dreams research paper

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What about dreams ? State of the art and open questions

Serena scarpelli.

1 Department of Psychology, Sapienza University of Rome, Rome Italy

Valentina Alfonsi

Maurizio gorgoni.

2 Body and Action Lab, IRCCS Fondazione Santa Lucia, Rome Italy

Luigi De Gennaro

Associated data.

Several studies have tried to identify the neurobiological bases of dream experiences, nevertheless some questions are still at the centre of the debate. Here, we summarise the main open issues concerning the neuroscientific study of dreaming. After overcoming the rapid eye movement (REM) ‐ non‐REM (NREM) sleep dichotomy, investigations have focussed on the specific functional or structural brain features predicting dream experience. On the one hand, some results underlined that specific trait‐like factors are associated with higher dream recall frequency. On the other hand, the electrophysiological milieu preceding dream report upon awakening is a crucial state‐like factor influencing the subsequent recall. Furthermore, dreaming is strictly related to waking experiences. Based on the continuity hypothesis, some findings reveal that dreaming could be modulated through visual, olfactory, or somatosensory stimulations. Also, it should be considered that the indirect access to dreaming remains an intrinsic limitation. Recent findings have revealed a greater concordance between parasomnia‐like events and dream contents. This means that parasomnia episodes might be an expression of the ongoing mental sleep activity and could represent a viable direct access to dream experience. Finally, we provide a picture on nightmares and emphasise the possible role of oneiric activity in psychotherapy. Overall, further efforts in dream science are needed (a) to develop a uniform protocol to study dream experience, (b) to introduce and integrate advanced techniques to better understand whether dreaming can be manipulated, (c) to clarify the relationship between parasomnia events and dreaming, and (d) to determine the clinical valence of dreams.

1. INTRODUCTION

Dreams have been extensively studied from many points of view, focussing on different aspects of the phenomenon. Dreaming is a composite experience occurring during sleep that includes images, sensations, thoughts, emotions, apparent speech, and motor activity. The oneiric production is a form of mental sleep activity that appears strictly related to memory processes and cognitive elaboration (Wamsley & Stickgold,  2010 ; Mangiaruga et al., 2018). In this respect, some investigations have highlighted that dream features mirror the development of cognitive processes (Mangiaruga et al., 2018; Scarpell et al.,  2019a ).

Additionally, a growing number of studies have suggested that dream experience might be considered an expression of human wellbeing (Fränkl et al.,  2021 ; Scarpelli et al.,  2022 ) and has a pivotal role in emotional regulation, as suggested by some neurobiological findings (Nielsen & Lara‐Carrasco,  2007 ). For instance, dream recall and nightmare frequency increase when subjects are exposed to adverse and traumatic events (e.g., Hartmann & Brezler,  2008 ; Nielsen et al.,  2006 ; Sandman et al.,  2013 ; Tempesta et al.,  2013 ). Also, the qualitative characteristics of dream reports change in parallel with the emotional charge of waking experiences (Schredl,  2006 ; Scarpelli et al.,  2021 ).

It should be highlighted that psychoanalysis had primacy in dream research until the discovery of the rapid eye movement (REM) sleep stage (Aserinsky & Kleitman,  1953 ). The interpretation of oneiric contents was one of the main focusses of the Freudian theories positing that dreaming allows access to the unconscious functions of the mind in neurosis treatment (Freud,  1953 ). Aserinsky and Kleitman ( 1953 ) observed specific intervals with rapid and recurrent eye movement and bursts of alpha activity comparable to those that occur during wakefulness. The enthusiasm linked to the discovery of REM sleep considerably influenced dreaming research in several ways, and the neuroscientific study of dreaming is relatively recent. Several studies have attempted to identify the neurobiological bases of dream experience through a neuropsychological approach (Solms,  1997 , 2000 ), neuroimaging (Maquet et al.,  1996 ) and electrophysiological techniques (Marzano et al.,  2011 ; Siclari et al.,  2017 ).

Although several studies provide compelling evidence for the existence of specific brain mechanisms predicting dream recall (e.g., Siclari et al.,  2017 ), many questions are still at the centre of the debate.

The present paper summarises the main open issues concerning the neuroscientific study of dream experience. Specifically, the review offers an overview about (a) the question related to the REM‐non‐REM (NREM) sleep dichotomy, (b) the state–trait‐like problem, (c) the relationship between waking and dreaming state and the manipulation of dreaming, (d) the issue concerning the access to dream experience, (e) the role of nightmares, and (f) the debate on dreamwork in psychotherapy.

