Eye-hand coordination: memory-guided grasping in a cluttered environment
Bibliographic record
Abstract
We often reach for remembered objects, such as when picking up a coffee cup from behind our laptop. In cases like this, we rely on visuospatial memory, encoded by the perceptual mechanisms of the ventral visual stream, to guide our actions, rather than on the real-time control of action by the dorsal visual stream (Milner & Goodale, 1995). Further, our motor plans must often accommodate for the messy spaces within which we act, avoiding irrelevant objects in our way. Little research has examined obstacle avoidance during memory-guided grasping, though it is likely obstacles perceived by the ventral stream as more salient will produce exacerbated avoidance maneuvers. This study examined how the availability of visual feedback altered eye-hand coordination in an obstacle avoidance paradigm. Eye and hand movements were monitored as subjects had to reach through a pair of obstacles in order to grasp a 3-D target object, under full visual feedback (visually-guided), immediately in the absence of visual feedback (memory-guided no-delay), or after a 2-s delay in the absence of visual feedback (memory-guided delay). Positions and widths of obstacles were manipulated, though their inner edges remained a constant distance apart. We expected the memory-guided delay group to exhibit exaggerated avoidance strategies due to a reliance on the perceptual mechanisms of the ventral steam. Results revealed successful obstacle avoidance and grasps of the target object in all groups, however different avoidance strategies emerged depending on the availability of visual feedback. The visually-guided and memory-guided no-delay groups used real-time visual information to alter the paths of the index finger and wrist and adjust final index finger positions on the target object, to account for positioned obstacles. Still, the no-delay group showed wider index finger paths and a failure to adjust final fixations, resulting from the inability to use visual information for the online control of action. Unexpectedly, the memory-guided delay group employed a rather moderate strategy for avoiding obstacles, in that positioned obstacles less often prompted alterations in the mechanics of the reach. In conclusion, obstacle avoidance when reaching to remembered objects adopts a more moderate, rather than exaggerative, strategy.
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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 | 0.000 | 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) | 0.001 | 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".