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
Selective Tuning (ST) presents a framework for modeling attention and in this work we show how it performs in covert visual search tasks by comparing its performance to human performance. Two implementations of ST have been developed. The Object Recognition Model recognizes and attends to simple objects formed by the conjunction of various features and the Motion Model recognizes and attends to motion patterns. The validity of the Object Recognition Model was first tested by successfully duplicating the results of Nagy and Sanchez. A second experiment was aimed at an evaluation of the model's performance against the observed continuum of search slopes for feature-conjunction searches of varying difficulty. The Motion Model was tested against two experiments dealing with searches in the visual motion domain. A simple odd-man-out search for counter-clockwise rotating octagons among identical clockwise rotating octagons produced linear increase in search time with the increase of set size. The second experiment was similar to one described by Thorton and Gilden. The results from both implementations agreed with the psychophysical data from the simulated experiments. We conclude that ST provides a valid explanatory mechanism for human covert visual search performance, an explanation going far beyond the conventional saliency map based explanations.
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 | 0.001 |
| 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