thesis about lucid dreams

ORIGINAL RESEARCH article

Findings from the international lucid dream induction study.

• 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]

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Dreams and Dreaming

Dreams and dreaming have been discussed in diverse areas of philosophy ranging from epistemology to ethics, ontology, and more recently philosophy of mind and cognitive science. This entry provides an overview of major themes in the philosophy of sleep and dreaming, with a focus on Western analytic philosophy, and discusses relevant scientific findings.

1.1 Cartesian dream skepticism

1.2 earlier discussions of dream skepticism and why descartes’ version is special, 1.3 dreaming and other skeptical scenarios, 1.4 descartes’ solution to the dream problem and real-world dreams, 2.1 are dreams experiences, 2.2 dreams as instantaneous memory insertions, 2.3 empirical evidence on the question of dream experience, 2.4 dreams and hallucinations, 2.5 dreams and illusions, 2.6 dreams as imaginative experiences, 2.7 dreaming and waking mind wandering, 2.8 the problem of dream belief, 3.1 dreaming as a model system and test case for consciousness research, 3.2 dreams, psychosis, and delusions, 3.3 beyond dreams: dreamless sleep experience and the concepts of sleep, waking, and consciousness, 4. dreaming and the self, 5. immorality and moral responsibility in dreams, 6.1 the meaning of dreams, 6.2 the functions of dreaming, 7. conclusions, other internet resources, related entries, 1. dreams and epistemology.

Dream skepticism has traditionally been the most famous and widely discussed philosophical problem raised by dreaming (see Williams 1978; Stroud 1984). In the Meditations , Descartes uses dreams to motivate skepticism about sensory-based beliefs about the external world and his own bodily existence. He notes that sensory experience can also lead us astray in commonplace sensory illusions such as seeing things as too big or small. But he does not think such cases justify general doubts about the reliability of sensory perception: by taking a closer look at an object seen under suboptimal conditions, we can easily avoid deception. By contrast, dreams suggest that even in a seemingly best-case scenario of sensory perception (Stroud 1984), deception is possible. Even the realistic experience of sitting dressed by the fire and looking at a piece of paper in one’s hands (Descartes 1641: I.5) is something that can, and according to Descartes often does, occur in a dream.

There are different ways of construing the dream argument. A strong reading is that Descartes is trapped in a lifelong dream and none of his experiences have ever been caused by external objects (the Always Dreaming Doubt ; see Newman 2019). A weaker reading is that he is just sometimes dreaming but cannot rule out at any given moment that he is dreaming right now (the Now Dreaming Doubt ; see Newman 2019). This is still epistemologically worrisome: even though some of his sensory-based beliefs might be true, he cannot determine which these are unless he can rule out that he is dreaming. Doubt is thus cast on all of his beliefs, making sensory-based knowledge slip out of reach.

Cartesian-style skeptical arguments have the following form (quoted from Klein 2015):

• If I know that p , then there are no genuine grounds for doubting that p .
• U is a genuine ground for doubting that p .
• Therefore, I do not know that p .

If we apply this to the case of dreaming, we get:

• If I know that I am sitting dressed by the fire, then there are no genuine grounds for doubting that I am really sitting dressed by the fire.
• If I were now dreaming, this would be a genuine ground for doubting that I am sitting dressed by the fire: in dreams, I have often had the realistic experience of sitting dressed by the fire when I was actually lying undressed in bed!
• Therefore, I do not know that I am now sitting dressed by the fire.

Importantly, both strong and weak versions of the dream argument cast doubt only on sensory-based beliefs, but leave other beliefs unscathed. According to Descartes, that 2+3=5 or that a square has no more than 4 sides is knowable even if he is now dreaming:

although, in truth, I should be dreaming, the rule still holds that all which is clearly presented to my intellect is indisputably true. (Descartes 1641: V.15)

By Descartes’ lights, dreams do not undermine our ability to engage in the project of pure, rational enquiry (Frankfurt 1970; but see Broughton 2002).

Dream arguments have been a staple of philosophical skepticism since antiquity and were so well known that in his objections to the Meditations , Hobbes (1641) criticized Descartes for not having come up with a more original argument. Yet, Descartes’ version of the problem, more than any other, has left its mark on the philosophical discussion.

Earlier versions tended to touch upon dreams just briefly and discuss them alongside other examples of sensory deception. For example, in the Theaetetus (157e), Plato has Socrates discuss a defect in perception that is common to

dreams and diseases, including insanity, and everything else that is said to cause illusions of sight and hearing and the other senses.

This leads to the conclusion that knowledge cannot be defined through perception.

Dreams also appear in the canon of standard skeptical arguments used by the Pyrrhonists. Again, dreams and sleep are just one of several conditions (including illness, joy, and sorrow) that cast doubt on the trusthworthiness of sensory perception (Diogenes Laertius, Lives of Eminent Philosophers; Sextus Empiricus, Outlines of Pyrrhonism) .

Augustine ( Against the Academics ; Confessions) thought the dream problem could be contained, arguing that in retrospect, we can distinguish both dreams and illusions from actual perception (Matthew 2005: chapter 8). And Montaigne ( The Apology for Raymond Sebond ) noted that wakefulness itself teems with reveries and illusions, which he thought were even more epistemologically worrisome than nocturnal dreams.

Descartes devoted much more space to the discussion of dreaming and cast it as a unique epistemological threat distinct from both waking illusions and evil genius or brain-in-a-vat-style arguments. His claim that he has often been deceived by his dreams implies he also saw dreaming as a real-world (rather than merely hypothetical) threat.

This is further highlighted by the intimate, first-person style of the Meditations . Their narrator is supposed to exemplify everyone’s epistemic situation, illustrating the typical defects of the human mind. Readers are further drawn in by Descartes’ strategy of moving from commonsense examples towards more sophisticated philosophical claims (Frankfurt 1970). For example, Descartes builds up towards dream skepticism by first considering familiar cases of sensory illusions and then deceptively realistic dreams.

Finally, much attention has been devoted to several dreams Descartes reportedly had as a young man. Some believe these dreams embodied theoretical doubts he developed in the Discourse and Meditations (Baillet 1691; Leibniz 1880: IV; Cole 1992; Keefer 1996). Hacking (2001:252) suggests that for Descartes, dream skepticism was not just a philosophical conundrum but a source of genuine doubt. There is also some discussion about the dream reports’ authenticity (Freud 1940; Cole 1992; Clarke 2006; Browne 1977).

In the Meditations , after discussing the dream argument, Descartes raises the possibility of an omnipotent evil genius determined to deceive us even in our most basic beliefs. Contrary to dream deception, Descartes emphasizes that the evil genius hypothesis is a mere fiction. Still, it radicalizes the dream doubt in two respects. One, where the dream argument left the knowability of certain general truths intact, these are cast in doubt by the evil genius hypothesis . Two, where the dream argument, at least on the weaker reading, involves just temporary deception, the evil genius has us permanently deceived.

One modernized version, the brain-in-a-vat thought experiment, says that if evil scientists placed your brain in a vat and stimulated it just right, your conscious experience would be exactly the same as if you were still an ordinary, embodied human being (Putnam 1981). In the Matrix -trilogy (Chalmers 2005), Matrixers live unbeknownst to themselves in a computer simulation. Unlike the brain-in-a-vat , they have bodies that are kept alive in pods, and flaws in the simulation allow some of them to bend its rules to their advantage.

Unlike dream deception, which is often cast as a regularly recurring actuality (cf. Windt 2011), brain-in-a-vat-style arguments are often thought to be merely logically or nomologically possible. However, there might be good reasons for thinking that we actually live in a computer simulation (Bostrom 2003), and if we lend some credence to radical skeptical scenarios, this may have consequences for how we act (Schwitzgebel 2017).

Even purely hypothetical skeptical scenarios may enhance their psychological force by capitalizing on the analogy with dreams. Clark (2005) argues that the Matrix contains elements of “industrial-strength deception” in which both sensory experience and intellectual functioning are exactly the same as in standard wake-states, whereas other aspects are more similar to the compromised reasoning and bizarre shifts that are the hallmark of dreams.

At the end of the Sixth Meditation , Descartes suggests a solution to the dream problem that is tied to a reassessment of what it is like to dream. Contrary to his remarks in the First Meditation , he notes that dreams are only rarely connected to waking memories and are often discontinuous, as when dream characters suddenly appear or disappear. He then introduces the coherence test:

But when I perceive objects with regard to which I can distinctly determine both the place whence they come, and that in which they are, and the time at which they appear to me, and when, without interruption, I can connect the perception I have of them with the whole of the other parts of my life, I am perfectly sure that what I thus perceive occurs while I am awake and not during sleep. (Meditation VI. 24)

For all practical purposes, he has now found a mark by which dreaming and waking can be distinguished (cf. Meditation I.7), and even if the coherence test is not fail-safe, the threat of dream deception has been averted.

Descartes’ remarks about the discontinuous and ad hoc nature of many dreams are backed up by empirical work on dream bizarreness (see Hobson 1988; Revonsuo & Salmivalli 1995). Still, many of his critics were not convinced this helped his case against the skeptic. Even if Descartes’ revised phenomenological description characterizes most dreams, one might occasionally merely dream of successfully performing the test (Hobbes 1641), and in some dreams, one might seem to have a clear and distinct idea but this impression is false (Bourdin 1641). Both the coherence test and the criterion of clarity and distinctness would then be unreliable.

