Speed, Accuracy, and Serial Order in Sequence Production
Bibliographic record
Abstract
The production of complex sequences like music or speech requires the rapid and temporally precise production of events (e.g., notes and chords), often at fast rates. Memory retrieval in these circumstances may rely on the simultaneous activation of both the current event and the surrounding context (Lashley, 1951). We describe an extension to a model of incremental retrieval in sequence production (Palmer & Pfordresher, 2003) that incorporates this logic to predict overall error rates and speed-accuracy trade-offs, as well as types of serial ordering errors. The model-assumes that retrieval of the current event is influenced by activations of surrounding events. Activations of surrounding events increase over time, such that both the accessibility of distant events and overall accuracy increases at slower production rates. The model's predictions were tested in an experiment in which pianists performed unfamiliar music at 8 different tempi. Model fits to speed-accuracy data and to serial ordering errors support model predictions. Parameter fits to individual data further suggest that working memory contributes to the retrieval of serial order and overall accuracy is influenced in addition by motor dexterity and domain-specific skill.
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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 | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".