Rapid category learning: Naturalized images to abstract categories
Bibliographic record
Abstract
Object categories are the perceptual glue that holds our visual world together. They allow us to recognize familiar instances and extend recognition to novel ones. Although object categorization has been studied using supervised learning techniques, less is known about how they are spontaneously acquired through unsupervised learning. In this study, we examined how temporal contiguity contributes to this spontaneous abstraction of object categories during passive viewing. We hypothesized that viewing exemplars of the same category closer in time would support better abstraction of the visual properties that distinguish one object category from another, and facilitate better category formation. Participants passively viewed a continuous sequence of 160 natural images of four warbler species (40 images per species). Images were presented serially for 500 ms per image with no visual masking. In a blocked condition, participants viewed images grouped by species (e.g., 40 images of Cape May warblers, followed by 40 images of Magnolia warbler, etc.). In a mixed condition, participants viewed images presented in random order. Participants then completed a "same/different" test using novel warbler images. A study image was presented for 500 ms, and then a test image was presented for 500 ms. Participants responded "same" if the images depicted warblers of the same species or "different" if they depicted different species. Participants in the blocked presentation condition performed reliably better on the same/different task (d' = 1.96) than participants in the mixed presentation condition (d' = 1.30, p < .05) and participants in a control condition who received no presentations prior to test (d' = 1.19, p < .01). Performance in the mixed presentation and control conditions did not reliably differ, p > .10. These results suggest that temporal contiguity may enhance the visual system's ability to rapidly extract statistical regularities involved in category learning. Meeting abstract presented at VSS 2016
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".