Stimulus-specificity of training with explicit or ambiguous diagnostic structure
Bibliographic record
Abstract
Perceptual learning (PL) is often due to observers becoming more sensitive to the diagnostic information in the specific stimuli used during training. Here, we asked how diagnostic information affects PL in a texture identification task. Specifically, we examined whether learning and stimulus generalization differed when diagnostic information was presented alone or embedded in an uninformative context. Texture stimuli were generated from low-frequency band-pass filtered white noise; to create stimuli with restricted diagnostic information, we applied a ±45 degree orientation filter centered on 0 (HORZ) or 90 (VERT) degrees. Explicit stimuli comprised HORZ and VERT orientation-filtered textures as well as an unfiltered FULL texture. Embedded stimuli comprised HORZ and VERT filtered textures summed with non-informative complementary orientation information from the average of all our texture stimuli. We measured accuracy in a 1-of-6 identification task: Observers were trained with either HORZ-explicit or HORZ-embedded stimuli, and tested with all stimulus types. When trained with HORZ-embedded stimuli, performance improvements could result from: i) increased reliance on the informative horizontal band, manifesting as improved accuracy for all conditions in which HORZ structure is informative, or ii) decreased reliance on the uninformative vertical band, manifesting as reduced accuracy for both VERT-embedded and VERT-explicit stimuli. When trained with HORZ-explicit stimuli, performance improvements can only result from increased sensitivity to horizontal structure. Our results reveal higher pre-training accuracy with explicit than embedded stimuli, demonstrating that uninformative structure impaired identification. Training with HORZ-embedded stimuli eliminated this effect, producing post-training accuracy similar to pre-training explicit stimuli. Further, accuracy for VERT stimuli was not decreased, suggesting that learning was not at a cost to the uninformative band. Finally, embedded and explicit learning were largely stimulus-specific, with minimal transfer to other conditions. Together, these results support an increased reliance on the trained orientation band, without suppression of non-informative orientations, but high context-specificity. Meeting abstract presented at VSS 2015
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".