Driving and Speaking: Revelations by the Head-Mounted Detection Response Task

Conti, Antonia S; Dlugosch, Carsten; Schwarz, Felix; Bengler, Klaus · 2013 · Crossref

DOI: 10.17077/drivingassessment.1513

archive: archived pipeline: cataloged verified

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Summary

This study addresses the need for an economic and simple method to measure the cognitive workload associated with speech-related activities, such as conversation and speech-interaction, while driving. As in-vehicle technology becomes increasingly cognitive, industry requires reliable metrics to assess how these tasks impact driver performance. The research specifically evaluates the Head-Mounted Detection Response Task (HDRT) as a tool to quantify this workload. The study aims to determine if the HDRT can detect increased cognitive load during various conversation types and compare these naturalistic tasks against an artificial cognitive benchmark, the n-back task. The experiment involved 22 licensed participants who performed a simulated driving task on a four-lane highway while concurrently engaging in cognitive tasks and the HDRT. The HDRT required participants to press a button upon detecting a red LED stimulus mounted on their cap, with reaction times (RTs) serving as the primary metric for workload. The cognitive conditions included a baseline (driving only), a 2-back task, and three conversation scenarios: talking to a passenger seated next to the driver, talking on a hands-free cell phone with a clear connection, and talking on a cell phone with a noisy, distorted connection. The driving simulation required maintaining lane position and following distance, while the HDRT stimuli were presented at random intervals. Results indicated that the HDRT was sensitive to additional cognitive workload, with reaction times significantly increasing in all conditions involving secondary tasks compared to the driving-only baseline. Specifically, the n-back task produced the highest mean RT (624.36 ms), followed by the cell phone conversations (clear: 519.40 ms; noisy: 516.79 ms) and the passenger conversation (478.47 ms). Statistical analysis revealed significant differences between the baseline and all cognitive task conditions. However, the HDRT did not discriminate between the different conversation types; there were no significant differences in RTs based on interlocutor location (passenger vs. remote) or connection quality (clear vs. noisy). Hit rates remained above 90% across all conditions. The findings confirm that the HDRT is a reliable measure for detecting general speech-related cognitive workload and replicates previous research suggesting that conversation itself, rather than the physical presence of an interlocutor, is the primary source of distraction. The study demonstrates that the HDRT can effectively compare naturalistic tasks like conversation with artificial benchmarks like the n-back task. While the method successfully identified increased workload, it could not differentiate between specific conversation nuances, suggesting that conversing imposes a relatively uniform cognitive demand regardless of context. This supports the use of the HDRT for standardizing workload assessments in vehicle design and human factors research.

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

Topics

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• workload measurement
• cognitive
• mental demand
• auditory
• dual task performance
• distraction detection algorithms

Information type

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• Empirical Findings: behavioral performance data, physiological data
• Methodological Resource: measurement protocol