Tag: methodology

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A Dream Research Contest

To help test the initial hypotheses arising from the SDDb baselines, I am offering a dream research contest, open to anyone willing to give it a try.  The first person to send me (at bulkeleyk@gmail.com) the correct answers will win a $50 gift certificate to Amazon.com.

 

The contest challenges you to look at the word usage frequencies of six sets of dreams (available here) and make the following three predictions for each set: a) Is the dreamer a male or female? b)  Is the dreamer younger or older than 18? c) Is this a set of most recent dreams (MRDs) or highly memorable dreams (MemDs)?  You will not be able to read any of the dreams–you only get to look at the numerical frequencies of how often they used certain types of words.

 

I’m going to make it easier by giving three big clues.  These clues are so big, in fact, that they will give you a clear map to reach the correct answers.

 

Clue #1: The six sets of dreams come from four individual dreamers, let’s call them A, B, C, and D.  Two individuals gave two sets of dreams each.  The other two individuals gave one set of dreams each.  This means the contest has a bonus question: Which sets of dreams come from which individual?

 

Clue #2: As discussed in previous posts, the SDDb baselines have identified a number of significant differences between male and female dreamers and between MRDs and MemDs.   These differences can serve as analytic tools of comparison for making the kinds of predictions I’m asking you to propose.  Below are several specific tests you can use to identify the dreamer’s gender (male or female) and age (older or younger than 18) and the type of dreams contained in the set (MRDs or MemDs).  (Note: The age tests are not drawn directly from the SDDb but from other sources of research on children’s dreams (e.g., David Foulkes, Calvin Hall).)

 

Clue #3: There are two cases out of the 18 total predictions (3 questions x 6 sets of dreams) in which the overall conclusion of the tests gives the wrong answer.  In other words, the tests will lead you to the right answer about 89% of the time.  To get 100% of the answers right, you’ll have to add some kind of insight above and beyond the tests.

 

Good luck!  And please “show your work”–explain to me how you derived your predictions.

 

Tests for Gender:

If Emotion is higher than the Female MRD baselines –> Female

If Emotion is lower than the Male MRD baselines –> Male

If Fear is higher than the Female MRD baselines –> Female

If Fear is lower than the Male MRD baselines –> Male

If Speech is higher than the Female MRD baselines –> Female

If Speech is Lower than the Male MRD baselines –> Male

If Characters is higher than the Female MRD baselines –> Female

If Characters is lower than the Male MRD baselines –> Male

If Family is higher than the Female MRD baselines –> Female

If Family is lower than the Male MRD baselines –> Male

If Friendliness is higher than the Female MRD baselines –> Female

If Friendliness is lower than the Male MRD baselines –> Male

If Physical Aggression is lower than the Female MRD baselines –> Female

If Physical Aggression is higher than the Male MRD baselines –> Male

If Sexuality is lower than the Female MRD baselines –> Female

If Sexuality is higher than the Male MRD baselines –> Male

 

Tests for Age:

If Cognition is higher than the MRD baselines –> Older than 18

If Animals is higher than the MRD baselines –> Younger than 18

If Family is higher than the MRD baselines –> Younger than 18

 

Tests for MRD vs. MemD

If Air is higher than the MemD baselines –> MemD

If Air is lower than the MRD baselines –> MRD

If Flying is higher than the MemD baselines –> MemD

If Flying is lower than the MRD baselines –> MRD

If Family is higher than the MemD baselines –> MemD

If Family is lower than the MRD baselines –> MRD

If Animals is higher than the MemD baselines –> MemD

If Animals is lower than the MRD baselines –> MRD

If Fantastic Beings is higher than the MemD baselines –> MemD

If Fantastic Beings is lower than the MRD baselines –> MRD

If Christianity is higher than the MemD baselines –> MemD

If Christianity is lower than the MRD baselines –> MRD

If Death is higher than the MemD baselines –> MemD

If Death is lower than the MRD baselines –> MRD

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The Distinguishing Features of Big Dreams

If someone presented you with two sets of dreams, one of most recent dreams and one of highly memorable dreams, you could predict with a high degree of confidence which type of dream was in which set, based only on word usage frequencies.

The set with more references to flying, air, family, animals, fantastic beings, Christianity, and death is more likely to consist of highly memorable dreams.

