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Record W4206351222 · doi:10.3389/fnmol.2021.806376

Multisensory Integration: Is Medial Prefrontal Cortex Signaling Relevant for the Treatment of Higher-Order Visual Dysfunctions?

2022· review· en· W4206351222 on OpenAlexaff
Miguel Skirzewski, Stéphane Molotchnikoff, Luis F Hernandez, José Fernando Maya‐Vetencourt

Bibliographic record

VenueFrontiers in Molecular Neuroscience · 2022
Typereview
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversité de MontréalUniversité de SherbrookeWestern University
Fundersnot available
KeywordsMultisensory integrationNeurosciencePrefrontal cortexStimulus modalitySensory systemPsychologyPerceptionModalitiesCognitive psychologySensory processingCognition

Abstract

fetched live from OpenAlex

In the mammalian brain, information processing in sensory modalities and global mechanisms of multisensory integration facilitate perception. Emerging experimental evidence suggests that the contribution of multisensory integration to sensory perception is far more complex than previously expected. Here we revise how associative areas such as the prefrontal cortex, which receive and integrate inputs from diverse sensory modalities, can affect information processing in unisensory systems via processes of down-stream signaling. We focus our attention on the influence of the medial prefrontal cortex on the processing of information in the visual system and whether this phenomenon can be clinically used to treat higher-order visual dysfunctions. We propose that non-invasive and multisensory stimulation strategies such as environmental enrichment and/or attention-related tasks could be of clinical relevance to fight cerebral visual impairment.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.092
GPT teacher head0.397
Teacher spread0.305 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2022
Admission routes1
Has abstractyes

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