An E-Prime Study on the Cognitive Mechanisms of English Predicative Metaphor Comprehension by Chinese EFL Learners
Bibliographic record
Abstract
Studies on predicative metaphors like The rumor flew through the office have not received due attention until recently. Through a behavioural experiment, this study investigates the cognitive mechanisms as well as the effects of familiarity on Chinese EFL learners’ comprehension of English predicative metaphors, adopting a two factors within-subject design: 2 (degree of familiarity: high-familiarity, low-familiarity) × 3 (priming condition: matching priming condition (MP), mismatching priming condition (MMP) and, no priming condition (NP)). Forty-five third-year Chinese undergraduate students participated in the experiment by completing a metaphor semantic comprehension test. Their reaction times (RTs) and accuracy rate of comprehension were recorded and a two-way ANOVA analysis of the results reveals that: Embodied simulation mechanism plays an important role in English predicative metaphor processing, especially when the metaphors being processed are unfamiliar. Yet its role diminishes when the metaphors being processed are highly familiar, which encourages the use of the language processing mechanism. To conclude, Chinese EFL learners make use of either the embodied simulation mechanism or the language processing mechanism in comprehending predicative metaphors, depending on their varying degrees of familiarity. These findings shed light on predicative metaphor instruction in L2 English teaching.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".