How considerations of empirical plausibility impact the dream argument continues to be a matter of debate. Grundmann (2002) appeals to scientific dream research to introduce an introspective criterion: when we introspectively notice that we are able to engage in critical reflection, we have good reason to think that we are awake and not dreaming. However, this assumes critical reasoning to be uniformly absent in dreams. If attempts at critical reasoning do occur in dreams and if they generally tend to be corrupted, the introspective criterion might again be problematic (Windt 2011, 2015a). There are also cases in which even after awakening, people mistake what was in fact a dream for reality (Wamsley et al. 2014). At least in certain situations and for some people, dream deception might be a genuine cause of concern (Windt 2015a).

2. The ontology of dreams

In what follows, the term “conscious experience” is used as an umbrella term for the occurrence of sensations, thoughts, impressions, emotions etc. in dreams (cf. Dennett 1976). These are all phenomenal states: there is something it is like to be in these states for the subject of experience (cf. Nagel 1974). To ask about dream experience is to ask whether it is like something to dream while dreaming, and whether what it is like is similar to (or relevantly different from) corresponding waking experiences.

Cartesian dream skepticism depends on a seemingly innocent background assumption: that dreams are conscious experiences. If this is false, then dreams are not deceptive experiences during sleep and we cannot be deceived, while dreaming, about anything at all. Whether dreams are experiences is a major question for the ontology of dreams and closely bound up with dream skepticism.

The most famous argument denying that dreams are experiences was formulated by Norman Malcolm (1956, 1959). Today, his position is commonly rejected as implausible. Still, it set the tone for the analysis of dreaming as a target phenomenon for philosophy of mind.

For Malcolm, the denial of dream experience followed from the conceptual analysis of sleep: “if a person is in any state of consciousness it logically follows that he is not sound asleep” (Malcolm 1956: 21). Following some remarks of Wittgenstein’s (1953: 184; see Chihara 1965 for discussion), Malcolm claimed

the concept of dreaming is derived, not from dreaming, but from descriptions of dreams, i.e., from the familiar phenomenon that we call “telling a dream”. (Malcolm 1959:55)

Malcolm argued that retrospective dream reports are the sole criterion for determining whether a dream occurred and there is no independent way of verifying dream reports. While first-person, past-tense psychological statements (such as “I felt afraid”) can at least in principle be verified by independent observations (but see Canfield 1961; Siegler 1967; Schröder 1997), he argued dream reports (such as “in my dream, I felt afraid”) are governed by different grammars and merely superficially resemble waking reports. In particular, he denied dream reports imply the occurrence of experiences (such as thoughts, feelings, or judgements) in sleep:

If a man had certain thoughts and feelings in a dream it no more follows that he had those thoughts and feelings while asleep, than it follows from his having climbed a mountain in a dream that he climbed a mountain while asleep. (Malcolm 1959/1962: 51–52)

What exactly Malcolm means by “conscious experience” is unclear. Sometimes he seems to be saying that conscious experience is conceptually tied to wakefulness (Malcolm 1956); other times he claims that terms such as mental activity or conscious experience are vague and it is senseless to apply them to sleep and dreams (Malcolm 1959: 52).

Malcolm’s analysis of dreaming has been criticized as assuming an overly strict form of verificationism and a naïve view of language and conceptual change. A particularly counterintuitive consequence of his view is that there can be no observational evidence for the occurrence of dreams in sleep aside from dream reports. This includes behavioral evidence such as sleepwalking or sleeptalking, which he thought showed the person was partially awake; as he also thought dreams occur in sound sleep, such sleep behaviors were largely irrelevant to the investigation of dreaming proper. He also claimed adopting a physiological criterion of dreaming (such as EEG measures of brain activity during sleep) would change the concept of dreaming, which he argued was tied exclusively to dream reporting. This claim was particularly radical as it explicitly targeted the discovery of REM sleep and its association with dreaming (Dement & Kleitman 1957), which is commonly regarded as the beginning of the science of sleep and dreaming. Malcolm’s position was that the very project of a science of dreaming was misguided.

Contra Malcolm, most assume that justification does not depend on strict criteria with the help of which the truth of a statement can be determined with absolute certainty, but “on appeals to the simplicity, plausibility, and predictive adequacy of an explanatory system as a whole” (Chihara & Fodor 1965: 197). In this view, behavioral and/or physiological evidence can be used to verify dream reports (Ayer 1960) and the alleged principled difference between dream reports and other first-person, past-tense psychological sentences (Siegler 1967; Schröder 1997) disappears.

Putnam noted that Malcolm’s analysis of the concept of dreaming relies on the dubious idea that philosophers have access to deep conceptual truths that are hidden to laypeople:

the lexicographer would undoubtedly perceive the logical (or semantical) connection between being a pediatrician and being a doctor, but he would miss the allegedly “logical” character of the connection between dreams and waking impressions. […] this “depth grammar” kind of analyticity (or “logical dependence”) does not exist. (Putnam 1962 [1986]: 306)

Nagel argued that even if one accepts Malcolm’s analysis of the concept of dreaming,

it is a mistake to invest the demonstration that it is impossible to have experiences while asleep with more import than it has. It is an observation about our use of the word “experience”, and no more. It does not imply that nothing goes on in our minds while we dream. (Nagel 1959: 114)

Whether dream thoughts, feelings or beliefs should count as real instances of their kind now becomes an open question, and in any case there is no conceptual contradiction involved in saying one has experiences while asleep and dreaming.

To ask about dream experience is also to ask whether there is something it is like to dream during sleep as opposed to there just being something it is like to remember dreaming after awakening. Dennett’s (1976, 1979) cassette theory says dreams are the product of instantaneous memory insertion at the moment of the awakening, as if a cassette with pre-scripted dreams had been inserted into memory, ready for replay. Dennett claims the cassette theory and the view that dreams are experiences can deal equally well with empirical evidence for instance on the relationship between dreaming and REM sleep. The cassette theory is preferable because it is more parsimonious, positing only an unconscious dream composition process rather than an additional conscious presentation process in sleep. For Dennett, the important point is that it is impossible to distinguish between the two rival theories based on dream recall; the question of dream experience should be settled by independent empirical evidence.

While Dennett shares Malcolm’s skepticism about dream experience, this latter claim is diametrically opposed to Malcolm’s rejection of a science of dreaming. For Dennett, the unreliability of dream recall also is not unique, but exemplifies a broader problem with memory reports: we generally cannot use retrospective recall to distinguish conscious experience from memory insertion (Dennett 1991; see also Emmett 1978).

An earlier and much discussed (Binz 1878; Goblot 1896; Freud 1899; Hall 1981; Kramer 2007:22–24) version of Dennett’s cassette theory goes back to Maury’s (1861) description of a long and complex dream about the French revolution that culminated in his execution at the guillotine, at which point Maury suddenly awoke to find that the headboard had fallen on his neck. Because the dream seemed to systematically build up to this dramatic conclusion, which in turn coincided with a sudden external event, he suggested that such cases were best explained as instantaneous memory insertions experienced at the moment of awakening. Similarly, Gregory (1916) described dreams are psychical explosions occurring at the moment of awakening.

The trustworthiness of dream reports continues to be contentious. Rosen (2013) argues that dream reports are often fabricated and fail to accurately describe experiences occurring during sleep. By contrast, Windt (2013, 2015a) argues that dream reports can at least under certain conditions (such as in laboratory studies, when dreams are reported immediately after awakening by trained participants) be regarded as trustworthy sources of evidence with respect to previous experience during sleep.

Unlike Malcolm, many believe that whether dreams are experiences is an empirical question; and unlike Dennett, the predominant view is that the empirical evidence does indeed support this claim (Flanagan 2000; Metzinger 2003; Revonsuo 2006; Rosen 2013; Windt 2013, 2015a).

A first reason for thinking that dreams are experiences during sleep is the relationship between dreaming and REM (rapid eye movement) sleep. Researchers in the 1950s discovered that sleep is not a uniform state of rest and passivity, but there is a sleep architecture involving different stages of sleep that is relatively stable both within and across individuals (Aserinsky & Kleitman 1953, 1955; Dement & Kleitman 1957). Following sleep onset, periods of non-REM (or NREM) sleep including slow wave sleep (so called because of the presence of characteristic slow-wave, high-voltage EEG activity) are followed by periods of high-frequency, low-voltage activity during REM sleep. EEG measures from REM sleep strongly resemble waking EEG. REM sleep is additionally characterized by rapid eye movements and a near-complete loss of muscle tone (Dement 1999: 27–50; Jouvet 1999).

The alignment between conscious experience on the one hand and wake-like brain activity and muscular paralysis on the other hand would seem to support the experiential status of dreams as well as explain the outward passivity that typically accompanies them. Reports of dreaming are in fact much more frequent following REM (81.9%) than NREM sleep awakenings (43%; Nielsen 2000). REM reports tend to be more elaborate, vivid, and emotionally intense, whereas NREM reports tend to be more thought-like, confused, non-progressive, and repetitive (Hobson et al. 2000). These differences led to the idea that REM sleep is an objective marker of dreaming (Dement & Kleitman 1957; Hobson 1988: 154).

Attempts to identify dreaming with mental activity during REM sleep have not, however, been successful, and many now hold that dreams can occur in all stages of sleep (e.g., Antrobus 1990; Foulkes 1993b; Solms 1997, 2000; Domhoff 2003; Nemeth & Fazekas 2018). In recent years there has been renewed interest in NREM sleep for the study of dreaming (Noreika et al. 2009; Siclari et al. 2013, 2017). This suggests the inference from the physiology of REM sleep to the phenomenology of dreaming is not straightforward.

