Observer detection and discrimination performance as a function of clutter: a signal detection approach
Bibliographic record
Abstract
In this paper we investigate the use of signal detection theory (SDT) in predicting target detection and discrimination in disorganized clutter. Two normal observers performed monocular visual search experiments at 25 cm, in the dark. They detected Gabor gratings on an achromatic background cluttered with 2000 or 500 random dots. The targets were displayed at pseudorandom locations from 0–20° and 20–47°, by method of constant stimuli. A contrast-based detection and orientation-based discrimination task was completed in a yes/no or 2-alternative-forced-choice (2AFC) task. The hit rate, false alarm rate, detectability, criterion and bias were analysed. The psychometric function indicated low detection and discrimination thresholds in low clutter that increased in high clutter. Increased clutter showed high hit rates and a false alarm rate that increased with low detectability and liberal criterion. In the detection task, low clutter showed high hit rates and low false alarm rates in the central field. Therefore, SDT proves useful to predict observer performance in visual scenes with disorganized clutter.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".