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. It is a crucial aspect of consciousness, indicating that an agent can understand the causal relationship between their actions and environmental changes and, more importantly, voluntarily influence the environment through their actions. There has been extensive debate about when children and which animals possess the sense of agency, primarily because they cannot easily report it. Observing actions might be a promising way to estimate the sense of agency without relying on introspection. However, despite actions containing rich information about human subjective feelings, there are very few ways to abstract such information. In the present study, we aim to clarify the relationship between actions and the sense of agency during control exploration using a dataset of 167 children (6-16 years old) acquired by Nobusako et al. (2022). We employ action plan analysis developed by Chang et al. (2024) to analyze motion sequences data which was self-generated by participants during a control detection task. The action plan analysis uses transformer-LSTM-based autoencoders to capture high-level, abstract representations of sequences of motor commands (referred to as action plans). This approach allows us to quantify action plan diversity that reflects control exploration behaviors. The results showed that action plan diversity can be a promising way to measure the sense of agency both within individuals and among them. This suggests 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".