,” there were concerns that the Week 2 L DRF Lucid rate may be inflated compared to the Week 1 L DRF Lucid rate. To control for this issue, analyses were repeated comparing mean L DRF Lucid rates based on only the first four logbook days of Week 1 and Week 2. L DRF Lucid was again significantly higher for all participants combined and for participants in all six of the Week 2 groups, confirming the effectiveness of the techniques. Independent samples Kruskal-Wallis tests showed that there were no significant group differences within Cohort 1 (χ 2 = 1.51, p = 0.471, r = 0.06) or Cohort 2 (χ 2 = 4.16, p = 0.125, r = 0.11) in Week 2 L DRF Lucid. The combined L DRF Lucid rate for the two MILD + WBTB groups that did RT during the day ( n = 88, M = 12.1%, SD = 20.4%) was compared to the combined rate for the two MILD + WBTB groups that did not do RT during the day ( n = 118, M = 19.4%, SD = 27.8%). Results from a Mann-Whitney test were non-significant ( Z = 1.94, p = 0.052, r = 0.14).

Table 4. Differences between week 1 and Week 2 lucid dreaming rates for all participants combined and for each of the six week 2 groups.

Relationships With Technique Practice Variables

Relationships between L DRF Lucid and variables that operationalize the way in which the lucid dreaming techniques were practiced were assessed using Spearman rho non-parametric correlations and are presented with descriptive statistics in Table 5 . All correlations were non-significant except for a weak correlation between L Fast cycles performed by participants in Group 5: SSILD + WBTB (no RT) and L DRF Lucid . The results remained non-significant in all cases when correlations were repeated for each group individually, except for a weak negative correlation observed between L Technique min and L DRF Lucid in Group 5: SSILD + WBTB (no RT) ( r s = -0.16, p = 0.013, n = 256).

Table 5. Spearman rho non-parametric correlations between Week 2 lucid dreaming rates and variables that operationalize the way in which the lucid dream induction techniques were practiced.

Participants turned on the light when they awoke to practice lucid dreaming techniques on 467 occasions (28.7%) as opposed to keeping the light turned off. A 2 × 2 Chi 2 test showed that this was not related to lucid dreaming: χ 2 (1, N = 1626) = 0.30, p = 0.582, V = 0.01. Participants got out of bed after the alarm went off and before practicing lucid dreaming techniques on 1140 occasions (70.1%) as opposed to staying in bed. A 2 × 2 Chi 2 test showed that this was not related to lucid dreaming: χ 2 (1, N = 1624) = 1.08, p = 0.298, V = 0.03. Participants fell asleep while performing lucid dreaming techniques on 1162 occasions (70.7%). A 2 × 2 Chi 2 test showed that this was not related to lucid dreaming: χ 2 (1, N = 1642) = 0.01, p = 0.966, V = 0.01.

A 2 × 2 Chi 2 test was conducted to assess the hypothesis that lucid dreaming rates would be significantly higher when participants took 5 min or less to fall asleep after practicing lucid dreaming techniques compared to when they took more than 5 min to fall asleep. Mean Week 2 L DRF Lucid was 17.5% ( SD = 38.1%) for 177 occasions when participants fell asleep within 5 min or less, compared to 13.8% ( SD = 34.6%) for 275 occasions when participants took more than 5 min to return to sleep. However, this difference was not significant: χ 2 (1, n = 452) = 1.14, p = 0.286, V = 0.05. Therefore, these findings did not support the hypothesis. To further explore the hypothesis, another 2 × 2 Chi 2 test was conducted using the criterion of 10 min or less instead of 5 min or less. Mean L DRF Lucid was 18.3% ( SD = 38.7%) for 263 occasions when participants fell asleep within 10 min or less, compared to 11.1% ( SD = 31.5%) for 189 occasions when participants took more than 10 min to return to sleep. This difference was statistically significant: χ 2 (1, n = 452) = 4.33, p = 0.037, V = 0.10. When this test was repeated for each of the six groups individually the results were non-significant in all cases. This may be due to insufficient statistical power.

Additional Exploratory Analyses

Mann-Whitney tests were conducted to further explore factors related to the success rate of the lucid dream induction techniques and are presented in Table 6 . On nights when participants were successful in inducing lucid dreams, they had significantly better sleep quality and significantly higher general dream recall compared to nights when they failed to induce lucid dreams. Participants in Group 5: SSILD + WBTB (no RT) also did more fast cycles on nights when they had lucid dreams. As noted in section “Relationships With Lucid Dreaming,” there was no significant correlation between P Lucid tech freq and Week 2 L DRF Lucid . Further to this, a Mann-Whitney test showed that there was no difference in Week 2 L DRF Lucid between participants who had prior lucid dream induction experience ( M = 15.3%, SD = 24.9%) and participants without prior experience ( M = 16.4%, SD = 25.7%): Z (355) = 0.75, p = 0.454, r = 0.04.

Table 6. Mann–Whitney tests for differences in week 2 logbook variables between nights when practice of lucid dream induction techniques was and was not followed by lucid dreaming.

General Discussion

Participants of the International Lucid Dream Induction Study (ILDIS; N = 355) completed a pre-test questionnaire, a baseline Week 1 logbook period, and then practiced one of six different combinations of lucid dream induction techniques in Week 2. All six technique combinations were effective.

Lucid Dream Induction Techniques

Reality testing (rt).

No significant correlations were observed between number of RT performed each day and lucid dreaming incidence. This replicates the lack of significant correlations in the RT only and the RT + WBTB + MILD groups of the NALDIS, and the lack of correlation reported by Konkoly and Burke (2019) . There was no significant difference in lucid dreaming rate between the MILD + WBTB groups that did and did not perform RT during the day. These findings are consistent with the NALDIS and studies by LaBerge (1988) and Taitz (2011) , in which RT was ineffective. It remains possible that RT is effective over longer periods of time, as found for 3 weeks in studies by Purcell et al. (1986) and Purcell (1988) , and 8 weeks in a study by Schlag-Gies (1992) . Many participants complained that performing RT was burdensome and difficult to remember. This burden may reduce motivation and compliance with more effective techniques when practiced in combination. Lucid dream induction studies should avoid daytime RT unless this technique is of specific interest. The present author believes that RT is still a valuable technique for confirming whether one is dreaming, and as a specialized lucid dreaming practice for cultivating mindfulness, which is associated with lucid dreaming ( Stumbrys et al., 2015 ).

The Mnemonic Induction of Lucid Dreams (MILD) Technique

The MILD technique was effective in four separate experimental groups, two of which involved performing RT during the day. As discussed above, the addition of RT did not result in higher lucid dreaming rates. The weighted average lucid dreaming rate for the four MILD technique groups was 16.5%. This is close to the success rate reported in the NALDIS of 17.4%. These findings replicate the NALDIS and several other studies that have shown the MILD technique to be effective ( LaBerge, 1988 ; Levitan, 1989 , 1990a , 1990b , 1991 ; Edelstein and LaBerge, 1992 ; Levitan et al., 1992 ; LaBerge et al., 1994 , 2018 ; Levitan and LaBerge, 1994 ; Saunders et al., 2017 ; Konkoly and Burke, 2019 ). Although there were no statistically significant differences between the effectiveness of the hybrid SSILD/MILD technique and the other techniques in Cohort 2, results show that the overall lucid dreaming rate in Week 2, the improvement in week 2 compared to Week 1, and the effect size were all lowest for the SSILD/MILD hybrid group.

The Senses Initiated Lucid Dream (SSILD) Technique

The SSILD technique was shown to be effective, with a large effect size and a Week 2 lucid dreaming rate of 16.9%. This rate is almost identical to the weighted average rate for the four groups that practiced the MILD technique ( M = 16.5%), as well as the RT + WBTB + MILD group of the NALDIS ( M = 17.4%). These findings indicate that the SSILD technique is similarly effective for inducing lucid dreams as the MILD technique. There are several possible explanations for how the SSILD technique may induce lucid dreams. One is that repeatedly focusing attention on the visual, auditory and kinesthetic sensory modalities causes a generally increased awareness of perceptual stimuli that persists into REM sleep, making it more likely that the practitioner will notice that they are dreaming, either through generally increased awareness, or through recognition of anomalies within the dream. This could also occur if repeated sensory modality shifts persist upon entering REM sleep. Indeed, one participant reported: “as I was drifting off to sleep, I found myself continuing to do the technique, even though I wasn’t trying to.” Another possible explanation is that repeatedly refocusing one’s attention on different types of perceptual stimuli causes a general increase in cortical activation that increases the likelihood of lucid dreaming.

Predictors and Effects of Lucid Dream Induction

Prior technique experience.

There was no relationship between Week 2 lucid dreaming and whether participants had ever practiced a lucid dream induction technique, nor with the frequency of practice for those who did have prior experience. This indicates that MILD and SSILD combined with WBTB can be used successfully regardless of baseline lucid dreaming or prior technique experience.

General Dream Recall

In Week 2, lucid dreaming rates were significantly correlated with general dream recall rates. Pre-test lucid dreaming was also correlated with pre-test general dream recall. Furthermore, participants recalled significantly more dreams on nights when lucid dreaming occurred following technique practice. General dream recall was significantly lower in Week 2 compared to Week 1, indicating that the increased lucid dreaming rates cannot be attributed to simply recalling more dreams of all types. Taken together, these findings provide further support for the theory that superior general dream recall is conducive to lucid dreaming (see Aspy et al., 2017 ) and that general dream recall is a strong predictor of lucid dreaming (see Erlacher et al., 2014 ).

Technique Practice Variables

Lucid dreaming was not related to any of the variables that operationalized the way in which the lucid dream induction techniques were practiced, except for a weak correlation with the number of fast cycles in the SSILD + WBTB (no RT) group. The explanation for this correlation is unclear. Type 1 error is a likely possibility ( p = 0.039).

Time Taken to Return to Sleep

In the NALDIS, lucid dreaming occurred 86.2% more often when participants fell asleep within 5 min of completing the MILD technique. This finding was not replicated in the ILDIS. However, upon further exploration, it was found that lucid dreaming occurred 64.9% more often on nights when participants of the ILDIS fell asleep within 10 min ( L DRF Lucid M = 18.3%) compared to nights when they took more than 10 min ( L DRF Lucid M = 11.1%). This effect is weaker than in the NALDIS. A possible explanation is that participants of the ILDIS were able to fall asleep more quickly in general due to being given suggestions for how to do this. Notwithstanding, findings from the ILDIS provide further support that lucid dreaming techniques are more effective when one can return to sleep quickly. For the MILD technique, this probably makes it more likely that the mnemonic intention to remember that one is dreaming will be recalled during REM sleep. For the SSILD technique, it may be due to increased cortical activation and/or increased awareness of perceptual stimuli being more likely to persist into REM sleep.

Effects of Lucid Dream Induction on Sleep

Sleep quality was superior on nights when participants successfully induced lucid dreams compared to nights when they failed to induce lucid dreams. Participants also reported significantly more time asleep and significantly less tiredness on waking in Week 2 compared to Week 1. These findings indicate that sleep quality was not adversely affected by successful induction of lucid dreams but may have been adversely affected by unsuccessful attempts. This would be expected if the probability of success is related to the amount of time taken to return to sleep. These findings are consistent with findings from the NALDIS, whereby successful lucid dream induction using the MILD technique was related to the amount of time taken to return to sleep and did not adversely affect sleep quality. Vallat and Ruby (2019) have recently drawn attention to the fact that increasing the frequency of lucid dreams may have unknown negative impacts on the usual processes that occur during REM sleep, due to the fact that lucid dreaming involves a brain state that is neurologically distinct from non-lucid REM sleep. They also raised concerns about potential negative health impacts of the sleep disruption inherent in many lucid dreaming techniques. Soffer-Dudek (2020) raised similar concerns about the effects of lucid dreaming on sleep as well as potential disruptions to reality-fantasy boundaries, which may be of particular concern to clinical populations with disorders such as pscyhosis. More research is needed to investigate the impacts of lucid dreaming generally, and lucid dreaming training specifically, on sleep quality.

Strengths and Limitations

Strengths include the wide range of measures used, the use of measures that operationalized the way in which lucid dream induction techniques were practiced, the comparison of six different lucid dream induction technique combinations, and the large and highly diverse international sample of participants that were mostly employed non-students (71.8%), with nearly equal proportions of people who did (54.9%) and did not (45.1%) have prior lucid dreaming technique experience. Indeed, the ILDIS is the largest study of lucid dream induction techniques to date. As with the NALDIS, the ILDIS has high ecological validity. Participants practiced the techniques in their own homes using written instructions, which reflects how cognitive lucid dream induction techniques are usually practiced. A limitation of the ILDIS is the high attrition rate from the initial sample that completed the pre-test questionnaire ( N = 1618) to the final sample ( N = 355). Findings are likely to be most generalizable to people who are highly motivated to learn lucid dreaming. The use of self-report measures is a potential limitation to the findings that lucid dream induction did not adversely affect sleep quality. This is because the excitement of having a lucid dream may have counteracted feelings of tiredness upon waking. Another limitation is that the large number of statistical tests increases the familywise error rate. Results that are only marginally significant should therefore be interpreted with caution.

Directions for Future Research

Further research is needed to gain a deeper understanding of the mechanisms through which the MILD and SSILD techniques work. This may yield potential avenues for refinement. One approach could be to ask participants to describe in detail exactly how they become lucid in each lucid dream, including whether they thought about or practiced the techniques in their dreams prior to becoming lucid. Sleep laboratory research could investigate whether the SSILD technique causes increased cortical activation and whether this activation is correlated with lucid dreaming. Further research is also needed to investigate the effectiveness of practicing the MILD, SSILD and RT techniques over longer periods of time than the single week used in the present study, and the effects of lucid dreaming training on sleep quality.

Findings provide further evidence that superior general dream recall is conducive to lucid dreaming. Thus, it may be possible to increase the effectiveness of cognitive lucid dream induction techniques using drugs and supplements that enhance dream recall. In a small pilot study by Ebben et al. (2002) , ingestion of vitamin B6 (pyridoxine hydrochloride) prior to sleep was found to significantly enhance dream recall compared to placebo. In a larger replication study ( Aspy et al., 2018 ), participants recalled 64.1% more dream content when they took 240 mg of vitamin B6 directly before bed compared to placebo. Future research should compare the effectiveness of cognitive lucid dream induction techniques both with and without vitamin B6 before bed.

