Effects of Aging on Small Target Selection with Touch Input
Bibliographic record
Abstract
Age-related declines in physical and cognitive function can result in target selection difficulties that hinder device operation. Previous studies have detailed the different types of target selection errors encountered, as well as how they vary with age and with input device for mouse and pen interaction. We extend this work to describe the types of age-related selection errors encountered with small touchscreen devices. Consistent with prior results, we found that older adults had longer target selection times, generated higher error rates, and encountered a broader range of selection difficulties (e.g., miss errors and slip errors) relative to a younger comparison group. However, in contrast to the patterns previously found with pen interaction, we found that miss error (i.e., both landing and lifting outside the target bounds) was a more common source of errors for older adults than slip error (i.e., landing on the target but slipping outside the target bounds before lifting). Moreover, aging influenced both miss and slip errors in our study of touch interaction, whereas for pen interaction, age has been found to influence only slip errors. These differences highlight the need to consider pen and touch interaction separately despite both being forms of direct input. Based on our findings, we discuss possible approaches for improving the accessibility of touch interaction for older adults.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".