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Goal interference occurs when you reach a decision to accomplish a specific goal (e.g., retrieve something from the refrigerator, complete a work assignment, engage in a conversation, drive your car) and something takes place to hinder the successful completion of that goal. The interference can either be generated internally, presenting as thoughts within your mind, or generated externally, by sensory stimuli such as restaurant chatter, beeps, vibrations, or flashing visual displays (figure 1.1). Goal interference, originating from either your internal or external environments (often both), can occur in two distinct varieties—distractions and interruptions—based on your decision about how you manage the interference.1
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Interruptions are the other major source of goal interference. The difference from distractions is that interruptions happen when you make a decision to concurrently engage in more than one task at the same time, and even if you attempt to switch rapidly between them.
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Our cognitive control abilities that are necessary for the enactment of our goals have not evolved to the same degree as the executive functions required for goal setting.
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Our cognitive control is really quite limited: we have a restricted ability to distribute, divide, and sustain attention; actively hold detailed information in mind; and concurrently manage or even rapidly switch between competing goals.
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The true core of the change to our mental landscape is that we are experiencing an elevation of information itself to the level of the ultimate commodity.
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multitask.” For example, a study by Dr. Rosen’s lab found that the typical teen and young adult believes that he or she can juggle six to seven different forms of media at the same time.7 Other studies have shown that up to 95 percent of the population report media multitasking each day, with activity in more than one domain occupying approximately a third of the day.8
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US adults and teenagers check their
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phone up to 150 times a day, or every six to seven minutes that they are awake.9
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A common explanation that is offered in response to this question is that it is simply more fun and rewarding to engage in multitasking compared to single tasking.
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In regard to rewards, researchers have shown that novelty is associated with reward processing in our brains.13
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In addition, the act of receiving an earlier reward is often more highly valued, even if a delayed reward has greater overall associated value.14 This phenomenon, known as the “temporal discounting of rewards,” is a strong influence on impulsive behaviors and so may also play a role in the inherent drive to seek the immediate gratification that comes from switching to new tasks sooner rather than later.
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The answer is that at our core we are information-seeking creatures, so behaviors that maximize information accumulation are optimal, at least from that viewpoint. This notion is supported by findings that molecular and physiological mechanisms that originally developed in our brain to support food foraging for survival have now evolved in primates to include information foraging.15 Data to support this assertion rest largely on observations that the dopaminergic system, which is crucial for all reward processing, plays a key role in both basic food-foraging behavior in lower vertebrates and higher-order cognitive behaviors in monkeys and humans that are often dissociated from clear survival benefits.16 The role of the dopamine system has actually been shown to relate directly to information-seeking behavior in primates. Macaque monkeys, for example, respond to receiving information similarly to the way they respond to primitive rewards such as food or water. Moreover, “single dopamine neurons process both primitive and cognitive rewards, and suggest that current theories of reward-seeking must be revised to include information-seeking.”17
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through increasing cortical connections, eventually used to forage
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for information.”
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Since humans seem to exhibit an innate drive to forage for information in much the same way that other animals are driven to forage for food, we need to consider how this “hunger” is now fed to an extreme degree by modern technological advances that deliver highly accessible information; yet another reason for why we are ancient brains living in a high-tech world.
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Neuroscience research has helped us understand that perception is not a passive process; sights, sounds, and smells of the world do not simply flood into our brain. Rather, the inward flow of information is sculpted and pruned by goals in much the same way that our actions are, resulting in our perceptions being an interpretation of reality and not a veridical representation. Those flowers you decide to pay attention to actually do look much redder to you and smell much sweeter than the ones you chose to ignore. Goals thus influence both sides of the cycle, perception and action.
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CONTROL PROCESSES Everyone knows what attention is. It is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought. Focalization, concentration, of consciousness are of its essence. It implies withdrawal from some things in order to deal effectively with others, and is a condition which has a real opposite in the confused, dazed, scatterbrained state which in French is called distraction, and Zerstreutheit in German. —WILLIAM JAMES9
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The mediators of our top-down goals comprise another amazing collection of abilities that fall under the umbrella of cognitive control. This includes three major faculties: (1) attention, (2) working memory, and (3) goal management, each consisting of subcomponent processes.
