Examining interactivity in speech perception : compensation for lexically-induced coarticulation
Bibliographic record
Abstract
Although the psycholinguistic literature is rife with examples of lexical influences on phoneme identification, a debate still rages among theorists over whether these influences reflect a top-down, interactive speech perception system, or are merely evidence of post-perceptual decision biases. The failure to resolve this debate may be attributed to the shortcomings of many of the previously used behavioral methodologies, which have been unable to ensure that subject responses in phoneme decision tasks have their locus in pre-lexical phonetic processes, rather than post-lexical phonological codes. However, one promising methodology that has been applied only sparsely to the interactivity debate is the low-level compensation-for-coarticulation effect (Mann, 1980; Mann & Repp, 1981). The power of this effect is that it is believed to demonstrate a process that occurs at a pre-lexical, phonetic-perceptual level; in fact, researchers from both sides of the debate have relied on this assumption to both support (Elman & McClelland, 1988) and refute (Pitt & McQueen, 1998) the interactivity hypothesis. The current study built upon this previous work by employing stimuli that provide a richer semantic context, yet also serve to provide a more stringent test of feedback from lexical to pre-lexical processes than has heretofore been used. Experiment 1 replicated the low-level compensation-for-coarticulation effect with fricative and liquid context phonemes that precede /k/-/t/ and /g/-/d/ continuum stimuli. Experiment 2 directly tested the interactivity hypothesis using illusory, lexically-implied fricative and liquid phonemes to evoke the compensation effect, and found evidence for lexical feedback only with the liquid context phonemes. Experiment 3 retested the fricative context stimuli from the preceding experiment with a new set of phonetic identification continua that possessed a greater degree of ambiguity, and found evidence of lexical feedbac
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".