Tag: Sleep and Dream Database

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Building the Dream Library

Construction is going well so far on the Dream Library, a stand-alone structure on a rural, forested property near Portland, Oregon. As many friends and colleagues know, the project has taken a long time to reach this stage, but at last it’s beginning to take actual shape. The building will provide a long-term archive for dream-related materials such as journals, books, and art. The journal & book collections of Jeremy Taylor and Patricia Garfield will form the core of the library, along with other donated materials and my own collections.

To help with future activities involving the library, I have established the Dream Library Foundation. The mission of the Dream Library Foundation is to promote dream research, dream-related art, and public education about dreaming. Everyone has innate potentials for dreaming, so everyone can potentially benefit from the Foundation’s mission. To enact this mission, the Foundation will focus its resources on four areas of activity:

1.    Maintaining a physical archive of dream journals, books, art, and other dream-related materials (the Dream Library);

2.     Maintaining a digital archive of sleep and dream-related information (the Sleep and Dream Database);

3.     Providing funding and support for researchers, artists, and educators working with dreams (Dream Library Grants);

4.     Sponsoring in-person gatherings on dream-related topics (Symposia at the Dream Library).

It’s likely that construction will not finish until summer of 2025. We have much to do between now and then…

 

 

 

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The Nightmares of Halloween

It’s more than a metaphor to say that Halloween is a time when our nightmares go on parade. The scary images, decorations, and costumes that take over the month of October have a direct psychological connection to the actual themes and patterns of people’s nightmares. If we look at current research on nightmares—who has them, what they’re about, what causes them—we can gain new insight into the unconscious creativity of our Halloween festivities.

Who has nightmares? Numerous studies have reached the same conclusion: children are especially prone to nightmares, and so are women. Let’s start with age. The younger you are, the more likely you experience nightmares. Ernest Hartmann’s 1984 book The Nightmare notes the frequency of nightmares in children between the ages of 3 and 6, and he suggests that bad dreams may begin even earlier than this: “It is quite likely that nightmares can occur as early as dreams can occur; that is, probably late in the first year of life.”

The age factor shows up clearly in the nightmare patterns of adults. In a 2010 survey (available in the Sleep and Dream Database) in which 2,993 American adults answered a series of questions about sleep and dreams, the following are the percentages of people in different age groups who answered “Yes” to the question, “Have you ever had a dream of being chased or attacked?”

Chasing/Attack dreams, by age:

18-24: 71%

25-34: 65%

35-54: 59%

55-69: 48%

70+:  40%

Now for gender. Women tend to have more nightmares than men do, although how much more seems to vary during the life cycle. A meta-analysis by Michael Schredl and Iris Reinhard in 2011 found a striking pattern: similar frequencies of nightmares for males and females in childhood and old age, but a significantly higher frequency of nightmares for females during adolescence and adulthood. There seems to be a “nightmare bump” during women’s lives that elevates their frequency of bad dreams consistently higher than men’s. Is this nature or nurture? Probably both. A similar pattern appears in the 2010 SDDb survey cited above, when analyzed in terms of gender.

Chasing/Attack dreams for men, by age:

18-24: 68%

25-34: 63%

35-54: 56%

55-69: 47%

70+:  37%

Chasing/Attack dreams for women, by age:

18-24: 82%

25-34: 71%

35-54: 65%

55-69: 50%

70+: 46%

No matter how we explain these differences—more on that below—the basic pattern seems clear. Nightmares are especially frequent early in life, and especially for women during adolescence and young adulthood.

What do we have nightmares about? The most common content is fear, of course. And yet, what terrifies one person may have no emotional impact on someone else, so it’s difficult to generalize about the contents of nightmares. Still, it is possible to identify a few typical elements. In the SDDb, I selected four types of dream text (“bad dream,” “nightmare,” “nightmares,” “worst nightmare”), of 25+ words in length, which yielded a set of 423 dreams. I analyzed these 423 dreams using a word-search method with a template of 40 categories of content. I then compared the nightmare results to the results for the SDDb Baselines, a collection of more than 5,000 dreams representing ordinary patterns of dream content.

