Listen before you drive: the effect of voice familiarity on listening comprehension and driving performance
Bibliographic record
Abstract
OBJECTIVE: Voice familiarity has been reported to reduce cognitive load in complex listening environments. The extent to which the reduction in listening effort allows for mental resources to be reallocated to other complex tasks needs further investigation. We sought to answer whether a familiar audiobook narrator provides benefits to (1) listening comprehension and/or (2) driving performance. DESIGN: A double-blind between-groups design was implemented. Participants were randomly assigned to the Familiar group or the Unfamiliar group. STUDY SAMPLE: = 18 female). Participants first listened to an audiobook read by either Voice 1 (Familiar condition) or Voice 2 (Unfamiliar condition). Then they completed a virtual reality driving task while listening to a second audiobook, always read by Voice 1. Audiobook comprehension (30-question multiple-choice test) and driving performance (number of driving errors made) were recorded. RESULTS: Participants in the Familiar group made fewer driving errors than participants in the Unfamiliar group. There were no differences in listening comprehension. CONCLUSIONS: Increased voice familiarity positively impacts behaviour (i.e. reduced driving errors) in normal-hearing adults. We discuss our findings in the context of effortful listening frameworks.
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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 | 0.006 |
| 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) | 0.004 | 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".