Passive Haptic Training to Improve Speed and Performance on a Keypad
Bibliographic record
Abstract
Learning text entry systems is challenging, yet necessary. Many layouts and keyboards exist, but they rely on laborious learning techniques. Passive haptic learning (PHL) has already demonstrated some benefit for learning the Braille text entry system. Could this computing-enabled technique be used to improve desktop keyboard typing skills? It is unknown whether passive haptic training can improve speed on a motor task (as opposed to initial learning). We use a randomized numeric keypad to examine users’ typing performance with or without passive haptic training. When users were prevented from looking at the keyboard, the PHL group demonstrated consistent accuracy (-0.011 KSPC) while those in the control group greatly increased their error (+1.26 KSPC on average). This result is consistent with the finding that PHL users looked significantly less at the keyboard. In a second, longer study, users exposed to PHL were found to significantly improve their typing speed (mean increase of 11 WPM) versus control (mean increase of 2.2 WPM).
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".