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Record W1530648300 · doi:10.1167/15.12.693

Measuring the flexibility of orientation selectivity in face processing by varying task demands

2015· article· en· W1530648300 on OpenAlexaff
Matthew V. Pachai, Allison B. Sekuler, Patrick Bennett

Bibliographic record

VenueJournal of Vision · 2015
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOrientation (vector space)Observer (physics)Stimulus (psychology)PsychologyComputer scienceArtificial intelligenceComputer visionCommunicationNoise (video)Contrast (vision)Cognitive psychologyMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

Observers preferentially process information conveyed by the horizontal orientation band when identifying faces (Dakin and Watt, J Vis 2009; Goffaux and Dakin, Front Psychol 2010). However, ideal observer analysis reveals that such horizontal selectivity is optimal in face identification tasks (Pachai et al, Front Psychol 2013). Therefore, it remains unclear whether horizontal selectivity results from a flexible system tuned to the most diagnostic band for a given task, or a general bias present during all face-related tasks. To disambiguate these hypotheses, we asked observers to perform two face-related tasks for which the diagnostic orientation band differed. On each trial, one of six identities was presented with the head turned slightly to the left or right. Observers were asked on different trials, either blocked or intermixed, to judge the stimulus identity or viewpoint direction. Stimuli were masked with high-contrast orientation-filtered noise (horizontal or vertical, bandwidth = 90 deg) and a low-contrast white noise to enable ideal observer analysis. The dependent measure was the d’=1 RMS contrast threshold, which should be elevated from baseline proportionally to the weight placed by the observer on the masked orientation band during the task in question. A simulated ideal observer confirmed the differential diagnosticity of orientation bands in the two tasks: more masking produced by horizontal noise in the face identification task, and more masking produced by vertical noise in the viewpoint direction task. However, human observers exhibited more masking from horizontal noise in both the identity and direction tasks, regardless of whether these tasks were blocked or intermixed. This result demonstrates an inability to preferentially process vertical facial structure even when it is optimal for the task at hand, and suggests that horizontal selectivity may represent a general face processing strategy. Meeting abstract presented at VSS 2015

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.108
GPT teacher head0.359
Teacher spread0.251 · 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
Published2015
Admission routes1
Has abstractyes

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