Age-Related Differences in Neuromagnetic Brain Activity Underlying Concurrent Sound Perception
Bibliographic record
Abstract
Deficits in parsing concurrent auditory events are believed to contribute to older adults' difficulties in understanding speech in adverse listening conditions (e.g., cocktail party). To explore the level at which aging impairs sound segregation, we measured auditory evoked fields (AEFs) using magnetoencephalography while young, middle-aged, and older adults were presented with complex sounds that either had all of their harmonics in tune or had the third harmonic mistuned by 4 or 16% of its original value. During the recording, participants were asked to ignore the stimuli and watch a muted subtitled movie of their choice. For each participant, the AEFs were modeled with a pair of dipoles in the superior temporal plane, and the effects of age and mistuning were examined on the amplitude and latency of the resulting source waveforms. Mistuned stimuli generated an early positivity (60-100 ms), an object-related negativity (ORN) (140-180 ms) that overlapped the N1 and P2 waves, and a positive displacement that peaked at approximately 230 ms (P230) after sound onset. The early mistuning-related enhancement was similar in all three age groups, whereas the subsequent modulations (ORN and P230) were reduced in older adults. These age differences in auditory cortical activity were associated with a reduced likelihood of hearing two sounds as a function of mistuning. The results reveal that inharmonicity is rapidly and automatically registered in all three age groups but that the perception of concurrent sounds declines with age.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".