relationship between problem solving and multitasking

Multitasking: Switching costs

What the research shows.

Doing more than one task at a time, especially more than one complex task, takes a toll on productivity. Although that shouldn't surprise anyone who has talked on the phone while checking E-mail or talked on a cell phone while driving, the extent of the problem might come as a shock. Psychologists who study what happens to cognition (mental processes) when people try to perform more than one task at a time have found that the mind and brain were not designed for heavy-duty multitasking. Psychologists tend to liken the job to choreography or air-traffic control, noting that in these operations, as in others, mental overload can result in catastrophe.

Multitasking can take place when someone tries to perform two tasks simultaneously, switch . from one task to another, or perform two or more tasks in rapid succession. To determine the costs of this kind of mental "juggling," psychologists conduct task-switching experiments. By comparing how long it takes for people to get everything done, the psychologists can measure the cost in time for switching tasks. They also assess how different aspects of the tasks, such as complexity or familiarity, affect any extra time cost of switching.

In the mid-1990s, Robert Rogers, PhD, and Stephen Monsell, D.Phil, found that even when people had to switch completely predictably between two tasks every two or four trials, they were still slower on task-switch than on task-repeat trials. Moreover, increasing the time available between trials for preparation reduced but did not eliminate the cost of switching. There thus appear to be two parts to the switch cost -- one attributable to the time taken to adjust the mental control settings (which can be done in advance it there is time), and another part due to competition due to carry-over of the control settings from the previous trial (apparently immune to preparation).

Surprisingly, it can be harder to switch to the more habitual of two tasks afforded by a stimulus. For example, Renata Meuter, PhD, and Alan Allport, PhD, reported in 1999 that if people had to name digits in their first or second language, depending on the color of the background, as one might expect they named digits in their second language slower than in their first when the language repeated. But they were slower in their first language when the language changed.

In experiments published in 2001, Joshua Rubinstein, PhD, Jeffrey Evans, PhD, and David Meyer, PhD, conducted four experiments in which young adults switched between different tasks, such as solving math problems or classifying geometric objects. For all tasks, the participants lost time when they had to switch from one task to another. As tasks got more complex, participants lost more time. As a result, people took significantly longer to switch between more complex tasks. Time costs were also greater when the participants switched to tasks that were relatively unfamiliar. They got up to speed faster when they switched to tasks they knew better.

In a 2003 paper, Nick Yeung, Ph.D, and Monsell quantitatively modeled the complex and sometimes surprising experimental interactions between relative task dominance and task switching. The results revealed just some of the complexities involved in understanding the cognitive load imposed by real-life multi-tasking, when in addition to reconfiguring control settings for a new task, there is often the need to remember where you got to in the task to which you are returning and to decide which task to change to, when.

What the research means

According to Meyer, Evans and Rubinstein, converging evidence suggests that the human "executive control" processes have two distinct, complementary stages. They call one stage "goal shifting" ("I want to do this now instead of that") and the other stage "rule activation" ("I'm turning off the rules for that and turning on the rules for this"). Both of these stages help people to, without awareness, switch between tasks. That's helpful. Problems arise only when switching costs conflict with environmental demands for productivity and safety.

Although switch costs may be relatively small, sometimes just a few tenths of a second per switch, they can add up to large amounts when people switch repeatedly back and forth between tasks. Thus, multitasking may seem efficient on the surface but may actually take more time in the end and involve more error. Meyer has said that even brief mental blocks created by shifting between tasks can cost as much as 40 percent of someone's productive time.

How we use the research

Understanding the hidden costs of multitasking may help people to choose strategies that boost their efficiency - above all, by avoiding multitasking, especially with complex tasks. (Throwing in a load of laundry while talking to a friend will probably work out all right.) For example, losing just a half second of time to task switching can make a life-or-death difference for a driver on a cell phone traveling at 30 MPH. During the time the driver is not totally focused on driving the car, it can travel far enough to crash into an obstacle that might otherwise have been avoided.

Meyer and his colleagues hope that understanding switching costs and the light they shed on "executive control" may help to improve the design and engineering of equipment and human-computer interfaces for vehicle and aircraft operation, air traffic control, and many other activities using sophisticated technologies. Insights into how the brain "multitasks" lend themselves to a range of settings from the clinic, helping to diagnose and help brain-injured patients, to the halls of Congress, informing government and industrial regulations and standards.

This research is also taken into account by states and localities considering legislation to restrict drivers' use of cell phones.

Sources & further reading

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Meuter, R. F. I. & Allport, A. (1999). Bilingual language switching in naming: Asymmetrical costs of language selection. Journal of Memory and Language, 40(1) , 25-40.

Meyer, D. E. & Kieras, D. E. (1997a). A computational theory of executive cognitive processes and multiple-task performance: Part 1. Basic mechanisms. Psychological Review, 104 , 3-65.

Meyer, D. E. & Kieras, D. E. (1997b). A computational theory of executive cognitive processes and multiple-task performance: Part 2. Accounts of psychological refractory-period phenomena. Psychological Review, 104 , 749-791.

Monsell, S., Azuma, R., Eimer, M., Le Pelley, M., & Strafford, S. (1998, July). Does a prepared task switch require an extra (control) process between stimulus onset and response selection? Poster presented at the 18th International Symposium on Attention and Performance, Windsor Great Park, United Kingdom.

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Rogers, R. & Monsell, S. (1995). The costs of a predictable switch between simple cognitive tasks. Journal of Experimental Psychology: General, 124, 207-231.

Rubinstein, J., Evans, J. & Meyer, D. E. (1994). Task switching in patients with prefrontal cortex damage. Poster presented at the meeting of the Cognitive Neuroscience Society, San Francisco, CA, March, 1994. Abstract published in Journal of Cognitive Neuroscience , 1994, Vol. 6.

Rubinstein, J. S., Meyer, D. E. & Evans, J. E. (2001). Executive Control of Cognitive Processes in Task Switching. Journal of Experimental Psychology: Human Perception and Performance, 27 , 763-797.

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• Published: 08 November 2022

Knowledge generalization and the costs of multitasking

• Kelly G. Garner 1 , 2 &
• Paul E. Dux   ORCID: orcid.org/0000-0002-4270-2583 1  

Nature Reviews Neuroscience volume  24 ,  pages 98–112 ( 2023 ) Cite this article

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• Cognitive control
• Human behaviour

Humans are able to rapidly perform novel tasks, but show pervasive performance costs when attempting to do two things at once. Traditionally, empirical and theoretical investigations into the sources of such multitasking interference have largely focused on multitasking in isolation to other cognitive functions, characterizing the conditions that give rise to performance decrements. Here we instead ask whether multitasking costs are linked to the system’s capacity for knowledge generalization, as is required to perform novel tasks. We show how interrogation of the neurophysiological circuitry underlying these two facets of cognition yields further insights for both. Specifically, we demonstrate how a system that rapidly generalizes knowledge may induce multitasking costs owing to sharing of task contingencies between contexts in neural representations encoded in frontoparietal and striatal brain regions. We discuss neurophysiological insights suggesting that prolonged learning segregates such representations by refining the brain’s model of task-relevant contingencies, thereby reducing information sharing between contexts and improving multitasking performance while reducing flexibility and generalization. These proposed neural mechanisms explain why the brain shows rapid task understanding, multitasking limitations and practice effects. In short, multitasking limits are the price we pay for behavioural flexibility.

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement no. 796329 awarded to K.G.G. and ARC Discovery Projects grants DP180101885 and DP210101977 awarded to P.E.D. The authors thank D. Lloyd for his work on the graphical representations of the concepts in this Perspective. The authors also thank C. Nolan, H. Bowman, J. Mattingley, Y. Wards and A. Renton for providing helpful feedback and insightful commentary on previous drafts.

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The More, The Merrier? What Happens In Your Brain When You Are Multitasking?

Have you ever thought about what happens in your brain when you engage in multiple tasks at the same time? Although we multitask often throughout the day, limits to our mental capacity prevent our brains from processing multiple tasks truly simultaneously. Instead, we actually switch between them, so rapidly that we may not even be aware of it. Two brain areas appear to be especially important for multitasking—the parietal cortex and the prefrontal cortex, whose complex interaction is an ongoing focus of scientific investigation. With the ever-increasing popularity of new technological devices like smartphones, which tempt us to multitask more frequently, it has become crucial to understand how multitasking affects the ability to complete a task quickly and correctly. While many questions remain about multitasking and its effects on the brain and our cognitive skills, currently available research points to the importance of developing smart multitasking habits.

Can More Become Too Much?

Sometimes, more is better. Picture yourself in your kitchen with friends, baking your favorite chocolate chip cookies. You cannot get enough of them, so you have decided to double the recipe today! While gathering the ingredients, your growling stomach reminds you of what you learned in your latest biology lesson, about the fascinating digestive roles of gut bacteria. You begin to share your new knowledge with your friends as you simultaneously try to calculate the doubled measurements and whisk the ingredients together. But wait—how much sugar do you need, exactly? To ensure that you are doing the math correctly, you realize that you need to pause your explanation of gut bacteria. Clearly, more is not always better: while your stomach may be able to handle the extra cookies, adding more tasks to be processed can sometimes prove too much for your brain.

You have likely done two things, such as baking or talking to friends, separately , many times, without any problems. However, if you attempt to do both at once , you might find yourself struggling to handle either task as well as you normally could. This is called the cost of multitasking, and it occurs when the same cognitive (brain) resources are required by two or more tasks at the same time.

Why are brain scientists interested in multitasking? Humans have likely always multitasked, but recently, new technologies have made it a more common feature of our daily activities. Smartphones, for example, have become our constant companions and are often used to do several things simultaneously. Increased multitasking brings up many questions and concerns.

Many parents and teachers worry that the tech-savvy younger generation divides its attention too much between different activities and may grow up to be a bunch of unfocused “scatterbrains.” Maybe you have even wondered about the effects of frequent multitasking yourself—is it “good” or “bad” for you? Before we address this question, let us look at what makes multitasking challenging and what happens in the brain.

A Bottleneck in the Brain?

Why is our ability to perform two tasks at once limited? Scientists have developed laboratory experiments comparing participants’ performances when they were performing only one task to when they performed two tasks simultaneously ( Figure 1 , based on the task described in [ 1 ]). In two separate tasks, participants concentrated on a series of single letters. By pressing a button, they had to indicate whether the letter was a consonant or a vowel in the first task, and whether the letter was capitalized or not in the second task. In the so-called single-task condition, participants performed only one task at a time, whereas in the dual-task condition they had to complete both tasks simultaneously. So, while just one button press was required for a response to each letter in the single-task condition, the dual-task condition required two button presses for each letter: one for the vowel/consonant decision and one for the capital letter task. In this experiment and others like it, participants tend to respond more slowly to each of the two tasks in the dual-task condition compared to the single-task condition.

• Figure 1 - Experimental setup.
• (A,B) In the single-task condition, participants focused on whether the presented letters were written in capital or lowercase, and whether the letters were consonants or vowels. These tasks were performed separately, during independent task sessions. (C) In the dual-task condition, participants had to focus on both categories simultaneously for each letter, with 2.5 s in between letters.

To explain this finding, scientists have developed several theories. One of the most influential is called the bottleneck theory [ 2 ]. Think of the neck of a bottle filled with marbles: only one marble at a time can pass through. Similarly, just one task at a time can move through the bottleneck of limited processing capacity in the brain. The other task must wait until the first one is completed.

