Musical Novices Are Unable to Judge Musical Quality from Brief Video Clips: A Failed Replication of Tsay (2014)
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
Research focusing on "thin slicing" suggests in making judgements of others' moods, personality traits, and relationships, we are able to make relatively reliable decisions based on a small amount of information. In some instances, this can be done in a matter of a few seconds. A similar result was found with regard to the judgement of musical quality of ensemble performances by Tsay (2014), wherein musical novices were able to reliably choose the winner of a music competition based on the visual information only (but not auditory or audiovisual information). Tsay argues that this occurs due to a lack of auditory expertise in musical novices, and that they are able to extract quality information based on visual movements with more accuracy. As part of the SCORE project (OSF, 2021), we conducted a direct replication of Tsay (2014). Findings showed that musical novices were unable to judge musical quality at a level greater than chance, and this result held for auditory, visual, and audiovisual presentation. This suggests that 6 s is not a sufficient amount of time for novices to judge the relative quality of musical performance, regardless of the modality in which they were presented.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it