The dreams in the Baselines average about 100 words per report, while the 423 nightmares have an average length of only 65 words per report. This means the Baselines will tend to have higher frequencies on all categories. That’s actually helpful for our purposes, because it makes it easy to spot the categories that are unusually high in the nightmares (Baselines included in parentheses):

Fire: 4.7% (4.3%)

Air:  6.4% (4.4%)

Falling: 10.9% (8.3%)

Death: 18.4% (8%)

Fantastic beings: 10.4% (2.1%)

Physical aggression: 42.1% (17.6%)

Religion: 7.1% (6.7%)

Weapons: 9.9% (3.9%)

These are the categories of content that seem to be over-represented in nightmares, appearing more often in bad dreams than in ordinary dreams. They are also the themes that characterize pretty much every horror movie ever made, and countless video games, and, of course, many of the costumes and decorations of Halloween.

Why do we have nightmares? Psychologists have offered several theories about this. For Sigmund Freud, a nightmare is a failure of the sleeping mind to contain the instinctual desires aroused in dreaming. Similarly, the neurocognitive theory of Ross Levin and Tore Nielsen explains nightmares as a failure of emotional regulation during sleep. Carl Jung viewed nightmares as reflections of inner conflict, and thus potential revelations of insight and guidance. Antti Revonsuo’s “threat simulation theory” focuses on chasing nightmares and their potentially beneficial role in preparing the individual for similar threats in waking life.

The simple fact that nightmares are so common seems to be evidence against a theory of dreaming as a form of play (such as I propose). How can a frightening experience be playful? Actually, a theory of dreaming as play has a good explanation for the prevalence of nightmares. Research on play in animals and humans has found that play-fighting is one of the most common forms of play among social species like ours. Although it’s not “real” fighting, play-fighting does involve real aggression, threats, and negative emotions, and it seems to have a valuable rehearsal/preparatory function similar to other forms of play. Paradoxically, play-fighting can also promote social bonding by creating a safe arena to work through interpersonal tensions.

This brings us back to the connection between nightmares and Halloween. Seen in this light, the many little rituals of Halloween are ways of playing with our nightmares, welcoming them into waking awareness, sharing them with others, and celebrating their wild creative energies. Once each year, we invite these energies into the community as a way of enlivening and strengthening our collective bonds, at a time when daylight is waning and the nights are growing colder. This is the psychological wisdom of Halloween, infusing us with a playful burst of unconscious vitality just as we’re preparing to survive through the coming darkness.

Note: this post first appeared in Psychology Today on October 21, 2021.

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Aggression in Dreams

Hitting, fighting, chasing, shooting, killing—these are not only common themes in the news each day, they are also recurrent features of our dreams at night. Few studies have focused specifically on aggression in dreaming, even though Sigmund Freud, the founder of psychoanalysis, claimed that “the inclination to aggression is an original, self-subsisting instinctual disposition in man” (Civilization and its Discontents, 1930). A combination of old and new methods of research can shed light on how this primal instinct plays out in our dreams.

Who Has Aggressive Dreams?

The Hall and Van de Castle system (1966) of dream content analysis has codes for three kinds of social interactions: friendly, sexual, and aggressive. Research using the HVDC system has suggested a few basic patterns in the frequency of aggression in dreams:

  • Men have more aggression, especially physical aggression, in their dreams than do women.
  • Women are more likely to be victims than initiators of aggression in dreams.
  • Children have more aggression in dreams than do adults, especially involving attacks by animals.
  • Older people have less aggression in dreams than do younger people.

Hundreds of studies have used the HVDC method over the past several decades, and their findings support the basic idea that aggression is an innate feature of human dreaming.

Why Do We Have Aggressive Dreams?

An additional perspective comes from using word search technologies to identify significant patterns of meaning in dream content. The Sleep and Dream Database (SDDb) has a template with a category for physical aggression, and a large collection of dreams to study for a specific theme like this.

The SDDb Baseline dreams are a good place to start—a set of 5,321 dreams (3,227 females, 2,094 males) that represent a composite portrait of dreaming in general (the reports were given in response to a question about “your most recent dream”). Although limited in many ways, the Baseline dreams offer an empirical basis for making comparisons across different sets of dreams. This can help in identifying trends and patterns that would be difficult to see otherwise.

