Factors that increase processing demands when listening to speech
Bibliographic record
Abstract
Listening effort is an increasingly important concept for applied hearing researchers, but it has not been well elaborated in the cognitive literature. We propose that listening effort is the product of two factors: the processing demands imposed by the listening situation, and the cognitive resources that an individual brings to bear, to compensate for demands. Whereas cognitive resources differ markedly among individuals, processing demands are generally constant in a given listening situation, at least for normal-hearing individuals, and fall into at least three different categories: (i) perceptual demands, (ii) linguistic demands, and (iii) concurrent task demands. Perceptual demands are increased when the speech is degraded, when concurrent interfering sounds are present, or when the listener is hearing impaired. Linguistic demands are increased, for example, when speech is semantically or syntactically complex, or when meaning is ambiguous. Finally, when a listener is performing another task (such as driving) while hearing speech, additional demands are present. A series of behavioral and neuroimaging experiments reveals the nature of these different demands, which appear to be largely independent but to interact in left inferior frontal cortex; fMRI activity in this area may serve as an objective, quantifiable, measure of listening effort. Interactions among different types of demand imply that they cannot be fully understood by studying them in isolation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".