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Record W3034690823 · doi:10.46278/j.ncacn.20190513

Harnessing the Brain’s Neuro-Compensatory Processes: Lessons from a High-Functioning Person with Complete Agenesis of the Corpus Callosum

2019· article· en· W3034690823 on OpenAlexvenueno aff
Krysta J. Trevis, Eugene McTavish, Taylor Winter, Yan Fu, Jessica McTavish, Benjamin Wilson, Jill Oliver, Elizabeth A. Franz

Bibliographic record

VenueNeuropsychologie clinique et appliquée · 2019
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsAgenesis of the corpus callosumCorpus callosumPsychologyNeuroscienceObject (grammar)AgenesisDevelopmental psychologyCognitive psychologyMedicineArtificial intelligenceAnatomyComputer science

Abstract

fetched live from OpenAlex

It remains elusive how and why some people born with profound brain structure abnormalities develop high levels of intellect and near normal behaviour, while others with what appears to be the same or similar structural abnormalities experience far more concerning phenotypical outcomes. To begin to address this issue, a high-functioning female (aged 17 years at testing) born with complete callosal agenesis (ACC1) was tested on a series of psychophysical tests requiring unimanual-sequential or bimanual object weight discrimination; the latter of which is believed to depend on the integrity of the corpus callosum. In all five variants of the weight-discrimination task, ACC1’s performance was well within two standard deviations of the sample distribution mean. Arguably within the normal range, her performance warrants further investigation. Results suggest that individuals like ACC1 hold the secret to future understanding of the elusive neuro-compensatory processes of the human brain.

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)0.001
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 score0.319
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.310
Teacher spread0.254 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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