1.1. The REM‐NREM sleep dichotomy

A classical view of the neurobiological basis of the oneiric activity postulates the existence of a close relationship between dream experience and REM sleep (Hobson et al.,  2000 ; Nielsen,  2000 ). This hypothesis was based on early electroencephalographic (EEG) observations showing that >70% of individuals awakened during REM sleep reported dreams, while dream recall at the awakening from other sleep stages was rare (Aserinsky & Kleitman, 1955 ). According to this view, the wake‐like high‐frequency EEG pattern characterising REM sleep would represent the ideal electrophysiological scenario for the occurrence of dream experiences, while the slow‐frequency activity characterising NREM sleep would be associated with the absence of oneiric activity. However, using different criteria to collect dream reports, several studies found that successful recall of a conscious experience can be frequently observed also after NREM awakenings, and in a minority of cases no dream experience was reported after REM awakenings (Foulkes,  1962 ; Nielsen,  2000 ). Moreover, dream recall is still possible after lesions in brain regions involved in REM sleep generation, while the total disappearance of dream recall can be observed after focal forebrain lesions without an impact on REM sleep (Solms,  2000 ). Also, dream experience is preserved after pharmacological suppression of REM sleep (Landolt et al.,  2001 ; Oudiette et al.,  2012 ). Finally, dream recall has been recently associated with a similar electrophysiological response after REM and NREM sleep (D'Atri et al.,  2019 ; Siclari et al.,  2017 ). These results suggest that (a) dream and REM sleep are controlled by distinct brain mechanisms, (b) the postulate of a clear distinction between presence and absence of dreaming respectively in REM and NREM has not a solid support, and therefore (c) dreams can occur in any sleep stage.

A dichotomy between NREM and REM sleep has been also hypothesised for the qualitative aspects of dreams. Indeed, it has been proposed that REM and NREM sleep exhibit different kinds of mental activity. According to this view, REM sleep is characterised by an emotional, vivid, and bizarre “dream‐like” mentation (Antrobus,  1983 ; Casagrande et al.,  1996 ; Foulkes,  1967 ; Foulkes & Schmidt,  1983 ; Waterman et al.,  1993 ), while NREM mental activity would be “thought‐like”, with reduced emotional load, greater fragmentation, and contents more similar to waking thoughts (Foulkes,  1967 ; Rechtschaffen et al.,  1963 ). Nevertheless, the existence of a clear‐cut REM‐NREM dichotomy has been questioned also in this case based on several findings: (a) “dream‐like” reports have been observed also after NREM sleep (Monroe et al.,  1965 ; Solms,  2000 ; Zimmerman,  1970 ) and (b) the qualitative differences between REM and NREM dream reports disappear when their length is equated (Antrobus,  1983 ; Cavallero et al.,  1992 ; Foulkes & Schmidt,  1983 ).

In light of these observations, the assumption that the presence/absence and the phenomenological aspects of dream experiences strictly depend on the sleep stage per se is simplistic. It is worth noting that a precise definition of the time‐coupling between the sleep stages and the actual occurrence of dream experience is difficult, as the access to sleep mentation is possible only in an indirect way through dream reports after the awakening (see the paragraph “What about direct access to dream experience?”). At the same time, the occurrence of dream experiences in both REM and NREM sleep, two physiological stages characterised by distinct electrophysiological and neurotransmitters patterns, appears paradoxical. Such considerations raised the question of what mechanisms facilitate/inhibit the recall of a conscious experience at the awakening from different sleep stages, and what factors can explain intra‐ and inter‐individual variability in the phenomenology of the oneiric activity.

1.2. State‐ and trait‐like facets of dreams

Stable individual characteristics (trait‐like factors) can impact dreams, explaining inter‐individual variability. Sociodemographic factors like gender (Schredl & Reinhard,  2008 ; Settineri et al.,  2019 ) and age (Mangiaruga et al.,  2018 ; Scarpelli et al.,  2019a ) can predict dream recall. Interest in dreams (Bealulieu‐Prevost & Zadra,  2007 ), visual imagery abilities (Cory & Ormiston,  1975 ), personality dimensions like openness to experience, absorption, psychological boundaries (Beaulieu‐Prevost & Zadra, 2007), and predisposition to suppress negative emotions and thoughts (Malinowski,  2015 ) appear related to individual differences in the oneiric activity.

Crucially, neuroimaging studies provided evidence about the relationship between dream features and stable brain anatomical and functional characteristics. Qualitative facets of dreams have been associated with volumetric and structural measures of the amygdala‐hippocampus complex in healthy subjects (De Gennaro et al.,  2011 ) and amygdala volume, dorsomedial prefrontal cortical thickness, and dopaminergic activity in patients with Parkinson's disease (De Gennaro et al.,  2016 ). Moreover, compared to low dream recallers, high dream recallers showed (a) greater medial prefrontal cortex white‐matter density (Vallat et al.,  2018 ); (b) higher regional cerebral blood flow in the temporo‐parietal junction during wakefulness, Stage 3, and REM sleep and in medial prefrontal cortex during wakefulness and REM sleep (Eichenlaub et al.,  2014a ); (c) enhanced functional connectivity within the default mode network (DMN) and between areas of the DMN and memory‐related regions immediately after the awakening (Vallat et al.,  2020 ); and (d) larger event‐related potentials to distracting sounds even during active listening, arguing for enhanced bottom‐up processing of irrelevant sounds but also an enhanced recruitment of top‐down attention as suggested by larger contingent negative variation during target expectancy and P3b to target sounds (Ruby et al.,  2021 ). Taken together, these findings highlight that stable individual features of the brain structure and activation patterns can explain inter‐individual differences in dream experience.

