Does the Brain Read Chinese or Spanish the Same Way It Reads English?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are at least 6,000 languages spoken in the world today [<xref ref-type="bibr" rid="B1">1</xref>]. 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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it