Category: Psychology and Brain-Mind Science

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Aggression in Dreams

Hitting, fighting, chasing, shooting, killing—these are not only common themes in the news each day, they are also recurrent features of our dreams at night. Few studies have focused specifically on aggression in dreaming, even though Sigmund Freud, the founder of psychoanalysis, claimed that “the inclination to aggression is an original, self-subsisting instinctual disposition in man” (Civilization and its Discontents, 1930). A combination of old and new methods of research can shed light on how this primal instinct plays out in our dreams.

Who Has Aggressive Dreams?

The Hall and Van de Castle system (1966) of dream content analysis has codes for three kinds of social interactions: friendly, sexual, and aggressive. Research using the HVDC system has suggested a few basic patterns in the frequency of aggression in dreams:

  • Men have more aggression, especially physical aggression, in their dreams than do women.
  • Women are more likely to be victims than initiators of aggression in dreams.
  • Children have more aggression in dreams than do adults, especially involving attacks by animals.
  • Older people have less aggression in dreams than do younger people.

Hundreds of studies have used the HVDC method over the past several decades, and their findings support the basic idea that aggression is an innate feature of human dreaming.

Why Do We Have Aggressive Dreams?

An additional perspective comes from using word search technologies to identify significant patterns of meaning in dream content. The Sleep and Dream Database (SDDb) has a template with a category for physical aggression, and a large collection of dreams to study for a specific theme like this.

The SDDb Baseline dreams are a good place to start—a set of 5,321 dreams (3,227 females, 2,094 males) that represent a composite portrait of dreaming in general (the reports were given in response to a question about “your most recent dream”). Although limited in many ways, the Baseline dreams offer an empirical basis for making comparisons across different sets of dreams. This can help in identifying trends and patterns that would be difficult to see otherwise.

Applying the word search category for physical aggression to the female Baselines, we find that 15.1% of the dreams include at least one word relating to physical aggression. Applying the same word search category to the male Baselines yields a result of 21.5% of the dreams with at least one reference to physical aggression. (The combined Baselines figure is 17.6%.) So this analysis confirms the finding of the HVDC system that men’s dreams, on average, seem to involve more physical aggression than do women’s dreams. The top ten words used in these dreams were the following: Hit, kill, fight, chasing, killed, shot, fighting, chased, war, shooting.

Turning to the dreams of individuals who have kept track of their dreams for a lengthy period of time, a great deal of variation appears in the frequency of physical aggression. For example, “Tanya,” a young woman, has a relatively high proportion of physical aggression in her dreams (25.4%, in 563 reports), about the same as “Lawrence,” an older man (25.7%, in 206 reports. Another young woman, “Jasmine,” has low physical aggression in her dreams (10.5%, in 800 reports), just like “RB,” an older man (11.8%, in 51 reports).

There is clear evidence that experiences with physical aggression in waking life can increase the frequency of its appearance in dreaming. The best examples are “Mike,” who served as a medic during the Vietnam War and whose collection of dreams includes a very high proportion of physical aggression (76.3%, in 97 reports). In the four sets of dreams from “Beverley” from 1986, 1996, 2006, and 2016, the first set has much more physical aggression (11.9%, in 253 reports) than in the other three (5.7%, in 687 reports), which accurately reflected her involvement in that earlier time period with a violent religious cult.

To help shed light on the role of culture in dreams of physical aggression, the SDDb also includes sets of dreams from non-Western people, which can be analyzed in the same way. For the Mehinaku people of the Amazonian rain forest, a collection of 383 dreams had 22.5% with at least one reference to physical aggression. For a group of Nepalese college students, their dreams (535) had 18.1% with a reference to physical aggression. Three groups of African church members reported dreams (142) with a 19% frequency of physical aggressions. These findings are close enough to the SDDb baselines overall figure of 17.6% to suggest that culture is not a decisive factor in this aspect of dream content.

Concluding Insights

Aggression appears to be a normal feature of human dream content, across different cultures.

Men seem to have more physical aggression in their dreams, although some women have high levels, too.

Dreams of physical aggression can accurately reflect actual aggressions in waking life, so an unusually high level of dream aggression, or a sudden change in dreams to a higher level of aggression, might be a therapeutically valuable sign.

Many dreams of physical aggression do not, however, reflect actual experiences of aggression. These dreams may use violence as a metaphor (e.g., a dream of physical attack as a metaphor of feeling emotionally vulnerable). They may reflect instances of fictional aggression (e.g., seen in a movie). They may be anticipations of violence that may happen at some point in the future (e.g. a threat simulation).

