The preattentive emperor has no clothes: A dynamic redressing.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Preattentive models of early vision have not been supported by the evidence. Instead, an input filtering system, which is dynamically reconfigured so as to optimize performance on the task at hand, is proposed. As a case in point, the authors examined Sagi and Julesz's (1985a) claim that detection tasks are processed preattentively and efficiently (shallow search slopes), whereas discrimination tasks require focal attention and yield inefficient steep slopes. In 5 visual search experiments, efficiency was found to depend not on the nature of the task but on whether the task is single or dual. The second component of a dual task, whether detection or discrimination, is performed inefficiently if it does not fit the configuration of the input system, which had been set optimally for the first component. But, even the second component is processed efficiently if there is enough time to reconfigure the system after processing the first component.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it