Evaluating the feasibility of using heart rate to measure auditory attention allocation during spoken language processing
Bibliographic record
Abstract
A deceleration in heart rate (HR), along with a lengthening of the time between heartbeats, has been associated with attentional engagement. Here, we investigated the feasibility of using changes in HR to estimate attentional engagement during spoken language processing. Prior neuroimaging studies suggest that speech processing of an unknown language is more cognitively demanding; we thus designed an experiment to test this finding through HR. In an active listening task, we measured cardiac responses in 66 native English speakers (34 monolingual, 32 simultaneous bilingual), who listened to spoken passages in two conditions: one in a familiar language and the other unfamiliar. Results demonstrated significant condition effects on participants' BPM (beats per minute) and IBI (interbeat interval). Listening to an unfamiliar language induced significantly lower BPM and a trend of longer IBI, particularly during the first five out of nine trials. Our finding aligns with previous neuroimaging evidence that processing an unfamiliar language demands more attention than a familiar one. Our analysis also revealed that the effects were independent of participants' bilingual experience or language and cognitive abilities. Overall, our results indicate that HR measurement has a potential in psycholinguistic and cognitive 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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".