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Record W2156147195 · doi:10.1162/jocn.2007.19.8.1275

Neural Substrates of Dynamic Object Occlusion

2007· article· en· W2156147195 on OpenAlexaff
Sarah M. Shuwairi, Clayton E. Curtis, Scott P. Johnson

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsYork University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthNational Eye InstituteBeatrice and Samuel A. Seaver Foundation
KeywordsObject (grammar)PsychologyPerceptionFunctional magnetic resonance imagingRepresentation (politics)CognitionVisibilityCognitive psychologyOcclusionPhenomenonSightCognitive scienceCognitive neuroscience of visual object recognitionTracking (education)NeuroscienceCommunicationComputer visionArtificial intelligenceComputer scienceMedicine

Abstract

fetched live from OpenAlex

In everyday environments, objects frequently go out of sight as they move and our view of them becomes obstructed by nearer objects, yet we perceive these objects as continuous and enduring entities. Here, we used functional magnetic resonance imaging with an attentive tracking paradigm to clarify the nature of perceptual and cognitive mechanisms subserving this ability to fill in the gaps in perception of dynamic object occlusion. Imaging data revealed distinct regions of cortex showing increased activity during periods of occlusion relative to full visibility. These regions may support active maintenance of a representation of the target's spatiotemporal properties ensuring that the object is perceived as a persisting entity when occluded. Our findings may shed light on the neural substrates involved in object tracking that give rise to the phenomenon of object permanence.

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)0.003
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 score0.190
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.300
Teacher spread0.274 · 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

Citations34
Published2007
Admission routes1
Has abstractyes

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