Visual search and the inhibitions of return
Bibliographic record
Abstract
In the early 1980's independent research streams were launched by two of the 20th century's leading attention researchers. Anne Treisman's research program is best-known for distinguishing empirically between serial and pop-out search and for proposing feature integration theory and the idea of an attentional operator, that sequentially inspects items or groups of items when search is difficult. Among his many contributions to psychological science, Michael Posner is well-known for pioneering simple model tasks that made the allocation of visual attention in space amenable to scientific inquiry. When one version of the Posner cuing paradigm was used to explore visuospatial orienting it was serendipitously discovered that an “inhibitory” effect occurred in the aftermath of events that captured visuospatial attention involuntarily. This “inhibitory” phenomenon became known as Inhibition of Return (IOR), and, as implied by its name, the underlying mechanisms were thought to bias attention away from previously explored places. These two research programs were linked in 1988 when Raymond Klein exploited the distinction between pop-out and serial search to test and verify Posner's proposal that this inhibition might be a novelty seeking mechanism that could improve search efficiency. Subsequent research has identified at least two different inhibitory mechanisms that bias attention toward novelty. We present evidence using several diagnostics (central vs. peripheral targets, joint consideration of speed and accuracy, and the locus of slack logic embedded in the psychological refractory period effect) to illustrate the dual natures of IOR. The input form operates on a salience map that influences what will capture our attention, while the output form operates on a priority map that influences what behaviors (including orienting) are likely to be executed. The input form is generated when the reflexive oculomotor system is suppressed while the output form is generated when this system is not suppressed. We believe that both forms of IOR can serve the novelty seeking (and search facilitating) function proposed by Posner and others. Yet, many questions remain unanswered. Some of the many remaining puzzles will be described and we hope that our review will stimulate research to solve them.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".