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Dream content analysis using Artificial Intelligence

Patrick Mcnamara, Kelly Duffy-Deno, Tom Marsh, Thomas Jr. Marsh

University Library Heidelberg June 26, 2018 DOI: 10.11588/ijodr.2019.1.48744 (opens in new tab)

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Study at a glance

AI-extracted from the abstract
Characteristics Observational study Peer reviewed
Sample size 635
Population Users of Dreamboard.com who posted dreams
Duration 4 years
Topics Dreaming
Keywords Dream Content measure theory Negative mood Content analysis Artificial intelligence Social science Psychotherapist
Citations 11
Key findings An AI algorithm trained on over 35,000 dreams identified 47 dream themes and found small but significant male-female differences in 34 themes, with female dreams showing higher incidence for most.

Abstract

We developed a dream content analysis system (DCAS) based on an artificial intelligence (AI) algorithm that was trained using a relatively large corpus of over 35,000 dreams. This sample of dreams were supplied by 424 female and 211 male users over 4 years who had posted them at the dream posting website and app Dreamboard.com. Building upon previous dream content ontologies developed by Hall, Van de Castle, Domhoff and Bulkeley, forty-seven reliably identified dream themes emerged from repeated application of algorithm and agent training procedures. DCAS reproduced most of the key dream content themes from these previous ontologies but also returned some unexpected findings. Mixed-model estimation detected significant male-female content differences for 34 dream themes, with female dreams evidencing higher incidence percentages for most themes, but effect sizes were small. Mixed-model logistic regression identified those themes that best predicted self-reported positive or negative mood associated with dreams. We conclude that the AI-based DCAS algorithm developed here is a promising tool for detailed analyses of dream content patterns.

Comparable studies

Other observational and cohort studies on dreaming, most cited first.

Study Year Design Participants
Lucid Dreaming: a State of Consciousness with Features of Both Waking and Non-Lucid Dreaming Student volunteers trained to become lucid and signal lucidity with horizontal eye... 2009 Observational study n = 6
Nightmare Prevalence, Nightmare Distress, and Self-Reported Psychological Disturbance Young adults with a mean age of 20 years 2002 Prospective observational study n = 116
Long-term effects of extreme situational stress on sleep and dreaming Survivors of traumatic events who complained of sleep disturbances, compared with... 1987 Case-control study n = 11
The relationship of nightmare frequency and nightmare distress to well‐being Adults who reported dreams 2004 Observational cohort n = 147
Dreaming in NREM Sleep: A High-Density EEG Study of Slow Waves and Spindles Healthy human subjects 2018 Observational cohort n = 14

Citations in the library

Cited by 3

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