Investigating stationary limb localization using psychophysics: Beware of proprioceptive drift
Bibliographic record
Abstract
Quantifying the accuracy and variance of hand localization without vision is integral to understand the reliability of the proprioceptive system. Psychophysical methods primarily compare the endpoint position of a passively moved limb to a stationary visual reference (e.g., Sadler & Cressman, 2019). The purpose of the current study was to understand the ability to localize one's stationary, rather than passively moved, limb without vision using psychophysical methods. Participants placed their unseen limb under a half-silvered mirror with their index and ring finger atop of tactors. One finger was stimulated and then a visual mask was presented followed by a briefly flashed comparison dot. Participants then dictated whether the comparison dot was left or right of their stimulated finger position. An adaptive staircase procedure used the participant's response to determine the position of the comparison dot on the next trial. The initial comparison position was either fixed (task 1) or based on the participant's initial perceived finger positions (tasks 2 and 3). Additionally, a proprioceptive cue presented every 5 trials had participants lift their arm, clinch their fist and isometrically flex their wrist and elbow flexors/extensors (task 3; Wann & Ibrahim, 1992). In all three tasks, the perception of participants' finger position drifted in the magnitude of ~3cm, resulting in unreliable psychophysical estimates of perceptual accuracy and variability. The results suggest that when isolating somatosensory cues and not allowing for any visual recalibration, researchers must be aware of and account for large perceptual drifts in one's stationary limb position.Acknowledgments: University of Toronto, Ontario Research Fund, Canadian Foundation for Innovation, National Sciences and Engineering Council
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".