Ruling out task difficulty in the context-generalization of texture perceptual learning
Bibliographic record
Abstract
Perceptual learning in a texture identification task reflects improved sensitivity to diagnostic features. We have studied perceptual learning using orientation-filtered textures that contain identity-specific information in a horizontal orientation band (i.e., Target) and non-diagnostic information (i.e., Context) in a vertical orientation band. Using Target-alone or Target+Context textures as training stimuli in a 1-of-6 texture identification task, Hashemi et al. (VSS 2015) demonstrated that learning was significantly more difficult in the Target+Context condition. Interestingly, the more difficult condition produced greater context generalization (Hashemi et al., VSS 2016): Target+Context trained observers generalized learning to the same Targets alone (i.e., presented without Context), but Target-alone trained observers did not transfer their overall strong learning to the same Targets with Context. These results may reflect a difference in perceptual strategies: Target-alone textures can be identified using any visible pixel, while Target+Context textures require observers to learn the selective extraction of target information in a specific orientation band and ignore the context. However, identifying Target+Context stimuli was significantly more difficult than Target-alone textures, so it is possible that the asymmetry in context generalization is a by-product of task difficulty and/or the magnitude of learning during training. Here, we tested that idea by adjusting stimulus contrast to make identification of Target-alone textures as difficult as Target+Context textures. We found that equating task difficulty did not eliminate the difference in context generalization: Target+Context training improved identification of both Target+Context and Target-alone textures, but Target-alone training improved identification only of Target-alone textures. We conclude that context-generalizable learning reflects a perceptual strategy learned when observers have to distinguish diagnostic from non-diagnostic information, and is not simply a by-product of task difficulty. Meeting abstract presented at VSS 2017
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".