Investigating verbal and nonverbal indicators of physiological response during second language interaction
Bibliographic record
Abstract
Abstract Second language (L2) researchers have long acknowledged that affective variables (e.g., anxiety, motivation, positive emotions) are essential in understanding L2 learner psychology and behavior, both of which influence communication and have implications for language learning. However, there is little research investigating affective variables during L2 interaction, particularly from a dynamic rather than a static, trait-oriented perspective. Therefore, this study examined 60 L2 English speakers’ affective responses in real time during a paired discussion task using galvanic skin response sensors to capture speakers’ anxiety. Analyses focused on speakers’ speech, their behavioral reactions, and the content of their discussion while experiencing anxiety episodes (high vs. low arousals). Findings revealed that speakers glanced away, blinked, and used self-adaption gestures (touching face, hair-twisting) significantly more frequently during high arousals than low arousals, whereas head nods were found to occur significantly more often during low arousals. In comparison to low arousals, a larger proportion of high arousals occurred while discussing personal topics. Implications are discussed in terms of the role of affective variables in communication processes.
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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 | 0.001 |
| 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 | 0.000 |
| 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".