East and West: A Role for Culture in the Acquisition of Nouns and Verbs
Bibliographic record
Abstract
Abstract This chapter presents a multifactor view of word learning, opting for a social explanation based in cultural factors. Using a modification of Gillette and Gleitman's human simulation paradigm, researchers asked adult subjects (Western students, Japanese students, and second-generation Japanese students) to guess the words an American mother was saying to her child in the play scenes. They did not specify the form class of the word to be supplied. The general prediction was that Japanese students would focus on actions more than nouns and vice versa for the Western students. They found that all three groups identified more nouns than verbs but that this effect was more pronounced with the Western students. However, there were no differences in the number of correct matches for nouns between the three groups or for the accuracy of the verbs guessed. These results suggest that cultural factors may indeed influence the English-speaking child to learn more nouns than verbs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".