Relational properties within a target set influence antisaccade amplitudes
Bibliographic record
Abstract
Prosaccades entail a response to the spatial location of a stimulus, whereas antisaccades require decoupling the normally direct relations between stimulus and response (i.e., 180° spatial transformation). Notably, prosaccades are characterized by an invariant undershooting bias: a finding linked to a movement time and/or an energy minimization strategy (Gillen et al. 2013: Exp Brain Res). It is, however, unclear whether antisaccades are similarly characterized by an invariant endpoint bias. To address that issue, we had participants (N=12) complete pro- and antisaccades to briefly presented stimuli in separate blocks (i.e., proximal and distal) that included an equal number of targets but differed with respect to the magnitude of their eccentricities. In the proximal block target eccentricities were 3, 5.5, 8, 10.5, and 13o, whereas in the distal block eccentricities were 10.5, 13, 15.5, 18, and 20.5o. Results for the proximal block indicated that antisaccades to the 3.0o and 5.0o targets produced larger amplitudes than their matched prosaccade counterparts, whereas the 10.5o and 13.0o targets produced the converse pattern (amplitudes for the 8o target did not differ between tasks). In turn, results for the distal block produced a null between-task difference for the 10.5o target; however, antisaccade amplitudes for the remaining targets were less than their prosaccade counterparts. Thus, pro- and antisaccades were characterized by distinct endpoint strategies. In particular, we propose that the top-down requirements of decoupling the spatial relations between stimulus and response renders the mediation of antisaccades via relational visual cues.
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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 | 0.007 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".