Detection Response Tasks: Using Remote, Headmounted and Tactile Signals to Assess Cognitive Demand While Driving

Harbluk, Joanne L.; Burns, Peter C.; Tam, Jane; Glazduri, Viliam

doi:10.17077/drivingassessment.1470

Cited by 20 publications

(19 citation statements)

References 7 publications

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“…Thus, according to the cognitive control hypothesis, the DRT should be sensitive to interference from cognitively loading secondary tasks. This prediction is confirmed by a large number of studies reporting that cognitively loading tasks increase DRT response times relative to a baseline (no-task) condition, with effects typically in the range of 100 to 300 ms (Bruyas & Dumont, 2013; Chong et al, 2014; Conti, Dlugosch, Vilimek, Keinath, & Bengler, 2012; Diels, 2011; Engström, Åberg, Johansson, & Hammarbäck, 2005; Engström, Larsson, & Larsson, 2013; Harbluk, Burns, Hernandez, Tam, & Glazduri, 2013; Mantzke & Keinath, 2015; Merat & Jamson, 2008; Merat, Kountouriotis, Tomlinson, Carsten, & Engström, 2015; Nilsson et al, 2017; Patten, Kircher, Östlund, & Nilsson, 2004; Ranney et al, 2011; Törnros & Bolling, 2005; Young, 2013; see further references in ISO, 2016).…”

Section: Experimental Effects Of CL On Driving Performancementioning

confidence: 70%

Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis

et al. 2017

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Objective: The objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework. Background: Although there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood. Method: Based on existing models of driver attention, an explanatory framework was outlined. This framework can be summarized in terms of the cognitive control hypothesis: Cognitive load selectively impairs driving subtasks that rely on cognitive control but leaves automatic performance unaffected. An extensive literature review was conducted wherein existing results were reinterpreted based on the proposed framework. Results: It was demonstrated that the general pattern of experimental results reported in the literature aligns well with the cognitive control hypothesis and that several apparent discrepancies between studies can be reconciled based on the proposed framework. More specifically, performance on nonpracticed or inherently variable tasks, relying on cognitive control, is consistently impaired by cognitive load, whereas the performance on automatized (well-practiced and consistently mapped) tasks is unaffected and sometimes even improved. Conclusion: Effects of cognitive load on driving are strongly selective and task dependent. Application: The present results have important implications for the generalization of results obtained from experimental studies to real-world driving. The proposed framework can also serve to guide future research on the potential causal role of cognitive load in real-world crashes.

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Section: Experimental Effects Of CL On Driving Performancementioning

confidence: 70%

Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis

et al. 2017

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“…However, as reviewed in [1,2] this effect appears to depend strongly on the type of response task used in the experiment. More specifically, cognitive load (CL) reliably impairs response performance on non-practiced, artificial, response tasks such as the Detection Response Task (DRT; [3][4][5][6][7][8]) or speeded and/or instructed responses to a lead vehicle's brake light onset [9][10][11][12][13][14][15][16][17][18][19]. However, CL appears to leave response performance more or less unaffected for more natural tasks, such as reacting to rapidly closing, visually looming (optically expanding) objects.…”

Section: Introductionmentioning

confidence: 99%

“…This idea is generally supported by the experimental literature on CL in driving. As reviewed above (and in further detail in [2]), CL has reliably been found to delay DRT responses [3][4][5][6][7][8] as well as responses to the brake light onset of a LV [9][10][11][12][13][14][15][16][17][18][19]. While the DRT is consistently mapped, it is an artificial task that is novel to most study participants and hence relies on cognitive control to be performed.…”

Section: Introductionmentioning

confidence: 99%

Simulating the effect of cognitive load on braking responses in lead vehicle braking scenarios

Engström

Markkula

Xue

et al. 2018

IET Intelligent Transport Systems

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Abstract:The recently proposed cognitive control hypothesis suggests that the performance of cognitively loading but nonvisual tasks such as cell phone conversation selectively impairs driving tasks that rely on top-down cognitive control while leaving automatized driving tasks unaffected. This idea is strongly supported by the existing experimental literature and we have previously outlined a conceptual model to account for the key underlying mechanisms. The present paper presents a mechanistically explicit account of the cognitive control hypothesis in terms of a computational simulation model. More specifically, it is shown how this model offers a straightforward explanation for why the effect of cognitive load on brake response time reported in experimental lead vehicle braking studies depends strongly on scenario kinematics, more specifically the initial time headway. It is demonstrated that this relatively simple model can be fitted to empirical data obtained from an existing meta-analysis on existing lead vehicle braking studies.

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“…It then requires a gold standard for measuring current cognitive workload on a fast enough timescale to capture these fluctuations. For this purpose, we use the detection response task (DRT), which is recommended by the International Standards Organization (ISO) (ISO 17488, 2016) for measuring the effects of cognitive workload in driving (Bengler et al, 2012;Bruyas and Dumont, 2012;Harbluk et al, 2012). The ISO DRT procedure involves presenting a simple stimulus (e.g., a light or vibrating buzzer) with a interstimulus interval that varies randomly and uniformly between 3 and 5 s and requiring participants to respond with a button press when they detect the stimulus.…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction of short-timescale fluctuations in cognitive workload

et al. 2021

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Human operators often experience large fluctuations in cognitive workload over seconds timescales that can lead to sub-optimal performance, ranging from overload to neglect. Adaptive automation could potentially address this issue, but to do so it needs to be aware of real-time changes in operators’ spare cognitive capacity, so it can provide help in times of peak demand and take advantage of troughs to elicit operator engagement. However, it is unclear whether rapid changes in task demands are reflected in similarly rapid fluctuations in spare capacity, and if so what aspects of responses to those demands are predictive of the current level of spare capacity. We used the ISO standard detection response task (DRT) to measure cognitive workload approximately every 4 s in a demanding task requiring monitoring and refueling of a fleet of simulated unmanned aerial vehicles (UAVs). We showed that the DRT provided a valid measure that can detect differences in workload due to changes in the number of UAVs. We used cross-validation to assess whether measures related to task performance immediately preceding the DRT could predict detection performance as a proxy for cognitive workload. Although the simple occurrence of task events had weak predictive ability, composite measures that tapped operators’ situational awareness with respect to fuel levels were much more effective. We conclude that cognitive workload does vary rapidly as a function of recent task events, and that real-time predictive models of operators’ cognitive workload provide a potential avenue for automation to adapt without an ongoing need for intrusive workload measurements.

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Detection Response Tasks: Using Remote, Headmounted and Tactile Signals to Assess Cognitive Demand While Driving

Cited by 20 publications

References 7 publications

Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis

Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis

Simulating the effect of cognitive load on braking responses in lead vehicle braking scenarios

Real-time prediction of short-timescale fluctuations in cognitive workload

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