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Record W2060603252 · doi:10.1152/jn.01156.2005

Modulation of V1 Activity by Shape: Image-Statistics or Shape-Based Perception?

2006· article· en· W2060603252 on OpenAlexaff
Serge O. Dumoulin, Robert F. Hess

Bibliographic record

VenueJournal of Neurophysiology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsPerceptionVisual cortexStimulus (psychology)Orientation (vector space)Artificial intelligenceVisual perceptionContrast (vision)Pattern recognition (psychology)PsychophysicsMathematicsComputer visionComputer sciencePsychologyCognitive psychologyGeometryNeuroscience

Abstract

fetched live from OpenAlex

It is current dogma that neurons in primary visual cortex extract local edges from the scene from which later visual areas reconstruct more meaningful shapes. Recent neuroimaging studies, however, have shown V1 modulations by the degree of structure in the image (shape). These V1 modulations due to the level of shape coherence have been explained in one of two possible ways: due to changes in image statistics or shape-based perceptual influences from higher visual areas. Here we compare both hypotheses using stimuli composed of Gabor arrays constructed to form circular shapes that can be successively degraded by manipulating the orientations of individual Gabors while maintaining local and global statistics. In a first experiment, we confirm that V1 responses are inversely correlated with the degree of structure in the image. In a second experiment, stimulus predictions are compared based on the degree of circular shape or change in the image statistic varied (orientation variance) in the image. We find that these V1 modulations to shape change are correlated with low-level changes in orientation contrast rather than shape perception per se.

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 score0.687
Threshold uncertainty score0.836

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)0.0010.000

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 head0.045
GPT teacher head0.328
Teacher spread0.283 · 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

Citations32
Published2006
Admission routes1
Has abstractyes

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