Bibliographic record
Abstract
Music has the capacity to affect humans’ affective, social and cognitive abilities in different ways. For example, music may be used to regulate a person’s mood after a stressful day driving home. Music may be used to connect with other people during rituals, or dancing parties. And many people use music to enjoy the different flavors of sound in aesthetic experiences at home, to learn about the quality of organized sounds and interpret their meanings along with its flow. The different ways of interacting with music have in common that music is most engaging and thus attractive and alluring. People get involved with music and want to be fully absorbed by it. Apparently, there are few things in our environment that touch human nature so profoundly. Why is it? And what is the possible benefit of this musical power? These questions turn out to be extremely difficult and hard to answer because musical experience is subtle and ineffable. Moreover, there are many variable factors that play a role, such as the energetic level or mood of the subject involved with music, the previous preoccupations and conditioning, personality, familiarity, cultural background, context, or educational level. In short, the elusive character of music and the variable context in which music is dealt with make it a challenging research topic. Nevertheless, the power of music touches the core of our human abilities and its understanding necessitates an interdisciplinary research approach. As a matter of fact a core factor of musical power is based on the listener’s ability to interact with music. And a major precondition of this ability is that music is perceived, that is, processed through the senses, perhaps on the basis of previous perception, perhaps involving awareness, conceptualization and interpretation. The concept of perception thus involves a range from sensation to cognition, emotion and even interpretation. In what follows we first consider music perception from a cognitive viewpoint. Then we provide a critique of this cognitive approach and we look for direct evidence for the hypothesis that music perception is integrated with other modalities of human behavior, such as movement and emotion. In the final section we present a dynamic framework in which music perception is tightly linked with body movement, action and environmental interaction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".