Currently, the most evidence-based substance for inducing lucid dreams is Galantamine, a widely used and well-tolerated acetylcholine-esterase inhibitor that influences the REM-on neurotransmitter acetylcholine ( LaBerge, 2004 ; Yuschak, 2006 ; Sparrow et al., 2016 , 2018 ; LaBerge et al., 2018 ). In the most recent study by LaBerge et al. (2018) , lucid dreaming occurred on 42% of nights when participants ingested 8 mg of Galantamine in addition to practicing the MILD technique and, in most cases, using an external LED light stimulation device. According to Yuschak (2006) , Galantamine is more effective when combined with Alpha-GPC, a form of choline that acts as a precursor to acetylcholine. It may be even more effective to take vitamin B6 before bed and then a combination of Galantamine and Alpha-GPC during a WBTB period 5 h after going to sleep, before practicing a cognitive lucid dream induction technique such as MILD or SSILD and then returning to sleep within 5–10 min. An external light stimulation device may further increase the success rate (see Mota-Rolim et al., 2019 ). This combination of cognitive, pharmacological and external stimulation techniques is currently the most promising approach to lucid dream induction.

Future studies should operationalize the way in which lucid dream induction techniques are practiced, use valid and reliable measures of dream recall, and avoid the many methodological limitations of prior lucid dream induction studies (see Stumbrys et al., 2012 ; Aspy et al., 2017 ). These methodological issues – especially the inconsistency in the way that lucid dreaming rates are operationalized – are a major impediment to research progress. The present author implores other researchers to, at minimum, report the L DRF Lucid rate based on daily logbook observations in all lucid dream induction studies, so that the effectiveness of techniques can be determined and compared (see section “Materials”).

Findings provide the strongest evidence to date that the MILD technique is effective for inducing lucid dreams. Findings indicate that the SSILD technique is similarly effective. In contrast, RT appears to be an ineffective lucid dream induction technique – at least for short periods such as 1 week in the present study.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the School of Psychology Human Research Ethics Committee at the University of Adelaide. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

DA-H was the sole author of this study and was solely responsible for all tasks involved. This includes experiment design, experiment management, data collection, data analysis, literature review, and manuscript authorship.

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.

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Keywords : lucid dreaming, lucid dream induction techniques, dream recall, reality test, sleep quality

Citation: Adventure-Heart DJ (2020) Findings From the International Lucid Dream Induction Study. Front. Psychol. 11:1746. doi: 10.3389/fpsyg.2020.01746

Received: 19 December 2019; Accepted: 24 June 2020; Published: 17 July 2020.

Reviewed by:

Copyright © 2020 Adventure-Heart. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Denholm Jay Adventure-Heart, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Science News

Here’s what lucid dreamers might tell us about our sleeping minds.

Dreams are one of the most universal yet elusive human experiences

illustration of a person wearing pajamas flying through the air with blue a pink hues

Most people rarely lucid dream. But some people can do it regularly and even gain control over these alternate realities.

RUNE FISKER

Are lucid dreams real?

Tales of lucid dreams date back to antiquity. Aristotle may have been the first to mention them in Western literature in his treatise On Dreams . “Often when one is asleep,” he wrote, “there is something in consciousness which declares that what then presents itself is but a dream.”

If Aristotle had lucid dreams often, though, he was probably an outlier. Only about half of people say they’ve ever had a lucid dream , while a mere 1 percent or so say they lucid dream multiple times a week. Modern enthusiasts use various techniques to boost their likelihood of lucid dreaming — such as repeatedly telling themselves before bedtime that they will have a lucid dream, or making a habit of checking whether they’re awake several times a day in the hopes that this routine carries over into their dreams, where a self-check may help them realize they’re asleep. But those practices don’t guarantee lucidity.

The rarity of lucid dreaming may be why modern science took some convincing that it’s even real. For millennia, lucid dreamers’ own testimonies were the only evidence that someone could be self-aware while catching z’s. Some scientists wondered if so-called lucid dreams were just brief waking hallucinations between bouts of sleep.

But within the last few decades, experiments have offered proof that lucid dreams are truly what they seem. It turns out, when someone in a dream purposely sweeps their gaze all the way left, then all the way right, their eyes can match those movements behind closed lids in real life. These motions, measured by electrodes near the eyes, stand out from the smaller optical jitters typical of REM sleep, when most lucid dreams happen. This gives dreamers a crude way to signal they’ve become lucid or send other messages to the outside world ( SN: 9/19/81, p. 183 ). Meanwhile, brain waves and muscle paralysis throughout the rest of the body confirm that the dreamer is indeed asleep.

Eyes on eye movements

A person’s eyes can smoothly track left and right movements when they are awake or in a lucid dream. But when someone closes their eyes and tries to imagine tracking that motion, their eyes pan in small jumps, suggesting that lucid dreams are experienced more like waking perception.

three graphs show the direction of eye movement during waking perception, lucid dreaming and imagination

Neuroscientists are just beginning to realize the potential of that line of communication. Lucid dream research “has been enjoying a renaissance over the last decade,” says neuroscientist Tore Nielsen. He directs the Dream & Nightmare Laboratory at the Center for Advanced Research in Sleep Medicine in Montreal. “This renaissance has made it one of the cutting-edge areas of dream study.”

One research team recently deployed experienced lucid dreamers to find out whether dream imagery is more like real-life visuals or imagined ones. While asleep, six lucid dreamers moved their thumbs in either a circle or a line (or both) and traced that motion with their eyes. Participants repeated the same task while awake with their eyes open and in their imaginations with their eyes closed. People’s gazes panned jerkily when they tracked the imagined movements, as though they were viewing something in low resolution. But in dreams, people’s eyes tracked the movements smoothly just as in real life, the team reported in 2018 in Nature Communications .

“It’s been debated really all the way back to the ancient Greeks, are dreams more like imagination, or is it more like perception?” says study coauthor Benjamin Baird, a cognitive psychologist and neuroscientist at the University of Texas at Austin. “The smooth tracking data suggests that, at least in that sense, the imagery is more like perception.”

This and other early experiments offer a taste of what dreamstronauts could teach us. But any conclusions based on just a handful of dreamers have to be taken with a grain of salt. “They’re more like proof-of-concept studies,” says Michelle Carr, a cognitive neuroscientist at the Center for Advanced Research in Sleep Medicine. “It needs to be studied in bigger samples.”

That means finding — or creating — more expert lucid dreamers.

Strategies for lucid dreaming

If you want to have a lucid dream, there are a few strategies you can use to up your chances. Besides regularly questioning whether you’re awake and setting an intention before bed to become lucid, you can keep a dream diary. Getting familiar with common characters, events or themes in your dreams may help you recognize when you’re dreaming. Some aspiring lucid dreamers also use a tactic called “wake-back-to-bed.” They wake up extremely early in the morning, stay up for a while, then get more shut-eye. That jolt of alertness right before tumbling back into REM sleep may help them become lucid in a dream.

Such techniques can be hit-or-miss, though. And data on their effectiveness are still pretty murky, Baird says. One study with about 170 Australians, for instance, suggested that checking if you’re awake, setting an intention to become lucid and doing wake-back-to-bed all together can increase your odds of lucid dreaming . But it wasn’t as clear if using just one or two of those practices worked.

Investigations by Baird and others have shown that the supplement galantamine promotes lucid dreaming , probably by fiddling with neurotransmitters involved in REM sleep. But galantamine can be saddled with side effects such as nausea. And although lucidity itself does not appear to spoil sleep quality , the long-term effects of using galantamine are not well-known. “Personally, I wouldn’t be mucking around with my neurotransmitters every night,” Baird says.

In 2020, Carr and colleagues reported that they’d coaxed 14 of 28 nappers to become lucid in the lab — including three people who’d never before lucid dreamed — no drugs necessary. Before falling asleep, participants learned to associate a cue, such as a series of beeps, with self-awareness. Hearing the same sound again while sleeping reminded them to become lucid. Carr is particularly interested in finding out whether lucid dreaming can help people conquer nightmares, but researchers at Northwestern use the sensory cue strategy to get more lucid emissaries to carry out dream tasks for their experiments.

Galantamine as a dream aid

For three nights, 121 people combined commonly used strategies for lucid dreaming with one of three doses of galantamine. Those who took higher doses of galantamine were more likely to have lucid dreams.

Effect of galantamine dose on likelihood of lucid dreaming

graph showing the effect of galantamine dose in milligrams on likelihood of lucid dreaming, measured by the percentage of study participants who reported at least one lucid dream

“Our method is kind of a shortcut,” says Northwestern cognitive neuroscientist Ken Paller. It doesn’t require a lot of mental training or the grueling sleep interruptions that some other lucid dreaming techniques do.

Another shortcut for researchers is to recruit dreamers from a special slice of the population: people with narcolepsy, who are liable to fall asleep suddenly during the day.

“They’re just champions at lucid dreams,” says Isabelle Arnulf, a sleep neurologist who heads the sleep disorders clinic at Pitie-Salpetriere University Hospital in Paris.

In 2018, Arnulf’s team reported a study where 18 of 21 narcolepsy patients signaled lucidity during lab naps . Even with those impressive numbers, a couple of lucid nappers still couldn’t control their dreams well enough to complete their assignment: to do something in a dream that made them briefly stop breathing, such as swimming underwater or speaking. One said after waking that they’d simply forgotten to stop breathing while diving off a cliff, while another said they tried to speak but couldn’t get any words out.

Staying lucid and successfully wrangling dream scenarios present challenges for lucid dreamers — and the scientists relying on them. In one study, lucid dreamers instructed to fill a dream room with objects, such as a clock and a rubber snake, ran into problems ; the clock spun wildly, or the snake slithered away. In another experiment, lucid dreamers asked to practice throwing darts were waylaid by only having pencils to throw or being pelted with darts by a nasty doll.

“It’s a lot harder than just passively lucid dreaming in your bed,” says Mazurek, who has participated in several lucid dream studies at Northwestern. “You realize, ‘OK, I have to stabilize the dream. I have to remember what the task is. I have to do the task without the dream falling apart.’ ”

Missions to the moon may be hard, but at least astronauts don’t have to worry about forgetting who or where they are, or their spaceship suddenly turning into a banana.

Despite these challenges, lucid dream expeditions are forging ahead — and fast. In fact, an international crew of dreamfarers, including Mazurek, recently embarked on their most ambitious mission yet.

An illustration of a patient lucid dreaming surrounded by scientists and charts. Swirling above are another depiction of the patient holding a clock with snakes and other dream figures swirling around.

Real-time dream science

When it comes to getting on-the-ground data, interviewing dreamers in real time is, well, the dream. Instead of just sitting back and watching dreamers do various activities, researchers could ask these agents about their experiences moment to moment, painting the realm of dreams in sharper detail than ever before.

“Reports of dreamed sensations, [such as] tasting certain foods, can be compared with those of actual sensations,” Nielsen says. “Similarly, one could test whether sexual pleasure, certain sounds or other types of experiences are accurately simulated.” These details, he says, might help “probe the limits and mechanisms of dream production.”

Karen Konkoly is especially excited about giving people assignments mid-dream. Say researchers want to know how much dreams help with creative problem-solving. If dreamers are assigned a problem before sleep, they’re liable to mull it over as they nod off. “Even if it feels like the lucid dream, maybe it’s really the time as you’re falling asleep that helped you solve the problem,” says Konkoly, a cognitive neuroscientist at Northwestern. Airdropping a puzzle straight into a dream could better isolate the usefulness of that specific part of sleep.

There’s a whole medley of theories about why people dream, from honing skills to tapping into creativity to processing memories or emotions. “But if you can’t control the dream in real time and then study the outcome, then you never know … if the dream is really doing anything,” Konkoly says. So a few years ago, she, Arnulf, Dresler and others decided to find out if people can receive and respond to outside input while dreaming.

Thirty-six people took snoozes at Northwestern, Arnulf’s lab, Dresler’s lab or another lab that was in Germany. Once sleepers signaled that they were lucid, researchers spoke yes-or-no questions or math problems in the sleepers’ ears. Or, for the Germans, lights flashing different colors conveyed math questions in Morse code. Before conking out, dreamers were told to answer whatever questions they received with eye signals or by smiling and frowning.

“Facial muscles are less inhibited than other muscles during REM sleep,” Arnulf explains. Someone smiling in a dream may not make that expression in real life, but electrodes on the face can register tiny corresponding muscle twitches.

Out of 158 attempts to interrogate lucid dreamers, 29 total correct responses came from six different people . Those six ranged from newbie to frequent lucid dreamers, including Mazurek, who heard scientists’ questions while dreaming he was in a Legend of Zelda game. The rest of the attempts yielded five wrong answers, 28 ambiguous ones and 96 nonresponses.

When Konkoly first saw someone correctly answer a question in their sleep, “my first reaction was to not believe it.” But for 26 of those 29 correct responses, a panel of independent sleep experts unanimously agreed that the dreamers were in the throes of REM sleep when they replied. Nearly 400 attempts to reach sleepers who hadn’t signaled lucidity netted a single correct response — bolstering the researchers’ confidence that correct answers from lucid dreamers weren’t flukes. The results appeared in 2021 in Current Biology .

Answering questions during a dream

While dreaming, Christopher Mazurek signaled the outside world by sweeping his eyes left and right. Electrodes on his face recorded those motions. On the graph below, Mazurek’s eye motions that indicate he is lucid appear as three big up-down sweeps. Eye signals answering “2” to researchers’ simple math question appear as two big up-down sweeps.

Lucid dreamer’s eye movements during a mid-dream conversation

“I was astonished,” says Robert Stickgold, a cognitive neuroscientist at Harvard Medical School who studies dreams but not lucid ones. “I had no question but that these people are in fact listening and are in fact having lucid dreams at the time of the communication — and that opens up all sorts of possibilities.”

Arnulf and others have since asked lucid dreamers to smile or frown as their dreams became more or less pleasant with the goal of understanding how dreamers experience emotion. Another study, not yet published, tracked when lucid dreamers answered or ignored researchers’ questions to see how people tuned in and out of the real world while dreaming. Knowing which signals break the dream-reality barrier could help “uncover the mechanism of the brain’s disconnection from the external world — which is huge,” Baird says. It could even be relevant for other states of unconsciousness, he adds, such as when someone is put under for surgery.

Limits of lucidity

Even if researchers get all the expert lucid dreamers they need to run all their desired experiments, there’s still one major sticking point to this whole field of study.

“The biggest issue is how far can you push these results to dreaming in general,” Stickgold says. Imagine, for instance, that lucid dreamers get better at a skill by practicing it in their dreams. It’s not clear that people who just happen to have normal dreams about doing those activities, without self-awareness, would reap the same rewards. “It’s a little bit like recruiting major league baseball players to give you some baseline data on how far people can throw balls,” Stickgold says.

Existing data do suggest that lucid dreamers may have access to parts of the brain that normal dreamers don’t. The lone case study comparing fMRIs of someone’s lucid and nonlucid REM sleep hints that brain areas linked with self-reflection and working memory are more active during lucidity. But those data come from just one person, and it’s not yet clear how such differences in brain activity would affect the outcomes of lucid dream experiments.

Brain clues to lucid dreams

Functional MRI scans of one sleeper’s brain during lucid and nonlucid sleep showed that some brain areas (highlighted) may be more active during lucid dreams than during normal sleep.