In our earlier example of baking, measuring sugar and telling your friends about your biology class can be pictured as two separate marbles attempting to move through the bottleneck. Mentally doubling the recipe measurements then adds a third marble that needs to pass. Some of these tasks are more complicated and thus need more of your capacity, represented by the larger size of the marble ( Figure 2 ). The task marbles cannot move through the bottleneck at the same time, so they are dealt with one after another. After partially completing one task, you rapidly switch to the next one and then back again to the first, repeatedly. Thus, you feel like you are doing multiple tasks simultaneously.

• Figure 2 - The bottleneck theory states that our brain has a limited capacity for processing and performing multiple tasks simultaneously.
• To envision this concept, imagine your brain was a bottle filled with marbles, each marble representing a task. The moment you perform one task, the corresponding marble has to pass through the bottleneck. But, only one marble at a time can pass through the bottleneck. Similarly, only one task at a time can be processed in your brain. In addition, some tasks require more brain capacity represented by the different sizes of the marbles.

Are There Multitasking “Hotspots” in the Brain?

With a technology called fMRI [ 3 ], scientists can measure how active the various regions of the brain are while someone performs a task. Using fMRI, scientists have identified two main brain areas where this switch between tasks happens during multitasking. The prefrontal cortex and the parietal cortex are more active when participants complete two tasks at once, compared to when they process a single task. These brain regions thus appear to be essential for multitasking. Using clever experiments, scientists have developed an idea of what these brain areas do when we multitask ( Figure 3 ). The parietal cortex stores the relationship between an external event, such as a picture or a sound (called a stimulus ), and the response that should happen because of that stimulus. This is called a stimulus-response-mapping or S-R-mapping . In our example experiment, the task rules of pressing the left yellow button if the letter is capitalized and the right blue button if it is a vowel are examples of S-R-mappings stored in the parietal cortex. The prefrontal cortex is essential for selecting the correct S-R-mappings and responding appropriately. So, if you see a capital “A” in the yellow single-task condition, the prefrontal cortex ensures that you press the left yellow button in response [ 4 ]. The prefrontal cortex may be the location of the bottleneck. Practicing multitasking intensively can increase the speed with which the prefrontal cortex processes multiple tasks [ 5 ] and can improve multitasking even for tasks that were not practiced, but we still do not know how long-lasting these effects are and whether practice also helps us improve multitasking skills in our daily lives.

• Figure 3 - Brain regions involved in multitasking.
• The prefrontal cortex (yellow) and the parietal cortex (green) are both active during multitasking. The parietal cortex stores S-R mappings, that describe the relationship between an external event and the response that is triggered by this event. The prefrontal cortex on the other hand, is essential for selecting the correct S–R-mapping for the task and responding appropriately.

Interestingly, some people can switch between tasks or sources of information more easily than others. While many factors influence the ability to multitask, age is especially important [ 6 ]. Perhaps you have observed that your younger siblings have a harder time doing multiple things at a time than you do, or that your own multitasking ability has improved since you were little. That is because the brain regions involved in multitasking and their connections with one another need time to develop, becoming the strongest in late adolescence or early adulthood. Just as your brain keeps changing after adolescence, so does your ability to multitask. As we approach our grandparents’ age, the brain regions responsible for task switching function less effectively, making multitasking more difficult again [ 6 ].

We Live in the Age of Media Multitasking

Multitasking can be challenging for people regardless of age, but it is also the “new normal” of our time. Even when the situation does not require it, our electronic devices can tempt us to multitask. You may, for example, enjoy listening to music and keeping an eye on social media while completing your homework. In general, research suggests that media multitasking like this can scatter attention and worsen memory, which may impair learning and performance on a task. Additionally, trying to do multiple things at once does not save more time than doing tasks one after the other—multitasking actually slows us down! This seems to be mostly true even for frequent media multitaskers with much “practice” and confidence in their ability to switch between tasks [ 7 ]. However, there may be some exceptions to this rule. Experienced video gamers appear to be able to switch between tasks better than non-gamers, at least if the tasks are not very similar [ 8 ]. This does not necessarily mean that video games improve multitasking skills—perhaps people who like to play them are better task switchers to begin with. Finally, researchers are still working to find out if frequent multitasking has any long-term effects, positive or negative, on our cognitive abilities or our brains [ 6 ].

Be Smart About Multitasking!

Although the bottleneck theory has received much support from scientific experiments, there are also competing explanations, and gaps still exist in our knowledge. More research is needed to reveal exactly how multitasking works and why it is challenging to our brains. Multitasking is here to stay, and concerns about its effects will likely persist until more scientific evidence becomes available. In the meantime, it is important to remember that multitasking is a human ability, neither “good” nor “bad.” Still, try to be mindful about what kinds of activities you combine. For example, if a task is important and requires your attention, such as studying for an exam, it is better to make it your sole focus and avoid multitasking. In contrast, if time is not of the essence and there is little at stake, multitasking will not do much harm. So, while you cannot trick your brain into processing more than one task simultaneously, keeping the bottleneck theory in mind can help you multitask smartly!

Cognitive : ↑ Relating to mental processes like learning, thinking, problem-solving, and memory, which are carried out by the brain.

Bottleneck Theory : ↑ It states that the brain has a limited capacity for processing multiple tasks simultaneously, just like only one marble can pass through the narrow neck of a bottle at once.

Functional Magnetic Resonance Imaging (fMRI) : ↑ fMRI uses a strong magnetic field to take images of the brain. For example, when human participants work on a task, it can show the brain regions that are activated for that task (learn more here: https://kids.frontiersin.org/article/10.3389/frym.2019.00086 ).

Prefrontal Cortex : ↑ A region at the front of the brain, which is involved in multitasking by selecting the correct S-R mapping for a task and thus for responding appropriately.

Parietal Cortex : ↑ A region at the upper back of the brain, which is involved in multitasking by storing the S-R mapping of each task.

Stimulus : ↑ An event or a cue, such as a picture or a sound, that activates sensory receptors and causes a response from an organism.

Stimulus-Response-Mapping (Short: S-R-mapping) : ↑ The relation between a stimulus and the response that is appropriate for this stimulus; it can be imagined as a roadmap from stimulus to response.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank our fellow members of the project Mechanisms and Sequential Progression of Plasticity for their insights and feedback on this article.

[1] ↑ Dreher, J. C., and Grafman, J. 2003. Dissociating the roles of the rostral anterior cingulate and the lateral prefrontal cortices in performing two tasks simultaneously or successively. Cereb. Cortex. 13:329–39. doi: 10.1093/cercor/13.4.329

[2] ↑ Pashler, H. 1984. Processing stages in overlapping tasks: evidence for a central bottleneck. J. Exp. Psychol. Human. 10:358–77. doi: 10.1037/0096.1523.10.3.358

[3] ↑ Hoyos, P., Kim, N., and Kastner, S. 2019. How is magnetic resonance imaging used to learn about the brain? Front. Young Minds. 7:86. doi: 10.3389/frym.2019.00086

[4] ↑ Worringer, B., Langner, R., Koch, I., Eickhoff, S. B., Eickhoff, C. R., and Binkofski, F. C. 2019. Common and distinct neural correlates of dual-tasking and task-switching: a meta-analytic review and a neuro-cognitive processing model of human multitasking. Brain Struct. Funct. 224:1845–69. doi: 10.1007/s00429-019-01870-4

[5] ↑ Dux, P. E., Tombu, M., Harrison, S., Rogers, B. P., Tong, F., and Marois, R. 2009. Training improves multitasking performance by increasing the speed of information processing in human prefrontal cortex. Neuron 63:127–38. doi: 10.1016/j.neuron.2009.06.005

[6] ↑ Courage, M. L., Bakhtiar, A., Fitzpatrick, C., and Brandeau, K. 2015. Growing up multitasking: the costs and benefits for cognitive development. Dev. Rev. 35:5–41. doi: 10.1016/j.dr.2014.12.002

[7] ↑ Uncapher, M. R., and Wagner, A. D. 2017. Minds and brains of media multitaskers: current findings and future directions. PNAS 115:9889–96. doi: 10.1073/pnas.1611612115

[8] ↑ Karle, J. W., Watter, S., and Shedder, J. M. 2010. Task switching in video game players: benefits of selective attention but not resistance to proactive interference. Acta Psychol. 134:70–8. doi: 10.1016/j.actpsy.2009.12.007

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How Multitasking Affects Productivity and Brain Health

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• Multitasking and Productivity

Brain Function in Multitaskers

• Break the Habit

Frequently Asked Questions

What is multitasking.

Multitasking involves working on two or more tasks simultaneously, switching back and forth from one thing to another, or performing a number of tasks in rapid succession.

Is multitasking a good thing? While multitasking seems like a great way to get a lot done at once, research has shown that our brains are not nearly as good at handling multiple tasks as we like to think they are. In fact, some research suggests that multitasking can actually hamper your productivity by reducing your comprehension, attention, and overall performance.

What is it that makes multitasking such a productivity killer? It might seem like you are accomplishing multiple things at the same time, but what you are really doing is quickly shifting your attention and focus from one thing to the next. Switching from one task to another may make it difficult to tune out distractions and can cause mental blocks that can slow you down.

Examples of Multitasking

• Starting two projects at the same time
• Listening to the radio while driving to work
• Talking on the phone while typing an assignment
• Watching television while responding to work emails
• Scrolling through social media while in a meeting
• Listening to a person talk while writing a to-do list

How Multitasking Hampers Productivity

Multitasking takes a serious toll on productivity . Our brains lack the ability to perform multiple tasks at the same time—in moments where we think we're multitasking, we're likely just switching quickly from task to task. Focusing on a single task is a much more effective approach for several reasons.

Multitasking Is Distracting

Multitaskers may feel more distracted than people who focus on one task at a time. This makes sense when you consider that, by habit, multitaskers constantly refocus on a new task, effectively distracting themselves from their original assignment.

Some research suggests that multitaskers are more distractible, and they may have trouble focusing their attention even when they're not working on multiple tasks at once.

Other research shows that while there may be a connection between multitasking and distraction, that link is smaller than originally thought and varies quite a bit from person to person.

Multitasking Slows You Down

While it may seem contrary to popular belief, we tend to work slower and less efficiently when we multitask. Multitasking leads to what psychologists call "task switch costs," or the negative effects that come from switching from task to task. We encounter task switch costs (like a slower working pace) because of the increased mental demand that's associated with jumping from one thing to another.

Changing our focus also keeps us from relying on automatic behaviors to finish tasks quickly. When we're focused on a single task that we've done before, we can work on "autopilot," which frees up mental resources. Switching back and forth bypasses this process, and we tend to work more slowly as a result.

Multitasking Impairs Executive Function

Multitasking is managed by executive functions in the brain . These control and manage cognitive processes and determine how, when, and in what order certain tasks are performed. There are two stages to the executive control process:

• Goal shifting : Deciding to do one thing instead of another
• Rule activation : Changing from the rules for the previous task to the rules for the new task

Moving through these stages may only add a few tenths of a second, but it can start to add up when people switch back and forth repeatedly. This might not be a big deal when you are folding laundry and watching television at the same time.

