Influence of Psycho-emotional Factors on Motor Control : Cerebral Mechanism and Behavioral Response Underlying (Motiv)Action
Bibliographic record
Abstract
The objective of this research topic is to regroup a collection of manuscripts investigating the relationship between the psycho-emotional components and motor control using multidisciplinary approaches.Movement performance can be influenced by internal or external factors that interfere with motor control. In this special issue, we want to highlight studies that examine how motivational components can influence motor function. These articles could include, but are not limited to, studies looking into the motor impact of psycho-emotional states or traits like fear of injury, fear of falling, and pain catastrophizing. Manuscript on how to assess this interaction from a fundamental or behavioral perspective, or on how to modulate motor control through motivational manipulation are welcomed.Research has demonstrated that emotions modulate our readiness to move. While neurophysiological studies report improved or decreased cortical activity, biomechanical studies report improved or impaired movement preparation or execution. Physical inactivity and reduced movement in the aging population and in patients with chronic diseases are linked to symptom aggravation and a loss of autonomy. While movement- based interventions are often a first-line treatment, there are several cognitive and motivational barriers that can lead individuals to avoid of physical activity. These factors can also influence athletes in their preparation or recovery from injury.A better understanding of the interactions between behavioral, neurophysiological and psycho-emotional components has important applications in the fields of rehabilitation, ergonomics, and sports performance. This special issue seeks to advance knowledge in the field to improve the care of patients, athletes and the general population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".