When What's Left Is Right: Visuomotor Transformations in an Aged Population
Bibliographic record
Abstract
BACKGROUND: There has been little consensus as to whether age-related visuomotor adaptation effects are readily observable. Some studies have found slower adaptation, and/or reduced overall levels. In contrast, other methodologically similar studies have found no such evidence of aging effects on visuomotor adaptation. A crucial early step in successful adaptation is the ability to perform the necessary transformation to complete the task at hand. The present study describes the use of a viewing window paradigm to examine the effects of aging in a visuomotor transformation task. METHODS: Two groups of participants, a young adult control group (age range 18-33 years old, mean age = 22) and an older adult group (age range 62-74, mean age = 68) completed a viewing window task that was controlled by the user via a computer touchscreen. Four visuomotor "flip" conditions were created by varying the relationship between the participant's movement, and the resultant on-screen movement of the viewing window: 1) No flip 2) X-Axis and Y-axis body movements resulted in the opposite direction of movement of the viewing window. In each of the 3) Flip-X and 4) Flip-Y conditions, the solitary X- or Y-axes were reversed. Response times and movement of the window were recorded. CONCLUSIONS: Older participants demonstrated impairments in performing a required visuomotor transformation, as evidenced by more complex scanning patterns and longer scanning times when compared to younger control participants. These results provide additional evidence that the mechanisms involved in visuomotor transformation are negatively affected by age.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".