However, if you are in a situation where safety or productivity is important, such as when you are driving in heavy traffic, even small amounts of time can prove critical.

Multitaskers Make Mistakes

Multitasking may lower your performance and make you more prone to making mistakes. Research has shown that students who multitask in class tend to have lower GPAs (and, if they continue multitasking at home, they often take longer to finish their homework).

Adults may also experience lower performance while multitasking. One 2018 study found that older adults were likely to make more mistakes while driving if they were multitasking.

Doing several different things at once can impair cognitive ability , even for people who multitask frequently. In fact, research suggests that people tend to overestimate their ability to multitask, and the people who engage in this habit most frequently often lack the skills needed to be effective at it.

Chronic multitaskers tend to show more impulsivity than their peers, and they may be more likely to downplay possible risks associated with tackling multiple things at once. They also seem to show lower levels of executive control and are often distracted easily.

Limited cognitive resources may be involved in this phenomenon. Several networks in the brain interact to guide our behavior whenever we set out to complete a task. This behavior includes:

• Setting a goal
• Identifying the information we need to achieve it
• Disregarding irrelevant distractions

When we try to engage in this process for multiple tasks at once, it can lead to cognitive errors. We might fail to disregard irrelevant information, for instance, which would lead to more distraction.

The research isn't clear on the exact relationship between multitasking and brain function. It's possible that chronic multitasking changes the brain over time, leading to more distractibility and problems with focus, or it may be that people with these traits are more likely to multitask in the first place.

Teens and Multitasking

The negative impact of chronic, heavy multitasking might be particularly detrimental to adolescent minds. At this age, brains are busy forming important neural connections. Spreading attention so thin and constantly being distracted by different streams of information might have a serious, long-term, negative impact on how these connections form.

Media Multitasking

Some research suggests that people who engage in media multitasking (using more than one form of media or type of technology at once) might be better at integrating visual and auditory information.

In one study, participants between the ages of 19 and 28 were asked to complete questionnaires regarding their media usage. The participants then completed a visual search task both with and without a sound to indicate when an item changed color.

Heavy multitaskers performed better on the search when the sound was presented, indicating that they were more adept at integrating the two sources of sensory information . Conversely, heavy multitaskers performed worse than light/medium multitaskers when the tone was not present.

Break the Multitasking Habit

If you feel like multitasking is negatively impacting your life, it is possible to make some changes that will increase your productivity and efficiency. Next time you find yourself multitasking, take a quick assessment of the various things you are trying to accomplish. Then, determine which task you need to focus on first. Try to:

• Limit the number of things you juggle at any given time to just one task . If you do need to work on multiple things at once, try to combine something automatic, like folding laundry, with something that requires more focus, like having a conversation.
• Use the "20-minute rule." Instead of constantly switching between tasks, try to fully devote your attention to one task for 20 minutes before switching to the other.
• Batch your tasks . If you're having trouble resisting the urge to check your email or engage in another distracting task, schedule a set time in your day to tackle it. By batching similar tasks together and setting a time to handle them, you can free your mind up to focus on something else.
• Limit distractions . This may mean seeking out a quieter place to work, switching your phone off, and turning off notifications and alarms.
• Practice mindfulness . Adding mindfulness to your daily routine may help you notice the times when you're multitasking. Mindfulness can also improve your ability to focus and pay attention to one thing at a time.

Working on one task at a time may help you become more productive and it may make each task more enjoyable.

Yes, it can be. Multitasking may reduce your ability to focus, increase feelings of stress, and exacerbate impulsiveness. It can also worsen your performance at work or school, which can lead to further negative feelings and anxiety.

It means that, like most of us, their brain isn't wired to work on multiple complex tasks simultaneously. We perform much better when we focus fully on one thing at a time.

You should consider whether or not you're really able to multitask before adding it to your resume. We have a tendency to overestimate our ability to multitask, and even people who think they're skilled in this area often make mistakes or work inefficiently.

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Introduction, section snippets, references (51), cited by (58).

Computers in Human Behavior

Mindful multitasking: the relationship between mindful flexibility and media multitasking.

• H1: Since trait mindfulness, intolerance of ambiguity, thinking style, complexity, positive affect, and negative affect are theoretically related to the construct of mindful flexibility, these factors should be correlated with each other.
• H2: Media multitasking performance will differ depending on whether individuals received an induction of high state mindfulness, an induction of low state mindfulness, or No Treatment .
• H3: The interaction between state mindfulness and trait mindfulness will predict media multitasking performance above and beyond other factors linked to mindful flexibility.

Participants

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Not all executive functions are related to intelligence

Psychological science, causes, effects, and practicalities of everyday multitasking, what else do college students "do" while studying an investigation of multitasking.

Deriving our hypotheses from theoretical models of cognitive resources, resource allocation, and task motivation, we expected higher NA and fatigue to be associated with greater multitasking behaviors, while we anticipated higher PA, homework task motivation, and self-efficacy to be linked to fewer multitasking behaviors. Although other studies have examined student media multitasking in a laboratory setting (e.g., Ie, Haller, Langer, & Courvoisier, 2012), they have typically done so using somewhat artificial laboratory tasks. To increase the external validity of our study results, we explored media multitasking in a less constrained environment, by having students complete their own homework during the study session.

Minds and brains of media multitaskers: Current findings and future directions

Examining creativity through a virtual reality support system, how has the internet reshaped human cognition, multidimensions of media multitasking and adaptive media selection.

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Can you accurately monitor your behaviors while multitasking? The effect of multitasking on metacognition

Affiliations.

• 1 School of Psychology, Liaoning Normal University, Dalian, 116029, People's Republic of China.
• 2 Institute of Brain and Psychological Sciences, Sichuan Normal University, Sichuan, People's Republic of China.
• 3 School of Psychology, Liaoning Normal University, Dalian, 116029, People's Republic of China. [email protected].
• 4 Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China.
• PMID: 37707650
• DOI: 10.1007/s00426-023-01875-z

With the pace of life accelerating, multitasking has become the norm in daily life. According to research, multiple cognitive processes, including numerical reasoning, comprehension, and writing, are negatively affected by multitasking. However, only a few studies have investigated the relationship between multitasking and metacognition. In this study, the effect of multitasking on metacognition was examined using a prospective monitoring paradigm (prediction of subsequent recall performance). In Experiment 1, the participants simultaneously studied word pairs (primary task) and differentiated between different sound pitches (secondary task) and then predicted their performance in a subsequent memory test for the studied word pairs (prospective metacognitive monitoring). The accuracy of metacognitive evaluation with multitasking was then compared with that without multitasking. In Experiment 2, sounds and icons of real-life applications were used to improve the ecological validity of the experiment in the secondary task. The results indicated that multitasking impaired metacognition in both artificial and real-life simulated scenarios. In addition, the participants who engaged in more media multitasking in their daily lives exhibited poorer metacognitive monitoring abilities in single tasks.

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Can you accurately monitor your behaviors while multitasking? The effect of multitasking on metacognition

• Published: 14 September 2023
• Volume 88 , pages 580–593, ( 2024 )

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• Ruiqiao Guo 1 , 2 ,
• Yan Liu 1 ,
• Hui Jing Lu 3 &
• Annan Jing 1  

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With the pace of life accelerating, multitasking has become the norm in daily life. According to research, multiple cognitive processes, including numerical reasoning, comprehension, and writing, are negatively affected by multitasking. However, only a few studies have investigated the relationship between multitasking and metacognition. In this study, the effect of multitasking on metacognition was examined using a prospective monitoring paradigm (prediction of subsequent recall performance). In Experiment 1, the participants simultaneously studied word pairs (primary task) and differentiated between different sound pitches (secondary task) and then predicted their performance in a subsequent memory test for the studied word pairs (prospective metacognitive monitoring). The accuracy of metacognitive evaluation with multitasking was then compared with that without multitasking. In Experiment 2, sounds and icons of real-life applications were used to improve the ecological validity of the experiment in the secondary task. The results indicated that multitasking impaired metacognition in both artificial and real-life simulated scenarios. In addition, the participants who engaged in more media multitasking in their daily lives exhibited poorer metacognitive monitoring abilities in single tasks.

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Availability of data and materials.

The datasets generated during the current study are available in the github ( https://github.com/rachelzzy/metacognition ).

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Ruiqiao Guo, Yan Liu & Annan Jing

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Ruiqiao Guo

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Hui Jing Lu

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Guo, R., Liu, Y., Lu, H.J. et al. Can you accurately monitor your behaviors while multitasking? The effect of multitasking on metacognition. Psychological Research 88 , 580–593 (2024). https://doi.org/10.1007/s00426-023-01875-z

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Media multitasking and memory: Differences in working memory and long-term memory

Melina r. uncapher.

1 Department of Psychology, Stanford University, Stanford, CA 94305, USA

3 Jordan Hall, Bldg 420, Stanford, CA 94305-2130, USA

Monica K. Thieu

Anthony d. wagner.

2 Neurosciences Program, Stanford University, Stanford, CA 94305, USA

Associated Data

Increasing access to media in the 21st century has led to a rapid rise in the prevalence of media multitasking (simultaneous use of multiple media streams). Such behavior is associated with various cognitive differences, such as difficulty filtering distracting information and increased trait impulsivity. Given the rise in media multitasking by children, adolescents, and adults, a full understanding of the cognitive profile of media multitaskers is imperative. Here we investigated the relationship between chronic media multitasking and working memory (WM) and long-term memory (LTM) performance. Four key findings are reported (1) heavy media multitaskers (HMMs) exhibited lower WM performance, regardless of whether external distraction was present or absent; (2) lower performance on multiple WM tasks predicted lower LTM performance; (3) media multitasking-related differences in memory reflected differences in discriminability rather than decision bias; and (4) attentional impulsivity correlated with media multitasking behavior and reduced WM performance. These findings suggest that chronic media multitasking is associated with a wider attentional scope/higher attentional impulsivity, which may allow goal-irrelevant information to compete with goal-relevant information. As a consequence, heavy media multitaskers are able to hold fewer or less precise goal-relevant representations in WM. HMMs’ wider attentional scope, combined with their diminished WM performance, propagates forward to yield lower LTM performance. As such, chronic media multitasking is associated with a reduced ability to draw on the past—be it very recent or more remote—to inform present behavior.

In a world that affords ubiquitous access to information, many people often multitask with multiple streams of media. The rapid rise in “media multitasking” ( Rideout, Foehr, & Roberts, 2010 ) has generated considerable scientific and societal interest in the relationship between this behavior and fundamental aspects of human cognition. Initial studies have examined aspects of cognitive control, finding that heavy media multitaskers (HMMs) perform poorly in tasks involving working memory ( Minear, Brasher, McCurdy, Lewis, & Younggren, 2013 ) and distractor filtering ( Cain & Mitroff, 2011 ; Ophir, Nass, & Wagner, 2009 ), with variable effects on task switching (c.f. Alzahabi & Becker, 2013 ; Minear et al., 2013 ; Ophir et al., 2009 ). Other studies have examined the relationship between media multitasking behavior and psychosocial variables such as trait impulsivity ( Minear et al., 2013 ; Sanbonmatsu, Strayer, Medeiros-Ward, & Watson, 2013 ; Shih, 2013 ). In general, greater self-reported media multitasking appears associated with higher self-reported measures of impulsiveness, either on Attention ( Sanbonmatsu et al., 2013 ) or Motor subscales ( Minear et al., 2013 ; Sanbonmatsu et al., 2013 ; c.f. Shih, 2013 ).

