How age and linguistic competence alter the interplay of perceptual and cognitive factors when listening to conversations in a noisy environment
Bibliographic record
Abstract
Multi-talker conversations challenge the perceptual and cognitive capabilities of older adults and those listening in their second language (L2). In older adults these difficulties could reflect declines in the auditory, cognitive, or linguistic processes supporting speech comprehension. The tendency of L2 listeners to invoke some of the semantic and syntactic processes from their first language (L1) may interfere with speech comprehension in L2. These challenges might also force them to reorganize the ways in which they perceive and process speech, thereby altering the balance between the contributions of bottom-up vs. top-down processes to speech comprehension. Younger and older L1s as well as young L2s listened to conversations played against a babble background, with or without spatial separation between the talkers and masker, when the spatial positions of the stimuli were specified either by loudspeaker placements (real location), or through use of the precedence effect (virtual location). After listening to a conversation, the participants were asked to answer questions regarding its content. Individual hearing differences were compensated for by creating the same degree of difficulty in identifying individual words in babble. Once compensation was applied, the number of questions correctly answered increased when a real or virtual spatial separation was introduced between babble and talkers. There was no evidence that performance differed between real and virtual locations. The contribution of vocabulary knowledge to dialog comprehension was found to be larger in the virtual conditions than in the real whereas the contribution of reading comprehension skill did not depend on the listening environment but rather differed as a function of age and language proficiency. The results indicate that the acoustic scene and the cognitive and linguistic competencies of listeners modulate how and when top-down resources are engaged in aid of speech comprehension.
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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".