Congruence-Association Model of music and multimedia: Origin and evolution
Bibliographic record
Abstract
Abstract The evolution of the Congruence-Association Model (CAM) is presented in a cognitive-scientific and historical context. Iteration 1 of CAM proposed a bottom-up process for analysis of musical and film stimuli in terms of ‘Congruence’ (audiovisual structural overlap) and ‘Association’ (contingency-base meaning). Iteration 2 added speech and top-down inference to create the best match with bottom-up processing. Adding sound effects and text, Iteration 3 referred to the best match between top-down and bottom-up information as the conscious ‘working narrative.’ A developmental version of CAM was also proposed to accommodate cohort-specific multimedia experience at critical periods. Iteration 4 incorporated a kinesthetic channel, supporting a mirror system and embodied meaning. The chapter illustrates how psychological experiments increase understanding of the role of music in multimedia, and aims to help set the stage for further empirical work and progress.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".