Effects of underscoring on the perception of closure and intensity in film excerpts
Bibliographic record
Abstract
In 3 experiments, we examined the influence of musical underscoring on judgments of closure in filmed events. In Experiment 1, a 12 s animated episode was judged to end with greater closure if underscoring was strongly closed than if it was weakly closed. This influence of music was implicit: When asked to justify their judgments, participants mainly cited only qualities of the visual information. Experiment 2 provided evidence that music can influence perceived closure in longer film episodes, but it also revealed that musical accompaniment does not always influence judgments of closure. Experiment 3 examined the effect of underscoring for 12 brief film excerpts from a commercial motion picture. Ratings of closure were obtained for 3 conditions: underscores only, films without underscores, and films with underscores. Again, underscoring influenced perceived closure in films. However, ratings of closure were more heavily influenced by visual information than by underscoring. Other effects of closure in film music are discussed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".