Beyond the influence of trait‐like factors, a growing number of studies also point to the role of the physiological milieu associated with the oneiric experience (state‐like factors). In other words, the specific regional features of the physiological background contingent with dreaming would facilitate or prevent dream recall, potentially explaining intra‐individual differences in dream reports. This possibility has been investigated mainly by assessing the sleep EEG pattern preceding dream recall. In this way, several studies found that a successful dream recall was associated with greater frontal theta oscillations before the awakening from REM sleep (Marzano et al.,  2011 ; Scarpelli et al.,  2015 ; Scarpelli et al.,  2019b ) and reduced parieto‐occipital alpha activity before the awakening from NREM sleep (Esposito et al.,  2004 ; Marzano et al.,  2011 ). As theta and alpha oscillations are associated with memory processes during wakefulness (Hsieh & Ranganath,  2014 ), these results suggest that wakefulness and sleep share the same neurobiological mechanisms for the elaboration of episodic memories (see the next paragraph).

On the other hand, a growing number of within‐subject investigations (which allows overcoming the possible influence of stable trait‐like factors) show that a more desynchronised EEG pattern is associated with dream recall in both NREM and REM sleep (Siclari et al.,  2017 ; D'Atri et al.,  2019 ; Scarpelli et al.,  2017 ; Scarpelli et al.,  2020a ; but see Wong et al.,  2020 ). In particular, dream experience would be facilitated by a pattern of reduced slow‐wave activity (SWA), most steadily in posterior regions (Siclari et al.,  2017 , 2018 ). Interestingly, lucid dreams, phenomenon characterised by conscious awareness during the oneiric experience, appear associated with greater EEG gamma activity (Baird et al.,  2022 ; Voss et al.,  2009 ). Furthermore, a transcranial current stimulation delivered in a lower gamma range during REM sleep can affect the ongoing electrophysiological activity and increase self‐reflective awareness in dreams (Voss et al.,  2014 ). These observations are consistent with “activation” theoretical models (Antrobus,  1991 ; Hobson & McCarley,  1977 ; Koulack & Goodenough,  1976 ), which postulate that dream recall would be facilitated by a greater level of arousal during sleep, represented at an electrophysiological level by higher brain activation. Indeed, the frequency of dream recall increases in association with a sleep pattern characterised by greater sleep fragmentation (van Wyk et al.,  2019 ), faster spindles, especially in central and posterior cortical areas (Siclari et al.,  2018 ), intra‐sleep wakefulness (De Gennaro et al., 2010 ; Eichenlaub et al.,  2014b ; Vallat et al.,  2017 ), and sleep arousal (Polini et al.,  2017 ; Schredl,  2009 ). Furthermore, a night of recovery sleep after a period of prolonged wakefulness, usually characterised by reduced awakenings, almost totally abolished dream recall after the final morning awakening (De Gennaro et al., 2010 ). The SWA represents a marker of sleep intensity (Borbély & Achermann,  1999 ), likely subserving the fading of consciousness during sleep. Thus, the pattern of local SWA reduction in association with dreaming activity may represent the electrophysiological marker of the greater arousal level needed for a successful dream recall. Moreover, this evidence provides a reliable explanation for the apparently paradoxical occurrence of dreams in states of consciousness (i.e., REM and NREM sleep) characterised by drastically different EEG patterns.

Overall, these findings highlight the crucial role of the physiological state preceding dream recall. However, several questions remain open. First, the influence of circadian and homeostatic factors on the oneiric experience and its electrophysiological pattern is not clear (Chellappa et al.,  2011 ; D'Atri et al.,  2019 ; Scarpelli et al.,  2017 ; Scarpelli et al.,  2020a ). Moreover, the impact of the regional distribution of SWA on qualitative dream facets needs to be fully investigated, as empirical preliminary evidence has been provided only by Siclari et al. ( 2017 ). Finally, the possible interaction between state‐ and trait‐like factors should be carefully considered.

1.3. Continuity between waking and dream experience

The above‐mentioned “activation hypothesis” represents one of the main theoretical frameworks on dreaming, along with the so‐called “continuity hypothesis” (Domhoff,  2017 ; Schredl & Hofmann,  2003 ). In the early 1970s, Bell and Hall ( 1971 ) firstly proposed that waking experiences may have continuity in sleep. The formulation of the original concept has gone through several re‐interpretations and adjustments since then.

