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Record W2119802329 · doi:10.1073/pnas.0802525105

Spatiotemporal object continuity in human ventral visual cortex

2008· article· en· W2119802329 on OpenAlexfundno aff
Do-Joon Yi, Nicholas B. Turk‐Browne, Jonathan Flombaum, Min‐Shik Kim, Brian J. Scholl, Marvin M. Chun

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2008
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsnot available
FundersNational Eye InstituteNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Science Foundation
KeywordsVisual cortexObject (grammar)Biological motionPerceptionTemporal cortexMotion (physics)Computer visionVisual perceptionPsychologyObject permanenceCortex (anatomy)NeuroscienceComputer scienceCognitionCommunicationArtificial intelligenceCognitive psychology

Abstract

fetched live from OpenAlex

Coherent visual experience requires that objects be represented as the same persisting individuals over time and motion. Cognitive science research has identified a powerful principle that guides such processing: Objects must trace continuous paths through space and time. Little is known, however, about how neural representations of objects, typically defined by visual features, are influenced by spatiotemporal continuity. Here, we report the consequences of spatiotemporally continuous vs. discontinuous motion on perceptual representations in human ventral visual cortex. In experiments using both dynamic occlusion and apparent motion, face-selective cortical regions exhibited significantly less activation when faces were repeated in continuous vs. discontinuous trajectories, suggesting that discontinuity caused featurally identical objects to be represented as different individuals. These results indicate that spatiotemporal continuity modulates neural representations of object identity, influencing judgments of object persistence even in the most staunchly "featural" areas of ventral visual cortex.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.097
GPT teacher head0.359
Teacher spread0.261 · 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

Citations77
Published2008
Admission routes1
Has abstractyes

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