Bibliographic record
Abstract
Abstract How does perception of abstract tonal patterns—music—lead to the pleasure we experience from these sounds? The answer presented in this book is that pleasure in music arises from interactions between cortical loops that enable processing of sound patterns and subcortical circuits responsible for reward and valuation. The auditory cortex and its ventral-stream connections encode acoustical features and their relationships, maintain them in working memory, and form internal representations of statistical patterns from which predictions are made about how sound patterns evolve in time. Disruption of this pathway leads to amusia. The auditory dorsal stream allows for sensory-motor transformations, music production, and metrical representation, leading to predictions of when events will occur. These predictive processes play a central role in creating expectancies about musical events that are transmitted to the dopaminergic reward system, where hedonic responses are generated according to how well an event fits with predictions. These responses are linked to the balance between predictability and surprise in musical patterns. Disruption of interactions between perceptual and reward systems leads to musical anhedonia. Engagement of the reward system is also related to movement and vocal cues, social factors, musical preference, and emotion regulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.022 |
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".