While the direction of causality is unknown—whether frequent media multitasking induces psychosocial and cognitive control differences or whether people with these differences gravitate toward more frequent media multitasking—the initial observations demand a deeper understanding of the cognitive costs (and benefits) associated with frequent media multitasking. This is especially urgent given that more and more young people, whose brains are still developing, are engaging in media multitasking ( Rideout et al., 2010 ).

Progress may come from a fuller investigation of how cognitive performance varies as a function of media multitasking behavior. For instance, while the aforementioned studies point to working memory (WM) differences, the conditions in which such differences are obtained remain underspecified. Using a signal detection decision-making framework, WM performance can be characterized by a discriminability parameter ( d ’) that indexes the precision or amount of information held in WM, and a bias parameter ( C ) that indexes the propensity to endorse that a signal was detected ( Green & Swets, 1966 ). Given that HMMs demonstrate higher trait impulsivity ( Minear et al., 2013 ; Sanbonmatsu et al., 2013 ), it remains an open question as to whether this population may require less evidence to reach a decision, which would manifest as a more liberal response bias when making WM judgments. Moreover, HMMs’ greater sensitivity to internal and external distraction may manifest as reduced WM performance even in the absence of external distractors.

A second line of open questions concerns whether the WM performance differences in HMMs have consequences for long-term memory (LTM). To date, investigations of media multitasking have focused on cognition directed to the present moment/very recent past or series of moments (e.g., visual working memory; n-back; task switching). It remains unknown whether the impairments in moment-by-moment cognition observed in HMMs have consequences for future cognition that depends on long-term memories for those moments.

To address these open questions, we measured discrimination and bias during WM performance, and then related these measures to corresponding measures during LTM performance (including measures of LTM for information encountered in one of the WM tasks) in a large sample of participants ( N = 143).

Participants

We recruited 143 participants (83 females; 18–35 years old, mean = 22.1 years, SD = 3.65 years) from the Stanford University community. Complete data were collected from 139 of the participants (data were lost from two participants due to equipment malfunction and two due to noncompliance). The experiment was performed in accordance with a protocol approved by the Stanford University IRB. All participants gave written informed consent and were remunerated $10/hr.

Participants completed a set of questionnaires and performed four cognitive tasks (see Supplemental Materials for details). The questionnaires included the Media Multitasking Index (MMI; Ophir et al., 2009 ) and inventories for impulsivity and ADHD. The cognitive paradigms included two visual WM tasks and two recognition memory tests. All significant effects are reported.

Working memory task: Rectangles

Participants first performed a standard visual WM task that required attentional filtering ( Vogel, McCollough, & Machizawa, 2005 ). Each trial consisted of an array of two target rectangles, colored red, along with 0, 2, 4, or 6 distractor rectangles, colored blue (see Fig. 1a ). Participants were instructed to first encode the orientations of the red rectangles—ignoring blue rectangles—during the encoding period, then remember these orientations over the delay period, and finally detect whether either of the targets changed orientation between encoding and test. Participants indicated they detected a change (right index finger button press) or no change (right middle finger button press).

Schematic of the working memory and long-term memory tasks. a . Participants first performed a standard version of a visual WM task that required attentional filtering at encoding ( Vogel et al., 2005 ). Participants first viewed an array of colored rectangles (red and blue) and were instructed to attend to the red and ignore the blue rectangles. Two red (target) rectangles always appeared, along with 0, 2, 4, or 6 blue (distracting) rectangles. Participants were instructed to detect whether either of the red (target) rectangles changed orientation from first to second presentation. b . The standard WM task was modified to include trial-unique common objects. Target and distractors (0, 2, 4, or 6) could appear in any of 8 positions in a circular annulus around fixation (NB, size of objects depicted relative to frame is not representative). Participants were again instructed to detect whether either of the red objects changed orientation from first to second presentation. c . The ability to retrieve the target objects encountered in the WM objects task was assessed by a recognition memory test, which interspersed objects that were targets in the WM objects task with novel objects. d . The ability to retrieve distractor objects encountered in the WM objects task was assessed by a recognition memory test, which interspersed objects that were distractors in the WM objects task with novel objects. (Color figure online.)

Working memory task: Objects

Participants next performed a modified version of the visual WM task, wherein rectangles were replaced with common objects arranged in a circle (see Fig. 1b ). Instructions were the same as in the rectangles task.

Recognition memory tasks

Participants next performed (a) an old/new recognition memory test for target objects from the WM task, interspersed with new objects (see Fig. 1c ) and then (b) a similar test for distractor objects from the WM task (see Fig. 1d ). Participants responded with an old/new judgment that included their confidence in the decision (high or low).

Questionnaires

Media multitasking index.

Across all 139 participants, the median MMI score was 4.34 (mean = 4.41 ± 1.91). We identified 36 HMMs (mean = 6.92 ± 1.23) and 36 LMMs (mean score = 2.19 ± 0.70).

Impulsivity index

The mean BIS-11 score was 61.38 (±10.57); HMMs did not significantly differ from LMMs across subscales, F (1, 201) = 2.40, p = .12; HMM All scales = 62.79 ± 10.81, LMM All scales = 59.86 ± 11.57.

The mean ADHD score was 2.41 (±1.59); HMMs scored significantly higher than LMMs, F (1, 54) = 9.30, p = .0033; HMM = 2.92 ± 1.61, LMM = 1.97 ± 1.65.

Relationship between MMI, impulsivity, and ADHD

Across all participants, MMI score positively correlated with ADHD, r 136 = .30, p = .00036, and impulsivity across subscales, r 136 = .17, p = .046. The relationship between impulsivity and MMI was driven by the Attention subscale ( r = .24, p = .0046), with no significant effects in the other subscales (Motor: r = .078, p = .36; Nonplanning: r = .065, p = .45). The ADHD and overall impulsivity scores significantly correlated, r 136 = .56, p = 1.1 * 10 −12 .

Working memory and long-term memory performance

We first examined group effects (HMMs vs. LMMs) on performance and then, for effects of interest, we further tested whether performance continuously scaled with MMI score (i.e., across all participants).

Working memory: Rectangles

We analyzed WM performance following Vogel et al. (2005) : K = S * ( H − F ), where K is WM capacity, S the size of the target array (2), H the proportion of correct changes detected (hit rate), and F the proportion of changes incorrectly reported (false alarm rate). As measured by K , LMMs were able to hold more task-relevant information in mind relative to HMMs (see Fig. 2a , left panel); Group (HMM, LMM) × Distractor Load (0, 2, 4, 6) ANOVA showed a main effect of Group: F (1, 256) = 4.88, p = .028. This difference was driven by a greater tendency for HMMs to incorrectly endorse a change, when none occurred (“false alarms”; FAs), ANOVA on FA rate showed a main effect of Group: F (1, 256) = 7.52, p = .0065. Hit rate did not significantly differ across Groups: F(1, 256) = 1.27, p = .26, and the Group × Hit/FA interaction was significant, F(1, 548) = 5.39, p = .021.

Performance on the working memory tasks. a . Light media multitaskers (LMMs; blue) exhibited better working memory performance ( K ; left panel) than heavy media multitaskers (HMMs; red). This was driven by better discriminability ( d ’; middle panel) to detect differences between the presence or absence of a change in orientation of the target rectangles, and not a more liberal decision bias to endorse a change ( C ; right panel). b . This overall pattern was similar when the WM task required trial-unique objects to be held in mind: WM performance ( K ; left panel) was better for LMMs than HMMs, and this performance was driven by discriminability ( d ’; middle panel) and not decision bias ( C ; right panel). (Color figure online.)

We also interrogated the data in a signal detection theory (SDT) framework ( Green & Swets, 1966 ) to determine whether HMMs’ reduced WM performance reflects (a) reduced discriminability to detect a change in the WM arrays, as measured by d ’ WM ( d ’ = Z Hits − Z False Alarms ), and/or (b) a different bias to report changes, as measured by C WM ( C = −½ [Z Hits + Z False Alarms ]). Relative to LMMs, HMMs had a poorer ability to discriminate between the presence versus absence of change (see Fig. 2a , middle panel), d ’ WM by Group and Distractor Load; main effect of Group: F(1, 256) = 5.92, p = .016. HMMs and LMMs did not differ in bias (see Fig. 2a , right panel), C WM by Group and Distractor Load; main effect of Group: F (1, 256) = 1.48, p = .23. Thus, reduced WM performance in people who frequently media multitask appears to be driven by discriminability differences: HMMs hold fewer or less precise representations of target information in WM.

To determine whether WM performance scales linearly across all levels of media multitasking, we regressed all 139 participants’ MMI scores against their d ’ WM . This revealed a significant negative relationship: The higher the MMI score, the lower the WM discriminability, d ’ WM ~ MMI, with Distractor Load as a factor (i.e., d ’ WM ~ MMI * Load): multiple regression r = .16; effect of MMI, t = −2.61, p = .0092. As was the case with group effects, the relationship between bias and MMI was not significant, C WM ~ MMI * Load: multiple regression r = .098; effect of MMI, t = −1.10, p = .27. Discriminability differences appeared to be due to FA rates and not hit rates: participants with higher MMI scores exhibited significantly higher FA rates, FA rate ~ MMI * Load: multiple regression r = .18; effect of MMI, t = 2.97, p = .0032, but not significantly lower hit rates, Hit rate ~ MMI * Load: multiple regression r = .094; effect of MMI, t = −1.20, p = .23.

Working memory: Common objects

A similar pattern of results was observed using the Objects variant of the WM task. Specifically, HMMs again exhibited significantly lower WM performance than LMMs (see Fig. 2b , left panel); K by Group and Distractor Load, main effect of Group: F (1, 272) = 5.45, p = .020, and this difference was due to a greater tendency to endorse a change when none occurred, FA rate by Group and Distractor Load, main effect of Group: F(1, 272) = 4.49, p = .035. Again, hit rate did not significantly differ across Groups (Hit rate by group and distractor load, main effect of Group: F (1, 272) = 2.19, p = .14. Finally, HMMs demonstrated reduced discrimination relative to LMMs (see Fig. 2b , middle panel); d ’ WM by Group and Distractor Load, main effect of Group: F (1, 272) = 4.56, p = .034, with no difference in bias (see Fig. 2b , right panel), C WM by Group and Distractor Load, main effect of Group: F (1, 272) < 1.

Across-participant regression revealed that while higher MMI scores numerically tended to be associated with lower WM discriminability, this relationship only trended toward significance, d ’ WM ~MMI * Distractor Load: multiple regression r = .16; effect of MMI, t = −1.64, p = .10. As in the rectangles task, this trend was associated with a slightly greater tendency to endorse a change when none occurred, although this relationship again only trended toward significance, FA rate ~ MMI * Load: multiple regression r = .16; effect of MMI, t = 1.64, p = .10. Finally, MMI again did not correlate across participants with hit rate, Hit rate ~ MMI * Load: multiple regression r =.15; effect of MMI, t = −.87, p = .39, or bias, C WM ~ MMI * Load: multiple regression r = .099, effect of MMI t = −.42, p = .68.

