Kick the Ball or Kicked the Ball? Perception of the Past Morpheme <i>–ed</i> by Second Language Learners
Bibliographic record
Abstract
Abstract: Explanations for the well-documented second language (L2) learning challenge of the English regular past include verb semantics (Bardovi-Harlig, 2000), phonetic properties (Goad, White, & Steele, 2003), and frequency factors (Collins, Trofimovich, White, Cardoso, & Horst, 2009). Difficulty perceiving past-tense morphology (i.e., hearing –ed in the input) has received less research attention. In this study, we explored the roles of three perceptual factors (phonological environment, speech rate, semantic clues) among 106 L2 learners and 81 English speakers of a similar age. Experiment 1 was a forced-choice auditory identification task contrasting perceptually ‘easy’ ([əd] + vowel) and ‘hard’ ([t] or [d] + consonant) regular past contexts at normal conversational speed. Experiment 2 contrasted easy and hard contexts at a slowed-down speech rate. Experiment 3 included time adverbials that matched or mismatched the tense marker (e.g., walked the dog now vs. walked the dog yesterday). The L2 learners behaved at just above chance at normal conversational speed in both contexts, and slowing speech down helped them in easy contexts only. The English speakers were more accurate in easy than in hard contexts regardless of speech rate. Both L2 learners and English speakers also relied on adverbials at the expense of the phonetic cue to past morphology (–ed). Implications of these findings for the roles of input and frequency in L2 learning, and for pronunciation teaching (i.e., setting reasonable learning goals) are discussed.
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 | 0.002 |
| 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) | 0.004 | 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".