Effects of Meditation on Temporal Processing and Speech Perceptual Skills in Younger and Older Adults
Bibliographic record
Abstract
The purpose of this study was to assess the temporal processing and speech perception abilities in older adults who were practicing meditation for more than five years. Participants were comprised of three groups, 30 young adults (“YA”) in the age range of 20–30 years, 30 older adults in the age range of 50–65 years who practiced meditation for a period of five years or more (effective meditators “EM”), and 51 age matched older adults who did not have any experience of meditation (non-meditators “NM”). Temporal processing was evaluated using gap detection in noise, duration discrimination, modulation detection, and backward masking and duration pattern tests. Speech perception was measured in presence of a four-talker babble at −5 dB signal to noise ratio and with the vocoded stimuli. Results revealed that EM group performed significantly better than NM group in all psychophysical and speech perception tasks except in gap detection task. In the gap detection task, two groups did not differ significantly. Furthermore, EM group showed significantly better modulation detection thresholds compared to YA. Results of the study demonstrate that the practice of meditation not only offsets the decline in temporal and speech processing abilities due to aging process but also improves the ability to perceive the modulations compared to young 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".