Keeping still doesn't “make sense”: examining a role for movement variability by stabilizing the arm during a postural control task
Bibliographic record
Abstract
Small-amplitude, higher frequency oscillations of the body or limb are typically observed when humans attempt to maintain the position of a body or limb in space. Recent investigations have suggested that these involuntary movements of the body during stance could be used as an exploratory means of acquiring sensory information. In the present study, we wanted to determine whether a similar phenomenon would be observed in an upper limb postural task that does not involve whole body postural control. Participants were placed in a supine position with the arm pointing vertically and were asked to maintain the position of the limb in space with and without visual feedback. The wrist was attached to an apparatus that allowed the experimenter to stabilize or “lock” movements of the arm without the participants' awareness. When participants were “locked,” the forces recorded predicted greater accelerations than those observed when the arm was freely moving with and without visual feedback. From unlocked to locked, angular accelerations increased in the eyes-closed condition and when participants were provided visual feedback of arm angular displacements. Irrespective of their origin, small displacements of the limb may be used as an exploratory means of acquiring sensory information from the surrounding environment. NEW & NOTEWORTHY The role of movement variability during a static limb position task is currently unknown. We tested whether variability remains in the absence of sensory-based error with an apparatus that stabilized the limb without the participant's knowledge during a static postural task. Increased forces observed during arm stabilization predicted movements greater than those observed when not externally stabilized. These results suggest movement variability during static postures could facilitate the gathering of sensory information from the surrounding environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".