Aggression in dreaming can be viewed as an internal form of play-fighting—the most common form of play in the animal kingdom, and very frequent among humans, too. Play-fighting functions as a way of preparing for future challenges, and also for diminishing and defusing emotional tensions that can lead to actual violence. The same psychological dynamics of play-fighting seem to be operative in dreaming, too.

 

Note: this post first appeared in Psychology Today, May 31, 2021.

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The Best Technology for Studying Dreams

It’s hard to beat the simple practice of keeping a dream journal.

Many exciting new tools are being developed to help us understand the nature and functions of our dreams. For example, researchers are developing technologies for generating a video “read-out” of a person’s dreams based on neural signals from the brain. They are devising methods to stimulate a sleeping person’s brain to instigate lucidity or consciousness during a dream, or even to prompt certain kinds of dream content.

However, none of these new technologies are as valuable for the study of dreams as one of the simplest tools available: the dream journal. A record of an individual’s dreams over time offers the most powerful tool we currently have for the study of dreams. Even compared to the most high-tech devices used by neuroscientists, the dream journal has big advantages in effectiveness, accessibility, and privacy.

Effectiveness

The new dream technologies mentioned above have very short track records. We still don’t know many details about their impact on brain functioning during sleep, nor do we know how the impact varies according to individual differences among people from across the demographic spectrum. And, all these tools rely on measurements of neural activity that have to be interpreted by the researchers and translated into meaningful mental content. That’s not an easy or purely objective process.

However, dream journals as a tool of studying and exploring dreams has a very long track record, going back many centuries (The Sacred Tales of Aelius Aristides in the 2nd century may be the oldest surviving example). We know from extensive psychological research that recording one’s dreams over time yields rich personal insights and self-knowledge. Psychologists have used dream journals starting with Freud’s own dreams in The Interpretation of Dreams, and continuing through Allan Hobson’s use of the “Engine Man’s” dreams in The Dreaming Brain, and now to the works of G. William Domhoff, Michael Schredl, and others who find that dream journals provide legitimate scientific insights into recurrent patterns of content. Only by tracking an individual’s dreams over time can these patterns be identified. Both for psychologists doing research and individuals seeking personal growth, the dream journal remains the most effective technology available.

Accessibility

The new dream technologies are generally used in hospitals or research laboratories. Some devices have been developed for home use, but they tend to be expensive and complicated to operate. Extensive training and preparation are required for the use of these tools, along with a sophisticated computer system and a reliable internet/electrical system. All of these factors have limited the accessibility of new dream technologies to a very small number of people.

The dream journal, by contrast, is available to virtually everyone. To keep a dream journal, you need no training or special preparation, and you don’t have to go to a laboratory or hospital. All that is required is a method of recording your dreams (e.g., by pen and paper, computer, voice-to-text), and a safe place to preserve them over time. This makes the dream journal by far the most accessible tool for studying dreams.

Privacy

Almost every type of new dream technology has connections to the internet that feed data from individual dreamer to the researchers and back again. Even if the researchers preserve the confidentiality of the individual’s data, which of course they should, the sheer presence of an outside observer peering into one’s dreaming experiences and reflections naturally heightens people’s concerns about personal privacy. Some of the new technologies, for example the dream-visualization tools and the dream-altering tools, clearly raise enormous ethical issues around protecting the privacy and integrity of one’s inner thoughts.

A dream journal has the advantage here of being a type of personal diary. Just as a diary provides a safe and private space for honest self-reflection, a dream journal offers the same kind of private space for exploring one’s dreams. A dream journal “works” as a tool without anyone else’s input. All you need is you, paying attention to your own dreams consistently over time. You can keep the results to yourself, and no one else needs to know anything about what you are doing.

None of this is to dismiss the exciting potentials of many new technologies to improve our understanding of dreams and perhaps even enhance our experience of dreaming in a meaningful way. But the enduring power and simplicity of the dream journal, and its advantages in effectiveness, accessibility, and privacy, suggests that a good strategy for new technologies is to build on the dream journal, amplifying what it can already do. Any new dream technology will be stronger if it is grafted onto a solid dream journal system as its roots.

 

Note: this post first appeared in Psychology Today on May 3, 2021.

