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Supertaskers: Profiles in extraordinary multitasking ability

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Theory suggests that driving should be impaired for any motorist who is concurrently talking on a cell phone. But is everybody impaired by this dual-task combination? We tested 200 participants in a high-fidelity driving simulator in both single- and dual-task conditions. The dual task involved driving while performing a demanding auditory version of the operation span (OSPAN) task. Whereas the vast majority of participants showed significant performance decrements in dual-task conditions (compared with single-task conditions for either driving or OSPAN tasks), 2.5% of the sample showed absolutely no performance decrements with respect to performing single and dual tasks. In single-task conditions, these "supertaskers" scored in the top quartile on all dependent measures associated with driving and OSPAN tasks, and Monte Carlo simulations indicated that the frequency of supertaskers was significantly greater than chance. These individual differences help to sharpen our theoretical understanding of attention and cognitive control in naturalistic settings.
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... The efficiency of handling multiple tasks in complex environments differs across individuals. A small fraction of the population, coined supertaskers, even seem to be capable of performing multiple tasks with no costs relative to a single task situation (Medeiros-Ward et al., 2015;Watson & Strayer, 2010). Such variations reflect, on the one hand, individual differences in the cognitive abilities that underlie multitasking behavior. ...
... Other authors used multiple multitasking measures such as , Martin et al. (2020). Watson and Strayer (2010) used a driving simulator and an operation span task simultaneously to assess multitasking ability. ...
... Moreover, Fischer and Hommel (2012) applied an experimental approach, where participants engaged in divergent thinking (flexible task processing mood), convergent thinking (focused task processing mode), and neutral control conditions, and studied shifting task in the context of a dualtask paradigm. The other experimental study, Watson and Strayer (2010) presented two conditions (single and dualtask conditions). ...
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Our ability to multitask—focus on multiple tasks simultaneously—is one of the most critical functions of our cognitive system. This capability has shown to have relations to cognition and personality in empirical studies, which have received much attention recently. This review article integrates the available findings to examine how individual differences in multitasking behavior are linked with different cognitive constructs and personality traits to conceptualize what multitasking behavior represents. In this review, we highlight the methodological differences and theoretical conceptions. Cognitive constructs including executive functions (i.e., shifting, updating, and inhibition), working memory, relational integration, divided attention, reasoning, and prospective memory were investigated. Concerning personality, the traits of polychronicity, impulsivity, and the five-factor model were considered. A total of 43 studies met the inclusion criteria and entered the review. The research synthesis directs us to propose two new conceptual models to explain multitasking behavior as a psychometric construct. The first model demonstrates that individual differences in multitasking behavior can be explained by cognitive abilities. The second model proposes that personality traits constitute a moderating effect on the relation between multitasking behavior and cognition. Finally, we provide possible future directions for the line of research.
... A contrasting view is a conception of today's students as fundamentally and uniquely different from previous generations who have grown up in a world which has honed their cognitive and neurological development to allow simultaneous and effective use of multiple streams of information (Brown, 2000;Negroponte, 1995;Prensky, 2001aPrensky, , 2001bVeen & Vrakking, 2006). This assessment is often echoed by mass media (Brooks, 2001;Meade, 2003;Wallis, 2006) and students, who describe themselves as bored and distracted when not working with multiple sources of information simultaneously (Bowman et al., 2010;Frand, 2000;Hammer et al., 2010;Watson & Strayer, 2010). According to this view, instructors should perhaps abandon the lecture in favor of developing new teaching approaches catering to the technological mastery and need for stimulation which distinguishes the new generation of digital natives (Prensky, 2001a; see also Kirschner & de Bruyckere, 2017, for an opposing view). ...
