Automatic gender detection of dream reports: A promising approach.
Christina Wong, Reza Amini, Joseph De Koninck
Consciousness and Cognition June 23, 2016 DOI: 10.1016/j.concog.2016.06.004 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study comparing human and automated classification of dream reports Peer reviewed |
|---|---|
| Sample size | 204 |
| Population | Home-collected dream diaries from 100 male and 100 female adolescent Anglophones |
| Measures | Hall and Van de Castle (HVDC) scales, DreamSAT |
| Topics | Dreaming |
| Keywords | Humans Reproducibility of results Dreams Sex factors Sex characteristics Students Artificial intelligence Adolescent Canada Female Young adult Self report Gender Automatic analysis Machine learning |
| Key findings | A computer program and four undergraduate raters both distinguished male from female dream reports above chance, at 74.5% and 75.8% pairwise accuracy respectively. Prediction held when dreams containing obvious gender identifiers were removed, and incorporating Hall and Van de Castle scale scores did not improve accuracy. |
Abstract
A computer program was developed in an attempt to differentiate the dreams of males from females. Hypothesized gender predictors were based on previous literature concerning both dream content and written language features. Dream reports from home-collected dream diaries of 100 male (144 dreams) and 100 female (144 dreams) adolescent Anglophones were matched for equal length. They were first scored with the Hall and Van de Castle (HVDC) scales and quantified using DreamSAT. Two male and two female undergraduate students were asked to read all dreams and predict the dreamer's gender. They averaged a pairwise percent correct gender prediction of 75.8% (κ=0.516), while the Automatic Analysis showed that the computer program's accuracy was 74.5% (κ=0.492), both of which were higher than chance of 50% (κ=0.00). The prediction levels were maintained when dreams containing obvious gender identifiers were eliminated and integration of HVDC scales did not improve prediction.
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 4
- A Novel Approach to Dream Content Analysis Reveals Links Between Learning-Related Dream Incorporation and Cognitive Abilities Frontiers in Psychology August 6, 2018
- Dreams are more “predictable” than you think Frontiers in Sleep July 23, 2025
- The influence of intentions on dream content SLEEP Advances 2024
- New strategies for the cognitive science of dreaming. Trends in Cognitive Sciences December 1, 2024
Cites 9
- Dream content analysis: Basic principles heiDOK (Heidelberg University) 2010
- Themes of Continuity International Journal of Dream Research 2011
- Predicting the dreamer’s gender from a single dream report: a matching study in a non-student sample heiDOK (Heidelberg University) 2010
- Determining the dreamer’s gender from a single dream report: a matching study heiDOK (Heidelberg University) 2008
- Digital dream analysis: a revised method. Consciousness and Cognition October 1, 2014
and 4 more in the library