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Remington Mallett

19 papers in the library · 250 citations · publishing 2020-2026

Papers

Real-time dialogue between experimenters and dreamers during REM sleep.

Current biology : CB April 12, 2021 Karen R Konkoly, Kristoffer Appel, Emma Chabani et al. 126 citations

People who are asleep and having a lucid dream—aware that they are dreaming—can perceive questions from an experimenter and answer them in real time using eye movements and facial muscle contractions. In a study of 36 individuals during REM sleep, including frequent lucid dreamers, a novice, and a patient with narcolepsy, participants performed perceptual analysis of new information, held information in working memory, computed simple answers, and gave volitional replies. Correct answers occurred on 29 occasions across 6 individuals, documented by four independent laboratories. This two-way communication channel allows real-time interrogation of dream cognition and characteristics.

Dream lucidity is associated with positive waking mood.

Consciousness and Cognition August 1, 2020 Abigail Stocks, Michelle Carr, Remington Mallett et al. 33 citations

Higher levels of lucidity during dreaming are associated with more positive dream content and a more positive mood the following day. Twenty participants completed a week-long online dream diary after practicing lucid dream induction techniques. The study found no link between lucidity and subjective sleep quality. The findings suggest that cultivating lucid dreams may help improve waking mood, though longer-term studies are needed.

Benefits and concerns of seeking and experiencing lucid dreams: benefits are tied to successful induction and dream control.

Sleep advances : a journal of the Sleep Research Society January 1, 2022 Remington Mallett, Laura Sowin, Rachel Raider et al. 22 citations

Lucid dreams can end nightmares and prevent their recurrence, but they can also induce harrowing dysphoric dreams. The realization of dreaming (lucidity) and dreams with high control were both associated with positive experiences. Negative outcomes primarily result from failed induction attempts or lucid dreams with low dream control; successfully inducing high-control lucid dreams poses low risk for negative outcomes. A process model describes the progression from lucid dream induction to waking benefit, identifying potential areas of concern. The findings provide new insights into possible negative repercussions and how to avoid them in future applications.

DREAM: A Dream EEG and Mentation database

May 16, 2023 William Wong, Kátia C. Andrade, Thomas Andrillon et al. 21 citations preprint

A new open-access database, DREAM, combines sleep magneto/electroencephalography (M/EEG) recordings with standardized dream reports to enable large-scale neurocognitive research on dreaming. The initial release includes 20 datasets from 561 participants and 2649 awakenings, each with at least 20 seconds of high-frequency M/EEG data and a classification of the subject's experience. Analyses demonstrate that features extracted from EEG can predict whether a person reports having had a conscious experience during both REM and NREM sleep. The database aims to overcome the limitations of small sample sizes and methodological variability in dream research, allowing new questions to be addressed at a scale unattainable by individual labs.

Partial memory reinstatement while (lucid) dreaming to change the dream environment.

Consciousness and Cognition August 1, 2020 Remington Mallett 14 citations

Lucid dreamers can often control dream events, but the limits of that control are unclear. In this study, participants briefly viewed a real-world scene and then, while lucid dreaming, tried to change their dream scenery to match that scene. Even when dreamers were aware during the dream that their reinstatement was inaccurate, the dream imagery remained incorrect. This dissociation between memory access and dream imagery indicates that detailed control over dream content is limited, despite the ability to broadly change the dream environment. The findings suggest that reinstating waking contexts during lucid sleep can be a method for studying sleep, dreams, and memory.

A dream EEG and mentation database.

Nature Communications August 13, 2025 William Wong, Rubén Herzog, Kátia Cristine Andrade et al. 10 citations

A new open database, the DREAM database, combines standardized sleep magneto/electroencephalography (M/EEG) recordings with dream reports from 505 participants across 20 datasets, totaling 2,643 awakenings. Each awakening includes at least 20 seconds of high-resolution sleep EEG (≥100 Hz, ≥2 electrodes) and a classification of the sleeper's reported experience. Analyses showed that reports of conscious experiences during sleep can be predicted from objective EEG features in both REM and NREM sleep. The database aims to overcome limitations of small sample sizes and methodological variability in dream research, enabling larger-scale investigations of the neurocognitive basis of dreaming.

Treating narcolepsy-related nightmares with cognitive behavioural therapy and targeted lucidity reactivation: A pilot study.

