Conditional Associative Memory for Musical Stimuli in Nonmusicians: Implications for Absolute Pitch
Bibliographic record
Abstract
A previous positron emission tomography (PET) study of musicians with and without absolute pitch put forth the hypothesis that the posterior dorsolateral prefrontal cortex is involved in the conditional associative aspect of the identification of a pitch. In the work presented here, we tested this hypothesis by training eight nonmusicians to associate each of four different complex musical sounds (triad chords) with an arbitrary number in a task designed to have limited analogy to absolute-pitch identification. Each subject under-went a functional magnetic resonance imaging scanning procedure both before and after training. Active condition (identification of chords)-control condition (amplitude-matched noise bursts) comparisons for the pretraining scan showed no significant activation maxima. The same comparison for the posttraining scan revealed significant peaks of activation in posterior dorsolateral prefrontal, ventrolateral prefrontal, and parietal areas. A conjunction analysis was performed to show that the posterior dorsolateral prefrontal activity in this study is similar to that observed in the aforementioned PET study. We conclude that the posterior dorsolateral prefrontal cortex is selectively involved in the conditional association aspect of our task, as it is in the attribution of a verbal label to a note by absolute-pitch musicians.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".