Grasping remembered objects: Pinpointing the transition between on-line and off-line visuomotor control modes
Bibliographic record
Abstract
The scaling of grip aperture in visually guided prehension is largely insensitive to size-contrast illusions, but the same is not true of grasping movements initiated after a delay. When does the transition between veridical and illusory size-scaling occur? In experiment one (No-delay), participants (N=10) viewed a target object and an adjacent flanker object for 500 msec, and reached to grasp the target object in response to a subsequent auditory cue. Unpredictably, vision was occluded either at cueing or at movement onset. In experiment two (Delay), the target array was viewed for 500 msec followed by a 2.5 sec period of visual occlusion and then an auditory initiation cue. Unpredictably, vision was restored either at cueing (and withdrawn at movement onset), or not at all. In both experiments, peak grip aperture was insensitive to flanker size when vision was available between cueing and movement onset. When vision was unavailable at this time, peak grip aperture was modulated by flanker size in a direction consistent with a perceptual size-contrast effect. The magnitude of this effect was similar for both experiments. We propose two discrete modes of visuomotor control. On-line visuomotor control accesses a veridical representation of object size, and requires vision at the time of movement programming (i.e., between cueing and movement onset). Off-line control accesses a perceptual representation of object size, and is engaged when vision is unavailable at the time of movement programming. The transition from on-line to off-line control occurs within one reaction time (approx. 310 msec) or less of visual occlusion.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".