Applying the word search category for physical aggression to the female Baselines, we find that 15.1% of the dreams include at least one word relating to physical aggression. Applying the same word search category to the male Baselines yields a result of 21.5% of the dreams with at least one reference to physical aggression. (The combined Baselines figure is 17.6%.) So this analysis confirms the finding of the HVDC system that men’s dreams, on average, seem to involve more physical aggression than do women’s dreams. The top ten words used in these dreams were the following: Hit, kill, fight, chasing, killed, shot, fighting, chased, war, shooting.

Turning to the dreams of individuals who have kept track of their dreams for a lengthy period of time, a great deal of variation appears in the frequency of physical aggression. For example, “Tanya,” a young woman, has a relatively high proportion of physical aggression in her dreams (25.4%, in 563 reports), about the same as “Lawrence,” an older man (25.7%, in 206 reports. Another young woman, “Jasmine,” has low physical aggression in her dreams (10.5%, in 800 reports), just like “RB,” an older man (11.8%, in 51 reports).

There is clear evidence that experiences with physical aggression in waking life can increase the frequency of its appearance in dreaming. The best examples are “Mike,” who served as a medic during the Vietnam War and whose collection of dreams includes a very high proportion of physical aggression (76.3%, in 97 reports). In the four sets of dreams from “Beverley” from 1986, 1996, 2006, and 2016, the first set has much more physical aggression (11.9%, in 253 reports) than in the other three (5.7%, in 687 reports), which accurately reflected her involvement in that earlier time period with a violent religious cult.

To help shed light on the role of culture in dreams of physical aggression, the SDDb also includes sets of dreams from non-Western people, which can be analyzed in the same way. For the Mehinaku people of the Amazonian rain forest, a collection of 383 dreams had 22.5% with at least one reference to physical aggression. For a group of Nepalese college students, their dreams (535) had 18.1% with a reference to physical aggression. Three groups of African church members reported dreams (142) with a 19% frequency of physical aggressions. These findings are close enough to the SDDb baselines overall figure of 17.6% to suggest that culture is not a decisive factor in this aspect of dream content.

Concluding Insights

Aggression appears to be a normal feature of human dream content, across different cultures.

Men seem to have more physical aggression in their dreams, although some women have high levels, too.

Dreams of physical aggression can accurately reflect actual aggressions in waking life, so an unusually high level of dream aggression, or a sudden change in dreams to a higher level of aggression, might be a therapeutically valuable sign.

Many dreams of physical aggression do not, however, reflect actual experiences of aggression. These dreams may use violence as a metaphor (e.g., a dream of physical attack as a metaphor of feeling emotionally vulnerable). They may reflect instances of fictional aggression (e.g., seen in a movie). They may be anticipations of violence that may happen at some point in the future (e.g. a threat simulation).

Aggression in dreaming can be viewed as an internal form of play-fighting—the most common form of play in the animal kingdom, and very frequent among humans, too. Play-fighting functions as a way of preparing for future challenges, and also for diminishing and defusing emotional tensions that can lead to actual violence. The same psychological dynamics of play-fighting seem to be operative in dreaming, too.

 

Note: this post first appeared in Psychology Today, May 31, 2021.

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A Database for Dreamers

The Sleep and Dream Database (SDDb) is a digital archive designed to promote an empirical, hands-on approach to dream research.  The SDDb enables users to apply basic tools of data analysis to identify meaningful dimensions of dreaming experience.  The goal of the SDDb is not to replace other modes of dream interpretation, but rather to complement and enrich them with new insights into the recurrent patterns of dream content.  Anyone who studies dreams, from whatever perspective and for whatever purpose, can benefit from knowing more about these basic patterns.

The SDDb is not the only online resource for this kind of approach to the study of dreams.  The Dreambank.net website run by G. William Domhoff and Adam Schneider also has a large online collection of dream reports gathered by various researchers that can be searched and analyzed in many ways.  The future will likely witness the development of many other online databases with valuable collections of dream material.  The focus here is on the SDDb, but the following discussion highlights important methodological principles that apply to all forms of digitally enhanced dream research.

The SDDb currently contains more than 30,000 dream reports of various types from a wide range of people.  Some of the reports come from individuals who have kept a dream journal for many years.  Some of the reports come from participants in surveys and questionnaires.  Some come from the studies of other researchers who have generously shared their data with me.  The SDDb also includes dream reports from anthropological studies, historical texts, literary sources, and media interviews.  (The database does not, however, contain dream reports that users have entered directly through an online portal. That feature awaits future development.)

