Attention modulates saccade latency but not kinematics
Bibliographic record
Abstract
Previous studies have observed that similar brain areas are activated during covert shifts of attention and during the execution of saccades, leading to the suggestion that the brain systems controlling these functions share similar neural substrates. In the present study we tested the extent of the functional overlapping between the two systems. In the first of two conditions (full attention) we instructed subjects (n=6) to make saccades from a central fixation point toward a target that appeared randomly at two different eccentricities (12, 24 degrees) to the left or to the right of the fixation point. The target could have six different contrast levels (0, 2, 4, 6 and 10%). In a second condition (divided attention) the subjects performed the same task but we additionally instructed them to signal the occurrence of a transient contrast change at the central fixation point. We found that in the divided attention condition the saccade latency was increased relative to the full attention condition; however the kinematics of the saccades (peak velocity vs. saccade amplitude) was the same in both conditions. We additionally found that changing the saccade target contrast in the full attention condition had a similar effect as in the divided attention condition, i.e., lowering saccade target contrast increased saccade latency but did not affect the kinematics. In general our results suggest that the level of attention directed to a stimulus influences visuomotor processing by modulating the relative saliency of that stimulus representation mainly during earlier stages of processing (similar to the effects of contrast), leaving the ultimate motor commands specifying the parameters for contracting the eye muscles relatively unchanged.
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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 | 0.002 |
| 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) | 0.002 | 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".