The lateral parietal cortex is involved in working memory.
The dorsolateral prefrontal cortex and frontopolar cortex are involved in working memory and introspection.
Activity near the temporal cortex may make lucid dreams brighter and more detailed than normal dreams.

Some researchers, including Dresler, resist the idea that lucid dreams are profoundly different from nonlucid ones. “Lucid dreaming is not a strict all-or-nothing phenomenon,” he says, with people often fluttering in and out of awareness. “That suggests that lucid and nonlucid dreaming are in principle something very similar on the neural level and not two completely different animals.”

Perhaps lucidity affects some aspects of the dream experience but not all of them, Baird adds. In terms of how dreams look, he says, “it would be very, very surprising if it was somehow completely different when you become lucid.”

A more thorough inventory of the differences in brain activity between lucid and nonlucid dreams might help settle these questions. But even if lucid dreams don’t represent dreams in general, Nielsen still thinks they’re worth studying. “It is a type of consciousness that has intrigued and amused people for centuries,” he says. “It would be important for science to understand how and why humans have this extraordinary capacity for intentional world simulation.”

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Open access
Published: 12 December 2018

Frequent lucid dreaming associated with increased functional connectivity between frontopolar cortex and temporoparietal association areas

Benjamin Baird 1 ,
Anna Castelnovo 1 , 2 ,
Olivia Gosseries 1 , 3 &
Giulio Tononi 1

Scientific Reports volume 8 , Article number: 17798 ( 2018 ) Cite this article

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Consciousness

Humans typically lack awareness that they are dreaming while dreaming. However, at times a remarkable exception occurs and reflective consciousness can be regained while dreaming, referred to as lucid dreaming. While most individuals experience lucid dreams rarely there is substantial variance in lucid dream frequency. The neurobiological basis of lucid dreaming is unknown, but evidence points to involvement of anterior prefrontal cortex (aPFC) and parietal cortex. This study evaluated the neuroanatomical/neurofunctional correlates of frequent lucid dreams and specifically whether functional connectivity of aPFC is associated with frequent lucid dreams. We analyzed structural and functional magnetic resonance imaging from an exceptional sample of fourteen individuals who reported ≥3 lucid dreams/week and a control group matched on age, gender and dream recall that reported ≤1 lucid dream/year. Compared to controls, the frequent lucid dream group showed significantly increased resting-state functional connectivity between left aPFC and bilateral angular gyrus, bilateral middle temporal gyrus and right inferior frontal gyrus, and higher node degree and strength in left aPFC. In contrast, no significant differences in brain structure were observed. Our results suggest that frequent lucid dreaming is associated with increased functional connectivity between aPFC and temporoparietal association areas, regions normally deactivated during sleep.

EEG microstates of dreams

Lucie Bréchet, Denis Brunet, … Christoph M. Michel

Repetitive negative thinking in daily life and functional connectivity among default mode, fronto-parietal, and salience networks

D. M. Lydon-Staley, C. Kuehner, … D. S. Bassett

Psilocybin enhances insightfulness in meditation: a perspective on the global topology of brain imaging during meditation

Singer Berit, Daniel Meling, … Milan Scheidegger

Introduction

For reasons not currently understood, humans are typically unaware that they are dreaming while dreaming. At times, however, a remarkable exception occurs and we can become aware of the fact that we are dreaming, a state referred to as lucid dreaming 1 . During lucid dreams, one becomes aware that one is dreaming while remaining physiologically asleep and immersed within a dream environment that often appears strikingly realistic. In addition to the metacognitive awareness of one’s state of consciousness, during lucid dreams it is also common to regain episodic memory for waking life as well as the ability to volitionally control actions within the dream (e.g. 2 , 3 ). Despite initial skepticism from some scientists and philosophers, lucid dreaming has been demonstrated to be objectively verifiable through volitional eye movement signals which can be recorded in the electrooculogram during polysomnography-verified REM sleep 4 (for replications and extensions see, e.g., refs. 5 , 6 , 7 ; for recent implementations see, e.g., refs. 8 , 9 , 10 ). For most individuals lucid dreams spontaneously occur infrequently, however 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 experience lucid dreams several times per week or in some cases every night 11 , 12 . This variation invites the question of whether the frequency of lucid dreams is related to individual differences in anatomical or functional properties of the brain.

The prefrontal cortex (particularly the lateral and rostrolateral regions), parietal cortex and lateral middle temporal cortex show low regional cerebral blow flow (rCBF) throughout sleep, including during REM sleep 13 , 14 , 15 , the stage of sleep most strongly associated with dreaming. Hypoactivity of these regions has been postulated to underlie the diminished self-awareness and volitional control during dreaming 15 , 16 . Consistent with this, a functional magnetic resonance imaging (fMRI) case study found increased BOLD signal in many of these same regions during lucid compared to non-lucid REM sleep, including the anterior prefrontal cortex (aPFC), bilateral inferior parietal lobule (IPL), precuneus and inferior/middle temporal gyrus (ITG/MTG) 9 . However, these results should be interpreted cautiously given that they are derived from a single subject, and no group-level fMRI study of lucid REM sleep has yet been undertaken. EEG studies have also reported increased activity in the beta band over parietal regions 17 or gamma band in frontal regions 18 during lucid compared to baseline REM sleep. However, overall EEG studies of lucid dreaming show considerable discrepancies and at the current time these results should be interpreted cautiously given methodological issues such as low statistical power 19 , 20 .

Despite these caveats, evidence linking frontopolar and parietal regions to lucid dreaming is consistent with the role of these regions in metacognitive functions. Across the literature, a convergence of evidence indicates that aPFC in particular is a critical part of the neuroanatomical basis of metacognitive processes. 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 21 , 22 . Individuals can also learn to voluntarily modulate activity in aPFC through a metacognitive awareness strategy 21 . Furthermore, inter-individual variance in metacognitive ability has also been linked to aPFC gray matter volume 23 , 24 and aPFC functional connectivity 24 . Finally, patients with damage to this region frequently display metacognitive deficits such as an inability to monitor disease symptoms or accurately appraise their cognitive functioning 25 , 26 , similar to the lack of metacognitive insight into the global state of consciousness characteristic of non-lucid REM sleep dreams 27 .

As the initiation of lucid dreaming requires one to achieve metacognitive awareness of the state of consciousness one is in, these findings motivate the hypothesis that individual differences in the anatomy or functional connectivity of aPFC could be associated with the frequency of lucid dreams. Indeed, lucid dreaming presents a unique experimental paradigm to further explore the link between aPFC and metacognitive awareness 28 , 29 . In further support of a connection between the metacognitive functions of aPFC and lucid dreaming, a recent study found increased gray matter volume in two regions of the frontal pole in individuals who scored higher on a scale assessing the frequency of lucid dreams and/or dream content hypothesized to be related to lucidity 30 . Additionally, these same regions also showed increased BOLD activation in the monitoring component of a metacognitive thought-monitoring task. However, a limitation of the study was a lack of specific assessment of lucid dream frequency in the “high lucidity” and “low lucidity” groups (lucid dream frequency for the two groups was not reported). Furthermore, the groups were distinguished based on a median split on scores to a composite measure that also included elements that may have varied with dream recall frequency, making it unclear whether the results could have been partly influenced by differences in dream recall. In summary, research points to the possibility that frontoparietal cortex, and aPFC in particular, could be associated with lucid dream frequency. However, an analysis of brain structure and function in individuals who experience frequent lucid dreams, while also controlling for dream recall frequency, is needed.

In the current research we evaluated an exceptional sample of individuals who reported lucid dreams spontaneously in the range of approximately every other night to multiple times per night compared to a control group matched on age, gender and dream recall frequency but who reported lucid dreams once per year or less. The primary aim of the study was to test whether differences in brain structure and/or functional connectivity are associated with frequent lucid dreams while also controlling for dream recall frequency. Based on the research reviewed above, our primary analysis investigated whether individuals who have frequent lucid dreams would show increased gray matter density and/or resting-state functional connectivity of aPFC. For analysis of structural data, we first employed a whole-brain voxel-based morphometry (VBM) analysis 31 , followed by a region-of-interest (ROI) analysis of the aPFC regions reported to be associated with lucid dream frequency in a previous study 30 . For resting-state functional connectivity (rsfcMRI) analysis, we employed seed-based whole-brain functional connectivity analysis of aPFC, based on the aPFC activation peak reported in the fMRI case study of lucid REM sleep 9 , which allowed us to explore differences in aPFC functional connectivity with all other brain regions between groups. We additionally employed a follow-up whole-brain graph-theoretic analysis to examine differences in functional network properties across all brain areas between groups in a data-driven approach, as well as evaluated differences in within-network and between-network connectivity in large-scale resting-state networks (LSNs) 32 . Finally, we evaluated several additional cognitive variables which have been hypothesized to be associated with lucid dreaming and have been linked to PFC function, including working memory capacity, trait mindfulness and prospective memory (e.g., refs. 2 , 33 , 34 ), in order to test for between-group differences and, if necessary, to be able to control for these variables in our MRI analysis.

Demographic and behavioral results

The mean age for both groups was 22.6 ± 5.4 [M ± SD] (range = 18–34) and both groups were composed of 5 males and 9 females. There was no significant difference in dream recall between the control group (median = 5–6 per week; IQR = 2) and lucid dream group (median = 7 per week; IQR = 1) [ Z = 1.70, p = 0.11, Mann-Whitney U-test; see Methods for details on dream recall case-control matching]. All 28 participants reported high dream recall (≥3–4 per week). The frequent lucid dream group reported significantly more lucid dreams (median = 5–6 per week; IQR = 1) compared to the control group (median = 0 per week; IQR = 0) [ Z = 4.68, p < 10 −6 , Mann-Whitney U-test]. The frequent lucid dream group reported a median of 75 lucid dreams in the last 6 months, a median of 90 lucid dreams for the highest number of lucid dreams in any 6-month period, and reported experiencing lucid dreams on average for 9.5 ± 5.8 [M ± SD] years. No significant differences between groups were observed for working memory capacity (OSpan, RotSpan, SymSpan), or questionnaire assessments of mind-wandering frequency, prospective or retrospective memory or trait mindfulness (all p ≥ 0.25, two-tailed independent samples t -test; Table 1 ).

Voxel-based morphometry (VBM)

No suprathreshold clusters were observed for either the frequent lucid dream group contrasted with the control group or the control group contrasted with the frequent lucid dream group at the whole brain level either for raw gray matter density values or after proportional scaling gray matter values by total intracranial volume (all p > 0.05, two-tailed independent samples t -test, corrected for multiple comparisons at the cluster level). No significant differences in gray matter density were observed for ROIs in left prefrontal cortex ( t (26) = −0.47, p = 0.65, two-tailed independent samples t -test), right prefrontal cortex ( t (26) = −0.36, p = 0.72, two-tailed independent samples t -test), or the left ( t (26) = −0.40, p = 0.69, two-tailed independent samples t -test) or right ( t (26) = −1.31, p = 0.20, two-tailed independent samples t -test) hippocampus based on the regions reported in ref. 30 . Total hippocampal volume (extracted from FreeSurfer segmentation) also showed no significant differences between groups for either left ( t (26) = 0.14, p = 0.89, two-tailed independent samples t -test) or right ( t (26) = 0.32, p = 0.75, two-tailed independent samples t -test) hippocampus.

Seed-based whole-brain resting-state functional connectivity

There were no significant differences in in-scanner head motion (mean framewise displacement) between the frequent lucid dream group ( M = 0.07, SD = 0.03) and control group ( M = 0.07, SD = 0.04) ( t (26) = 0.72, p = 0.48, two-tailed independent samples t -test). As shown in Fig. 1 and Table 2 , compared to the control group, the frequent lucid dream group showed significantly increased functional connectivity between left aPFC and five clusters: the left and right inferior parietal lobule (IPL), left and right middle temporal gyrus (MTG) and right inferior frontal gyrus (IFG) (all p < 0.05, two-tailed independent samples t -test, corrected for multiple comparisons at the cluster level; Table 2 ). The frequent lucid dream group also displayed reduced functional connectivity between left aPFC and the bilateral insula (all p < 0.05, two-tailed independent samples t -test, corrected for multiple comparisons at the cluster level; Table 2 ). No significant differences in functional connectivity were observed between groups for right aPFC (all p ≥ 0.22, two-tailed independent samples t -test corrected for multiple comparisons at the cluster level). Although aPFC connectivity was the main target of investigation in the current study, we also performed a supplementary seed-based functional connectivity analysis on other regions identified in ref. 9 to increase BOLD signal during lucid REM sleep, including left/right IPL, MTG and precuneus. The frequent lucid dream group showed increased connectivity between left IPL and left MTG, right lingual gyrus; right IPL and left aPFC, right PCC; right MTG and left aPFC, left MFG, and decreased connectivity between right IPL and right MFG, left insula, left precentral gyrus and left SMC (all p < 0.05, two-tailed independent samples t -test, corrected for multiple comparisons at the cluster level; Supplementary Table 1 ). No other suprathreshold clusters were identified.

Seed-based resting-state functional connectivity differences between frequent lucid dream and control groups. Top panel: ( a ) Seed region of left aPFC with significant differences between groups. To estimate connectivity, spherical ROIs of 6 mm radius were defined in aPFC based on the peak voxel reported in Dresler et al . 9 which had increased fMRI BOLD signal response during signal-verified lucid REM sleep dreaming. (b) The frequent lucid dream group showed increased resting-state functional connectivity between left aPFC and the bilateral angular gyrus (AG), bilateral middle temporal gyrus (MTG) and right inferior frontal gyrus (IFG). All clusters are significant at p < 0.05, corrected for multiple comparisons at the cluster level. Middle panel: Volume slices illustrating bilateral MTG and IFG results. Bottom panel: Volume slices illustrating bilateral AG results.

IPL/IPS subdivision analysis

We performed a follow-up analysis on the clusters in left and right IPL in order to characterize the overlap between these clusters and anatomical subdivisions of the angular gyrus (PGa/PGp) and intra-parietal sulcus (hlP1, hlP2 and hlP3) (see Methods: Angular gyrus (AG)/intra-parietal sulcus (IPS) subdivision analysis) . The cluster peak for right parietal cortex was in the anterior AG (PGa) and the overlap between the functional cluster and the cytoarchitectonic maps was 47.3% for PGa, 24.7% for PGp, 4.2% for hlP1 and 0.6% for hlP3. The cluster peak for left parietal cortex was also in PGa and the overlap between the functional cluster and the cytoarchitectonic maps was 34.3% for PGa, 19.7% for PGp, 6.7% for hlP1 and 0.2% for hlP3 (Fig. 2 ). Frequent lucid dreamers showed significantly increased mean functional connectivity between left aPFC and left PGa ( t (26)3.20, p = 0.004, two-tailed independent samples t -test), right PGa ( t (26) = 2.46, p = 0.02, two-tailed independent samples t -test) and right hlP1 ( t (26) = 2.59, p = 0.02, two-tailed independent samples t -test). No other anatomical subdivisions of AG/IPS showed significant differences between groups (all p ≥ 0.06, two-tailed independent samples t -test).

