How Age, Linguistic Status, and the Nature of the Auditory Scene Alter the Manner in Which Listening Comprehension Is Achieved in Multitalker Conversations
Bibliographic record
Abstract
PURPOSE: We investigated how age and linguistic status affected listeners' ability to follow and comprehend 3-talker conversations, and the extent to which individual differences in language proficiency predict speech comprehension under difficult listening conditions. METHOD: Younger and older L1s as well as young L2s listened to 3-talker conversations, with or without spatial separation between talkers, in either quiet or against moderate or high 12-talker babble background, and were asked to answer questions regarding their contents. RESULTS: After compensating for individual differences in speech recognition, no significant differences in conversation comprehension were found among the groups. As expected, conversation comprehension decreased as babble level increased. Individual differences in reading comprehension skill contributed positively to performance in younger EL1s and in young EL2s to a lesser degree but not in older EL1s. Vocabulary knowledge was significantly and positively related to performance only at the intermediate babble level. CONCLUSION: The results indicate that the manner in which spoken language comprehension is achieved is modulated by the listeners' age and linguistic status.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".