Surprisal in reading: language models predict the N400 for L2 readers
Bibliographic record
Abstract
Research on L1 reading has demonstrated context-dependent predictive/integrative processes during reading a word in context. This study examined these processes in proficient L2 speakers by measuring surprisal effects during reading English texts and compared the findings with those of native English speakers. We used ERPs to examine whether surprisal derived from different probabilistic language models affects the N400 response in L2 readers. Overall, the results showed that surprisal is a strong predictor of the N400 in L2 reading, suggesting that predictive/integrative processes play an important role during L2 reading. Further analyses suggested that both syntactic and lexical predictive/integrative processes are functional in L2 reading. Crucially, L2 predictive/integrative processes incorporate hierarchical syntactic information. Additionally, the language models that best predicted N400 differed between L2 and L1 readers, potentially reflecting differences in cognitive processing between native and non-native speakers of English.
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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 | 0.001 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".