Examining the Equivalence Between Imagery and Execution—Does Imagery Comprise the Intended Spatial Trajectory?
Bibliographic record
Abstract
The functional equivalence model suggests a common internal representation initiates both imagery and execution. This suggestion is supported by the mental chronometry effect, where there is a positive relation between task difficulty (as defined by the Index of Difficulty; ID) and imagined movement time. The present study extends this logic by examining whether imagery captures the spatial trajectory. Participants were initially tasked with the imagery and execution of a rapid aiming movement under different IDs. These initial attempts were adapted to configure auditory tones at early (25%) and late (75%) intervals for a separate set of imagery trials. If a tone had sounded, participants had to estimate post-trial where their imagined limb would have been located. The findings revealed increases in ID that coincided with increases in imagined and executed movement times. However, participant mean and standard deviation of estimated locations revealed limited differences between the early and late tones. Further inspection revealed some evidence for these estimated locations shifting further along in space following more rapid imagined movements. While equivalence is clearly evident within the temporal domain, there is comparatively little to suggest that this logic extends to the resolution required for simulating the spatial characteristics of movement.
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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 | 0.025 |
| 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 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".