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Record W2973319110 · doi:10.1167/19.10.216a

Link between initial fixation location and spatial frequency utilization in face recognition

2019· article· en· W2973319110 on OpenAlexaffabout
Amanda Estéphan, Carine Charbonneau, Virginie Leblanc, Daniel Fiset, Caroline Blais

Bibliographic record

VenueJournal of Vision · 2019
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsArtificial intelligenceFixation (population genetics)Computer scienceFacial recognition systemEye movementVisual processingFace (sociological concept)Pattern recognition (psychology)Computer visionPerceptionPsychologyMedicine

Abstract

fetched live from OpenAlex

Recent face perception studies have explored individual differences with regard to visual processing strategies. Two main strategies, associated with distinct eye movement patterns, have been highlighted: global (or holistic) face processing involves fixations near the center of the face to facilitate simultaneous peripheral processing of key facial features (i.e. eyes and mouth); local (or analytic) face processing involves fixations directed to those facial features (Chuk et al, 2014; Miellet et al, 2011). Since it has been shown that peripheral processing entails lower spatial resolution (Goto et al, 2001), global face processing may theoretically be linked to lower spatial frequency (SF) sampling. By contrast, local face processing may allow the extraction of higher SFs. However, the link between eye movements and SF extraction has not yet been empirically verified for face recognition. Thus, the current study proposes to investigate this question. The eye movements of 21 Canadian participants were monitored while they completed an Old/New face recognition task. Subsequently, the SF Bubbles method (Willenbockel et al., 2010) was used to measure the same participants’ SF utilization during a face identification task. Fixation duration maps were computed for each participant using the iMap4 toolbox (Lao et al., 2017), and participants’ individual SF tuning peaks, obtained with SF Bubbles, were calculated. In line with previous studies, our participants’ initial fixations generally landed near the center of the face, with varying degrees of proximity to this location (Euclidean distances: from 25.76 to 146.03 pixels; SD of 32.08 pixels). Crucially, SF peaks significantly correlated with the location of the first fixation (r = −0.46; p = 0.037): participants using higher SFs initially gazed closer to the left eye than participants using lower SFs. These results suggest that greater reliance on high SFs is associated with early fixations toward the left eye region.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.092
GPT teacher head0.356
Teacher spread0.264 · 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
Published2019
Admission routes2
Has abstractyes

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