Attentional influences on cue weighting in vowel perception: Examining prosodic prominence and informational masking
Bibliographic record
Abstract
Beyond sources of listener-external variability such as variation in talker and acoustic context, listener-internal variation also plays a role in speech perception and cue weighting. The present study examines the effects of prosodic prominence, signaled by F0, and multi-talker babble noise as methods of boosting and decrementing listeners' attention, respectively. Listeners categorized four English vowel contrasts, including two high vowel contrasts and two non-high vowel contrasts, with both formant cues and vowel duration varying along a continuum. In Experiment 1, results showed that prominence boosted formant cue usage, whereas babble noise was detrimental to formant cue usage, aligning with predicted roles in modulating listener attention. Listeners' use of vowel duration, a secondary cue to the contrasts, was also impacted by prominence or babble noise. In Experiment 2, two methods of eliciting F0-based prominence, off-target (contextual) and on-target (target-internal), were investigated. Results showed that off-target prominence showed a very limited effect in boosting formant cue usage. Results are discussed in terms of the role of prosodic prominence in speech perception, and the role of attention in perceptual processing. The data and code for the experiments is available on the OSF at: https://osf.io/52khc/ .
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".