Two Apparent âCounterexamplesâ To Marcus: A Closer Look
Bibliographic record
Abstract
Marcus, Vijayan, Bandi Rao, Vishton's experiment (1999) concerning infant ability to discriminate between simple syntactic structures has prompted many connectionists to strive to demonstrate that certain types of neural networks can replicate those results.In this paper we take a closer look at two such attempts: Shultz & Bale (2001) and Altmann & Dienes (1999).We were not only interested in how well these two models matched the infants' reported results, but also whether or not they were able to learn the grammars involved in this process.After performing an extensive set of experiments, we found that, at first blush, Shultz & Bale's model replicated the infant's known data, but the model largely failed to learn the grammars.We also discovered serious problems with Altmann & Dienes' model, which failed to match most of the infant's results and to learn the syntactic structure of the input patterns.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".