Explicit and Implicit Learning: Exploring Their Simultaneity and Immediate Effectiveness
Bibliographic record
Abstract
Do adults learn the same syntactic second language (L2) form explicitly and implicitly simultaneously during meaning-based exposure, and does the type of learning (explicit and/or implicit) affect subsequent performance. In this study, 81 anglophones completed comprehension tasks providing incidental exposure to a semi-artificial language (English lexis, German syntax). A surprise grammaticality-judgement test (GJT) measured performance with the novel syntax. Source attributions and verbal reports provided information on type of learning (implicit and/or explicit), which were analysed in an exploratory fashion to classify within-participant implicit and explicit learning. Sixty-three participants demonstrated both types of learning, which suggests that extant binary classifications of type of learning may be inadequate. Furthermore, there were no significant performance differences on the GJT based on type of learning despite poor performance overall. The interpretation of the findings considers within-participant explicit and implicit learning measures and research issues when comparing the effectiveness of initial explicit learning and implicit learning on immediate performance.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".