Clusters in lateral parietal cortex showing increased resting-state functional connectivity with aPFC in the frequent lucid dream group overlaid with cytoarchitectonic subdivisions of IPL/IPS. The angular gyrus can be subdivided into anterior (PGa; blue outline) and posterior (PGp; white outline) subdivisions based on cytoarchitecture. IPS can be divided into three subdivisions (hlP1 on the posterior lateral bank- yellow outline, hlP2 which is anterior to hIP1- purple outline, and hlP3 which is posterior and medial to both subdivisions- green outline). The cluster peak as well as maximal cluster extent localized bilaterally to a dorsal segment of the anterior angular gyrus (PGa). Region-of-interest (ROI) analysis revealed increased connectivity between left aPFC and bilateral PGa (blue outline) [all p < 0.05].

Large-scale functional resting-state networks analysis

We next tested whether connectivity within and between established LSNs differed between groups. We first computed the average connectivity (Fisher-transformed correlation coefficients) within and between all pairs of nodes within 7 distinct systems identified in a meta-analysis 32 (see Methods: Large-scale networks analysis ). No significant differences in connectivity were observed between groups within any LSN (all p ≥ 0.29, two-tailed independent samples t -test) (Supplementary Fig. 1a ). There were also no differences in between-network connectivity between groups (all p ≥ 0.16, two-tailed independent samples t -test). Next, we evaluated the overlap between our seed-based functional connectivity results and a 17-network parcellation of human brain connectivity 35 . The regions identified in our functional connectivity analysis overlapped with both default mode network (DMN) and frontoparietal control networks (FPCN), with the strongest overlap occurring within a subsystem of the FPCN (Supplementary Fig. 1b ). We followed up this spatial overlap analysis by evaluating the connectivity within the FPCN subsystem that showed the largest overlap with the functional connectivity results, based on a 400 node parcellation of the 17 networks 36 . However, no significant difference in average network connectivity (average across all FPCN subsystem nodes) was observed within this network between groups ( t (26) = −1.08, p = 0.29, two-tailed independent samples t -test). Thus, while the frequent lucid dream group showed increased functional connectivity of left aPFC with regions of IPL and MTG that overlapped with this FPCN subsystem, there was no difference in the average connectivity of this subsystem between groups.

Whole-brain graph-theoretic analysis

To evaluate whole-brain differences in network and topological properties, we next parcellated the brain into 1015 regions according to the Lausanne 2008 atlas 37 , 38 and performed graph-theoretic analysis. Graphs were thresholded over a range of connection densities (0.05 ≤ δ ≤ 0.35) for which the area under the curve (AUC) was computed for each node. Multiple comparisons were corrected against a max t distribution across all nodes in the network (see Methods: Graph-theoretic network analysis ). Node degree and strength showed significant differences between groups in left aPFC after correcting for multiple comparisons, with higher node degree ( t obs = 4.58, p obs = 0.0003, p corr = 0.03, two-tailed independent samples t -test, max t corrected) and node strength ( t obs = 4.40, p obs = 0.0003, p corr = 0.04, two-tailed independent samples t -test, max t corrected) in the frequent lucid dream group compared to the control group (Fig. 3 ). No differences in betweenness centrality or eigenvector centrality were observed between groups for any node (all p > 0.05, two-tailed independent samples t -test, max t corrected).

Whole-brain graph-theoretic network differences between frequent lucid dream and control groups. ( a ) aPFC node (red sphere) with significantly higher degree ( k ) and strength ( s ) in the frequent lucid dream group from axial (top panel) and left sagittal (bottom panel) views. ( b ) Left panel: Mean node degree (top row) and strength (bottom row) over density (cost factor) thresholds 0.05 ≤ δ ≤ 0.35 (step size 0.01) for frequent lucid dream (blue triangles) and control groups (red circles) for significant node shown in panel a. Shaded regions show 95% confidence intervals for each δ. Right panel: boxplots of area under the curve (AUC) for frequent lucid dream and control groups. The bottoms and tops of the boxes show the 25th and 75th percentiles (the lower and upper quartiles), respectively; the inner white band shows the median; and the whiskers show the most extreme data points not considered outliers (outliers are plotted separately with red squares). Asterisks indicate significant differences ( p < 0.05) between conditions with a nonparametric bootstrap test after correcting for multiple comparisons against a surrogate max t distribution across all nodes.

Summary of main findings

To the best of our knowledge, the current study is the first to evaluate differences in brain structure and functional connectivity of individuals who experience lucid dreams with high frequency. We found that compared to a control group matched on age, gender and dream recall frequency, individuals who reported lucid dreams spontaneously approximately every other night or more had increased resting-state functional connectivity between the left anterior prefrontal cortex (aPFC) and the bilateral angular gyrus (AG), bilateral middle temporal gyrus (MTG) and right inferior frontal gyrus (IFG). 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. In contrast to these functional changes, we did not observe any differences in brain structure (gray matter density) in any brain area between groups (c.f. ref. 30 ). Furthermore, no differences were observed between frequent lucid dream and control groups in behavioral or questionnaire measures of working memory capacity, prospective memory, mind-wandering frequency or trait mindfulness.

Our results converge with a recent fMRI case study of lucid dreaming, which found that a highly similar network of brain areas increased fMRI BOLD signal during lucid compared to baseline REM sleep, including bilateral aPFC, bilateral ITG/MTG, and bilateral medial/lateral parietal cortex (including AG) 9 . These same brain areas, particularly aPFC and IPL/AG, show reduced regional cerebral blood flow (rCBF) 13 , 14 , 39 during REM sleep compared to waking (see ref. 15 for a review). Hypoactivity of these regions coupled with preserved or increased activity in limbic/paralimbic structures and extrastriate cortices has been postulated to facilitate a mode of brain function conducive to hallucinatory dream mentation but diminished higher-order consciousness/self-awareness 40 , 41 . The current results suggest that increased functional integrity during wakefulness between aPFC and temporoparietal association areas—all regions that show suppressed activity in REM sleep and increased activity during lucid REM sleep—is associated with the tendency to have frequent lucid dreams.

Lucid dreaming and brain connectivity

Becoming lucid during REM sleep dreaming involves making an accurate metacognitive judgment about the state of consciousness one is in, often by recognizing that the correct explanation for an anomaly in the dream is that one is dreaming 1 , 2 . The finding that changes in the functional connectivity of aPFC is associated with lucid dream frequency is therefore consistent with a large literature linking this region to metacognitive functions, including the evaluation of one’s thoughts and feelings 21 , 42 and variance in the capacity to make accurate metacognitive judgments 23 , 24 .

Given the link to metacognition, it has been speculated that lucid dreaming is linked to neural systems that regulate executive control processes, in particular the frontoparietal control network (FPCN) 27 , 29 . The FPCN is a large-scale brain network that is interconnected with both the default mode network (DMN), which is linked to internal aspects of cognition, such as autobiographical memory 43 , 44 , spontaneous thought 45 , 46 , and self-referential processing 47 , and the dorsal attention network (DAN), which is involved in visuospatial perceptual attention 48 , 49 . Being spatially interposed between these two systems, the FPCN is postulated to integrate information coming from the opposing DMN and DAN systems by switching between competing internally and externally directed processes 49 .

Based on a parcellation of 17 resting-state networks in the human brain, which distinguished potentially separable FPCN networks 35 , a recent study found that the FPCN could be fractionated using hierarchical clustering and machine learning classification into two distinct subsystems: FPCNa, which is more strongly connected to the DMN than the DAN and is linked to introspective processes, and FPCNb, which is more strongly connected to the DAN than the DMN and is linked to regulation of perceptual attention 50 . The current results show that frequent lucid dreams are associated with increased functional connectivity between aPFC and a network of regions that showed substantial overlap with the FPCN sub-network corresponding most closely to FPCNa 35 , 50 . However, neither connectivity within FPCN broadly defined through meta-analysis nor connectivity within FPCN sub-networks as defined through parcellation of resting-state networks was significantly associated with frequent lucid dreaming in the current study. This may be attributed to both the partial overlap of the regions that showed increased aPFC connectivity in lucid dreamers with FPCN networks, as well as the fact that lucid dream frequency was associated with increased connectivity between these regions and aPFC in the left hemisphere, but not to increased connectivity between these regions and right aPFC, or broadly increased connectivity between other regions of FPCN to each other (outside of aPFC).

The strongest increase in functional connectivity in the frequent lucid dream group was observed between left aPFC and IPL, which localized to a dorsal segment of the anterior subdivision of the angular gyrus (PGa) bilaterally, as measured by overlap with cytoachitectonic probability maps. While many neuroimaging studies have treated the regions that comprise IPL as a homogenous region, cytoarchitectonic mapping studies have shown that these regions can be subdivided 51 , 52 , and these subdivisions show distinct patterns of structural and functional connectivity 53 . Specifically, PGa shows increased functional connectivity with the caudate, anterior cingulate, and bilateral frontal poles compared to PGp, whereas PGp shows increased connectivity with regions of the DMN, including precuneus, medial prefrontal cortex and parahippocampal and hippocampal gyri 53 . Cognitive or clinical correlates of altered functional connectivity between the frontal pole and this specific subdivision of AG (PGa) have to our knowledge not yet been identified, since much of the cognitive neuroscience literature on this region lacks anatomical specificity. However, a meta-analysis of 120 neuroimaging studies of language and semantic processes found that the left AG had the densest concentration of activation foci across studies, with a significant clustering of activation foci also in MTG 54 . The authors also note that these regions are greatly expanded in humans compared to non-human primates, suggesting a role in the development of language. Moreover, PGa is more closely linked to the semantic system that PGp, and analysis of the connectivity and cognitive functions associated with this region suggests that it is positioned at the top of a processing hierarchy for concept retrieval and conceptual integration 53 .

In line with these observations, we would like to offer a speculative hypothesis regarding our findings, which relates these results and the overlap with semantic/conceptual systems to the difference between lucid and non-lucid dreaming in terms of consciousness. Specifically, non-lucid dreams exhibit reduced working memory function, reduced ability to engage in behavioral control and planning, and reduced reflective consciousness 55 , 56 , 57 . Thus, while dreams are rich in primary consciousness of perception and emotion, consciousness during dreams typically lacks important aspects of what Edelman referred to as secondary or higher-order consciousness, which enables a creature to escape the “remembered present” of primary consciousness and to be conscious of being conscious 58 , 59 . In contrast, gaining lucidity during dreaming sleep involves regaining cognitive abilities associated with higher-order consciousness, including the ability to be explicitly aware of oneself and one’s state 55 . The distinction between primary and higher-order consciousness is thought to depend on the linguistic abilities that separate humans from other species 58 . While language processes also occur during non-lucid dreams 60 , 61 , they are nevertheless linked to the remembered present and apparently lack the conceptual structure that allows for full self-awareness. We speculatively propose that the aPFC-AG-MTG network identified here may be part of the neural circuitry enabling the integration between heteromodal metacognitive and linguistic/conceptual systems (in particular, the availability of AG-MTG semantic/conceptual content to anterior prefrontal regions) that allows one to be aware of oneself and one’s current state (i.e., “ I am dreaming! ”) 55 .

Limitations, methodological considerations and future directions

The measurement of individual differences in lucid dream frequency has been done in inconsistent ways and could be improved in future research. In the current research we used a scale with a range of response categories, from “none” to “multiple times per night” 62 (see Supplementary Methods: Dream and lucid dream frequency questionnaire ). While this questionnaire provides a straightforward coarse assessment of lucid dream frequency, a limitation of this measure is that it does not measure variation in the length or “degree of awareness” of lucid dreams. Indeed, lucid dreams can range from a realization about the fact that one is dreaming followed by a loss of lucidity shortly thereafter to more extended lucid dreams in which an individual can maintain lucidity for prolonged periods of time 63 . Likewise, lucid dreams can be characterized by varying degrees of clarity of thought. Evaluating the duration of lucid dreams as well as the degree of awareness during lucid dreams will be valuable to relating brain structural and functional measures to lucid dream frequency in future studies. An extended discussion of this issue is beyond the scope of the present article; however, overall these remarks emphasize the need for the development of standardized measures that can be used to assess individual differences in frequency of lucid dreams that simultaneously measure the duration and degree of lucidity during dreams.

Another limitation of the current study is that our measurement of lucid dream frequency relied on questionnaire responses and participant interviews. There are established methods for the objective validation of individual lucid dreams in a sleep laboratory setting using volitional eye-movement signals 4 , but there are no protocols for physiologically validating the frequency of lucid dreams. While questionnaire measures of lucid dream frequency have shown high test-retest reliability 64 , 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. We think that additional validations such as this would potentially be valuable to incorporate in future studies. Nevertheless, 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 as used in the current study. 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 or forgetting the intention would be missed by this procedure.

In contrast to the observed differences in functional connectivity described above, in the current study we did not observe any significant differences in brain structure (gray matter density) between groups. This result contrasts with a study that found that two regions of aPFC had increased gray matter density in a “high-lucidity” group compared to a “low-lucidity” group 30 . As noted in the introduction, a limitation of that study is that the high-lucidity group was not a sample of frequent lucid dreamers, but rather individuals from a database that scored above the group median on a composite measure of dreaming, which 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 57 , 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. However, the fact that the study found that these aPFC regions also showed increased BOLD activity during the monitoring component of a thought-monitoring task lends additional plausibility to the results. It is important to note that issues of statistical power could also account for the discrepant findings of these two studies. Unfortunately, no statistics or estimates of effect size have been reported for this effect and as a result we were unable to perform a power analysis to determine the adequate sample size for testing this effect. However, a single study that fails to reject the null hypothesis does not provide good evidence for the absence of an effect, especially with relatively small sample sizes. Overall, therefore, more research addressing this question using larger sample sizes will be needed before firm conclusions can be drawn.

Here we studied individuals who reported spontaneous lucid dreaming with high frequency without engaging in training to have lucid dreams. In our questionnaire samples, the proportion of individuals who reported spontaneous lucid dreams on close to a nightly basis constituted approximately 1 in 1,000 respondents. While frequent spontaneous lucid dreams are uncommon, evidence indicates that lucid dreaming is a learnable skill that can be developed by training in strategies such as metacognitive monitoring (i.e., “reality testing”) and, especially, prospective memory 65 , 66 . While it is plausible that the neurophysiological correlates of spontaneous frequent lucid dreaming are the same as frequent lucid dreaming that occurs as a result of training, this has not yet been studied. Future longitudinal training studies would be valuable in order to evaluate within-subject changes in brain connectivity as a result of training to have lucid dreams and to compare how such changes relate to the functional network associated with frequent lucid dreaming identified here.

