Zeo Sleep Data and the Ur-Patterns of Dream Content

Zeo Sleep Data and the Ur-Patterns of Dream Content by Kelly BulkeleySo 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.


Dreams That Matter: Egyptian Landscapes of the Imagination

Dreams That Matter: Egyptian Landscapes of the Imagination by Kelly BulkeleyI’ve just read an excellent new book, Dreams That Matter: Egyptian Landscapes of the Imagination, by Amira Mittermaier (University of California Press, 2011).  The following two paragraphs are from a review that will appear in the next issue of the IASD magazine Dream Time.


Important new books on dreams and Islam have been coming out faster than this reviewer can keep up with them. In the last issue of Dream Time I wrote about Iain Edgar’s The Dream in Islam: From Qur’anic Tradition to Jihadist Inspiration. In this issue I review Dreams That Matter: Egyptian Landscapes of the Imagination by Amira Mittermaier, an Assistant Professor of Religion at the University of Toronto. Mittermaier’s work won the American Academy of Religion’s 2011 book award for Excellence in the Study of Religion in the Analytic-Descriptive Category. Based on her dissertation fieldwork, the book explores the lively yet hotly contested terrain of dreaming in contemporary Egypt. Whereas Edgar’s work takes a broad historical view of dreaming in Islam and then focuses on a particular type of dream experience (jihadist inspiration dreams), Mittermaier’s approach is to delve deeply into the ethnographic details of particular people’s lives in Egyptian culture and then open the discussion to broader issues and debates in Islamic teachings about dreams, revelation, and the imagination.

Mittermaier brings a unique personal background to her research topic. She was raised in Bavaria by a German father who was a neurologist and by an Egyptian mother who was a Jungian psychotherapist. Her upbringing gave her a deep familiarity with the ideas of Freud and Jung—and a conviction that the individual-centered theories of psychoanalysis were missing something important about the transpersonal dimensions of dreaming: “Intrigued by this apparent tension between Elsewhere-oriented and psychoanalytic dream models, I wanted to explore what it means for dreamers in the world today to be extended outward as opposed to inward.” (14)

I’ll post the rest of the review once the Dream Time issue appears, some time in the next few weeks.


Comparing Dream Content and Zeo Sleep Data

Comparing Dream Content and Zeo Sleep Data by Kelly BulkeleyAn advanced feature of the Sleep and Dream Database is the ability to analyze dream content using sleep stage measurements from the Zeo Sleep Manager as search constraints. So far, the SDDb has only one series with both dream reports and Zeo sleep data from the same nights (KB DJ 2009-2010). In coming months I will be pursuing new studies with other participants using a combination of dream journaling and the Zeo device. (If you’re interested in contributing to this research, please let me know!)

Using the word search template of the SDDb, I analyzed 135 dream reports with Zeo data in terms of total REM sleep, total light sleep, total deep sleep, total time awake during the night, and total ZQ (an aggregate number measuring overall sleep quality). For each of these five Zeo variables I divided the 135 reports into three or four subgroups of roughly equal number and average word length, then searched each subgroup to determine its frequency of using the seven word classes and forty word categories available in the SDDb.

At this very early stage of working with dream and Zeo data, my goal is to learn enough to be able to ask more refined questions in future research. The small size of these subgroups (28 the smallest, 52 the largest) means that the statistics are not definitive and surely include a fair amount of noise. The variation in average word length of the reports in each subset (105.53 the shortest, 142.49 the longest) is another reason to view these results cautiously. Some of the reports provide only a brief mention of sexual activity, omitting additional details for privacy reasons.  The KB DJ 2009-2010 series has 182 total dreams, but 47 of the reports do not have corresponding Zeo data.

If patterns in the sleep data do correlate with patterns in dream content, I suspect the effects are likely to appear at the extremes, at the high and low ends of each measurement scale. Unusual frequencies may be nothing more than random noise, but they may also be genuine signals of interaction between sleep physiology and dream content. I’m hoping to identify where these signals might be appearing in data.

The spreadsheet with all the results can be found on Google docs.

Over the next few weeks I’ll post some comments about these data and what I think they mean. For anyone who repeats the SDDb word searches I did on the KB DJ 2009-2010 series and finds an error in my spreadsheet, I’ll send you a free book!