Video Game Play Effects on Dreams: Self-Evaluation and Content Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent dreams were collected over a year’s period from college undergraduates. In addition to providing self-evaluations of the dreams, participants were also asked to answer a variety of media use questions. These were both in terms of their media use the day before the dream and in terms of their historical media use with the most interactive and absorbing media available today, video games. High-end gamers’ dreams were content-analyzed using the Hall and Van de Castle system. These were compared to dreams from a similar population that were collected by interview but were not necessarily recent. There was some replication and some differences in these two different dream samples from individuals with the same gamer history. The second analysis examined day before electronic media use more specifically by loading all the gamer history and media use information with two types of dream variables: sum scores from the Hall and Van de Castle scale and self-evaluations of the dream. Seven of nine factors loaded some combination of media and dream content. This study further supports the idea that general electronic media use and game play in particular are affecting how we process and store information by demonstrating changes at the source of such processes, in dreams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it