Memory Constraints in Motor Sequences: Typing and Music Performance
Bibliographic record
Abstract
Abstract Humans across cultures exhibit incredibly flexible and fast typing on computer keyboards, cell phones, and other input devices, often at rates faster than the content can be spoken. Many people in Western cultures can play a musical instrument, also at faster rates than the same content can be sung. These flexible and fluent tasks exhibit several similarities in the role of instance-based memory and motor experiences, especially for comparisons that are text based (typing or performing from notation). We consider the contributions of Gordon Logan's hierarchical organization of memory processes as applied to typing, compared with theoretical developments in music performance. Based on Logan's contributions, we identify similarities and differences in the goal-directed behaviors of typing and music, including schemas in motor learning, with an emphasis on eye–hand coordination metrics observed in typing and music; serial ordering processes in action sequences, with consideration of similarity-based constraints on the types of errors observed; and speed–accuracy tradeoffs identified in both domains. Despite the fact that typing is a speeded task with a goal to produce events as quickly as possible, whereas music performance is a rhythmic task with a goal to produce events at a specific time, both tasks exhibit important shared constraints of instance-based memory and motor learning. We end with a discussion of Logan's work and its application to future research in music cognition.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".