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However, the effectiveness of attention relies on much more than whether the spotlight is focused or not. It is also critical when, where, and how long the spotlight is wielded. These three aspects of attention that build on its selectivity are known as expectation, directionality, and sustainability.
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Attention, complete with selectivity, expectation, sustainability, and directionality, is an undeniably powerful mediator of our goals.
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Goal management serves the critical function of our mental traffic controller. Of course, success in actually accomplishing multiple goals is dependent upon integrating goal management with attention—the spotlight—and working memory—the bridge—in a fluid and flexible manner.
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Decades of research have now shown that while focusing on relevant information is of course critical to accomplishing our goals, ignoring irrelevant information is just as important.
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The fact that ignoring is an active process is critical to understanding the Distracted Mind because it emphasizes that it takes resources to filter out what is irrelevant.
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This means that your goal of focusing on a conversation in a restaurant may be successful, but your ability to ignore the chatter all around you may be failing. If so, you will find yourself susceptible to one of the two types of goal interference: distraction.
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This experiment thus generated important evidence that the prefrontal cortex causally induces top-down modulation of activity in the visual cortex and that this activity modulation (both enhancement and suppression) underlies selective attention that is necessary for working memory performance. Thus
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Even though we gave no instruction to participants to switch between these two tasks, we see that is actually what is happening in their brain. They do not maintain the memory network at the same level when the selective attention network is engaged. Rather, they dynamically switch between these two cognitive control networks. The results of our study are consistent with many other studies that have shown when we simultaneously pursue multiple goals that compete for cognitive control resources, our brains switch between tasks—they do not parallel process.
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As we will discuss in the next chapter, this act of switching, whether we make the decision to switch or not, diminishes our performance on tasks. This is the basis for the other type of goal interference: Interruption.
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we need to rapidly fire our cognitive resources at targets selected to be most relevant to our goals. Simultaneously, we must block out the vast, rapidly changing stream of goal-irrelevant information that flows around us.
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Those stimuli with the strongest bottom-up factors of novelty and salience are the most formidable at involuntarily usurping our attention from our goals; they are the source of external distraction and a major aspect of goal interference.
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selectivity depends as much on neural processes involved in ignoring goal-irrelevant information as it does on processes that facilitate the focus on goal-relevant information.
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Neural data show that when two objects are simultaneously placed in view, focusing attention on one pulls visual processing resources away from the other. But when the stimulus sitting outside of the area of our goals has intrinsic characteristics that induce strong bottom-up attention, then the competition is not so easily won by our goals. Just
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This experiment revealed that focus was not the primary determinant of high-level working memory performance; rather, memory depended more on effectively ignoring distractions.
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failure to effectively ignore irrelevant information has direct consequences for our success at holding relevant information in mind for brief periods of time.3
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prefrontal cortex networks, which serve a critical role in selective attention, are quite vulnerable to being disrupted by these low-level bottom-up influences.
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The results showed that the negative impact of passively viewing the pictures on memory recall was even worse if we disrupted prefrontal cortex function, thus supporting our hypothesis that prefrontal cortex networks support memory recall by reducing distractions.
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A clever research study used an iPhone app to randomly present questions to college students asking if at that very moment they were focusing their attention on what they were doing or if their mind was wandering. Strikingly, the study revealed that 47 percent of randomly sampled moments throughout the day were occurrences of mind wandering.8 In addition, they found that people were generally less happy while mind wandering, seemingly independent of the type of activity that they were engaged in at the time. Mind wandering has been shown to have a negative impact on cognitive performance, and it has been associated with deficits in working memory, fluid intelligence, and SAT performance.9
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Externally generated, bottom-up influences by sights and sounds and internally generated mind wandering both blunt the sharpness of our attentional selectivity.
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In general, our ability to distribute our attention is quite limited.
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children diagnosed with ADHD (attention deficit hyperactivity disorder) had difficulties sustaining attention when they were assessed using standard boring lab tests, but not when playing engaging video games.
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Another aspect of limitations on the speed of attentional processing is that it not only takes time to allocate our attention when we want to, but it also takes time to disengage our attention if it was captured by bottom-up influences.
