The Surfeit of the Stimulus: Analytic Biases Filter Lexical Statistics in Turkish Laryngeal Alternations
Bibliographic record
Abstract
In an experimental task with novel words, we find that some lexical statistical regularities of Turkish phonotactics are productively extended in nonce words, while others are not. In particular, while laryngeal alternation rates in the lexicon can be predicted by the place of articulation of the stem-final stop, by word-length, and by the preceding vowel quality, this laryngeal alternation is only productively conditioned by place of articulation and word-length. Speakers' responses in a novel word task demonstrate that although they are attuned to the place of articulation and size effects, they ignore preceding vowels, even though the lexicon contains this information in abundance. We interpret this finding as evidence that speakers distinguish between phonologically motivated generalizations and accidental generalizations. We propose that universal grammar (UG), a set of analytic biases, acts as a filter on the generalizations that humans can make: UG contains information about possible and impossible interactions between phonological elements. Omnivorous statistical models that do not have information about possible interactions incorrectly reproduce accidental generalizations, thus failing to model speakers' behavior.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".