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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".