Top-Down Biases for Lexicality and Frequency in Both Monosyllabic and Disyllabic Stimuli: Evidence from Cantonese
Bibliographic record
Abstract
Also known as the Ganong effect, a lexicality bias effect- i.e., the bias to interpret an ambiguous sound as the phoneme that yields a real word in its context - has been widely replicated. The search for a similar frequency bias effect, on the other hand, has yielded mixed results: In English, a bias has been observed such that listeners tend to interpret an ambiguous sound as the phoneme that yields a higher-frequency word, but this has failed to be replicated in Mandarin. One difference between these studies is the use of monosyllabic vs. disyllabic stimuli. To determine the factors that influence the presence of a bias effect, the present study tested for both frequency and lexicality bias effects using monosyllabic and disyllabic stimuli in Cantonese. Results show that the lexicality and frequency bias effects can be elicited in both monosyllabic and disyllabic stimuli, but the frequency effect is weaker.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".