Tag: word searches

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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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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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Dystopian Dreaming

While sitting in the audience and taking notes during the recent IASD conference in Berkeley, I found myself marking several instances where something the presenter said triggered my dystopian imagination.  I confess to being a long-time fan of science fiction and fantasy stories about frightening future worlds controlled by alien invaders, zombie hordes, inhuman technologies, totalitarian governments, and/or rapacious capitalists (I made a list of some favorites below).  I enjoy these stories as literary nightmares: vivid, emotionally intense simulations of real psycho-cultural threats, looming now and in our collective future.

 

At the IASD conference I realized I could turn this interpretive process inside out.  I began to look at dream research from the genre perspective of dystopian fiction.  What would an uber-villain in such stories find appealing in state-of-the-art dream research?

 

Let me be clear, these are my own shadowy speculations and in no way reflect anything directly said or intended by the presenters!

 

Sleep paralysis induction.  There is now a proven technique for inducing the nightmarish experience of sleep paralysis–that is, causing someone to enter a condition in which their bodies are immobilized but their minds are “awake” and vulnerable to terrifying images, thoughts, and sensations.   I can imagine this technique being put to nefarious use by military intelligence agents, state-controlled psychiatrists, and cybernetic overlords.  The ability to trap a person within a state of sleep paralysis would be a horribly useful tool for anyone bent on total mind control.

 

Transcranial magnetic stimulation.  This technology enables the direct manipulation of neural activity during REM sleep, targeting specific regions of the brain.  If the technology were refined with malevolent purposes in mind, it could potentially disrupt people’s normal dreaming patterns, controlling what they do and don’t dream about.  An evil scientist could thus invent a kind of anti-dream weapon, a magnetic beam aimed at the head of a sleeping person and programmed to stun, control, or destroy.

 

Disrupting PTSD memory formation.  Trauma victims can diminish the symptoms of PTSD if they perform a series of distracting cognitive tasks with six hours of the trauma, thereby disrupting the formation of long-term traumatic memories.  The future militarization of this method seems inevitable.  Anything that alters memory can be used by evil governments to manipulate people against their will, either to do things they don’t want to do (black ops soldiers) or forget things that have been done to them (massacre survivors).

 

Remote monitoring of a person’s sleep.  The Zeo sleep monitoring system (which I’ve used for three years) has now developed a wireless version that instantly relays the user’s sleep data from the headband via a bedside mobile phone to the Zeo database.  This kind of technology opens the door to real-time remote monitoring of people’s sleeping experience, and potentially the ability to reverse the flow of data and influence/shape/guide people while they sleep.  If enough people were linked into the system, it could serve police states as a valuable tool in 24-hour mind-body surveillance.

 

My interest in these morbidly malevolent scenarios is not entirely theoretical.  Over the past few years of developing the Sleep and Dream Database I’ve been thinking of the darker possible applications of this technology, less Star Trek and more Blade Runner.  If it’s true, as most researchers at the IASD are claiming, that dreams are accurate expressions of people’s deepest fears, desires, and motivations, then it’s also true a real potential exists to put that dream-based information to ill use.

 

Projecting even farther forward, I wonder if there might be some kind of future inflection point where the amount of data we gather suddenly reveals much bigger patterns and forms of intelligence than we had previously been able to recognize or scientifically document.  What would happen if this leap of knowledge enabled our collective dreaming selves to somehow unite to challenge the dominance (one might say totalitarian regime) of waking consciousness?

 

I think about all this as I continue building up the SDDb, trying to make good decisions and avoid the nightmare pitfalls.  Dystopian fantasies help me clarify what’s at stake, where the dangers lurk, and how the future may unfold.

 

You may be familiar with Arthur C. Clarke’s 1953 science fiction short story “The Nine Billion Names of God.”  If so, you’ll understand why, as I work on developing new database technologies for dream research, I meditate on the phrase, “The Nine Billion Dreams of God.”

 

 

 

Dystopian Films and TV: Blade Runner, 12 Monkeys, Children of Men, Logan’s Run, The Matrix, Soylent Green, V for Vendetta, Battlestar Galactica, The Prisoner, Gattica, Terminator, Alien, Total Recall, 28 Days

 

Dystopian Novels: The Hunger Games, Fahrenheit 451, Neuromancer, 1984, Brave New World, The Time Machine

 

 

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Word Searching as a Tool in the Study of Dreams, or, Dream Research in the Era of Big Data

I’m giving a presentation with that title on Saturday, June 23, at the annual conference of the International Association for the Study of Dreams, held in Berkeley, California.  The presentation is part of a panel session, “What’s New in the Scientific Study of Dreams.”  I’m giving an overview of the word searching method I’ve been developing over the past several years, with a special focus on four “blind analysis” studies I’ve performed with the help of Bill Domhoff.  A youtube video preview of the presentation can be found here.

