Motor Intention Determines Sensory Attenuation of Brain Responses to Self-initiated Sounds
Bibliographic record
Abstract
One of the functions of the brain is to predict sensory consequences of our own actions. In auditory processing, self-initiated sounds evoke a smaller brain response than passive sound exposure of the same sound sequence. Previous work suggests that this response attenuation reflects a predictive mechanism to differentiate the sensory consequences of one's own actions from other sensory input, which seems to form the basis for the sense of agency (recognizing oneself as the agent of the movement). This study addresses the question whether attenuation of brain responses to self-initiated sounds can be explained by brain activity involved in movement planning rather than movement execution. We recorded ERPs in response to sounds initiated by button presses. In one condition, participants moved a finger to press the button voluntarily, whereas in another condition, we initiated a similar, but involuntary, finger movement by stimulating the corresponding region of the primary motor cortex with TMS. For involuntary movements, no movement intention (and no feeling of agency) could be formed; thus, no motor plans were available to the forward model. A portion of the brain response evoked by the sounds, the N1-P2 complex, was reduced in amplitude following voluntary, self-initiated movements, but not following movements initiated by motor cortex stimulation. Our findings demonstrate that movement intention and the corresponding feeling of agency determine sensory attenuation of brain responses to self-initiated sounds. The present results support the assumptions of a predictive internal forward model account operating before primary motor cortex activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".