Exploring age differences in absorption and enjoyment during story listening
Bibliographic record
Abstract
Using naturalistic spoken narratives to investigate speech processes and comprehension is becoming increasingly popular in experimental hearing research. Yet, little is known about how individuals engage with spoken story materials, and how listening experiences change with age. We investigated absorption in the context of listening to spoken stories, explored predictive factors for engagement, and examined the utility of a scale developed for written narratives to assess absorption for auditory materials. Adults aged 20-78 years (N = 216) participated in an online experimental study. Participants listened to one of ten stories intended to be engaging to different degrees and rated the story in terms of absorption and enjoyment. Participants of different ages rated the stories similarly absorbing and enjoyable. Further, higher mood scores predicted higher absorption and enjoyment ratings. Factor analysis showed scale items approximately grouping according to the original scale dimensions, suggesting that absorption and enjoyment experiences may be similar for written and spoken stories, although certain items discriminated less effectively between stories intended to be more or less engaging. The present study provides novel insights into how adults of different ages engage in listening and supports the use of naturalistic speech stimuli in the context of hearing research.
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 | 0.005 |
| 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 | 0.000 | 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".