Journal of Sleep Research June 1, 2025 Jennifer M Mundt, Kristi E. Pruiksma, Karen R Konkoly et al. 7 citations

A small trial tested cognitive behavioral therapy for nightmares (CBT-N), adapted for people with narcolepsy, with or without targeted lucidity reactivation (TLR) to enhance lucid dreaming. Six adults who had frequent nightmares (at least 3 per week) received seven treatment sessions. Nightmare frequency dropped from an average of 8.38 per week to 2.25 per week, a large improvement. Nightmare severity and symptoms such as sleep paralysis, hallucinations, and dream enactment also improved. The three participants who received TLR all recalled dreams related to their rescripted nightmare. Participants reported reduced shame and anxiety about sleep and nightmares. The findings offer preliminary evidence that CBT-N and TLR may help manage narcolepsy-related nightmares.

Exploring the range of reported dream lucidity

Philosophy and the Mind Sciences April 15, 2021 Remington Mallett, Michelle Carr, Martin Freegard et al. 7 citations

Lucid dreaming—being aware that one is dreaming—occurs on a spectrum rather than as an all-or-nothing state. Participants used mnemonic training methods at home for one week and rated their dream awareness on a 5-point scale each morning. About half reported at least one lucid dream, and about half of all dreams included some lucidity, but success rates varied with the minimum criteria used. The amount of mnemonic rehearsal during a brief early awake period predicted lucidity level. Lucidity levels correlated positively with dream control, dream bizarreness, and next-morning positive affect. Qualitative analysis of 'semi-lucid' dreams (intermediate ratings) explored why participants chose those levels, highlighting methodological implications for lucid dreaming research.

Provoking lucid dreams at home with sensory cues paired with pre-sleep cognitive training.

Consciousness and Cognition October 1, 2024 Karen R Konkoly, Nathan W Whitmore, Remington Mallett et al. 4 citations

A smartphone-based procedure called Targeted Lucidity Reactivation (TLR) can increase lucid dreaming without requiring laboratory equipment. In two experiments, participants reported more lucid dreams when they received sounds during REM sleep that they had heard during pre-sleep training, compared to a prior week without TLR or to blinded control procedures on alternate nights. The findings indicate that the sounds strengthen a link formed during training between the cues and a mindset of carefully analyzing one's current experience, which carries into dreams and boosts lucidity.

Using Real-time Reporting to Investigate Visual Experiences in Dreams

Journal of Cognitive Neuroscience September 30, 2025 Karen R Konkoly, Saba Al-Youssef, Christopher Y Mazurek et al. 3 citations

Alpha oscillations, a hallmark of waking visual perception when eyes close, do not reliably increase during lucid dreams when dreamers signal that their dream-eyes are closed. In 150 signals from 11 lucid dreamers, dream-eye closure was associated with fading visual content only about half the time. In three participants where visual content presence versus absence could be compared, increased alpha power accompanied momentary lack of visual content. Real-time reporting via sniffing patterns enables dynamic investigation of dream perception.

Cognitive control and semantic thought variability across sleep and wakefulness

March 14, 2023 Remington Mallett, Yasmeen Nahas, Kalina Christoff et al. 2 citations preprint

The flow of thought is persistent and sometimes unpredictable, and understanding what influences how thoughts shift from moment to moment has implications for disorders like schizophrenia and recurrent nightmares. This study examined whether cognitive control limits moment-to-moment content shifts across sleep and wakefulness. Thought variability was measured as semantic incoherence between sequential thought phrases in dreaming and waking reports. During wakefulness, on-task conditions showed reduced thought variability compared to off-task conditions, and thought variability was greater when thoughts wandered freely. During sleep, lucid dreams, which involve higher cognitive control, showed reduced thought variability compared to non-lucid dreams. These results suggest that cognitive control may limit thought variability across the 24-hour cycle of thought generation, consistent with the Dynamic Framework of Thought.

Dream Content Discovery from Reddit with an Unsupervised Mixed-Method Approach

arXiv (Cornell University) July 9, 2023 Anubhab Das, Sanja Šćepanović, Luca Maria Aiello et al. 1 citation

A new data-driven method using natural language processing identifies 217 dream topics grouped into 22 larger themes from 44,213 dream reports posted on Reddit's r/Dreams subreddit. The approach overcomes limitations of traditional dream analysis, which relies on retrospective surveys and lab studies with over 130 scales. The topics were validated against the Hall and van de Castle scale. The method can detect unique patterns in nightmares or recurring dreams, assess topic importance and connections, and track changes in collective dream content over time, including shifts during the COVID-19 pandemic and the Russo-Ukrainian war.

