13 Considerations for the Neuropsychological Assessment of Verbal Abilities in Japanese Speakers
Bibliographic record
Abstract
Objective: Although the majority of Japanese speakers live in Japan, there are also large populations of Japanese speakers in the United States of America and Brazil, with more than a million Japanese speakers across the two countries. Only 53% of foreign-born Japanese individuals in the United States report proficiency in English. Although there has been increasing attention to the neuropsychological assessment of linguistically diverse patients broadly in recent years, there are specific considerations unique to Japanese that clinicians and researchers should be aware of when working with Japanese speakers outside of Japan. The aim of the present study is to present considerations and appropriately normed assessments of verbal abilities for Japanese patients. Participants and Methods: A systematic review of cognitive screeners and assessments of verbal fluency, verbal memory, and verbal academic skills that have been translated and normed for use with Japanese speaking populations was conducted. Studies published in both English and Japanese were reviewed. Test content modifications, administration modifications, and relevant cultural and linguistic considerations were synthesized and summarized. Results: One consideration in translation is the use of words that are linguistically and culturally comparable across the two languages. Multiple cognitive screeners and verbal learning/memory tasks have been translated with cultural equivalency considerations (e.g., for the Montreal Cognitive Assessment, velvet, church, and daisy were changed to silk, shrine, and lily). In Japanese, there is a one-to-one correspondence between sound (syllable) and graphemes (kana script), compared to one-to-many associations in alphabet-based languages like English. This impacts normative expectations on letter fluency tasks. The hiragana letters, A, Ka, and Shi (fc, fr, L) are recommended because there are relatively large number of words that start with these letters and the number of words generated with these letters showed close to normal distributions in previous research. Unlike letter fluency, semantic fluency is believed to be relatively culture-free and independent of language systems. The Japanese writing system utilizes both phonographic systems where written symbols map onto sounds, and logographic systems, where written symbols map onto concepts. This is in contrast to English, which has a solely phonographic written system. These two separate writing systems complicate the assessment of reading among Japanese-speaking individuals, as there may be a dissociation between abilities in reading in the phonographic versus logographic systems. Acculturation has been shown to impact performance on certain verbal task performances, along with demographic variables such as immigration generation status and bilingualism. Conclusions: Neuropsychologists should be familiar with linguistic differences between English and Japanese such as the one-to-one correspondence between sound and grapheme in Japanese and the use of both phonographic and logographic systems in written Japanese. Neuropsychologists should also be careful to use tests that are translated for cultural equivalence rather than direct translations, and that have been normed for use with Japanese speakers. Finally, general cross-cultural considerations in assessment such as the evaluation of bilingualism, familiarity with the testing environment, and other factors remain essential.
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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 | 0.075 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".