Perceived tonal continuity through two noise bursts separated by silence
Bibliographic record
Abstract
Three experiments measured the perceived continuity of two pure tones "flankers" through a masker containing a silence. Experiment 1 used a 2I-2AFC procedure; one interval contained two noise bursts separated by a silent gap, and the other contained two noise bursts separated by a tone of the same duration as the silence. Discrimination between masker conditions was very accurate when the flankers were absent but was impaired substantially when the flankers were present. This was taken as evidence that illusory flanker continuity during the silent gap was heard as similar to the physical presence of a tone in the gap. In experiment 2, performance remained poor when the flankers were frequency glides aligned along a common trajectory. Performance improved significantly when the flankers were misaligned in trajectory. In experiment 3, listeners rated directly perceived flanker continuity. Strong continuity was reported in the silent gap conditions for which poor performance had been observed in experiments 1 and 2. These findings show that continuity may be heard through a masker that cannot mask a physically continuous tone but can mask the flankers' offset and onset. The results are explained in terms of the perceptual grouping of onsets and offsets of the flankers.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".