A second line of evidence comes from lucid dreams, or dreams in which one knows one is dreaming and often has some level of dream control (Voss et al. 2013; Voss & Hobson 2015; Baird et al. 2019). The term lucid dreaming was coined by van Eeden (1913), but Aristotle ( On Dreams ) already noted that one can sometimes be aware while dreaming that one is dreaming.

Scientific evidence that lucid dreaming is real and a genuine sleep phenomenon comes from laboratory studies (Hearne 1978; LaBerge et al. 1981) showing lucid dreamers can use specific, pre-arranged patterns of eye movements (e.g., right-left-right-left) to signal in real-time that they are now lucid and engaging in dream experiments. These signals are clearly identifiable on the EOG and suggest a correspondence between dream-eye movements and real-eye movements (as predicted by the so-called scanning hypothesis ; see Dement & Kleitman 1957; Leclair-Visonneau et al. 2010). Retrospective reports confirm that the dreamer really was lucid and signalled lucidity (Dresler et al. 2012; Stumbrys et al. 2014).

Signal-verified lucid dreams have been used to study muscular activity accompanying body movements in dreams (Erlacher et al. 2003; Dresler et al. 2011), for advanced EEG analysis of brain activity during lucid dreaming (Voss et al. 2009), and imaging studies (Dresler et al. 2011, 2012). Eye signals can also be used to measure the duration of different activities performed in lucid dreams; contrary to the cassette theory, lucid dreams have temporal extension and certain dream actions even seem to take slightly longer than in waking (Erlacher et al. 2014). There have also been attempts to induce lucidity through non-invasive electrical stimulation during sleep (Stumbrys et al. 2013; Voss et al. 2014). The combination of signal-verified lucid dreaming with volitional control over dream content, retrospective report, and objective sleep measures has been proposed to provide controlled conditions for the study of conscious experience in sleep and a new methodology for investigating the relationship between conscious experience and neurophysiological processes (Baird et al.2019).

A third line of evidence (Revonsuo 2006: 77) comes from dream-enactment behavior (Nielsen et al. 2009), most prominently in patients with REM-sleep behavior disorder (RBD; Schenck & Mahowald 1996; Schenck 2005; Leclair-Visonneau et al. 2010). Due to a loss of the muscular atonia that accompanies REM sleep in healthy subjects, these patients show complex, seemingly goal-directed outward behaviors such as running or fighting off an attacker during REM sleep. Retrospective dream reports often match these behaviors, suggesting that patients literally act out their dreams during sleep.

While persuasive, these lines of evidence might not satisfy skeptics about dream experience. They might worry that results from lucid dreaming and dream enactment do not generalize to ordinary, non-lucid dreams; they might also construe alternative explanations that do not require conscious experience in sleep. There are also methodological concerns, for instance about how closely sleep-behaviors actually match dream experience. A key issue is that to support the experiential status of dreams, evidence from sleep polysomnography, signal verified lucid dreams, or sleep behavior requires convergence with retrospective dream reports. This means trusting dream reports is built into any attempt to empirically resolve the question of dream experience – which then invites the familiar skeptical concerns. Again, an anti-skeptical strategy may be to appeal to explanatory considerations. In this view, the convergence of dream reports and objective polysomnographic or behavioral observations is best explained by the assumption that dreams are experiences in sleep, and this assumption is strengthened by further incoming findings. This strategy places dream reports at the center of scientific dream research while avoiding the contentious claim that their trustworthiness, and with it the experiential status of dreams, can be demonstrated conclusively by independent empirical means (Windt 2013, 2015a).

Even where philosophers agree dreams are experiences, they often disagree on how exactly to characterize dreaming relative to wake-state psychological terms. Often, questions about the ontology of dreaming intersect with epistemological issues. Increasingly, they also incorporate empirical findings.

The standard view is that dreams have the same phenomenal character as waking perception in that they seemingly put us in contact with mind-independent objects, yet no such object is actually being perceived. This means dreams count as hallucinations in the philosophical sense (Crane & French 2017; Macpherson 2013). Even if, in a particularly realistic dream, my visual experience was exactly as it would be if I were awake (I could see my bedroom, my hands on the bed sheets, etc.), as long as my eyes were closed during the episode, I would not, literally, be seeing anything.

There is some controversy in the psychological literature about whether dreams should be regarded as hallucinations. Some believe the term hallucination should be reserved for clinical contexts and wake-state pathologies (Aleman & Larøi 2008: 17; but see ffytche 2007; ffytche et al. 2010).

The view that dreams involve hallucinations is implicit in Descartes’ assumption that even when dreaming,

it is certain that I seem to see light, hear a noise, and feel heat; this cannot be false, and this is what in me is properly called perceiving ( sentire ). (Descartes 1641: II.9)

It also lies at the heart of Aristotle’s ( On Dreams ) assumption that dreams result from the movements of the sensory organs that continue even after the original stimulus has ceased. He believed that in the silence of sleep, these residual movements result in vivid sensory imagery that is subjectively indistinguishable from genuine perception (see also Dreisbach 2000; Barbera 2008).

The assumption of phenomenological equivalence between dream and waking experience can also be found in Berkeley’s (1710: I.18) idealist claim that the existence of external bodies is not necessary for the production of vivid, wake-like perceptual experience. Similarly, Russell defended sense-data theory by noting that in dreams,

I have all the experiences that I seem to have; it is only things outside my mind that are not as I believe them to be while I am dreaming. (Russell 1948: 149–150)

Elsewhere, he argued dreams and waking life

must be treated with equal respect; it is only by some reality not merely sensible that dreams can be condemned. (Russell 1914: 69)

Hume was less clear on this matter, proposing that dreams occupy an intermediate position between vivid and largely non-voluntary sensory impressions and ideas, or “the faint images of previous impressions in thinking and reasoning” (Hume 1739: 1.1.1.1). On the one hand, as mere creatures of the mind, Hume wanted to categorize dreams as ideas. On the other hand, he acknowledged that in sleep, “our ideas can approach the vivacity of sensory impressions” (Hume 1739: 1.1.1.1). Dreams do not fit comfortably into Hume’s attempt to draw a dichotomous distinction between impressions, including perception, and ideas, including sensory imagination (Ryle 1949; Waxman 1994; Broughton 2006).

Phenomenologists often focus not so much on the quality of dream imagery as on the overall character of experience, noting that dreams are experienced as reality; as in waking perception, we simply feel present in a world. This also sets dreams apart from waking fantasy and daydreams (Husserl 1904/1905; Uslar 1964; Conrad 1968; Globus 1987: 89.

At its strongest, the hallucination view claims that dreaming and waking experience are identical in both the quality of sensory imagery and their overall, self-in-a-world structure (Revonsuo 2006: 84). This claim is central to the virtual reality metaphor , according to which consciousness itself is dreamlike and waking perception a kind of online hallucination modulated by the senses (Llinás & Ribary 1994; Llinás & Paré 1991; Revonsuo 2006; Metzinger 2003, 2009).

This seems to be empirically supported. Neuroimaging studies (Dang-Vu et al. 2007; Nir & Tononi 2010; Desseilles et al. 2011) show that the predominance of visual and motor imagery as well as strong emotions in dreams is paralleled by high activation of the corresponding brain areas in REM sleep, which may exceed waking; at the same time, the cognitive deficits often thought to characterize dreams such as the loss of self-awareness, the absence of critical thinking, delusional reasoning, and mnemonic deficits fit in well with the comparative deactivation of frontal areas (Hobson et al. 2000). Hobson (1988, Hobson et al. 2000) has argued that the vivid, hallucinatory character of dreaming results from the fact that in REM sleep, the visual and motor areas are activated in the same way as in waking perception, the sole difference being dreams’ dependence on internal signal generation. Horikawa and colleagues (2013) used neuroimaging data from sleep onset to predict the types of objects described in mentation reports, which they took to support the perceptual equivalence between dreaming and waking.

Generally, versions of the hallucination view that suggest dreams replicate all aspects of waking perception are too vague to be informative. Especially for subtle perceptual activities (such as visual search), we might not know enough about dream phenomenology to make any strong claims (Nielsen 2010). Specifying points of similarity leads to a more informative and precise, but likely also more nuanced view. Dreams are heterogeneous, and some might be more perception-like while others resemble imagination (Windt 2015a). There might also be differences between or even within specific types of imagery. For example, visual imagery might be quite different from touch sensations, which tend to be rare in dreams (Hobson 1988). Visual dream imagery might overall resemble waking perception but lack color saturation, background detail and focus (Rechtschaffen & Buchignani, 1992). Classifying dreams as either hallucinatory or imaginative is further complicated by the fact that there is strong overlap in cortical activity associated with both visual imagery and perception (Zeidman & Maguire, 2016). This means even a strong overlap in cortical activity between, say, visual dream imagery and visual perception does not necessarily set dreaming apart from waking imagination.

This is also true for evidence on eye movements in dreams. LaBerge and colleagues (2018) recently showed that eye tracking of objects is smooth in lucid dreaming and perceiving, but not in imagining. Drawing from this evidence, Rosen (forthcoming) suggests many dreams mimic the phenomenology of interacting with a stable world, including eye movements and visual search. Others argue we should not analogize dream imagery to mind-independent, scannable objects and that eye movements might instead be implicated in the generation of dream imagery (Windt 2018).

Another way to make sense of the claim that dreaming has the same phenomenal character as waking perception is to say some kinds of dream imagery are illusory: they involve misperception of an external object as having different properties than it actually has (cf. Smith 2002; Crane & French 2017). The illusion view disagrees with the hallucination view on whether dreams have a contemporaneous external stimulus source.

