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fMRI reveals greater within‐ than between‐hemifield integration in the human lateral occipital cortex

2008· article· en· W2092049320 on OpenAlexaff
Mary‐Ellen Large, Jody C. Culham, A. Kuchinad, Adrian Aldcroft, Tutis Vilis

Bibliographic record

VenueEuropean Journal of Neuroscience · 2008
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsWestern University
Fundersnot available
KeywordsNeurosciencePsychologyCortex (anatomy)Occipital lobeVisual cortexCognitive psychology

Abstract

fetched live from OpenAlex

Early visual areas within each hemisphere (V1, V2, V3/VP, V4v) contain distinct representations of the upper and lower quadrants of the contralateral hemifield. As receptive field size increases, the retinotopy in higher-tier visual areas becomes progressively less distinct. Using functional magnetic resonance imaging (fMRI) to map the visual fields, we found that an intermediate level visual area, the lateral occipital region (LO), contains retinotopic maps with a contralateral bias, but with a combined representation of the upper and lower visual field. Moreover, we used the technique of fMRI adaptation to determine whether neurons in LO code for both the upper and lower contralateral quadrants. We found that even when visual stimulus locations are equivalent across comparisons, the LO was more sensitive to location changes that crossed hemifields than location changes within a hemifield. These results suggested that within high-tier visual areas the increasing integration of visual field information is a two-stage process. The upper and lower visual representations are combined first, in LO, then the left and right representations. Furthermore, these results provided evidence for a neural mechanism to explain behavioral findings of greater integration within than between hemifields.

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)0.000
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 score0.918
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.128
GPT teacher head0.327
Teacher spread0.199 · 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

Citations20
Published2008
Admission routes1
Has abstractyes

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