Video Game Play Effects on Dreams: Self-Evaluation and Content Analysis
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".