Taken together, these two WM studies indicate that—regardless of the nature of the information (common objects or rectangles)—HMMs demonstrate a deficit in WM that reflects a reduction in the number or precision of task-relevant representations that they can encode and/or maintain in WM.

Long-term memory: Target objects

Paralleling the effects observed in WM, HMMs, relative to LMMs, exhibited reduced LTM performance, manifested as a reduced ability to discriminate the previously encountered WM targets from novel objects (see Fig. 3a , left panel); d ’ LTM by Group, Distractor Load, and Confidence (high vs. low), main effect of Group: F (1, 532) = 9.39, p = .0023. Here HMMs’ poorer discrimination was accompanied by a more liberal decision bias when looking across all trials, with HMMs demonstrating a stronger bias to endorse objects as recognized, C LTM by Group, Distractor Load, and Confidence, main effect of Group: F (1, 532) = 5.83, p = .016. However, when confined to high confidence responses only, HMMs and LMMs were equally conservative, F (1, 267) = 1.31, p = .25. Across participants, higher MMI scores correlated with reduced LTM performance, d ’ LTM ~ MMI * Distractor Load * Confidence: multiple regression r = .65; effect of MMI, t = −2.67, p = .008, even when confined to high confidence retrieval responses, multiple regression r = .16; effect of MMI, t = −2.47, p = .014.

Performance on the long-term memory tasks, for target and distractor objects encountered in WM objects task. a . Target objects encountered in the WM objects task were better remembered by LMMs than HMMs (left panel), and, across participants, WM object performance predicted later LTM for the target objects (right panel). b . Distractor objects encountered in the WM objects task were also better remembered by LMMs than HMMs (though they were poorly remembered, overall, by both groups; left panel), and, across participants, WM object performance predicted later LTM for the distractor objects (right panel). (Color figure online.)

To test whether WM performance—using the standard K metric—predicted LTM performance, we regressed all participants’ LTM discrimination scores ( d ’ LTM ) onto their performance on the WM objects task. There was a significant positive relationship between the ability to hold objects in WM and the ability to later recognize those previously encountered objects (NB, this pattern was significant when LTM performance was assessed collapsed across decision confidence), r 136 = .31, p = 2.3 * 10 −4 , as well as when restricted to high confidence decisions, r 136 = .33, p = 8.6 * 10 −5 ; thus, we report high confidence outcomes henceforth (see Fig. 3a , right panel, green ).

This relationship between the ability to encode and maintain common objects in WM and the ability to later retrieve those objects from LTM is important, and yet does not adjudicate between alternative hypotheses about whether impaired WM acts to reduce (a) the encoding of information into LTM, or (b) task performance more generally, perhaps by reducing the ability to hold information online during LTM tasks . A first step toward adjudicating between these alternatives may come from assessing whether WM performance predicts LTM performance for completely different information. Here, we tested this hypothesis by determining whether WM performance on the rectangles task predicted LTM performance (for the objects), and found that the predictive relationship held (see Fig. 3a , right panel, orange ); r 132 = .22, p = .0093. Because WM performance for the two types of material was correlated, we further examined whether performance on the rectangles task provided predictive information about LTM above and beyond that which was provided by the objects task. A multiple regression analysis revealed a strong predictive relationship, even after removing variance associated with the WM objects task, multiple regression r =.29; effect of K rectangles , t = 3.52, p = .00046, suggesting that WM performance may have a more general impact on LTM.

Taken together, the foregoing results show that people who frequently engage with multiple media streams during their daily lives demonstrate worse LTM for previously encountered target information. Importantly, HMMs’ diminished LTM and WM performance occurred for information that was encountered while the participants were ostensibly single-tasking .

Long-term memory: Distractor objects

A final question concerned the LTM fate of distractor objects encountered during the WM objects task, as the answer may shed light on mechanisms underlying how HMMs manage competing representations in the WM task. We predicted two possible scenarios: (1) at encoding, HMMs attend to distractor objects at the expense of target objects, resulting in better representation of distractor objects in WM for HMM vs. LMMs, and ultimately leading to better LTM of distractor objects for HMMs, or (2) the ability to interrogate representations held in mind, whether during WM or LTM tasks, is reduced in HMMs, manifesting as worse LTM performance in HMMs than LMMs, for both targets and distractor objects.

An ANOVA of distractor LTM performance revealed a trend favoring the second scenario, in that HMMs remembered the distractors more poorly than LMMs (see Fig. 3b , left panel); d ’ LTM by Distractor Load (2, 4, 6), Group, and Confidence: main effect of Group: F (1, 399) = 3.47, p = .063. Interestingly, the number of times a distractor was displayed in the array (i.e., Distractor Load) had no effect on LTM for distractors, F(1, 399) < 1.

We next examined whether WM performance predicts LTM performance for the distractor objects (as it did for target objects). To do so, we regressed all participants’ ability to retrieve distractors from LTM ( d ’ LTM ) onto their performance in the WM objects task ( K , the index of how well target information was held in mind). We found a positive relationship between K and the ability to later confidently recognize distractor objects (see Fig. 3b , right panel, green ); K objects ~ d ’ LTM-distractors * Confidence: multiple regression r = .72, effect of K objects , t = 4.48, p = 1.1 * 10 −5 . This relationship was similar across WM tasks, with WM performance in the rectangles task also predicting long-term memory for distractor objects (see Fig. 3b , right panel, orange ); K rectangles ~ d ’ LTM-distractors * Confidence: multiple regression r = .33, effect of K rectangles t = 2.11, p = .036, although the relationship was not significant after removing variance associated with WM performance for the objects task, likely due to floor effects, multiple regression r = .32; effect of K rectangles , t < 1.

Together, these findings show that WM performance in general—across different tasks (rectangles/objects) and different information (target/distractor objects)—predicts LTM performance, suggesting that WM deficits are likely exerting their effects at both encoding and retrieval.

Relationship between task performance and impulsivity

Given the observed relationship between impulsivity and MMI score—driven by the Attentional Impulsivity subscale—we examined whether this subscale predicted task performance ( d ’ and C in WM and LTM tasks). Across all participants, the Attentional subscale negatively predicted d ’ in both WM tasks, rectangles: attentional impulsivity ~ d ’ WM * Load: multiple regression r = .14, effect of d ’, t = −2.15, p = .032; objects: multiple regression r = .15, effect of d ’, t = − 2.75, p = .0062, but did not show a relationship with d’ in the LTM task ( p > .6) or with C in any task (all p s > .05). Thus, higher self-reported attentional impulsivity was associated with worse discrimination in both WM tasks.

The study yielded four important findings. First, in two independent tasks, HMMs showed reduced WM performance regardless of whether external distractors were present or absent. This performance decline was evident in reduced K and d ’ measures of WM ability. Moreover, when media multitasking was treated as a continuous variable, a negative relationship between chronic media multitasking behavior and WM performance was observed. Second, there was a coupling between WM and LTM, with LTM performance predicted by WM abilities more broadly (across different WM tasks and different content). This pattern suggests that WM deficits likely exert effects on LTM at both encoding and retrieval rather than selectively reducing the fidelity of the representations encoded into LTM. Third, the observation that discriminability and not decision bias accounted for differences in WM and LTM performance suggests that HMMs’reduced amount or precision of information held in mind—whether during WM or LTM tasks—drives performance differences. Finally, in contrast to our predictions, the higher impulsivity of HMMs correlated with their reduced WM discrimination but not with a tendency to require less evidence to reach a decision.

A small but growing number of studies have investigated task performance of heavy and light media multitaskers or have correlated MMI score with task performance, revealing various behavioral differences. For instance, HMMs were observed to have difficulty (a) filtering distracting information, whether the information came from the environment (external distraction) or from memory (internal distraction; Ophir et al., 2009 ), and (b) ignoring attention-capturing information, regardless of whether or not they were instructed to ignore the information ( Cain & Mitroff, 2011 ). HMMs were observed to adopt a split visuospatial attention mode (the allocation of attention to multiple locations), whereas LMMs adopt a more unitary mode ( Yap & Lim, 2013 ), and individuals with higher media multitasking scores exhibit enhanced multisensory integration ( Lui & Wong, 2012 ). Other studies investigating task-switching abilities have reported equivocal results, showing that, relative to LMMs, HMMs were worse ( Ophir et al., 2009 ; Sanbonmatsu et al., 2013 ), better ( Alzahabi & Becker, 2013 ), or equivalent ( Alzahabi & Becker, 2013 ; Minear et al., 2013 ).

One mechanism proposed to underlie the differences associated with chronic media multitasking is that HMMs exhibit a broader attentional scope ( Cain & Mitroff, 2011 ; Lui & Wong, 2012 ; Ophir et al., 2009 ). A wider scope may change the manner in which available information is filtered in order to optimize task goals, manifesting as attention to both goal-relevant and goal-irrelevant information. As a consequence, goal-irrelevant information may compete with goal-relevant information, reducing task performance.

Here, a wider attentional bias at encoding (i.e., WM objects task) would result in competing WM representations of targets and distractors, giving rise to lower fidelity LTM representations of both, as was observed. However, the amount of external distraction present during WM did not differentially affect HMMs (c.f., Ophir et al., 2009 ), which suggests that their lower performance may be a result of continual distraction by information not under experimental control. Additionally, the present data suggest that lower fidelity encoding is not the only mechanism contributing to HMMs’ poor LTM performance: that performance on an entirely different WM task (WM rectangles) predicted LTM for both targets and distractors suggests that HMMs exhibit a generalized reduction in the ability to hold or interrogate precise representations in mind, whether during WM or LTM tasks. Thus, the pattern of findings suggest that HMMs’ reduced discrimination in WM and LTM may be a result of a wider attentional scope at both encoding and retrieval, allowing task-irrelevant information to continually compete with task-relevant information. This wide scope first serves to reduce the amount or precision of goal-relevant information held in mind and therefore encoded into LTM; during the subsequent retrieval from LTM, the wider attentional scope may result in the intrusion of task-irrelevant information, further degrading the ability to make accurate retrieval decisions.

Further bolstering the idea that a wider attentional scope impacts cognition at both encoding and retrieval is the finding that LTM for study distractors was worse, rather than better, for HMMs. To the extent that a wider attentional scope at encoding allowed more distractor information into WM for HMMs, distractors could have been better encoded by HMMs than by LMMs, which should have then led to better distractor memory. Instead, here we found distractor memory to be slightly worse in HMMs, suggesting that the seemingly wider attentional scope of HMMs has an impact on task performance more generally. It will be important in future investigations to determine just how extensively WM deficits impact cognition in HMMs.

Our findings additionally revealed that attentional impulsivity positively related to the degree to which participants multitasked with media. The BIS-Attention subscale has been shown to index self-reported factors of attention (“focusing on the task at hand”) and cognitive instability (“thought insertions and racing thoughts”; Patton et al. 1995 ). These factors may describe well the phenomenology associated with adopting a broad attentional scope/reduced filter (see Supplement for further discussion ).

