Sensory attenuation of self-produced feedback and the Lombard Effect
Bibliographic record
Abstract
The Lombard effect describes the automatic and involuntary increase in vocal intensity that speakers exhibit in noisy environments. Literature studying the Lombard effect has typically focused on the role of audition. Interestingly, previous studies of discrete, repetitive finger forces have noted similar automatic and involuntary increases in output, when visual feedback of force level is removed, which have been attributed to mechanisms of sensory attenuation affecting perceptions of self-generated somatosensory feedback. In two experiments we tested the hypothesis that sensory attenuation mechanisms also underlie the Lombard effect. First, we sought to replicate the Lombard effect using a repetitive vocalization task developed as a vocal analog to previous studies of repetitive force production (cf. Therrien, Richardson & Balasubramaniam, 2011). Second, we studied the role of somatosensory feedback in vocal intensity control by having participants perform the same repetitive vocalization task while auditory and visual feedback stimuli were manipulated. We hypothesized that providing a visual reference of participants' voice level would serve to calibrate somatosensory-based judgments of vocal intensity and result in reduced expression of the Lombard effect when auditory feedback was masked. Results confirmed our hypothesis, suggesting a prospective role for sensory attenuation mechanisms in situations where force production extends beyond discrete motor output.
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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 | 0.010 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".