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Record W2006232713 · doi:10.1002/hbm.21507

Visual cognition in disorders of consciousness: From V1 to top‐down attention

2012· article· en· W2006232713 on OpenAlexaff
Martin M. Monti, John D. Pickard, Adrian M. Owen

Bibliographic record

VenueHuman Brain Mapping · 2012
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsWestern University
FundersMedical Research CouncilEuropean CommissionJames S. McDonnell Foundation
KeywordsConsciousnessPsychologyMinimally conscious stateCognitionFunctional magnetic resonance imagingCognitive psychologyPersistent vegetative stateNeuroscienceVisual perceptionConsciousness DisordersFocus (optics)Perception

Abstract

fetched live from OpenAlex

What is it like to be at the lower boundaries of consciousness? Disorders of consciousness such as coma, the vegetative state, and the minimally conscious state are among the most mysterious and least understood conditions of the human brain. Particularly complicated is the assessment of residual cognitive functioning and awareness for diagnostic, rehabilitative, legal, and ethical purposes. In this article, we present a novel functional magnetic resonance imaging exploration of visual cognition in a patient with a severe disorder of consciousness. This battery of tests, first developed in healthy volunteers, assesses increasingly complex transformations of visual information along a known caudal to rostral gradient from occipital to temporal cortex. In the first five levels, the battery assesses (passive) processing of light, color, motion, coherent shapes, and object categories (i.e., faces, houses). At the final level, the battery assesses the ability to voluntarily deploy visual attention in order to focus on one of two competing stimuli. In the patient, this approach revealed appropriate brain activations, undistinguishable from those seen in healthy and aware volunteers. In addition, the ability of the patient to focus one of two competing stimuli, and switch between them on command, also suggests that he retained the ability to access, to some degree, his own visual representations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.562
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.064
GPT teacher head0.362
Teacher spread0.299 · 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 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".

Quick stats

Citations76
Published2012
Admission routes1
Has abstractyes

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