Individual responses to a method of cursor assistance
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate a new click assistance technique, Steady Clicks, designed to help computer users with motor impairments to click more accurately using a mouse. Specifically, Steady Clicks suppresses two types of errors: slipping while clicking and accidentally clicking. Steady Clicks suppresses these errors by freezing the cursor during mouse clicks, preventing overlapping button presses and suppressing clicks made while the mouse is moving at a high velocity. METHOD: Eleven individuals with motor impairments participated in a repeated-measures evaluation of Steady Clicks. This evaluation involved performing a clicking task both with and without Steady Clicks, while time-stamped log files detailing each participant's cursor movements and mouse button presses were recorded. RESULTS: When using Steady Clicks, five of the 11 participants were able to select targets using significantly fewer attempts, and had significantly improved overall task performance times. Blocking of overlapping and high velocity clicks also shows promise as an error filter. For some participants, Steady Clicks had effects beyond a simple reduction in errors and error correction time, including faster positioning times with fewer, shorter pauses. Two participants slipped a greater distance when using Steady Clicks, suggesting they were taking advantage of the support. Two were much less fatigued, and one was able to start using a simpler clicking strategy. Nine participants preferred Steady Clicks to the unassisted condition. CONCLUSION: Large individual differences were found not only in performance but also in the ways that individuals reacted to and benefited from the cursor assistance. The results showed that Steady Clicks can be used to effectively block slipping and accidental clicks by individuals for whom this is a problem. This assistance could be used in conjunction with existing techniques for cursor positioning, to enable faster and more effective mouse use for those who currently struggle with the standard computer mouse.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".