Performing E-mail Tasks While Driving: The Impact of Speech-Based Tasks on Visual Detection
Bibliographic record
Abstract
Drivers listened and responded to e-mail messages presented in ahuman voice and two types of synthetic speech (concatenative and formant) whiledriving a simulator. Their performance for visual event detection, vehicle control,and message responses was assessed. Results indicated that the type of speechoutput system affected drivers’ detection of visual changes in the drivingenvironment; they were poorer at detecting these events when either of thesynthetic speech systems was used. Drivers detected fewer visual changes duringthe difficult messages than during the baseline driving. No effects of the speechsystem type or e-mail message difficulty were observed on the vehicle controlmeasures. Drivers were also less accurate when responding to message content formessages presented in synthetic speech (concatenative) compared with recordedhuman voice. Subjective ratings indicated that listening to the synthetic speechrequired more mental effort than listening to the recorded human voice.Preference ratings for the interfaces decreased as mental effort increased. Theresults indicated that although drivers were not required to direct their attentionaway from the road, using the speech-based interfaces reduced drivers’ visualevent detection and their response accuracy to messages themselves.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".