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Record W2111700131 · doi:10.1093/cercor/bhn216

Visual Awareness, Emotion, and Gamma Band Synchronization

2008· article· en· W2111700131 on OpenAlexaff
Qian Luo, Derek Mitchell, Xi Cheng, Krystal Mondillo, Daniel McCaffrey, Tom Holroyd, Frederick W. Carver, Richard Coppola, James Blair

Bibliographic record

VenueCerebral Cortex · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsSTART Clinic
FundersNational Institutes of Health
KeywordsStimulus (psychology)PsychologyNeurosciencePosterior cingulateCognitive psychologyCognition

Abstract

fetched live from OpenAlex

What makes us become aware? A popular hypothesis is that if cortical neurons fire in synchrony at a certain frequency band (gamma), we become aware of what they are representing. We tested this hypothesis adopting brain-imaging techniques with good spatiotemporal resolution and frequency-specific information. Specifically, we examined the degree to which increases in event-related synchronization (ERS) in the gamma band were associated with awareness of a stimulus (its detectability) and/or the emotional content of the stimulus. We observed increases in gamma band ERS within prefrontal-anterior cingulate, visual, parietal, posterior cingulate, and superior temporal cortices to stimuli available to conscious awareness. However, we also observed increases in gamma band ERS within the amygdala, visual, prefrontal, parietal, and posterior cingulate cortices to emotional relative to neutral stimuli, irrespective of their availability to conscious access. This suggests that increased gamma band ERS is related to, but not sufficient for, consciousness.

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)0.003
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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 head0.024
GPT teacher head0.251
Teacher spread0.227 · 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".

Quick stats

Citations119
Published2008
Admission routes1
Has abstractyes

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