Detargeting the target in phoneme detection: aiming the task at phonological representations rather than backgrounds
Bibliographic record
Abstract
One challenge of learning a second or additional language (L2+) is learning to perceive and interpret its sounds. This includes acquiring the target language (TL) contrastive phonemic inventory, the sounds' systematic behavior in the TL phonology, and novel relationships between spelling and sound (GPCs; grapheme-phoneme correspondences). Many perception tasks require stipulation of written labels for target speech sounds (e.g., phoneme detection). Listening for this target is not necessarily, or even frequently, an equivalent cognitive task between participant groups. The incongruence of phonological and orthographic domains and their GPCs poses a methodological challenge for L2+ research. The author argues that phoneme detection tasks should avoid the phone of investigative interest ( x ) as the direct target of listener attention and redirect focus to an adjacent listening target ( y ). Ideally, this target should not trigger or otherwise be implicated in the phonological process or phonotactic constraint under investigation. The careful choice of listening target ( y ) with both a familiar sound and a congruent orthographic label for both (or all) language groups of the experiment yields an equivalent task and better indicates implicit knowledge of the phenomenon under study. This approach opens up potential choices of phonological objects of interest ( x ). The two phoneme detection experiments reported here employ this novel adjacent-congruent listening target approach, which the author calls the Persean approach. Experiment 1 establishes baseline performance in two assimilation types and replicates processing inhibition in first-language (L1) German speakers in response to violations of regressive nasal assimilation. It also uses [t] as the Persean listening target to test sensitivity to preceding violations of progressive dorsal fricative assimilation (DFA). Experiment 2 investigates sensitivity to violations of DFA in both L1 German speakers and L1 English L2+ German learners. Experiment 2 also uses the Persean method for the first phoneme detection investigation demonstrating sensitivity to violation of a prosodic/phonotactic constraint banning /h/ in syllable codas. The study demonstrates that phoneme detection with Persean listening targets is a viable instrument for investigating regressive and progressive assimilation, prosodic/phonotactic constraints, and prelexical perceptual repair strategies in different language background groups and proposes statistical best practices for future phoneme detection research.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".