No significant differences were observed between groups in working memory capacity, or questionnaire assessments of prospective memory or trait mindfulness. It has been suggested that a sufficient level of working memory is required in order to become lucid during dreaming sleep 2 and thus it might be predicted that frequent lucid dreams could be associated with a higher baseline level of working memory capacity. Likewise, an effective method of lucid dream induction, the Mnemonic Induction of Lucid Dreams (MILD) technique 63 , relies on the use of prospective memory to become lucid, and thus it might be predicted that frequent lucid dreams could be associated with increased prospective memory ability. While we did not find evidence in support of a relationship between these variables and spontaneous frequent lucid dreams, it is worth noting that the relation between lucid dreaming and working memory has been discussed primarily in the context of successfully being able to “activate the pre-sleep intention to recognize that one is dreaming” during a dream 2 , and the relation to prospective memory is mostly considered in the context of learning to have lucid dreams by remembering to recognize that one is dreaming. However, spontaneous frequent lucid dreamers neither necessarily need to activate a pre-sleep intention nor use prospective memory to remember to recognize that they are dreaming; instead, their lucid dreams tend to occur spontaneously without engaging in specific methods for inducing them. Thus, it remains plausible that there could be a relationship between working memory and prospective memory and (successful) training in lucid dreaming despite a lack of a relationship between these variables and spontaneous frequent lucid dreams. In future work it would be interesting to explore whether individuals with higher baseline scores on these measures show increased propensity in successfully training to have lucid dreams, as well as to quantify the association between potential improvements in these skills and lucid dream frequency as a result of training. Finally, the finding that there was no significant difference in mindfulness in frequent lucid dreamers is consistent with other research, which has found that outside of meditators, there does not appear to be an association between trait mindfulness and lucid dream frequency in the facets of mindfulness studied here (decentering and curiosity) 34 , 67 , 68 .

In future work it would be intriguing to build on these findings to evaluate whether high frequency lucid dreamers show increased functional connectivity and/or higher metabolism or BOLD signal in these regions during REM sleep. If so, this would suggest that it may be possible to bias these networks toward increased metacognitive awareness of dreaming during REM sleep, for example through techniques to increase activation of these regions. Notably, a recent double blind, placebo-controlled study found that cholinergic enhancement with galantamine, an acetylcholinesterease inhibitor (AChEI), increased the frequency of lucid dreams in a dose-related manner when taken late in the sleep cycle and combined with training in the mental set for lucid dream induction 62 . While the relationship between cholinergic modulation and frontoparietal activation is complex and depends on the task context and population under study (see ref. 69 for a review), 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 69 . Given that frontoparietal activity is typically suppressed during REM sleep, an intriguing follow-up to these findings based on the current results would be to examine whether AChEIs, and galantamine in particular, may facilitate lucid dreaming through increasing activation within the network of fronto-temporo-parietal areas observed here.

In line with the above ideas, several studies have attempted to induce lucid dreams through electrical stimulation of the frontal cortex during REM sleep. One study tested whether transcranial direct current stimulation (tDCS) applied to the frontal cortex would increase lucid dreaming 70 . While tDCS resulted in a small numerical increase in self-ratings of the unreality of dream objects, it did not significantly increase the number of lucid dreams as rated by judges or confirmed through the eye-signaling method. Another study tested whether applying transcranial alternating current stimulation (tACS) in the low gamma band (25 Hz and 40 Hz) to frontal regions would induce lucid dreams 71 . While it was reported that lucid dreams could be induced with a high success rate (58% with 25 Hz stimulation and 77% with 40 Hz stimulation), there are concerns about how lucid dreams were defined. Specifically, lucid dreams were not dreams that participants self-reported as lucid, nor dreams that were objectively verified to be lucid through the eye-movement signaling method. Instead, dreams were inferred to be lucid based on higher scores to questionnaire items measuring the amount of insight or dissociation 57 . Given that dissociation (i.e. “seeing oneself from the outside” or a “3rd person perspective”) has never been considered a defining feature of lucid dreams (e.g., refs 1 , 72 , 73 ), it is controversial to classify dreams as lucid based on higher ratings of dissociation. Furthermore, 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. However, the scale anchors ranged from 0 (strongly disagree) to 5 (strongly agree), indicating that, on average, in the 25 Hz and 40 Hz stimulation conditions, participants disagreed that their dreams had increased insight. In summary, it remains unclear whether electrical brain stimulation techniques could be effective for inducing lucid dreams (see refs 19 , 62 for further discussion). Nevertheless, given the current findings, stimulation of aPFC and temporoparietal association areas appears to be a worthwhile direction for future research attempting to induce lucid dreaming. Future studies might consider testing a wider range of stimulation parameters, particularly applied to aPFC, as well as combining stimulation with training in the appropriate attentional set for lucid dream induction.

Participants

In total, 28 right-handed participants (18 females, age = 22.6 ± 5.4 (mean ± SD), range 18–34) participated in the study. Participants were recruited via mass emails sent to University of Wisconsin-Madison faculty, staff and students. The study was described broadly as a study on brain structure and dreaming. Exclusion criteria for all participants included pregnancy, severe mental illness or any contraindications for MRI (e.g., metal implants or pacemakers). To determine study eligibility, participants completed a questionnaire that measured their dream recall and lucid dreaming frequency (described below). For the frequent lucid dream group, we recruited individuals who reported a minimum of 3–4 lucid dreams per week, or approximately one lucid dream every other night without engaging in training to have lucid dreams. We recruited control participants who were 1-to-1 matched to participants in the frequent lucid dream group on age, gender and dream recall frequency variables but who reported lucid dreams never or rarely. Specifically, for each participant in the frequent lucid dream group, we recruited a matched control participant that was the same age (date of birth <12 months apart), the same gender, a similar level of dream recall (see below) and lucid dream frequency of 1 per year or less. Signed informed consent was obtained from all participants before the experiment, and ethical approval for the study was obtained from the University of Wisconsin–Madison Institutional Review Board. The study protocol was conducted in accordance with the Declaration of Helsinki.

Individual differences in lucid dreaming and dream recall frequency

Participants completed a questionnaire that measured their dream recall and lucid dreaming frequency ( Supplementary Methods: Dream and lucid dream frequency questionnaire ). Dream recall was measured with a 15-pt scale ranging from 0 (never) to 15 (more than one dream per night). Lucid dream frequency was measured with a 15-pt scale ranging from 0 (no lucid dreams) to 15 (multiple lucid dreams per night). To help ensure clear understanding of the meaning of lucid dreaming, participants were provided with a written definition along with the scale as follows: “Lucid dreaming is a special sort of dream in which you know that you are dreaming while still in the dream. Typically, you tell yourself ‘I’m dreaming!’ or ‘This is a dream!’” (See Snyder & Gackenbach 12 for the importance of providing a definition in the assessment of individual differences in lucid dreaming frequency). Participants were also provided with a short excerpt of a written report of a lucid dream (see Supplementary Methods for full text of the definition and example of lucid dreaming provided on the questionnaire measure).

Several additional checks were made to ensure that participants had a clear understanding of the meaning of lucid dreaming. First, participants were asked to provide a written example of one of their lucid dreams, including how they knew they were dreaming. Second, participants were interviewed by the experimenters before being enrolled in the study to ensure that they had a clear understanding of the meaning of lucid dreaming. During the interview participants described several recent lucid dreams and confirmed the frequency with which they experienced lucid dreams through follow-up questions. Only participants who demonstrated unambiguous understanding of lucidity and met the frequency criteria as confirmed by both written and oral responses were enrolled in the frequent lucid dream group. The frequent lucid dream group also reported several additional variables related to their experiences with lucid dreaming, including the number of lucid dreams they had in the last six months, the most lucid dreams they had ever had in a six-month period, whether they had engaged in training to have lucid dreams and their general interest in the topic.

As noted above, we aimed to match dream recall between the frequent lucid dream group and control group as closely as possible in order to control for this potentially confounding variable. However, it was not always possible to recruit a matched control participant that was exactly matched on age, gender and dream recall. For each participant in the frequent lucid dream group, we therefore sought to recruit the closet matched pair control participant of the same age and gender, with the constraint that dream recall had to be within at least 3 rank order values on the questionnaire measure. In 7 cases, we were able to obtain an exact match between control participants and frequent lucid dream participants on dream recall, in 5 cases within 1 rank value, in 1 case within 2 rank values and in 1 case within 3 rank values. In 4 out of the 5 cases that were within 1 rank value, the difference in reported dream recall frequency was between 7 dreams recalled per week and 5–6 dreams recalled per week, and in the remaining case the difference was between 3–4 dreams recalled per week and 5–6 dreams recalled per week. Overall this method ensured that the frequent lucid dream group and control group were closely matched on dream recall frequency.

Behavioral and questionnaire assessment

Participants completed several additional assessments that measured cognitive variables which have been hypothesized to be associated with lucid dreaming and have been linked to PFC function, including working memory capacity (WMC), trait mindfulness and prospective memory (e.g., refs 2 , 33 , 34 ). To measure WMC, participants completed automated versions of the operation span task (OSpan), rotation span task (RotSpan) and symmetry span task (SymSpan) 74 . These tasks have been validated to yield a reliable measure of WMC 75 , 76 . In brief, each task presents to-be-remembered stimuli in alternation with an unrelated processing task. In the OSpan the to-be-remembered stimuli are letters and the unrelated task is verifying the accuracy of an equation; in the SymSpan the to-be-remembered stimuli are locations of red squares in a 4 × 4 grid and the unrelated task is verifying the vertical symmetry of an image; in the RotSpan the to-be-remembered stimuli are arrows pointing in one of eight different directions and the unrelated task is whether a rotated letter is presented correctly. Participants completed two blocks of each task, which together provide a reliable measure of an individual’s WMC 75 . Following standard scoring procedures, span scores were calculated as the total number of items recalled in correct serial order across all trials 76 .

Participants also completed a questionnaire battery that assessed several additional variables of interest: their mind-wandering frequency, memory function in everyday life and trait mindfulness. Mind-wandering frequency was assessed with the Daydreaming Frequency subscale of the Imaginal Process Inventory (IPI) 77 . Memory function was assessed with the Prospective and Retrospective Memory Questionnaire (PRMQ) 78 , which measures self-report scores of the frequency of both prospective and retrospective memory errors in everyday life (see ref. 79 for normative data). Trait mindfulness was measured with the Toronto Mindfulness Scale (TMS) 80 . The TMS measures two factor-analytically derived components of mindfulness: Curiosity and Decentering. The Curiosity factor corresponds to an “an attitude of wanting to learn more about one’s experiences”, whereas the Decentering factor corresponds to “awareness of one’s experience with some distance and dis-identification rather than being carried away by one’s thoughts and feelings” 80 .

MRI acquisition

Resting-state functional MRI scans were collected on a 3.0 Tesla GE MRI scanner at the Wisconsin Institute for Sleep and Consciousness/HealthEmotions Research Institute (Department of Psychiatry) at the University of Wisconsin - Madison. A T2*-weighted echo-planar imaging (EPI) sequence was used (TR = 2000 ms; TE = 25 ms; flip angle = 60°; acquisition matrix = 64 × 64; FOV = 204 mm; acquisition voxel size = 3.75 × 3.75 × 4.00 mm; 40 interleaved slices, number of volumes = 300, duration = 10 minutes). During the resting-state scan, participants were instructed to stay awake and relax, to hold as still as possible, and to keep their eyes open. Before the functional scan, high-resolution T1-weighted anatomical scans were acquired (BRAVO, TR = 9180 ms; TE = 3.68 ms; TI = 600 ms; flip angle = 10°; FOV = 256 mm; acquisition voxel size = 1 × 1 × 1 mm).

Structural (T1) data processing

T1 anatomical scans were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using SPM12 (Statistical Parametric Mapping, Wellcome Trust Centre for Neuroimaging, London). A diffeomorphic non-linear registration algorithm (diffeomorphic anatomical registration through exponentiated lie algebra; DARTEL) 81 was used to iteratively register the images to their average. The resulting flow fields were combined with an affine spatial transformation to generate Montreal Neurological Institute (MNI) template spatially normalized and smoothed Jacobian-scaled gray matter images. Spatially normalized images were smoothed using an 8 mm full width at half maximum (FWHM) Gaussian kernel. We additionally evaluated average gray matter density between groups in the two regions of prefrontal cortex and bilateral hippocampus observed by ref. 30 to show increases in a “high lucidity” group. We defined spherical ROIs of 4 mm radius in MNI152 space centered on the peak voxels reported in ref. 30 : right prefrontal (MNI: 4, 57, 31), left prefrontal (MNI: −30, 51, 6), left hippocampus (MNI: −21, 31, 3) and right hippocampus (MNI: 21, 31, 3). Total hippocampal volume was also extracted from an updated routine for automated segmentation of the hippocampal subfields implemented in FreeSurfer version 6.0 82 .

Resting-state fMRI (EPI) data processing

Resting-state fMRI data were processed based on a workflow described previously 24 . To remove potential scanner instability effects, the first four volumes of each EPI sequence were removed. This was followed by slice timing and rigid-body motion correction to the mean EPI image in AFNI 83 . To compare head motion between groups, head motion was calculated by mean framewise displacement (FD) using Jenkinson’s relative root mean square (RMS) algorithm 84 . Affine transformation from mean EPI image to T1 volume was calculated using BBRegister 85 and nonlinear transformation from T1 to the 2 mm MNI152 template was calculated using Advanced Normalization Tools (ANTs) 86 . Brain mask, cerebrospinal fluid (CSF) mask and white matter (WM) mask were parcellated using FreeSurfer 87 , 88 , 89 , 90 and transformed into EPI space and eroded by 2 voxels in each direction to reduce partial volume effects. Realigned timeseries were masked using the brain mask. Differences in global mean intensity between functional sessions were removed by normalizing the mean of all voxels across each run to 100. Simultaneous surface and volume smoothing was applied using FreeSurfer: Cortical voxels were sampled to the surface and smoothed in surface space with a 10 mm FWHM Gaussian kernel while subcortical voxels were smoothed separately in volume space with a 5 mm FWHM Gaussian kernel. Outliers in the EPI sequence were discovered based on intensity and motion parameters using ArtDetect ( http://www.nitrc.org/projects/artifact_detect ). This was followed by nuisance regression of motion-related artifacts using a GLM with six rigid-body motion registration parameters and outlier scans as regressors. Principal components of physiological noise were estimated using the CompCor method 91 . Joined WM and CSF mask and voxels of highest variance were used to extract two sets of principal components (aCompCor and tCompCor). Timeseries were then denoised using a GLM model with 10 CompCor components as simultaneous nuisance regressors. Note that global signal regression was not performed because this processing step can induce negative correlations in group-level results 92 . Finally, timeseries data were temporally filtered (high-pass = 0.01 Hz, low-pass = 0.1 Hz).

