Learning to predict and predicting to learn: Before and beyond the syntactic bootstrapper
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Young children can exploit the syntactic context of a novel word to narrow down its probable meaning. This is syntactic bootstrapping. A learner that uses syntactic bootstrapping to foster lexical acquisition must first have identified the semantic information that a syntactic context provides. Based on the semantic seed hypothesis, children discover the semantic predictiveness of syntactic contexts by tracking the distribution of familiar words. We propose that these learning mechanisms relate to a larger cognitive model: the predictive processing framework. According to this model, we perceive and make sense of the world by constantly predicting what will happen next in a probabilistic fashion. We outline evidence that prediction operates within language acquisition and show how this framework helps us understand the way lexical knowledge refines syntactic predictions and how syntactic knowledge refines predictions about novel words’ meanings. The predictive processing framework entails that learners can adapt to recent information and update their linguistic model. Here we review some of the recent experimental work showing that the type of prediction preschool children make from a syntactic context can change when they are presented with contrary evidence from recent input. We end by discussing some challenges of applying the predictive processing framework to syntactic bootstrapping and propose new avenues to investigate in future work.
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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 | 0.000 | 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) | 0.001 | 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 it