Absorption of reliable spectral characteristics in auditory perception
Bibliographic record
Abstract
Several experiments are described in which synthetic monophthongs from series varying between /i/ and /u/ are presented following filtered precursors. In addition to F(2), target stimuli vary in spectral tilt by applying a filter that either raises or lowers the amplitudes of higher formants. Previous studies have shown that both of these spectral properties contribute to identification of these stimuli in isolation. However, in the present experiments we show that when a precursor sentence is processed by the same filter used to adjust spectral tilt in the target stimulus, listeners identify synthetic vowels on the basis of F(2) alone. Conversely, when the precursor sentence is processed by a single-pole filter with center frequency and bandwidth identical to that of the F(2) peak of the following vowel, listeners identify synthetic vowels on the basis of spectral tilt alone. These results show that listeners ignore spectral details that are unchanged in the acoustic context. Instead of identifying vowels on the basis of incorrect acoustic information, however (e.g., all vowels are heard as /i/ when second formant is perceptually ignored), listeners discriminate the vowel stimuli on the basis of the more informative spectral property.
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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 | 0.017 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".