Extensive training of orientation filtered textures increases generalization of learning
Bibliographic record
Abstract
Previously, we (Hashemi et al., VSS 2015 & VSS 2016) investigated perceptual learning in a texture identification task using textures that contain diagnostic information (Target) in one orientation band and non-diagnostic information (Context) in a perpendicular orientation band. We found that, compared to Target-alone (i.e., no Context) textures, training in a 1-of-6 identification task with Target+Context textures resulted in lower accuracy, less learning, but greater generalization of learning. Specifically, training with Target+Context patterns generalized to familiar Target-alone textures, but training with Target-alone stimuli did not generalize to familiar Target+Context textures. Nevertheless, even with Target+Context training, we found no evidence of generalization to novel targets, regardless of context. Here we investigated whether greater generalization of learning could be obtained by significantly increasing the amount of training with Target+Context stimuli from 960 to 4200 trials. Before and after training, we assessed identification accuracy on trained and novel Targets with and without Context. We also tested accuracy on textures where the Target and Context orientations were swapped, using both novel and trained Targets. Results varied across observers: During training, accuracy increased by at least 50% in half of the participants, but only by ~20% in the others. Our post-training assessment found 1) all participants improved on the trained Target+Context textures; and some participants generalized learning to 2) familiar and novel Target-alone textures; 3) novel Target+Context textures; and/or 4) orientation-swapped Target+Context textures. Finally, the different patterns of generalization were not related in any simple way to the change in accuracy that occurred during training. Our results indicate that perceptual learning of orientation filtered textures varies significantly across individuals, but that it can be generalized to novel and familiar targets in novel contexts. These findings may have implications for perceptual learning in applied settings in which generalization of learning is a critical component of training. 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".