Do individual differences correlate across speech perception tasks?
Bibliographic record
Abstract
Speech perception requires listeners to take into account acoustic cues as well as lexical context and phonetic (coarticulatory) context. Individuals have been shown to vary in how they integrate these factors. To better understand the sources of these differences, we conducted three phoneme categorization tasks on speech continua with 82 native Canadian English speakers. Task 1 (lexical + coartic) embedded a /s-ʃ/ continuum in lexically biasing contexts (e.g., a(s)ume, a(ʃ)ure) followed by different coarticulatory contexts (rounded or unrounded vowels). Task 2 (lexical) had only lexical context cues for /ɛ/-/ɪ/ vowel continua (e.g., v(ɛ)st, k(ɪ)t). In task 3 (coartic), a /d/-/g/ stop continuum in nonsense syllables followed different coarticulatory contexts (/ar/ or /al/). We found those who used lexical context more used coarticulatory context less in task 1, consistent with prior research. However, this correlation disappears when examined across tasks 2 and 3. We also found no correlation between individual use of lexical and coarticulatory context across tasks, suggesting task dependency. Participants’ use of acoustic continua was positively correlated across tasks, indicating an individual trait for utilizing acoustic cues.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".