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Record W2062349210 · doi:10.1097/wnr.0b013e32833312c7

Comparing neuronal and behavioral thresholds for spiral motion discrimination

2009· article· en· W2062349210 on OpenAlexafffund
Antonio Rodrı́guez-Sánchez, John K. Tsotsos, Stefan Treue, Julio Martínez-Trujillo

Bibliographic record

VenueNeuroreport · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchBundesministerium für Bildung und Forschung
KeywordsSpiral (railway)Projection (relational algebra)NeuroscienceMotion perceptionMotion (physics)PerceptionArtificial intelligenceBiological motionPhysicsComputer visionCommunicationComputer scienceBiologyPsychologyMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

As we move, the projection of moving objects on our retinas generates an array of velocity vectors known as optic flow. One class of optic flow is spiral motion, defined by the angle between a local vector direction and the direction of the steepest increase in local speed. By discriminating among such angles, an organism could discern between different flow patterns and effectively interact with the environment. In primates, spiral-selective neurons in medial superior temporal area are thought to provide the substrate for this ability. We found that these cells show higher discrimination thresholds than found behaviorally in humans, suggesting that when discriminating spiral motions the brain integrates information across many of these neurons to achieve its high perceptual performance.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.104
GPT teacher head0.325
Teacher spread0.220 · 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 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

Citations6
Published2009
Admission routes2
Has abstractyes

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