Task-generated processes in second language speech production: <i>Exploring the neural correlates of task complexity during silent pauses</i>
Bibliographic record
Abstract
Abstract The last three decades have seen significant development in understanding and describing the effects of task complexity on learner internal processes. However, researchers have primarily employed behavioral methods to investigate task-generated cognitive load. Being the first to adopt neuroimaging to study second language (L2) task effects, we aimed to provide novel insights into the neural correlates of task-related variation in L2 oral production. To advance research methodology, we also tested the utility of a neuroimaging technique, functional magnetic resonance imaging (fMRI), in examining the impact of task-related variables on L2 speech production when combined with cognitive–behavioral tools (speech analysis, expert and learner judgments). Our research focus was the effects of task complexity on silent pausing. Twenty-four Japanese learners of English completed eight simple and complex versions of decision-making tasks, half in their first language and half in their L2. The dataset for the present study included the L2 speech and fMRI data, expert judgments, and participants’ difficulty ratings of the L1 and L2 tasks they completed. Based on our findings, we concluded that brain imaging and L1 task difficulty ratings were more sensitive to detecting task complexity effects than L2 self-ratings and pausing measures. These results point to the benefits of triangulating cognitive and neural data to study task-based neurocognitive 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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".