Ecologically Grounded Observations Offer Unique Insights into the Embodied and Embedded Nature of Infant Learning
Bibliographic record
Abstract
Scientists and laypeople alike have long been inspired by infants' acquisition of skills-from walking to talking to interacting with objects. Indeed, complementary aims of developmental science are to characterize infants' changing skills and identify the experiences that propel learning. Our observations of infants' unconstrained behavior in the ecologically-valid home environment have led to new insights on learning that integrate theory at the intersection of Piaget's writings on embodied action and Vygotsky's writings on the socially embedded nature of learning. These insights formed the basis of my plenary talk at the Jean Piaget Society conference, at which I underscored the need to: (1) surmount soundbites that early learning is rapid and effortless by characterizing the immense amount of practice that infants generate across domains such as playing, walking, and talking; (2) reinforce Piaget's observation that cognition is grounded in sensorimotor action; (3) challenge assumptions that controlling the environment is the only way to identify learning mechanisms; and (4) elucidate the shared and unique developmental contexts that frame infants' learning across cultural communities. Objective documentation of the natural behaviors, cultural niches, and learning opportunities of infants around the world is fundamental to science.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".