No morphological connections between L2 past-tense and present-tense verbs for low-proficient bilinguals: Evidence from masked priming
Bibliographic record
Abstract
Aims and objectives: Masked priming lexical decision research involving relatively high-proficient Japanese–English bilinguals suggests that past-tense and present-tense morphological connections (e.g., fell-FALL and looked-LOOK) are represented in their L2 (English) lexicons in a way that is similar to how they are represented in L1 (English) lexicons. The goal of the present research was to determine whether the same is true for low-proficient Japanese–English bilinguals. Methodology: Seventy-seven low-proficient Japanese–English bilinguals were tested in the masked priming lexical decision task. We manipulated the morphological or orthographic similarity between L2 English prime-target pairs. Data and analysis: We analyzed response latencies and error rates using (generalized) linear mixed-effects models. Findings: Although participants responded significantly faster to targets preceded by past-tense primes (e.g., fell-FALL and looked-LOOK) when compared to unrelated primes (e.g., slow-FALL and danger-LOOK), those priming effects were the same size as priming effects produced by orthographically similar primes (e.g., fill-FALL and lonely-LOOK), suggesting that the facilitation from past-tense primes is likely orthographic in nature. Nevertheless, the low-proficient bilinguals showed significant L2-L2 repetition priming (e.g., fall-FALL and look-LOOK), suggesting that, for those individuals, L2 (English) words are at least represented at the lexical level. Originality: The present study empirically confirmed a prediction, derived from a post hoc exploratory analysis in our previous research, that masked morphological priming effects are no larger than orthographic priming effects in low-proficient bilinguals. This indicates that a certain level of functional proficiency is required to observe morphological priming effects for Japanese–English bilinguals. Implications: Our results suggest that morphological connections in L2 are not yet established for low-proficient bilinguals, even when L2 words are lexically represented in their mental lexicon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".