1 To appear in the Canadian Journal of Lingustics, special 50th anniversary issue Syntactic Dependencies as Memorized Sequences in the Brain
Bibliographic record
Abstract
I would like to consider a unification of two areas in cognitive neuroscience: Investigations of working memory (WM), and the study of syntactic representation and processing. I would like to think about the functional neuroanatomy of these seemingly unrelated systems, and entertain the possibility that they may be much more closely related than previously supposed. Think of dependency relations in syntax: It is clear that their computation requires a memory. A sentence like (0), to take an extreme example, requires several memories, each with different properties: (0) [Which of the papers that he1 gave to Ms. Brown2]3 did every student1 hope t’3 that she2 will read t3 Here, not only does each pronoun relate to a different antecedent {1:(every student, he), 2:(Ms. Brown, she)}, but also, the parenthesized expression to the left must be linked to two different positions 3:(Which of the papers that he gave to Ms. Brown, t’,t). This is a truly complex structure, aspects of which will be ignored here (like quantifier scope, precedence relations among syntactic operations, etc., cf. Fox, 1999). Suffice it to note that we have at least three separate links, each with its own structural properties, each requiring a memory to hold linked parts temporarily during processing. Perhaps, I will
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.494 | 0.199 |
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".