Early cognitively‐oriented studies focussed on the continuity between dream contents and waking events, personal concerns, thoughts, behaviours, and emotions, suggesting that waking‐life experiences are reflected into subsequent dreams (Nielsen & Powell,  1992 ; Schredl,  2006 ; Blagrove,  2011 ; Vallat et al.,  2017 ). Compelling evidence also showed the key role of the personal and emotional salience in mediating the preferential incorporation of waking‐life aspects during mental sleep activity (Malinowski & Horton,  2014 ).

Further, different time intervals between waking experiences and related dream contents could represent “day‐residue effect” or “dream‐lag effect” as a function of the elapsed period (i.e., 1–2 days and 5–7 days, respectively) (Eichenlaub et al.,  2017 ). Specifically, the delayed incorporation of waking life events (“dream‐lag effect”) was selectively observed during REM sleep and for personally significant events (Van Rijn et al.,  2015 ).

A complementary field of study posits the continuity between waking state and mental sleep activity from a neurophysiological perspective. Namely, a growing body of evidence suggests that brain mechanisms underlying cognitive and emotional functioning remain the same across different states of consciousness (e.g., Marzano et al.,  2011 ; Eichenalub et al., 2018).

The involvement of alpha (8–12 Hz) and theta (5–7 Hz) oscillations in memory‐related neural processes during wakefulness are well‐established, especially as regards episodic‐declarative memory (Klimesch,  1999 ). In particular, the increase in the frontal theta activity and the alpha power decrease during the encoding phase of episodic memories were found to play a pivotal role in the subsequent recall of stored information (Hsieh & Ranganath,  2014 ; Klimesch,  1999 ).

Over the last two decades, several studies were conducted under the assumption that dream encoding and recall could represent a peculiar form of episodic memory (Fosse et al.,  2003 ). As previously mentioned, a successful dream recall has been linked to higher frontal theta activity during REM sleep (Marzano et al.,  2011 ; Scarpelli et al.,  2015 ) and lower alpha activity over the temporo‐parietal region during NREM (Esposito et al.,  2004 ; Marzano et al.,  2011 ; Takeuchi et al.,  2003 ). Moreover, the topographical distribution of the above‐mentioned frequency bands resembles brain regions involved in encoding and retrieval mechanisms during wakefulness.

A large body of experimental studies have also shown the continuity between dreaming and emotional processing (for a review, see Scarpelli et al.,  2019c ). First of all, as described in the previous paragraph, neuroimaging studies showed the relationship between qualitative and quantitative stable aspects of dream experience and structural parameters of limbic areas (De Gennaro et al.,  2011 ). Consistently, subjects reporting higher levels of fear in their dreams showed a concomitant higher activation of the medial prefrontal cortex, responsible for reduced activation of the amygdala, insula, and midcingulate cortex both during sleep and wakefulness (Phelps et al.,  2004 ; Sterpenich et al.,  2020 ). Further, the main brain circuits involved in emotional processing during wake are highly activated during REM sleep, such as the limbic system (Nir & Tononi,  2010 ) and reward system (Perogamvros & Schwartz,  2012 ). Notably, a recent simultaneous EEG‐functional magnetic resonance imaging study demonstrated the privileged re‐emergence during sleep of patterns of brain activity associated with a recent rewarding (compared to a non‐rewarding) waking experience during sleep (Sterpenich et al.,  2021 ).

Starting from these findings, many researchers stated that dream activity might have a crucial role in processing emotional events experienced during wakefulness (see Scarpelli et al.,  2019c ). More in‐depth, the theta (Nishida et al.,  2009 ; Boyce et al.,  2016 ; Sopp et al.,  2018 ) and gamma activities (Van Der Helm et al.,  2011 ) were identified as the EEG markers of emotional memory processing. Selective sleep deprivation protocols provided experimental evidence about the lack of emotional memories consolidation in the absence of REM sleep stage (Spoormaker et al.,  2014 ; Wagner et al.,  2001 ), supporting the notion that dreaming represents the privileged scenario for the offline reprocessing of waking emotional stimuli.

Keeping in mind the unitary perspective across waking and sleep state, several investigations aimed to overcome the boundaries between different states of consciousness directly influencing sleep mentation by different kinds of sensory stimuli administered pre‐ or during sleep. Pre‐sleep stimulation methods have been used since the very beginning of dream research. The pioneering study by Dement and Wolpert ( 1958 ) showed the relation between the 24‐h fluid restriction in participants and their subsequent REM dream content. Sensory stimulation through pre‐sleep visual stimuli affected dream content by using stressful films (Goodenough et al.,  1965 ) or visual inverting prisms (Corsi‐Cabrera et al.,  1986 ).

