Orienting of attention without awareness is affected by measurement-induced attentional control settings
Bibliographic record
Abstract
McCormick (1997) concluded that peripheral cues presented below a threshold of awareness could nevertheless attract attention because they facilitated target processing near the cue shortly after its presentation. Yet, whereas an exogenous shift of attention typically exhibits a biphasic pattern (initial facilitation followed by inhibition of return [IOR]), at late cue-target onset asynchronies, IOR was not observed by McCormick. In our study, targets requiring a detection response were preceded by masked and nonmasked, uninformative cues presented under two conditions: one in which the cue was ignored (no report) and one in which the cue was detected and localized following the response to the target (cue report). When participants were required to make cue judgments at the end of each trial, we replicated McCormick's pattern, finding facilitation (but not IOR) following both masked and nonmasked cues. When there was no requirement to judge the presence or location of the cues, IOR was present with and without masks, whereas facilitation was observed only when the cues were not masked. That the assessment of cue awareness increases attentional facilitation and prevents (or delays) the onset of IOR is attributed to attentional control settings put in place to perform the cue-awareness assessments in the cue-report condition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".