Automatic categorical abstraction during visual statistical learning in children and adults
Bibliographic record
Abstract
One of the primary goals of the visual system is to make predictions about upcoming sensory events, which requires extracting and learning regularities from the environment. Research on visual statistical learning demonstrated that humans can not only learn these regularities from very brief exposure (Fisher & Aslin, 2001), but that this learning can occur at the categorical level; when images are always different, but regularities are present across categories (Brady & Oliva, 2008). In the present set of studies, we ask whether this category-level generalization of learning occurs even with exposure to repeated items. Additionally, we ask whether children learn categorical-level regularities like adults. Given evidence that children are more sensitive to features of individual items than adults (Sloutsky & Fisher, 2004) they may or may not show learning of regularities at the category level. We tested this question by performing several statistical learning experiments with adults (18-22 yo) and children (6-9 yo). Participants were exposed to a stream of animal images, which consisted of four sets of triplets, randomly distributed. Each triplet consisted of three animals appearing in the same order. Critically, the images were always novel exemplars from a given category. Observers were tested using a 2AFC Familiarity Judgment task between triplets of images, which either maintained or violated the temporal predictability from the exposure phase. We observed that adults and children showed familiarity to triplets in the exposed sequence, which suggests that they learned the regularities at the category level. To determine whether this categorical abstraction is automatic, we exposed children and adults to an image stream with regularities at the item level and tested with multiple novel exemplars. We found that both adults and children could still learn the statistical regularities at the category level even with single item exposure, suggesting that they can generalize item-level learning to categories. Meeting abstract presented at VSS 2018
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".