Perceptual learning of detection of band-limited noise patterns
Bibliographic record
Abstract
Perceptual learning has most frequently been studied for discrimination and identification tasks, in which learning is specific to stimuli exposed throughout practice. Here, we asked whether practice improves detection of textures in noise, and whether the improvements, if any, are stimulus specific when stimulus features are not easily identified. We used two external noise levels, following previous studies showing different patterns of improvement in discrimination in low and high noise. Two groups of observers practiced detection of five noise textures (2-4 cpi) on two consecutive days. The texture was presented at one of eight contrasts, including a zero-contrast (signal absent) condition. The observer's task was to detect whether the texture was present or absent on each trial (yes/no). One group practiced the task with the same five textures on both days, and the other group switched to five novel textures on Day 2. Noise levels were blocked, and the five textures were randomly presented throughout the session. We calculated d' at each contrast, and the false alarm rate for each observer in each noise level on both days. Performance improved for both groups in high noise, but the improvement was larger for the same texture group, particularly at high contrasts. Performance improved slightly in low noise for the novel texture group, but not for the same texture group. The improvement in low noise was significantly associated with a reduction in false alarms. The results suggest that learning of texture detection generalizes to novel textures from the same bandwidth, but there is an advantage in high noise for previously exposed textures (i.e., stimulus specificity) despite absence of identification. The low noise data are consistent with work suggesting that threshold improvements in certain yes-no tasks are accompanied by changes in the decision criterion. Meeting abstract presented at VSS 2014
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".