Age-related increased frontal activation in sentence comprehension reflects inefficiency, not compensation
Bibliographic record
Abstract
Cognitive aging is associated with increased prefrontal cortex (PFC) activity, often interpreted as either a compensatory mechanism or a sign of neural inefficiency. In the context of speech-in-noise perception, it remains unclear whether this increase supports or impairs performance, as findings across studies are mixed. This study investigated age-related differences in PFC activity during sentence comprehension in noise using functional near-infrared spectroscopy. Fifty-seven participants (22 younger adults, 35 older adults) listened to sentences ending in either a high- or low-predictability word under two signal-to-noise ratio (SNR) conditions. Older adults showed increased PFC activity as SNR decreased, whereas younger adults showed no significant modulation. Among older adults, lower performers exhibited the greatest right-lateralized PFC activity, suggesting the recruitment of suboptimal neural resources. At the trial level, incorrect responses were associated with greater bilateral PFC activity in both age groups. Mediation analyses revealed that the negative effect of age on performance was partially explained by increased bilateral PFC activity, indicating that overactivation contributes to age-related speech-in-noise difficulties. Hearing loss and cognitive ability did not predict overall PFC activity but moderated the effect of SNR on PFC activity. Specifically, older adults with better hearing or higher cognitive scores showed increased PFC activity in the difficult SNR condition compared to the easier one, whereas those with more hearing loss or lower cognition showed similar activity across conditions. No effects of sentence predictability were observed. These findings support a neural inefficiency framework and highlight the importance of addressing PFC overactivation to improve speech-in-noise communication in older adults.
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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 | 0.000 | 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".