The illusion view has fallen out of favor but has a long history. The Ancients believed dreams have bodily sources. This idea underlies the practice of using dreams to diagnose illness, as practiced in the shrines at Epidaurus (Galen On Diagnosis in Dreams ; van de Castle 1994). Aristotle ( On Dreams ) thought some dreams are caused by indigestion, and Hobbes adopted this view, claiming different kinds of dreams could be traced to different bodily sensations. For instance, “lying cold breedeth Dreams of Feare, and raiseth the thought and Image of some fearfull object” (Hobbes 1651: 91).

Appeals to the bodily sources of dreaming became especially popular in the 19 th and early 20 th centuries. Many believed specific dream themes such as flying were linked to sleeping position (Macnish 1838; Scherner 1861; Vold 1910/1912; Ellis 1911) and realizing, in sleep, that one’s feet are not touching the ground (Bergson 1914).

There were also attempts to explain the phenomenology of dreaming by appealing to the absence of outward movement. The lack of appropriate feedback and of movement and touch sensations was thought to cause dreams of being unable to move (Bradley 1894) or of trying but failing to do something (Gregory 1918).

Some proponents of the “ Leibreiztheorie ” (or somatic-stimulus theory) of dreaming attempted to go beyond anecdotal observations to conduct controlled experiments. Weygandt (1893) investigated the influence of various factors including breathing, blood circulation, temperature changes, urge to urinate, sleeping position, and visual or auditory stimulation during sleep on dream content (see Schredl 2010 for details). Singer (1924) proposed experiments on stimulus incorporation in dreams can inform claims on the ontology of dreaming: If dreams are sensations, a particular auditory stimulus should increase the frequency of dreams in nearby sleepers as well as the frequency of sound in their dreams, and it should decrease the range of quality and intensity of these dreams, making them overall more similar and predictable.

Newer studies provide evidence for the incorporation of external stimuli in dreams, including light flashes, sounds, sprays of water applied to the skin (Dement & Wolpert 1958), thermal (Baldridge 1966), electrical (Koulack 1969), and verbal stimuli (Berger 1963; Breger et al. 1971; Hoelscher et al., 1981), as well as blood pressure cuff stimulation on the leg (Nielsen et al. 1995; Sauvageau et al. 1998).

Muscular activity also often leaves its mark on dreams. It occurs throughout sleep but is especially frequent in REM sleep, mostly in the form of twitching but occasionally also in the form of larger, seemingly goal-directed movements (Blumberg 2010; Blumberg & Plumeau 2016). The relation between outward and dream movements is complex: in some cases, outward movements might mirror dream movements, while in others, sensory feedback might prompt dream imagery (Windt 2018).

Generally, it seems external and bodily stimuli can be related to varying degrees to dream and sleep onset imagery (Nielsen 2017; Windt 2018; Windt et al. 2016). Some of these cases appear to fit the concept of illusion, as in when the sound of the alarm clock is experienced, in a dream, as a siren, or when blood pressure cuff inflation on the leg leads to dreams of wearing strange shoes (Windt 2018; for these and other examples, see Nielsen et al. 1995). In other cases, such as when blood pressure cuff stimulation on the leg prompts a dream of seeing someone else’s leg being run over, describing this as illusory misperception might be less straightforward.

Saying that dreams can be prompted by external stimuli and that in some cases these are best described as illusions is different from the stronger claim, sometimes advanced by historical proponents of somatic-stimulus theory, that dreams generally are caused by external or bodily stimuli. As an example of the stronger claim, consider Wundt’s proposal that the

ideas which arise in dreams come, at least to a great extent, from sensations, especially from those of the general sense, and are therefore mostly illusions of fancy, probably only seldom pure memory ideas which hence become hallucinations. (Wundt 1896: 179)

This claim is likely too strong. It is also likely that appeals to external or bodily stimuli on their own cannot fully explain dream imagery, including when and how external stimuli are incorporated in dreams. Sensory incorporation in dreams is often hard to predict and indirect; associated imagery seems related not just to stimulus intensity, but also to short- and long term memories. A full explanation of dream content additionally has to take the cognitive and memory sources of dreaming into account (Windt 2018; Nielsen 2017; cf. Silberer 1919).

The most important rival to the hallucination view is that dreams are imaginative experiences (Liao & Gendler 2019; Thomas 2014). This can mean dream imagery involves imaginings rather than percepts (including hallucinations or illusions; McGinn 2004), that dream beliefs are imaginative and not real beliefs (Sosa 2007), or both (Ichikawa 2008, 2009). An important advantage is that by assimilating dreams to commonplace mental states such as waking fantasy and daydreaming, rather than a rare and often pathological occurrence such as hallucinations, it provides a more unified account of mental life (Stone 1984). However, the reasons for adopting the imagination view are diverse, and dreams have been proposed to resemble imaginings and differ from perception along a number of dimensions (e.g. McGinn 2004, 2005a,b; Thomas 2014). This issue is complicated by the fact that there is little agreement on the definition of imagination and its relation to perception (Kind 2013).

One way is to deny dreams involve presence or the feeling of being in a world, which many believe is central to waking perception. Imagination theorists compare the sense in which we feel present in our dreams to cognitive absorption, as when we are lost in a novel, film, or vivid daydream (Sartre 1940; McGinn 2004; but see Hering 1947; Globus 1987). Some argue that reflexive consciousness or meta-awareness (as in lucid dreams) interrupts cognitive absorption and terminates the ongoing dream (Sartre 1940), essentially denying lucid dreams are possible.

Another issue is whether dreams are subject to the will (Ichikawa 2009). Imagination is often characterized as active and under our control (Wittgenstein 1967: 621, 633), involving “a special effort of the mind” (Descartes 1641: VI, 2), whereas perception is passive. Because dreams just seem to happen to us without being under voluntary control, they present an important challenge for the imagination view. Ichikawa (2009) argues lucid control dreams show dreams are generally subject to the will even where they are not under deliberate control.

Dreams are widely described as more indeterminate than waking perception (James 1890: 47; Stone 1984). In scientific dream research, vagueness is regarded as one of three main subtypes of bizarreness (Hobson 1988; Revonsuo & Salmivalli 1995). An example are dream characters who are identified not by their behavior or looks, but by just knowing (Kahn et al. 2000, 2002; Revonsuo & Tarkko 2002). Dreams are also attention-dependent and lack foreground-background structure (Thompson 2014); while it is tempting to construe the dream world as rich in detail, there is no more to dreams than meets the eye, and many think dream experience is exhausted by what is the focus of selective attention (Hunter 1983; Thompson 2014).

Indeterminacy is also related to the question of whether we dream in color or in black and white. Based on a review of historical and recent studies, Schwitzgebel (2002, 2011) argues there has been a shift in theories on dream color that coincides with the rise first of black-and-white and then color television. He argues it is unlikely that dreams themselves changed from colored to black and white and back to colored, proposing that a change in opinion is a more plausible explanation. Maybe dreams were either black and white or colored all along; or maybe they are indeterminate with respect to color, as may be the case for imagined or fictional objects; were this the case, it would strengthen the imagination view (Ichikawa 2009). Schwitzgebel’s main point is that reports of colored dreaming are unreliable and our opinions about dreams can be mistaken (but see Windt 2013, 2015a). This relates to Schwitzgebel’s (2011; Hurlburt & Schwitzgebel 2007) general skepticism about the reliability of introspection.

The issue of dream color has led to a number of follow-up studies (Schwitzgebel 2003; Schwitzgebel et al. 2006; Murzyn 2008; Schredl et al. 2008; Hoss 2010). They suggest most people dream in color and a small percentage describe grayscale or even mixed dreams (Murzyn 2008) or dreams involving moderate color saturation (Rechtschaffen and Buchignani 1992). Indeterminacy is rarely reported.

The imagination view has consequences for Cartesian dream skepticism. If dream pain does not feel like real pain, there is a fail-safe way to determine whether one is now dreaming: one need only pinch oneself (Nelson 1966; Stone 1984; but see Hodges & Carter 1969; Kantor 1970). As Locke put it,

if our dreamer pleases to try, whether the glowing heat of a glass furnace, be barely a wandering imagination in a drowsy man’s fancy, by putting his hand into it, he may perhaps be wakened into a certainty greater than he could wish, that it is something more than bare imagination. (Locke 1689: IV.XI.8)

If dreaming feels different from waking, this raises the question why we tend to describe dreams in the same terms as waking perception. Maybe this is because most people haven’t thought about these matters and they would find the imagination view plausible if they considered it (Ichikawa 2009). Or maybe

it is just because we all know that dreams are throughout un like waking experiences that we can safely use ordinary expressions in the narration of them. (Austin 1962: 42)

Some authors classify dreams as imaginings while acknowledging they feel like perceiving. For example, Hobbes describes dreams as “the imaginations of them that sleep” (Hobbes 1651: 90), and imagination as a “ decaying sense ” (Hobbes 1651: 88). Yet he also uses the concepts of imagination and fancy to describe perception and argues “their appearance to us is Fancy, the same waking, that dreaming” (Hobbes 1651: 86).

