A cross-linguistic study of informational masking: English versus Chinese
Bibliographic record
Abstract
The amount of release from informational masking in monolingual English (Toronto, Canada), and Chinese (Beijing, China) listeners was measured using the paradigm developed by Freyman et al. [J. Acoust. Soc. Am. 106, 3578–3588]. Specifically, psychometric functions relating percent-correct word recognition to signal-to-noise ratio were determined under two conditions: (1) masker and target perceived as originating from the same position in space; (2) masker and target perceived as originating from different locations. The amount of release from masking due to spatial separation was the same for English and Chinese listeners when the masker was speech-spectrum noise or cross linguistic (Chinese speech masking English target sentences for English listeners or English speech masking Chinese target sentences for Chinese listeners). However, there was a greater release from masking for same-language masking of English (English speech masking English target sentences) than for same-language masking of Chinese (Chinese speech masking Chinese target sentences). It will be argued that the differences in same-language masking between English and Chinese listeners reflect structural differences between English and Mandarin Chinese. [Work supported by China NSF and CIHR.]
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".