Validation of scrambling methods for vocal affect bursts
Bibliographic record
Abstract
Studies on perception and cognition require sound methods allowing us to disentangle the basic sensory processing of physical stimulus properties from the cognitive processing of stimulus meaning. Similar to the scrambling of images, the scrambling of auditory signals is aimed at creating stimulus instances that are unrecognizable but have comparable low-level features. In the present study, we generated scrambled stimuli of short vocalizations taken from the Montreal Affective Voices database (Belin et al., Behav Res Methods, 40(2):531-539, 2008) by applying four different scrambling methods (frequency-, phase-, and two time-scrambling transformations). The original stimuli and their scrambled versions were judged by 60 participants for the apparency of a human voice, gender, and valence of the expressions, or, if no human voice was detected, for the valence of the subjective response to the stimulus. The human-likeness ratings were reduced for all scrambled versions relative to the original stimuli, albeit to a lesser extent for phase-scrambled versions of neutral bursts. For phase-scrambled neutral bursts, valence ratings were equivalent to those of the original neutral burst. All other scrambled versions were rated as slightly unpleasant, indicating that they should be used with caution due to their potential aversiveness.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".