An investigation of the dual mechanism model of past tense formation : does the model apply to non-native speakers?
Bibliographic record
Abstract
The purpose of this research is to further investigate the ongoing debate between the Dual Mechanism Model and the Connectionist Model of language processing by investigating how knowledge of second language (L2) inflectional morphology is represented and processed by learners of English. Specifically, do second language learners of English use the same Dual Mechanism Model that Prasada and Pinker (1993) have argued is a universally applicable model, or does the Connectionist Model of language processing better explain L2 learning and language processing? The participants in this study were students in a Montreal area CEGEP. The instrument used to gather data was the Prasada and Pinker pseudo-verb list, with modifications suggested by Lee (1994) to create a revised list. Participants were asked to create past tense forms of pseudo verbs. In addition to this task, four participants were asked to do a simultaneous verbal think aloud, orally explaining their responses to the stimulus presented in the study. The results of the studies indicate that English second language learners used both a rule based mechanism and an associative mechanism in the formation of both regular and irregular English verbs. This result provides support for the claims of the Connectionist model of past tense formation of English verbs, but also supports some of the claims of the Dual Mechanism Model. There are possible implications for the teaching and learning of English as a Second Language (ESL). This study also raises further research questions involving rule vs. associative learning in the teaching and learning of language. (Abstract shortened by UMI.)
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".