A long-overdue review of empirical uncertainties in the “fatigue” found through simultaneous dichotic loudness balance
Bibliographic record
Abstract
SDLB uses equalization of the loudnesses of stimuli at the two ears (one stimulus intermittent, one constant) to measure the alleged “fatigue” over time of the loudness of the constant stimulus (Hood, 1950). Hood found 50 dB of “fatigue”, and SDLB remained influential for over a quarter-century. However, the interpretation of SDLB was long questioned; recently, a novel model uniting the physiology and the behavior emerged (Nizami 2012, Int. Soc. Psychophys., Ottawa, Canada), and others independently re-measured “fatigue”. Classically, the stimulus waveforms at the two ears were similar, sometimes identical, permitting two equalization techniques when the stimuli coincided in time: centering of the sound between the ears (lateralization), or matching the loudness contributions from each ear (loudness-matching). Further, “fatigue” was habitually expressed as across-listeners averages. But careful scrutiny reveals that (1) over the “fatiguing” duration, lateralization may give way to loudness-matching, (2) dedicated loudness-matching may nonetheless yield only half as much “fatigue” as lateralization, and (3) the standard deviation of “fatigue” can be half its mean value, such that some listeners would not have “fatigued.” In sum, the magnitude of “fatigue” is remarkably uncertain, and is likely to remain so until auditory physiology is compellingly integrated into explanations of SDLB.
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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 | 0.020 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".