Object- and location-based inhibition of return to superimposed surfaces
Bibliographic record
Abstract
Previous studies have suggested the existence of spatial and object-based Inhibition of Return (IOR) effects, and proposed that they are driven by separate mechanisms. These studies have exclusively used objects occurring in spatially separate locations. Thus the object-based effects could be mediated by a location-based mechanism. To control for location, we superimposed two objects (random dot kinetograms). This study examines whether IOR is present for objects that are superimposed or requires that the objects are separated in space. We modified the traditional dynamic IOR displays (Tipper et al, 1991) by placing 2 superimposed surfaces in each of two peripheral locations (left vs right). Location-based IOR was observed regardless whether the target appeared on the cued or uncued surface. Critically, object-based IOR was not present; instead we found evidence of object-based facilitatory effects. Thus location-based but not object-based IOR is found with superimposed surfaces. In Experiment 2, we asked whether spatial separation is necessary throughout the trial or at time of cueing. These results have implications for the relative roles of subcortical oculomotor (e.g. superior colliculus) and cortical substrates for mediating IOR.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".