Numbers and counting: Silent gesture and artificial language learning do not always reflect typological patterns
Bibliographic record
Abstract
In classifier languages, a sequence consisting of a noun (N), a numeral (Num), and a numeral classifier (CL) could in principle occur in one of six possible word orders. However, the cross-linguistic distribution of these word orders is highly uneven. Specifically, classifier languages tend to use Num-CL orders and, furthermore, N-medial orders are completely unattested in the world’s languages. We use an artificial language learning paradigm (Experiment 1) and a silent gesture paradigm (Experiment 2) to test the hypothesis that typological patterns arise from cognitive biases at the level of individual speakers. In contrast to studies that examined coarser grained ordering effects, our results do not align with typological preferences. We consider the possibility that cognitive biases might not play a role in “finer grained” ordering phenomena involving units such as classifiers, whose role in an utterance is more about grammatical well-formedness than a strong contribution to meaning.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".