This is a testable hypothesis that emerges out of a comparison of the SDDb baselines for most recent dreams (MRDs) and highly memorable dreams (MemDs), available here.  To be clear, it’s a prediction of probability, not certainty.  Some highly memorable dreams have none of these elements, while some most recent dreams have several of them.  But according to the SDDb baselines, it is the statistical tendency of highly memorable dreams to contain significantly more of these elements than we generally find in most recent dreams.

Whether or not this hypothesis has any practical application, it adds new evidence in support of the theoretical claim that dreams are meaningfully structured not just for the individual dreamer but also in relation to each other.  There really are different types of dreams, and their differences can be expressed in increasingly precise terms.

Other researchers such as Harry Hunt and Don Kuiken have proposed psychological models to account for different types of dreams (what Hunt calls “the multiplicity of dreams”).  I am not yet at the point with the SDDb baselines to feel comfortable engaging directly with their approaches, but that is definitely a long-term goal.

At this stage I want to look more closely at the higher-frequency MemD elements and try to understand what they might contribute to the dreams’ long-term impact on waking awareness.

Flying: Not all dream references to flying involve magical powers–some relate to the flight of birds, or flying on airplanes, or floating in water, or time “flying” by.  But many of the references are indeed about people flying magically, and I think it makes good sense that overall, MemDs have significantly more flying references than MRDs.  I would be surprised if it were otherwise, based on the recurrence of magical flying dreams through cross-cultural history.  Genuine flying dreams tend to be quite vivid and realistic, and it’s reasonable to assume that such unusually stimulating sensations would make a lasting impact on waking awareness.

Air: Some of the air references occur in flying dreams, but in most MemDs the air references appear in different contexts: the dreamer is struggling to breathe, or facing a tornado, or noticing the wind blowing.  I don’t know about dreams of wind, but certainly with dreams of tornados and potential suffocation the memorability of the experience is likely to be very high.  A tornado is the most powerfully destructive form of air in nature, and suffocation is a perennial threat to human life, perhaps especially in sleep for people who snore or have apnea.

Family: References to family members appear often in MRDs; they appear even more often in MemDs.   I think it’s fair to say that most people’s strongest emotional relationships (both positive and negative) are with family members.  Thus it makes sense that their appearance in a dream correlates with high memorability.  Looking in more detail at the word search results, references to parents (e.g., mother, father, mom, dad) tend to be the highest, suggesting that dreams in which the individual is cast as a child or in a child’s role are more likely to be memorable.

Animals: Based on any of several different theories (psychoanalytic, developmental, evolutionary), it could be expected that MemDs would have a higher proportion of animals than MRDs.  Psychoanalytically, animals symbolize powerful instinctual energies. Developmentally, animals appear more often in children’s than in adults’ dreams, and MemDs are often recalled from early in childhood. In evolutionary terms, animals in dreams may reflect ancestral threats that we are innately primed to notice and remember.

Fantastic Beings: This category by definition includes characters who are not “real,” so their appearance in dreams naturally arouses some degree of heightened awareness and emotional impact.  Many of them are perceived as extremely frightening and dangerous to the life of the dreamer.  I was surprised by the SDDb baseline results that the MemDs do not have more fear-related emotions than the MRDs, but perhaps what makes some MemDs different is the supernatural source of the fear. There is a connection to be made here with the notion of “minimally counterintuitive supernatural agents” as used in the cognitive science of religion–dreaming is a rich experiential source of people’s religious and spiritual beliefs about such beings.

Christianity: Many references to Christianity in both MRDs and MemDs are relatively trivial references to Christmas, or mild oaths, or a person’s name.  But more often in the MemDs there are direct references to interactions with Jesus, battles with demons, visiting heaven, and worshipping in church.  In a majority-Christian country like the U.S., where all the SDDb baseline participants reside, this seems like an expectable result.  Insofar as Christianity, like most religions, is concerned with deep questions of morality, suffering, and faith, any dream that refers to religious teachings is likely to register more memorably in the dreamer’s awareness.

Death: Whether considered in religious or secular terms, death surely counts as a major existential concern of human life.  Dreaming itself has long been mythologically associated with death, and cultural traditions all over the world have stories about dreams as a portal to the afterlife.  In MemDs the theme of death takes many forms: other characters dying or being killed right in front of the dreamer, dead relatives appearing as if alive (i.e., visitation dreams), and, more rarely, the dreamer him or herself dying.  When the prospect of mortality arises in a dream, it’s not surprising that the individual takes notice and remembers.

