Evaluating age-related sensorimotor transformation differences when programming movements to somatosensory targets
Bibliographic record
Abstract
While older adults experience declines in somatosensory acuity, this loss does not appear to increase movement accuracy above that of younger adults (e.g., Helsen et al., 2016). Alternatively, older adults exhibit longer reaction times than younger adults when initiating upper-limb reaches to somatosensory targets (Goodman et al., 2018). One possible explanation for longer reaction time could be the result of increased cortical processing spent performing sensorimotor transformations when planning the movement (e.g., Jeannerod, 1991, Sarlegna & Sainburg, 2007). To determine if somatosensory information about the reaching limb contributes to these transformations, tendon vibration was applied to the effector. Younger and older adults performed reaches to visual, somatosensory and bimodal targets, employed by lights and vibro-tactile sensation to the left fingers. All trials were performed without vision of the limb, and one block of trials was performed with tendon vibration presented between-trials. Both older and younger adults exhibited lower endpoint precision when tendon vibration was applied. Also, only older adults showed significantly lower endpoint precision when making movements to somatosensory targets, versus both visual and bimodal targets. Further, older adults also showed the greatest increases in reaction time when vibration was applied, specifically when they were creating movements to the somatosensory targets. Thus, perturbing the effector limb created significantly greater delays in movement initiation to somatosensory targets in the older versus younger adults. These results provide further evidence that older adults have difficulties programming movements utilizing somatosensory information when reaching to somatosensory targets, including somatosensory cues from the effector.Acknowledgments: University of Toronto, Ontario Research Fund (ORF), Canada Foundation for Innovation (CFI), Natural Sciences and Engineering Research Council of Canada (NSERC)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".