Motorists often engage in secondary tasks unrelated to driving that increase cognitive workload, resulting in fatal crashes and injuries. An International Standards Organization method for measuring a driver’s cognitive workload, the detection response task (DRT), correlates well with driving outcomes, but investigation of its putative theoretical basis in terms of finite attention capacity remains limited. We address this knowledge gap using evidence-accumulation modeling of simple and choice versions of the DRT in a driving scenario. Our experiments demonstrate how dual-task load affects the parameters of evidence-accumulation models. We found that the cognitive workload induced by a secondary task (counting backward by 3s) reduced the rate of evidence accumulation, consistent with rates being sensitive to limited-capacity attention. We also found a compensatory increase in the amount of evidence required for a response and a small speeding in the time for nondecision processes. The International Standards Organization version of the DRT was found to be most sensitive to cognitive workload. A Wald-distributed evidence-accumulation model augmented with a parameter measuring response omissions provided a parsimonious measure of the underlying causes of cognitive workload in this task. This work demonstrates that evidence-accumulation modeling can accurately represent data produced by cognitive workload measurements, reproduce the data through simulation, and provide supporting evidence for the cognitive processes underlying cognitive workload. Our results provide converging evidence that the DRT method is sensitive to dynamic fluctuations in limited-capacity attention.

When the web became popular, people had to develop ways to talk and think about it. In the mid-1990s, we analyzed spatial language in "web talk." We found that people described pages as places, and search as motion, both passive and active motion. Here we investigate web talk nearly two decades later. Our analysis reveals that some spatial language has stayed the same, and some has changed. Of special interest is how far fewer motion verbs are used nowadays. We argue that people naturally produce spatial metaphors when talking about new technological domains, and that over time, the most useful elements persist.

With the emergence of vehicle-based technologies that could compete for attention due to visual and cognitive workloads in a driving environment, it is important to accurately assess the various components of potential distractions. Current Detection Response Task (DRT) measurements are sensitive to overall mental workload, but may not be useful for assessing visual workload. This study seeks to examine the ability of two unique extensions of DRTs to assess levels of cognitive and visual load in a lateral steering tracking task. Each DRT was tested in conditions that manipulated cognitive load, visual load, the combination of cognitive and visual load, and normal driving conditions. The data suggest that an altered design of the DRT may allow for reliable assessment of cognitive and visual loads simultaneously during a driving task. Measuring the components of different types of workload that lead to driver distraction may inform industry standards for assessing driver distraction in the vehicle.

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Objective: A set of 4 driving related tasks were used to evaluate the potential for a modified Detection Response Task (DRT) to simultaneously measure visual and cognitive task demands. Background: The accurate assessment of cognitive and visual tasks demands in driving has become increasingly important. As of yet, no simple, cost effective approach has been found to measure visual demands in complex, multimodal tasks. Methods: Two experiments are presented which evaluate an extension of the standard DRT methodology. The discriminate sensitivity of the experiments is tested using an integrated testing configuration, which systematically increased visual demand across four conditions. Results: Results suggest that the standard DRT configurations are highly tuned to selectively evaluate cognitive demand but that a variant of the system may be able to simultaneously evaluate changes in both visual and cognitive task demands. Conclusions: These data suggest that the simple, rapid, and reliable assessment of both visual and cognitive task demands is possible, even in highly fluid systems with non-constant visual task requirements.