Learning to generalize stimulus-specific learning across contexts
Bibliographic record
Abstract
Perceptual learning (PL) in a texture identification task occurs because observers become more sensitive to diagnostic stimulus components, but the particular components that are learned vary across observers. Here, we encouraged observers to adopt a specific processing strategy by manipulating the diagnostic orientation structure of textures. Six targets were generated by applying a 0±30 deg orientation-filter to band-pass filtered white noise. Targets were embedded in non-diagnostic contexts created by filtering novel textures with a 90±30 deg orientation band. The remaining empty orientations served as potential cues for distinguishing the target and context. In a 1-of-6 identification task, observers were trained with the horizontal Target alone (hT-only) or in a vertical Context (hT+vC). Before and after training, observers were tested with hT-only and hT+vC stimuli and controls (vT-only and vT+hC). Training with hT-only produced strong stimulus- and context-specific learning; hT+vC training led to weak but stimulus-specific and context-generalizable learning. Next, we examined if context-generalization reflected a broader, stimulus-transferable learning by testing hT+vC trained observers with novel hT textures. Again, learning was minimal: almost half of the observers did not exhibit learning. For those who learned, we found no evidence of context-generalization or stimulus-transfer. Finally, to make it easier to distinguish targets and context during training, target contrast was held constant while context contrast was varied with a 1up/1down staircase. Significant learning occurred: across training blocks, observers tolerated increasingly higher levels of context contrast. Unlike previous experiments, training improved performance for both the same hT+vC and hT-only stimuli; however, training did not affect performance with novel hT or vT stimuli. Thus, learning was context-generalizable but not stimulus-transferable. In conclusion, learning to identify structure in a specific orientation is difficult when that information is embedded in non-informative orientation information, but this difficulty can be overcome by providing cues to distinguish components. Meeting abstract presented at VSS 2016
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".