The forgotten role of absorption in music reward
Bibliographic record
Abstract
Interindividual differences in music-related reward have been characterized as involving five main facets: musical seeking, emotion evocation, mood regulation, social reward, and sensory-motor. An interesting concept related to how humans decode music as a rewarding experience is music transcendence or absorption (i.e., music-driven states of complete immersion, including momentary loss of self-consciousness or even time-space disorientation). Here, we investigated the relation between previously characterized facets of music reward and individual differences in music absorption. A first sample of participants (N = 370) completed both the Barcelona Music Reward Questionnaire (BMRQ) and the Absorption in Music Scale (AIMS). Results showed that both constructs were highly interrelated (r = 0.78, p < 0.001), indicating that higher music reward sensitivity is associated with a greater tendency to music-related absorption states. In addition, four items from the AIMS were identified as suitable to be added to an extended version of the BMRQ (eBMRQ). A second sample (N = 550) completed the eBMRQ for a validation study. Exploratory and confirmatory factor analyses on the whole sample (N = 920) showed the reliable psychometric properties of the eBMRQ and suggested that taking into account an absorption facet could contribute to a better characterization of individual differences in the sensitivity to experience music-related reward and pleasure.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".