Contextual influences: Perception of sentences in noise is facilitated similarly in young and older listeners by meaningful semantic context; neural correlates explored via functional magnetic resonance imaging (fMRI)
Bibliographic record
Abstract
Meaningful semantic context has been demonstrated to improve comprehension of spoken sentences by young and old adults, especially in difficult listening conditions. Evidence for this benefit is based largely on data collected using SPIN sentences, highly structured sentences with a predictable or unpredictable final word. We asked young (14 participants, aged 18-25) and older adults (20 participants, aged 60-75) to report entire sentences which were less structured in nature and contained either a meaningful or anomalous global semantic context. Sentences were mixed with signal-correlated noise, at 9 signal-to-noise ratios (-6 to +2 dB), and also presented without noise. Comprehension by both groups benefited from meaningful context, without a clear overall difference in the amount of benefit obtained. We used fMRI to look at neural activity associated with deriving benefit from meaningful context. Whole-brain EPI data were acquired from young (16 participants, aged 19-26) adults using a sparse imaging design. Word-report data obtained in the scanner were used as intelligibility measures in data analysis. In addition to activity associated with meaningful context, we observe a large left inferior-frontal region in which BOLD signal correlated more strongly with the intelligibility of anomalous compared to coherent prose, presumably reflecting challenged semantic integration processes.
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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 | 0.002 |
| 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".