The SDDb also includes, in addition to dream reports, the answers given by survey participants to a variety of questions about their sleep and dreaming, for example how often they remember their dreams, how often they experience insomnia, have they ever had a dream of flying or lucid dreaming, etc.  The data also include people’s responses to various demographic questions about their gender, age, race/ethnicity, education, religious practices, political beliefs, etc.

This combination of a large number of narrative dream reports plus a large amount of quantitative survey data makes the SDDb an especially deep and varied resource for the study of dreaming.

The SDDb offers two basic functions for exploring this material.  One, “Survey Analysis,” enables you to compare answers to questions posed on a survey or questionnaire.  For example, you can create a statistical table to compare the dream recall frequencies of people from different age groups, or the insomnia frequencies of people with different political views, or the occurrence of lucid dreams among men and women.

The other function, “Word Searching,” enables you to sift through large numbers of dreams for particular words and phrases.  You can search the dreams by choosing your own word strings, or you can also use the built-in word search templates to search for typical categories of dream content.  This function allows you, for example, to search a set of dreams for all the references to water, or colors, or fear, or the names of famous people or places.

Background and Methodology

The development of the SDDb began in the early 2000’s in consultation with G. William Domhoff and Adam Schneider, who helped me understand how to use their Dreambank.net website.  With their encouragement I started designing a new, complementary database that would 1) include both dream reports and survey data, 2) allow for the use of built-in word search templates, and 3) have the flexibility to enable a wide range of searches and analyses.  In 2009 I worked with Kurt Bollacker, a software designer and engineer from San Francisco with expertise in digital archiving practices, to build the first version of the database.  In 2014 I began working with Graybox, a web technology company in Portland, to expand the scope of the SDDb and improve its user interface.  A major upgrade of the database was completed by Graybox in the spring of 2020.

The word search approach has many advantages as a mode of dream research include its speed, transparency, replicability, flexibility, and power to analyze very large quantities of material.  The process is fairly easy to learn, and sites like the SDDb and Dreambank.net provide free and open access for users to engage in their own study projects aided by these new digital tools.

This approach has several disadvantages, too.  They include deemphasizing the qualitative aspects of dreaming, overemphasizing the measurability of dream content, and leaving open the key question of how to connect the numerical frequencies of word usage with the waking life concerns of the dreamer.

These disadvantages can be diminished by using quantitative analysis as one method among others in a multidisciplinary approach to dreams.  There is no reason in principle why word search methods cannot work in coordination with other methods using qualitative insights and evaluations.  Indeed, I would argue the future prosperity of dream research depends on developing better interdisciplinary models for integrating the results of multiple methods of study.  The users of the SDDb can help to make progress in creating those models.

To address the challenge of how to connect the word usage frequencies with relevant aspects of the dreamer’s life, two principles should be kept in mind.  These principles suggest paths for exploring the potentially meaningful connections between the dream and the individual’s waking situation.

One principle is the continuity hypothesis: the relative frequency with which something appears in a person’s dream can be a reflection of its importance as a meaningful concern in the person’s waking life.  In other words, the more often something (a character, setting, activity) shows up in dreams, the more emotionally important it’s likely to be in waking life. To be clear, the continuity does not need to be literal or physical; it’s more what people care and think about in their waking lives.

As an example, one of the dream series in the SDDb comes from “Bea,” a young woman whose anxious, sad dreams were continuous not with her actual life, which was quite safe at the time, but with her worries about possible bad things that might happen to her family or to the students in her care as dormitory resident assistant.

The other principle is the discontinuity hypothesis: infrequent and anomalous elements of dream content can be spontaneous expressions of playful imagination, occurring at any point in life but especially in times of crisis, change, or transition.  Something that appears very rarely and is dramatically discontinuous with typical patterns of dream content can reflect the mind’s concerted effort to go beyond what is to imagine what might be.

As an example, the “Nan” series in the SDDb comes from a woman who had suffered a horrible car crash, followed by several months in the hospital. Most of the dreams in her series have negative, nightmarish quality (as would be expected from the continuity hypothesis), but one dream is unusual in having multiple colors, a good fortune, and a reference to beauty. Nan singled this dream out as having an especially important impact on her during her recovery from the accident, giving her a sense of hope that one day she would regain her health and creative spirits (which she eventually did).