In the scientific literature, the imagination view is complemented by cognitive theories. Foulkes (1978: 5) describes dreaming as a form of thinking with its own grammar and syntax, but allows that dream imagery is sufficently perception-like to deceive us. Domhoff’s neurocognitive model of dreaming (2001, 2003) emphasizes the dependence of dreaming on visuospatial skills and on a network including the association areas of the forebrain. The theory draws from findings on the partial or global cessation of dreaming following brain lesions (cf. Solms 1997, 2000), evidence that dreaming develops gradually and in tandem with visuospatial skills in children (Foulkes 1993a, 1999; but see Resnick et al. 1994), and results from dream content analysis supporting the continuity of dreaming with waking concerns and memories (the so-called continuity hypothesis ; see Domhoff 2001, 2003; Schredl & Hofmann 2003; Schredl 2006; see also Nir & Tononi 2010).

A number of researchers have begun to consider dreaming in the context of theories of mind wandering. Mind wandering is frequent in waking and involves spontaneous thoughts that unfold dynamically and are only weakly constrained by ongoing tasks and environmental demands (Schooler et al. 2011; Smallwood & Schooler 2015; Christoff et al. 2016). Based on phenomenological and neurophysiological similarities, dreams have been proposed to be an intensified form of waking mind wandering (Pace-Schott 2007, 2013; Domhoff 2011; Wamsley 2013; Fox et al. 2013). This basic idea seems to have been anticipated by Leibniz, who noted that the spontaneous formation of visions in dreams surpasses the capacity of our waking imagination (Leibniz, Philosophical Papers and Letters , Vol. I, 177–178).

The analogy between dreams and waking mind wandering has been discussed in the context of cognitive agency. Metzinger (2013a,b, 2015) describes dreams and waking mind wandering as involving a cyclically recurring loss of mental autonomy, or the ability to deliberately control one’s conscious thought processes. Dreams and waking mind wandering are not mental actions but unintentional mental behaviors, comparable to subpersonal processes such as breathing or heartbeat. Because dreaming and waking mind wandering make up a the majority of our conscious mental lives, he argues that cognitive agency and mental autonomy are the exception, not the rule.

This raises the question of how to make sense of lucid control dreams, which involve both meta-awareness and agency. Windt and Voss (2018) argue that in such cases, spontaneous processes including imagery formation co-exist alongside more deliberate, top-down control; they also argue metacognitive insight and control themselves can have spontaneous elements. This suggests spontaneity and control are not opposites, but a more complex account is needed. Possibly, certain dreams and instances of waking mind wandering can be both spontaneous and agentive.

The analogy with mind wandering might help move forward the debate on the ontology of dreaming. In this debate, a common assumption is that dreams can be categorized as either hallucinatory or imaginative. Yet the application of these terms to dreams quickly runs into counterexamples and it is unclear they are mutually exclusive. One option is pluralism (Rosen 2018b), in which some aspects of dreaming are hallucinatory, others imaginative, and yet again others illusory. Another is that dreams are sui generis, combining aspects associated with wake states such as hallucinating, imagining, or perceiving in a novel manner without mimicking them completely. Windt (2015a) proposes that mind wandering, which describes a range of mental states loosely characterized by their spontaneous and dynamic character, might be particularly suitable for the characterization of dreaming precisely because that term leaves open more specific questions on the phenomenology of dreaming, allowing for variation in control, determinacy, and so on. This might be a good starting point for describing what is unique about dreaming while also acknowledging continuities across sleep-wake states and capitalizing on the strengths of the hallucination, illusion, imagination, and cognitive views.

The second strand of the imagination view argues that dream beliefs are not real beliefs, but propositional imaginings. This may or may not be combined with the claim that dream imagery is imaginative rather than perceptual (Sosa 2007; Ichikawa 2009).

Denying that dream beliefs have the status of real beliefs only makes sense before the background of a specific account of what beliefs are and how they are distinguished from other mental states such as delusions or propositional imaginings. For instance, Ichikawa (2009) argues that if we follow interpretationist or dispositionalist accounts of belief, dream beliefs fall short of real beliefs. He claims dream beliefs lack connection with perceptual experience and fail to motivate actions; consequently, they do not have the same functional role as real beliefs. Moreover, we cannot ascribe dream beliefs to a person by observing them lying asleep in bed. Dream beliefs are often inconsistent with longstanding waking beliefs and acquired and discarded without any process of belief revision (Ichikawa 2009).

This analysis of dream beliefs has consequences for skepticism. If dream beliefs are propositional imaginings, then we do not falsely believe while dreaming that we are now awake, but only imagine that we do (Sosa 2007). It is not clear though that this protects us from deception. If dream beliefs fall short of real beliefs, this might even make the specter of dream deception more worrisome: in mistaking dream beliefs for the real thing, we would now be deceived about the status of our own mental states (Ichikawa 2008).

It is also not clear whether the same type of argument extends to mental states other than beliefs. As Lewis points out, a person might

in fact believe or realize in the course of a dream that he was dreaming, and even if we said that, in such case, he only dreamt that he was dreaming, this still leaves it possible for someone who is asleep to entertain at the time the thought that he is asleep. (Lewis 1969: 133)

Mental states other than believing such as entertaining, thinking, or minimally appraisive instances of taking for granted might be sufficient for deception (Reed 1979).

The debate about dream beliefs is paralleled by a debate about whether delusions are beliefs or imaginings (see Currie 2000; Currie & Ravenscroft 2002; McGinn 2004; Bayne & Pacherie 2005; Bortolotti 2009; Gendler 2013). Both debates might plausibly inform each other, especially as dreams are sometimes proposed to be delusional (Hobson 1999).

3. Dreaming and theories of consciousness

Dreams are a global state of consciousness in which experience arises under altered behavioral and neurophysiological conditions as compared to standard wakefulness; unlike other altered states of consciousness (such as drug-induced or deep meditative states) and pathological wake states (such as psychosis or neurological syndromes), dreams occur spontaneously and regularly in healthy subjects. For both reasons, many regard dreams as a test case for theories of consciousness or even an ideal model system for consciousness research (Churchland 1988; Revonsuo 2006).

Existing proposals differ on the phenomenology of dreaming: referring to dream bizarreness, Churchland describes dream experience as robustly different from waking, whereas Revonsuo argues dreaming is similar to waking and the purest form of experience:

the dreaming brain brings out the phenomenal level of organization in a clear and distinct form. Dreaming is phenomenality pure and simple, untouched by external physical stimulation or behavioural activity. (Revonsuo 2006: 75)

Revonsuo argues dreaming reveals the basic, state-independent structure of consciousness to be immersive: “dreaming depicts consciousness first and foremost as a subjective world- for-me ” (Revonsuo 2006: 75). This leads him to introduce the “world-simulation metaphor of consciousness”, according to which consciousness itself is essentially simulational and dreamlike. This is taken to support internalism about conscious experience.

This latter claim is also contentious. Noë (2004: 213) argues that phenomenological differences between dreaming and waking (such as greater instability of visual dream imagery) result from the lack of dynamic interaction with the environment in dreams. He proposes this shows that neural states are sufficient for dreaming but denies they are also sufficient for perceptual experience.

A possible problem for both views is their reliance on background assumptions about the phenomenology of dreaming and its disconnection from environmental stimuli and bodily sensations. Windt (2015a, 2018) argues both internalism and externalism mistakenly assume dreams to be isolated from external sensory input and own-body perception; she believes both the phenomenology of dreaming and its correlation with external stimuli are complex and variable. She argues the analysis of dreaming does not clearly support either side in the debate on internalism vs externalism (but see Rosen 2018a). Generally, in the absence of a well worked out theory of dreaming and its sleep-stage and neural correlates, proposals for using dreaming as a model system or test case run the risk of relying on an oversimplified description of the target phenomenon (Windt & Noreika 2011).

Recent accounts appealing to generative models and predictive processing (Clark 2013b; Hohwy 2013) suggest a new, unified account of perception, imagination, and dreaming. In these accounts, different mental states, including perception and action, embody different strategies of hypothesis testing and prediction error minimization. Perception is the attempt to model the hidden external causes of sensory stimuli; action involves keeping the internal model stable while changing the sensory input. Clark argues that on such a model,

systems that know how to perceive an object as a cat are thus systems that, ipso facto , are able to use a top-down cascade to bring about the kinds of activity pattern that would be characteristic of the presence of a cat. […] Perceivers like us, if this is correct, are inevitably potential dreamers and imaginers too. Moreover, they are beings who, in dreaming and imagining, are deploying many of the very same strategies and resources used in ordinary perception. (Clark 2013a: 764)

Predictive processing accounts have also been used to explain specific features of dreaming. Bizarreness has been associated with the comparative lack of external stimulus processing, implying dream imagery is relatively unconstrained by prediction errors (cf. Hobson & Friston 2012; Fletcher & Frith 2008; Bucci & Grasso 2017). Windt (2018) suggests a predictive processing account of dream imagery generation that links bodily self-experience to own-body perception and subtle motor behaviors such as twitching in REM sleep (Blumberg 2010; Blumberg & Plumeau 2016). She argues that movement sensations in dreams, in relation to REM-sleep related muscle twitching, involve a form of bodily self-sampling in which coordinated muscular activity contributes to the generation and maintenance of a body model. This is important because in predictive processing accounts neither the bodily nor the external causes of sensory inputs are known; at the same time, having an accurate body model is a prerequisite for action, requiring the system to disambiguate between self- and other generated changes to sensory inputs. Especially in early development, sleep might provide the ideal conditions for exploring one’s own body via subtle but coordinated muscular activity while processing of visual and auditory stimuli is reduced.

