Attention switching between global and local elements: Distractor category and the level repetition effect
Bibliographic record
Abstract
When selecting information at global and local levels of hierarchical stimuli, there is a robust effect of level repetition in which performance is more efficient when a target is presented at the same level as the previous target. Moreover, the effect is symmetrical; it affects global and local processing equally. Evidence exists to suggest the effect may be automatic; however, we show here that the level repetition effect requires some amount of competition from the ignored level, and that the nature of the irrelevant information can determine whether the level-repetition effect is symmetrical (global and local responses are affected equally) or asymmetrical (global responses are more greatly affected than local responses). In Experiment 1, the level-repetition effect was eliminated when information at the distracting level was invariant across trials; effects of hemisphere bias and level repetition were observed only when suppression or filtering of distractor information was required. Experiment 2 demonstrated that simple featural variance is sufficient to produce the level repetition effect and that the symmetry of the level-repetition effect is sensitive to Garner-type interference that affects global processing to a greater extent than local processing. In Experiment 3, we showed that the absence of a level-repetition effect in the invariant distractor condition persists when the position of relevant stimuli is random within a block, a manipulation which should greatly reduce the contribution of controlled attention. We conclude that simple featural variance at the ignored level is critical to produce the advantage of level repetition, and that the size of the effect can be asymmetrical.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".