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The Four Factors that Shape Our Dreams

In Sigmund Freud’s pioneering book The Interpretation of Dreams (1900), he explained how dreams are formed by referring to four factors that make up what he called the “dream-work.” Even after the passage of more than a century, many of Freud’s key concepts are still valid and useful in the practice of dream interpretation. This includes his observation that four specific factors are central to the construction of dreams. These factors are fairly easy to identify, not only in dreams but in other areas of people’s lives, too. With this claim, Freud dramatically raised the stakes for the science of dreaming—more than just providing knowledge about the sleeping mind, it can also become a source of insight into other arenas of human symbolic behavior and expression.

The first mechanism of the dream-work is condensation. Even a very simple dream image can bring together a large number of feelings, thoughts, associations, and memories. In his “Dream of Irma’s Injection,” from chapter 2 of ID, Freud identified references to eight different women in the single dream character of Irma. Complex dream images can be condensations of even greater numbers of people, places, and activities. Freud regarded this as one of the sleeping mind’s greatest powers: creating singular images that contain a surprising multitude of emotional meanings.

The second mechanism of the dream-work is displacement. This factor accounts for why dreams can seem so bizarre and out of sync—for example, your feelings don’t match your activities in the dream, people behave like they’re someone else, or an object from one place appears in a totally different setting. Freud believed this was intentional misdirection, a way of hiding uncomfortable truths from conscious awareness. It’s also possible that instances of displacement are highlighting unusual connections that waking awareness had not previously noticed. Either way, displacement is a frequent factor in shaping the symbolic and metaphorical content of dreaming.

Third of the dream-work mechanisms is their visual quality. Not all dreams are visual in their primary content, but most of them are. The imagistic language of dreaming differs in fundamental ways from the rational, linear language of waking thought. The sleeping mind channels its abstract thoughts and feelings into the more concrete modality of image, shape, movement, and color.

The fourth factor in the dream-work is secondary revision, which is Freud’s term for all the little things we do, consciously and unconsciously, to smooth out the dream and make it more comprehensible for the waking mind. This starts happening during the dreaming process itself, almost like a preparation for the dream’s eventual emergence into consciousness upon awakening. The influence of secondary revision makes it difficult to know if we have ever remembered the “true” dream, before the smoothing process took over.

Here’s an example of what these four factors look like in an actual dream. A couple nights ago I dreamed the following:

I am in a forest, standing outside the dark opening of a cave. I toss a pebble inside, and I am surprised at how deep the cave must be. Then I see the outline of a big cat looking out, coming towards me. I step back in surprise, fear, and awe.

The big cat is a condensation of several felines in my life, two of whom recently passed away. The emotions I feel in response to seeing the big cat reflect feelings from other parts of my life, in addition to kitties living and dead, domestic and wild. As a displacement, the dream changes the setting of my turbulent feelings about recent political developments in the U.S., shifting the scenario from media abstraction to visceral experience in nature. The visual quality emerges most vividly with the looming shape of the big cat, which gives a beautiful imagistic expression to these interwoven parts of my life. An instance of secondary revision appears with the tossing of the pebble into the cave, and the subsequent idea that the cave was surprisingly deep. As I think back on the dream, I assume that I noticed how long it took for the pebble to hit the ground and then made the reasonable inference about the cave’s unusual depth. But Freud would likely say that’s secondary revision at work, smoothing over the gaps and rough spots and making everything seem more coherent. With that in mind, I now wonder if perhaps the pebble-tossing has other meanings I’m overlooking.

From the beginning of his career, Freud realized that learning how to interpret dreams can reveal new ways of interpreting other phenomena in our lives—jokes, slips of the tongue, obsessive memories, symptoms of mental illness, romantic fantasies, religious rituals, political ideologies, and works of art, to name a few. The same four factors of the unconscious mind that shape our dreams also shape our expressions and behaviors in these other realms, too.

 

Note: this post first appeared in Psychology Today, January 29, 2021.

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A Database for Dreamers

The Sleep and Dream Database (SDDb) is a digital archive designed to promote an empirical, hands-on approach to dream research.  The SDDb enables users to apply basic tools of data analysis to identify meaningful dimensions of dreaming experience.  The goal of the SDDb is not to replace other modes of dream interpretation, but rather to complement and enrich them with new insights into the recurrent patterns of dream content.  Anyone who studies dreams, from whatever perspective and for whatever purpose, can benefit from knowing more about these basic patterns.

