The Units of Gating and Access to Lexical Representations During Spoken Word Recognition
Bibliographic record
Abstract
Word recognition models such as Cohort have long relied on the gating paradigm to investigate how acoustic-phonetic information maps onto lexical representations. We report on a methodological study investigating (a) whether the recognition point of a spoken word is affected by the speech variables employed in the gating paradigm, and (b) which distributional properties of a words’ linguistic and social usage pattern affect its recognition point. We addressed the first question by contrasting the traditional “brute-force” gating paradigm (i.e., employing incremental segments of 50 ms) to “phonetically-driven” gating paradigms. Three methodologies were employed for determining phonemic segments: (1) articulatory measures, relying on the peak velocity of articulatory gestures, (2) acoustic measures, relying on the acoustic energy of consonants and vowels, and (3) brute-force measures, relying on 50 ms increments. We addressed the second question by relying on four social measures of lexical strength, which were attained from a corpus of 57 billion words from Reddit: word frequency (WF), contextual diversity (CD), discourse contextual diversity (DCD), and user contextual diversity (UCD). Results showed that the traditional brute-force gating method yielded significantly faster word recognition times, in comparison to articulatory and acoustically driven gating methods. Our results also showed that CD is a superior measure of lexical strength than WF, UCD, and DCD. Overall, our results suggest that the traditional gating paradigm is a reliable method for investigating spoken word recognition, given that spoken word recognition may rely on the gradual accumulation of phonetic information over time, rather than relying solely on the recovery of categorical phonetic features that are distributed non-linearly in time. We also suggest that the lexical system may be organized as a function of usage-based contextual measures of lexical items.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| 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".