The eye dominates in guiding attention during simultaneous eye and hand movements
Bibliographic record
Abstract
Prior to the onset of a saccade or a reach, attention is directed to the goal of the upcoming movement. However, it remains unknown whether attentional resources are shared across effectors for simultaneous eye and hand movements. Using a 4-AFC shape discrimination task, we investigated attentional allocation during the planning of a saccade alone, reach alone, or combined saccade and reach to one of five peripheral locations. Target discrimination was better when the probe appeared at the goal of the impending movement than when it appeared elsewhere. However, discrimination performance at the movement goal was not better for combined eye-hand movements compared to either effector alone, suggesting a shared limited attentional resource rather than separate pools of effector-specific attention. To test which effector dominates in guiding attention, we then separated eye and hand movement goals in two conditions: (1) cued reach/fixed saccade--subjects made saccades to the same peripheral location throughout the block, while the reach goal was cued and (2) cued saccade/fixed reach--subjects made reaches to the same location, while the saccade goal was cued. For both conditions, discrimination performance was consistently better at the eye goal than the hand goal. This indicates that shared attentional resources are guided predominantly by the eye during the planning of eye and hand movements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".