Cognitive Flexibility in Bilinguals and Multilinguals and Its Implications for Dementia
Bibliographic record
Abstract
BACKGROUND: Verbal fluency (VF) is crucial for language processing and cognitive flexibility, involving selective attention, inhibition, set shifting, response generation, and self-monitoring. VF assessment includes two distinct tasks, i.e., phonemic and semantic VF. It assesses long-term verbal semantic memory, phonological awareness, lexical-semantic access, and executive function. Semantic VF is mainly based on generating semantic associations, whereas phonemic VF is based majorly on executive functions instead of lexicon-semantic networks. Even though the VF involves several neural substrates majorly is coordinated by superior frontal lobe and temporal lobe for phonemic and semantic VF, respectively. This study aims to assess VF in linguistically diverse young adults of bilingual and multilingual individuals and its contributing insights into language-cognition interplay. METHOD: A prospective cross-sectional research study recruited 120 participants, divided into two groups: bilinguals (proficient in at least two languages;n = 60) and multilinguals (proficient in at least three languages;n = 60), identified through the Language Experience and Proficiency Questionnaire. Inclusion criteria encompassed participants aged 20-50, of both genders, with standard education up to graduation. Participants with neurological or psychiatric language disorders and those scoring ≤25 on the Montreal Cognitive Assessment were excluded from the study. Research-specific assessments, including Spontaneous speech of Western Aphasia Battery, Addenbrooke's Cognitive Examination-III for cognitive functions and VF tests (phonemic & semantic VF, phonemic & semantic switching tasks, alternating language task, controlled semantic association task), were conducted in L1, L2 for bilinguals and L1, L2, and L3 for multilinguals. Statistical evaluation was done using SPSS-version-29.0.10, utilizing descriptive statistics, Likelihood ratio, Chi-square, and Independent sample t'-tests. RESULTS: Participants recruited showed no significant difference between groups for age, gender and education highlighting linguistic-diversity of participants which is essential for comprehending potential variations in research findings and their general applicability. The results revealed that bilinguals outperformed multilinguals in semantic VF tasks while multilinguals outperformed bilinguals in phonemic verbal VF tasks. CONCLUSION: The adaptability in switching between semantic fields and linguistic-dimensions of temporal lobe, particularly evident in bilinguals, underscores the cognitive advantages of bilingualism. These findings are crucial for formulating targeted interventions tailored to the unique needs of linguistically-diverse dementia patients, to strengthen neural-networks which can account for the principles of neuroplasticity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".