Concerning sensory stimulation delivered during REM or NREM sleep stages, early studies described the incorporation of meaning verbal stimuli (Berger,  1963 ; Hoelscher et al.,  1981 ). Also, somatosensory stimulation (e.g., water on the skin, thermal stimulation, pressure cuff, electrical pulses) (Baldridge et al.,  1965 ; Dement & Wolpert,  1958 ; Koulack,  1969 ; Nielsen,  1993 ) or vestibular stimulation (Leslie & Ogilvie,  1996 ) were found to affect dream content. As expected, these types of stimulation increased vividness and bodily sensation in the dream contents.

Recent studies using olfactory stimulation during sleep showed the influence on the emotional content of dreams as a function of the hedonic characteristic of stimuli (Schredl et al.,  2009 ) and the reactivation of the odour‐associated images (Schredl et al.,  2014 ). The strong effect of olfactory stimulation on dream emotional aspects is interpreted in terms of direct connections to the limbic system (Smith & Shepherd,  2003 ).

In the last few years, a promising field of research explored the shared neural circuits between wake and sleep mentation by directly manipulating dream activity via transcranial electrical stimulation techniques. Some studies showed that interfering with cortical areas that are notably involved in a specific function during wakefulness influenced the dream content accordingly (Jakobson et al.,  2012 ; Noreika et al.,  2020 ).

Taken together, these results strengthen the hypothesis of shared mechanisms between the awake and sleeping brain from both psychological and neurobiological perspectives and through experimental manipulations. However, the intrinsic restraint due to the impossibility of directly investigating the dream content represents a common limitation of these studies.

1.4. What about direct access to dream experience?

The issue concerning dream access is definitively the most complex to address. Indeed, the real object of study in the abovementioned investigations (e.g., Chellappa et al.,  2011 ; Marzano et al.,  2011 ; Scarpelli et al.,  2015 , 2017 ; Scarpelli et al.,  2020a ; Scarpelli et al.,  2019b ; Siclari et al.,  2017 ) is “dream recall” and not the dream experience itself . In other words, dreaming is not directly observable, and researchers are able to obtain information about the oneiric activity just requiring a dream report to the individual when he is awake. Also, we have already discussed that detecting the exact moment in which the dreams are produced during sleep is very difficult.

From a methodological point of view, three approaches to collect dreaming are well‐known: (a) retrospective, (b) prospective, and (c) provoked awakenings with subsequent dream reports. While the retrospective method allows researchers to collect dreaming through interviews or questionnaires in large samples quickly, the prospective protocol (i.e., dream diaries; longitudinal dream report collection) is less prone to memory biases (Robert & Zadra,  2008 ). These two strategies allow classifying people in high and low recallers, helping to investigate the neurobiological trait‐like features of dreamers (e.g., Eichenlaub et al.,  2014b ; Eichenlaub et al.,  2014a ; Ruby et al.,  2021 ; van Wyk et al.,  2019 ). However, the most accurate approach is represented by the provoked awakenings associated with the polysomnography (PSG) of one or more sleep nights in a laboratory. Generally, participants are awakened to explore the presence of a dream report and to compare the recall and non‐recall condition (Scarpelli et al.,  2017 ; Scarpelli et al.,  2020a ; Siclari et al.,  2017 ) or the report's qualitative features (Scarpelli et al.,  2020b ), correlating them with the specific EEG patterns preceding the awakening. It is worth noting that the narration of dream contents could be influenced by many biases after awakenings, such as the experimental setting (Schredl,  2008 ), the physiological background of waking‐life and by individual variables, such as personality, cognitive functions, censure/omissions and socio‐cultural features (Nir & Tononi,  2010 ), making dream reports not always completely reliable.

How can we overcome this obstacle? In this regard, recent studies have suggested that viable access to mental sleep activity is represented by dream‐enacting behaviours (DEBs; Baltzan et al.,  2020 ). Any acting out of a dream during sleep characterised by motor, emotional or verbal components may be considered a direct observation of dream experience while the subject is asleep (Nielsen et al.,  2009 ). In this view, the study of parasomnias or parasomnia‐like events, i.e., REM behaviour disorder (RBD), sleep walking, nightmares, and sleep talking, may provide new insights about dreaming. Interestingly, some investigations highlighted a strong level of congruence between the body movements, verbal or emotional expressions during sleep and the subsequent components of dream recall (Arkin et al.,  1970 ; Leclair‐Visonneau et al.,  2010 ; Oudiette et al.,  2009 ; Rocha & Arnulf,  2020 ).

