Speed and direction changes induce the perception of animacy in 7-month-old infants
Bibliographic record
Abstract
A large body of research has documented infants' ability to classify animate and inanimate objects based on static or dynamic information. It has been shown that infants less than 1 year of age transfer animacy-specific expectations from dynamic point-light displays to static images. The present study examined whether basic motion cues that typically trigger judgments of perceptual animacy in older children and adults lead 7-month-olds to infer an ambiguous object's identity from dynamic information. Infants were tested with a novel paradigm that required inferring the animacy status of an ambiguous moving shape. An ambiguous shape emerged from behind a screen and its identity could only be inferred from its motion. Its motion pattern varied distinctively between scenes: it either changed speed and direction in an animate way, or it moved along a straight path at a constant speed (i.e., in an inanimate way). At test, the identity of the shape was revealed and it was either consistent or inconsistent with its motion pattern. Infants looked longer on trials with the inconsistent outcome. We conclude that 7-month-olds' representations of animates and inanimates include category-specific associations between static and dynamic attributes. Moreover, these associations seem to hold for simple dynamic cues that are considered minimal conditions for animacy perception.
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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 | 0.002 |
| 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 | 0.000 | 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".