Spatiotemporal dynamics in inhibition of return
Bibliographic record
Abstract
Once attention has been disengaged from a non-predictive exogenous cue, processing of a target subsequently presented at the cued location is slowed relative to that of targets at uncued locations. This effect has been termed inhibition of return (IOR). Previous studies of IOR using multiple cueing paradigms have focused on either temporal or spatial dynamics in isolation. We argue, however, that space and time have to be considered in conjunction to understand the processes governing the allocation of attention. To test this notion, the present work examines the spatiotemporal distribution of IOR in a sequential cueing task, in which three cues were presented sequentially for 500ms each at three of six possible locations along an imaginary circle with 5° radius. Following a central reorienting cue of 500ms duration, a target appeared for 100ms at any of the six possible locations, which participants were instructed to detect by button press as rapidly as possible. In line with previous work, we found greatest IOR at the most recently cued locations, with decreasing magnitudes of IOR at locations cued earlier in the sequence. Importantly, the magnitude of IOR was affected by the spatial arrangement of the cues, such that targets appearing at the vector average of all cued locations showed greater IOR than those appearing at the edges of the spatial distribution. Therefore, we conclude that IOR is a function of both the positions and the timing of sequential cues, indicating that attention integrates spatial and temporal properties to optimize responses to new information.
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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) | 0.000 | 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) | 0.001 | 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".