An Eye in the Palm of Your Hand: Alterations in Visual Processing Near the Hand, a Mini-Review
Bibliographic record
Abstract
Feedback within the oculomotor system improves visual processing at eye movement end points, also termed a visual grasp. We do not just view the world around us however, we also reach out and grab things with our hands. A growing body of literature suggests that visual processing in near-hand space is altered. The control systems for moving either the eyes or the hands rely on parallel networks of fronto-parietal regions, which have feedback connections to visual areas. Since the oculomotor system effects on visual processing occur through feedback, both through the motor plan and the motor efference copy, a parallel system where reaching and/or grasping motor-related activity also affects visual processing is likely. Areas in the posterior parietal cortex, for example, receive proprioceptive and visual information used to guide actions, as well as motor efference signals. This trio of information channels is all that would be necessary to produce spatial allocation of reach-related visual attention. We review evidence from behavioral and neurophysiological studies that support the hypothesis that feedback from the reaching and/or grasping motor control networks affects visual processing while noting ways in which it differs from that seen within the oculomotor system. We also suggest that object affordances may represent the neural mechanism through which certain object features are selected for preferential processing when stimuli are near the hand. Finally, we summarize the two effector-based feedback systems and discuss how having separate but parallel effector systems allows for efficient decoupling of eye and hand movements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".