Modulation of Auditory Responses to Speech vs. Nonspeech Stimuli during Speech Movement Planning
Bibliographic record
Abstract
Previously, we showed that the N100 amplitude in long latency auditory evoked potentials (LLAEPs) elicited by pure tone probe stimuli is modulated when the stimuli are delivered during speech movement planning as compared with no-speaking control conditions. Given that we probed the auditory system only with pure tones, it remained unknown whether the nature and magnitude of this pre-speech auditory modulation depends on the type of auditory stimulus. Thus, here, we asked whether the effect of speech movement planning on auditory processing varies depending on the type of auditory stimulus. In an experiment with nine adult subjects, we recorded LLAEPs that were elicited by either pure tones or speech syllables when these stimuli were presented prior to speech onset in a delayed-response speaking condition vs. a silent reading control condition. Results showed no statistically significant difference in pre-speech modulation of the N100 amplitude (early stages of auditory processing) for the speech stimuli as compared with the nonspeech stimuli. However, the amplitude of the P200 component (later stages of auditory processing) showed a statistically significant pre-speech modulation that was specific to the speech stimuli only. Hence, the overall results from this study indicate that, immediately prior to speech onset, modulation of the auditory system has a general effect on early processing stages but a speech-specific effect on later processing stages. This finding is consistent with the hypothesis that pre-speech auditory modulation may play a role in priming the auditory system for its role in monitoring auditory feedback during speech production.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".