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These results demonstrate that overprocessing irrelevant information diminishes the fidelity of a working memory trace. Cognitive scientist Dr. Edward Vogel and colleagues showed that distraction also influences working memory capacity, in that individuals who exhibit greater distractibility by irrelevant information have a lower capacity.23
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This failure of our brain to truly multitask at a neural level represents a major limitation in our ability to manage our goals. The process of neural network switching is associated with a decrease in accuracy, often for both tasks, and a time delay compared to doing one task at a time. Known as multitasking or task-switching costs, these decrements in performance occur for both types of goal management. You can think of these costs as the price you pay for trying to do more than one thing at a time.
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life. How did they pull this off? The reason is simple: This is not 100% true multitasking. All system resources are available to all applications, with the system assuming the role of a traffic controller, giving preference to some tasks and less preference to others as needed.25 This is essentially how our brains manage multiple tasks that require cognitive control: the prefrontal cortex serves as a traffic controller to facilitate goal management by rapidly switching between neural networks associated with each of the tasks. It is fascinating to read Apple’s description for why they went this route, rather than implementing true multitasking in their phones: Free-for-all multitasking will consume way too many resources, especially memory. This will make the system choke, given the limited memory available in these devices. The CPU would also be taxed, and it would deplete the battery life quicker while slowing down applications running on the foreground.26 Such a description could easily have been written about our brains, rather than our iPhones. Perhaps a reason why our brains did not evolve true multitasking capabilities is that the competition for resources involved in cognitive control would also choke the system and create an energy drain.
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However, you do not have to be multitasking, or attempting to simultaneously complete two tasks, to exhibit performance costs. We experience costs even when we explicitly decide to switch between tasks. This is true even for simple tasks, as long as they demand some degree of cognitive control. You can observe this for yourself.
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Where older adults suffered a deficit was in suppressing the irrelevant information. Thus, we discovered that their main attentional issue was that they are more distractible than younger adults.13 The Gazzaley Lab published the results of this study as evidence that selectivity impairments in older adults were the result of a neural deficit in the mechanisms responsible for goal-directed, top-down suppression.
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There is a growing consensus of research studies that support the conclusion that attentional deficiencies in selectivity are not the consequence of an inability of older adults to focus on their goals, but rather are the consequence of a selective deficit in ignoring distractions. We have recently shown that this selectivity deficit is associated with age-related alterations in prefrontal cortex networks; and not just functional changes, but also structural changes in the volume of a region in the middle part of the prefrontal cortex, as well as diminished integrity of the white matter that connect this area with other brain structures. We also found that older adults with these brain changes were more distractible on a working memory test.16
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Older adults, however, do not show neural signs of suppressing the brain activity associated with a distracting face until at least half a second. These results suggest that if distractions are not suppressed almost immediately, they have time to create interference with the processing of relevant information, in turn degrading both working memory and long-term memory performance.18 In other words, our distraction filter needs to stop the flow of noise from entering our brain at the entrance gate.
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Unfortunately, selectivity deficits along with accompanying delays in processing speed are not even the whole story; limitations in sustainability and distribution of attention also contribute to attention challenges experienced by older adults.
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Interestingly, the brain changes that underlie goal interference deficits experienced by older adults—distraction and interruption—are mechanistically distinct: distractibility is caused by an inefficient filter that results in excessive processing of irrelevant information in the visual cortex, whereas multitasking impairments are caused by a failure to effectively switch between networks involved in performing two tasks.
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surmised from observations of your own mental sluggishness after a bad night’s sleep, acute sleep deprivation negatively impacts cognitive control in a major way. Notably, it has been shown to impair sustained attention.
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One study, for example, took brain scans of adults three and a half years apart and found that those who had the most sleep difficulties showed a more rapid decline in brain volume.35 More specifically related to cognitive control, however, is research showing that just one night of poor sleep can lead to less efficient filtering out of important information from junk as well as inefficient visual tracking, both of which, of course, underlie the Distracted Mind.
