Bibliographic record
Abstract
The extent to which capture by uninformative peripheral cues is contingent on the top-down goals of the participant has been a hotly debated issue. Most studies have investigated if uninformative cues whose features are irrelevant to the current task goals capture attention. Here (as did Ishigami and colleagues in earlier work), we ask a slightly different, but related question: Do uninformative cues whose locations are irrelevant capture attention? Ruthruff and Gaspelin (2018) presented an abrupt-onset cue among four placeholders (above, below, left & right of fixation). Each participant was asked to find a colour target letter (red or green) among four letters (E/H) in either the horizontal or the vertical axis (by instruction, only one axis was task-relevant) and report its identity; thus cues could be spatially relevant (on the relevant axis) or irrelevant (on the irrelevant axis). Response Times (RTs) on irrelevant-cue trials and absent-cue trials were equivalent suggesting “immunity from attention capture at ignored locations”. We hypothesized that the RTs on absent-cue trials may have been overestimated due to the absence of alerting benefit compared to the cue present trials, and tested this hypothesis in a registered replication study. Experiment 1 replicated the task of Ruthruff and Gaspelin (2018). RTs on irrelevant-cue trials were faster compared to the absent-cue trials lending support to the original conclusion by Ruthruff and Gaspelin that performance does not suffer on irrelevant-cue trials. In Experiment 2, we additionally included a warning signal on every trial to equate all cue conditions on the alerting component. Here, RTs on irrelevant-cue trials were significantly slower than on absent-cue trials suggesting that the irrelevant-cues captured attention, at least to some degree. The results underscore the importance of using an appropriate baseline in attention capture studies.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".