The impact of nonverbal information on subtitle translators’ cognitive effort: An eye-tracking and key-logging study
Bibliographic record
Abstract
This study investigates the impact of nonverbal information on the cognitive effort of translators in audiovisual translation (AVT), specifically in subtitling. While the importance of nonverbal elements in AVT is widely recognised, understanding how the amount of nonverbal information directly affects translators’ cognitive effort remains a challenge. To address this, methods from Translation Process Research (TPR) are employed, including eye-tracking, keystroke-logging and subjective reflection. Based on the multimodal transcription method, texts are differentiated according to their level of nonverbal information by analysing key features such as kinesics, camera content and acoustic diegesis. Through the experiment, the cognitive processes and perceived effort of translators are examined throughout the subtitle translation process, including video watching. Results show that an increase in the amount of nonverbal information does not lead to increased cognitive effort among subtitle translators, as evidenced by reduced fixations, keystroke operations and pauses. The perceived effort by translators aligns with this finding. Additionally, the distribution of attention exhibits a consistent pattern, with only minor differences due to varying levels of nonverbal information. Thus, this study highlights the potential to examine the role of nonverbal information in shaping translators’ cognitive processes and confirms the viability of incorporating TPR methods into AVT process research.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".