Development of binaural hearing in humans: Behavioral and physiological factors
Bibliographic record
Abstract
Auditory sound localization and separation of sources in noisy environments depend on the integrity of binaural processes. Throughout development, binaural hearing undergoes changes that can be captured with behavioral and physiological assays. Although it may be somewhat easier to capture binaural abilities, and their development, by employing measures of sensitivity, i.e., just noticeable difference (JND) thresholds, perception cannot be fully understood by relying on threshold estimates. Rather, binaural development can be more fully understood by capturing features of decision-making such as time to decision, certainty with which the listener arrives at a decision and task-related attentional resources that are harnessed depending on the salience of the auditory stimulus. We probe binaural perceptual phenomena through a combination of psychophysics, pupillometry, eye tracking, and electroencephalography (EEG), showing that beyond the JND, critical information about perception is captured through a multi-dimensional approach. We investigate binaural development in individuals with typical hearing and in cochlear-implant users, the latter being vulnerable to disruption of establishment of binaural circuits. Our studies test hypotheses regarding limitations during development placed by CI signal processors, which discard temporal fine structure (TFS) of sounds and encode acoustic information by the envelope modulations (ENV).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".