The Effect of Cognitive Load on Auditory Susceptibility During Automated Driving

Van der Heiden, Remo M. A.; Kenemans, J. Leon; Donker, Stella F.; Janssen, Christian P. · 2021 · Crossref

DOI: 10.1177/0018720821998850

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study investigates how cognitive load from nondriving tasks affects auditory susceptibility during automated driving. As automation levels increase, drivers are expected to monitor the system and intervene when necessary, often relying on auditory alerts. While previous research established that automated driving itself reduces susceptibility to auditory stimuli compared to stationary conditions, it remained unclear how concurrent cognitive tasks—common in semi-automated vehicles—further impact this susceptibility. The authors aimed to determine if performing additional cognitive tasks during automated driving significantly diminishes the brain’s ability to process auditory alerts, using the frontal P3 (fP3) event-related potential as a neurophysiological marker of susceptibility. The experiment employed a within-subjects design with 24 participants in a high-fidelity driving simulator. Participants experienced four conditions: stationary baseline, automated driving without additional tasks, automated driving with a noun repetition task, and automated driving with a verb generation task. The verb generation task was selected to induce higher cognitive load than simple repetition. Throughout the trials, participants listened to a stream of standard tones interspersed with novel environmental sounds (oddball probes). Electroencephalography (EEG) was used to record brain activity, specifically analyzing the fP3 response, which indicates the brain’s readiness to detect and respond to novel stimuli. Speech response times were also measured to validate the cognitive load manipulation. Results indicated that the fP3 amplitude was significantly lower during automated driving combined with cognitive tasks compared to automated driving alone. Specifically, both the noun repetition and verb generation conditions resulted in reduced fP3 responses relative to the automated-only condition, demonstrating that any concurrent task diminishes auditory susceptibility. However, there was no significant difference in fP3 amplitude between the repetition and generation tasks, suggesting that the mere presence of a secondary task, rather than its specific difficulty level, drove the reduction in susceptibility. Speech response times confirmed the load manipulation, as participants took significantly longer to generate verbs than to repeat nouns. Additionally, the fP3 response in the automated-only condition did not differ significantly from the stationary baseline, indicating that automated driving alone did not substantially reduce susceptibility in this specific setup. The findings imply that engaging in nondriving tasks during automated driving creates additional cognitive load that reduces a driver’s susceptibility to auditory alerts. This has critical implications for the design of semi-automated vehicles (SAE levels 3 and 4), where human intervention may be required. Designers must account for the reduced effectiveness of auditory warnings when drivers are distracted by other tasks. The study highlights that even low-load tasks can impair the brain’s processing of critical auditory information, necessitating robust alerting strategies or interface designs that mitigate the risks associated with divided attention in automated driving environments.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• auditory
• mental demand
• workload measurement
• music audio mood
• cognitive
• cognitive capacity variation

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: physiological data
• Theoretical Contribution: theory or model, conceptual framework