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Record W4387495859 · doi:10.15557/pipk.2023.0016

An analysis of intelligence in a 32-lingual man

2023· article· en· W4387495859 on OpenAlexaboutno aff
Mateusz Jan Dudka

Bibliographic record

VenuePsychiatria i Psychologia Kliniczna · 2023
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsnot available
FundersUniwersytet Marii Curie-Skłodowskiej
KeywordsPsychologyFluid and crystallized intelligenceExecutive functionsContext (archaeology)TemperamentCognitionTest (biology)Developmental psychologyCognitive psychologyComprehensionPersonalityFluid intelligenceWorking memoryLinguisticsSocial psychology

Abstract

fetched live from OpenAlex

The presented case study is a description of the specifics of functioning of a 69-year-old man who learned 31 foreign languages over a period of nine years. Previous research focused on personality traits, temperament and the level of development of executive functions in the context of learning multiple foreign languages. The purpose of the analysis was to determine the man’s level of cognitive functioning. The following three research tools were used in this project: Cattell Culture Fair Intelligence Test (CFT-20-R), the Word Comprehension Test in the Advanced Version (Test rozumienia słów, wersja dla zaawansowanych, TRS-Z), and the Montreal Cognitive Assessment score (MoCA). The results obtained indicate a very high level of fluid and crystallised intelligence; however, the MoCA scores fell within the lower limit of normal, suggesting deficits in delayed verbal and auditory memory. Based on the data collected, it can be concluded that the man’s level of intelligence (fluid and crystallised) was the base for the development of his outstanding linguistic abilities. The issues related to the learning of verbal and non-verbal material remain unclear.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.437
Teacher spread0.363 · 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.

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
Published2023
Admission routes1
Has abstractyes

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