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one study of seven- to eleven-year-old children in Quebec, Canada, asked parents of one group to have their children go to bed earlier than normal (averaging a bit more than half an hour of additional nightly sleep) while the other half went to bed an hour later than normal. Classroom teachers rated their behavior without knowing which group they were in and found that the sleep-deprived children showed reduced cognitive control, particularly in the areas of attention, increased impulsivity, and frustration.
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nearly two-thirds of the work episode interruptions were self-generated, and most of those involved some form of mediated communication using a technological device. In fact, of the approximately eighty-six daily changes in an employee’s work activity, the workers themselves generated sixty-five of them internally, with the vast majority involving “checking in”
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We are self-interrupting and not even aware of how often we are diverting our attention from our main task—in this case, our job—to another task that may be completely unrelated to work.
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According to the study’s authors, “This is worrisome because students begin to feel like they need to have the TV on or they need to continually check their text messages or computer while they do their homework. It’s not helping them, but they get an emotional reward that keeps them doing it.”35
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a major challenge. Unless they are returning older
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Minute-by-minute observations showed that the typical student couldn’t stay focused on work for more than three to five minutes.
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Strikingly, the predictors of a lower GPA from extensive data collected about the students were: percentage of time on-task, studying strategies, total media time during a typical day, and preference for task-switching rather than working on a task until it was completed. In addition, by examining the websites that students visited during that fifteen-minute sample, we uncovered a fifth predictor of a lower GPA. Only one website visited predicted a lower GPA: Facebook. And it did not matter whether the students visited it once or fifteen times. Once was enough to predict lower school performance.
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The authors propose that “the difference between the effects of passive listening to music and active engagement in texting or social media highlights a major shift in the intrusion of media in everyday life. Traditional media, such as radio, television or music, which can be ignored as background noise, are fundamentally different from human interactions via text message or
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According to many research studies, most students send and receive text messages during class, and those who do get lower grades.9 In a study by Dr. Rosen’s lab, students were sent varying amounts of text messages at crucial points of a videotape lecture and asked to respond.10 Those who received eight text messages during the thirty-minute lecture performed an entire grade lower in a test of the lecture material than the average of those who received no texts or only four texts. Interestingly, according to another study, students are aware of the potential downsides of such interruptions.
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One study found that those students who used cell phones and texted more often during class showed more anxiety, had lower GPAs, and were less satisfied with life than students who used phones and texted less frequently.
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Overall, it appears that college students who use inessential technology either during class sessions or while studying face difficulties on both an academic and personal level.14
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When asked directly if they saw a clown, still only one in four of the cell-phone-using students reported seeing it compared with half of single walkers, 61 percent of music listeners, and 71 percent of walking pairs. Whatever was happening between the user and his or her phone appears to have inhibited their ability to identify such a strong bottom-up event in the immediate neighborhood.
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David Strayer, a professor at the University of Utah and an expert on the impact of technology on driving, compared cell phone drivers and drunk drivers and discovered that a person using a cell phone while driving and a person with a blood alcohol level above the legal limit have an equal chance of being in a traffic accident.27 According to the CDC, 69 percent of adult drivers
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Given that research shows that the incidence of accidents with handsfree phones and handheld phones are equivalent, it is likely that the primary cause is neither physical nor visual but rather an issue of attention—one of our major cognitive control abilities with distinct limitations.29
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interruptions by asserting that “we humans are Pavlovian; even though we know we’re just pumping ourselves full of stress, we can’t help frantically checking our e-mail the instant the bell goes ding.”37
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Finally, a content analysis of twenty-seven open-office studies identified auditory distractions, job dissatisfaction, illness, and stress as major ramifications of this type of workplace.42
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Strikingly, as the authors concluded, “the mere presence of mobile phones inhibited the development of interpersonal closeness and trust, and reduced the extent to which individuals felt empathy and understanding from their partners.”
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Yet another similar study by researchers at the University of Southern Maine found that “simply the presence of a cell phone and what it might represent (i.e., social connections, broader social network, etc.) can be similarly distracting and have negative consequences in a social interaction.”
