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Record W7120184067 · doi:10.54389/mwaz9065

Differences in Cognitive Functions based on English Language Proficiency in Young Adults

2025· article· W7120184067 on OpenAlexaboutno aff
Methmi Liyanage, Avishka Gamaathige, Sanithma Hewagama, Taveesha Perera, Nilusha Goonetilleke

Bibliographic record

Venue˜The œproceedings of sliit international conference on advancements in science and humanities · 2025
Typearticle
Language
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionLanguage proficiencyYoung adultMontreal Cognitive AssessmentSample (material)English languageLimited English proficiencyConfounding

Abstract

fetched live from OpenAlex

Cognitive assessments rely heavily on language-based tasks, potentially confounding cognitive performance with language proficiency, particularly in multilingual settings. This study investigated the influence of English language proficiency on cognitive functioning among young adults in Sri Lanka, a linguistically diverse country. 51 participants aged 18–26 were assessed using the Montreal Cognitive Assessment (MoCA) and grouped by first-language English and non-first-language status. Standardised administration protocols and ethical guidelines were followed. Data analysis using an independent sample t-test revealed a significant difference in overall MoCA scores between the two groups, suggesting that language proficiency may influence MoCA performance. These findings emphasise the importance of culturally and linguistically appropriate tools and the need for caution when interpreting cognitive assessments in multilingual contexts. Keywords: Cognitive function; English proficiency; MoCA; Bilingualism; Cognitive assessment

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)0.001
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 score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.061
GPT teacher head0.380
Teacher spread0.319 · 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
Published2025
Admission routes1
Has abstractyes

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