Directional interactions between current and prior saccades
Bibliographic record
Abstract
One way to explore how prior sensory and motor events impact eye movements is to ask someone to look to targets located about a central point, returning gaze to the central point after each eye movement. Concerned about the contribution of this return to center movement, Anderson et al. (2008) used a sequential saccade paradigm in which participants made a continuous series of saccades to peripheral targets that appeared to the left or right of the currently fixated location in a random sequence (the next eye movement began from the last target location). Examining the effects of previous saccades (n-x) on current saccade latency (n), they found that saccadic reaction times (RT) were reduced when the direction of the current saccade matched that of a preceding saccade (e.g., two left saccades), even when the two saccades in question were separated by multiple saccades in any direction. We examined if this pattern extends to conditions in which targets appear inside continuously marked locations that provide stable visual features (i.e., target "placeholders") and when saccades are prompted by central arrows. Participants completed 3 conditions: peripheral targets (PT; continuous, sequential saccades to peripherally presented targets) without placeholders; PT with placeholders; and centrally presented arrows (CA; left or right pointing arrows at the currently fixated location instructing participants to saccade to the left or right). We found reduced saccadic RT when the immediately preceding saccade (n-1) was in the same (vs. opposite) direction in the PT without placeholders and CA conditions. This effect varied when considering the effect of the previous 2-5 (n-x) saccades on current saccade latency (n). The effects of previous eye movements on current saccade latency may be determined by multiple, time-varying mechanisms related to sensory (i.e., retinotopic location), motor (i.e., saccade direction), and environmental (i.e., persistent visual objects) factors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".