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Record W3195477922 · doi:10.1027/0269-8803/a000285

Insular Cortex Response to Static Visual Sexual Stimuli

2021· article· en· W3195477922 on OpenAlexaff
Mathieu Brideau-Duquette, Olivier Boucher, Julie Tremblay, Manon Robert, Alain Bouthillier, Franco Leporé, Dang Khoa Nguyen

Bibliographic record

VenueJournal of Psychophysiology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsInsulaPsychologyEmotional valenceElectroencephalographyInsular cortexAudiologyNeuroscienceSalientCognitive psychologyDevelopmental psychologyCognitionMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract. According to previous research, the insula is important for processing salient and emotional stimuli, but its precise role remains elusive. By combining high spatial and temporal resolution, intracranial electroencephalography (iEEG) might contribute to filling this gap. Four drug-resistant epileptic patients with intracranial electrodes in the insula were instructed to watch and rate pictures with sexual content and neutral pictures. Event-related potentials (ERPs) were computed separately for both types of stimuli. Ninety-three percent of the anterior insula (AI) and 85% of the posterior insula (PI) contacts showed differences between ERPs. AI-positive deflections tended to have an earlier onset than PI-positive deflections. The results suggest that the AI generates a P300-like response and contributes to the early phase of the late positive potential, both components found enhanced while viewing emotional stimuli in the ERP literature. The present findings are interpreted as congruent with the role of the AI in maintaining attention to salient stimuli.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.030
GPT teacher head0.337
Teacher spread0.307 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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