Electroencephalography Shows Effects of Age in Response to Oddball Auditory Signals: Implications for Semi-Autonomous Vehicle Alerting Systems for Older Drivers
Bibliographic record
Abstract
This research considers the efficacy of auditory alert systems for semi-autonomous vehicles from the perspective of the neurological processing of multi-modal information.While semiautonomous vehicles are growing in popularity, there is much to be discovered concerning driver safety.For example, understanding how the brain integrates multi-model information is essential to determining the efficacy of auditory alerting systems in semi-autonomous vehicles.The present work reports on how the auditory processing of deviant and standard stimuli is impacted by age and workload conditions at regions of the brain involved in the auditory processing pipeline.Electroencephalography (EEG) and behavioural data from five older (57-78) and five younger (18-26) participants were collected.Participants completed a visual memory task with low-and high-workload conditions along with a novel paired-click paradigm.Behavioural results showed the expected negative effects of age on visual task accuracy and response times.EEG results showed that in the low-workload visual task condition both younger and older adults partially overcame the redundancy effect of the second paired tone when a highly salient stimulus was presented.In contrast, P200 neural responses to these oddball tones were attenuated in older adults in the high-workload conditions of the visual memory task.These findings have implications for how alerting systems are implemented in semi-autonomous vehicles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".