Can adult learners sense L2 emotional words automatically? The role of L2 use on the emotional Stroop effect
Bibliographic record
Abstract
The present study investigated whether adult learners of second language (L2) can automatically activate emotional connotation during emotional word recognition as compared native (L1) users and whether L2 use plays a significant role in it. The automaticity of activation was measured through the emotional Stroop task. In this task, emotional words and neutral words were displayed in two different colors, and the participants were asked to indicate the color by button press. Results showed a delay in L2 learners’ response to emotional words (the emotional Stroop effect) without significant differences from L1 users’ response, indicating comparable automaticity in activating emotional connotation in performing the task. Further analyses on the effect of L2 use revealed its significant role in increasing the emotional Stroop effect. Specifically, L2 learners with higher amount of L2 use in daily life produced a significant emotion Stroop effect comparable to L1 users, while L2 learners with lower L2 use did not. We discuss the importance of L2 use in actual context in automatic processing of L2 emotional words, especially among adult learners who began L2 learning in adulthood in a case of underrepresented languages as L2.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".