Assessing REMs in patients with RBD, Leclair‐Visonneau et al. ( 2010 ) found a concordance between limbs, head, and eye movements during the REM behaviour episode. The authors suggested that REMs may imitate the scanning of the dream scenario according to the so‐called “scanning hypothesis” (Arnulf,  2011 ; Leclair‐Visonneau et al.,  2010 ). Moreover, Oudiette et al. ( 2009 ) revealed that during sleepwalking or sleep terror episodes, subjects show complex motor behaviours strictly related to their oneiric scenes. The same group has demonstrated that sleepwalkers are able to replay the recently trained behaviour during the parasomnia episode, supporting the idea that dream enactment may have a pivotal role in memory processing during sleep (Oudiette et al.,  2011 ).

More recently, Rivera‐García et al. ( 2019 ) investigated the activation of facial muscles during REM sleep among healthy women. They considered facial expressions during sleep on a par with DEBs and an index of emotional dreams. Consistently, the previous literature shows that DEBs are more frequent during intense emotional dreams, such as nightmares (Nielsen et al., 2009 ). Indeed, the authors revealed that the activation of corrugator and zygomatic muscles are highly associated with dreams featured by negative affect (Rivera‐García et al. ( 2019 )).

Also, sleep talking could be considered an additional non‐pathological parasomnia‐like event related to dreaming (Alfonsi et al.,  2019 ; Mangiaruga et al., 2021). During sleep, the audible verbalisations may represent access to oneiric contents (Arkin et al.,  1970 ; Alfonsi et al.,  2019 ). In this regard, some studies showed different degrees of correspondence between sleep talking and dreaming (Arkin et al.,  1970 ; Rechtschaffen et al.,  1962 ). Arkin et al. ( 1970 ) reported different orders of concordance between sleep speech and later dream reports. Some authors investigated the presence of dialogical components within the dream reports proposing an overlapping between the neural mechanisms underlying linguistic production in dreams and those responsible for language during waking state (Shimizu & Inoue,  1986 ; Hong et al.,  1996 ; Siclari et al.,  2017 ). Specifically, Hong et al. ( 1996 ) found a reduction of the alpha activity focussed on Broca's and Wernicke's language regions, proportional to the amount of expressive and receptive language reported in dreams (Hong et al.,  1996 ; Shimizu & Inoue,  1986 ). In addition, Noreika et al. ( 2015 ) demonstrated a decrement in the theta and alpha activity in a single‐case study associated with linguistic hypnagogic hallucination. Consistently, a recent study revealed that similar EEG patterns predict intelligible verbalisations during sleep (Mangiaruga et al., 2022 ).

Overall, both findings in subjects suffering from parasomnias and those related to “benign” phenomena (e.g., facial expressions, sleep talking), suggest that parasomnia‐like episodes may open a new frontier in dream research making the oneiric production more accessible.

1.4.1. Nightmares

Nightmares are disturbing mental sleep activity characterised by negative emotions and often considered a clinical symptom causing significant distress. They are frequently associated with a high level of arousal and somatic manifestations that are capable to awake the dreamer from REM sleep. The repeated occurrence of this event is categorised as parasomnia, i.e., “nightmare disorder”, according to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM‐5; American Psychiatric Association, 2013 ).

On the one hand, this disturbance is frequently related to post‐traumatic stress disorder (PTSD; Germain,  2013 ), but it could also be a reaction to stress conditions (Scarpelli et al.,  2022 ). On the other hand, also idiopathic nightmares, i.e., without a known cause, should be considered. For instance, this kind of mental sleep activity is quite common in children tending to disappear during adulthood, and it is more frequent among females (Nielsen & Levin,  2007 ).

From a neurobiological perspective, a recent investigation shows that the activation of the autonomic nervous system may be linked to nightmares (Paul et al.,  2019 ). Some studies revealed REM‐specific alterations in nightmare sufferers such as longer REM latency, increased skipping of early REM periods and cycle length, and more frequent REM periods (Nielsen et al.,  2010 ). Furthermore, some EEG findings highlighted the presence of slow frontal and central theta activity during REM sleep in a group of nightmare recallers (Marquis et al.,  2017 ). Further studies reported evidence for reduced slow‐wave sleep and greater intra‐sleep wakefulness (Simor et al.,  2012 ), increased alpha power during REM sleep, and higher levels of EEG desynchronisation in NREM sleep of students with frequent nightmares (Simor et al.,  2013 ). In other words, as already mentioned for dream recall, a higher autonomic and electrophysiological activation may provide the physiological background to the nightmare occurrence (Fisher et al.,  1970 ; Nielsen & Zadra,  2005 ). This is consistent with the self‐reported experience of greater emotional and physical activations during the nightmare occurrence.

Fear is the predominant emotion included in nightmares (Zadra et al.,  2006 ), suggesting that nightmares could be linked to fear‐dysfunction disturbances, i.e., phobias, generalised or social anxiety (Nielsen & Levin,  2007 ; Walker,  2010 ). In other words, nightmares could be related to the dysfunction in the hippocampal–amygdala prefrontal system that controls fear memory formation and extinction (Marquis et al.,  2017 ; Nielsen & Levin,  2007 ). Nevertheless, the functional role of nightmares is still debated. Considering the early theories of dream function emphasising roles for REM sleep and dreaming in promoting adaptation to stress, nightmares could be interpreted as a failure of this process (Wright & Koulack,  1987 ).

