Attention is predominantly guided by the eye during concurrent eye-hand movements
Bibliographic record
Abstract
Attention is directed to the upcoming goal location of both saccades and reaches . It remains unknown however, how attention is allocated during simultaneous eye and hand movements. We investigated attentional allocation through a 4-alternative forced-choice shape discrimination task (Deubel & Schneider, 1996) while subjects made either a saccade or a reach (or both) when cued by an arrow to one of five peripheral locations. The discrimination shape appeared during the latency period either at the goal (50% of the time) or at one of the other 4 locations. We found that target discrimination was better when the discrimination stimulus appeared at the movement goal than when it appeared elsewhere. Discrimination performance at the movement goal was not better in the combined condition compared to either effector alone, suggesting limited shared attentional resources rather than separate attentional resources specific to each effector. To test which effector dominated in guiding attentional resources, we then separated the goals for the hand and the eye. This was done using two paradigms, 1) cued reach/constant saccade - subjects made a saccade to the same peripheral location throughout the block, while the reach goal was cued by the arrow and 2) cued saccade/constant reach - subjects made a reach to the same location, while the saccade goal was cued. During both eye-hand goal dissociation paradigms, discrimination performance was consistently better at the eye goal than the hand goal. This indicates that limited attentional resources are guided predominantly by the eye during 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 | 0.000 | 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".