Spatial Negative Priming, but Not Inhibition of Return, with Central (Foveal) Displays
Bibliographic record
Abstract
The view persists that the inhibition of return (IOR) and the spatial negative priming (SNP) phenomena may be produced by a common “orientation inhibition” mechanism (e.g., Christie & Klein, 2001), held to arise during the processing of peripherally delivered (parafoveal) visual events. Both IOR and SNP effects are present when responding to recently to-be-ignored distractor events is delayed. Since an SNP effect has been produced using centrally located distracters (visual angle of about 2.5? or less), a common mechanism view would require that these locations generate orientation inhibition, which then cause of the SNP effect. We report past results and an experiment that reject the common mechanism view. Subjects completed four tasks; two, 1-response tasks, using either central (Task 1) or peripheral (Task 2: IOR) event locations, and two, 4-response tasks, again, using central (Task 3: SNP-central) or peripheral (Task 4: SNP-peripheral) locations. Trials occurred in pairs; first the prime (a target or a distractor), then the probe (target only). Critically, neither distractor- nor target-occupied prime locations produced either inhibitory (SNP effect) or positive after-effects, respectively, in Task 1. Seemingly, centrally located events do not generate orientation inhibition and so, unlike the IOR effect, this inhibition does not cause the SNP-central phenomenon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".