Learning to move, moving to learn: A quarter century of insights into infant motor development
Bibliographic record
Abstract
Over the past quarter century, the field of infant motor development has undergone a profound conceptual shift from viewing motor behavior as a biologically preprogrammed sequence to understanding it as a dynamic, emergent process shaped by interaction, feedback, and prediction. This review traces that evolution across three key eras: the rise of Dynamic Systems Theory (DST) in the 2000s, which emphasized real-time coordination across bodily and environmental systems, the developmental cascades framework of the 2010s, which demonstrated how early motor milestones shape broader developmental trajectories, and the emergence of predictive, mechanistic models in the 2020 s, inspired by advances in artificial intelligence and robotics. Building on this trajectory, we propose a unifying framework termed Reinforcement from Sensorimotor Predictability (RSP, which posits that infants repeat actions not because they are goal-directed, but because those actions produce consistent and expected feedback. We present preliminary findings from a gaze-contingent eye-tracking study, along with a large-scale longitudinal project that applies machine learning to track sensorimotor trajectories in early infancy. Together, these lines of work suggest that predictability itself may serve as an intrinsic reinforcer, thus laying the groundwork for learning, agency, and the emergence of intentional behavior.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".