Digital Dream Analysis: A New Article on Word Search Methods

Digital Dream Analysis: A New Article on Word Search Methods by Kelly BulkeleyThe latest issue of the journal Consciousness and Cognition has an article of mine titled “Digital dream analysis: A revised method,” that’s the fruition of several years of data-driven work.  It lays out the latest developments in testing and refining the word search template programmed into the Sleep and Dream Database, a digital archive and search engine designed to promote scientific dream research.  The original article I wrote using this word search method was in a 2009 issue of Consciousness and Cognition, titled “Seeking patterns in dream content: A systematic approach to word searches.”  The new article builds on that earlier piece and extends it in two ways.

First, it presents a revised, 2.0 version of the word search template that has many improvements on the 1.0 version presented in the 2009 article.  I’m sure there will be more refinements in the future, and hopefully more researchers developing their own templates as well.  But for now, the 2.0 version is useful as a well-tested and fairly comprehensive tool for analyzing dream content simply, quickly, and reliably.

Second, the article applies the 2.0 word search template to a number of previously studied collections of dreams from very high quality sources (Calvin Hall and Robert Van de Castle, J. Allan Hobson, and G. William Domhoff).  In doing so I followed the advice of Kurt Bollacker, database engineer for the SDDb, who suggested I take “classic” studies in dream research from the past and try applying my new method to their same data.  That’s what I have done in this article: use the word search method to analyze the same sets of dreams those researchers studied, so we can see what the new method can and cannot tell us about meaningful patterns in dream content.

Here is the abstract for the article.  The whole thing, I’m told, is available for free download until November 22, 2014.

“This article demonstrates the use of a digital word search method designed to provide greater accuracy, objectivity, and speed in the study of dreams.  A revised template of 40 word search categories, built into the website of the Sleep and Dream Database (SDDb), is applied to four “classic” sets of dreams: The male and female “Norm” dreams of Hall and Van de Castle (1966), the “Engine Man” dreams discussed by Hobson (1988), and the “Barb Sanders Baseline 250” dreams examined by Domhoff (2003).  A word search analysis of these original dream reports shows that a digital approach can accurately identify many of the same distinctive patterns of content found by previous investigators using much more laborious and time-consuming methods. The results of this study emphasize the compatibility of word search technologies with traditional approaches to dream content analysis.”

The Social Networks of Dreaming

The Social Networks of Dreaming by Kelly BulkeleyDigital technologies have given us new ways to socialize and to track, measure, and reflect upon our socializing.  Humans have always been social animals, as Aristotle said long ago, but now we’re social animals with smart phones, email, Facebook, Tubmlr, and Twitter.  It’s not just that these tools and platforms dramatically expand the range of our social interactions—they do—but more importantly they enable us to observe those interactions over time in finely grained detail and analyze them for personal insight.

 

An unusual but promising development in this area is the use of social network analysis in the study of dreams.  Despite their occasionally strange and otherworldly content, people’s dreams offer a surprisingly accurate source of information about their most important emotional concerns in waking life, including their relationships with other people.

Richard Schweickert, a professor of psychology at Purdue University, has done pioneering work in demonstrating the validity of applying the latest tools of social network analysis to dream content.  Schweickert (with the help of G. William Domhoff) analyzed the lengthy dream journals of three participants, 2 women and a man.  He identified all the characters that appeared in the dreams and created maps of “affiliation networks” to indicate how often the various characters appeared in the same dreams together.

His results showed that all three dream series had a nonrandom “small world structure,” meaning that certain characters appeared together in the dreams far more frequently than would be predicted by chance alone.

Schweickert’s research is more than another piece of evidence supporting the notion that dreams are meaningfully structured psychological phenomena, not just random neural nonsense from the sleep-addled brain.  His findings cast new light on the profoundly social nature of human dreaming, showing that dreams can be a potentially valuable mirror revealing the people who matter to us the most.

For example, the participant known as “Merri” dreamed more often of her recently deceased sister than of any living person in her current waking life. This suggests that we dream about people who are especially meaningful, not necessarily the people with whom we spend the most time.

Schweickert also noticed that another of the participants had many dreams of family members and of work colleagues, but rarely dreams including people from both those spheres of his life.  This participant was an insect taxonomist by profession, prompting Schweickert to speculate that “perhaps his cognitive style is to focus”; this might account for the mutually exclusive categories of social interaction in his dreams.

What’s most exciting about these findings is that they open the door to deeper and more sophisticated examinations of our social networks.  Who are the interlinked communities of people we dream about the most often?  Which of our dream characters serve as mediators connecting different communities?  What happens when characters we personally know appear in the same dreams as celebrities we’ve never actually met (e.g., actors, musicians, athletes)?  Who are the “lone wolves” of our dream life, people who only appear by themselves and never with other characters?  How do the social networks of dreaming relate to other aspects of dream content such as emotions, colors, and settings?  Do we only dream of some people in happy situations, and other people only in frightening scenarios?

The technology needed to answer these questions is emerging rapidly.   Better than counting Facebook friends or Twitter followers, the big data of dreaming offers a valuable source of honest, accurate insight into the intricate web of social relationships that shape our lives.

 

Note: this post also appears on the Huffington Post, as of September 3, 2013.