Behavioral Assessment of Listening Effort Using a Dual-Task Paradigm
Bibliographic record
Abstract
Published investigations ( n = 29) in which a dual-task experimental paradigm was employed to measure listening effort during speech understanding in younger and older adults were reviewed. A summary of the main findings reported in the articles is provided with respect to the participants' age-group and hearing status. Effects of different signal characteristics, such as the test modality, on dual-task outcomes are evaluated, and associations with cognitive abilities and self-report measures of listening effort are described. Then, several procedural issues associated with the use of dual-task experiment paradigms are discussed. Finally, some issues that warrant future research are addressed. The review revealed large variability in the dual-task experimental paradigms that have been used to measure the listening effort expended during speech understanding. The differences in experimental procedures used across studies make it difficult to draw firm conclusions concerning the optimal choice of dual-task paradigm or the sensitivity of specific paradigms to different types of experimental manipulations. In general, the analysis confirmed that dual-task paradigms have been used successfully to measure differences in effort under different experimental conditions, in both younger and older adults. Several research questions that warrant further investigation in order to better understand and characterize the intricacies of dual-task paradigms were identified.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".