The antisaccade task: Vector inversion contributes to a statistical summary representation of target eccentricities
Bibliographic record
Abstract
Antisaccades require the top-down suppression of a stimulus-driven prosaccade (i.e., response suppression) and the inversion of a target's spatial location to mirror-symmetrical space (i.e., vector inversion). Moreover, recent work has shown that antisaccade amplitudes are characterized by a statistical summary representation (SSR) of the target eccentricities included in a stimulus-set--a result suggesting that antisaccades are supported via the same relative visual information as perceptions. The present investigation sought to determine whether response suppression and the disruption of real-time control or vector inversion contribute to a SSR in oculomotor control. Participants completed pro- and antisaccades (target eccentricities of 10.5°, 15.5°, and 20.5°) in blocks of trials that differed with regard to the frequency that individual target eccentricities were presented. The manipulation of target eccentricity frequency was used to determine whether the most frequently presented target within a stimulus-set (i.e., the SSR) influences saccade amplitudes. Moreover, we disrupted the real-time control of prosaccades by requiring participants to suppress their response for a brief visual delay (i.e., 2000 ms: so-called delay prosaccade). As expected, antisaccades and delay prosaccades produced equivalent reaction times. In turn, amplitudes for delay prosaccades were refractory to the manipulation of target eccentricity frequency, whereas antisaccades were biased in the direction of the most frequently presented target within a stimulus-set. Accordingly, we propose that vector inversion contributes to the mediation of target eccentricities via a SSR and that such a phenomenon provides convergent evidence that a relative visual percept mediates antisaccades.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".