Infants Parse Dynamic Action
Bibliographic record
Abstract
As observers of human behavior, infants are faced with a complex flow of motion in which pauses are rare and only occasionally coincide with boundaries between intentional actions. Two studies investigated whether, despite such complexity, 10- to 11-month-old infants (N = 16 for each study) possess skills for parsing ongoing behavior along boundaries correlated with the initiation and completion of intentions. After being familiarized with digitized sequences of continuous everyday action, infants showed renewed interest in test versions in which motion paused in the midst of an actor's pursuit of intentions (interrupting test videos). In contrast, pauses that suspended motion at intention boundary points (completing test videos) sparked no such renewed interest on infants' part. Moreover, basic salience differences between the two types of test videos were not the source of infants' increased interest when intentions were interrupted (Study 2). These findings demonstrate that infants readily detect disruptions of the structure inherent in intentional action, and hence parse ongoing behavior with respect to such structure. Such parsing skill is likely a prerequisite to the development of genuine intentional understanding.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".