When is inhibition of return input- or output-based? It depends on how you look at it.
Bibliographic record
Abstract
Two important diagnostics have been used to infer whether the effect of inhibition of return, when preceded by a saccade, is primarily upon input (i.e., attentional/perceptual level) or output (i.e., response/decision level) processes. Data from antisaccade paradigms involving luminance targets in peripheral vision suggest input effects whereas data from spatially compatible manual responses to centrally presented arrow targets suggest output effects. Here, we combine these diagnostics to resolve the discrepancy. In separate conditions participants made a pro- or antisaccade to a peripheral stimulus. Upon returning gaze to the original fixation, left and right manual responses were made to left- and right-pointing arrows at fixation, respectively. The primary objective of the prosaccade condition was to determine whether an eye movement toward a visual stimulus that was not associated with a manual localization response would bias spatially compatible manual responses against the prior saccade vector. Manual responses were slowest in the direction of the prior saccade, consistent with an output-based attribution (e.g., Posner, Rafal, Choate, & Vaughan, 1985). The primary objective of the antisaccade condition was to determine whether an eye movement away from a visual stimulus would also bias subsequent manual responses. No apparent response bias was detected, consistent with an input-based attribution (e.g., Fecteau, Au, Armstrong, & Munoz, 2004). Collectively, the findings indicate that there are 2, dissociable forms of inhibition depending on saccadic response demands. Converging evidence from other paradigms is discussed. (PsycINFO Database Record
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".