The Study of a 32-Year Long Dream Journal

The Study of a 32-Year Long Dream Journal by Kelly BulkeleyThe latest series to be uploaded into the Sleep and Dream Database (SDDb) is the biggest yet: the “Brianna Journal 1984-2016,” 2,448 dream reports from a woman who kept a journal fairly consistently for 32 years.  This series offers an amazing opportunity to observe in unusually close detail the emotional contours of an individual’s life as she makes her way through a challenging and often dangerous world.

Brianna (not her real name) shared these dreams with me and Deirdre Barrett last year, which we initially studied for a presentation at the 2016 conference of the International Association for the Study of Dreams.  Using the word search functions of the SDDb, I performed a “blind analysis” on three subsets of Brianna’s dream journals, meaning I 1) tabulated the frequencies of word usage for several categories of dream content, 2) compared her frequencies with baseline averages for each category, and 3) made inferences, based on nothing other than her dream patterns, about her concerns and activities in waking life.  For instance, I inferred that Brianna is closer to her mother than her father, is interested in books and writing, is not interested in sports, and has significant involvement with issues of death and dying.  Brianna herself, who attended the conference presentation, confirmed these and other inferences, which helped demonstrate the general idea that patterns in dreaming can accurately reflect people’s waking life concerns.

Now I have finally uploaded the complete collection of dreams Brianna shared with me, which provides a broader overview of her dreaming experiences over the span of more than three decades.  I will share more details from my analysis at the upcoming 2017 IASD conference (held in Anaheim, California, June 16-20).  For now, here are some of the initial findings of my study of this remarkable series.

Length: This is a long series in at least three ways: total number of dreams (2,448), time span covered by the journals (32 years), and average number of words per report (292).  The median word length is 168 words, meaning half the reports are shorter than that, and half the reports are longer.  Looking at the distribution of word lengths in the series as a whole, 851 of the dreams have between 1 and 99 words, 794 of the dreams have between 100 and 299 words, and 803 of the dreams have 300 or more words.  A series with this many dreams at both the short and long ends of the spectrum poses special challenges for analysis.  For now, I will study the series as a whole, but at some point I will look at subsets of varying lengths (e.g., the dreams of 50-300 words in length, of which there are 1,192).

Cognition: The series as a whole has a remarkably high frequency of dreams with at least one word relating to thinking (71%), speaking (56%), and reading/writing (19%).  The dreams have lots of strange, irrational material, too, but much of the content is oriented around normal cognitive activities that are also important in her waking life (Brianna is, in fact, a literate, well-educated, and sociable person).  The high proportion of cognition references could be a result of the unusual length of her dreams, and/or it could be an accurate reflection of her waking personality.  Either way, this is a topic worth further investigation.

Death: One out of every seven (15%) of Brianna’s dreams has a reference to death.  That is quite high compared to other dream series I have studied, and it strongly suggests that death and dying are major concerns in Brianna’s waking life.  I know enough about her to confirm the general accuracy of this inference, and now I am curious to look more closely at how this theme weaves its way through her series as a whole.

Religion: The frequency of references to religion is also unusually high in this series, and the list of specific words used in the dreams makes it fairly easy to accurately infer that Brianna is Jewish.  In previous studies I have found that patterns in dreaming offer good clues to a person’s beliefs and attitudes towards religion.  The Brianna series seems to be another illustration of that premise, and through deeper analysis I hope to understand better how religious and spiritual themes in the dreams track with Brianna’s waking life interests, concerns, and experiences.

Note: this post was originally published in Psychology Today, March 10, 2017.

Donald Trump: The Sleep Deprivation Hypothesis

Donald Trump: The Sleep Deprivation Hypothesis by Kelly BulkeleyIt seems that every pundit on the planet has taken a shot at explaining the phenomenal rise and mercurial character of Donald Trump, currently the leading contender to become the Republican nominee for US President. A recent op-ed piece by Timothy Egan in the New York Times, “A Unified Theory of Trump,” suggested a novel and I believe entirely plausible explanation for Trump’s behavior as a candidate: he is chronically sleep deprived.

Egan pointed to Trump’s comments last November in which he boasted about his disinterest in sleep. As reported by Nara Schoenberg in the Chicago Tribune, Trump told a crowd in Springfield, Illinois that “I’m not a big sleeper, I like three hours, four hours, I toss, I turn, I beep-de-beep, I want to find out what’s going on.” A few days later Trump told Henry Blodget in an interview for Business Insider that he can get by on as little as one hour of sleep. Here is an excerpt from the interview; the sleep discussion comes at the very beginning:

HENRY BLODGET, CEO AND EDITOR-IN-CHIEF OF BUSINESS INSIDER: You have an incredible work ethic, which is clearly part of your success. You’re tweeting at 3 o’clock in the morning, you’re up all night.