The SDDb is not the only online resource for this kind of approach to the study of dreams.  The Dreambank.net website run by G. William Domhoff and Adam Schneider also has a large online collection of dream reports gathered by various researchers that can be searched and analyzed in many ways.  The future will likely witness the development of many other online databases with valuable collections of dream material.  The focus here is on the SDDb, but the following discussion highlights important methodological principles that apply to all forms of digitally enhanced dream research.

The SDDb currently contains more than 30,000 dream reports of various types from a wide range of people.  Some of the reports come from individuals who have kept a dream journal for many years.  Some of the reports come from participants in surveys and questionnaires.  Some come from the studies of other researchers who have generously shared their data with me.  The SDDb also includes dream reports from anthropological studies, historical texts, literary sources, and media interviews.  (The database does not, however, contain dream reports that users have entered directly through an online portal. That feature awaits future development.)

The SDDb also includes, in addition to dream reports, the answers given by survey participants to a variety of questions about their sleep and dreaming, for example how often they remember their dreams, how often they experience insomnia, have they ever had a dream of flying or lucid dreaming, etc.  The data also include people’s responses to various demographic questions about their gender, age, race/ethnicity, education, religious practices, political beliefs, etc.

This combination of a large number of narrative dream reports plus a large amount of quantitative survey data makes the SDDb an especially deep and varied resource for the study of dreaming.

The SDDb offers two basic functions for exploring this material.  One, “Survey Analysis,” enables you to compare answers to questions posed on a survey or questionnaire.  For example, you can create a statistical table to compare the dream recall frequencies of people from different age groups, or the insomnia frequencies of people with different political views, or the occurrence of lucid dreams among men and women.

The other function, “Word Searching,” enables you to sift through large numbers of dreams for particular words and phrases.  You can search the dreams by choosing your own word strings, or you can also use the built-in word search templates to search for typical categories of dream content.  This function allows you, for example, to search a set of dreams for all the references to water, or colors, or fear, or the names of famous people or places.

Background and Methodology

The development of the SDDb began in the early 2000’s in consultation with G. William Domhoff and Adam Schneider, who helped me understand how to use their Dreambank.net website.  With their encouragement I started designing a new, complementary database that would 1) include both dream reports and survey data, 2) allow for the use of built-in word search templates, and 3) have the flexibility to enable a wide range of searches and analyses.  In 2009 I worked with Kurt Bollacker, a software designer and engineer from San Francisco with expertise in digital archiving practices, to build the first version of the database.  In 2014 I began working with Graybox, a web technology company in Portland, to expand the scope of the SDDb and improve its user interface.  A major upgrade of the database was completed by Graybox in the spring of 2020.

The word search approach has many advantages as a mode of dream research include its speed, transparency, replicability, flexibility, and power to analyze very large quantities of material.  The process is fairly easy to learn, and sites like the SDDb and Dreambank.net provide free and open access for users to engage in their own study projects aided by these new digital tools.

This approach has several disadvantages, too.  They include deemphasizing the qualitative aspects of dreaming, overemphasizing the measurability of dream content, and leaving open the key question of how to connect the numerical frequencies of word usage with the waking life concerns of the dreamer.

These disadvantages can be diminished by using quantitative analysis as one method among others in a multidisciplinary approach to dreams.  There is no reason in principle why word search methods cannot work in coordination with other methods using qualitative insights and evaluations.  Indeed, I would argue the future prosperity of dream research depends on developing better interdisciplinary models for integrating the results of multiple methods of study.  The users of the SDDb can help to make progress in creating those models.

To address the challenge of how to connect the word usage frequencies with relevant aspects of the dreamer’s life, two principles should be kept in mind.  These principles suggest paths for exploring the potentially meaningful connections between the dream and the individual’s waking situation.

One principle is the continuity hypothesis: the relative frequency with which something appears in a person’s dream can be a reflection of its importance as a meaningful concern in the person’s waking life.  In other words, the more often something (a character, setting, activity) shows up in dreams, the more emotionally important it’s likely to be in waking life. To be clear, the continuity does not need to be literal or physical; it’s more what people care and think about in their waking lives.

As an example, one of the dream series in the SDDb comes from “Bea,” a young woman whose anxious, sad dreams were continuous not with her actual life, which was quite safe at the time, but with her worries about possible bad things that might happen to her family or to the students in her care as dormitory resident assistant.

The other principle is the discontinuity hypothesis: infrequent and anomalous elements of dream content can be spontaneous expressions of playful imagination, occurring at any point in life but especially in times of crisis, change, or transition.  Something that appears very rarely and is dramatically discontinuous with typical patterns of dream content can reflect the mind’s concerted effort to go beyond what is to imagine what might be.

