Bibliographic record
Abstract
We report a series of investigations into the effects of common names, physical identity, and physical similarity on visual detection time. The effect of these factors on the capacity of the system processing the signals was also examined. We used a redundant targets design with separate testing of the target-distractor (single target), target-target (redundant targets), and distractor-distractor (no targets) displays. When a target and a distractor share names, detection of the target is slower than it is in a situation in which the two do not go by a common name. Nevertheless, the gain reaped by redundant targets in this situation is larger and signal processing is of increased capacity compared with those in a situation in which the target and the distractor are coded by different names. The results also highlight the role of physical identity of targets: Detection is disproportionately efficient when reproductions of a given signal are presented. Together, the results provide guiding principles for a model of visual detection by a context-sensitive human detector.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".