Tag: SDDb Research Notes

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Creating a Baseline for Studying Patterns in Dream Content (Part 1)

Compared to what?

 

That’s a question I’ve learned from Tracey Kahan to ask whenever I study a set or series of dreams.  If I find, for example, that 13% of a given collection of dreams include words related to fire, I can only assess the significance of that number in comparison to some other collection of dreams.  Maybe 13% is unusually high, maybe it’s unusually low; we can’t say for sure unless we have some kind of standard or baseline against which to compare it.

 

For the past half century, the Hall and Van de Castle (HVDC) Norm Dreams have been used as a general baseline to compare the content analysis findings of other sets or series of dreams.   No disrespect to Hall or Van de Castle, but I’ve always thought it would be good for the field of dream studies to develop a baseline that includes input from more than just 200 college students from 1950’s Ohio. We have indeed learned a great deal from that set of dreams, and now it’s time to widen our perspective.  One of my goals with the SDDb is to expand the HVDC approach by creating a bigger and better baseline for studying patterns in dream content.

Any dream research baseline, short of a total collection of all human dreams ever experienced, will inevitably be partial and limited, a tiny fraction of the totality of human dreaming.  This fact imposes an obligation of humility on those who pursue this kind of research.  A baseline is a pragmatic tool we create and use to help answer our questions, not a perfect representation of objective reality.

That said, it is not only possible but extremely important to make reasonable distinctions between better and worse baselines.

The bigger and more broadly based, the better.  The larger the database, the more likely the patterns in content are genuine and not just statistical noise (though we can never be absolutely sure).

Always, always, quality of data is essential–garbage in, garbage out, no matter how big your N.

The baseline’s sources should be very transparent, so researchers can make informed decisions about how much weight to give the results comparing their data with the baseline.

The HVDC Norm Dreams are divided by gender, and I think this is a good practice to continue for a couple of reasons.  First, there do seem to be significant differences between male and female dreaming, so creating a baseline for each gender offers a more precise tool for comparative research.  Second, many studies have a drastic imbalance in the gender of their participants, specifically a much higher proportion of female than male dream reports.  Hence the practical importance of offering a baseline for each gender, to facilitate the analysis of these kinds of imbalanced sets. (Why it’s easier to gather female than male dreams is a separate topic of discussion.)

Baseline frequencies for dream content will be sensitive to the word counts of the reports.  A collection of extremely long dreams will likely have higher frequencies of ALL categories of content, while a collection of extremely short dreams will likely have lower frequencies across the board.  The HVDC set draws the line at 50 words minimum and 300 words maximum.  I’m willing for now to go along with that policy, though eventually I want to return to consider what we may be losing by excluding shorter and longer dream reports.

What types of dreams should be included in a general baseline for dream research?  That’s a trickier question.  Should it blend together many different types of dreams, or should it concentrate on a single generic type of dream?

Many researchers have opted for the latter approach. The HVDC Norm Dreams include five dream reports from each participant, presumably recent dreams from the previous few nights, although several of the dreams are recurrent and/or come from an earlier time of life.  It’s not a “pure” set, but it purports to be a reasonable selection of the average dreams of this group of people.

Sleep laboratory researchers like David Foulkes have argued that dreams gathered in a home setting are too unreliable and only dream reports gathered in a controlled laboratory setting with accompanying sleep stage data should be considered when assessing basic patterns in dream content.  However, Bill Domhoff has made the case that dream reports gathered outside the lab setting can also be a valid source of insight, especially questionnaires asking people to describe their “most recent dreams.”

The difficulty in defining what counts as the most generic type of dream makes this approach problematic.  Another drawback is the under-reporting of the incidence of rare but intense and highly memorable types of dreams–nightmares, lucid dreams, visitation dreams, recurrent childhood dreams, etc.  These exceptional types of dreams may not occur as frequently as ordinary dreams, and thus they do not appear as often when people are asked to describe their most recent dreams.  But these unusual dream types are widely experienced and reflect important features of the dreaming mind that we need to account for in any general theory of dream psychology.  We lose sight of those features when we focus only on allegedly “average” dreams.

