Process Dissociation Procedure Improves Assessment of Motor Imagery Ability Using Implicit Sequence Learning
Bibliographic record
Abstract
For motor imagery (MI) to be effective for motor learning and rehabilitation, one must be able to perform it. The covert nature of MI makes it difficult to objectively assess MI ability. Assessment of MI ability is particularly pertinent in clinical populations, where brain damage can preclude the ability to perform it. To aid assessment of MI ability, we developed MiScreen, a mobile application that uses MI-based training through which individuals implicitly learn. The logic behind MiScreen is that if an individual can learn via MI, they must be able to perform it. Here we apply process dissociation procedure (PDP) to the data resulting from the MI-based training underlying MiScreen to address the limitations of MiScreen that reduce its applicability. Our results show that the use of PDP increases the number of users for which MiScreen would be applicable, demonstrating added value. Incongruence between PDP and current analysis procedures highlights the need for future work to identify the optimal analysis that best represents MI-based learning, and thus MI ability.
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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 | 0.001 | 0.001 |
| 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".