MétaCan
Menu
Back to cohort
Record W1972827232 · doi:10.3389/fnhum.2014.00128

Are visual impairments responsible for emotion decoding deficits in alcohol-dependence?

2014· article· en· W1972827232 on OpenAlexaff
Fabien Dâ€TMHondt, Franco Leporé, Pierre Maurage

Bibliographic record

VenueFrontiers in Human Neuroscience · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPsychologyEmotion perceptionCognitive psychologyFacial expressionPerceptionVisual processingVisual perceptionEmotional expressionNeuroscienceCommunication

Abstract

fetched live from OpenAlex

Emotional visual perception deficits constitute a major problem in alcohol-dependence. Indeed, the ability to assess the affective content of external cues is a key adaptive function, as it allows on the one hand the processing of potentially threatening or advantageous stimuli, and on the other hand the establishment of appropriate social interactions (by enabling rapid decoding of the affective state of others from their facial expressions). While such deficits have been classically considered as reflecting a genuine emotion decoding impairment in alcohol-dependence, converging evidence suggests that underlying visual deficits might play a role in emotional alterations. This hypothesis appears to be relevant especially as data from healthy populations indicate that a coarse but fast analysis of visual inputs would allow emotional processing to arise from early stages of perception. After reviewing those findings and the associated models, the present paper underlines data showing that rapid interactions between emotion and vision could be impaired in alcohol-dependence and provides new research avenues that may ultimately offer a better understanding of the roots of emotional deficits in this pathological state.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.149
GPT teacher head0.402
Teacher spread0.253 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Human NeuroscienceSame topicNeural and Behavioral Psychology StudiesFrench-language works237,207