How Age, Linguistic Status, and the Nature of the Auditory Scene Alter the Manner in which Listening Comprehension is Achieved in Multi-talker Listening Situations
Bibliographic record
Abstract
Conversations in noise challenge the perceptual and cognitive capabilities of older adults and those listening in their second language (L2), and might force them to alter the balance between the contributions of bottom-up versus top-down processes involved in spoken language comprehension. We investigated the extent to which individual differences in vocabulary and reading comprehension skills are related to individual differences in spoken language comprehension. In Experiments 1 and 2 younger and older L1s as well as young L2s listened to conversations in English played against a babble background and answered questions regarding their content. Individual hearing differences were compensated for by creating the same degree of difficulty in recognizing spoken words in babble. In Experiment, 1 two-talker conversations were played with or without either real or virtual spatial separation between the talkers and masker. The results showed that all listeners performed better when there appeared to be spatial separation. The contribution of vocabulary to dialogue comprehension was larger when spatial location was virtual rather than real, whereas the contribution of reading comprehension differed as a function of age and language proficiency. In Experiment 2, three-talker conversations, with or without spatial separation, were played in either quiet or against moderate or high babble. The contribution of individual differences in vocabulary and reading comprehension skills differed among the groups, and the babble level. In both experiments compensating for differences in spoken word recognition, minimized the differences in conversation comprehension among the groups. In addition, the manner in which spoken language comprehension is achieved is modulated by the auditory scene, and the listeners' age and linguistic status.Experiment 3 investigated how the perceived compactness of sound sources affects spoken language recognition. Younger L1s, older L1s and young L2s were asked to repeat meaningless sentences played in noise, babble or speech, either with or without contrast of diffuseness between target and masker. Results showed a release of masking due to contrast, which was greater when the masker was speech compared with noise. Individual differences in speech recognition were related to individual differences in vocabulary and reading comprehension in young L2s and older L1s only.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".