Un acercamiento neurocientífico a la relatividad lingüística
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
Between the 1920s and the 1950s, linguists Benjamin Whorf and Edward Sapir shaped a hypothesis that suggests that the world we perceive is distorted by the language we speak: We see the world through a linguistic filter. This hypothesis has been interpreted and discussed countless times in the last fifty years from anthropology, sociology, linguistics and cognitive science. To Whorf, the words of our language determine the way we see the world: in the case of the rainbow, the bands of different colors that emerge from the light continuum would actually be a product of the way in which we have subdivided and named the spectrum. Color discrimination is a bad example of this theory, since it is not the result of linguistic but innate filters -product of biological mechanisms in our retinas and brains. But the “rainbow” phenomenon is relevant as an example of Categorical Perception, in which categories determine or distort our perception beyond mere physical differences: we see two shades of red that are 100 nm apart as the most similar than one shade of red and a shade of yellow at the same distance on the spectrum. Even if colors are innate categories, most of the words in our language are the names of categories that we learn through experience. The question then is if learning these categories generates changes in our perception like those that occur with the colors of the rainbow. Supported by methods that measure brain activity before, during and after learning new categories and their names, cognitive neuroscience brings new elements to study linguistic relativity from a scientific perspective. This essay recounts these approaches in order to stimulate multidisciplinary dialogues around this controversial hypothesis.
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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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