Learning to predict and predicting to learn: Before and beyond the syntactic bootstrapper
Bibliographic record
Abstract
Young children can exploit the syntactic context of a novel word to narrow down itsprobable meaning. This is syntactic bootstrapping. A learner that uses syntacticbootstrapping to foster lexical acquisition must first have identified the semanticinformation that a syntactic context provides. Based on the semantic seed hypothesis,children discover the semantic predictiveness of syntactic contexts by tracking thedistribution of familiar words. We propose that these learning mechanisms relate to a largercognitive model: the predictive processing framework. According to this model, weperceive and make sense of the world by constantly predicting what will happen next in aprobabilistic fashion. We outline evidence that prediction operates within languageacquisition, and show how this framework helps us understand the way lexical knowledgerefines syntactic predictions and how syntactic knowledge refines predictions about novelwords’ meanings. The predictive processing framework entails that learners can adapt torecent information and update their linguistic model. Here we review some of the recentexperimental work showing that the type of prediction preschool children make from asyntactic context can change when they are presented with convincing contrary evidencefrom recent input. We end by discussing some challenges of applying the predictiveprocessing framework to syntactic bootstrapping and propose new avenues to investigatein future work.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".