What do these seven higher-frequency MemD elements have in common?

For one thing, several of them involve “counter-factuals,” i.e., phenomena that are literally impossible in ordinary waking life.  Magically flying in the air, encountering fantastic beings, seeing people who are dead appear as if alive–these are strikingly anomalous experiences that stand out from ordinary life and make a big impression on memory.

Secondly, several of the MemD themes involve dire threats to the individual’s life and well-being.  Dreams of death, demons, monsters, wild animals, suffocation, and tornados naturally arouse a host of psychological and physiological responses that can literally seize the dreamer’s attention and hold it long after waking.

Thirdly, a few MemD themes relate closely to the prominent themes of children’s dreams generally, with more animals and higher family references.  As I noted earlier, the SDDb baseline for MemDs includes numerous childhood-era dreams reported by children and adults, so it is definitely skewed toward children’s dream content.  That means the differences between MRDs and MemDs could be explained as artifacts of the differences between adults and children.  I grant there will be a large degree of overlap between highly memorable dreams and children’s dreams–precisely because the most memorable dreams people often recall are dreams from childhood.

Not all children’s dreams are big dreams–but many big dreams are dreams that have been remembered from childhood.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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A Primal Difference (Part 3 of Creating a Baseline for Studying Patterns in Dream Content)

What makes unusually memorable dreams different from average, ordinary dreams?  Putting it in Jungian terms, how are big dreams different from little dreams?

 

The SDDb baselines for most recent dreams (MRDs) and memorable dreams (MemDs) give a very precise and empirically based answer to this question.

 

MRDs tend to use more words relating to perception, emotion, cognition, social interactions, and culture.

 

MemD tend to use more words relating to flying, air, family, animals, fantastic beings, Christianity, and death.

Overall, MRDs are more anchored in present waking circumstances, while MemDs seem to have less connection to current social reality.   MRDs reflect more of daily life, while MemDs express deeper existential themes.

These results derive from 828 female MRDs and 691 male MRDs, compared to 801 female MemDs and 504 male MemDs.  You can see the spreadsheet here.

With the help of Dominic Luscinci at Far West Research, I analyzed these four sets of dreams in terms of their similarities and differences, adjusting the levels of statistical significance to account for multiple tests in each word class to protect against type 1 errors.  Fisher’s Exact Test was used in cases where the criteria for chi-square testing were not present.

Before getting into the MRD vs. MemD comparison, I wanted to know what gender differences were most significant. I found that female MRDs and MemDs are more likely than male reports of both types to include references to emotions (especially fear), characters (especially family), speech, and friendly social interactions.  Female MRDs have more perception and cognition, while male MRDs have more physical aggression and sexuality.

There are fewer gender differences in the MemDs than the MRDs.

Then I looked at the MRDs and the MemDs to see what differences show up for both males and females.  I found that MRDs for both genders have more references to emotion, cognition, social interactions, and culture.  MemDs have more references to nature (especially air and flying), characters (family, animals, fantastic beings), Christianity, and death.  The female MemDs have more fire, falling, and physical aggression words.  The male MemDs have more chromatic and achromatic colors.

There are many more differences between MRDs and MemDs than between the males and females.  The comparison with the fewest differences was female MemDs vs. male MemDs; these two sets of dreams were the most like each other.

My first reaction to these findings was surprise that the MRDs had more words relating to perception and emotion, since I expected these indices of intensity and vividness would be more frequent in highly memorable dreams.

But I also felt good because these results basically replicate a 2011 study I did with Ernest Hartmann on big dreams.  In the conclusion of that article we wrote “people’s big dreams are distinguished by a tendency toward ‘primal’ qualities of form and content: more intense imagery, more imagery picturing nightmarish emotions, more nature references, more physical aggression, more family characters, more fantastic/imaginary beings, and more magical happenings, along with less high-order cognition and less connection to ordinary daily surroundings.” (p. 165)

These findings are very similar to the SDDb baseline results.  They give me confidence that these differences between MRDs and MemDs are real and not the result of random variations in the data.

The comparison with the 2011 study is not perfect, since a) that project did not adjust the dream reports for word length, including reports of less than 50 and more than 300 words, unlike the SDDb baselines, b) some aspects of the conclusion (e.g., intense imagery, nightmarish emotions) were derived from Hartmann’s Central Image scoring system and did not emerge from the word search analysis, and c) the participant pool had a big gender imbalance (147 female, 15 male).  However, the mostly female composition of the 2011 study actually points to an even closer alignment with the SDDb female results because the female MemDs (but not the male MemDs) have higher frequencies of fire, falling, and physical aggression, all of which seem consistent with the 2011 study’s conclusion.

