A Comparison of Touchscreen and Mouse for Real-World and Abstract Tasks with Older Adults
Bibliographic record
Abstract
Computer technology is increasingly being used to facilitate the timely identification of cognitive impairment in older adults. Our Cognitive Testing on Computer (C-TOC) project aims to develop a self-administered online test for older adults to take at their home. Due to the freedom of devices they can use, it is important to investigate whether different input devices can impact test performance. We compared touchscreen and mouse input on both abstract and real-world pointing and dragging tasks: classic Fitts’s Law tasks and tasks drawn from C-TOC. The abstract and real-world tasks were designed to require equivalent motor skills. Our research goals were to determine (1) if performance on computerized cognitive tasks are affected by input device, and (2) if performance differences due to input device can be explained by those observed on Fitts’s Law tasks. Sixteen older adults completed both types of tasks using a touchscreen and a mouse. We found that input device affected speed on three out of four cognitive tasks while only affecting accuracy on one task. Secondarily, our results suggest that Fitts’s Law results of differences in mouse and touch cannot be used to predict device differences in the performance on C-TOC tests. As an additional research goal, we looked into the movement patterns of one real-world dragging task—the C-TOC Pattern Construction task—to see if they could provide richer performance measures, beyond speed and accuracy. Such measures could compensate for the lack of a clinician observer who is typically present in comparable paper-based cognitive tests. We found that older adults naturally adopted different movement patterns on the two devices: they tended to make shorter moves and a greater number of moves on a touchscreen than with a mouse. Altogether, our results suggest that careful device-based performance calibration will be needed in computerized tests.
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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 | 0.016 |
| 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 | 0.001 | 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".