Auditory-orthographic integration at the onset of L2 speech acquisition
Bibliographic record
Abstract
Recent studies have provided evidence for both a positive and a negative effect of orthography on second language speech learning. However, not much is known about whether orthography can trigger a McGurk-like effect (McGurk & MacDonald, 1976) in second language speech learning. This study examined whether exposure to auditory and orthographic input may lead to a McGurk-like effect in naïve English-speaking participants learning a second language with Spanish phonology and orthography. Specifically, it reports on (a) production of non-target-like combinations such as [lj] as in [poljo] for -[pojo], where the auditory Spanish [j] and the first language English [l] that correspond to the shared digraph are integrated, and (b) fusion quantified in terms of [z] devoicing such as [z̥apito] for -[zapito]. Moreover, the effects of (a) type of grapheme-to-sound correspondence, (b) position in the word, and (c) condition of training and testing were examined. Participants were assigned to four groups: (a) auditory only, (b) orthography at training and production, (c) orthography at training, and (d) orthography at production. The positions included word-initial and word-medial. The grapheme-to-sound correspondences consisted of -[b], -[δ], -[s] and -[j]. Results were indicative of a McGurk-like effect only for the Spanish digraph . The highest rate of combination productions was attested in the orthography-training condition in the word-medial position.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".