As an example, the “Nan” series in the SDDb comes from a woman who had suffered a horrible car crash, followed by several months in the hospital. Most of the dreams in her series have negative, nightmarish quality (as would be expected from the continuity hypothesis), but one dream is unusual in having multiple colors, a good fortune, and a reference to beauty. Nan singled this dream out as having an especially important impact on her during her recovery from the accident, giving her a sense of hope that one day she would regain her health and creative spirits (which she eventually did).

A New Feature: The SDDb Baselines

The recent upgrade of the SDDb included the addition of a new feature that allows users to compare the results of word searches with a large set of more-or-less “average” dreams. This feature helps to determine the significance of the word search results. For example, I said above that most of Nan’s dreams have a “negative, nightmarish” quality. How can I support that claim? By using the baselines feature.

The baselines are two curated sets of “most recent dreams” from 2,094 males and 3,227 females, gathered by several researchers from a variety of populations between the 1950’s and the present (including the Hall and Van de Castle “norm” dreams). They are aggregated here to represent typical densities of the appearance of key words or phrases in ordinary dreaming.

In Nan’s case, her dreams indicate she definitely did feel strong concerns at this time, in a mostly negative direction.  Of her 26 dreams, 8 of them (31%) have at least one reference to fear.  The corresponding figure for the female baselines is 25%. She has references to death in 19% of her dreams, versus 9% for the female baselines; references to physical aggression in 23%, versus 15% for the female baselines; and zero references to happiness, versus 8% in the baselines.

These frequencies accurately reflect the frightened and vulnerable quality of Nan’s feelings in waking life. Even if we knew nothing about Nan’s personal life, we could use these variations of her dreams from the baselines to make the prediction that she is suffering through a difficult and frightening situation.

This is the foundation for the “blind analysis” method I have been using in several papers and IASD conference presentations (see below). Now the tools I use to make those analyses are available to everyone.

 

Further reading:

  1. The Meaningful Continuities Between Dreaming and Waking: Results of a Blind Analysis of a Woman’s Thirty-Year Dream Journal. Dreaming 28: 337-350.
  2. Using the LIWC Program to Study Dreams. Dreaming 28: 43-58. (Co-authored with Mark Graves)
  3. The Digital Revolution in Dream Research. In Dream Research: Contributions to Clinical Practice (edited by Milton Kramer and Myron Glucksman) (Routledge).
  4. Dreaming in Adolescence: A “Blind” Word Search of a Teenage Girl’s Dream Series. Dreaming 22: 240-252.

 

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The New Dream Studies and the Wall Street Journal

Dream researchers are creatively deploying a variety of big data technologies to open a new era of oneiric discovery.

An article appeared earlier today by Robert Lee Hotz, science reporter for the Wall Street Journal, titled “New Insights into Dreams and What They Say About Us.” It’s a great article, well-written and thoroughly researched, and quite fair-minded towards the scientific study of dreams. (The article can be found here, if you have WSJ access.)

Here is my favorite line:

“While still highly experimental, the new dream studies underscore the power of data mining to assemble unexpected insights by sifting through large data sets of seemingly unrelated information.”

That is very well put. Exciting possibilities beckon on the horizon, and yet much more work needs to be done in mapping the multidisciplinary terrain between here and there. Hopefully others who read the article will recognize these potentials and contribute their insights to this dynamic, though still “highly experimental” realm of inquiry.

I always want to get people more enthused about the study of dreams—but not too enthused. To my great relief, Hotz concludes the WSJ article with some cautionary words (my own included) about the need for greater ethical evaluation and awareness of the possibly harmful abuses of these technologies.

Two follow-up notes from the article.

First, the survey of dreams in relation to the Black Lives Matter movement and recent protests against racial injustice involved 4,947 American adults, completing an online questionnaire designed by me and administered by YouGov on June 15-19, 2020. I am currently working with Michael Schredl on an article analyzing the responses to this survey. An early preview of the results appeared in a post I wrote for Psychology Today on June 25, 2020. The data from this survey are not yet available in the Sleep and Dream Database, but they will be soon.

Second, to the question of “How many dream reports from how many people are in the SDDb?” I gave the estimate of more than 26,000 dreams from more than 11,000 people. I obtained those figures by using the SDDb’s advanced word search tool and defining the data set as all reports with a minimum word count of 5, which yields a result of 26,498 dreams from 11,346 participants. There are surely many additional dreams in the database of less than five words, but many of those reports include “non-dream” answers (such as “no,” “don’t remember any”), which are important to preserve but shouldn’t be counted in overall tallies of the actual dreams. There are also some non-dreams of more than 5 words, but not enough to alter the basic estimate of 26,000 dream reports currently in the database.

