Auditory-orthographic integration at the onset of L2 speech acquisition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <pollo>-[pojo], where the auditory Spanish [j] and the first language English [l] that correspond to the shared digraph <ll> are integrated, and (b) fusion quantified in terms of [z] devoicing such as [z̥apito] for <zapito>-[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 <v>-[b], <d>-[δ], <z>-[s] and <ll>-[j]. Results were indicative of a McGurk-like effect only for the Spanish digraph <ll>. The highest rate of combination productions was attested in the orthography-training condition in the word-medial position.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.004 | 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 it