The advent of database technology makes it easier than ever to try the former approach: Creating a baseline that accepts rather than denies the “multiplicity of dreams” (in Harry Hunt’s terms), a baseline that blends together many different types of dreams and seeks a dynamic balance representing the varied phenomenology of dreaming across the widest possible range of its occurrence.

In Part 2 I’ll describe how I’m trying to develop this kind of blended baseline using data in the SDDb.

 

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Work Dreams, Lucid Dreams, Visitation Dreams: New Data from the Demographic Survey 2012

Now available in the Sleep and Dream Database are hundreds of new dream reports gathered as part of a demographic survey of 2252 American adults, conducted via online questionnaires administered by Harris Interactive.  I designed the survey to focus on three types of dreams that people often report with special frequency and/or intensity: Work dreams, lucid dreams, and visitation dreams.  I’ve just begun reading through the narratives, and they’re fascinating–I invite anyone who’s curious to take a look at the dreams for yourself, and let me know what patterns you see. (Update: I’m having some server issues, if you can’t access the site I’m sorry, please try again later and I should have it fixed.)

 

The work dreams are answers to the question, “Have you ever dreamed about your job or a situation at work?”  I’ve created a sample word search for the female work dreams and male work dreams, including all reports of five or more words.  For the most part these do not seem to be happy dreams.

 

The lucid dreams are answers to the question, “Have you ever dreamed that you were aware of being within a dream?” I’ve created a sample word search for the female lucid dreams and male lucid dreams, including all reports of five or more words.  At a minimum, these dreams testify to the frequency of lucid dreaming experiences among the general American public.

 

The visitation dreams are answers to the question, “Have you ever dreamed about someone who is dead appearing as if they were still alive?” I’ve created a sample word search for the female visitation dreams and male visitation dreams, including all reports of five or more words.  These kinds of dreams have played a big role in cross-cultural religious history, and I’m interested to study their occurrence among present-day Americans.

 

The survey also included questions about dream recall, nights of insomnia per week, and several other questions about demographic background (age, race, education, income, political ideology, religious worship, etc.).  These data, too, are available for you to study however you wish (although you may find it a little tricky–I’m still working on bugs in the SDDb system).  I’ll write soon about my initial findings with these demographic variables as they relate to patterns of sleep and dreaming.

 

 

 

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Hall and Van de Castle Norm Dreams Now in the SDDb

Thanks to the help of Bill Domhoff and Adam Schneider (and of course Kurt Bollacker), the set of 981 Hall and Van de Castle male and female “Norm Dreams” are now in the SDDb and available for study using the database tools.  Long available on the Dreambank.net website, the Norm Dreams have been widely cited in research literature for many decades, and it’s a big boost to the SDDb to include this historically significant dream collection.

Calvin Hall gathered these dreams from 100 female and 100 male college students from two colleges near Cleveland, Ohio, from 1947-1950.  Each student provided five dream reports of no less than 50 words and no more than 300 words in length.  The complete set of 1000 dreams served as the foundation for Hall’s book with Robert Van de Castle, The Content Analysis of Dreams in 1966.  Hall and Van de Castle called them the Norm Dreams because their content frequencies could be used as a basis for comparison with other groups, as a measuring stick to determine what counts as normal or abnormal proportions of dream content.

That’s a strong claim, of course, too strong perhaps, but only because Hall and Van de Castle’s data were relatively limited.  The goal of trying to identify large-scale, widely distributed patterns in dreaming remains a worthwhile pursuit, and now we have much more data and much better tools than Hall and Van de Castle had to seek them out.

The first thing I did once the Norm Dreams were in the SDDb was to try a series of identical word searches in the Dreambank and the SDDb.  I wanted to insure that the original texts (981 remain, 19 were lost some time ago) were exactly the same in both databases and that their search results were directly comparable.

