Bibliographic record
Abstract
Visual prior entry effects, as measured by temporal order judgments (TOJs), are a sensitive measure of attentional capture. In the present study, we investigated if face stimuli tend to capture attention more effectively than non-face stimuli. To do so, we used a novel TOJ paradigm in which participants were presented with pairs of stimuli on either side of fixation cross arriving at different SOAs (12ms – 132ms) without any preceding cue. The task was to simply indicate which stimulus item had the first onset. If faces do show enhanced prior entry compared to non-face stimuli, then greater accuracy for face stimuli should be observed at short SOAs. First, we compared an innocuous abstract object against a neutral schematic face and, somewhat surprisingly, found the abstract object had a greater prior entry effect at SOAs of 12 and 24 ms. To further investigate this finding, a second experiment contrasted a schematic neutral face and a schematic mad face as earlier research indicates that attention is biased towards emotional faces compared to non-emotional faces. Here no significant difference at any SOA was found, with performance remaining at chance for shorter SOA. A third experiment that masked both stimulus items 100 ms after the second stimulus onset once again contrasted a mad face with an inverted neutral face, again revealing no prior entry effects for the mad face stimulus. A final experiment varied the spatial location of the stimulus onsets confirming the abstract object's ability to show visual prior entry over the face stimulus. These findings suggest that attention is not reflexively biased towards the detection of faces.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".