In a future post I will look at the SDDb’s high-frequency MemD elements–flying, air, family, animals, fantastic beings, Christianity, death–to try and discern what each of them adds to the dream’s memorability and impact on the dreamer.

 

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SDDb Baselines for Recent Dreams and Memorable Dreams (Part 2 of Creating a Baseline for Studying Patterns in Dream Content)

“Dreams are not mysterious, supernatural, or esoteric phenomena.  They are not messages from the gods nor are they prophecies of the future.”  That’s what Calvin Hall said in his 1966 book The Meaning of Dreams (New York: McGraw-Hill, revised edition, p. 120).  Hall’s secular beliefs may or may not be justified, but what’s certain is that his baseline of “norm dreams” was designed to explain normal, average, ordinary types of dreams.  He was not interested in unusual, extraordinary types of dreams involving “esoteric phenomena.”  As a result, the baseline he developed gives what I call a homogenized view of dreams, privileging the theoretical significance of common, recently remembered dreams and denying the scientific relevance of rare but extremely memorable types of dreams from earlier times of life.

This is why I’ve created not one SDDb baseline, but two–one for most recent dreams (MRDs), and one for highly memorable dreams (MemDs).  You can find a spreadsheet with the baseline word usage frequencies here.  As always, I offer the caveat that this is a work in progress and will surely grow and change in the future.  My focus for now is to clarify some of the basic features of different types of dreams.  I’d like to know how MRDs and MemDs are similar, because that could tell us something interesting about how the sleeping mind operates consistently across varying dream types.  I’d also like to know how MRDs and MemDs are different, because that could tell us something interesting about the complexity of the mind in sleep and the creative potentials of the nocturnal imagination.  Setting up two baselines will, I hope, help the cause of answering these questions and provide a more sophisticated resource for the comparative analysis of other collections of dreams.

MRD Baseline: This includes 828 female dream reports and 691 male dream reports, all from the USA, all between 50 and 300 words in length, drawn from three sources.  The Hall and Van de Castle norm dreams (491 male, 490 female) form one component of the baseline.  This enables future analyses to maintain a solid “backwards compatibility” with the traditional standard of measurement in the dream research field, even as we continue trying to expand and improve beyond it. The additional dreams come from two SDDb sources: The Demographic Survey 2010, which included a “most recent dream” question, and the SCU Sleep/Wake Study 2008, which asked each participant to keep a dream journal and provide their two most recent dreams.  The SCU participants were college students like the HVDC norm dreams participants.  Those in the Demographic Survey were considerably older; I don’t yet have a detailed analysis of the age data, but I’m pretty sure the majority of participants were 50+ years of age.

MemD Baseline: This includes 801 female reports and 504 male reports, all from the USA, all between 50 and 300 words in length, drawn from four SDDb sources.  One is a question asking participants in the Demographic Survey 2010 to describe the earliest dream from childhood they can still remember.  Second is a question asking those same participants to describe the worst nightmare they can recall from any time in their life.  Third is a survey of children ages 8-18 asking them to describe the most memorable dream they’ve ever had.  Fourth is a survey of adults asking them to describe the most memorable dream they’ve ever had.  Unlike the MRD baseline, this one includes reports from children and reports answering different types of questions.  I have grouped these sources into a single baseline because they all fit comfortably under the heading “highly memorable dreams.”  Two of the sources use exactly that phrase in their questions, and the other two asked questions implicitly seeking reports of dreams with unusual memorability.  The inclusion of children’s reports is justified, I believe, because so many highly memorable dreams come from childhood, and thus children themselves may be in an especially good position to recall these dreams and describe them in detail.

At the far right of the spreadsheet you can see the word usage frequencies for each of these constituent sources of the two baselines.  As I said in the previous post, two important principles for creating a useful baseline are transparency and flexibility.  The baselines I’ve created have their limits, but they offer a great deal of transparency–you can see exactly where the reports are coming from–and flexibility–you can change or revise the baselines to suit your own purposes.

In the next post of this series, I’ll talk about some of the initial patterns I see in comparing the MRD and MemD baselines.  I invite your thoughts and observations! And corrections where I’ve gotten something wrong…

 

 

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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.