Phew!  Every word I searched for in the Norm Dreams in the SDDb yielded the same results as a search for the same word in the Norm Dreams in the Dreambank. (Individual words being searched in the Dreambank have to be framed with^ ^.  For example, to search for the word anger, the term must be typed ^anger^.)

Next, I wanted to check the Norm Dreams for their frequencies on the SDDb 40-category template and compare these results to the frequencies I found using an earlier prototype of this template in my 2009 paper in Consciousness and Cognition, where I reported word search findings on the Norm Dreams in the Dreambank.  I have made several minor changes and additions to the 40 categories since 2009, so I expected the results now to be slightly higher but essentially the same.

Again, the results were reassuring (although I didn’t have the counts from 2009, just the percentages).  When I searched the Norm Dreams for each of the SDDb’s 40 word categories, the frequencies were the same or slightly higher as the frequencies I found in 2009 applying similar categories to the Norm Dreams in the Dreambank.  The Earth and Transportation categories had the biggest increase between the two analyses, due to the addition of several new terms to these two categories when I originally programmed the SDDb’s template.

The one exception was the Weather category, which initially showed a lower frequency in the SDDb analysis compared to the earlier Dreambank analysis.  When I investigated the differing results more closely, I found I had not done a very good job translating all the weather-related words into the SDDb template.  Several words were missing from Weather category in the SDDb template that I had used in the Dreambank analysis.

Doh!

When I performed an adjusted SDDb search including these previously excluded words, the results were back in line with the expected similarity between the two databases. (This makes me think I’ll need to re-check all the categories when I next get a chance to upgrade the template.)

These initial findings have given me confidence that the Hall and Van de Castle Norm Dreams can be studied using the word search tools of the SDDb in a way that’s consistent, reliable, and open to comparison with analyses from the Dreambank or any other research project making use of the Norm Dreams.

All of this means it’s getting easier and easier to make apples-to-apples comparisons of dream content using word search technology.

I doubt the dreams of 200 college students from 1940’s Ohio can give us a complete representation of all human dreaming (though there are actually many intriguing “big dream” experiences in the set).  But I share Hall and Van de Castle’s goal of identifying broad patterns of dream content.  I’m hopeful that word search methods, applied to larger collections of data from more diverse groups of people, will help us move closer to that goal.

Note: the statistical table I created with the frequencies for the 40 categories can be found here.

 

 

 

 

 

 

 

 

 

 

 

 

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Research Suggestions Welcome

The basic functionality of the Sleep and Dream Database is now in place and ready to use.  Some aesthetic tweaks still need to be made, and better export options are in the works, but I’m finally starting to turn my attention from the architecture of the database to its contents.  In coming weeks I will upload several new data sets, including the Hall and Van de Castle norm dreams, a new demographic survey from Harris Interactive, and a collection of reports from a small group of people who have been wearing the Zeo sleep manager device while keeping dream journals.

 

Looking farther ahead, I’d like to collect dream reports from distinctive individuals and/or groups whose waking life concerns could be studied in light of patterns in their dreams.  For example, I’d love to study the dreams of serious athletes to learn about their visions of victory and fears of injury or defeat.  It would be fascinating to look at the dreams of avid gun owners to understand better the psychological roots of their passion for firearms.  I’d be curious to explore the dreams of both prison inmates and prison guards, to get a deeper sense of life on both sides of the penal system.

Now the SDDb is up and running, these kinds of projects are easier than ever to pursue.

If you have suggestions about types of people you think would be good prospects for new research, please let me know.  Better yet, if you would like to collaborate in gathering and analyzing dreams from specific groups, I’d like to hear what you have in mind.

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Animals in Dreams

Below is the section on animal dreams from my video talk for the IASD Australian Regional Conference held last week in Sydney.  I would be very interested in hearing from people whose dreams include types of animals NOT mentioned in my findings, to help us develop an even broader sense of oneiro-zoology (yes that’s a made up word!).