A New Feature: The SDDb Baselines

The recent upgrade of the SDDb included the addition of a new feature that allows users to compare the results of word searches with a large set of more-or-less “average” dreams. This feature helps to determine the significance of the word search results. For example, I said above that most of Nan’s dreams have a “negative, nightmarish” quality. How can I support that claim? By using the baselines feature.

The baselines are two curated sets of “most recent dreams” from 2,094 males and 3,227 females, gathered by several researchers from a variety of populations between the 1950’s and the present (including the Hall and Van de Castle “norm” dreams). They are aggregated here to represent typical densities of the appearance of key words or phrases in ordinary dreaming.

In Nan’s case, her dreams indicate she definitely did feel strong concerns at this time, in a mostly negative direction.  Of her 26 dreams, 8 of them (31%) have at least one reference to fear.  The corresponding figure for the female baselines is 25%. She has references to death in 19% of her dreams, versus 9% for the female baselines; references to physical aggression in 23%, versus 15% for the female baselines; and zero references to happiness, versus 8% in the baselines.

These frequencies accurately reflect the frightened and vulnerable quality of Nan’s feelings in waking life. Even if we knew nothing about Nan’s personal life, we could use these variations of her dreams from the baselines to make the prediction that she is suffering through a difficult and frightening situation.

This is the foundation for the “blind analysis” method I have been using in several papers and IASD conference presentations (see below). Now the tools I use to make those analyses are available to everyone.

 

Further reading:

  1. The Meaningful Continuities Between Dreaming and Waking: Results of a Blind Analysis of a Woman’s Thirty-Year Dream Journal. Dreaming 28: 337-350.
  2. Using the LIWC Program to Study Dreams. Dreaming 28: 43-58. (Co-authored with Mark Graves)
  3. The Digital Revolution in Dream Research. In Dream Research: Contributions to Clinical Practice (edited by Milton Kramer and Myron Glucksman) (Routledge).
  4. Dreaming in Adolescence: A “Blind” Word Search of a Teenage Girl’s Dream Series. Dreaming 22: 240-252.

 

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The New Dream Studies and the Wall Street Journal

Dream researchers are creatively deploying a variety of big data technologies to open a new era of oneiric discovery.

An article appeared earlier today by Robert Lee Hotz, science reporter for the Wall Street Journal, titled “New Insights into Dreams and What They Say About Us.” It’s a great article, well-written and thoroughly researched, and quite fair-minded towards the scientific study of dreams. (The article can be found here, if you have WSJ access.)

Here is my favorite line:

“While still highly experimental, the new dream studies underscore the power of data mining to assemble unexpected insights by sifting through large data sets of seemingly unrelated information.”

That is very well put. Exciting possibilities beckon on the horizon, and yet much more work needs to be done in mapping the multidisciplinary terrain between here and there. Hopefully others who read the article will recognize these potentials and contribute their insights to this dynamic, though still “highly experimental” realm of inquiry.

I always want to get people more enthused about the study of dreams—but not too enthused. To my great relief, Hotz concludes the WSJ article with some cautionary words (my own included) about the need for greater ethical evaluation and awareness of the possibly harmful abuses of these technologies.

Two follow-up notes from the article.

First, the survey of dreams in relation to the Black Lives Matter movement and recent protests against racial injustice involved 4,947 American adults, completing an online questionnaire designed by me and administered by YouGov on June 15-19, 2020. I am currently working with Michael Schredl on an article analyzing the responses to this survey. An early preview of the results appeared in a post I wrote for Psychology Today on June 25, 2020. The data from this survey are not yet available in the Sleep and Dream Database, but they will be soon.

Second, to the question of “How many dream reports from how many people are in the SDDb?” I gave the estimate of more than 26,000 dreams from more than 11,000 people. I obtained those figures by using the SDDb’s advanced word search tool and defining the data set as all reports with a minimum word count of 5, which yields a result of 26,498 dreams from 11,346 participants. There are surely many additional dreams in the database of less than five words, but many of those reports include “non-dream” answers (such as “no,” “don’t remember any”), which are important to preserve but shouldn’t be counted in overall tallies of the actual dreams. There are also some non-dreams of more than 5 words, but not enough to alter the basic estimate of 26,000 dream reports currently in the database.

