Bibliographic record
Abstract
We examined whether the effects of practice on visual detection are stimulus specific and whether practice alters response bias. Eighty-four subjects performed yes-no detection of band-limited noise patterns (textures) in two levels of external noise, on two consecutive days. On day 2, one-half of the observers switched to novel textures. Texture contrast was varied on signal-present trials using the method of constant stimuli. The signal was present on 50% of the trials. We measured d', detection thresholds, and two measures of response criterion: a global criterion that was based on sensitivity at all signal levels (Jones et al., 2015) and a local criterion computed at a hit rate of 70% or 80% (Wenger & Rasche, 2006). Performance improved for both groups on day 2, indicating that improvement transferred to novel textures. Increases in d' were associated with a decrease in false alarms across days. The global criterion became less liberal and became more optimal (i.e., less biased) with practice; however, this effect was small and was not statistically significant in all conditions. The local criterion measure also became slightly less liberal with practice in most conditions, becoming more or less optimal depending on the hit rate at which it was computed. Overall, the effects of practice on sensitivity in a visual detection task generalized to novel patterns. In addition, we found that practice had relatively small effects on response criterion, and the precise effects on response bias differed between global and local measures of criterion.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".