Explicit and Implicit Second Language Training Differentially Affect the Achievement of Native-like Brain Activation Patterns
Bibliographic record
Abstract
It is widely believed that adults cannot learn a foreign language in the same way that children learn a first language. However, recent evidence suggests that adult learners of a foreign language can come to rely on native-like language brain mechanisms. Here, we show that the type of language training crucially impacts this outcome. We used an artificial language paradigm to examine longitudinally whether explicit training (that approximates traditional grammar-focused classroom settings) and implicit training (that approximates immersion settings) differentially affect neural (electrophysiological) and behavioral (performance) measures of syntactic processing. Results showed that performance of explicitly and implicitly trained groups did not differ at either low or high proficiency. In contrast, electrophysiological (ERP) measures revealed striking differences between the groups' neural activity at both proficiency levels in response to syntactic violations. Implicit training yielded an N400 at low proficiency, whereas at high proficiency, it elicited a pattern typical of native speakers: an anterior negativity followed by a P600 accompanied by a late anterior negativity. Explicit training, by contrast, yielded no significant effects at low proficiency and only an anterior positivity followed by a P600 at high proficiency. Although the P600 is reminiscent of native-like processing, this response pattern as a whole is not. Thus, only implicit training led to an electrophysiological signature typical of native speakers. Overall, the results suggest that adult foreign language learners can come to rely on native-like language brain mechanisms, but that the conditions under which the language is learned may be crucial in attaining this goal.
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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 | 0.000 | 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 | 0.001 |
| 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".