The latencies of prosaccades are prolonged by both executed and planned (but not executed) prior antisaccades
Bibliographic record
Abstract
Background: Studies that mix trials with prosaccades and antisaccades have shown that there are carry-over effects between one trial and the next. In particular, a preceding antisaccade leads to increased latencies of the following response, particularly if it is a prosaccade. Whether this antisaccade effect results from effects generated in the execution of the antisaccade or possibly in the planning and preparation process is unknown. Objective: We studied whether prolongation of the latencies of subsequent prosaccades after antisaccade trials depended upon execution of the antisaccade or occurred regardless of whether the antisaccade was actually performed. Methods: 9 subjects were tested using blocks of randomly ordered prosaccades and antisaccades. An instructional cue at fixation indicated whether a prosaccade or antisaccade was required when the target appeared 2 seconds later. On 20% of the antisaccade trials, the target did not actually appear (antisaccade cue/no-target trials). We analysed the latencies of all correct prosaccades or antisaccades that were preceded by correctly executed trials. Results: As expected the latencies of prosaccade trials preceded by prosaccades were 20ms shorter (mean 189ms) than the latencies of prosaccade trials preceded by antisaccades (mean 209ms). Prosaccades preceded by trials where antisaccades were cued but not executed because the target did not appear also showed prolonged latencies (mean 214ms). These differed from the latencies of trials with preceding prosaccades but not from those of trials with preceding antisaccades. Conclusion: The effects of a prior antisaccade in prolonging the latencies of subsequent saccades is generated by the planning of the antisaccade. This may reflect persistence of the known pre-target preparatory activity seen in neural recordings of the superior colliculus and frontal eye field. Meeting abstract presented at VSS 2014
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.006 | 0.001 |
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".