You Go This Way and I’ll Go That Way: Developmental Changes in Infants’ Detection of Correlations among Static and Dynamic Features in Motion Events
Bibliographic record
Abstract
Four experiments utilizing the habituation procedure examined 10- to 18-month-olds' ability to detect and encode correlations among features in a motion event (N = 136). Infants were habituated to two events in which objects-with distinct parts and a distinct body-moved across a screen along a rectilinear or curvilinear motion path. Infants were then tested with one familiar event and three events in which one feature of the object (parts, body, or motion path) was presented in a novel combination with the other features. The results of the experiments revealed that 10-month-olds process independently static features in an event, but do not process correlations among dynamic features; whereas 14-month-olds detect the correlation between an object's parts and its motion trajectory, but only when the movement of parts is correlated with the motion of the object. Further, the data show that 18-month-olds detect correlations between all three features when the parts of the object move, but they detect only the relation between parts and motion path when the parts do not move. It is proposed that infants develop representations for the static and dynamic properties of objects through a sensitive perceptual system that detects individual features, whole objects, and movement properties, and a domain-general associative learning mechanism that encodes independent features and correlations among features.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".