Sensory Attenuation of the Auditory P2 Differentiates Self- from Partner-Produced Sounds during Joint Action
Bibliographic record
Abstract
Successful human interaction relies on people's ability to differentiate between the sensory consequences of their own and others' actions. Research in solo action contexts has identified sensory attenuation, that is, the selective perceptual or neural dampening of the sensory consequences of self-produced actions, as a potential marker of the distinction between self- and externally produced sensory consequences. However, very little research has examined whether sensory attenuation distinguishes self- from partner-produced sensory consequences in joint action contexts. The current study examined whether sensory attenuation of the auditory N1 or P2 ERPs distinguishes self- from partner-produced tones when pairs of people coordinate their actions to produce tone sequences that match a metronome pace. We did not find evidence of auditory N1 attenuation for either self- or partner-produced tones. Instead, the auditory P2 was attenuated for self-produced tones compared to partner-produced tones within the joint action. These findings indicate that self-specific attenuation of the auditory P2 differentiates the sensory consequences of one's own from others' actions during joint action. These findings also corroborate recent evidence that N1 attenuation may be driven by general rather than action-specific processes and support a recently proposed functional dissociation between auditory N1 and P2 attenuation.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".