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Record W2753926392 · doi:10.3389/fnsys.2017.00064

From Cortical Blindness to Conscious Visual Perception: Theories on Neuronal Networks and Visual Training Strategies

2017· review· en· W2753926392 on OpenAlexafffund
Vanessa Hadid, Franco Leporé

Bibliographic record

VenueFrontiers in Systems Neuroscience · 2017
Typereview
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsCortical blindnessNeuroscienceInattentional blindnessBlindnessPerceptionVisual cortexPsychologyVisual perceptionBlindsightCognitive psychologyMedicineOptometry

Abstract

fetched live from OpenAlex

Homonymous hemianopia (HH) is the most common cortical visual impairment leading to blindness in the contralateral hemifield. It is associated with many inconveniences and daily restrictions such as exploration and visual orientation difficulties. However, patients with HH can preserve the remarkable ability to unconsciously perceive visual stimuli presented in their blindfield, a phenomenon known as blindsight. Unfortunately, the nature of this captivating residual ability is still misunderstood and the rehabilitation strategies in terms of visual training have been insufficiently exploited. This article discusses type I and type II blindsight in a neuronal framework of altered global workspace, resulting from inefficient perception, attention and conscious networks. To enhance synchronization and create global availability for residual abilities to reach visual consciousness, rehabilitation tools need to stimulate subcortical extrastriate pathways through V5/MT. Multisensory bottom-up compensation combined with top-down restitution training could target pre-existing and new neuronal mechanisms to recreate a framework for potential functionality.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.159
GPT teacher head0.415
Teacher spread0.257 · 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

Citations21
Published2017
Admission routes2
Has abstractyes

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