Dreams have also been suggested as a test case for whether phenomenal consciousness can be divorced from cognitive access (e.g., Block 2007; but see Cohen & Dennett 2011). Sebastián (2014a) argues that dreams provide empirical evidence that conscious experience can occur independently of cognitive access. This is because during (non-lucid) REM-sleep dreams, the dorsolateral prefrontal cortex (dlPFC) as the most plausible mechanism underlying cognitive access is selectively deactivated (see also Pantani et al. 2018). This would challenge theories linking conscious experience to access, such as higher-order-thought theory (Sebastián 2014b). However, both the hypoactivation of the dlPCF in REM sleep and its association with cognitive access have been debated. Fazekas and Nemeth (2018) suggest that certain kinds of cognitive access may be independent of dlPFC activation, necessitating a more complex account.

Dreaming has been suggested as a model system not just of waking consciousness in general, but also of psychotic wake states in particular. The analogy between dreaming and madness has a long philosophical history (Plato, Phaedrus ; Kant 1766; Schopenhauer 1847) and finds particularly stark expression in Hobson’s claim that “dreaming is not a model of a psychosis. It is a psychosis. It’s just a healthy one” (Hobson 1999: 44). Gottesmann (2006) proposes dreaming as a neurophysiological model of schizophrenia. There is a rich discussion on the theoretical and methodological implications of dream research for psychiatry (see Scarone et al. 2007; d’Agostino et al. 2013; see Windt & Noreika 2011 as well as the other papers in this special issue) and a number of studies have investigated differences in dream reports from schizophrenic and healthy subjects (Limosani et al. 2011a,b).

Rather than likening dreaming to waking in general or specific wake states such as psychosis, there have also been attempts to compare specific dream phenomena to wake-state delusions. Gerrans (2012, 2013, 2014) focuses on character misidentification in dreams and delusions of hyperfamiliarity (such as the Frégoli delusion, in which strangers are mistakenly identified as family members, and déjà vu ) to argue that anomalous experience and faulty reality testing both play a role in delusion formation. Rosen (2015) analyzes instances of thought insertion and of auditory hallucinations, which are key symptoms of schizophrenia, to raise broader questions about the altered sense of agency in dreams as compared to waking.

Philosophers have focused almost exclusively on dreaming, largely leaving to the side questions about dreamless sleep including whether it is uniformly unconscious. In recent years there has been a surge of interest in the possibility of dreamless sleep experience and foundational issues about the definition of sleep and waking. This has been paralleled by growing interest in dreaming in NREM sleep.

Conceptually, interest in dreamless sleep experience has been facilitated by the precise definition of dreaming offered by simulation views (Revonsuo et al. 2015). If dreams are immersive sleep experiences characterized by their here -and- now structure, it makes sense to ask whether this is true for all or just a subset of sleep-related experiences and whether non-immersive sleep experiences exist. By contrast, if dreaming is broadly identified with any conscious mentation in sleep (Pagel et al. 2001), there is no conceptual space for dreamless sleep experience.

Following Thompson's (2014, 2015) discussion of dreamless sleep in Indian and Buddhist philosophy, Windt and colleagues (2016; see also Windt 2015b) introduce a framework for different kinds of dreamless sleep experience ranging from thinking and isolated imagery, perception, or bodily sensations, where these lack integration into a scene, to minimal kinds of experience lacking imagery or specific thought contents. A possible example of minimal phenomenal experience in sleep are white dreams, where people report having had experiences during sleep but cannot remember any details. Taken at face value, some white dream reports might describe experiences that lack reportable content (Windt 2015b); others might describe forgotten dreams or dreams with degraded content (Fazekas et al. 2018). Another example are reports of witnessing dreamless sleep, as described in certain meditation practices. This state is said to involve non-conceptual awareness of sleep, again in the absence of imagery or specific thought contents, and loss of sense of self (Thompson 2014, 2015). Some schools in Buddhist philosophy explain claims of deep and dreamless sleep by saying we never fully lose consciousness in sleep (Prasad 2000, 66; and Thompson 2014, 2015).

Empirically, interest in dreamless sleep experience is paralleled by increasing interest in experiences in NREM sleep (Fazekas et al. 2018). Most researchers now accept that dreaming is not confined to REM sleep, but also occurs at sleep onset and in NREM sleep. The deeper stages of NREM sleep are particularly interesting as they involve roughly similar proportions of dreaming, unconscious sleep, and white dreams (Noreika et al. 2009: Siclari et al. 2013, 2017). In the search for the neural correlates of dreaming vs unconscious dreamless sleep, this makes comparisons within the same sleep stage possible and avoids confounds involved in comparing presumably dreamful REM sleep with presumably dreamless NREM sleep. Findings suggest that activity in the same parietal hot zone underlies dreaming in both NREM and REM sleep (Siclari et al. 2017).

Where sleep and dream research have traditionally tried to identify the sleep stage correlates of dreaming, newer research suggests local changes occurring independently of sleep stages might in fact be more relevant. Traditionally regarded as global, whole-brain phenomena, there is now increasing evidence that sleep itself is locally driven, and local changes in sleep depth might be associated with changes in sleep-related experience (Siclari & Tononi 2017; Andrillon et al. 2019). While sleep and dream research are often considered as separate fields, changes in how sleep in general and sleep stages in particular are defined appear closely associated with changes in the theoretical conception of dreaming and its empirical investigation.

Historically, discoveries about dreaming have precipitated changing conceptions of sleep (for an excellent history of the study of sleep and dreaming, see Kroker 2007). Following Aristotle ( On Sleeping and Waking ), sleep was traditionally defined in negative terms as the absence of wakefulness and perception. This is still reflected in Malcolm’s assumption that “to a person who is sound asleep, ‘dead to the world’, things cannot even seem” (Malcolm 1956: 26). With the discovery of REM sleep, sleep came to be regarded as a heterogeneous phenomenon characterized by the cyclic alteration of different sleep stages. REM sleep was now considered as “neither sleeping nor waking. It was obviously a third state of the brain, as different from sleep as sleep is from wakefulness” (Jouvet 1999: 5). The folk-psychological dichotomy between sleep and wakefulness now seemed oversimplified and empirically implausible. At the same time dreaming, which had previously been considered as an intermediate state of half-sleeping and half-waking, came to be regarded as a genuine sleep phenomenon, but narrowed to REM sleep. Today, the framework for describing dreams and other sleep-related experiences is more precise, but dreaming has also been cast adrift from REM sleep.

A closely associated issue is how to define waking. Crowther’s (2018) capacitation thesis casts waking consciousness as a state in which the individual is fully switched on to their environment, but also to their own epistemic (cf. O’Shaugnessy 2002) and agentive potential; the waking individual is empowered to act and think in certain ways, though this potential need not be actualized. By contrast, dreaming is an “imagining-of consciousness” (O’Shaughnessy 2002: 430) and consciousness is conceptually tied to wakefulness. Because in lucid dreams, the epistemic and agentive profile of waking is at least partly realized, they might, according to Crowther, be regarded as closer to waking than nonlucid dreams.

This account of waking and sleep may also have consequences for the imagination model of dreaming and dream skepticism (Soteriou 2017). As in the imagination model, dreaming would be passive and action, including cognitive agency, would be tied to waking. If dreaming nonetheless involved passive episodes of imagining oneself to be active, one would be unable to tell that one were dreaming and imagining, as this insight would require the exercise of real agency. The sceptical consequence would be that when dreaming, one would lose agency as well as the capacity to gain insight into one’s current state. Yet our ability to know we are waking when waking would be unscathed; according to Soteriou, waking would thus have an epistemic function connected to the capacity to exercise agency over our mental lives.

Finally, definitions of consciousness themselves are bound up with conceptions of sleep and dreaming. As dreaming went from a state whose experiential status was doubted to being widely recognized as a second global state of consciousness, consciousness sometimes came to be defined contrastively as that which disappears in deep, dreamless sleep and reappears in waking and dreaming (Searle 2000; Tononi 2008). In light of dreamless sleep experience, such definitions are problematic (Thompson 2014, 2015; Windt 2015b; Windt et al. 2016). Dreamless sleep experience has been proposed to be particularly relevant for understanding minimal phenomenal experience, or the conditions under which the simplest kind of conscious experience arises (Windt 2015b). The investigation of dreamless sleep might thus shed light on the transition from unconscious sleep to sleep-related experience.

We almost always have a self in dreams, though this self can sometimes be a slightly different (e.g. older or younger) version of our waking self or even a different person entirely. Dreams therefore raise interesting questions about the identity between the dream and waking self. Locke (1689) invites us to imagine two men alternating in turns between sleep and wakefulness and sharing one continuously thinking soul (Locke 1689: II.I.12). He argues that if one man retained no memory of the soul’s thoughts and perceptions while it was linked to the other man’s body, they would be distinct persons. His position is that personal identity depends on psychological continuity, including recall: in the absence of recall, as illustrated by the toy example of two people sharing one soul, continuous conscious thinking does not suffice for identity. Locke also rejects the possibility of unrecalled dreams and the idea that we dream throughout sleep, remembering only a small proportion of our dreams (Locke 1689: II.I.19).

Valberg distinguishes between the subject of the dream (i.e., the dream self) and the sleeping person who is the dreamer of the dream and recalls it upon awakening (Valberg 2007). He argues that awakening from a dream involves crossing a chasm between discrete worlds with discrete spaces and times; it does not make sense to say that “the ‘I’ at these times [is] a single individual who crosses from one world to the other” (Valberg 2007: 69). According to Valberg, this is relevant to dream skepticism because there is no simple way to make sense of the claims that it is I who emerge from a dream or that I was the victim of dream deception.

