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Record W1996348618 · doi:10.1007/s13295-012-0034-9

The spotlight of attention: shifting, resizing and splitting receptive fields when processing visual motion

2012· article· en· W1996348618 on OpenAlexaff
Stefan Treue, Julio Martínez-Trujillo

Bibliographic record

Venuee-Neuroforum · 2012
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsReceptive fieldFlexibility (engineering)Focus (optics)Visual spaceComputer scienceSet (abstract data type)Visual processingVisual cortexCommunicationPsychologyVisual fieldOrientation (vector space)Motion (physics)Computer visionSurround suppressionCognitive psychologyNeuroscienceArtificial intelligenceVisual perceptionPhysicsMathematicsOptics

Abstract

fetched live from OpenAlex

Abstract In the visual system receptive fields repre­sent the spatial selectivity of neurons for a given set of visual inputs. Their invariance is thought to be caused by a hardwired in­put configuration, which ensures a stable ‘la­beled line’ code for the spatial position of vi­sual stimuli. On the other hand, changeable receptive fields can provide the visual system with flexibility for allocating processing re­sources in space. The allocation of spatial at­tention, often referred to as the spotlight of attention, is a behavioral equivalent of visu­al receptive fields. It dynamically modulates the spatial sensitivity to visual information as a function of the current attentional focus of the organism. Here we focus on the brain sys­tem for encoding visual motion information and review recent findings documenting in­teractions between spatial attention and re­ceptive fields in the visual cortex of primates. Such interactions create a careful balance be­tween the benefits of invariance with those derived from the attentional modulation of information processing according to the cur­rent behavioral goals.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.316
Teacher spread0.272 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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