Seed-based whole-brain functional connectivity

To estimate connectivity, spherical regions of interest (ROIs) of 6 mm radius were defined in the MNI152 space (Fig. 1a ) based on the peak voxel (MNI: −26, 62, 10; and homologous (x-flipped) coordinate) in aPFC reported in ref. 9 to show increased BOLD signal during lucid compared to non-lucid REM sleep. In order to ensure that the spheres were contained within the pial surface of the cortex, spheres were shifted by two voxels in the x and y dimensions yielding a final MNI coordinate of x = ± 24, y = 64, z = 10. Although aPFC functional connectivity was the main target of the current investigation, we also performed supplementary seed-based functional connectivity analysis on other regions identified in ref. 9 to increase BOLD signal during lucid REM sleep, based on the peak voxel coordinates in left inferior parietal lobule (IPL) (MNI: −50, −52, 52), right IPL (MNI: 38, −62, 52), left inferior temporal gyrus/middle temporal gyrus (ITG/MTG) (MNI: −54, −60, −16), right ITG/MTG (MNI: 64, −38, −14), left precuneus (MNI: −10, −68, 42) and right precuneus (MNI: 8, −78, 48). ROI masks were transformed back to each subject EPI space using inverse nonlinear MNI152 to T1 transform and affine T1 to EPI (thresholded after interpolation at 0.5). Translated ROIs were restricted within the cortical ribbon mask. ROI timeseries were estimated by averaging voxels within each ROI. Full brain connectivity (correlation) maps were calculated using AFNI 83 . Connectivity maps were z-transformed using Fisher’s r- to- z transform and then spatially transformed into MNI152 space. Group-level analysis was conducted using the general linear model (GLM) framework implemented in SPM12 (Wellcome Trust Department of Imaging Neuroscience, University College London, UK). Voxelwise independent samples t -tests were performed between groups. Whole-brain analyses were conducted, correcting for multiple comparisons using topological FDR 93 at the cluster level. Cluster forming threshold was set at p < 0.0075 and cluster size threshold was set at p < 0.05 (cluster corrected). Surface rendering was performed using FreeSurfer and Surf Ice ( https://www.nitrc.org/projects/surfice/ ).

Angular gyrus (AG)/intra-parietal sulcus (IPS) subdivision analysis

Cytoarchitectonic mapping studies have shown that AG can be divided into anterior (PGa) and posterior (PGp) subdivisions and IPS can be divided into three distinct subdivisions (hlP1 on the posterior lateral bank, hlP2 which is anterior to hIP1, and hlP3 which is posterior and medial to both subdivisions) 51 , 52 . The subdivisions of AG and IPS have been shown to have distinct structural and functional connectivity patterns 53 . We performed a follow-up analysis on the functional clusters identified in our seed based functional connectivity analysis in order to characterize the overlap between these clusters and the anatomical subdivisions of these regions. Five regions of interest (ROIs) were constructed using maximum probability maps (MPMs) with the atlas probability maps from the Anatomy Toolbox v1.8 in SPM 94 . MPMs create non-overlapping regions of interest from the inherently overlapping cytoarchitectonic probability maps 94 , 95 . The anatomical boundaries of these maps are described in detail in previous publications 51 , 52 , 95 . Mean connectivity values from each binarized mask were exacted using the MarsBar toolbox 96 .

Large-scale networks (LSNs) analysis

In order to compare whether connectivity within and between established large scale resting-state brain networks showed differences between groups, we extracted timecourses from a set of 166 nodes from a meta-analysis by Power, et al . 32 corresponding to 7 different systems: the default mode network (DMN; 58 nodes), the cingulo-opercular network (CO; 14 nodes), the frontoparietal control network (FPCN; 25 nodes), the salience network (SN; 18 nodes), the ventral attention network (VAN; 9 nodes), the dorsal attention network (DAN; 11 nodes) and the visual system (VIS; 31 nodes). For each network, we calculated the mean correlation between all nodes within the network (within-network connectivity) as well as the mean correlation between all nodes of a given network and all the nodes of each other network (between-network connectivity). Correlation values were z-transformed using Fisher’s r- to- z transform. We also evaluated the overlap between our seed-based functional connectivity results and a 17-network parcellation of human brain connectivity networks 35 . The 17-network parcellation in MNI space was down-sampled from 1 mm isotropic to 2 mm isotropic to match the space of the functional connectivity results and the spatial overlap of all functional connectivity clusters with each network was calculated as the percentage of significant (cluster corrected) voxels within each network. We followed up this network overlap analysis by evaluating the connectivity between all nodes within the frontoparietal control subsystem that showed the largest overlap with the functional connectivity results, based on a 400 node parcellation of the 17 functional networks 36 .

Graph-theoretic network analysis

To construct functional networks for graph-theoretic analysis, anatomical scans were segmented using FreeSurfer and parcellated into 1015 regions according to the Lausanne 2008 atlas included in the connectome mapping toolkit 37 , 38 . Parcellation masks were transformed back to each subject EPI space using the BBRegister affine T1 to EPI transform. Voxel-level fMRI timeseries in each subject’s native space within each mask were averaged and correlated to all other regions, yielding an adjacency matrix A whose entries A ij reflect the functional connectivity between region i and region j for each subject. Resting-state fMRI data pre-processing was identical to the procedures described above (see Resting-state fMRI data processing ) with the exception that no spatial smoothing was applied, as spatial smoothing can distort network measures derived from average timeseries within parcellated regions (e.g., ref. 97 ). All network metrics were computed in Matlab v 9.1 (The MathWorks Inc., Natick, MA, 2008) using the Brain Connectivity Toolbox 98 . For each node in the network we analyzed the degree ( k ), strength ( s ), betweenness centrality (BC) and eigenvector centrality (EC). These metrics are described in detail elsewhere (see refs 98 , 99 for reviews). In brief, k quantifies the total number of connections of a node, while s quantifies the sum of the weights of all connections to a node. BC and EC are different measures of centrality of nodes: BC is the fraction of all shortest paths in the network that contain a given node and EC quantifies nodes connected to other densely connected nodes as having high centrality.

In order to compare network and topological properties between groups it is important to ensure that graphs contain the same number of edges 99 . This can be achieved by thresholding A by the connection density (δ), also known as cost factor, of the network, which is the number of existing connections over the total number of possible connections 100 , 101 . Following recommended practice 99 , rather than apply a single threshold to graphs, which would limit any findings to a single arbitrary connection density, we thresholded graphs over a range of connection densities (0.05 ≤ δ ≤ 0.35) in steps of 0.01. For all measures except node strength, for which we computed undirected weighted matrices, network metrics were calculated on binarized thresholded matrices for each value of δ by setting all connections ≥δ to 1 and all connections <δ to 0. In order to compare groups over the range of thresholds, we calculated the area under the curve (AUC) of the δ-thresholded data by integrating the curve over the specified density range for each graph metric, as has been applied in previous studies (e.g., refs 101 , 102 ). To test the null hypothesis of no difference in AUC between groups, we used a nonparametric bootstrapping procedure in which we randomly reassigned groups with replacement 10,000 times and computed a bootstrapped t -value for each node. To correct for multiple comparisons, the maximum t -value across all nodes for each surrogate distribution was recorded to obtain a maximum t distribution and the level of statistical significance was set against the maximum distribution at α = 0.05. This statistical approach has been used in previous studies and allows for strong control over type I error 103 , 104 .

Data Availability

The data that support the findings of this study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank Stephen LaBerge for helpful discussion. This work was supported by NIH/NCCAM P01AT004952 and the Tiny Blue Dot Foundation (to G.T.). B.B. was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award F32NS089348 from the NINDS. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. OG is post-doctoral researcher at the Belgian National Funds for Scientific Research (FRS-FNRS) and is supported by the Belgian National Funds for Scientific Research (FRS-FNRS), the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2), and the Luminous project (EU-H2020-fetopen-ga686764).

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Benjamin Baird, Anna Castelnovo, Olivia Gosseries & Giulio Tononi

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B.B. and G.T. designed research; B.B., A.C. and O.G. collected data; B.B. analyzed data; B.B., A.C., O.G. and G.T. wrote the paper.

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Baird, B., Castelnovo, A., Gosseries, O. et al. Frequent lucid dreaming associated with increased functional connectivity between frontopolar cortex and temporoparietal association areas. Sci Rep 8 , 17798 (2018). https://doi.org/10.1038/s41598-018-36190-w

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The cognitive neuroscience of lucid dreaming

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1 Wisconsin Institute for Sleep and Consciousness, University of Wisconsin-Madison, Madison, WI, USA. Electronic address: [email protected].
2 Brain Institute, Physiology Department and Onofre Lopes University Hospital - Federal University of Rio Grande do Norte, Natal, Brazil.
3 Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands.
PMID: 30880167
PMCID: PMC6451677
DOI: 10.1016/j.neubiorev.2019.03.008

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.

Keywords: Consciousness; Dreaming; Lucid dreaming; Meta-awareness; REM sleep.

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Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Awareness / physiology
Brain / physiology*
Brain Mapping
Brain Waves
Cognitive Neuroscience / methods
Dreams / physiology*
Magnetic Resonance Imaging
Metacognition / physiology
Transcranial Direct Current Stimulation

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New Frontiers in Lucid Dreaming

Lucid dreaming is being used as a research tool to better understand dreams..

Posted March 31, 2021 | Reviewed by Matt Huston

Why Is Sleep Important?
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A lucid dream is one in which the dreamer is consciously aware that they are in a dream.
Experiments have shown that it is possible to communicate with individuals in the lucid dream state.
A recent study has extended significantly the methods for communication with lucid dreamers.

Dreams are a strange realm that we all visit, and yet we often have only the vaguest memories of the unusual places and events we experience there. In the last century, methods such as polysomnography emerged that allowed for a more scientific study of dreams. But progress is difficult because we have to rely on reports of dreams that are given after the dreamer awakens, and we lose detail as soon as we emerge from the unconscious into the conscious state. Memory of dreams tends to rapidly decay.

For some time researchers have tried to devise ways to more directly access the dream world and get real-time rather than after-the-fact information about dreaming. As it turns out, lucid dreaming may offer a portal into this other realm.

What is lucid dreaming?

Lucid dreaming is the conscious awareness of being in a dream and potentially having some ability to control the dream as if the dreamer were the director of a movie. This phenomenon has been investigated at least since the 1800s and was significantly advanced by research carried out by Stephen LaBerge in the 1970s and 1980s. He developed methods for helping people learn to have lucid dreams and techniques for potentially accessing the dreaming person’s consciousness.

Have you ever experienced lucid dreaming? Having a dream in which you are vividly aware that you are dreaming? If you have had this experience you may also have had the sense that you could shape the dream, change it, and change the feelings you are having in the dream. This ability to be aware of and change dreams has been useful in helping people with PTSD deal with the frequent nightmares that are often symptoms of post- traumatic stress .

Is it possible to communicate with dreaming people?

It appears to be possible to communicate with people who are having lucid dreams while they are in the lucid dream state. LaBerge developed a research technique in which participants who were consciously aware that they were dreaming could signal researchers using eye movements. Later research built upon this work to further investigate the nature of lucid dreaming (e.g., Erlacher & Schredl, 2004). As you can imagine, most of these studies have had small numbers of participants, given the difficulty of finding participants who are proficient lucid dreamers, and the challenges of conducting the research. In these studies, the dreamers are able to use prearranged eye movements to send signals to researchers when they have entered the lucid dream state and have the awareness of being in the dream.

New research is showing unexpected possibilities for lucid dreaming .

A recent study by Konkoly et al (2021) has significantly extended previous research. This study was complex and was conducted by scientists in the United States, Germany, France, and the Netherlands. Each group used somewhat different experimental techniques but all used polysomnography to verify REM sleep. REM sleep has certain characteristics that help to distinguish it from both the waking state and from deep sleep. While the EEG patterns in REM sleep are similar to waking patterns, there are also characteristic rapid eye movements as well as the loss of voluntary muscle tone (to prevent acting out the dream). By using polysomnography and blinded raters it was possible to detect both REM sleep and the responses of the participants.

There were 36 participants in the study. Most were either frequent lucid dreamers or had at least some experience of lucid dreaming. There was one participant, in France, who had been diagnosed with narcolepsy, a neurological disorder that is characterized by excessive daytime sleepiness, parasomnias such as hypnagogic imagery and sleep paralysis, and cataplexy (temporary paralysis that occurs during the day, usually in emotionally charged situations).

Each of the groups used various methods to elicit lucid dreams. The American researchers used a method to train lucid dreaming in which sensory stimulation was provided prior to sleep and then again during sleep. To obtain lucid dreams, the German researchers used the method of waking the participants from sleep for a period of time and when the participants fell back to sleep it was with the intention to have a lucid dream. The French participant with narcolepsy was an accomplished lucid dreamer prior to the study. Like the American group, the researchers in the Netherlands used sensory stimulation prior to sleep and then again during sleep.

The American group worked with 22 participants who had reported remembering one or more dreams a week. They used spoken math problems as the task presented to the participants and eye movements as the signals from the participants. The German group worked with 10 experienced lucid dreamers. They used tones and lights to present math problems to the participants and eye movements as the output signal from the dreamers. The French group worked with the participant with narcolepsy and asked yes or no questions or asked for discrimination of light, touch, and speech stimuli and facial muscle contractions were used as the output answers. They obtained spontaneous lucid dreams experienced by the participant. The group in the Netherlands worked with three experienced lucid dreamers. Spoken math questions were used as the task, and eye movements were used for the participant output.

Altogether, 82 sessions with attempts at two-way communication were tried. Of these, 57 sessions had REM sleep. Of these, 15 sessions were found to have signal-verified lucid dreaming. The eye movements and muscle contractions used as responses to the tasks were scored by three raters and one experimenter. At least three of the four had to agree on whether or not a signal was present. Across 158 trials, there were 29 correct answers, 5 incorrect answers, 28 ambiguous answers, and 96 non-responses.

While this would not be a very impressive response rate for these same participants while awake, it is rather amazing that correct answers could actually be obtained from sleeping people at all. For example, one participant signaled that they were in a lucid dream using the prearranged eye movements. Alternating colored lights were given in the form of the math problem “4 – 0” in Morse-code. The participant used left and right eye movements to give the answer.

The subjective experiences reported by participants offer some insight into how this process works. Upon awakening, participants reported that the questions could seem to be coming from outside the dream or were superimposed onto the dream. They also described how the questions could become part of the dream itself as if, for example, the question were coming out of a radio. Clearly, more work will be needed to better understand the process of taking in, processing, and reporting out information from the dream state.

Exploring the dreaming mind

Evidence was found that lucid dreamers could analyze novel perceptual information, keep information in working memory, and answer questions. Upon hearing about this study, a colleague of mine worried that this could lead to new demands for workers. As he said, “I just hope that no one tries to monetize this, e.g., ‘well, since you can do work in your sleep, I’ll pay you $1.50 per hour to come up with a new logo for the company!’” For now, this seems an unlikely concern as there is no fully reliable way to assure that a dreamer will enter the state of lucid dreaming, the methods of communication are thus far crude and cumbersome, and the responses are limited in the amount of information that can be processed and communicated.