In conclusion, the present findings point to a parsimonious and mechanistic explanation for many of the performance differences observed in the growing literature investigating chronic media multitaskers. That chronic media multitasking is associated with deficits in cognitive abilities that are critical for successful navigation through life—including holding information in mind and retrieving information from memory—calls for systematic investigations into what is cause and what is effect. Our increasingly media-saturated world may be nudging us toward an increasingly wider scope of attention, in which case how we choose to interact with media may significantly impact cognitive performance. On the other hand, adopting healthy media hygiene may make no difference if one’s media multitasking behavior is due to a cognitive predisposition (e.g., impulsivity) that leads to, rather than is caused by, such multitasking. The relationship between media multitasking and academic outcomes also remains unknown, in college-age adults, as well as in younger students. Given the increasing understanding of the importance of WM and LTM to academic achievement, future studies should aim to determine whether and how media multitasking behavior relates to academic outcomes. Poorer WM and LTM could give rise to reduced classroom-based learning and testing performance. By contrast, there may be instances where the cognition associated with HMM behavior gives rise to superior academic outcomes. For example, if a broader attentional scope allows for reinstatement of related memories (e.g., Kuhl et al. 2011 ; Shohamy & Wagner, 2008 ), this may support the generation of cognitive schemas that facilitate learning of academic content. Recommendations for parents, educators, students, and policymakers will depend on understanding the direction of causality between media multitasking and cognitive differences in students as well as in the general population.

Supplementary Material

712443_sup1, acknowledgements.

This work was supported by NIMH grant R21-MH099812.

Electronic supplementary material The online version of this article (doi:10.3758/s13423-015-0907-3) contains supplementary material, which is available to authorized users.

Conflict of Interest The authors declare no competing financial interests.

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• > Handbook of Adolescent Digital Media Use and Mental Health
• > The Effects of Digital Media and Media Multitasking on Attention Problems and Sleep

Book contents

• Handbook of Adolescent Digital Media Use and Mental Health
• Copyright page
• About the Editors
• Contributors
• Acknowledgments
• Introduction
• Part I Theoretical and Methodological Foundations in Digital Media Research and Adolescent Mental Health
• Part II Digital Media in the Adolescent Developmental Context
• Part III Digital Media and Adolescent Mental Disorders
• 9 Depression and Anxiety in the Context of Digital Media
• 10 The Role of Digital Media in Adolescents’ Body Image and Disordered Eating
• 11 Digital Media in Adolescent Health Risk and Externalizing Behaviors
• 12 Problematic Digital Media Use and Addiction
• 13 The Effects of Digital Media and Media Multitasking on Attention Problems and Sleep
• 14 Digital Media, Suicide, and Self-Injury
• Part IV Intervention and Prevention in the Digital Age

13 - The Effects of Digital Media and Media Multitasking on Attention Problems and Sleep

from Part III - Digital Media and Adolescent Mental Disorders

Published online by Cambridge University Press:  30 June 2022

Past years have seen studies examining effects of digital media on attention problems and sleep in adolescents. The majority of these studies support that using digital media is related to attention problems and lower sleep quantity, and sleep quality in adolescents. The chapter overviews studies in the field, and avenues for future research. It is still unclear whether the link between digital media, attention, and sleep is causal. Recent media effects theories suggest these relationships are complex and dynamic. To answer questions on the effects of digital media use on attention and sleep, we need more research investigating the cause-and-effect nature of the relationship (e.g., longitudinal designs, intervention studies, field studies). Future studies should use more objective measures (i.e., tracking apps/wearables). Instead of focusing on the general effects of “social media” or “smartphones” we need a better understanding of which content within these media types are problematic for which individuals.

With the rise of social and mobile media, not only has the amount of media use changed but also how and when adolescents use media. Almost half of US American adolescents claim that they are almost always online (Anderson & Jiang, Reference Anderson and Jiang 2018 ). Being constantly online also leads to new forms of media use, such as media multitasking. Media multitasking is commonly defined as using two types of media simultaneously, or using media while engaging in other non-media activities, such as using media while doing homework, during dinner, or during face-to-face conversations (Jeong & Hwang, Reference Jeong and Hwang 2012 ; van der Schuur et al., Reference Van der Schuur, Baumgartner, Sumter and Valkenburg 2015 ). Media multitasking is highly prevalent, particularly among young people (Carrier et al., Reference Carrier, Rosen, Cheever and Lim 2015 ).

The rise of digital media and media multitasking has led to concerns whether these forms of media use deteriorate adolescents’ attention. The main assumption is that if adolescents get used to using media wherever they are and whenever they want, they might have difficulties sustaining their attention, for example when doing their homework or when attending school (Ralph et al., Reference Ralph, Thomson, Seli, Carriere and Smilek 2015 ). Moreover, the constant use of digital media has been linked to sleep problems among adolescents (Hale et al., Reference Hale, Li, Hartstein and LeBourgeois 2019 ). Since sleep is crucial for the healthy development of adolescents, including their attention and level of sleepiness in school, it is important to understand the ways in which digital media affects sleep. This chapter provides an overview of the current state of the field on the effects of digital media and media multitasking on attention and sleep.

Digital Media and Attention Problems: What Do We Know?

There is a long tradition in media effects research studying the effects of media on attention problems and ADHD-related behaviors. The focus was long on the effects of watching television or playing video games that have been the most popular forms of media use among adolescents in the past. For example, a meta-analysis from 2014 shows that there is indeed a small but significant association between the time children and adolescents spent watching TV and video games and ADHD-related behaviors (Nikkelen et al., Reference Nikkelen, Valkenburg, Huizinga and Bushman 2014 ). This is further supported in a more recent review of the literature (Beyens et al., Reference Beyens, Valkenburg and Piotrowski 2018 ). The effects of TV and video games on attention problems have been typically attributed to two main characteristics of these media types: their fast-paced and potentially violent content. It has been assumed that both of these characteristics might lead to higher arousal states to which adolescents potentially habituate (e.g., see Beyens et al., Reference Beyens, Valkenburg and Piotrowski 2018 ). In the past decade, however, the media landscape and the types of media that are popular among adolescents have changed dramatically. This has resulted in a research shift away from the effects of traditional types of media (i.e., TV and video games) toward understanding the potential effects of social media and media multitasking on attention.

Media Multitasking and Attention

In 2009, Ophir, Nass, and Wagner published a seminal paper on differences in cognitive processing styles between heavy and light media multitaskers. Specifically, heavy media multitaskers were more easily distracted than light media multitaskers during a cognitive task they performed in the laboratory. It was the first study explicitly investigating the potential effects of media multitasking on cognitive processes. The authors interpreted their findings as an indication that people who multitask with media frequently have a completely different processing style than people who do this less frequently. Following this study, a plethora of studies have been conducted to understand the relationship between media multitasking and various aspects of attention (for reviews, see Uncapher & Wagner, Reference Uncapher and Wagner 2018 ; van der Schuur et al., Reference Van der Schuur, Baumgartner, Sumter and Valkenburg 2015 ). The literature can be differentiated into studies using self-report-based measures of attention in everyday life, and studies using cognitive tasks to measure the level of sustained attention in laboratory settings. It is, however, important to note that most of these studies focused on young adults (i.e., university students), and very few studies focused specifically on adolescents.

Studies using self-reports for attention problems in everyday life have consistently shown that adolescents who media multitask more frequently have more problems focusing their attention (for a review see van der Schuur et al., Reference Van der Schuur, Baumgartner, Sumter and Valkenburg 2015 ). For example, media multitasking is positively related to increased attentional failures and mind wandering in young adults (i.e., undergraduate students; Ralph et al., Reference Ralph, Thomson, Cheyne and Smilek 2013 ). Moreover, adolescents who media multitask more frequently have more attention problems and higher levels of impulsivity (Baumgartner et al., Reference Baumgartner, Weeda, van der Heijden and Huizinga 2014 , Reference Baumgartner, van der Schuur, Lemmens and te Poel 2018 ). A recent meta-analysis supported these findings by showing that media multitasking and attention problems in everyday life are significantly positively related, with small to moderate effect sizes (Wiradhany & Koerts, Reference Wiradhany and Koerts 2019 ).

In contrast to the studies on everyday functioning, studies that tested differences in sustained attention with cognitive tasks in the laboratory show more mixed results. Whereas some find no differences between heavy and light media multitaskers on various tasks related to sustained attention or distractibility (e.g., Baumgartner et al., Reference Baumgartner, Weeda, van der Heijden and Huizinga 2014 ; Ralph et al., Reference Ralph, Thomson, Seli, Carriere and Smilek 2015 ; Wiradhany et al., Reference Wiradhany and Koerts 2019 ), others find small effects (e.g., Cain & Mitroff, Reference Cain and Mitroff 2010 ; Madore et al., Reference Madore, Khazenzon and Backes 2020 ; Moisala et al., Reference Moisala, Salmela and Hietajärvi 2016 ). Overall, the findings based on cognitive tasks are less consistent than those based on self-reports, and are more difficult to compare as different cognitive tasks are used across studies. Although the existing findings are rather mixed, a recent review of the literature concludes that for tasks measuring sustained attention, evidence points toward performance detriments for heavy media multitaskers in comparison to light media multitaskers (Uncapher & Wagner, Reference Uncapher and Wagner 2018 ).

Despite rather mixed findings for performance differences in cognitive tasks, overall, the existing studies support the idea that adolescents who media multitask more frequently show more attention problems in their everyday lives. However, almost all of these studies are cross-sectional and therefore conclusions about the direction of the effect cannot be drawn. Notably, it is also possible that media multitasking does not lead to attention problems, but that adolescents who are more easily distracted in their everyday lives are more likely to engage in media multitasking. To date, only a few longitudinal studies exist that tried to establish the causal direction of these effects. One longitudinal study found that adolescents who used media more often during academic activities (such as while doing homework) reported increased difficulties in focusing their attention during academic activities over time (van der Schuur et al., Reference Van der Schuur, Baumgartner, Sumter and Valkenburg 2015 ). Another study found effects of media multitasking on attention problems only among early adolescents (12–13 years old) but not among middle adolescents (Baumgartner et al., Reference Baumgartner, van der Schuur, Lemmens and te Poel 2018 ). Thus, there is some but limited evidence for long-term effects of media multitasking on attention. In line with media effects theories, such as reinforcing spiral models (Slater, Reference Slater 2007 ), it has been proposed that the effects of media multitasking on attention problems might be reciprocal, with adolescents suffering from attention problems being more drawn to media multitasking, and media multitasking in the long run further exacerbating their attention problems (Baumgartner et al., Reference Baumgartner, van der Schuur, Lemmens and te Poel 2018 ). However, more longitudinal research is needed to empirically test this proposition.

Social Media Use and Attention

Evidence for a relationship between social media use and attention is even more scarce. Only a few studies to date have specifically examined the relationship between the frequency of social media use and attention problems. These studies tentatively point toward a relationship between the use of social media and inattentiveness with adolescents using social media more frequently showing more signs of attention problems (Barry et al., Reference Barry, Sidoti, Briggs, Reiter and Lindsey 2017 : Boer et al., Reference Boer, Stevens, Finkenauer and van den Eijnden 2020 ). The evidence for a relationship between attention problems and problematic or addictive social media use is more compelling. Several studies showed that adolescents who use social media in obsessive or problematic ways, also report more attention problems (e.g., Boer et al., Reference Boer, Stevens, Finkenauer and van den Eijnden 2020 ; Mérelle et al., Reference Mérelle, Kleiboer and Schotanus 2017 ; Settanni et al., Reference Settanni, Marengo, Fabris and Longobardi 2018 ; Yen et al., Reference Yen, Ko, Yen, Wu and & Yang 2007 ). For example, one study found associations between problematic social media use and hyperactivity among a large sample of more than 20,000 Dutch adolescents (Mérelle et al., Reference Mérelle, Kleiboer and Schotanus 2017 ), and another study found cross-sectional correlations between problematic social media use and attention deficits, impulsivity, and hyperactivity (Boer et al., Reference Boer, Stevens, Finkenauer and van den Eijnden 2020 ).

