Dynamic workload measurement and modeling: Driving and conversing
DOI: 10.1037/xap0000431
archive: archived pipeline: cataloged verified
Summary
Castro, Heathcote, Cooper, & Strayer extended Tillman et al. (2017) by separately measuring the cognitive workload of drivers and non-drivers (passengers or remote cell-phone partners) while their speaking and listening turns in a natural conversation were tracked. Forty-four University of Utah undergraduates ran in 22 dyads on a DS-600 DriveSafety simulator (19-mile highway scenario, ~15-min blocks). Yoked vibrotactile DRT devices (ISO 17488) were fitted to the driver's and non-driver's left collarbones; lights flashed every 3-5 s and microswitch responses were time-locked to speaking vs. listening intervals. DRT response time was fitted with a Linear Ballistic Accumulator with Omissions (LBAO) to decompose workload into drift rate (evidence accumulation), response threshold (caution), and non-decision time.
Key finding
Drivers showed elevated DRT RT relative to non-drivers, and within both members of the dyad RT was higher when speaking than when listening, with conversational turn-taking producing reciprocal workload trade-offs. Unlike Tillman et al. (2017), who attributed dual-task cost solely to increased response threshold, LBAO modelling here showed that conversing while driving altered both the response threshold and the rate of evidence accumulation, consistent with a combined response-caution and capacity-sharing account; aggregating across speaking and listening in earlier work likely masked the drift-rate effect.
Methodology
Simulator dual-task experiment: 22 dyads (44 participants) drove a DS-600 simulator while conversing in person or via hands-free cell phone. Yoked DRT devices (ISO 17488) recorded RT and miss rates separately for driver and non-driver, segmented by speaking vs. listening turns. Linear mixed-effects models compared conditions; LBAO evidence-accumulation models were fit to decompose workload into drift rate, response threshold, and non-decision time.
Sample size: N=44 (22 dyads, 23 female, M age=21.1, SD=3.4), University of Utah undergraduates
Quality score: 5 / 5