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Record W2569354150 · doi:10.1167/16.12.733

Does shrinking the perceptual field of view affect horizontal tuning in upright face identification?

2016· article· en· W2569354150 on OpenAlexaff
Vincent Barnabé-Lortie, Gabrielle Dugas, Jessica Royer, Justin Duncan, Caroline Blais, Daniel Fiset

Bibliographic record

VenueJournal of Vision · 2016
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversité du Québec en OutaouaisUniversity of Ottawa
Fundersnot available
KeywordsInversion (geology)PerceptionComputer visionArtificial intelligenceComputer sciencePsychologyOrientation (vector space)Horizontal planeMathematicsPattern recognition (psychology)CommunicationGeometryGeology

Abstract

fetched live from OpenAlex

The face inversion effect (FIE) is characterized by an important drop in recognition performance when facial stimuli are rotated by 180° in the picture plane. Pachai and coll. (2013) showed that inversion disrupts the processing of horizontal information (see also Goffaux & Dakin, 2010) and reported a significant positive correlation between horizontal tuning and the magnitude of the face inversion effect. Recently, Van Belle & Rossion (2015) showed that face inversion reduces the size of the perceptual field of view (PFV). This offers an elegant explanation for the performance drop with inverted faces since a small PFV restricts feature extraction to only a few (maybe one) at a time; a proposition reminiscent of the holistic hypothesis. To make the link between the lack of horizontal tuning with inverted faces and the PFV hypothesis, we measured orientation tuning in five participants for upright faces presented either through a small aperture (a gaze-contingent approach), or as a whole. First, the participants were asked to learn the face-name association for 10 identities. They practiced in each condition until they reached an accuracy of 95%. In the second phase, images were randomly filtered in the orientation domain with orientation bubbles (Duncan et al., 2014) to precisely reveal orientation utilization. Participants performed 400 trials per condition. The signal-to-noise ratio was adjusted so that the same performance level (55%) was obtained in both conditions. Congruently with what was observed for FIE, the signal-to-noise ratio was significantly higher when faces were presented through a small aperture than as a whole [t(4) = 12.9, p < 0.001]. Despite this large effect, the small aperture condition is not linked to a decrease in horizontal tuning. Our results show that the smaller PFV associated with the FIE cannot explain the lack of horizontal tuning with inverted faces. Meeting abstract presented at VSS 2016

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.333
Teacher spread0.305 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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