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Record W3203839086 · doi:10.3390/brainsci11101312

The Self and Its Right Insula—Differential Topography and Dynamic of Right vs. Left Insula

2021· article· en· W3203839086 on OpenAlexaff
Andrea Scalabrini, Angelika Wolman, Georg Northoff

Bibliographic record

VenueBrain Sciences · 2021
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of Ottawa
FundersHorizon 2020
KeywordsInsulaPsychologyContext (archaeology)NeuroscienceBiology

Abstract

fetched live from OpenAlex

Various studies demonstrate a special role of the right compared to the left anterior insula in mediating our self. However, the neural features of the right insula that allow for its special role remain unclear. Presupposing a spatiotemporal model of self-"Basis model of self-specificity" (BMSS)-we here address the following question: what spatial-topographic and temporal-dynamic features render neural activity in the right insula to be more suitable in mediating self-specificity than the left insula? First, applying fMRI, we demonstrate that the right insula (i) exhibits higher degrees of centrality in rest, and (ii) higher context-dependent functional connectivity in a self-specific task among regions of distinct layers of self (intero-, extero-proprioceptive, and mental). Second, using EEG in rest and task, we show that the right insula shows longer autocorrelation window (ACW) in its neural activity than both left insula and other regions of the different layers of self. Together, we demonstrate special topographic, i.e., high functional connectivity, and dynamic, i.e., long ACW, neural features of the right insula compared to both left insula and other regions of the distinct layers of self. This suits neural activity in the right insula ideally for high functional integration and temporal continuity as key features of the self including its intero-, extero-proprioceptive, and mental layers.

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)0.004
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 score0.491
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.015
GPT teacher head0.258
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.

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

Citations43
Published2021
Admission routes1
Has abstractyes

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