Memory for musical tones: The impact of tonality and the creation of false memories
Bibliographic record
Abstract
Although the relation between tonality and musical memory has been fairly well-studied, less is known regarding the contribution of tonal-schematic expectancies to this relation. Three experiments investigated the influence of tonal expectancies on memory for single tones in a tonal melodic context. In the first experiment, listener responses indicated superior recognition of both expected and unexpected targets in a major tonal context than for moderately expected targets. Importantly, and in support of previous work on false memories, listener responses also revealed a higher false alarm rate for expected than unexpected targets. These results indicate roles for tonal schematic congruency as well as distinctiveness in memory for melodic tones. The second experiment utilized minor melodies, which weakened tonal expectancies since the minor tonality can be represented in three forms simultaneously. Finally, tonal expectancies were abolished entirely in the third experiment through the use of atonal melodies. Accordingly, the expectancy-based results observed in the first experiment were disrupted in the second experiment, and disappeared in the third experiment. These results are discussed in light of schema theory, musical expectancy, and classic memory work on the availability and distinctiveness heuristics.
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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 | 0.006 | 0.041 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".