Bibliographic record
Abstract
In Brief This article reviews the temporal aspects of human hearing as measured using the auditory evoked potentials. Interaural timing cues are essential to the detection and localization of sound sources. The temporal envelope of a sound—how it changes in amplitude over time—is crucially important for speech perception. Time is taken to integrate, identify, and dissolve auditory streams. These temporal aspects of human hearing can be examined using the auditory evoked potentials, which measure the millisecond-by-millisecond activity of populations of neurons as they form an auditory percept. Important measurements are the time taken to localize sounds on the basis of their interaural time differences as measured by the cortical N1 wave, the contribution of the vocal cord frequency and phonemic frequency to the perception of speech sounds as indicated by the envelope-following responses, the temporal integration of sound as assessed using the steady state responses, and the duration of auditory memory as shown in the refractory periods of the slow auditory evoked potentials. Disorders of temporal processing are a characteristic feature of auditory neuropathy, a significant component of the hearing problems that occur in the elderly, and a possible etiological factor in developmental dyslexia and central auditory processing disorders. Auditory evoked potentials may help in the diagnosis and monitoring of these disorders. The auditory evoked potentials can be used to evaluate the temporal processing of sounds in the human brain. The N1 wave of the transient response to changes in interaural timing can measure binaural interaction; envelope-following responses can evaluate the processing of speech; and the auditory steady state responses can track temporal integration in both brain stem and cortex. The evoked potentials can thus provide objective information that might help in the diagnosis and monitoring of disorders of temporal processing.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".