Long-term retention of proprioceptive recalibration
Bibliographic record
Abstract
Proprioception is recalibrated following reaches with misaligned visual feedback of the hand, such that one's sense of felt hand position is shifted in the direction of the visual feedback provided (Cressman and Henriques 2009). In this study we examined the ability of proprioceptive recalibration to be retained over an extended period of time (i.e. 4 days), and the benefits of additional training on retention in the form of recall and savings (i.e. faster re-learning on subsequent testing days). 24 participants trained to reach to a target while seeing a cursor that was rotated 30° clockwise relative to their hand on an initial day of testing. Half of the participants then completed additional reach training trials on 4 subsequent testing days (Training group), whereas the second half of participants did not complete additional training (Non-Training group). Participants provided estimates of their felt hand position on all 5 testing days to establish retention of proprioceptive recalibration. Results revealed that proprioceptive recalibration was recalled 24 hours after initial training and that there was no benefit of additional training. Retention in the form of savings was observed on all days for the Training group and on Day 5 in the Non-Training group. These results reveal that proprioceptive recalibration does not benefit from additional training but is retained in the form of recall and savings. Moreover, the different time scales (i.e. 1 day for recall versus 4 days for savings), suggest that distinct processes may underlie recall and savings of proprioceptive recalibration.Acknowledgments: NSERC
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".