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Record W3093564437 · doi:10.1093/texcom/tgaa076

Intersubject Spatial Pattern Correlations During Movie Viewing Are Stimulus-Driven and Nonuniform Across the Cortex

2020· article· en· W3093564437 on OpenAlexafffund
Angela Zhang, Reza Farivar

Bibliographic record

VenueCerebral Cortex Communications · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsStimulus (psychology)PsychologyCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

A fundamental step to predicting brain activity in healthy and diseased populations is characterizing the common spatio-temporal response to a shared experience. Multivoxel pattern analysis allows us to investigate information encoding through these patterns; however, we have yet to explore local, stimulus-driven, patterns of cortical activity during naturalistic stimulation. We sought to examine these patterns with minimum interpolation-excluding functional alignment-to characterize the most basic degree of shared response between subjects. We used an unbiased analytic approach, combined with rich, naturalistic, and nonsemantic stimulation to estimate shared spatial patterns in functional magnetic resonance imaging responses across a large group. We found that meso-scale spatial patterns were shared nonuniformly across the visual cortex and represent information distinct from the shared temporal response. Shared spatial patterns were stimulus-driven, modulated by pattern size, and more sensitive to the contrast of 3D versus 2D stimulus differences than the temporal signals. Although the grand functional structure of the brain is understood to be common, these results suggest that even at a meso-scale, we share common spatial structures with anatomical alignment alone. The strength of this similarity varies across the cortex, suggesting some spatial structures are innately organized, whereas others are shaped by factors such as learning and plasticity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.680
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.091
GPT teacher head0.334
Teacher spread0.243 · 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.

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

Citations4
Published2020
Admission routes2
Has abstractyes

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