Morphological processing is gradient not discrete in L1 and L2 English masked priming
Bibliographic record
Abstract
Abstract In recent years, evidence has emerged that readers may have access to the meaning of complex words even in the early stages of processing, suggesting that phenomena previously attributed to morphological decomposition may actually emerge from an interplay between formal and semantic effects. The present study adds to this line of work by deploying a forward masked priming experiment with both L1 (Experiment 1) and L2 (Experiment 2) speakers of English. Following recent research trends, we view morphological processing as a gradient process emerging over time. In order to model this, we used a large within-item stimulus design combined with advanced statistical methods such as generalised mixed models (GAMM) and quantile regression (QGAM). L1 GAMM analyses only showed priming for true morpho-semantic relations (the identity ‘bull’, inflected ‘bulls’ and derived conditions ‘bullish’), with no priming observed in the case of other relations (the pseudo-complex ‘bully’ or the stem-embedded ‘bullet’ conditions). Furthermore, with respect to the time-course of effects, we found significant differences between conditions were present from very early on as revealed by the QGAM analyses. In contrast, L2 speakers showed significant facilitation across all five conditions compared to the baseline condition, including the stem-embedded condition, suggesting early L2 processing is only dependant on the form.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".