Inhibition of return at multiple locations and its impact on visual search
Bibliographic record
Abstract
Previous research has shown that when attention is directed sequentially to multiple locations, inhibition of return (IOR) can be observed at each location, with a larger magnitude of IOR at the more recently attended locations. In the present study we asked whether this “multiple IOR” effect influences search only for simple feature targets, as has been shown in the past, or whether it generalizes to more complex, attentionally demanding conjunction search situations. The results demonstrated that IOR effects (1) occur for more complex conjunction search environments, (2) are larger for the attentionally demanding conjunction search, and (3) occur at more locations for conjunction search than feature search. Together these data provide a clear demonstration of the robustness and responsiveness of the IOR effect across search situations—which is precisely what is expected of a phenomenon posited to facilitate efficient visual search of real-world environments. Nevertheless, these data do not firmly establish that IOR effects established by the cueing paradigm before search is implemented are the same as the IOR effects that are assumed to be established during search itself. We suggest that this disconnection between paradigms highlights a fundamental limitation of laboratory-based research.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".