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Record W2108813565 · doi:10.1177/0956797610387440

It’s Alive!

2010· article· en· W2108813565 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychological Science · 2010
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsAnimacyMotion (physics)Biological motionPsychologyPerceptionCommunicationMotion perceptionVisual fieldVisual perceptionCognitive psychologyArtificial intelligenceComputer scienceComputer visionNeuroscience

Abstract

fetched live from OpenAlex

Across humans' evolutionary history, detecting animate entities in the visual field (such as prey and predators) has been critical for survival. One of the defining features of animals is their motion-self-propelled and self-directed. Does such animate motion capture visual attention? To answer this question, we compared the time to detect targets involving objects that were moving predictably as a result of collisions (inanimate motion) with the time to detect targets involving objects that were moving unpredictably, having been in no such collisions (animate motion). Across six experiments, we consistently found that targets involving objects that underwent animate motion were responded to more quickly than targets involving objects that underwent inanimate motion. Moreover, these speeded responses appeared to be due to the perceived animacy of the objects, rather than due to their uniqueness in the display or involvement of a top-down strategy. We conclude that animate motion does indeed capture visual attention.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0490.006

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 head0.079
GPT teacher head0.434
Teacher spread0.356 · 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