Finding events in a continuous world: A developmental account
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Event segmentation is a fundamental process of human cognition that organizes the continuous flux of activity into discrete, hierarchical units. The mechanism of event segmentation in infants seems to parallel the mechanism studied in adults, which centers on action predictability. Statistical learning appears to bootstrap infants' event segmentation by generating action predictions without relying on prior knowledge. Infants' first-hand experiences with goal-directed actions further enhance their prediction of others' actions. Scaffolds for event segmentation are available in the input, with caregivers providing redundant cues to event boundaries through the use of motionese and acoustic packaging. Research points to the importance of developing event segmentation skills for development in other areas of cognition, including memory, social competence, and language, though more work is needed to capture the directionality of effects. Although event segmentation is a relatively new area of focus in cognition, this process illuminates how children make sense of an ever-changing world.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.018 |
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 it