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our relationship with technology has spawned a variety of “conditions” that include phantom pocket vibration syndrome, FOMO (fear of missing out), and nomophobia (fear of being out of mobile phone contact), all of which are centered on a need to be connected constantly. Phantom vibrations are an interesting phenomenon. A mere ten years ago, if you felt a tingling near your pants pocket you would reach down and scratch the area to relieve the presumed itch. Now the very same neuronal activity promotes a need for us to check our smartphone—sometimes even if we are not carrying one in our pocket at the time—as it is assumed that our phone just vibrated, signaling an incoming alert or notification. Two studies have discovered that nearly everyone experiences these false vibrations often.49
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Overall, symptoms of psychiatric disorders were predicted by some combination of daily technology use and preference for multitasking even after factoring out the impact of anxiety about missing out on technology and technology-related attitudes.
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A recent article in Wired that summarizes research on touchscreen use by children includes the following quote from the author in his role as a parent: But these screens have a weird dual nature: They make us more connected and more isolated at the same time. When I hand my daughter an iPad with an interactive reading app, she dives in and reads along. But she also goes into a trance. It’s disturbing because, frankly, it reminds me of myself. I’m perpetually distracted, staring into my hand, ignoring the people around me. Hit Refresh and get a reward, monkey. Feed the media and it will nourish you with @replies and Likes until you’re hungry and bleary and up way too late alone in bed, locked in the feedback loop. What will my daughter’s loop look like? I’m afraid to find out.12
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with ADHD are facing what the researchers have termed a “bottleneck,” in which executive functions and cognitive control are stalled.
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We propose that the right side of the model is also strongly influenced by internal factors that modulate the slope of the curve, independent of actual diminishing information resources (the external factors). It seems that at least two internal factors exert a major influence in flattening the curve while we actively engage in information foraging: boredom and anxiety (seen on the right side of figure 9.2). We hypothesize that the accumulation of these two internal signals with the passage of time while engaged in an information patch—reflected physiologically as diminished arousal and increased stress, respectively—lowers the peak of this curve. This, in turn, drives the “optimal time in source” to the left (i.e., reduced time in source), thus leading to more rapid switching between information patches. Figure 9.2 Increased boredom/anxiety have flattened the slope of the resource intake curve to result in diminished time at an information source, and thus more rapid task switching.
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This contributes to a pervasive pattern of shallower resource intake curves, and thus more frequent media multitasking behavior. Under these conditions, switching would take place even if there were remaining tasty information treats to be consumed at the original
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source. In other words, internal factors of boredom and anxiety influence the perceived benefits of being in a patch, even if only subconsciously, to offset the value of consuming important information in a sustained manner.
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The left-side shift is a decrease in the expected transit time to reach a new source as a result of a dramatic increase in accessibility to new information patches, particularly because of one of our game changers—the smartphone—which offers an infinite supply of beckoning patches that sit in our pockets and are available at the tap of an icon. This results in even more rapid switching between information sources, despite the negative consequences associated with this behavior. Figure 9.3 Increased accessibility to information have decreased the perceived transit time to a new source and resulted in more rapid task switching.
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In summary, we propose that these influences of modern media and high-tech—increased rate of accumulation of anxiety and boredom, and increased accessibility of information—have
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driven the behaviors in humans described throughout Part II of this book.
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That means that these Stanford University students were switching screens roughly five times a minute.
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Across all switches, Yeykelis and his colleagues discovered that the arousal level started rising an average of twelve seconds prior to a switch but, more importantly, early arousal was most prominent when switching from “work-related” screens such as word processing or Internet information searches to “entertainment-related” screens including watching a video, gaming, and, of course, Facebook. In fact, while looking at work-related screens, arousal was quite low and the anticipatory increase was quite pronounced as the student prepared to leave the boring schoolwork and find something more stimulating such as an entertainment-related screen.
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boredom is “the aversive experience of wanting, but being unable, to engage in satisfying activity.”5 Eastwood and his colleagues go further in clarifying boredom as an aversive state that occurs when we • are not able to successfully engage attention with internal (e.g., thoughts or feelings) or external (e.g., environmental stimuli) information required for participating in satisfying activity, • are focused on the fact that we are not able to engage attention and participate in satisfying activity, and • attribute the cause of our aversive state to the environment. Eastwood further clarifies that a bored person is not just one who has nothing to do; rather, he or she wants to be stimulated and is unable to be. He calls it having an “unengaged mind.”6
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one task to another before the first was completed.13 Getting a text message was the most popular response (68 percent), followed by boredom (63 percent), both of which represent the two causes of interruption: external and internal.
