Background music of varying tempi produces differing facial emotional expressions and performance in music-majors and non-majors as they complete a reading comprehension test
Bibliographic record
Abstract
This study examined how music major and non-music major participants’ performances and emotional expressions differed when exposed to background music of varying tempi while completing a reading comprehension task. Two questions were addressed: (1) What effect does background music of varying tempi have on the performance of music and non-music majors while completing a reading comprehension task? and (2) What real-time expressed emotions and self-report measures may predict the reading comprehension scores between music and non-music majors? A total of 34 participants completed a reading comprehension task under each of three musical test conditions while facial-muscular emotion software analyzed their facial expressions, to understand the effect on performance. Results indicated that music majors displayed significantly different ( p < .05) emotional expressions of joy and contempt, which corresponded to no significant differences in performance scores, in comparison to non-majors, whose performance varied greatly across the background music test conditions that were given. These findings support the growing literature surrounding the possible effects of music training and how differences between trained and non-musicians may be understood through emotion detection software.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".