Inhibition of return at multiple locations and its impact on visual search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it