How are prosodic features processed cognitively? A case study of the multimodal cognitive dynamics in simultaneous interpreting with text
Bibliographic record
Abstract
This paper examines the effect of prosodic features on trainee interpreters’ cognitive effort, cognitive coordination and interpreting output in simultaneous interpreting with text. Sixteen trainee interpreters were recruited and randomly divided into two conditions to simultaneously interpret 27 sentences from English (L2) to Chinese (L1), with controlled prosodic features. Their interpreting output and eye-movements were recorded with the Eyelink 1000 Plus eye-tracking system. Various eye-metrics and temporal measures (i.e., ear-voice span, eye-voice span and ear-eye span) were analysed. The Simplified Chinese LIWC2015 Dictionary was employed to analyse the impact of prosodic features on the interpreting output. The findings indicate that prosodic features mitigate cognitive effort and facilitate the integration of auditory and visual inputs, as evidenced by the eye-tracking measures. While some prosodic features function as a predictive cue for content shifts, enhancing cognitive coordination and information processing efficiency, others hinder the coordination process due to their signalling of uncertainty or emphasis. This study reveals the role of prosodic features in enabling interpreters to produce linguistically and thematically rich outputs, indicating that interpreters interpret not only the linguistic meaning but also the underlying sense.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".