Action Plan Diversity in Children During Control Exploration: Link Between Action and Sense of Agency
Bibliographic record
Abstract
The sense of agency refers to the subjective feeling of controlling one’s own actions and, through them, external events. Despite actions containing rich information about human subjective feelings, there are very few ways to abstract such information. The present study aims to use a new method of action analysis to examine whether action plans contain information for measuring the sense of agency both across different conditions (i.e., within participants) and among different individuals. The present study employed an action plan analysis utilizing transformer-LSTM-based autoencoders on a movement dataset of 167 children in a control detection task collected previously in a published paper. This analysis can capture high-level, abstract representations of sequences of motor commands (referred to as action plans) and quantify control exploration behaviors. The action plan diversity showed a sigmoid-like function of control, indicating that actions indeed contain rich information regarding the sense of agency. Furthermore, the individual slope of action plan diversity against control, referred to as action plan sensitivity, significantly correlated with individual control detection accuracy, suggesting that this index can also be used as an inter-individual measure of sense of agency. Our results suggest that simply observing how actions change under different control conditions can quantitatively reflect the emergence of the sense of agency in children. The findings and methodology provide a highly novel and useful tool for studying the sense of agency in broader populations and species in future studies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".