 

Animals: I searched the SDDb for many different types of animal-related words, but I’m sure I missed some, so this is an area needing improvement.  What I found in this study [of 2087 total dreams, 1232 female and 855 male] was 16% of the female reports and 14% of the male reports including at least one animal reference.  Consistent with what previous researchers have found, the children’s dreams in my sample have a higher percentage of animal references (24% for the girls, 20% for the boys).  Does this mean children are “closer” to nature than adults?  Perhaps.  It does seem that a higher proportion of animals in children’s dreams (or should we say a diminished proportion of animals in modern Western adults’ dreams?) is a stable pattern across many studies.

The animals that appeared most often were, in order, dogs, cats, horses, bears, fish, snakes, birds, and insects.  The first three—dogs, cats, and horses—are among the most familiar domestic animals.  Bears are NOT domestic animals, and they actually appear most often to be aggressive, threatening creatures in dreams.  Among different types of fish, sharks appear frequently like bears, as frightening predators, putting the dreamer in the harrowing position of prey, the hunted.  In other dreams, however, ocean dwelling creatures like whales and dolphins reveal an amazing intelligence that teaches the dreamer something new about the natural world.

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Zeo Sleep Data and the Ur-Patterns of Dream Content

So far I’ve done word search analyses on 20 series of dreams from individuals and 9 sets of dreams from large groups of people, a total of more than 18,000 dream reports. It’s too early to say anything definite about the patterns that have emerged from this data. More reports need to be gathered from a wider variety of people, and more improvements need to be made in the SDDb word search template.

Still, a few basic patterns have appeared in nearly all the collections I’ve studied. I’m calling them ur-patterns because they seem to represent deep structural elements of dream content (ur- as in “original” or “primal”). That’s my general hypothesis, anyway, and each new set of dreams is another chance to test and refine it.

Here are the ur-patterns I’ve identified so far:

  1. Of the five senses, sight words are used most often, smell and taste the least.
  2. Of the five major emotions (fear, anger, sadness, confusion, happiness), fear words are used most often.
  3. Of all the categories of cognitive activity, speech words are used most often.
  4. Of the four natural elements, water words are used most often.
  5. Falling words are used more often than flying words.
  6. There are more references to family characters than animal characters, and more to animals than to fantastic beings.
  7. There are more references to friendliness than physical aggression.

Looking at the KB DJ 2009-2010 series with Zeo sleep data (available at google docs), a scan for these patterns finds good but not perfect evidence for each one.

Vision-related words are used more frequently across all the Zeo measurements, with smell and taste words almost entirely absent. Fear words are used more frequently than other emotion words. Speech words are the most used among the cognition categories, and water is the highest among the natural elements, though earth is a consistently high second. The usage of falling words is always higher than, or equal to, flying words.

The family > animals pattern > fantastic beings was not as clear-cut. Fantastic beings always had the lowest word usage, but animals were not always lower than family. When the names of the dreamer’s immediate family were added to the search for characters, the total frequency of family-related words rose higher than the usage of animal words in 15 of the 17 subgroups.

The friendliness > physical aggression pattern was not perfectly evident either. In part this is due to a “false positive” problem in the SDDb template. The word search category for physical aggression includes the word “bit,” which the dreamer used in almost 10% of all the reports as a term meaning “small amount,” not a physical bite. I’ll provide revised numbers once I’ve fixed this. For now, looking at how often the word “bit” is used in each Zeo subgroup, it appears the physical aggression frequencies will drop below the friendliness frequencies in most, but not all, subgroups.

In sum, the ur-patterns appear across virtually all the subgroups of Zeo sleep measurement. No matter what aspect of sleep was measured, the dream reports used the same basic frequencies of words in several major categories. High or low proportions of sleep did not correlate with any major change of dream content, at least at this level of analysis.

In future posts I’ll look at the few variations from these patterns (high physical aggression, animal, flying, and earth references) in relation to the dreamer’s waking life concerns, taking the possibility of metaphorical meaning into account.

I will also look at each of the five types of Zeo data and see if I can identify any particular variations that rise to the level of statistically significant correlation. If any such correlations emerge, they may guide us toward specific areas where a measurable aspect of sleep does interact with basic patterns of dream content.