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One possibility for why the rate of boredom accumulation has increased in recent times is the influence of pervasive short timescale reward cycles in modern media. From decades of research on learning and behavior, we know that the shorter the time between reinforcements (rewards), the stronger the drive to complete that behavior and gain the reward.
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It is worth considering that frequent exposure to such rapid rewards in video games may alter the boredom profile that is experienced when we engage in less stimulating information such as reading websites, which have considerably longer time-scale reward structures. There is also no reason to believe that
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In other words, this may all be cyclical: boredom drives frequent switching to new tasks → rapidly induced rewards → increased rate of boredom in nonstimulating information sources → rapid flattening of resource intake curve → quicker switch times → and so on.
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of “intermittent reinforcement” and showed that when someone is reinforced only some of the time, and particularly when that occurs on a variable (unpredictable) schedule, the behavior itself becomes resistant to extinction.
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The power of social media to increase our anxiety has been shown in numerous studies.
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In one survey of 3,800 adults, Cisco Systems reported that nine in ten adults under the age of thirty fear not having their mobile phone.
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That factor is accessibility. The more readily available a new patch of information is (or even seems to be), the earlier the time someone will disengage from his or her current source. It
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In summary, technology-powered influences on the right side of the model—boredom and anxiety—interact with a technology-powered influence on the left side—accessibility—to result in a shifted intercept between them, which is expressed as a much earlier “optimal time in source.”
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This lack of metacognition—awareness and understanding of one’s own thought processes—impacts the MVT model in two ways. On the right side: not understanding the benefits of remaining in an information patch and not appreciating our internal states of anxiety and boredom.
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Over the years more evidence has accrued that meditation techniques improve cognitive control, including sustained attention, speed of processing, and working memory capacity.15 In addition, one recent study took a step toward documenting real-world impact by showing meditation-induced improvements in the Graduate Record Examination (GRE) reading-comprehension test.16
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We found that Beepseeker training resulted in both older adults and older rats improving their ability to resist the negative impact of distraction. Neural recordings from both species showed that their brains had an enhanced ability to suppress distractors. Importantly, we also found that engaging in this attention exercise resulted in improvements in working memory. These results confirmed our hypothesis that adaptivity is a powerful tool to target a cognitive exercise to a specific process that needs to be improved, as well as suggesting that this exercise might also benefit other cognitive control abilities.
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In addition to the various aspects of attentional enhancement shown in the 2003 paper, evidence surfaced of improved selective attention for objects, space and time, sustained attention, bottom-up attention, working memory, task switching, and multitasking.29 Many of these cognitive control enhancements have even been shown to last for many months after game play was discontinued.30 Neural recordings to understand the mechanisms that underlie these
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effects revealed that gamers exhibit a superior ability to detect targets, at least in part as a result of being better able to suppress distractions.31 An fMRI study further revealed that gamers did not activate prefrontal cortex areas to the same degree as those who did not play video games in response to increasing attentional demands on a searching task.32 This suggests that gamers allocate their cognitive control resources more efficiently.
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ART proposes that cognitive fatigue can be most effectively and rapidly restored by relaxing the mind from the top-down demands through engagement in a strong bottom-up driven activity, which captures attention not based on goals, but because of the characteristics of the stimuli.
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These findings have been complemented by an onslaught of fascinating data regarding neural changes induced by exercise, which span the gamut from increases in brain volume (both gray and white matter), nerve growth factors, blood flow, functional and structural connections, and even new neurons being born.58 Perhaps not surprisingly, this neural plasticity is accompanied by a host of cognitive benefits, a claim that has been supported by several meta-analysis studies.59
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The general conclusion is that “lower-fit children may have more difficulty than higher-fit children in the flexible modulation of cognitive control processes to meet task demands.”61 This same relationship has been shown to exist for college-age adults.
