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Record W3214865659 · doi:10.1590/2317-1782/20212020365

Clustering and switching in verbal fluency: a comparison between control and individuals with brain damage

2021· article· en· W3214865659 on OpenAlexaboutno aff
Karina Carlesso Pagliarin, Eduarda Giovelli Fernandes, Maryndia Diehl Müller, Caroline Rodrigues Portalete, Róchele Paz Fonseca, Raira Fernanda Altmann

Bibliographic record

VenueCoDAS · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsVerbal fluency testFluencyAphasiaPsychologyAudiologyLateralization of brain functionRight hemisphereCognitive psychologyCognitionDevelopmental psychologyMedicineNeuropsychologyNeuroscience

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study is to analyze and compare the performance and strategies used by control subjects and patients with unilateral brain damage on phonemic and semantic Verbal Fluency tasks. METHODS: The sample consisted of 104 participants divided into four groups (26 with left hemisphere damage and aphasia- LHDa, 28 with left hemisphere damage and no aphasia- LHDna, 25 with right hemisphere damage- RHD and 25 neurologically healthy control subjects). All participants were administered the phonemic ("M" letter-based) and semantic (animals) verbal fluency tasks from the Montreal-Toulouse Language Assessment Battery (MTL-BR). RESULTS: Patients in the LHDa group showed the worst performance (fewer words produced, fewer clusters and switches) in both types of fluency task. RHD group showed fewer switching productions when compared with controls and LHDna had fewer words productions than controls in the first 30 seconds block. CONCLUSION: Our findings suggest that the LHDa group obtained lower scores in most measures of SVF and PVF when compared to the other groups.

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 score0.285
Threshold uncertainty score0.387

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 head0.025
GPT teacher head0.295
Teacher spread0.271 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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Same venueCoDASSame topicNeurobiology of Language and BilingualismFrench-language works237,207