The Processing of Interlexical Homographs in Translation Recognition and Lexical Decision: Support for Non-Selective Access to Bilingual Memory
Bibliographic record
Abstract
In three experiments we looked at the processing of interlexical homographs by Dutch-English bilinguals. In Experiment 1 we employed the translation recognition task, a task that forces the participants to activate both language systems simultaneously. In this task the processing of interlexical homographs was inhibited substantially compared to the processing of matched control words, especially when the homograph reading to be selected was the less frequent of the homograph's two readings. In Experiments 2 and 3 we used the lexical decision task: In one condition we asked the participants to categorize letter strings as words or nonwords in Dutch; in a second condition we asked them to do so in English. The makeup of the stimulus set in Experiment 2 permitted the participants to ignore the instructions and to instantiate the task in a language-neutral form--that is, to categorize the letter strings as words in either Dutch or English. Under these circumstances a small, frequency-dependent inhibitory effect for homographs was obtained, but only in condition Dutch. In Experiment 3 the participants were forced in a language-specific processing mode by the inclusion of "nonwords" that were in fact words in the non-target language. Large frequency-dependent inhibitory effects for homographs were now obtained in both language conditions. The combined results are interpreted as support for the view that bilingual lexical access is non-selective.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".