Seeing your own or someone else's hand moving in accordance with your action: The neural interaction of agency and hand identity
Bibliographic record
Abstract
Forward models can predict sensory consequences of self-action, which is reflected by less neural processing for actively than passively generated sensory inputs (BOLD suppression effect). However, it remains open whether forward models take the identity of a moving body part into account when predicting the sensory consequences of an action. In the current study, fMRI was used to investigate the neural correlates of active and passive hand movements during which participants saw either an on-line display of their own hand or someone else's hand moving in accordance with their movement. Participants had to detect delays (0-417 ms) between their movement and the displays. Analyses revealed reduced activation in sensory areas and higher delay detection thresholds for active versus passive movements. Furthermore, there was increased activation in the hippocampus, the amygdala, and the middle temporal gyrus when someone else's hand was seen. Most importantly, in posterior parietal (angular gyrus and precuneus), frontal (middle, superior, and medial frontal gyrus), and temporal (middle temporal gyrus) regions, suppression for actively versus passively generated feedback was stronger when participants were viewing their own compared to someone else's hand. Our results suggest that forward models can take hand identity into account when predicting sensory action consequences.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".