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Classroom response systems (i.e., clickers) have become increasingly popular to facilitate student learning. Unfortunately, the common practice of pausing a lecture to ask questions takes up precious time to cover content. Asking questions “on the fly” without pausing is a possible solution. But can students both attend to lecture and answer questions simultaneously? Is this multitasking detrimental to student learning? In three experiments, we examined the effects of relevant and irrelevant “on-the-fly” questions and note taking on lecture retention. Undergraduates watched a video of a classroom lecture while either taking notes or not and receiving 0, 6, 18, or 36 questions that were either relevant or irrelevant to the lecture and then took a test. Students performed better on the test when receiving relevant rather than irrelevant questions. As for an optimal number of questions or whether note taking should also be allowed, there were no obvious advantages. Thus, when considering using “on the fly” clicker questions during a lecture vs. having no such questions, our evidence indicates no clear interference. Rather, such activities such as clickers may counter lecture boredom by allowing students to multitask with relevant activities.
... The battery implements ten measures of cognitive control: working memory (both single and dual n-back tasks [16]), response inhibition (the stop-signal task [17]), attention control (Simon [18], Flanker [19] and Stroop [20] conflict tasks), multi-tasking [21], task switching [22] and using prior information [23]. Each task, and the way in which it measures a cognitive control construct, is described in detail in the next section. ...
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Over the last few years, it has become accepted that reliable measurement of individual cognitive abilities requires participants to complete many more trials and/or to use tasks with larger effect sizes than are typical of existing cognitive batteries. This project develops a battery of cognitive control tests enabling efficient and reliable measurement of cognitive control abilities crucial for high performance under time pressure. The test battery is implemented in the Unity game engine, and accessible online using only a web browser with no installation. Gaming mechanics (e.g., variety, feedback, rewards, and a leader board) and an integrated story line maintain engagement over extended and demanding testing sessions. The battery implements most prominent measures of cognitive control including: 1) working memory (single and dual n-back tasks), 2) response inhibition (stop-signal task), 3) conflict tasks (Simon, Flanker and Stroop tasks), 4) multi-tasking, and 5) task switching. The different measures can be flexibly combined within a coherent "room-clearing" narrative, and self-contained tutorials enable easily deployed online testing. Novel versions of the conflict tasks were developed to increase effect sizes and reliability, and they were tested in an online experiment. We develop a rigorous methodology for quantifying the ability of the tests to produce reliable measurements of individual differences and report the results of applying it to data from the A Flexible Gaming Environment for Reliably Measuring Cognitive Control 14-2 STO-MP-HFM-334 experiment. We conclude that these new conflict tasks produce much more reliable measurement than has previously been achieved.
... A parallel line of inquiry has attempted to identify individual and situational factors that might reduce the deleterious effects of multitasking, linking general multitasking ability (Himi et al., 2019;Watson and Strayer, 2010) and the nature of the dual-tasks themselves (e.g., design of user interfaces in vehicles; Feng et al., 2018) to performance on concurrent tasks. Some recent research (Han & Broniarczyk, 2021) has focused on identifying instances when consumers choose to multitask rather than engage in tasks independently. ...
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Despite the ubiquity of multitasking, prior research has ignored potential carryover effects of concurrent task performance on the way individuals interpret subsequent consumption behaviors and decisions. This study evinces that extensive use of updating, an executive function central to multitasking and necessary for construing actions abstractly, reduces the likelihood that it will be used on subsequent tasks. Accordingly, the results of three studies show that extensive employment of updating (via multitasking) reduces an individual's propensity to construe behaviors (consumption or otherwise) in abstract (vs. concrete) terms, influencing consumer choices. Specifically, we show that individuals are less likely to choose healthy food items (i.e., those consistent with long‐term health goals) and favor feasibility over desirability following episodes of multitasking. Further, we rule out general cognitive depletion and mood as alternative explanations for such carryover effects.
... While the Wickens' (2002) multiple resource model provides a useful means of characterizing the sources of interference produced when drivers concurrently engage in conversation, it does not account for the dynamically shifting demands of conversational exchanges over the course of a driving task. After all, driving and conversation are both activities that take place in time (Watson & Strayer, 2010), and thus involve the performance of tasks that vary in sequence, duration, and frequency of execution (Hollnagel et al., 2003;Salvucci et al., 2009). To address this, Salvucci and Taatgen (2008) presented threaded cognition, an integrated theory of multitasking implemented within the ACT-R cognitive framework (Anderson et al., 2004). ...
