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Record W2007213401 · doi:10.1167/15.7.4

From orientations to objects: Configural processing in the ventral stream

2015· review· en· W2007213401 on OpenAlexafffund
Hugh R. Wilson, Frances Wilkinson

Bibliographic record

VenueJournal of Vision · 2015
Typereview
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsYork University
FundersCanadian Institutes of Health Research
KeywordsReceptive fieldNeurophysiologyFunctional magnetic resonance imagingArtificial intelligencePsychologyObject (grammar)HierarchyIllusory contoursNeuroscienceVisual fieldCognitive neuroscience of visual object recognitionVisual systemDorsumCurse of dimensionalityPsychophysicsPattern recognition (psychology)Computer scienceVisual cortexComputer visionPerceptionBiologyAnatomyOptical illusion

Abstract

fetched live from OpenAlex

The ventral or form vision hierarchy comprises a sequence of cortical areas in which successively more complex visual attributes are extracted, beginning with contour orientations in V1 and culminating in face and object representations at the highest levels. In addition, ventral areas exhibit increasing receptive field diameter by a factor of approximately three from area to area, and conversely neuron density decreases. We argue here that this is consistent with configural combination of adjacent orientations to form curves or angles, followed by combination of these to form descriptions of object shapes. Substantial data from psychophysics, functional magnetic resonance imaging (fMRI), and neurophysiology support this organization, and computational models consistent with it have also been proposed. We further argue that a key to the role of the ventral stream is dimensionality reduction in object representations.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.334

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 studies0.0000.000
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 head0.150
GPT teacher head0.451
Teacher spread0.300 · 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 designOther design
Domainnot available
GenreReview

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

Citations40
Published2015
Admission routes2
Has abstractyes

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