Does unattended information facilitate change detection?
Bibliographic record
Abstract
Changes between alternating visual displays are difficult to detect when the successive presentations of the displays are separated by a brief temporal interval. To assess whether unattended changes attract attention, observers searched for the location of a change involving either a large or a small number of features, in pairs of displays consisting of 4, 7, 10, 13, or 16 letters (Experiment 1) or digits (Experiments 2 and 3). Each display in a pair of displays was presented for 200 ms, and either a blank screen (Experiments 1 and 2) or a screen of equal luminance to the letters and digits (Experiment 3) was presented for 80 ms between the alternating displays. In all experiments, the search function for locating the larger change was shallower than the search function for locating the smaller change. These results indicate that unattended changes play a functional role in guiding focal attention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".