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Record W4386242638 · doi:10.1167/jov.23.9.5146

Percepts of biological motion disappear in slow-moving displays: Evidence for domain-specific agent perception

2023· article· en· W4386242638 on OpenAlexaff
Merve Erdoğan, Nikolaus F. Troje, Brian J. Scholl

Bibliographic record

VenueJournal of Vision · 2023
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsYork University
Fundersnot available
KeywordsBiological motionPerceptionMotion (physics)Motion perceptionComputer visionCommunicationComputer scienceLimit (mathematics)Artificial intelligencePoint (geometry)PsychologyCognitive psychologyMathematicsNeuroscienceGeometryMathematical analysis

Abstract

fetched live from OpenAlex

The most important stimuli we perceive may be other agents, given their direct effects on our fitness. Accordingly, perception may be specialized for processing agents, and one of the best-studied examples may be *biological motion*: displays of surprisingly few moving dots (‘point-light walkers’; PLWs) nevertheless give rise to rich percepts of locomoting agents — even when static frames from such displays appear as meaningless jumbles of dots. Does this reflect a distinct, domain-specific form of perception, or is it (merely) a complex instance of general motion/shape perception? Inspired by the fact that humans tend to move at certain minimum speeds, we explored this by simply asking how *slow* PLWs can be before percepts of biological motion are impaired or destroyed. Are those limits similar to lower-level motion perception thresholds? Or might biological motion have its own domain-specific lower “speed limit”? Observers viewed PLWs moving in place, embedded in noise (with extra irrelevant moving dots). Animations moved at typical speeds, or at speeds that were considerably slower but still very far above motion perception thresholds. And for slow displays, we always tested both duration-matched versions (with less overall motion) and trajectory-matched versions (that simply lasted longer). Across several experiments, observers tried to discriminate various properties — such as the direction of locomotion, the walker’s apparent gender, or even whether a PLW was present or absent in the first place. We always obtained the same results (which were also apparent as powerful phenomenological demonstrations): discrimination of each of these aspects of biological motion was greatly impaired (often to chance level) in the slower displays, despite the fact that the motion was still readily visible. This demonstrates the utility of exploring ‘slow visual cognition’, and supports the characterization of biological motion perception as a domain-specific form of visual processing.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.169
GPT teacher head0.421
Teacher spread0.252 · 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
Published2023
Admission routes1
Has abstractyes

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