The time-course of rapid stimulus-specific perceptual learning
Bibliographic record
Abstract
Practice in perceptual tasks over hundreds or thousands of trials often leads to long-lasting improvements in performance that generalize only partially to new stimuli. However, the time courses of the general and stimulus-specific aspects of learning are still debated. Some researchers argue that general aspects of the task are learned first in an initial rapid phase of learning and that stimulus-specificity emerges more slowly. In contrast, Hussain et al. (Front Psychol. 2012; 3:226) reported that 105 trials in a 10-AFC face identification task on Day 1 was sufficient to produce stimulus-specific learning in a test phase on Day 2, suggesting that stimulus-specific improvements can emerge rapidly. The current experiments extend these findings by examining 1) whether similar, rapid stimulus-specific learning occurs in a 10-AFC texture identification task; 2) if this rapid stimulus-specificity is long-lasting by increasing the interval between Days 1 and 2 from 24 hours to 1 week; and 3) the effects of reducing practice on Day 1 from 840 to just 21 trials. On Day 1, subjects performed a 10-AFC identification task with band-pass random textures embedded in three levels of external noise. The textures were presented at 7 contrasts that spanned the threshold range; hence the signal-to-noise ratio varied significantly across trials. On Day 2, subjects performed the task with the same or a novel set of textures. The dependent variable was response accuracy, and stimulus-specificity was measured by comparing performance with the same and novel textures on Day 2. We found stimulus-specific learning in subjects who received 840, 105, and 63 trials of practice, but not in subjects who received 21 trials of practice. Our results are consistent with the idea that stimulus-specific learning can emerge rapidly during practice and that this rapid learning is long lasting. Meeting abstract presented at VSS 2013
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".