Along this vein, some authors proposed that a certain degree of awareness of our dream contents and the possibility of altering them may be beneficial for nightmares sufferers (Kellner et al.,  1992 ; Krakow et al.,  2001 ; Neidhardt et al.,  1992 ). In particular, compelling evidence highlighted that imagery rehearsal therapy (IRT) is very effective in reducing chronic nightmares within 6–12 weeks of therapy (Germain et al.,  2004 ; Kellner et al.,  1992 ; Krakow et al.,  2001 ; Neidhardt et al.,  1992 ). This technique consists of modifying the plot of the recurring nightmare during the wakefulness by an imaginal rehearsal of a new dream without disturbing items (Kellner et al.,  1992 ). The nightmare sufferers learn to change the nightmares scenes by creating a less unpleasant ending and including mastery elements in the new dream scenario (Germain et al.,  2004 ).

Interestingly, lucid dreaming induction could represent a useful intervention to reduce nightmares (Zadra & Pihl,  1997 ; Spoormaker & Van Den Bout,  2006 ; Rak et al.,  2015 ). It has been hypothesised that lucid dreaming could be a sort of coping strategy to face unpleasant stimuli during a dream experience (Schiappa et al.,  2018 ). Actually, lucid dream therapy is a cognitive technique that allows patients to learn to be aware of and modify their mental sleep activity during their nightmares through daily exercises (Spoormaker & Van Den Bout,  2006 ; Zadra & Pihl,  1997 ).

More recently, eye movement desensitisation and reprocessing (EMDR; Shapiro,  1989 ) has been employed for nightmares treatment in PTSD. Starting from the view that nightmares are the manifestations of adverse events registered in a dysfunctional form, this technique aimed to promote the recall of distressing images while activating one type of bilateral sensory input (e.g., hand tapping or side‐to‐side eye movement). The protocol allows subjects to identify and reprocess the targeted disturbing memories and experiences in order to formulate insight and adaptive behaviour.

In conclusion, it should be underlined that studies on PSG abnormalities and specific macro‐ and micro‐structural features correlated to nightmares are still missing. Further, efficacy studies on nightmare treatment (i.e., IRT, lucid dream therapy, EMDR) are scarce and fragmentary. Future research should be conducted to fill this gap and explore the effectiveness of the above‐mentioned interventions for nightmare disorders.

1.4.2. What role for dreamwork in modern psychotherapy?

An interesting open issue concerns the possible usefulness of the oneiric experience as a tool in clinical practice, also in light of the neuroscientific knowledge on dreams.

Classically, Freud (1953) proposed two main functions of dreams: the expression of repressed infantile wishes and the protection of sleep. The antimoral nature of such wishes implies the need of a distortion through the dream censor to be acceptable, allowing their partial expression while protecting the continuity of sleep. Freud distinguished the manifest and the latent content of dream, the latter containing the true meaning of the dream. Free associations would represent the “royal road” to uncover the latent dream content, and the analyst provide his/her dream interpretation on the basis of the patient's dynamics.

The role of dream interpretation in modern psychoanalytic models has been significantly redefined compared to the initial Freudian conceptualisation (Pesant & Zadra,  2004 ). Crucially, several authors focussed their attention to the intrinsic validity of the manifest facets of dreams and their relationship with the diurnal experience. According to different approaches, the role of dream has been conceptualised in terms of reorganisation of the experience (Fosshage,  2002 ), adaptation to reality (Gazzillo et al.,  2020 ), and co‐construction of the intersubjective reality (Jiménez,  2012 ).

Although several authors underline a “marginalisation” of dream in modern clinical psychological practice (Leonard & Dawson,  2018 ), it is worth noting that dreams have become an object of study also in clinical paradigms different from the psychoanalytical models (Pesant & Zadra,  2004 ; Velotti & Zavattini,  2019 ). Among the others, the evolution of the debate about dreaming in the cognitivist framework (Rosner et al.,  2004 ) represents an interesting example of the redefinition of dreamwork in psychotherapy based on novel experimental data, theoretical models, and clinical observations. Beck ( 1971 ) proposed that dreams reflect the individual conception (and biases) about the self, the world, and the future, and may represent and indicator of changes in the emotional status. Nevertheless, the initial need to move away from the psychoanalytical framework and the pressure to adopt an empirically verifiable clinical model led to a common disuse of oneiric activity in cognitive‐behavioural psychotherapy. Dreams were mainly considered as psychologically meaningless epiphenomena of sleep, useless for the dreamer and in turn for the therapeutic process. More recently, the progress in the scientific understanding of dreams has led to the reintegration of dreams among the object of interest from different epistemological paradigms in the cognitivist framework. From a rationalist perspective, starting from the hypothesis that dreams are subjected to the same cognitive distortions that characterise the waking experience, it has been proposed that dreamwork can help to detect cognitive biases and maladaptive thought patterns (Barrett,  2002 ; Freeman & White,  2002 ; Hill,  1996 , 2003 ) and promote cognitive reconstructing. On the other hand, the constructivist paradigm moved the focus on the narrative facets of dreams and the co‐construction of meaning between patient and therapist (Bara,  2012 ; Rezzonico & Bani,  2015 ; Rosner et al.,  2004 ), with the aim to promote the emergence of relevant aspects of the personal meaning and increase the level of awareness of the patient.

