Observer detection and discrimination performance as a function of clutter: a signal detection approach
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
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.
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 | 0.000 | 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 | 0.001 |
| 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