Learning unfamiliar words and perceiving non-native vowels in a second language: Insights from eye tracking
Bibliographic record
Abstract
One of the challenges in second-language learning is learning unfamiliar word forms, especially when this involves novel phoneme contrasts. The present study examines how real-time processing of newly-learned words and phonemes in a second language is impacted by the structure of learning (discrimination training) and whether asking participants to complete the same task after a 16-21 h delay favours subsequent word recognition. Specifically, using a visual world eye tracking paradigm, we assessed how English listeners processed newly-learned words containing non-native French front-rounded [y] compared to native-sounding vowels, both immediately after training and the following day. Some learners were forced to discriminate between vowels that are perceptually similar for English listeners, [y]-[u], while others were not. We found significantly better word-level processing on a variety of indices after an overnight delay. We also found that training [y] words paired with [u] words (vs. [y]-Control pairs) led to a greater decrease in reaction times during the word recognition task over the two testing sessions. Discrimination training using perceptually similar sounds had facilitative effects on second language word learning with novel phonemic information, and real-time processing measures such as eyetracking provided valuable insights into how individuals learn words and phonemes in a second language.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".