Modeling color naming in bilinguals: Computational mechanisms of crosslinguistic influence
Bibliographic record
Abstract
When bilingual speakers name stimuli such as colors or objects, their naming patterns can differ from those of monolingual speakers. Three accounts have been proposed to explain these differences – conceptual change, online lexical coactivation, and L1 footprint – yet these have not been empirically evaluated against each other. In this study, we propose a novel computational cognitive model which operationalizes each of these proposals as a mechanism of crosslinguistic influence, such that we can study their individual and combined effects on the model’s behavior. We focus on the domain of color in which we model existing experimental data collected from Navajo and English monolinguals and Navajo–English bilinguals. Our color learning model extends a statistical learning procedure for mixture models to the acquisition of labelled categories, and achieves bilingual learning by maintaining two sets of color categories and associated color words, which are connected in varying ways according to the three crosslinguistic mechanisms. We test the combinations of mechanisms in a color naming task, and analyze the match between the naming patterns of the model and the differences between bilingual and monolingual human speakers. Our results suggest that gradual conceptual change following crosslinguistic transfer at the initial learning stage can best capture the observed differences in human color naming patterns. While lexical coactivation combined with initial transfer can account for some of the empirical data, this mechanism consistently performs less well than that of conceptual change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".