How much practice is needed to produce perceptual learning?
Bibliographic record
Abstract
Extended practice — usually consisting of several hundred trials — substantially improves performance on a face-identification task. In this study we examined how many trials are necessary for this improvement, or perceptual learning, to occur. On consecutive days, observers performed a ten-alternative forced choice face identification task. Faces were presented at one of seven different contrast levels in three levels of external noise for a total of 21 stimulus conditions. The critical manipulation was the number of trials presented in each condition on Day 1. Separate groups completed either 1, 5, 10, 20, or 40 trials per stimulus condition, for a total number of trials that ranged from 21 to 840. The amount of time spent in the lab on Day 1 was equated across subjects by requiring everyone who completed fewer than 840 trials to perform an unrelated visual (filler) task. On Day 2, all observers completed 840 trials. Observers in a zero-trials condition (i.e. the control group) did only the filler task during the first session. Comparisons of face identification thresholds measured on Days 1 and 2, using only subjects that received at least 210 trials on Day 1, showed that thresholds decreased significantly with practice, indicating that perceptual learning occurred. Surprisingly, with the exception of the control group, thresholds did not differ across groups on Day 2. In other words, thresholds for subjects receiving 21–840 trials of practice did not differ significantly, and were significantly lower than thresholds for control subjects. These results suggest that relatively few trials are needed to produce perceptual learning. We currently are conducting experiments to determine what aspects of the brief exposure to our stimuli and/or task are necessary for learning to occur.
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.
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 | 0.010 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".