Effect of Tactile Feedback on Movement Speed and Precision During Work-Related Tasks Using a Computer Mouse
Bibliographic record
Abstract
Effects of tactile feedback on movement accuracy and speed were studied. Younger and older participants performed three tasks (1, select and drag word; 2, menu navigation; 3, select and drag cell) using commercial software and a mouse with or without tactile feedback. Task time and error number were recorded. Tasks were divided according to presence or absence of tactile feedback, and participants were divided into subgroups (high, average, low) based on Task 1 performance. Overall, older participants took longer (p < .0001) and made more errors (p < .001) than younger participants. There was an effect of feedback by task in younger participants for all six outcomes (p < 0.02). At the task level, with feedback, younger participants reduced performance time (13%) and errors (24%) on Task 1. Low- and average-performance younger participants benefited most from feedback for Task 1. Older low-performance participants also benefited from feedback for Task 1. For Task 3, older participants tended to take more time and make more errors with feedback. Tactile feedback may enhance performance when feedback is event related. Older people may not integrate sensation as well as younger individuals to enhance performance. Potential applications of this research include the development of tactile feedback interfaces to facilitate computer use.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".