Implicit and Explicit Processes in Language Acquisition and Learning: A Systematic Review of Neuroimaging Studies
Bibliographic record
Abstract
The acquisition of English as a non-native language can occur in either second language (L2) or foreign language (FL) contexts, which differ significantly in the cognitive and neural processes involved. While L2 acquisition tends to rely on implicit and procedural mechanisms, FL learning is based on explicit and declarative processes. From a cognitive neuroscience perspective, several studies have explored whether these contextual differences lead to distinct patterns of brain activation specific to English learning. A systematic review was conducted of studies published between 2015 and 2024 in the Scopus, PubMed, and ScienceDirect databases. Empirical investigations employing neuroimaging techniques (DTI, EEG, ERP, fMRI, fNIRS) to analyze linguistic processing in English L2 or FL contexts were included. After applying inclusion and exclusion criteria, and refining the selection to focus exclusively on English, 18 primary studies were selected following the PRISMA 2020 guidelines. The reviewed studies indicate that English L2 acquisition in natural contexts predominantly activates multimodal networks associated with procedural memory, whereas English FL learning in formal educational settings involves greater activation of executive networks and declarative memory. Furthermore, differences in functional connectivity patterns were observed depending on the type of learning context. The evidence suggests that immersion contexts favor more automatic, holistic, and socially integrated linguistic processing, whereas classroom contexts promote more controlled and analytical processing. These findings underscore the need to adapt pedagogical strategies to the neurocognitive dynamics specific to each English learning context and highlight the importance of clearly distinguishing between L2 and FL in future research.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.003 | 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".