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We report results from a driving simulator paradigm we developed to test the fine temporal effects of verbal tasks on simultaneous tracking performance. A total of 74 undergraduate students participated in two experiments in which they controlled a cursor using the steering wheel to track a moving target and where the dependent measure was overall deviation from target. Experiment 1 tested tracking performance during slow and fast target speeds under conditions involving either no verbal input or output, passive listening to spoken prompts via headphones, or responding to spoken prompts. Experiment 2 was similar except that participants read written prompts overlain on the simulator screen instead of listening to spoken prompts. Performance in both experiments was worse during fast speeds and worst overall during responding conditions. Most significantly, fine scale time-course analysis revealed deteriorating tracking performance as participants prepared and began speaking and steadily improving performance while speaking. Additionally, post-block survey data revealed that conversation recall was best in responding conditions, and perceived difficulty increased with task complexity. Our study is the first to track temporal changes in interference at high resolution during the first hundreds of milliseconds of verbal production and comprehension. Our results are consistent with load-based theories of multitasking performance and show that language production, and, to a lesser extent, language comprehension tap resources also used for tracking. More generally, our paradigm provides a useful tool for measuring dynamical changes in tracking performance during verbal tasks due to the rapidly changing resource requirements of language production and comprehension.
... Most people are poor at multitasking, suffering performance decrements to one or more of the concurrently performed tasks, even though they may think they are good at it (Sanbonmatsu et al., 2013;Strayer et al., 2011;Watson & Strayer, 2010). To date, most research has focused on the concurrent aspects of dual-task performance with much less attention given to performance following a multitasking episode (i.e., when people stop multitasking and return to the single task). ...
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We examined the hidden costs of intermittent multitasking. Participants performed a pursuit-tracking task (Experiment 1) or drove in a high-fidelity driving simulator (Experiment 2) by itself or while concurrently performing an easy or difficult backwards counting task that periodically started and stopped, creating on-task and off-task multitasking epochs. A novel application of the Detection Response Task (DRT), a standardized protocol for measuring cognitive workload (ISO 17488, 2016), was used to measure performance in the on-task and off-task intervals. We found striking costs that persisted well after the counting task had stopped. In fact, the multitasking costs dissipated as a negatively accelerated function of time with the largest costs observed immediately after multitasking ceased. Performance in the off-task interval remained above baseline levels throughout the 30-s off-task interval. We suggest that loading new procedures into working memory occurs fairly quickly, whereas purging this information from working memory takes considerably longer. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Motivated by behavioural and psychological phenomena that occur in human operators, we study single-machine multitasking scheduling with job efficiency promotion. In traditional multitasking scheduling, the primary task is assumed to be interrupted by every waiting task. In this paper we take into account job efficiency promotion that helps reduce the actual interruption time. We propose two functions to model job efficiency promotion based on the job positions in a given schedule. The objective is to minimize the makespan, total completion time, and total absolute difference in completion times. We show that the problem is polynomially solvable for each objective. We also provide efficient solutions for some special cases.
Chapter
Students increasingly control their learning as university instructors shift away from lecture formats, courses are offered online, and the internet offers near infinite resources for student-controlled informal learning. Students typically make effective choices about learning, including what to learn, when to learn, and how to learn, but sometimes make less-than-optimal study choices, including trying to study while multi-tasking. Dividing attention among various tasks impairs both learning and learners' control over their learning because secondary tasks divert cognitive resources away from learning and metacognition. This chapter reviews recent studies explaining how dividing attention affects students' metacognition, including their assessments of their own learning and the study choices that they make. This chapter reviews the fundamentals of metacognition, describes the impact of dividing attention on the effectiveness of learners' metacognition, and provides suggestions about how to enhance the efficacy of metacognition when students' attentional resources are limited.
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