On the other hand, this does appear to be an interesting new research tool for exploring the dreamworld and better understanding the abilities of the mind, which consistently seem greater than we could ever have expected.

Erlacher D., Schredl M. (2004). Time required for motor activity in lucid dreams. Perceptual and Motor Skills . 99 (3 Pt 2),1239-1242. doi: 10.2466/pms.99.3f.1239-1242. PMID: 15739850.

Konkoly, K. R., Appel, K., Chabani, E., Mangiaruga, A., Gott, J., Mallett, R., Caughran, B., Witkowski, S,, Whitmore, N. W., Mazurek, C. Y., Berent, J. B., Weber, F. D., Tu ̈ rker, B., Leu-Semenescu, S., Maranci, J., Pipa, G., Arnulf, I., Oudiette, D., Martin Dresler, M., & Paller, K. A. (2021). Real-time dialogue between experimenters and dreamers during REM sleep, Current Biology . 31 , 1–11, https://doi.org/10.1016/j.cub.2021.01.026 (in press).

John Cline, Ph.D. , is a clinical psychologist, Diplomate of the the American Board of Sleep Medicine, a fellow of the American Academy of Sleep Medicine and a clinical professor at Yale University.

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LUCID DREAM RESEARCH

This is a network for lucid dreamers and scientists to share research information and lucid dream experiences, connect for future studies, and expand the knowledge of the lucid dream phenomenon.

Lucid Dreaming In NREM Sleep

Personality Traits Of Lucid Dreamers

A Study Examining The Relation Between Type 2 Thinking (Calculating, Conscious Thoughts) And Lucid Dreaming

High Functioning Autism and Lucid Dreaming – By Ian Jaydid

Effective Means of Prolonging A Lucid Dream

Lucid Dreaming And Virtual Reality

About The Changing Sense Of Time, Space And Reality In A Lucid Dream…

The Sub-Frequency – Connecting via Lucidity with Loved Ones Who Have Passed On – By Ian Jaydid

Statistics About General Knowledge Of Lucid Dreaming And Lucid Dream Induction Techniques

About Lucid Dreaming And Its Relation To Mental Health, Personality, Self-Confidence, Behavior Control…

Information/news.

Excerpts from a study initially published in the International Journal Of Dream Research – modified by addition of pictures – Stumbrys, T., & Erlacher, D. (2012). Lucid dreaming during NREM sleep: Two case reports. International Journal of Dream Research, 5(2), 151–155. https://doi.org/10.11588/ijodr.2012.2.9483 “Lucid Dreaming During NREM Sleep: Two Case Reports Tadas Stumbrys1[…]

Excerpts from a study initially published in the International Journal Of Dream Research – modified by addition of pictures – Shafiei, B. (2019). Big five personality traits and dream recall frequency in spontaneous vs. self-trained lucid dreamers, 12(2), 8–13 – https://doi.org/10.11588/ijodr.2019.2.59710 “Big Five Personality Traits And Dream Recall Frequency In[…]

An interesting study on the relation between “Type 2″ thinking and lucid dreaming… Hopefully, more studies relating to this field will follow… LDF – Lucid Dream Frequency CRT – Cognitive Reflections Test DLPFC – Dorsolateral Prefrontal Cortex Locus Of Control – “…the degree to which people believe that they have[…]

I wasn’t tested for autism until I was already working in the field of mental health for several years as a case-manager for clients with autism and other similar conditions. One might wonder how I didn’t figure out my own diagnosis after studying and working directly with these traits for[…]

Excerpts from a study by Stephen LaBerge – Lucidity Institute: “Prolonging Lucid Dreams… By Stephen LaBerge Experiment Lucidity Institute members were invited to compare each of the following… A. SPINNING… B. GOING WITH THE FLOW When subjects were in a lucid dream and the dream began to fade, they were[…]

Below is an excerpt from an article by Kevin Healey – THE BLOG – 04/27/2016 – HUFFPOST “Dreaming the Virtual: Why Lucid Dreamers Should Steer the Digital Economy… … Beyond simple aesthetics, lucid dreaming and VR share some of the same therapeutic applications. VR can be used to treat PTSD, for example. Lucid[…]

Excerpts from a study initially published in the International Journal Of Dream Research – modified by addition of pictures – Schredl, M. (2014). Abstracts of the 31th Annual Conference of the International Association for the Study of Dreams June 4 – June 8, 2014 Berkeley, California, USA, 7. https://doi.org/10.11588/ijodr.2014.0.15743 “The Relationship Between Energy and[…]

By Ian Jaydid The practice of contacting loved ones who have passed via “psychic” channels is as old as religion itself, but my aim here is to examine the core dynamics of this phenomenon in a manner anyone, from any background or philosophy should be able to make use of.[…]

Excerpts from a study initially published in the International Journal Of Dream Research – modified by addition of picture(s) – Neuhäusler, A., Schredl, M., & Göritz, A. S. (2018). General knowledge about lucid dreaming and lucid dream induction techniques: An online study, 11(2), 179–185 – https://doi.org/10.11588/ijodr.2018.2.50491 “General knowledge about lucid dreaming and[…]

Excerpts from a study initially published in the International Journal Of Dream Research – Doll, E., Gittler, G., & Holzinger, B. (2009). Dreaming, Lucid Dreaming and Personality, 2(2), 52–57 – https://doi.org/10.11588/ijodr.2009.2.142 “DREAMING, LUCID DREAMING AND PERSONALITY… …Our investigation focused on the frequency of the lucid dream experience and its relationship to mental[…]

MIXING PSYCHOLOGY AND REALITY CHECKS – Using Awareness Of Emotions To Induce Reality Checks and Rational Thinking

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“The Avatar Approach” by Ian Jaydid

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My New Adventure

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About Brain Activity Causing Consciousness and Vivid Colors In Lucid Dreams

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Increased Abilities Of Lucid Dreaming

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YouTube Channel For Educational Lucid Dreaming / Sleep Science Content

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COCOON – A Modern Lucid Dream Machine – By the Massachusetts Institute of Technology (MIT)

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Commonalities Between Sleep Paralysis And Lucid Dreaming

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The Analysis And Assessment Of The CIA Gateway Experience

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DORMIO – A DEVICE TO INDUCE CREATIVITY, INFLUENCE MICRODREAMS AND EXTRACT INFORMATION DURING HYPNAGOGIA

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SPATIAL ANGLE MODULATION (SAM) – The Future Replacement Of Binaural Beats To Gain Altered States Of Consciousness, Such As Lucid Dreaming, Through Audio Technology?

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Frontal Parts Of The Brain Click Back In During Lucid Dreams Despite Them Normally Being Off During REM Sleep

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The World of Lucid Dreaming Sex

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MARIJUANA AND LUCID DREAMING

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The Power Of Intent and Mnemonic Techniques

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Neuroscience of Lucid Dreams and Amount of Frontopolar Cortex Grey Matter in Lucid Dreamers

Differences in brain consistency between lucid dreamers and non-lucid dreamers “The Neuroscience of Lucid Dreams” – Scientific American – By Susana Martinez-Conde “…The research showed that the brains of people with high and low dream lucidity were different. Subjects with high lucidity had greater gray matter volume in the frontopolar[…]

About Lucid Dreaming In Children

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Lucid Dreaming And Its Potential to Create Compassion…And Changing The World With It!

“Mindfulness, Compassion & Lucid Dreaming w/ Paul Gilbert & Charlie Morley”…“What is the connection between Mindfulness, Compassion and Lucid Dreaming? What is the point of cultivating compassion during waking and dreaming states? Learn more with Prof Paul Gilbert and Lucid Dreaming and Shadow Work teacher Charlie Morley.” – Youtube –[…]

The Relation Of Metacognition And Lucid Dreams; And The Possible Treatment Approaches That Come With It

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The Phenomenon Of False Awakenings

Excerpts from a study initially published in the International Journal Of Dream Research – modified by addition of pictures – Buzzi, G. (2011). False awakenings in light of the dream protoconsciousness theory: a study in lucid dreamers, 4(2), 110–116, https://doi.org/10.11588/ijodr.2011.2.9085 “False Awakenings In Light Of The Dream Protoconsciousness Theory: A Study In Lucid[…]

OBE/Astral Projection And Lucid Dreaming Teams – How About Those Two Groups Coming Together To Synergize Instead Of Being At War?

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Hypnagogia And Hypnopompia

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An Interview with Ryan Hurd on Raw Talk With Sheena

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Hypnagogic Hallucinations

“Hypnagogia is the experience of the transitional state between wakefulness and sleep in humans: the hypnagogic state of consciousness, during the onset of sleep. Mental phenomena that occur during this “threshold consciousness” phase include lucid thought, lucid dreaming, hallucinations, and sleep paralysis…. …Sights Among the more commonly reported,[9][10] and more thoroughly researched, sensory features of hypnagogia are phosphenes which can manifest as seemingly random speckles,[…]

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Lucid Dreaming And Locus Of Control

“In personality psychology, locus of control is the degree to which people believe that they have control over the outcome of events in their lives, as opposed to external forces beyond their control.” – Wikipedia “Internal vs. External Locus of Control People who base their success on their own work[…]

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Why Dreams Are Predominantly Experienced In Imagistic Terms

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About How Ions Control The Neuronal Firing Patterns During the Transition From Sleep to Wakefulness

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The Connection Between LSD And Dreams

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Does Lucid Dreaming Affect Your Emotions, Possibly Fear, Of Dying?

Does lucid dreaming affect your emotions, possibly fear, of dying?

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Have You Ever Asked Your Lucid Dream ‘Self’ Questions And Gotten Answers?

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Science - Pre-frontal Cortex and Lucid Dreaming

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L-Alpha Glycerylphosphorylcholine and Lucid Dreaming

Research Indicates That L-Alpha Glycerylphosphorylcholine Does Not Increase The Likelihood Of A Lucid Dream

“L-Alpha glycerylphosphorylcholine is a natural choline compound found in the brain. It is also a parasympathomimetic acetylcholine precursor…” – Wikipedia “Summarizing, our results illustrate that cholinergic activation via α-GPC did not alter the dream experience significantly nor did it facilitate the induction of lucid dreams.” – http://www.dropbox.com/s/bqtvxsrjybapjdx/manuscript.pdf

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A Review Of Lucid Dream Induction Techniques

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Lucid Dream Induction Devices and Technology

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WHY Can The Subconscious Mind In A Non-Lucid Dream Not Relate More Directly To What We Experienced In The Waking Physical Reality?

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The Various States of Lucidity

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SLEEP PARALYSIS – Who Has Something Positive And Encouraging To Share About It?

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Lucid Dreaming and the Multidimensional Brain

The Brain And Its Multidimensional Universe

Interesting new findings in neuroscience… “As above so below: Scientists have found a multidimensional universe inside our brain” …Human brains are estimated to be home to a staggering 86 billion neurons…forming a super-vast cellular network that SOMEHOW makes us capable of thought and consciousness…” The research is published in Frontiers in[…]

https://www.lucidgearup.com/research – A Resource For Lucid Dream Research

A Resource For Lucid Dream Research: https://www.lucidgearup.com/research

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Front Psychol

Is It a Good Idea to Cultivate Lucid Dreaming?

Raphael vallat.

1 Department of Psychology, Center for Human Sleep Science, University of California, Berkeley, Berkeley, CA, United States

2 DYCOG Team, Lyon Neuroscience Research Center, CNRS UMR 5292 - INSERM U1028 - Lyon 1 University, Bron, France

Perrine Marie Ruby

Introduction.

Lucid dreaming (LD) is the process of being aware that one is dreaming while dreaming. In some cases, the dreamer may even gain control over a part of the dream plot and scenery. The scientific investigation of LD (experience already mentioned in Antiquity) did not start before the nineteenth century (de Saint-Denys , 1867 ) , and the use of objective methods to study LD only emerged a few decades ago (e.g., LaBerge, 1979 , 1980 , 1988 ; LaBerge and Rheingold, 1991 ; Levitan and LaBerge, 1994 ). Recently, LD gained visibility: surveys showed that 1/4 of all participants ( N = 1,380) had heard of LD, that LD research is no longer seen as esoteric, and that the public has a generally positive view on LD (Lüth et al., 2018 ; Neuhäusler et al., 2018 ).

With the emergence of a digital lifestyle in rich countries and hyper-realistic video games, it became obvious to an ever-increasing amount of people that LD is the ultimate form of immersive experience. Indeed, it offers a (free) unique and fantastic world in which everything may become possible or controllable and feels real without putting the dreamer at risk. These characteristics (fantastic sensory and emotional experience) make LD indubitably highly desirable (e.g., Stumbrys et al., 2014 ).

There is however a problem preventing most of the population from enjoying LD: spontaneous LD is not frequent. About 50% of individuals have experienced at least one lucid dream in their lifetime, and only 11% report having two or more lucid dreams per month (Schredl and Erlacher, 2011 ; Saunders et al., 2016 ; Vallat et al., 2018 ).

It is not surprising, in this context, that numerous training methods and devices aiming at increasing LD frequency and the level of control within the dream have been developed and commercialized in recent years. The various LD induction methods can be classified in three categories: (1) cognitive techniques, (2) external stimulation during sleep and, (3) intake of specific substances (Stumbrys et al., 2012 ; Dyck et al., 2017 ; Bazzari, 2018 ; LaBerge et al., 2018 ). Reviews highlighted that none of these induction techniques were verified to induce LD reliably and consistently. However, for lack of anything better, individuals who want to increase their LD frequency may use one of these methods.

Sleep Disruption Risk Due to LD Induction Methods

Several of the LD induction methods deliberately (or incidentally) alter sleep architecture or duration. In the cognitive technique category, this is especially true of the widely-used mnemonic induction of lucid dreams technique (MILD; Levitan and LaBerge, 1994 ; Neuhäusler et al., 2018 ). The MILD is indeed more efficient if the trainee awakens during the night, stays awake for 30–120 min and then goes back to sleep (Stumbrys et al., 2012 ). This observation led to the development of the Wake-up-back-to-Bed technique, a LD induction method based solely on forced awakenings and periods of wake during the night. Those methods disturb sleep by increasing its fragmentation, modifying its architecture and decreasing its duration. Likewise, the dream re-entry method recommends counting while falling asleep after a short awakening, which may prevent trainees from actually falling asleep (Stumbrys et al., 2012 ).

Regarding the stimulation methods category, the principle is to deliver stimuli during sleep to trigger lucidity. Such stimulation is intrinsically associated with the risk of awakening (or arousing) the participants, and thus of decreasing sleep depth, disrupting sleep architecture and/or shortening sleep duration. The combination of the MILD techniques with external stimulation has also been tested because it was considered promising to induce LD (LaBerge, 1988 ; Levitan and LaBerge, 1994 ). In this case the risk of sleep disruption of the two techniques is cumulative.

