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Record W2051698893 · doi:10.1037/0096-1523.34.5.1129

The effects of face inversion on the perception of long-range and local spatial relations in eye and mouth configuration.

2008· article· en· W2051698893 on OpenAlexafffund
Alla Sekunova, Jason J.S. Barton

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2008
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Mental HealthCanadian Institutes of Health ResearchCanada Research ChairsMichael Smith Health Research BC
KeywordsSpatial relationInversion (geology)Horizontal and verticalPerceptionFace perceptionPsychologyGeometryMathematicsGeologyCommunicationArtificial intelligenceComputer scienceNeurosciencePaleontology

Abstract

fetched live from OpenAlex

A recent study hypothesized a configurational anisotropy in the face inversion effect, with vertical relations more difficult to process. However, another difference in the stimuli of that report was that the vertical but not horizontal shifts lacked local spatial references. Difficulty processing long-range spatial relations might also be predicted from a relevance-interaction explanation, which proposes that in inverted faces, spatial relations are processed efficiently only within high-relevance local regions. The authors performed 2 experiments to distinguish between these hypotheses. Experiment 1 showed that the inversion effect for vertical shifts of the eyes alone was more similar to that for horizontal eye shifts than for vertical shifts of the eyes and eyebrows. In Experiment 2, focused attention reduced the inversion effect for vertical mouth position more than that for vertical shifts of the eyes and brows. The authors concluded that face inversion impairs the perception of both local spatial relations in low-relevance regions and long-range spatial relations extending across multiple facial regions, consistent with a loss of efficient whole-face processing of the spatial relations between features.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.324
Teacher spread0.285 · 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 designBench or experimental
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

Citations62
Published2008
Admission routes2
Has abstractyes

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