The emergence of Cognitive Hearing Science
Bibliographic record
Abstract
Cognitive Hearing Science or Auditory Cognitive Science is an emerging field of interdisciplinary research concerning the interactions between hearing and cognition. It follows a trend over the last half century for interdisciplinary fields to develop, beginning with Neuroscience, then Cognitive Science, then Cognitive Neuroscience, and then Cognitive Vision Science. A common theme is that an interdisciplinary approach is necessary to understand complex human behaviors, to develop technologies incorporating knowledge of these behaviors, and to find solutions for individuals with impairments that undermine typical behaviors. Accordingly, researchers in traditional academic disciplines, such as Psychology, Physiology, Linguistics, Philosophy, Anthropology, and Sociology benefit from collaborations with each other, and with researchers in Computer Science and Engineering working on the design of technologies, and with health professionals working with individuals who have impairments. The factors that triggered the emergence of Cognitive Hearing Science include the maturation of the component disciplines of Hearing Science and Cognitive Science, new opportunities to use complex digital signal-processing to design technologies suited to performance in challenging everyday environments, and increasing social imperatives to help people whose communication problems span hearing and cognition. Cognitive Hearing Science is illustrated in research on three general topics: (1) language processing in challenging listening conditions; (2) use of auditory communication technologies or the visual modality to boost performance; (3) changes in performance with development, aging, and rehabilitative training. Future directions for modeling and the translation of research into practice are suggested.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".