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Basic Patterns in Dreaming

The basic patterns of dream content are coming into sharper focus, thanks to new technologies of digital analysis. By using these tools to study large and diverse collections of high-quality dream data, and then making those tools and data publicly available, we can illuminate recurrent frequencies of dream content that others can easily review, replicate, and verify for themselves. The more we know about these basic patterns, the more we can gain helpful insights from people’s dreams regarding their mental and physical health, social relations, cultural interests, and even spiritual beliefs.

When I began this line of research in the mid-2000’s, I used the resources of the Dreambank.net, a site managed by G. William Domhoff and Adam Schneider. In a paper from 2009, “Seeking patterns in dream content: A systematic approach to word searches,” drawing on the resources of the Dreambank, I included this passage in the conclusion:

“Until researchers have gathered many more high-quality reports from a wide variety of people (ideally accompanied by multiple sources of biographical data), we cannot make any definitive declarations about the universal features of human dreaming. But the results of this study suggest several testable hypotheses:

  1. Dreaming perception is primarily visual, with less hearing and touch and almost no smell or taste.

  2. All emotions are represented in dreams, with fear the most frequent.

  3. Many types of cognitive activity occur in dreaming, especially those associated with awareness and social intelligence.

  4. Aggression is more frequent than sexuality, and both are more frequent for men than for women.”

Today, these same hypotheses can easily be tested with the resources of the Sleep and Dream Database (SDDb). The simplest method is to use the SDDb’s built-in word search template of keywords. The word search function has a template of forty categories of dream content, including categories for specific types of perception, emotion, cognitive activity, and social interaction. Starting on the “Advanced Search” page, I would define the data set for this purpose by setting a word limit of 25 words, and then select a category from the keywords menu. Looking at perceptions first, the following results can be generated in a few moments:

Out of a total of 20,510 dream reports of at least 25 words in length, reported by a total of 7,335 people, a word relating to visual perception appeared at least once in 34.6% of the reports. For hearing, the figure was 10.7%, for touch, 13%, and smell and taste combined only 2.7%. Eleven years later, I would still stand by that first hypothesis.

Turning to emotions, the results of the same simple search process (define the data set as having a minimum of 25 words, and selecting a category from the keywords menu) are just as predicted. A word relating to fear appears at least once in 18.2% of the dreams. Anger appears in 7.1%, sadness in 3.7%, happiness 6.5%, and wonder/confusion 14.4%. This hypothesis seems pretty solid, too.

Cognition in dreaming is harder to study for various reasons, but the word search method can still offer some interesting results. A word relating to thinking appears at least once in 41.9% of the dreams. Some kind of speech or verbal communication appears in 37.6%, and a reference to reading or writing in 7.6%. These findings support the basic idea that dreaming has a fair amount of cognitive activity, with plenty of social communication, though more detailed studies are needed to tease out the variations between dreaming and waking cognition. The third hypothesis is worth keeping.

Social interactions in dreaming are also difficult to study, so the results here should be regarded with extra caution. Indeed, the hypothesis from 2009 may not bear contemporary scrutiny, particularly around gender differences. (When defining the data set, gender can be selected as a search variable from the constraints menu.) The SDDb word search approach yields a finding of at least one reference to physical aggression in 20.8% of the male dreams and 17.2% of the female dreams. That’s a difference, but not a huge one. With the category of sexuality, the male dreams had at least one reference in 5.8% of the reports, versus 6.6% for the female dreams. This is the reverse of the predicted difference. The results of this quick analysis confirm that overall references to physical aggression occur much more frequently than references to sexuality, but the results do not support the 2009 hypothesis about higher frequencies of both kinds of content in men’s dreams.

There are other ways to study these questions with the tools of the SDDb. For example, the “baselines” function provides the frequencies on all 40 categories for a specially curated subset of 2,094 male dreams and 3,227 female dreams. These baseline frequencies provide a kind of measuring stick for dream researchers—a more precise way of determining the average frequencies of particular types of dream content and comparing them to other sets of dreams, which might have content features that vary from the baseline patterns in interesting ways. That shall be a topic for another post.

Note: This post first appeared in Psychology Today on September 4, 2020.