The question of causality across these studies is key. Does the use of social media deteriorate adolescents’ attention capacities or are those adolescents who have difficulties sustaining their attention more drawn to social media? Due to the scarcity of longitudinal studies in this realm this question cannot yet be conclusively answered. One longitudinal study investigating the reciprocal relationships between ADHD and social media use found no evidence for an effect of social media use frequency on ADHD over time but an effect of addictive social media use on ADHD (Boer et al., Reference Boer, Stevens, Finkenauer and van den Eijnden 2020 ). This indicates that not the frequency of use per se but more problematic usage patterns (such as uncontrollability of usage or displacement of social activities) might be detrimental to adolescents’ attention. Although this study found no evidence for attention problems being a predictor of developing problematic social media use patterns, another study found that ADHD symptoms in adolescents were the strongest predictor for developing internet addiction two years later (Ko et al., Reference Ko, Yen, Chen, Yeh and Yen 2009 ).

Taken together, it seems likely that adolescents with attention problems are more drawn to social media in general, and that they are also more likely to show problematic usage patterns. The stimulating and arousing nature of digital media is particularly appealing to individuals showing symptoms of ADHD as they have a higher need for stimulation (Weiss et al., Reference Weiss, Baer, Allan, Saran and Schibuk 2011 ). Digital media might provide the optimal level of stimulation to them. However, it is still unknown how far the (problematic) use of digital media further increases attention problems. The existing studies indicate that there is indeed a possibility that problematic usage patterns further deteriorate attention. However, due to the small amount of longitudinal studies, it is difficult to draw definite conclusions .

How Do Social Media and Media Multitasking Affect Attention?

To understand how social media and media multitasking affect attention problems among adolescents, it is important to identify theoretical explanations for such effects. Three potential explanations have been put forward to explain the potential effects of media multitasking on attention: 1) habituation to high arousal levels, 2) becoming increasingly sensitive to irrelevant information, and 3) deterioration of attentional control processes (see Baumgartner et al., Reference Baumgartner, van der Schuur, Lemmens and te Poel 2018 ).

Similarly to the mechanism that was proposed for the effects of violent and fast-paced TV on attention, habituation to high arousal levels might also play a role in the effects of media multitasking and social media use on attention. Media multitasking is considered an arousing activity, and it has been shown that switching between media activities increases arousal levels (Yeykelis et al., Reference Yeykelis, Cummings and Reeves 2014 ). Thus, it can be assumed that when adolescents engage frequently in media multitasking, they habituate to these rather high arousal levels. This in turn makes them favor stimulating and arousing activities in the future. That individuals can habituate to media stimuli has been previously shown for video games with gamers physiologically habituating to arousal levels after repeated video game play (Grizzard et al., Reference Grizzard, Tamborini and Sherry 2015 ). In the context of media multitasking this could mean that adolescents habituate to the arousing nature of multitasking, and as a consequence find less stimulating single-task environments less appealing (e.g., sitting in class or listening to a lecture).

The second potential explanation is that media multitasking affects basic cognitive processes. Ever since Ophir et al. ( Reference Ophir, Nass and Wagner 2009 ) showed differences in cognitive processing among heavy and light media multitaskers, it has been suspected that engaging in media multitasking may cause these different processing patterns. Engaging in media multitasking requires individuals to attend to multiple streams of information. It has thus been argued that this type of information processing may train the brain to become more sensitive to irrelevant information (Ophir et al., Reference Ophir, Nass and Wagner 2009 ). If individuals get used to continuously attending to several streams of information, they might be more easily distracted by irrelevant external (and potentially internal) distractions (Adler & Benbunan-Fich, Reference Adler and Benbunan-Fich 2012 ).

The third mechanism that has been suggested is that by engaging in media multitasking, adolescents deteriorate their basic attentional control processes. This has been called the “deficit-producing hypothesis” (Ralph et al., Reference Ralph, Thomson, Cheyne and Smilek 2013 ). The main assumption is that media multitasking might deteriorate adolescents’ ability to regulate their attention internally as they get used to external stimulations. A similar mechanism has previously been assumed for the effects of fast-paced TV content for which it was suggested that fast-paced content captures attention in a bottom-up fashion and does not train adolescents’ volitional attention processes (e.g., Lillard & Peterson, Reference Lillard and Peterson 2011 ). Thus, by engaging in media multitasking frequently, adolescents might not train their ability to guide their attention. This may lead to deficits in these attentional control processes over time (Rothbart & Posner, Reference Rothbart and Posner 2015 ).

Next to these three cognitive mechanisms, others have argued that digital media use may increase symptoms of ADHD among adolescents by replacing time spent with more developmentally beneficial activities (Weiss et al., Reference Weiss, Baer, Allan, Saran and Schibuk 2011 ). Thus, even if digital media use has no direct effect on cognitive processes, it may still interfere with the healthy development of these skills because it replaces developmentally important activities, such as playing or having conversations with friends and family (Pea et al., Reference Pea, Nass and Meheula 2012 ).

Importantly, although all of these mechanisms are theoretically plausible, empirical research assessing the mediating role of these mechanisms is still missing. Understanding the underlying mechanisms, however, is crucial as this will help to develop intervention studies that target the problematic aspects of digital media use rather than restricting digital media use in general.

Are There Any Positive Effects of Digital Media on Attention?

If digital media has the potential to affect attentional processes, the question is warranted whether digital media use may not also have positive effects on cognition and attention. Indeed, it has been argued that engagement in media multitasking may also train attentional processes (i.e., trained attention hypothesis: Kobayashi et al., Reference Kobayashi, Oishi and Yoshimura 2020 ; van der Schuur et al., Reference Van der Schuur, Baumgartner, Sumter and Valkenburg 2015 ). It has been assumed that people who engage frequently in media multitasking may improve their task switching skills and lower their switching costs by training these skills. Evidence for this trained attention hypothesis for media multitasking is scarce. However, one brain imaging study found some evidence for improved attentional brain activity among heavy media multitaskers (Kobayashi et al., Reference Kobayashi, Oishi and Yoshimura 2020 ), and another study found better task switching performance among heavy media multitaskers (Alzahabi et al., Reference Alzahabi and Becker 2013 ). Interestingly, it has been recently suggested that there are curvilinear relationships in that intermediate media multitaskers have better attentional control than low or heavy media multitaskers (Cardoso-Leite et al., Reference Cardoso-Leite, Kludt, Vignola, Ma, Green and Bavelier 2016 ). More research is needed to establish whether such positive or curvilinear effects do indeed occur.

In contrast to the rather mixed findings on potential beneficial effects of media multitasking, research on the positive effects of playing action video games are more consistent. These studies show positive effects of playing action video games on several attentional skills, such as focused attention, selected attention, and sustained attention (for a recent meta-analysis, see Bediou et al., Reference Bediou, Adams, Mayer, Tipton, Green and Bavelier 2018 , and for a review focusing specifically on attention, see C. S. Green & Bavelier, Reference Green and Bavelier 2012 ). These effects were shown for cross-sectional studies but also for intervention studies that showed improvements in these cognitive skills after playing games for 20–40 hours. Most of these studies focused on young adults; however, a few also corroborated these effects for children and adolescents (Dye et al., Reference Dye, Green and Bavelier 2009 ). Action video games pose a high demand on divided attention, information filtering, and motor control. It is therefore assumed that engaging in these games trains these attentional processes and can therefore benefit attentional control (e.g., Bediou et al., Reference Bediou, Adams, Mayer, Tipton, Green and Bavelier 2018 ).

In sum, there is some evidence that digital media has positive effects on attention skills. However, this highly depends on the content and type of media used. Particularly, first-person action video games seem to be beneficial. Moreover, effects are dependent on the amount of time spent with particular media. Extant literature suggests possible curvilinear relationships with moderate amounts of exposure being more beneficial than no exposure or too much exposure (Cardoso-Leite et al., Reference Cardoso-Leite, Kludt, Vignola, Ma, Green and Bavelier 2016 ; Schmidt & Vandewater, Reference Schmidt and Vandewater 2008 ).

Future Research Directions for the Effects of Digital Media on Attention

Overall, research so far has found supporting evidence for a relationship between the amount of media multitasking and social media on the one hand and attention problems on the other hand. Adolescents who engage more frequently in media multitasking and who show more problematic social media use patterns, are also more likely to have attention problems in their everyday lives. The key endeavor for future research is to establish the causality of this relationship. It is yet unclear whether adolescents with attention problems are more drawn to engage in media multitasking, or whether media multitasking affects attention over time. Tentative evidence suggests a reciprocal relationship in that adolescents with attention problems are more drawn to specific types of media and media use patterns, and that spending too much time with these digital media further increases their attention problems (Baumgartner et al., Reference Baumgartner, van der Schuur, Lemmens and te Poel 2018 ).

Next to the fundamental question of causality, it is crucial to understand the characteristics and affordances of digital media that lead to potential effects on attention. Which characteristics of social media and media multitasking impair attention, and how do these differ from other types of media? Understanding these characteristics is important for several reasons. First, this may help our understanding of the underlying mechanisms through which they are at work. Despite several theoretical assumptions about these mechanisms, empirical evidence is clearly lacking. Understanding these mechanisms might help adolescents to find more beneficial ways to use digital media without banning these completely from their lives. Moreover, a theoretical understanding of which characteristics are problematic would have crucial advantages in the current fast-changing media landscape. Currently, research lags behind new technological developments, and the same questions emerge with every new type of media. To create a more sustainable research agenda it would be helpful to understand the key characteristics of media that drive these effects, and compare and differentiate these among different media types (Orben, Reference Orben 2020 ).

Digital Media Use and Sleep: What Do We Know?

Sleep plays a critical role in the development of adolescents. Insufficient sleep has been linked to decreased cognitive functioning, increased risk of obesity, and diminished well-being, such as depressive symptoms and perceived stress (e.g., Shochat et al., Reference Shochat, Cohen-Zion and Tzischinsky 2014 ; Short et al., Reference Short, Gradisar, Lack and Wright 2013 ). From late childhood to early adolescence sleep-related problems increase (Mitchell et al., Reference Mitchell, Morales and Williamson 2020 ), with approximately 75% of students in their last year of high school getting insufficient sleep in comparison to only 16% of 6th graders (i.e., fewer than eight hours per night; National Sleep Foundation, 2006 ). Due to the importance of sleep for healthy psychological and physical development, it is concerning that so many adolescents today get insufficient sleep. Digital media are often seen as one of the main culprits for insufficient sleep and sleep problems, especially among adolescents (e.g., Bhat et al., Reference Bhat, Pinto-Zipp, Upadhyay and Polos 2018 ; Mireku et al., Reference Mireku, Barker and Mutz 2019 ). Particularly smartphones and social media are used extensively by adolescents, and frequently when already in bed or even during the night (e.g., Scott & Woods, Reference Scott and Woods 2019 ; van den Bulck, Reference Van den Bulck 2003 , Reference Van den Bulck 2007 ).

