Analysis of cell diversity in human and mouse basal ganglia by single-cell RNA sequencing
Bibliographic record
Abstract
The issues related to the bilingual lexical processing are primarily concerned with the rapid involvement of phonological codes in the process of silent word reading.Here, we investigated bilinguals' neurolinguistic activities during L1 (Korean) and L2 (English) translation as well as language-switching during the same or different sensory type of linguistic stimuli.Subjects performed the translation task consisted of four experimental conditions, KK (Korean-Korean), KE (Korean-English), EE (English-English), and EK (English-Korean).Four words on the screen of visual stimulus and the sound of one word out of four words of auditory stimulus were given simultaneously and the condition was randomly selected in each trial.Subjects were instructed to translate what they see and hear in a brief time and to choose the correct answer as fast as they could.The result from a behavioral experiment showed significant correlations between the reaction time and accuracy in EK and EE.Additionally, the negative correlation was found in each condition accompanying speed-accuracy tradeoffs especially when the type of language was different such as EK and KE.Along with the results from a behavioral study, strong BOLD signals in middle frontal gyrus, precuneus, inferior occipital gyrus, BA 18, BA 9 and BA 39 regions were found in contrasts between conditions in fMRI experiment.We found that the brain took a similar route for translation as well as reading and listening to the word irrespective of the types of language.Our findings indicated when the given linguistic stimulus is less exposed one, especially when the stimulus is a sound, more regions are required to translate it into their familiar language.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".