DEVELOPING IDEA OF UNIVERSAL GRAMMAR VIA NONINVASIVE IMAGING TECHNIQUES
Bibliographic record
Abstract
Our ability to speak, write, understand speech and read is critical to our ability to function in today's society. As such, psycholinguistics, or the study of how humans learn and use language, is the central topic of cognitive science. Since 1950s concept of grammatical structure, an elaboration of Humboldt’s ideas but harkens back to earlier efforts, was risen once again. Noam Chomsky, a leading figure in modern development of the idea of universal grammar, identifies precursors in the writings of Panini, Plato, and both rationalist and romantic philosophers, such as René Descartes (1647), Claude Favre de Vaugelas (1647), César Chesneau DuMarsais (1729), Denis Diderot (1751), James Beattie (1788), and Humboldt (1836) [6]. Noam Chomsky’s linguistic research in the 1950s aimed to understand the tools and means through which children acquire language [1]. Of course, it was not Noam Chomsky who first raised the question of first language acquisition, but he brought it back in terms of universal grammar. He proposed a system of principles and parameters that suggested a child’s innate understanding of syntax and semantics. But while Chomsky and his followers have been searching for rules and algorithms how UG can work, I would transfer attention to those researches who continue studying brainwork while producing and perception of speech. In my opinion, acquisition of lexically and grammatically correct language is based on understanding of the psychophysiological nature of speech. The psychophysiological basis of speech is analytic and synthetic activity of brain and, first of all, functioning of the first and second signal systems [3]. What we have in ourselves as impressions, feelings and representations from the surrounding external environment as all-natural, and from our social, excepting the word heard and seen is the first signal system of reality and it works the same both for us and for animals. But the word has made the second signal system of reality, being a signal of the first signals [2].
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".