Absorption and enjoyment during listening to acoustically masked stories
Bibliographic record
Abstract
Comprehension of speech masked by background sound requires increased cognitive processing, which makes listening effortful. Research in hearing has focused on such challenging listening experiences, in part because they are thought to contribute to social withdrawal in people with hearing impairment. Research has focused less on positive listening experiences, such as enjoyment and absorption/engagement, despite their potential importance in motivating effortful listening. Moreover, the artificial speech materials – such as disconnected, brief sentences – commonly utilized to investigate speech intelligibility and listening effort may be ill-suited to capture positive experiences when listening is challenging. Here, we investigate how listening to naturalistic spoken stories under acoustic challenges influences the quality of listening experiences. We show (1) that story absorption and enjoyment are only minimally affected by moderate speech masking although listening effort increases; (2) that familiarity with the Harry Potter books and/or movies, providing thematic knowledge, increases absorption and enjoyment and reduces listening effort when listening to the audio of a Harry-Potter summary presented in multi-talker babble; and (3) that absorption and enjoyment increase and effort decreases over time as individuals listen to several stories successively in multi-talker babble. Our research indicates that naturalistic, spoken stories can reveal several concurrent listening experiences and that familiarity with a narrative can increase engagement and reduce effort. Our work also demonstrates that, although listening effort may increase with speech degradation, listeners may still find the experience both absorbing and enjoyable.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".