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Record W2793250218 · doi:10.21037/aes.2018.ab053

AB053. Oscillatory activity specific to peripheral emotional treatment induced by a visual steady state

2018· article· en· W2793250218 on OpenAlexaff
Caroline Grand-Maître, Vanessa Hadid, Michèle W. MacLean, Marie-Charlotte Higgins, Simon Faghel Soubeyrand, Franco Leporé

Bibliographic record

VenueAnnals of Eye Science · 2018
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFusiform gyrusFusiform face areaStimulus (psychology)SubconsciousFacial expressionPsychologySuperior temporal sulcusSulcusPerceptionCognitive psychologyGyrusAmygdalaPresentation (obstetrics)NeuroscienceEmotional expressionFace perceptionCommunicationMedicineFunctional magnetic resonance imaging

Abstract

fetched live from OpenAlex

Background: Research suggests that the analysis of facial expressions by a healthy brain would take place approximately 170 ms after the presentation of a facial expression in the superior temporal sulcus and the fusiform gyrus, mostly in the right hemisphere. Some researchers argue that a fast pathway through the amygdala would allow automatic and early emotional treatment around 90 ms after stimulation. This treatment would be done subconsciously, even before this stimulus is perceived and could be approximated by presenting the stimuli quickly on the periphery of the fovea. The present study aimed to identify the neural correlates of a peripheral and simultaneous presentation of emotional expressions through a frequency tagging paradigm. Methods: The presentation of emotional facial expressions at a specific frequency induces in the visual cortex a stable and precise response to the presentation frequency [i.e., a steady-state visual evoked potential (ssVEP)] that can be used as a frequency tag (i.e., a frequency-tag to follow the cortical treatment of this stimulus. Here, the use of different specific stimulation frequencies allowed us to label the different facial expressions presented simultaneously and to obtain a reliable cortical response being associated with (I) each of the emotions and (II) the different times of presentations repeated (1/0.170 ms =~5.8 Hz, 1/0.090 ms =~10.8 Hz). To identify the regions involved in emotional discrimination, we subtracted the brain activity induced by the rapid presentation of six emotional expressions of the activity induced by the presentation of the same emotion (reduced by neural adaptation). The results were compared to the hemisphere in which attention was sought, emotion and frequency of stimulation. Results: The signal-to-noise ratio of the cerebral oscillations referring to the treatment of the expression of fear was stronger in the regions specific to the emotional treatment when they were presented in the subjects peripheral vision, unbeknownst to them. In addition, the peripheral emotional treatment of fear at 10.8 Hz was associated with greater activation within the Gamma 1 and 2 frequency bands in the expected regions (frontotemporal and T6), as well as desynchronization in the Alpha frequency bands for the temporal regions. This modulation of the spectral power is independent of the attentional request. Conclusions: These results suggest that the emotional stimulation of fear presented in the peripheral vision and outside the attentional framework elicit an increase in brain activity, especially in the temporal lobe. The localization of this activity as well as the optimal stimulation frequency found for this facial expression suggests that it is treated by the fast pathway of the magnocellular 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0070.001

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 head0.229
GPT teacher head0.522
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2018
Admission routes1
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