Rhythmic masking release: Contribution of cues for perceptual organization to the cross-spectral fusion of concurrent narrow-band noises
Bibliographic record
Abstract
The contribution of temporal asynchrony, spatial separation, and frequency separation to the cross-spectral fusion of temporally contiguous brief narrow-band noise bursts was studied using the Rhythmic Masking Release paradigm (RMR). RMR involves the discrimination of one of two possible rhythms, despite perceptual masking of the rhythm by an irregular sequence of sounds identical to the rhythmic bursts, interleaved among them. The release of the rhythm from masking can be induced by causing the fusion of the irregular interfering sounds with concurrent "flanking" sounds situated in different frequency regions. The accuracy and the rated clarity of the identified rhythm in a 2-AFC procedure were employed to estimate the degree of fusion of the interferring sounds with flanking sounds. The results suggest that while synchrony fully fuses short-duration noise bursts across frequency and across space (i.e., across ears and loudspeakers), an asynchrony of 20-40 ms produces no fusion. Intermediate asynchronies of 10-20 ms produce partial fusion, where the presence of other cues is critical for unambiguous grouping. Though frequency and spatial separation reduced fusion, neither of these manipulations was sufficient to abolish it. For the parameters varied in this study, stimulus onset asynchrony was the dominant cue determining fusion, but there were additive effects of the other cues. Temporal synchrony appears to be critical in determining whether brief sounds with abrupt onsets and offsets are heard as one event or more than one.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".