MétaCan
Menu
Back to cohort
Record W2161967412 · doi:10.1073/pnas.2333622100

Computational evidence for a rivalry hierarchy in vision

2003· article· en· W2161967412 on OpenAlexaff
Hugh R. Wilson

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2003
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
Fundersnot available
KeywordsBinocular rivalryRivalryVisual cortexPerceptionNeuroscienceStimulus (psychology)PsychologyComputer scienceVisual perceptionBinocular neuronsCognitive scienceArtificial intelligenceCognitive psychologyEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Cortical-form vision comprises multiple, hierarchically arranged areas with feedforward and feedback interconnections. This complex architecture poses difficulties for attempts to link perceptual phenomena to activity at a particular level of the system. This difficulty has been especially salient in studies of binocular rivalry alternations, where there is seemingly conflicting evidence for a locus in primary visual cortex or alternatively in higher cortical areas devoted to object perception. Here, I use a competitive neural model to demonstrate that the data require at least two hierarchic rivalry stages for their explanation. This model demonstrates that competitive inhibition in the first rivalry stage can be eliminated by using suitable stimulus dynamics, thereby revealing properties of a later stage, a result obtained with both spike-rate and conductance-based model neurons. This result provides a synthesis of competing rivalry theories and suggests that neural competition may be a general characteristic throughout the form-vision hierarchy.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.211
GPT teacher head0.435
Teacher spread0.224 · 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 designSimulation or modeling
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

Citations422
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicVisual perception and processing mechanismsFrench-language works237,207