I’m giving a presentation with that title on Saturday, June 23, at the annual conference of the International Association for the Study of Dreams, held in Berkeley, California. The presentation is part of a panel session, “What’s New in the Scientific Study of Dreams.” I’m giving an overview of the word searching method I’ve been developing over the past several years, with a special focus on four “blind analysis” studies I’ve performed with the help of Bill Domhoff. A youtube video preview of the presentation can be found here.
Here’s how I define blind analysis in the paper:
A blind analysis involves an exclusive focus on word usage frequencies, bracketing out the narrative reports and personal details of the dreamer’s life and making inferences based solely on statistical patterns in word usage—not reading the dreams at all, and basing one’s analysis strictly on numerical data. The aim is to assess the patterns of dream content with the fewest possible preconceptions, as objectively as possible, before reading through the narratives and learning about the individual’s waking activities and concerns.
Good presentation.- especially on what could be seen as the weakness of the approach. A whole lot of work is done (easily, by computer) and you end up with a dozen or more facts about the dreamer, and 2 or 3 of these may turn out to be misses. People are never satisfied unless you get everything correct!
I’ve been working with co-occurrence of features (word lists or categories), trying to guess at very basic information about the dreamer; gender, age, sexual orientation. For the later I match sexual words and then expand by +/- 5 or 6 words around the matches. This gives me a ‘neighborhood’ text that I then search for two expressions that match generic character gender (he|him|his|man|men|boy|guy…) and (she|her|woman|women|…). This works very well for sexuality.
I also generalize the method by using two expressions for positively and negatively valenced words in the neighborhoods of a third, target expression. For instance, in the hvdc norms an expression for unknown males has overall negative valence for the male dreamers, but positive valance for the females. The valences are reversed when the target is unknown females.
I ran the same search with male vs female dreams from your Sleep and Dreams Database. The unknown males target showed negative valence for male dreamers, positive for females, but the unknown females target was positively valenced for both male and female dreamers in that set..
Good presentation – especially on what could be seen as the weakness of the approach. A whole lot of work is done (easily, by computer) and you end up with a dozen or more facts about the dreamer, and 2 or 3 of these may turn out to be misses. People are never satisfied unless you get everything correct!
I’ve been working with co-occurrence of features (word lists or categories), trying to guess at very basic information about the dreamer; gender, age, sexual orientation. For the later I match sexual words and then expand by +/- 5 or 6 words around the matches. This gives me a ‘neighborhood’ text that I then search for two expressions that match generic character gender (he|him|his|man|men|boy|guy…) and (she|her|woman|women|…). This works very well for sexuality.
I also generalize the method by using two expressions for positively and negatively valenced words in the neighborhoods of a third, target expression. For instance, in the hvdc norms an expression for unknown males has overall negative valence for the male dreamers, but positive valance for the females. The valences are reversed when the target is unknown females.
I ran the same search with male vs female dreams from your Sleep and Dreams Database. The unknown males target showed negative valence for male dreamers, positive for females, but the unknown females target was positively valenced for both male and female dreamers in that set..