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Basic Patterns in Dreaming

The basic patterns of dream content are coming into sharper focus, thanks to new technologies of digital analysis. By using these tools to study large and diverse collections of high-quality dream data, and then making those tools and data publicly available, we can illuminate recurrent frequencies of dream content that others can easily review, replicate, and verify for themselves. The more we know about these basic patterns, the more we can gain helpful insights from people’s dreams regarding their mental and physical health, social relations, cultural interests, and even spiritual beliefs.

When I began this line of research in the mid-2000’s, I used the resources of the Dreambank.net, a site managed by G. William Domhoff and Adam Schneider. In a paper from 2009, “Seeking patterns in dream content: A systematic approach to word searches,” drawing on the resources of the Dreambank, I included this passage in the conclusion:

“Until researchers have gathered many more high-quality reports from a wide variety of people (ideally accompanied by multiple sources of biographical data), we cannot make any definitive declarations about the universal features of human dreaming. But the results of this study suggest several testable hypotheses:

  1. Dreaming perception is primarily visual, with less hearing and touch and almost no smell or taste.

  2. All emotions are represented in dreams, with fear the most frequent.

  3. Many types of cognitive activity occur in dreaming, especially those associated with awareness and social intelligence.

  4. Aggression is more frequent than sexuality, and both are more frequent for men than for women.”

Today, these same hypotheses can easily be tested with the resources of the Sleep and Dream Database (SDDb). The simplest method is to use the SDDb’s built-in word search template of keywords. The word search function has a template of forty categories of dream content, including categories for specific types of perception, emotion, cognitive activity, and social interaction. Starting on the “Advanced Search” page, I would define the data set for this purpose by setting a word limit of 25 words, and then select a category from the keywords menu. Looking at perceptions first, the following results can be generated in a few moments:

Out of a total of 20,510 dream reports of at least 25 words in length, reported by a total of 7,335 people, a word relating to visual perception appeared at least once in 34.6% of the reports. For hearing, the figure was 10.7%, for touch, 13%, and smell and taste combined only 2.7%. Eleven years later, I would still stand by that first hypothesis.

Turning to emotions, the results of the same simple search process (define the data set as having a minimum of 25 words, and selecting a category from the keywords menu) are just as predicted. A word relating to fear appears at least once in 18.2% of the dreams. Anger appears in 7.1%, sadness in 3.7%, happiness 6.5%, and wonder/confusion 14.4%. This hypothesis seems pretty solid, too.

Cognition in dreaming is harder to study for various reasons, but the word search method can still offer some interesting results. A word relating to thinking appears at least once in 41.9% of the dreams. Some kind of speech or verbal communication appears in 37.6%, and a reference to reading or writing in 7.6%. These findings support the basic idea that dreaming has a fair amount of cognitive activity, with plenty of social communication, though more detailed studies are needed to tease out the variations between dreaming and waking cognition. The third hypothesis is worth keeping.

Social interactions in dreaming are also difficult to study, so the results here should be regarded with extra caution. Indeed, the hypothesis from 2009 may not bear contemporary scrutiny, particularly around gender differences. (When defining the data set, gender can be selected as a search variable from the constraints menu.) The SDDb word search approach yields a finding of at least one reference to physical aggression in 20.8% of the male dreams and 17.2% of the female dreams. That’s a difference, but not a huge one. With the category of sexuality, the male dreams had at least one reference in 5.8% of the reports, versus 6.6% for the female dreams. This is the reverse of the predicted difference. The results of this quick analysis confirm that overall references to physical aggression occur much more frequently than references to sexuality, but the results do not support the 2009 hypothesis about higher frequencies of both kinds of content in men’s dreams.

There are other ways to study these questions with the tools of the SDDb. For example, the “baselines” function provides the frequencies on all 40 categories for a specially curated subset of 2,094 male dreams and 3,227 female dreams. These baseline frequencies provide a kind of measuring stick for dream researchers—a more precise way of determining the average frequencies of particular types of dream content and comparing them to other sets of dreams, which might have content features that vary from the baseline patterns in interesting ways. That shall be a topic for another post.

Note: This post first appeared in Psychology Today on September 4, 2020.