Speed, Accuracy, and Serial Order in Sequence Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it