Several substances have also been used to stimulate LD (via intracerebral acetylcholine increase), often in combination with the MILD technique (e.g., LaBerge et al., 2018 ; Baird et al., 2019 ). In this case, in addition to the previously mentioned risk, there is also the risk of disturbing the balance between the serotonergic and cholinergic systems which are jointly involved in regulating sleep. Disturbing this balance may impact sleep structure integrity (i.e., increased sleep fragmentation, time awake during the night, and sleep paralysis) and have adverse effects on health (Stumbrys et al., 2012 ; Biard et al., 2015 , 2016 ).

Considering the gigantic amount of scientific evidence linking poor-quality or insufficient sleep to adverse health outcomes (including shorter life expectancy), and especially of sleep fragmentation in altered physical and cognitive health (e.g., Stepanski, 2002 ; Bonnet and Arand, 2003 ; Mullington et al., 2009 ; Mary et al., 2013 ; Walker, 2017 , 2019 ; Ahuja et al., 2018 ; Barnes and Watson, 2019 ; Brauer et al., 2019 ; Pichard et al., 2019 ), one may seriously question the health consequences of regularly practicing LD induction methods.

The Modified Cerebral State During LD

The experimental investigation of LD is challenging given the difficulty to get LD in the lab. Indeed, LD is rare and unpredictable even for frequent lucid dreamers, especially in an unfamiliar experimental setting. Nonetheless, by applying the method of LD objective detection (pre-determined ocular signaling, LaBerge and Rheingold, 1991 ) to EEG and fMRI, some determined neuroscientists have managed to get a glimpse of the cerebral correlates of LD. In a pioneering EEG study, Voss et al. ( 2009 ) succeeded in recording the brain activity of three dreamers while they were experiencing a lucid dream. They observed an increased activity in the gamma frequency band in the frontal lobe in lucid rapid eye movement (REM) sleep as compared to non-lucid REM sleep and concluded that LD constitutes a hybrid state of consciousness in-between sleep and wake (Hobson, 2009 ), with definable and measurable differences from waking and from REM sleep, particularly in frontal areas. This is coherent with the fact that most LD induction methods promote an increase of the arousal level during sleep, and suggest that anything susceptible to awaken the subject gradually, including nightmares, might favor or induce LD (e.g., Schredl and Erlacher, 2004 ). In line with this idea, a case fMRI study showed that lucid REM sleep was associated with a reactivation of areas that are normally deactivated during REM sleep, such as bilateral precuneus, parietal lobules, and prefrontal and occipito-temporal cortices (Dresler et al., 2012 ). These regions are involved in higher cognitive functions such as self-awareness and executive functions, and their reactivation during LD could account for the resurgence of a certain level of self-awareness and voluntary control (Hobson, 2009 ; Zink and Pietrowsky, 2015 ). In support to this hypothesis, an increased level of self-reflective awareness during dreaming was induced by fronto-temporal transcranial alternating current stimulation (tACS) (Bray, 2014 ; Voss et al., 2014 ). This study encouraged people to use tACS to induce LD, which again raises questions about safety notably of chronically using a method that affect cortical electrical activity (there are currently no clinical information on chronic or repeated use of tACS).

Sleep Disruption Risk Due to an Increase of LD Frequency

In the case of a spontaneous increased LD frequency without any use of LD induction methods, one may still wonder what is the impact of “replacing” a regular sleep stage by a hybrid sleep stage on general health and notably on the function of sleep, given the well-known involvement of good sleep in good health and especially of REM sleep in emotional regulation and memory consolidation (e.g., Rauchs et al., 2005 ; Walker and van der Helm, 2009 ; Perogamvros and Schwartz, 2013 ; Plailly et al., 2019 ). Since there are now evidences that the brain is not functioning in the same way during lucid and non-lucid REM sleep (Voss et al., 2009 , 2014 ; Dresler et al., 2012 ), one cannot exclude that an increase of lucid REM to the detriment of non-lucid REM may alter or diminish the outcome of regulation processes known to be at play during non-lucid sleep (Walker and van der Helm, 2009 ; Perogamvros and Schwartz, 2013 ; Ahuja et al., 2018 ; Lewis et al., 2018 ; Tempesta et al., 2018 ).

There are several reasons to fear an adverse effect on sleep and health of a regular use of LD induction methods or of an increased LD frequency, since (1) LD induction methods alter sleep integrity and (2) the brain state during LD is neither that of wake nor that of REM sleep, but rather a hybrid one that is naturally infrequent. Such concerns regarding the possible danger of LD training for sleep integrity are acknowledged on the web. On Google Search's top listing 1 (at the time of writing) for “lucid dreaming,” one can read “ Another concern is that engaging in lucid dreaming requires focus and effort, which might mean that the sleeper does not get enough rest.” Yet, such acknowledgment are mostly absent from the current scientific literature, and only a handful of studies have investigated the potential downsides of LD. The few existing experimental works are not visible and confirm the feared prediction by showing a significant relationship between LD frequency and poor sleep quality (Schadow et al., 2018 ; N = 1824). Similarly, Mota et al. ( 2016 ) showed that LD practice may further empower deliria and hallucinations in a psychotic population.

Our goal is therefore to draw attention to the fact that, as of today, we do not have a well-educated and clear idea of the consequence that training and cultivating LD may have on sleep integrity and more generally on health. This is even more important to highlight that there is a tendency in scientific and lay publications toward encouraging LD and not mentioning the possible side effects of LD training methods (e.g., Hobson, 2009 ; Mota-Rolim and Araujo, 2013 ; Stumbrys et al., 2016 ; Dyck et al., 2017 ). For example, Dyck et al. ( 2017 ) encourage to increase LD induction methods duration without mentioning possible adverse effect on sleep “ Future studies should extend the training period and increase participants' motivation by using social media technology in order to evaluate what techniques might be beneficial in a home setting for a group of participants not specifically selected for high interest in lucid dreaming .” One can further read in Mota-Rolim and Araujo ( 2013 ): “ LD may allow for motor imagery during dreaming with possible improvement of physical rehabilitation ,” and in Stumbrys et al. ( 2016 ): “ Lucid dreaming practice provides a more realistic simulation of the waking environment than mental practice and could be alternatively used when an athlete is injured, unable to practice physically or actions are dangerous […] While only a limited number of athletes have lucid dreams on a frequent basis, there is a wide range of techniques that can be used for lucid dream induction .” In these two latter publications LD is encouraged to achieve what could be done as effectively by motor imagery during wake (i.e., improved motor performance, as shown by the authors in Stumbrys et al., 2016 ), and without mentioning the possible side effects of LD practice on sleep. LD is also recommended in several publications (e.g., Mota-Rolim and Araujo, 2013 ; Morgenthaler et al., 2018 ; Sparrow et al., 2018 ) as a possible way to diminish nightmare frequency, even though several behavioral techniques preserving sleep are working very efficiently for this matter (e.g., Krakow and Zadra, 2006 ; Casement and Swanson, 2012 ; Putois et al., 2019 ; Imagery Rehearsal Therapy).

Our opinion is thus that one needs to be cautious and responsible regarding recommendations to practice LD training methods and a state (LD) whose consequences on health are unknown and understudied. To improve the safety of experimental use of LD in research or as a recreational activity, future studies would need to investigate the above-discussed downsides of LD induction methods practice and of LD frequency increase, and characterize them.

In this opinion paper, we draw the attention to the possible adverse effect of LD on sleep and health. There are several reasons leading to fear that LD, and especially training to increase LD frequency, may be detrimental to normal sleep and notably to the sleep-related regulation processes. Our aim is to encourage future studies to recognize the lack of knowledge regarding possible side effects of LD induction methods or LD frequency increase, as well as to investigate such side effects to better characterize what they are and in which context they appear.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

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

Abbreviations

1 https://www.medicalnewstoday.com/articles/323077.php#12

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IMAGES

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Lucid Dreaming: Definition, Techniques, Uses
Lucid Dreams: What They Are, and Why They're Awesome
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STUDIES » LUCID DREAM RESEARCH

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COMMENTS

The cognitive neuroscience of lucid dreaming
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 ...
Findings From the International Lucid Dream Induction Study
These widespread limitations are a major impediment to lucid dream research and make it difficult to compare the effectiveness of techniques across studies. Several additional lucid dream induction studies have been published since the publication of Stumbrys et al. (2012). Taitz (2011) found that daily RT for 2 weeks was ineffective.
Here's what lucid dreamers might tell us about our sleeping minds
Some aspiring lucid dreamers also use a tactic called "wake-back-to-bed.". They wake up extremely early in the morning, stay up for a while, then get more shut-eye. That jolt of alertness ...
The neuroscience of lucid dreaming: Past, present, future
Lucid dreaming research has come a long way since Aristotle, from detailed self-descriptions in the late 19 th and early 20 th centuries and its neuroscientific validation in the 1970s and 1980s to the momentum it experiences today, with an increasing number of research groups worldwide entering the field. Witnessing this ongoing maturation as ...
Frequent lucid dreaming associated with increased functional
The frequent lucid dream group reported a median of 75 lucid dreams in the last 6 months, a median of 90 lucid dreams for the highest number of lucid dreams in any 6-month period, and reported ...
Induction of lucid dreams: A systematic review of evidence
Finally, the overall trend regarding the number of studies carried out in lucid dream research is alarming. Out of 37 manuscripts included in this review, two were published in 1970s, 16 in 1980s, 15 in 1990s and only four in 2000s. After a "golden age" of lucid dream research in 1980s and 90s, the scientific interest in lucid dreams seems ...
The cognitive neuroscience of lucid dreaming
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 ...
New Frontiers in Lucid Dreaming
Later research built upon this work to further investigate the nature of lucid dreaming (e.g., Erlacher & Schredl, 2004). As you can imagine, most of these studies have had small numbers of ...
Research
Research. Knowledge is gained through science so research plays a critical role at our organization. Overall, our aim is studying the induction of lucid dreams and any associated risks/benefits. To accomplish this research, we utilize an empirical approach which means we use evidence. and scientific principles when drawing our conclusions.
How to Lucid Dream: Expert Tips and Tricks
Some studies have linked these characteristics to elevated cortical activity. In sleepers who have been observed during lucid dream studies, prefrontal cortex activity levels while they are engaged in lucid dreaming are comparable to levels when they are awake. For this reason, lucid dreaming may be referred to as a "hybrid sleep-wake state."
Organization for Lucid Dream Studies
Research. Dive into the science of lucid dreaming and learn how to. get involved as a participant, a researcher, or a supporter. Find additional information on lucid dreaming research being conducted by our organization. Learn More. A 501 (c)3 nonprofit organization dedicated to lucid dreaming. We provide educational programs as well as conduct ...
Dream Studies Portal
A balanced and diverse gateway into dream research, consciousness studies, and culture including integral perspectives for lucid dreaming, dream interpretation, and cognitive anthropology. This doorway into dream research and consciousness studies focuses on dream work, sleep, holistic wellness and the healing arts. ...
Bridging lucid dream research and transpersonal psychology: Toward
Despite the fact that lucid dream research and transpersonal psychology have common grounds, overlapping interests and a great potential to contribute to each other, the two fields over the recent decades evolved rather separately. The present article aims to renew the mutual dialogue by introducing the recent advancements of lucid dream research to the transpersonal community, discussing the ...
(PDF) Bridging lucid dream research and transpersonal psychology
This empirical study aimed to explore the relationship between lucid dreaming and spirituality, taking into account the role of mystical lucid dream experience, in an online sample of 471 ...
Lucid Dreaming Research Archive
Gackenbach, J. (2010). Psychological considerations in pursuing lucid dreaming research: Commentary on "The neurobiology of consciousness: Lucid dreaming wakes up" by J. Allan Hobson. International Journal of Dream Research, 3(1), 11-12. ... Hickey, D. A. (1988a). A psychophysiological and self-report study of lucid dreams in school-age ...
Key Concepts in Dream Research: Cognition and Consciousness Are
Introduction. Whilst lucid dreaming (LD) is defined as being aware of dreaming whilst dreaming, a misconception exists in the public domain as a referral to controlling dream content and plot (Neuhäusler et al., 2018).This misconception reflects a number of widely-held beliefs about the nature of dreaming, which in part this commentary will seek to explain and rectify.
Research Surveys
Surveys. Gain a deeper understanding of yourself by completing research surveys designed by scientists from around the world. Your participation helps push the boundaries of science and uncover potential applications of lucid dreaming in various fields like mental health, learning, and creativity. Our research relies on the invaluable insights ...
LUCID DREAM RESEARCH » This is a network for lucid dreamers and
This is a network for lucid dreamers and scientists to share research information and lucid dream experiences, connect for future studies, and expand the knowledge of the lucid dream phenomenon. ... An interesting study on the relation between "Type 2″ thinking and lucid dreaming… Hopefully, more studies relating to this field will follow ...
Is It a Good Idea to Cultivate Lucid Dreaming?
In line with this idea, a case fMRI study showed that lucid REM sleep was associated with a reactivation of areas that are normally deactivated during REM sleep, ... The public perception of lucid dreaming and its research. Int. J. Dream Res. 11, 186-196. 10.11588/ijodr.2018.2.51105 [Google Scholar]

ORIGINAL RESEARCH article

Introduction

Materials and Methods

Pre-test Questionnaire

Lucid Dream Induction Technique Documents

Group 1: MILD + WBTB (No RT)

Group 2: MILD + WBTB + RT Breath

Group 3: MILD + WBTB + RT Hands

Group 4: MILD + WBTB (No RT)

Group 5: SSILD + WBTB (No RT)

Group 6: SSILD/MILD Hybrid + WBTB

Preliminary Analyses

Relationships With Lucid Dreaming

Lucid Dream Induction

Relationships With Technique Practice Variables

Additional Exploratory Analyses

General Discussion

Lucid Dream Induction Techniques

The Mnemonic Induction of Lucid Dreams (MILD) Technique

The Senses Initiated Lucid Dream (SSILD) Technique

Predictors and Effects of Lucid Dream Induction

General Dream Recall

Technique Practice Variables

Time Taken to Return to Sleep

Effects of Lucid Dream Induction on Sleep

Strengths and Limitations

Directions for Future Research

Data Availability Statement

Ethics Statement

Author Contributions

Conflict of Interest

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Introduction

Demographic and behavioral results

Voxel-based morphometry (VBM)

Seed-based whole-brain resting-state functional connectivity

IPL/IPS subdivision analysis

Large-scale functional resting-state networks analysis

Whole-brain graph-theoretic analysis

Summary of main findings

Lucid dreaming and brain connectivity

Limitations, methodological considerations and future directions

Participants

Individual differences in lucid dreaming and dream recall frequency

Behavioral and questionnaire assessment

MRI acquisition

Structural (T1) data processing

Resting-state fMRI (EPI) data processing

Seed-based whole-brain functional connectivity

Angular gyrus (AG)/intra-parietal sulcus (IPS) subdivision analysis

Large-scale networks (LSNs) analysis

Graph-theoretic network analysis

Data Availability

Acknowledgements