Identifying Referent Control Variables Underlying Goal-Directed Arm Movements
Bibliographic record
Abstract
The referent control theory (RCT) for action and perception is an advanced formulation of the equilibrium-point hypothesis. The RCT suggests that rather than directly specifying the desired motor outcome, the nervous system controls action and perception indirectly by setting the values of parameters of physical and physiological laws. This is done independently of values of kinematic and kinetic variables including electromyographic patterns describing the motor outcome. One such parameter-the threshold muscle length, λ, at which motoneurons of a given muscle begin to be recruited, has been identified experimentally. In RCT, a similar parameter, the referent arm position, R, has been defined for multiple arm muscles as the threshold arm position at which arm muscles can be quiescent but activated depending on the deflection of the actual arm position, Q, from R. Changes in R result in reciprocal changes in the activity of opposing muscle groups. We advanced the explanatory power of RCT by combining the usual biomechanical descriptions of motor actions with the identification of the timing of R underlying arm movements made with reversals in three directions and to three different extents. We found that in all movements, periods of minimization of the activity of multiple muscles could be identified at ∼61%-86% of the reaching extent in each direction. These electromyographic minimization periods reflect the spatial coordinates at which the R and Q overlap during the production of movements with reversals. The findings support the concept of the production of arm movement by shifting R.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".