Comparing three portable, tablet-based visuomotor tasks to laboratory versions: An assessment of test validity
Bibliographic record
Abstract
The assessment of visuomotor function can provide important information about neurological status. Many tasks exist for testing visuomotor function in the laboratory, but the availability of portable, easy-to-use versions that allow reliable, accurate, and precise measurement of movement timing and accuracy has been limited. We developed a tablet application that uses three laboratory visuomotor tests: the double-step task, interception task, and stop-signal task. We asked the participants to perform both the lab and tablet versions of each task and compared their response patterns across equipment types to assess the validity of the tablet versions. On the double-step task, the participants adjusted to the displaced target adequately in both the lab and tablet versions. On the interception task, the participants intercepted nonaccelerating targets and performed worse on accelerating targets in both versions of the task. On the stop-signal task, the participants successfully inhibited their reaching movements on short stop-signal delays (50–150 ms) more frequently than on long stop-signal delays (200 ms) in both versions of the task. Our findings suggest that the tablet version of each task assesses visuomotor processing in the same way as their respective laboratory version, thus providing the research community with a new tool to assess visuomotor function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".