A large corpus of lucid and non-lucid dream reports

arXiv (Cornell University) March 27, 2026 Remington Mallett

Lucid dreams—dreams in which the dreamer is aware they are dreaming—are difficult to study because they are rare and hard to induce, leaving their characteristics unclear. A large corpus of 55,000 dream reports from 5,000 contributors was assembled by scraping ten years of publicly available anonymous dream journals from an online forum. Users optionally labeled their dreams as lucid, non-lucid, or nightmare, providing 10,000 lucid, 25,000 non-lucid, and 2,000 nightmare labels. Analysis confirmed that language patterns in lucid-labeled reports match known features of lucid dreams. This corpus supports broad dream research, and the labeled subset enables new discoveries about lucid dreaming.

Dream content discovery from social media using natural language processing

EPJ Data Science May 23, 2025 Anubhab Das, Sanja Šćepanović, Luca Maria Aiello et al.

Dreams remain a poorly understood aspect of human life. Traditional methods for analyzing dream content rely on over 130 rating scales and are limited by small samples and retrospective surveys. To address these limitations, researchers used natural language processing to identify topics in 44,213 dream reports from Reddit's r/Dreams subreddit. The analysis uncovered 217 topics grouped into 22 broader themes—the largest collection of dream topics to date. The topics were validated against the established Hall and van de Castle scale.

New strategies for the cognitive science of dreaming.

Trends in Cognitive Sciences December 1, 2024 Remington Mallett, Karen R Konkoly, Tore Nielsen et al.

A review of recent interdisciplinary advances that overcome historical methodological barriers in dream research. Three frameworks are described: observable dreaming, using neural decoding and real-time reporting to measure dream content more directly; dream engineering, using targeted stimulation and lucidity to experimentally manipulate dream content; and computational dream analysis, generating and exploring large dream-report databases to identify patterns. These innovations enable systematic observation, engineering, and analysis of dreams, heralding a new era in dream science.

0112 Lucid Dreaming Associated with Positive Waking Mood

May 27, 2020 M. Carr, A. Stocks, Remington Mallett et al.

Lucid dreaming is associated with more positive dream content and improved waking mood, without harming subjective sleep quality. In an open-label study, 32 participants practiced lucid dream induction techniques for a week and kept daily diaries. Higher lucidity correlated with greater positive emotion in dreams and more positive mood after waking. No significant relationship was found between lucidity and sleep quality or negative emotion. The authors suggest lucid dreaming may be a short-term intervention to improve wellbeing.

A pilot investigation into brain-computer interface use during a lucid dream

Remington Mallett preprint

A brain-computer interface (BCI) trained during wakefulness can be controlled from within a lucid dream. Three participants learned to use an Emotiv EPOC+ headset to map a mental motor command (imagining pushing a block) to a computer action (a graphic moving forward). After training, they wore the headset while attempting lucid dreaming and performed the same mental command during lucidity. In two participants, subjective reports of task completion matched video footage of the computer graphic moving. These preliminary results suggest that wake-trained BCI control is possible from sleep, pointing toward future dream communication and research.

Partial memory reinstatement while (lucid) dreaming to change the dream environment

Remington Mallett preprint

Lucid dreamers can control dream events, but the limits of that control are unclear. In this study, participants briefly viewed an experimental scene and then, while lucid dreaming, tried to change their dream scenery to match that real-world scene. Qualitative analysis showed that successful dream scene reinstatements were overwhelmingly inaccurate compared to the original scene, even when the dreamer was aware of the inaccuracies during the dream. This suggests a dissociation between memory access while dreaming and dream imagery. The ability to change the dream environment demonstrates high lucid dream control, but the inaccuracies reveal a lack of detailed control.

Dream Content Discovery from Social Media Using AI

Anubhab Das, Sanja Šćepanović, Luca Maria Aiello et al.

A new data-driven method using natural language processing identified 217 dream topics grouped into 22 larger themes from 44,213 dream reports posted on Reddit's r/dreams subreddit, the most extensive collection of dream topics to date. The topics were validated against the widely-used Hall and van de Castle scale. The method revealed unique patterns in different dream types, such as nightmares and recurring dreams, and showed that indoor location settings were more predominant in Reddit dreams than previously stipulated. It also detected changes in collective dream experiences over time and around major events like the COVID-19 pandemic and the Russo-Ukrainian war.