Examining the Bilingual Mental Lexicon through Associative Priming
Bibliographic record
Abstract
Research examining the associations between words in the monolingual versus bilingual mind has employed various models to examine differences in lexical organization, with varying degrees of success. The paradigms used have primarily been word association and semantic priming with a Lexical Decision Task (LDT). This thesis research has focused on the latter method, with an online data collection method using Testable. One distinction of this thesis research has been the types of semantic associations used for priming, namely syntagmatic and paradigmatic associations, which refer to either word context in a sentence, or word categories respectively. The control condition used from which facilitation effects were calculated was unrelated primes. In addition, a phonetic (or “clang”) priming condition was included as it was felt that it might tap into an important aspect of lexical organization for those who have English as a second language (L2). Recruitment was for native English-speaking monolinguals, native English-speaking bilinguals (who also speak a variety of other languages), and non-native English-speaking bilinguals (also from a range of language backgrounds) to participate. Results indicated that the paradigm was successful in gathering information about lexical associations in all three language groups. There was significant semantic facilitation across all language groups for both syntagmatic and paradigmatic associative primes, with these effects not differing from each other. Interestingly, only the L2 group showed significant facilitation from clang primes. Overall, the absolute priming effect was smaller than anticipated, despite reaching statistical reliability, suggesting possibilities to refine the display times of primes or targets. Other hypotheses concerned potential effects of participants’ context for L2 language learning and also attempts to address the main research question with the use of a classic word association task; however, both fell victim to the vagaries of online data collection. Nevertheless, the method and the software provide some hope for continued research in some aspects of the monolingual versus bilingual mental lexicon.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".