Bibliographic record
Abstract
Bimodal visual-tactile receptive fields have been found attached to body parts, such as the hand. Later studies have shown that visual attention has been modulated by the nearby presence of a hand. Based on the evidence for oculomotor control of visual spatial attention, we asked whether the attentional modulation produced near the hand is integrated into the oculomotor system/saliency map. Subjects were tasked with fixating a spot on the screen. That spot disappeared as another appeared and subjects shifted their gaze to the new spot. In one experiment, on half of the trials, the subject's hand was placed near the midpoint of the saccade trajectory, palm facing towards it. On the other half of the trials, the subject's hand was placed on the lap. In a second experiment, on half of the trials the subject's hand was placed near the midpoint of the saccade trajectory, palm facing towards it. On the other half of the trials, the hand was again placed near the midpoint of the saccade, however the palm faced away from it. We analyzed the metrics of the saccades to see if hand proximity and palm/back of hand facing affected saccade trajectories. If attentional modulation produced by the hand is independent of oculomotor control of visual spatial attention, then hand proximity and facing should have no effect on saccade trajectories. However, we find effects of hand position on saccade trajectories, suggesting that the bimodal visual-tactile receptive fields are part of an integrated visual spatial attention mechanism controlled by the oculomotor system.
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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".