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hit by cars while distracted by their phones.63 This study found that higher-fit children were more successful in street crossing under all testing conditions. Moreover, higher-fit children were not negatively impacted by the phone and music distractions, while the lower-fit children showed worsened performance when either listening to music or talking on the phone compared to undistracted crossing.64
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They have shown that aerobic exercise training for children results in improvements on cognitive control tasks.65
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A landmark meta-analysis of exercise interventions published in 2003 showed that there was an overall cognitive benefit of physical exercise in older adults, with the largest effects being in the domain of cognitive control.
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In terms of the neural mechanisms of these effects, an fMRI study revealed greater activation in the prefrontal cortex in high-fit versus lower-fit older adults while they were performing an interference challenge. Importantly, the same results were found in an intervention study with a group of older adults who trained aerobically for six months versus a control group who engaged in stretching and toning (a similar result was found for overweight children).71 The study showed not only an increase in brain activity in the prefrontal cortex, but also a decrease in impact of distraction on task performance.
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Most interestingly, by the end of the week they also showed improvements on a cognitive control challenge of mental rotation compared to a control group.
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Recall the cognitive control limitations that we presented in chapter 5 in the domains of attention (selectivity, distribution, sustainability, processing speed), working memory (capacity, fidelity), and goal management (multitasking, task switching). As described, high-tech influences stress these limitations in just about every possible way: they challenge our attention abilities via frequent distractions, fragment our working memory and diminish its fidelity through interruptions, and drive us to excessive multitasking and task switching, all of which introduce performance costs.
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One study that looked at the impact of interference on boredom established that external interruptions (alerts, notifications) reduced boredom on simple tasks, but if the task was complex, or even if it was simple but required sustained attention, external interruptions actually increased task-related boredom.1 We recommend that you think about the task in front of you and decide whether it needs your undivided attention.
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by taking the following steps (figure 11.3): 1. Improve metacognition by increasing your understanding of the cost of multitasking/task switching (left side) and the value of remaining at an information patch (right side). • One of the main objectives of this book has been to accomplish this very goal by sharing a wealth of information on the underlying basis of our Distracted Mind. 2. Limit accessibility to new information sources (left side). • We’ll outline some simple strategies to keep these temptations a little further away. 3. Decrease your boredom when focusing on a single goal (right side). • We’ll discuss approaches to make engagement in tasks more fun and productive without jeopardizing the primary goal. 4. Reduce anxiety that prompts a switch to something new (right side). • We’ll describe actions and technologies that help prevent FOMO from a social perspective. Figures 11.3 By modifying several factors—increasing metacognition, decreasing accessibility, diminishing boredom, and reducing anxiety—we can diminish counterproductive behavior.
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Here are some ideas based on research studies for planning restorative, stress-reducing breaks, each of which will take you only a few minutes. • Exercise—even for only twelve minutes—facilitates brain function and improves attention, as discussed in detail in chapter 10.21 • Train your eyes using the 20–20–20 rule: every twenty minutes take a twenty-second break and focus on objects twenty feet away. This changes your focal distance from inches to many feet and requires blood flow to brain areas that are not related to constant attention.22 • Expose yourself to nature. Consider using at least part of your break to get away from technology and spend a few minutes in a natural setting. Research has shown that just ten minutes in a natural environment can be restorative; even viewing pictures of nature can be restorative, as discussed in chapter 10.23 • Daydreaming, staring into space, doodling on paper, or any activity that takes you away from performing a specified task activates the “default mode network”—a network of interacting brain areas that most often indicate that you are daydreaming, thinking creatively, or just mind wandering—which is restorative for attention.24 • Short ten-minute naps have been shown to improve cognitive function. Longer naps work, too, as seen in a study of pilots who improved their reaction time after taking a thirty-minute nap.25 • Talking to other human beings, face to face or even on the telephone, reduces stress and has been shown to improve work performance.26 • Laugh! Read a joke book, look at comic strips, read a funny blog. A Loma Linda University study found that older adults who watched a funny video scored better on memory tests and showed reduced cortisol and increased endorphins and dopamine, meaning less stress and more energy and positive feelings.27 • Grab something to drink and a small snack.28 • Read a chapter in a fiction book. Recent research shows major brain shifts when reading immersive fiction.29
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