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Record W1977527028 · doi:10.1167/14.12.10

What causes the facing-the-viewer bias in biological motion?

2014· article· en· W1977527028 on OpenAlexaff
Séamas Weech, Maura McAdam, Sophie Kenny, Nikolaus F. Troje

Bibliographic record

VenueJournal of Vision · 2014
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsCanadian Institute for Advanced ResearchQueen's University
Fundersnot available
KeywordsBiological motionPerceptionOrientation (vector space)Computer visionPsychologyMotion (physics)ConvexityArtificial intelligenceCognitive psychologyMathematicsComputer scienceGeometry

Abstract

fetched live from OpenAlex

Orthographically projected biological motion point-light displays are generally ambiguous with respect to their orientation in depth, yet observers consistently prefer the facing-the-viewer interpretation. There has been discussion as to whether this bias can be attributed to the social relevance of biological motion stimuli or relates to local, low-level stimulus properties. In the present study we address this question. In Experiment 1, we compared the facing-the-viewer bias produced by a series of four stick figures and three human silhouettes that differed in posture, gender, and the presence versus absence of walking motion. Using a paradigm in which we asked observers to indicate the spinning direction of these figures, we found no bias when participants observed silhouettes, whereas a pronounced degree of bias was elicited by most stick figures. We hypothesized that the ambiguous surface normals on the lines and dots that comprise stick figures are prone to a visual bias that assumes surfaces to be convex. The local surface orientations of the occluding contours of silhouettes are unambiguous, and as such the convexity bias does not apply. In Experiment 2, we tested the role of local features in ambiguous surface perception by adding dots to the elbows and knees of silhouettes. We found biases consistent with the facing directions implied by a convex body surface. The results unify a number of findings regarding the facing-the-viewer bias. We conclude that the facing-the-viewer bias is established at the level of surface reconstruction from local image features rather than on a semantic level.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.107
GPT teacher head0.383
Teacher spread0.276 · 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 teacher head, 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

Citations22
Published2014
Admission routes1
Has abstractyes

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