The interest in the clinical use of dreams led to the development of different articulated models of dreamwork in psychotherapy, like the Description, Memory Sources, and Reformulation (DMR) model (Montangero,  2009 ) and the cognitive‐experiential model (Hill,  1996 , 2003 ). Overall, Eudell‐Simmons and Hilsenroth ( 2005 ) identify four main functions of dreams in psychotherapy: (a) facilitate the therapeutic process, (b) increase patient insight and self‐awareness, (c) provide clinical information relevant for the therapist, and (d) provide a measure of therapeutic change.

Clearly, a further research effort is needed to provide support for the objective and efficacy of dreamwork in psychotherapy. Nevertheless, the ongoing debate on this topic has led to several models of the clinical valence of dreams that appear consistent with experimental findings on oneiric activity, mainly moving from standardised symbolic interpretations of dreams to approaches based on the relationship of dreaming with individual experience and cognitive/emotional/behavioural functioning.

2. CONCLUSIONS

From the discovery of REM sleep to the present day, empirical investigations have considerably increased our understanding of neural mechanisms underlying dream recall.

Although compelling evidence converges in providing support to the so‐called activation hypothesis and continuity hypothesis, considerable efforts are still needed to fully understand the neurobiological bases of oneiric processes.

Overall, we believe that (a) some results are still heterogeneous due to the application of different protocols, so a more consistent approach is needed; (b) the use of advanced techniques such as high‐density EEG or source localisation methods should be encouraged to better understand the relationship between specific oscillations and dream features; (c) further studies on experimental manipulation of dreaming should be carried out, also considering the implementation of brain stimulation techniques to promote dream recall or its specific characteristics; and (d) DEBs could be used as a model to observe dream contents overcoming the problem regarding the correspondence between specific time/stage of sleep and dream production, offering new insights about the neural correlate of dreaming.

Lastly, it is worth noting that recent pandemic studies have “elected” dream activity (and nightmares) as a reliable index of our emotional and psychological health (Fränkl et al.,  2021 ; Scarpelli et al.,  2022 ). Considering this, we underline that a translational view is needed to systematically explore the potential role of neurobiological and experiential facets of dreaming in a clinical context.

AUTHOR CONTRIBUTIONS

All the authors contributed equally.

CONFLICT OF INTEREST

All authors report no conflict of interest.

ACKNOWLEDGEMENTS

Open Access Funding provided by Universita degli Studi di Roma La Sapienza within the CRUI‐CARE Agreement. [Correction added on 26 May 2022, after first online publication: CRUI funding statement has been added.]

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• Published: 14 October 2019

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

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

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

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

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Introduction.

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

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

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

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

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

Descriptive statistics and intercorrelations of tested variables

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

Multilevel models predicting dream valence as outcome

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

Distribution of dream emotional valence for 1653 dream reports.

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

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

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

Post Hoc multilevel models predicting dream valence as an outcome variable

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

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

Additional multilevel models predicting perceived stress as outcome

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

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

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

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

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

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

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

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

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

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

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

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

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

Participants

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

Retrospective measures

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

Boundary thinness

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

Trait anxiety

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

Youth trauma

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

Posttraumatic stress disorder

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

Prospective measures

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

Dream affect and content

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

Perceived stress

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

Statistical analyses

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

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

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

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

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

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

Data Availability

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

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Acknowledgements

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

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E.S.D. contributed to study design, conducted the study, analysed the results and wrote the manuscript draft. S.H.J. contributed to the study design and data collection. D.B.P. contributed to the statistical analyses and reviewed the manuscript. A.Z. obtained the grants, designed and supervised the study and reviewed the manuscript.

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Samson-Daoust, E., Julien, SH., Beaulieu-Prévost, D. et al. Predicting the affective tone of everyday dreams: A prospective study of state and trait variables. Sci Rep 9 , 14780 (2019). https://doi.org/10.1038/s41598-019-50859-w

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July 26, 2011

The Science Behind Dreaming

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

By Sander van der Linden

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

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

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

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

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

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

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

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

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

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

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

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