2020
DOI: 10.3758/s13423-020-01741-8
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How is multi-tasking different from increased difficulty?

Abstract: With the advancement of technologies like in-car navigation and smartphones, concerns around how cognitive functioning is influenced by "workload" are increasingly prevalent.Research shows that spreading effort across multiple tasks can impair cognitive abilities through an overuse of resources, and that similar overload effects arise in difficult single-task paradigms. We developed a novel lab-based extension of the Detection Response Task, which measures workload, and paired it with a Multiple Object Trackin… Show more

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Cited by 37 publications
(51 citation statements)
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References 60 publications
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“…With (23), the authors obtained the adjusted human error rates, and the detailed results are in Table XIV…”
Section: ) Numerical Results Of Risk Cubementioning
confidence: 99%
See 2 more Smart Citations
“…With (23), the authors obtained the adjusted human error rates, and the detailed results are in Table XIV…”
Section: ) Numerical Results Of Risk Cubementioning
confidence: 99%
“…The attention required in the air traffic, a complex situation, can exceed the controller's available attention causing the increasing workload [22]. The increasing mental workload can decrease reaction speed [23]. The excessive mental workload can lengthen the controller's reaction time [24] to increase the operational risk.…”
Section: Context Learning and Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…From the analysis of the survey and the literature review the nexus between cognitive capacity, workload, and the indicators of a loss of SA should be explored through experimentation. Such an experiment has two main requirements: First, an experimental platform that allows to systematically change the complexity, or load, of the task (Howard et al, 2020) and second, an objective way for measuring operators' workload. The Detection Response Task (DRT) is a well established methodology for observing changes in cognitive workload and capacity, especially in the automotive industry (Strayer et al, 2006;Conti et al, 2012).…”
Section: Experimental Platform For Assessing Samentioning
confidence: 99%
“…where x is the evidence, W is the Wiener process (noise from the standard normal distribution that evolves with the square root of time), and c is the diffusion coefficient, with c being fixed to 0.1 to solve a scaling property within the model. The ability to estimate these components of the decision-making process has made the diffusion model one of the most useful tools within cognitive psychology, being used to address questions in areas such as such as intelligence (van Ravenzwaaij, Brown, & Wagenmakers, 2011;Lerche et al, 2020), performance optimality (Starns & Ratcliff, 2012;Evans & Brown, 2017;Evans, Bennett, & Brown, 2019), cognitive workload (Ratcliff & Strayer, 2014;Tillman, Strayer, Eidels, & Heathcote, 2017;Howard, Evans, Innes, Brown, & Eidels, 2020), ageing (Ratcliff et al, 2001;, emotion processing (Lerche, Bucher, & Voss, 2019), alcohol consumption (van Ravenzwaaij, Dutilh, & Wagenmakers, 2012), and ego depletion (Lin, Saunders, Friese, Evans, & Inzlicht, 2020), just to name a few.…”
Section: Review Of Existing Modelsmentioning
confidence: 99%