Goal-directed reaching: Allocentric target representations result in an offline mode of control
Bibliographic record
Abstract
Everyday activities such as copying, drawing, and imitative gestures require allocentric representations of space for successful movement completion. Notably, the top-down nature of allocentric spatial representations is thought to render motor output via a slow and offline mode of cognitive control mediated via visuoperceptual networks. The present investigation sought to test this hypothesis by providing detailed trajectory analyses of allocentric and target-directed reaching tasks performed with and without concomitant limb vision. Allocentric tasks required reaches to a location defined by the distance between a target and reference stimulus, whereas target-directed tasks required reaches to a target's veridical location. To examine the extent to which tasks were controlled via feedback-based trajectory amendments (i.e., online) or central planning mechanisms (i.e., offline), we computed the proportion of variance explained (i.e., R2) by the spatial position of the limb at 75% of movement time relative to each response's ultimate movement endpoint for distance and direction axes. Results showed that target-directed limb visible trials produced smaller R2 values and decreased endpoint variability compared to their limb occluded counterparts. In turn, the latter trial-type exhibited R2 values and endpoint variability commensurate with allocentric limb visible and occluded trials (which did not differ). Accordingly, we propose that the presence of limb vision in a target-directed task affords an online mode of control supported via 'fast' visuomotor networks. In contrast, the absence of limb vision or presence of allocentrically defined endpoints is proposed to render a primarily slow and offline mode of cognitive control mediated via visuoperceptual networks.Acknowledgments: Natural Science and Engineering Council of Canada
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".