Vicarious dreams, or dreams in which the protagonist of the dream seems to be a different person from the dreamer, are particularly puzzling with respect to identity. They may even raise the question of whether the dream self has an independent existence (Rosen & Sutton 2013: 1047). Such dreams are superficially similar to cases in which we imagine being another person, but according to Rosen and Sutton require a different explanation: in the case of dreaming, the imagined person’s thoughts are not framed as diverging from one’s own and one does not retain one’s own perspective in addition to the imagined one; in nonlucid dreams, only the perspective of the dream’s protagonist is retained.

The dream self is also at the center of simulation views of dreaming, which define dreaming via its immersive, here and now character as the experience of a self in a world. This leads to further questions about the phenomenology of self-experience in dreams and how it is different from waking self-experience. Different versions of the simulation view focus on different aspects of self- and world experience in dreams, ranging from social simulation (Revonsuo et al. 2015) to the typical features of selfhood in dreams (Revonsuo 2005, 2006, Metzinger 2003, 2009) to the minimal conditions for experiencing oneself as a self in dreams and what this tells us about minimal phenomenal selfhood in general (Windt 2015a, 2018). Yet these different versions of the simulation view are largely complementary and together have forged unity in a field that was previously hampered by lack of agreement about the definition of dreaming. They also integrate the philosophy of dreaming and scientific dream research.

As so often in debates about dreaming, there is disagreement about basic phenomenological questions. Revonsuo (2005) describes self-experience including bodily experience in dreams as identical to waking, whereas Metzinger (2003, 2009; see also Windt & Metzinger 2007) argues that important layers of waking self-experience (such as autobiographical memory, agency, a stable first-person perspective, metacognitive insight, and self-knowledge) are missing in nonlucid dreams. He argues this is due to the cognitive and mnemonic deficit that characterizes nonlucid dreams (cf. Hobson et al. 2000). Windt (2015a) analyzes the range of cognitive and bodily self-experience in dreams, both of which she describes as variable. She argues that in a majority of cases, dreams are weakly phenomenally embodied states in which bodily experience is largely related to movement sensations but a detailed and integrated body representation is lacking; instead, bodily experience in dreams is largely indeterminate (for an attempt to test this empirically, see Koppehele-Gossel et al. 2016). She proposes this is because dreams are also weakly functionally embodied states, in which the specific pattern of bodily experience reflects altered processing of bodily sensations (as in the illusion view). She also analyzes instances of bodiless dreams, in which dreamers say they experienced themselves as disembodied entities, to argue that self-experience can be reduced to pure spatiotemporal-self-location (Windt 2010); she proposes these cases can help identify the conditions for the emergence of minimal phenomenal selfhood (Blanke & Metzinger 2009; see also Metzinger 2013b).

How the phenomenology of dreaming compares to waking and what to say about how the dream self relates to the waking self bears on questions about the moral status of dreams. For Augustine ( Confessions ) dreams were a cause of moral concern because of their indistinguishability from waking life. What particularly worried him about dreams of sexual acts was their vividness, as well as the feeling of pleasure and seeming acquiescence or consent on the part of the dreamer. He concluded, however, that the transition from sleep to wakefulness involves a radical chasm, enabling the dreamer to awaken with a clear conscience and absolving them from taking responsibility for their dream actions.

What exactly Augustine thought the chasm between dreaming and waking consists in allows for different interpretations (Matthews 1981). Firstly, if the dream and waking self are not identical, then waking Augustine is not morally responsible for dream-Augustine’s actions. Secondly, actions performed in dreams might be morally irrelevant because they did not really happen. And thirdly, assuming that moral responsibility requires the ability to act otherwise, dreams provide no grounds for moral concern because we cannot refrain from having certain types of dreams.

The issue of dream immorality may also present a choice point between different accounts of moral evaluation. Where internalists assume the moral status of a person’s actions is entirely determined by internal factors such as intentions and motives, externalists look beyond these to the effects of actions. Driver (2007) argues that the absurdity of dream immorality itself should count against purely internalist accounts; yet she also acknowledges this absurdity is not a necessary feature of dreams.

Central to the question of dream immorality is the status of dreams as actions rather than mere behaviors. Mullane (1965) argues that while we don’t have full control over our dreams, they are not completely involuntary either; as is the case for blushing, considerable effort is required to attain control over our dreams and in some cases they can even be considered as actions. That lucid dream control is, to some extent, a learnable skill (Stumbrys et al. 2014) lends some support to this claim.

6. The meaning of dreams and the functions of dreaming

Philosophical discussions of dreaming tend to focus on (a) dream deception and (b) questions about the ontology of dreaming, its moral status, etc., that tend to intersect with dream skepticism. By contrast, the main source of interest in dreams outside of philosophy traditionally has been dream interpretation and whether dreams are a source of knowledge and insight. Historically, the epistemic status of dreams and the use of prophetic and diagnostic dreams was not just a theoretical, but a practical problem (Barbera 2008). Different types of dreams were distinguished by their putative epistemic value. Artemidorus, for instance, used the term enhypnion to refer to dreams that merely reflect the sleeper’s current bodily or psychological state and hence do not merit further interpretation, whereas he reserved the term oneiron for meaningful and symbolic dreams of divine origin.

The practice of dream interpretation was famously attacked by Aristotle in On Prophecy in Sleep . He denied that dreams are of divine origin, but allowed that occasionally, small affections of the sensory organs as might stem from distant events that cannot be perceived in waking are perceptible in the quiet of sleep. He also believed such dreams were mostly likely to occur in dullards whose minds resemble an empty desert – an assessment that was not apt to encourage interest in dreams (Kroker 2007: 37). A similarly negative view was held by early modern philosophers who believed dreams were often the source of superstitious beliefs (Hobbes 1651; Kant 1766; Schopenhauer 1847).

In Freudian dream theory, dream interpretation once more assumed a prominent role as the royal road to knowledge of the unconscious. This was associated with claims about the psychic sources of dreaming. Freud (1899) also rejected the influence of external or bodily sources, as championed by contemporary proponents of somatic-stimulus theory.

In the neuroscience of dreaming, Hobson famously argued that dreams are the product of the random, brain-stem driven activation of the brain during sleep (Hobson 1988) and at best enable personal insights in the same way as a Rorschach test (Hobson et al. 2000). Dennett (1991) illustrates the lack of design underlying the production of dream narratives through the “party game of psychoanalysis”, which involves an aimless game of question-and-answer. In the game, players follow simple rules to jointly produce narratives that can seem symbolic and meaningful, even though no intelligent and deliberate process of narration was involved.

Even if we grant that dreams are not messages from a hidden entity in need of decoding, this does not imply that dream interpretation cannot be a personally meaningful source of insight and creativity (Hobson & Wohl 2005). Whether and under which conditions, and following which methods, dream interpretation can lead to personally significant insights is an empirical question that is only beginning to be investigated systematically (see Edwards et al. 2013).

Finally, throughout history, views on the epistemic status of dreams and the type of knowledge to be gained from dream interpretation (e.g., knowledge about the future, diagnosis of physical illness, or insights about one’s current concerns) often changed in tandem with views on the origin and sources of dreaming, which gradually moved from divine origins and external sources, via the body, to the unconscious, and finally to the brain.

Different theories on the functions of dreaming have been proposed and the debate is ongoing. An important distinction is between the functions of sleep stages and the functions of dreaming. Well-documented functions of REM sleep include thermoregulation and the development of cortical structures in birds and mammals, as well as neurotransmitter repletion, the reconstruction and maintenance of little-used brain circuits, the structural development of the brain in early developmental phases, as well as the preparation of a repertoire of reflexive or instinctive behaviors (Hobson 2009). Yet none of these functions are obviously linked to dreaming. An exception is protoconsciousness theory, in which REM sleep plays an important role in foetal development by providing a virtual world model even before the emergence of full-blown consciousness (Hobson 2009: 808) .

Numerous studies have investigated the contribution of sleep to memory consolidation, with different sleep stages promoting different types of memories (Diekelmann et al. 2009; Walker 2009). However, only a few studies have investigated the relationship between dream content and memory consolidation in sleep (for a review, see Nielsen & Stenstrom 2005). Dreams rarely involve episodic replay of waking memories (Fosse et al. 2003). The incorporation of memory sources seems to follow a specific temporal pattern in which recent memories are integrated with older but semantically related memories (Blagrove et al. 2011). Nielsen (2017) presents a model of how external and bodily stimuli on one hand and short- and long-term memories on the other hand form seemingly novel, complex, and dreamlike images at sleep onset; he proposes these microdreams shed light on the formation and sources of more complex dreams. There is also some evidence that dream imagery might be associated with memory consolidation and task performance after sleep, though this is preliminary (Wamsley & Stickgold 2009, 2010; Wamsley et al. 2010).

Prominent theories on the function of dreaming focus on bad dreams and nightmares. It has long been thought that dreaming contributes to emotional processing and that this is particularly obvious in the dreams of nightmare sufferers or in dreams following traumatic experiences (e.g., Hartmann 1998; Nielsen & Lara-Carrasco 2007; Levin & Nielsen 2009; Cartwright 2010; Perogamvros et al. 2013). Based on the high prevalence of negative emotions and threatening dream content, threat simulation theory suggests that the evolutionary function of dreaming lies in the simulation of ancestral threats and the rehearsal of threatening events and avoidance skills in dreams has an adaptive value by enhancing the individual’s chances of survival (see Revonsuo 2000; Valli 2008). A more recent proposal is social simulation theory, in which social imagery in dreams supports social cognition, bonds, and social skills. (Revonsuo et al. 2015).