There is consensus in the field that digital media use is linked to insufficient sleep in adolescents. Several reviews and meta-analyses support this notion (see, e.g., Carter et al., Reference Carter, Rees, Hale, Bhattacharjee and Paradkar 2016 ; Hale et al., Reference Hale, Li, Hartstein and LeBourgeois 2019 ; LeBourgeois et al., Reference LeBourgeois, Hale, Chang, Akacem, Montgomery-Downs and Buxton 2017 ). For example, a meta-analysis on the effects of mobile media devices on sleep, concluded – based on 20 studies with a total of more than 125,000 children and adolescents – that the use of media devices was consistently linked to insufficient sleep quantity, lower sleep quality, and increased daytime sleepiness (Carter et al., Reference Carter, Rees, Hale, Bhattacharjee and Paradkar 2016 ). Similarly, in a more recent review of the literature, digital media use was related to adolescents going to bed later, needing more time to fall asleep, waking up during the night, showing signs of sleep problems, and daytime sleepiness (Hale et al., Reference Hale, Li, Hartstein and LeBourgeois 2019 ). These effects have been shown for the general time that adolescents spent with media, but particularly for bedtime media use (Hale et al., Reference Hale, Li, Hartstein and LeBourgeois 2019 ) and are consistent across various countries and cultural backgrounds (Hale et al., Reference Hale, Li, Hartstein and LeBourgeois 2019 ).

Despite this strong evidence for cross-sectional relationships between digital media use and sleep, there are only a few longitudinal and experimental studies, and evidence from these studies is rather mixed. Some longitudinal studies found that digital media use was related to less sleep one or two years later (Johnson et al., Reference Johnson, Cohen, Kasen, First and Brook 2004 ; Mazzer et al., Reference Mazzer, Bauducco, Linton and Boersma 2018 ; Poulain et al., Reference Poulain, Vogel, Buzek, Genuneit, Hiemisch and Kiess 2019 ). In contrast, others did not find longitudinal effects of media use on sleep (Tavernier & Willoughby, Reference Tavernier and Willoughby 2014 ), or only for specific subgroups (van der Schuur et al., Reference Van der Schuur, Baumgartner, Sumter and Valkenburg 2018 ). For example, media multitasking was over time only related to increased sleep problems among girls but not among adolescent boys (van der Schuur et al., Reference Van der Schuur, Baumgartner, Sumter and Valkenburg 2018 ).

To further establish the causality of the relationship, a few intervention studies exist that encouraged adolescents or young adults to reduce the use of specific media before bedtime to examine whether this improves sleep length and quality. These studies typically show improvements in sleep quality during intervention. For example, engaging in a smartphone app-based slow-breathing exercise improved subsequent sleep in comparison to using social media before going to bed (Laborde et al., Reference Laborde, Hosang, Mosley and Dosseville 2019 ). Similarly, reducing adolescents’ screen time after 9pm on school nights was related to increased sleep duration and improved daytime vigilance (Perrault et al., Reference Perrault, Bayer and Peuvrier 2019 ). A recent meta-analysis on 11 intervention studies concluded that interventions can be successful in reducing screen time and improving sleep time (on average by 11 minutes per day) among children and adolescents (Martin et al., Reference Martin, Bednarz and Aromataris 2020 ). These studies are promising as they show that reducing screen time can have beneficial effects on sleep. Longer intervention studies, however, are needed to further test the long-term effectiveness and willingness to comply among adolescent samples .

Why and How Do Digital Media Affect Sleep?

Three underlying mechanisms are typically put forward in the literature to explain the effects of digital media use on sleep (e.g., Bartel & Gradisar, Reference Bartel, Gradisar, Nevšímalová and Bruni 2017 ). First, the use of digital media before bedtime or when already in bed might displace sleep time. Second, the blue light emitted from digital devices might interfere with the secretion of the sleep hormone, melatonin. Third, the arousing content of digital media might make it difficult for adolescents to fall asleep after media use.

Sleep displacement may occur in two stages: it may lead adolescents to go to bed later and, once in bed, media use may delay the time when adolescents close their eyes and try to fall asleep (Exelmans & van den Bulck, Reference Exelmans and van den Bulck 2017a ). Evidence for sleep displacement is consistent for adolescent samples, and has been shown to occur for various types of digital media, such as smartphone, social media, video games, and TV (e.g., Hysing et al., Reference Hysing, Pallesen, Stormark, Jakobsen, Lundervold and Sivertsen 2015 ; Kubiszewski et al., Reference Kubiszewski, Fontaine, Rusch and Hazouard 2013 ). Overall, the literature clearly points toward later bedtimes for adolescents who use digital devices in the evening. Delayed bedtimes and sleep times might be particularly problematic for adolescents who have strict school starting times and cannot easily sleep in. For adult samples, it has been shown that digital media use might lead to later bedtimes but in turn also to later rise times (Custers & van den Bulck, Reference Custers and van den Bulck 2012 ).

Particularly for the use of smartphones, sleep displacement might also occur after sleep onset during the night, when incoming messages interrupt sleep. Several studies reported that smartphones lead to nighttime awakenings (Fobian et al., Reference Fobian, Avis and Schwebel 2016 ; van den Bulck, Reference Van den Bulck 2003 ), and these nighttime awakenings might negatively influence sleeping patterns in the long run (Foerster et al., Reference Foerster, Henneke, Chetty-Mhlanga and Röösli 2019 ). Therefore, adolescents who take their devices to bed might not only fall asleep later but might also be awakened by these devices during the night. Based on the existing literature, it is very likely that sleep displacement is a contributing factor for the detrimental impact of digital media on sleep. However, it is likely not the only factor because sleep displacement can only account for effects on sleep quantity but to a lesser account for the effects on sleep quality.

The bright screen light emitted by electronic devices has also been considered one of the main culprits for the effects of digital media on sleep. It has been argued that the artificial light emitted by electronic devices may lead to a disruption of the circadian rhythm, leading to increased alertness, and deteriorating sleep quality (Cho et al., Reference Cho, Ryu, Lee, Kim, Lee and Choi 2015 ). When considering the effects of artificial light on sleep at least three factors need to be considered: the intensity of the emitted light, the duration of light exposure, and the type of light (Cho et al., Reference Cho, Ryu, Lee, Kim, Lee and Choi 2015 ). Bright light is more disruptive for sleep, as well as short-wave and blue light. Electronic devices, such as smartphones, emit short-wave blue light that is said to suppress the production of the hormone melatonin, which plays an important role in making people sleepy and supporting healthy sleep.

Several studies found negative effects of screen light on subsequent sleepiness and sleep quality (Cajochen et al., Reference Cajochen, Frey and Anders 2011 ; Chang et al., Reference Chang, Aeschbach, Duffy and Czeisler 2015 ; A. Green et al., Reference Green, Cohen-Zion, Haim and Dagan 2017 ). For example, exposure to a very bright LED-backlit computer screen affected melatonin levels and sleepiness of male adults (Cajochen et al., Reference Cajochen, Frey and Anders 2011 ). Similarly, negative effects of reading an e-reader before going to sleep were found (Chang et al., Reference Chang, Aeschbach, Duffy and Czeisler 2015 ). Importantly, the effects of screen light might be stronger for adolescents than for adults, as adolescents seem to be more affected by short-wave light than adults (Nagare et al., Reference Nagare, Plitnick and Figueiro 2019 ).

Despite several studies finding effects of screen light on sleep quality, it is still highly debated in the field whether the light emitted from tablets, e-readers, TVs, and smartphones is bright enough to interfere with melatonin secretion and sleep. In a recent study, no or only very small and clinically insignificant effects of a bright tablet screen were found (Heath et al., Reference Heath, Sutherland and Bartel 2014 ). Moreover, in those studies that found effects on melatonin secretion and/or sleep, sample sizes were rather small, and participants were exposed to rather extreme artificial light conditions, such as five hours of an extremely bright screen (Cajochen et al., Reference Cajochen, Frey and Anders 2011 ), or four hours of a bright e-reader screen (Chang et al., Reference Chang, Aeschbach, Duffy and Czeisler 2015 ). The clinical relevance of these findings is therefore still debatable. Overall, it is rather unlikely that the light emitted from digital devices is the only or even the most influential mechanism in explaining the effects of digital media on sleep.

The final mechanism that has been put forward is arousal. It is assumed that specific media content might lead to increased physiological arousal, which in turn makes it difficult for people to fall asleep after media use. This mechanism has received the least research attention, and a comprehensive theoretical conceptualization is missing. More specifically, we lack a clear conceptualization of which content characteristics lead to which effects on which mediator (e.g., physiological arousal, cognitive alertness). Bedtime media use might differ widely among adolescents, and from a media psychological perspective it is likely to assume that not all content is equally detrimental to all adolescents’ sleep. Although adolescents might use media for the same amount of time before going to bed, their usage patterns might differ tremendously, and their sleep might be differentially affected by their use. For example, one teenager might be listening to relaxing music on their smartphone when in bed, while another teen is actively posting and reacting on their social media accounts. It is likely that these different types of media use lead to very different effects on arousal and sleep.

Moreover, not only the type of content that adolescents consume might have an effect on sleep but also how these media are used. For example, interactive media (i.e., video games) seem to have a stronger negative impact on sleep than the passive use of media (i.e., watching a DVD; McManus et al., Reference McManus, Underhill, Mrug, Anthony and Stavrinos 2020 ; Weaver et al., Reference Weaver, Gradisar, Dohnt, Lovato and Douglas 2010 ). Similarly, engaging in media multitasking is also related to sleep problems among adolescents (van der Schuur et al., Reference Van der Schuur, Baumgartner, Sumter and Valkenburg 2018 ). These studies stress the importance of investigating not only screen time but examining more specifically the types of digital media use and the ways digital media are used.

There is limited understanding about the mechanisms that link varying content types and usage behaviors to sleep quantity and quality. So far, it has been frequently suggested that digital media use leads to heightened physiological arousal (Exelmans & van den Bulck, Reference Exelmans and van den Bulck 2017b ). However, specific types of media content may not necessarily increase physiological arousal but might lead to increased cognitive alertness that prohibits sleep (Weaver et al., Reference Weaver, Gradisar, Dohnt, Lovato and Douglas 2010 ; Wuyts et al., Reference Wuyts, De Valck and Vandekerckhove 2012 ). Empirical investigations of these mechanisms for digital media are largely missing. One study showed small effects of video game play on alertness but not on arousal, stressing the importance of differentiating between these two processes (Weaver et al., Reference Weaver, Gradisar, Dohnt, Lovato and Douglas 2010 ).

In sum, our current understanding of which digital media content factors are related to sleep, and through which mechanisms, is very limited. We know very little about whether specific content and usage patterns affect the varying sleep indicators differently and through which underlying mechanisms content affects sleep (see also Hale & Guan, Reference Hale and Guan 2015 ).

Future Research Directions for Digital Media and Sleep

Although concerns that media negatively affect the sleep of adolescents have a long tradition, these worries are exacerbated with the rise of smartphones and social media as these media types are used more than any other type of media by youth, and are often carried with them to bed. To avoid negative effects of digital media on sleep, the standard advice to adolescents is not to use any types of digital media in the two hours before going to bed (LeBourgeois et al., Reference LeBourgeois, Hale, