Feature-focusing constraints on implicit learning of function word and meaning associations
Bibliographic record
Abstract
Abstract Recent research has begun to investigate implicit learning at the level of meaning. The general consensus is that implicitly linking a word with a meaning is constrained by existing linguistic knowledge. However, another factor to consider is the extent to which attention is drawn to the relevant meanings in implicit learning paradigms. We manipulated the presence of cue saliency during implicit rule learning for a grammatical form (i.e., articles) linked to meaning (i.e., animacy vs. varying notions of size). In a series of experiments, participants learned four novel words but did not know that article usage also depended on a hidden rule, creating an opportunity for implicit rule learning. We found implicit learning through the use of a highly salient meaning (Experiment 1) or if image size was made salient by being explicitly cued (Experiment 3), but not in a low salient paradigm for intrinsic object size (Experiment 2). The findings suggest that implicit learning of semantic information might not be as constrained as previously argued. Instead, implicit learning might be additionally influenced by feature-focusing cues that make the meaning contrasts more salient and thereby more readily available to learning.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".