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Record W7597523

Is Schematic Biological Motion an Animacy Cue in Infancy

2014· dissertation· af· W7597523 on OpenAlexfundno aff
John Corbit

Bibliographic record

Venuenot available
Typedissertation
Languageaf
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAssociation for Psychological Science
KeywordsAnimacyBiological motionSchematicMotion (physics)PsychologyStimulus (psychology)Cognitive psychologyCommunicationPerceptionArtificial intelligenceComputer scienceNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

The goal of the present research was to investigate whether schematic biological motion serves as a cue to the concept of animacy in infancy. In order to present motion cues in the absence of bodily form cues, Michotte’s (1963) schematic biological motion stimuli (i.e., shape rhythmically expanding/contracting in the direction of movement) were used. The video animations displayed an amorphous shape moving in this way behind a screen (i.e., the shadow) and assessed looking patterns when the screen was removed to reveal either an animate or inanimate exemplar in the test phase. In Experiment 1, familiar exemplars of animate entities (i.e., dog, cow) were used as test items. In Experiment 2, the test objects were unfamiliar category exemplars associated with this type of motion (i.e., worm, caterpillar). Infants (10- and 18-months) looked longer when biological motion cues were congruent with the test items in Experiment 1, but 10-month-olds did not show differential looking across congruent and incongruent trials in Experiment 2. These findings suggest that the schematic biological motion stimulus does not serve as a primitive cue to the concept of animacy in infancy.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.357
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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