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Emotional arousal in agenesis of the corpus callosum

2006· article· en· W1966859307 on OpenAlexaff
Lynn K. Paul, Aaron Lautzenhiser, Warren S. Brown, Arch Hart, Dirk Neumann, Michael Spezio, Ralph Adolphs

Bibliographic record

VenueInternational Journal of Psychophysiology · 2006
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsBritish Columbia Children's Hospital
Fundersnot available
KeywordsArousalPsychologyCorpus callosumValence (chemistry)Agenesis of the corpus callosumAudiologyAlexithymiaStimulus (psychology)CognitionEmotionalityNeuroscienceCognitive psychologyDevelopmental psychologyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

While the processing of verbal and psychophysiological indices of emotional arousal have been investigated extensively in relation to the left and right cerebral hemispheres, it remains poorly understood how both hemispheres normally function together to generate emotional responses to stimuli. Drawing on a unique sample of nine high-functioning subjects with complete agenesis of the corpus callosum (AgCC), we investigated this issue using standardized emotional visual stimuli. Compared to healthy controls, subjects with AgCC showed a larger variance in their cognitive ratings of valence and arousal, and an insensitivity to the emotion category of the stimuli, especially for negatively-valenced stimuli, and especially for their arousal. Despite their impaired cognitive ratings of arousal, some subjects with AgCC showed large skin-conductance responses, and in general skin-conductance responses discriminated emotion categories and correlated with stimulus arousal ratings. We suggest that largely intact right hemisphere mechanisms can support psychophysiological emotional responses, but that the lack of interhemispheric communication between the hemispheres, perhaps together with dysfunction of the anterior cingulate cortex, interferes with normal verbal ratings of arousal, a mechanism in line with some models of alexithymia.

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)0.000
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 score0.581
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.008
GPT teacher head0.261
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

Citations60
Published2006
Admission routes1
Has abstractyes

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