Bibliographic record
Abstract
Inhibition of Return (IOR) is a phenomenon where responses to targets appearing at previously attended locations are slower after a certain temporal interval, enhancing visual search efficiency by discouraging attention from revisiting examined areas. This study investigates whether the spatial frequency of cues and targets modulates IOR by selectively engaging either the magnocellular (sensitive to low spatial frequencies; LSFs) or the parvocellular (sensitive to high spatial frequencies; HSFs) visual pathways. We conducted a typical visual cueing task (two horizontally aligned cue/target locations with placeholders ) with participants completing one of four cueing conditions: LSF cues, HSF cues, LSF targets, or HSF targets. These cues and targets were circles filled with black and white bars at 3 cpd (LSF) or 12 cpd (HSF). The control cues and targets were a white outline with a solid black circle. The inter-stimulus interval (ISI) between cue and target ranged from 100 ms to 1400 ms in order to examine the time course of IOR in each condition. In the spatial frequency cue conditions, we found that LSF and HSF cues generated typical – and equivalent – IOR effects and time courses. In the spatial frequency target conditions, we observed a surprising result; the lack of IOR with both spatial frequencies across all ISIs . Across all conditions, we additionally found the expected decrease in RTs synonymous with longer ISIs, indicating that participants attended to both cues and targets. Overall, the evidence from this study is indicative of a stronger effect centered around relationship between the physical features of cues and targets rather than what pathway is primed to process the stimuli.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".