Here’s how I define blind analysis in the paper:

A blind analysis involves an exclusive focus on word usage frequencies, bracketing out the narrative reports and personal details of the dreamer’s life and making inferences based solely on statistical patterns in word usage—not reading the dreams at all, and basing one’s analysis strictly on numerical data.  The aim is to assess the patterns of dream content with the fewest possible preconceptions, as objectively as possible, before reading through the narratives and learning about the individual’s waking activities and concerns.

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More Black & White vs. Color in Dreams

Mary Walsh, a psychotherapist and grad student at the GTU, offered an intriguing idea about color variations in dreams: “I wonder if the change in our waking experience of color impacts our dream experience. Photopic vision functions only in good illumination which we have more of for longer periods of time nowadays. Scotopic, or night vision, I think, provides the ability to distinguish between black and white. Could the fact that we see more color for more hours each day and use our photopic vision more cause us to dream in color more often? Maybe dreams have changed.”

I think Mary’s right that more attention to the neurophysiology of vision and the cultural/technological changes of modernity will be helpful in making better sense of this question.

Also, Bob Van de Castle reminded me that his 1994 book Our Dreaming Mind has a good discussion of color dreams (pp. 253-256 and 298).  After reviewing several experimental studies, Van de Castle concludes that “color appears in dreams with much greater frequency than is generally acknowledged.  The saturation or intensity of color in dreams seems to vary along a continuum.” (p. 255)

Bob Hoss is another IASD member who has done especially detailed investigations of color in dreams.

I’ve just read Eric Schwitzgebel’s longer paper, “Why did we think we dreamed in black and white?” in 2002, and I’m grateful for his extensive research on this topic.  He admits that he has larger philosophical fish to fry–he says “I write in service of the broader thesis that people generally have only poor knowledge of their own conscious lives, contrary to what many philosophers have supposed.” (p. 649), an argument he elaborates in his recent book Perplexities of Consciousness (2011).  I don’t think I’d want to argue with him about that general idea.  And I agree that “our knowledge of the phenomenology of dreaming is much shakier than we ordinarily take it to be” (p. 649).

But I suspect Schwitzgebel views this as an insoluble problem because of the fundamental limits of introspection and conscious self-knowledge.  I see it as a problem that can be solved by better empirical research that builds our knowledge of dream phenomenology on  firmer foundations.

Looking at some of the initial data I’ve drawn from the SDDb, it seems clear that most people dream fairly often, but by no means always, of colors and black and white.

Here’s a link to an SDDb search for reports of 25+ words with references to achromatic colors.  472 reports show up, out of 5193 reports of that length.  White appears most often, black next, gray third.

And here’s a link to an SDDb search for reports of 25+ words with references to chromatic colors.  476 reports show up, out of 5193 reports of that length.  Red appears most often, followed by blue, green, yellow, orange, and purple.

In studies of people who have kept long-term dream journals, I’ve found lots of variation in this area.  Some people have more chromatic color references in their dreams, and other people have more achromatic references.  Some people have very high overall frequencies (e.g., Merri, whose dream series of 315 dreams is available on the Dreambank, has by my count 44.4% of her dreams with at least one chromatic reference and 40% with at least one achromatic reference) and others quite low (Paul, whose series of 136 dreams is available on the SDDb, has 0% chromatic and 1.47% achromatic references).

I don’t know of any theoretical perspective that can encompass all this data.  Isn’t it paradoxical to think about the colors we see when we’re asleep and our eyes are closed?  Perhaps we need a new paradigm entirely to make adequate sense of the visual qualities of dreaming experience.

But I still hold to my “Dorothy Hypothesis”: This whole question in mid-20th century psychology of whether we dream in color or black & white was generated by the 1939 release of The Wizard of Oz, with its dramatic contrast between the drab black & white (sepia, really) of Kansas and the gaudy, transcendent technicolor of Oz.

 

Note: Schwitzgebel’s article appeared in Studies in History and Philosophy of Science 33 (2002), 649-660.

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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.