DONALD TRUMP: It’s part of my campaign. [Conservative radio host] Mark Levin said to me last night, I had a dinner-show at 8:30. He says, “I saw you on ‘Morning Joe’ at 7, I saw you in the debate. Where do you get the energy?” he said. I said, “Mark, you know what, I got one hour of sleep last night. Because I flew from Milwaukee at 2:30 in the morning. You know, by the time you’re finished up with all the stuff and the interviews.” It was a successful debate, so I stayed around.

I then flew, I went to New Hampshire. I went to a hotel, I stayed for one hour, because I got there at 5. And by the time I got there, I had to get up to get out at 6:30 something. So I slept for one hour over there.

He said, “Where do you get the energy?”

HB: So where do you get it? Where does it come from?

DT: Genetically. My father was very energetic, my mother was very energetic. He lived to a very old age and so did my mother. I believe that I just have it from my father, from my parents. They had wonderful energy.

In her Huffington Post commentary, “Can Sleep Deprivation Explain Donald Trump’s Behavior?” Krithika Varagur noted that in his 2004 book Trump: Think Like a Billionaire, he “claimed to sleep only from 1:00 a.m. to 5:00 a.m., in order to gain a competitive advantage in his dealings. He advised readers, ‘Don’t sleep any more than you have to. … No matter how brilliant you are, there’s not enough time in the day.’”

I won’t speculate about Trump’s genetics, but I agree with Schoenberg, Egan, and Varagur that his behavioral patterns are characteristic of someone with chronic sleep deprivation, the symptoms of which include emotional imbalance, sudden mood swings, cognitive deficits, poor judgment, memory loss, irritability in social situations, increased appetites, loss of creativity, the tendency to continue with an error despite contrary evidence, and an inability to recognize and adjust to new conditions. Most of these symptoms do seem applicable to Trump. As Egan put it,

“His judgment is off, and almost always ill informed. He has trouble processing basic information. He imagines things. He shows a lack of concentration… in addition, Trump is given to inchoate bursts of anger and profanity. He creates feuds. In his speeches, he picks up on the angry voice in the mob and then amplifies it.”

But if this theory about Trump is true, then his political success seems even more bizarre than ever. How can someone who flaunts his psychological dysfunction be winning the fervent support of a large portion of the American electorate?

The answer may be embedded in the question. Trump’s supporters themselves may have a tendency to chronic sleep deprivation.

The behavioral signs are consistent with this idea. People who support Trump are remarkably unyielding in their attachment to him; nothing anyone says will change their minds. As Trump himself commented in January, “I could stand in the middle of 5th Avenue and shoot somebody and I wouldn’t lose voters.” His supporters seem to include many people who are angry, suspicious of reason, socially irritable, and uncreative in the sense of seeking a return to an earlier, simpler time, when America used to be great.

Stronger evidence comes from demographic studies of sleep. Trump’s supporters tend to be people at the lower end of the income scale, less educated, and, in their own words, feeling besieged by outside forces threatening to overwhelm the country. Empirical research has shown that people in precisely those demographic conditions are more prone to suffer insomnia and problems sleeping. For example, Sara Arber at the University of Surrey has shown correlations in the British population between poor sleep and low socio-economic status. Here is how I describe her findings in chapter 4 of my book Big Dreams:

“Research by Sara Arber and her colleagues at the Center for the Sociology of Sleep at the University of Surrey has found clear connections between socioeconomic status and sleep quality. In a study based on interviews with 8,578 British men and women between the ages of 16 and 74, Arber and her colleagues identified several social and economic factors associated with increased sleep problems: unemployment, low household income, low educational achievement, and living in rented or public housing. Women had worse sleep problems than men, and divorced or widowed people had worse sleep problems than married people. Overall, their study found that disadvantages in social and economic life were strongly correlated with poor quality sleep. Noting the negative health consequences of sleep deprivation, Arber and her colleagues suggested that “disrupted sleep may potentially be one of the mechanisms through which low socioeconomic status leads to increased morbidity and mortality.”

The last point bears emphasis. Poor socioeconomic conditions can lead to poor sleep, which in turn can lead to increased health problems and a shorter lifespan. Sleep seems to be a pressure point where adverse social forces can directly and negatively impact a person’s physiological health.

My research with the Sleep and Dream Database has also found that people at the low end of the economic scale tend to have more insomnia and trouble sleeping. In a 2007 survey I found, consistent with Arber et al.’s research, that people with higher education and higher annual income tended to have less insomnia than people with lower education and lower annual income. A 2010 survey found the same pattern: people without college degrees had somewhat worse insomnia than people with a college degree. On the personal finances question, people with the lowest annual income reported having worse insomnia than did the people with the highest annual incomes. (I discuss these surveys at greater length in chapter 4 of Big Dreams.)

Most Americans are sleep deprived not by choice or genetics, but because of the relentless stress and pressure of modern life. For those Americans at the lower end of the economic scale, with fewer opportunities and more anxieties about the worsening condition of the country, it becomes difficult to preserve normal, healthy patterns of sleep.

