Guidelines for Reporting Action Simulation Studies (GRASS): proposals to improve reporting of research in Motor Imagery and Action Observation
Bibliographic record
Abstract
Researchers from multiple disciplines have studied the simulation of actions through motor imagery, action observation, or their combination. Procedures used in these studies vary considerably between research groups, and no standardized approach to reporting experimental protocols has been proposed. This has led to under-reporting of critical details, impairing the assessment, replication, synthesis, and potential clinical translation of effects. We provide an overview of issues related to the reporting of information in action simulation studies, and discuss the benefits of standardized reporting. We propose a series of checklists that identify key details of research protocols to include when reporting action simulation studies. Each checklist comprises A) essential methodological details, B) essential details that are relevant to a specific mode of action simulation, and C) further points that may be useful on a case-by-case basis. We anticipate that the use of these guidelines will improve the understanding, reproduction, and synthesis of studies using action simulation, and enhance the translation of research using motor imagery and action observation to applied and clinical settings.
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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 | 0.773 | 0.893 |
| Meta-epidemiology (narrow) | 0.007 | 0.011 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.043 | 0.036 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.019 | 0.022 |
| Research integrity | 0.029 | 0.028 |
| Insufficient payload (model declined to judge) | 0.009 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".