Inhibition of return at multiple locations in visual search: When you see it and when you don't
Bibliographic record
Abstract
Using a novel sequential task, Danziger, Kingstone, and Snyder (1998) provided conclusive evidence that inhibition of return (IOR) can co-occur at multiple non-contiguous locations. They argued that their findings depended crucially on the allocation of attention to cued locations. Specifically, they hypothesized that because subjects could not predict whether an onset event was a target or a non-target, all onset events had to be attended. As a result, non-targets were tagged with inhibition. The present study tested this hypothesis by manipulating whether target onset was predictable or not. In support of Danziger et al., three experiments revealed that multiple IOR was only observed when attention had to be directed to the cued locations. Interestingly, when attention did not need to be allocated to the cued locations, and multiple IOR was abolished, an IOR effect was still observed at the most recently cued location. Two possible accounts for this single IOR effect were presented for future investigation. One account attributes the effect to motor-based inhibition as hypothesized by Klein and Taylor (1994). The alternative account attributes the effect to weak attentional capture by a peripheral cue. Together the data support the view that multiple IOR is an attentional phenomenon and, as hypothesized by Tipper, Weaver, and Watson (1996), its presence or absence is largely under the control of the observer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".