And then Donald Trump comes along and says sleep deprivation is nonsense, that’s just what losers think when they see a high-energy individual with a strong work ethic. Trump shows people how to re-brand their loss of sleep as a badge of honor, reconceive their misfortune as a virtuous strength, and transform their diminished inner life into an outward projection of aggressive confidence. It seems to work for him, and the implicit promise of his campaign is that it will work for his supporters, too.

 

References:

Arber, Sara, Marcos Bote, and Robert Meadows. “Gender and socio-economic patterning of self-reported sleep problems in Britain,” Social Science & Medicine 68 (2009): 281-289.

Arber, Sara, Robert Meadows, and S. Venn. “Sleep and Society,” in The Oxford Handbook of Sleep and Sleep Disorders (Charles Morin and Colin Espie, ed.s). New York: Oxford University Press, 2012, 223-247.

 

Note: this essay first appeared in the Huffington Post on March 9, 2016.

Big Data and the Study of Religion: Can a Google Search Lead to God?

Big Data and the Study of Religion: Can a Google Search Lead to God? by Kelly BulkeleyA recent essay in the Sunday Review Section of the New York Times made several observations about religion in contemporary America by analyzing a huge collection of Google search data. In “Googling for God,” economist Seth Stephens-Davidowitz examined the search results for various religious terms and questions in relation to where the people lived and when they performed the searches. Stephens-Davidowitz’s work offers an excellent illustration of the pros and cons of using big data analytics to study religion. Three quotes from his essay show where the biggest challenges can be found.

  1. “If people somewhere are searching a lot about a topic, it is overwhelming evidence those people are very interested in that topic.”

This is the key methodological principle used in Stephens-Davidowitz’s analysis: the frequency of Google searches correlates to the intensity of personal interest. At one level this seems like a reasonable premise. In fact, this principle is very close to the “continuity hypothesis” used by dream researchers to correlate frequencies of dream content with personal concerns in waking life. Many dream researchers, myself included, have pursued studies of dream content using the continuity hypothesis to make inferences about people’s waking lives—if a person dreams a lot about sports, for example, we can confidently predict that sports are an important concern in the person’s waking life.

Stephens-Davidowitz does something similar when he connects Google search data to people’s religious concerns and questions. The problem, however, is defining “very interested.” What exactly can we infer about a person based on their entry of a Google search term? They are “interested,” of course, but interested in what way, and how strongly? What prompted their search? Is there anything distinctive about people’s searches for religious terms compared to non-religious terms?

Until these kinds of questions can be answered (ideally with lots of systematically analyzed empirical evidence, not just one-off studies), the use of Google search data to draw conclusions about religion remains on shaky ground.

In dream research we have many decades of studies that have helped us hone in on “emotional concerns” as a primary point of continuity between dreaming and waking. We also have statistical baselines of typical dream content to help us identify meaningful variations in the frequency of certain aspects of dreaming (see, for example, the Dreambank of G. William Domhoff and Adam Schneider, and the Sleep and Dream Database (SDDb) that I direct). If the use of Google search data included these kinds of analytic aids, the results would be much stronger and more convincing.

  1. “Sometimes Google search data, because of Google’s status as a kind of universal question service, is perfectly suited to give us fresh insights into our offline lives.”

The idea of Google as a “universal question service” has great appeal, not the least because so much of the information is easily accessible for public study. This is one of the great boons of the era of big data, and new studies of this treasure trove of information are bound to increase in future years.

A potential problem, however, is a tendency to blur the distinction between a) what Google offers its users and b) who those users are. The fact that Google enables people to ask all kinds of questions does not mean that all kinds of people are asking those questions. Google users are not necessarily representative of the US population as a whole, and we do not know how representative the Google users are who are searching specifically for religious terms. We do know that when people perform a Google search they are connected via technology to the internet, they are interacting with a global corporation, and they are being shown numerous commercial responses to their search. These circumstances should qualify our assumptions about who uses Google and how they engage with the search function.

  1. “There are 4.7 million searches every year for Jesus Christ. The pope gets 2.95 million. There are 49 million for Kim Kardashian.”

This quote comes at the end of the essay, and it perfectly encapsulates the difficulty of explaining the significance of Google search results. According to the findings cited by Stephens-Davidowitz, Kim Kardashian gets ten times the search results of Jesus Christ. What exactly does that mean? That Kim Kardashian is ten times more interesting than Jesus? That she is ten times more popular, or more important, or more influential?

The problem is that Google search data do not meaningfully measure any one thing, other than the tautological fact of having entered a specific search term. The results of analyzing these data seem admirably clear and quantitative—4.7 million vs. 49 million!—but they do not easily or self-evidently map onto the actual beliefs, feelings, and attitudes of the general population.

The good news is that these are tractable problems. Real progress can be made by more detailed studies and more systematic correlations of the data with genuinely meaningful aspects of people’s lives. This fascinating essay by Seth Stephens-Davidowitz helps people who study religion see where these new analytic endeavors can be most fruitfully pursued.

 

Note: first published September 24, 2015 in the Huffington Post.

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!