An evolutionary perspective can also be fruitfully applied to specific aspects of dream phenomenology. According to the vigilance hypothesis , natural selection disfavored the occurrence of those types of sensations during sleep that would compromise vigilance (Symons 1993). Dream sounds, but also smells or pains might distract attention from the potentially dangerous surroundings of the sleeping subject, and the vigilance hypothesis predicts that they only rarely occur in dreams without causing awakening. By contrast, because most mammals sleep with their eyes closed and in an immobile position, vivid visual and movement hallucinations during sleep would not comprise vigilance and thus can occur in dreams without endangering the sleeping subject. Focusing on the stuff dreams are not made of might then be at least as important for understanding the function of dreaming as developing a positive account.

Finally, even if dreaming in general and specific types of dream content in particular were found to be strongly associated with specific cognitive functions, it would still be possible that dreams are mere epiphenomena of brain activity during sleep (Flanagan 1995, 2000). It is also possible that the function of dreams is not knowable (Springett 2019).

A particular problem for any theory on the function of dreaming is to explain why a majority of dreams are forgotten and how dreams can fulfill their putative function independently of recall. Crick and Mitchinson (1983) famously proposed that REM sleep “erases” or deletes surplus information and unnecessary memories, which would suggest that enhanced dream recall is counterproductive. Another problem is that dreaming can be lost selectively and independently of other cognitive deficits (Solms 1997, 2000).

Some of the problems that arise for theories on the functions of dreaming can be avoided if we do not assume that dreaming has a specific function, separate from the function(s) of conscious wakeful states. This depends on the broader taxonomy of dreaming in relation to wakeful states. For example, if dreaming is continuous with waking mind wandering, imagination, and/or own-body perception, we should not expect it to have a unique function, but rather to express a similar function as these wakeful states, perhaps to varying degrees. Nor should we expect dreams to have a single function; the functions of dreaming might be as varied and complex as those of consciousness, and given the complexity of the target phenomenon, the failure to pin down a single function should not be surprising (Windt 2015a).

Questions about dreaming in different areas of philosophy such as epistemology, ontology, philosophy of mind and cognitive science, and ethics are closely intertwined. Scientific evidence from sleep and dream research can meaningfully inform the philosophical discussion and has often done so in the past. The discussion of dreaming has also often functioned as a lens on broader questions about knowledge, morality, consciousness, and self. Long a marginalized area, the philosophy of dreaming and of sleep is central to important philosophical questions and increasingly plays an important role in interdisciplinary consciousness research, for example in the search for the neural correlates of conscious states, in conscious state taxonomies, and in research on the minimal conditions for phenomenal selfhood and conscious experience.

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belief | Berkeley, George | delusion | Descartes, René: epistemology | imagination | Locke, John | perception: the problem of | personal identity | personal identity: and ethics | Plato: on knowledge in the Theaetetus | sense data | skepticism | skepticism: and content externalism

Acknowledgments

I want to thank Regina Fabry and two anonymous reviewers for helpful comments and constructive criticism on an earlier version of this manuscript. And as always, I am greatly indebted to Stefan Pitz for his support.

Copyright © 2019 by Jennifer M. Windt < jennifer . windt @ monash . edu >

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

Benjamin baird.

1 Wisconsin Institute for Sleep and Consciousness Department of Psychiatry, University of Wisconsin, Madison, USA

Anna Castelnovo

2 Sleep and Epilepsy Center Neurocenter of Southern Switzerland, Civic Hospital (EOC) of Lugano, Lugano, Switzerland

Olivia Gosseries

3 Coma Science Group GIGA-Consciousness, University of Liege, Liege, Belgium

Giulio Tononi

Associated data.

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

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.

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 – 7 ; for recent implementations see, e.g., refs. 8 – 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 – 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 ).

Demographic, behavioral and questionnaire data for the frequent lucid dream group and control group.

Note . OSpan = Operation Span, SymSpan = Symmetry Span, RotSpan = Rotation Span, IPI = Imaginal Process Inventory, PRMQ = Prospective and retrospective memory questionnaire, TMS = Toronto Mindfulness Scale.

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.

Whole-brain seed-based resting-state functional connectivity for left aPFC between groups.

Note . IPL = Inferior parietal lobule; AG = angular gyrus; MTG = middle temporal gyrus, IFG = inferior frontal gyrus. All clusters significant at p  < 0.05, cluster corrected. L: left, R: right.

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 – 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 – 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 .

Electronic supplementary material

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).

Author Contributions

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.

Data Availability

Competing interests.

The authors declare no competing interests.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Benjamin Baird, Email: ude.csiw@driabb .

Giulio Tononi, Email: ude.csiw@inonotg .

Supplementary information accompanies this paper at 10.1038/s41598-018-36190-w.

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Lucid dreams : an elecro-physiological and psychological study.

M. T. Hearne, K. (1978) Lucid dreams : an elecro-physiological and psychological study. PhD thesis, University of Liverpool.

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'Whale Fall' centers the push-and-pull between dreams and responsibilites

Elizabeth O'Connor's spare and bracing debut novel Whale Fall opens with an isolated Welsh island on a precipice. It is September 1938, and the community's fishermen have begun encountering the Royal Navy out at sea.

When a whale washes ashore, the minister, who shares developments from out-of-date newspapers at mass, suggests that submarine radar could explain its fate. To the elders, the beached whale seems to be an omen, though they're not sure if it portends good or bad. Either way, "it felt as though something was circling us, waiting to land against the shore," O'Connor writes.

We gain entrée to this remote, superstitious world through Manod Llan, Whale Fall 's gimlet-eyed, 18-year-old narrator. Her family is one of 12 left on the tiny fictional island, where livelihood revolves around the roiling sea — men like Manod's father, a lobsterman, do the fishing, and women prepare the catch for sale on the mainland. Each year, some men are lost at sea, and some young people move to the mainland for the promise of a better life.

Manod dreams of such a life. She has newly completed her studies at the island's single-room schoolhouse, where she learned English from reading the Bible and distinguished herself as especially bright. But in her culture, as her mother often lamented before her death, "There's no job for a woman to get except wife." Manod's life is even further circumscribed — with her mother gone, she must raise her 12-year-old sister Llinos and tend her father's house. Images from magazines left behind in the chapel fill her daydreams of the kind of life she could lead on the mainland.

When the whale beaches, Manod's options appear to broaden. A pair of English ethnographers from Oxford University soon arrive, eager to see the whale and to document the island's customs. Edward and Joan barely speak Welsh, so they employ Manod as a translator, giving her newfound power through language and stoking her desire to lead a worldlier life. But she struggles with being an object of their anthropological gaze, with their romantic misrepresentations of her culture, and with what it would mean to leave the island — and Llinos — behind. In bringing us to this world through Manod's eyes, Whale Fall provides a stark reckoning with what it means to be seen from the outside, both as a person and as a people, and a singular, penetrating portrait of a young woman torn between individual yearning and communal responsibilities.

In a note on the text, O'Connor writes that she based her fictional island on her research into "an amalgamation of islands around the British Isles," including Bardsey Island off the coast of the Llŷn Peninsula in Wales, where the long-term population in 2019 was just 11. As she told Publishers Weekly , she was also inspired by her "family connection to people who live with the sea and shore," particularly grandparents who were raised in coastal enclaves in Ireland and Wales and moved to English cities during World War II.

From this solid foundation, O'Connor constructs her setting with precise, atmospheric detail that captures a world slowly being eroded. Damp invades everything from the moss-covered chapel to a romance novel whose pages are "shaped in waves." The sea is close enough "to spray the house with water at high tide, and eat away at the paint." Month by month, the whale's body decays on the beach. It invades the women's dreams, where it appears alongside "a woman coming out of the water"; it animates the children's play, as they place flowers around its body and paint pictures of it.

Joan and Edward find the islanders' customs and myths charming, and over their months-long stay, they make phonograph recordings of songs about shipwrecks and tales about the sea jealously stealing daughters and returning them as whales, which Manod translates and O'Connor intersperses between short, impressionistic chapters. For all their efforts to meticulously document, the ethnographers' assumptions about the island and its people cloud their depictions from the start. In her first conversation with Manod, Joan compares the island to Treasure Island , which she presumes Manod has never heard of (Manod has read it). The island fulfills Joan's dream "of a place untouched by cities, where the people were like wildflowers" — a gross simplification of the arduous way of life there.

Through Manod's relationship with Joan, O'Connor grapples with the dark side of idealizing isolation. Manod initially looks up to Joan for her university education and fine clothing — she represents the kind of feminine role model Manod lost when her mother died. She thrills to Joan's attention, and strives to represent herself and the island in the best possible light, lying that she "was named after a kind of coastal herb" and concocting inaccurate tableaus for Joan's photographs. Gradually, though, Manod becomes aware of that Joan's pride in Britain and its Isles — and her conscious refusal to see the island as it truly is — is rooted in fascism. By exploring the looming threats of World War II through the personal, O'Connor concretizes the stakes for the island, avoiding what might otherwise be a plodding rehashing of history.

In the end, Manod is pulled between her feelings of being seen by Edward and Joan and being wholly misunderstood by them, between her yearning to leave the island and her obligations to protect her family, her community, and her culture from exploitation and even extinction. It all makes for a haunting and lucid exploration of the moments leading up to immense change.

Kristen Martin is working on a book on American orphanhood for Bold Type Books. Her writing has also appeared in The New York Times Magazine, The Believer, The Baffler, and elsewhere. She tweets at @kwistent .

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