Does the Brain Read Chinese or Spanish the Same Way It Reads English?
Bibliographic record
Abstract
There are at least 6,000 languages spoken in the world today [ 1 ]. The world’s languages are represented by a variety of writing systems called “orthographies.” Orthographies are the symbols used to represent spoken language. You are looking at one type of orthography now, as you read this! So, an orthography consists of the symbols used to turn a spoken language into a written form. However, orthographies differ in the size of the sound unit that is represented by each symbol. For example, in alphabetic orthographies, such as English, Spanish, and Russian, each symbol represents an individual sound called a phoneme (e.g., the/b/sound in “book” is one phoneme). In non-alphabetic orthographies, such as Chinese or Cherokee, the symbol represents a larger sound unit such as a syllable (e.g., such as “pro” in the word “project”). Over 400 orthographies exist today. Each orthography can be classified as alphabetic, such as English, or non-alphabetic, such as Chinese. In this article, we will first learn about the characteristics of different orthographies. Then, we will use these characteristics to help understand how different writing systems affect the process of reading. We will then learn about the brain regions involved in reading.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".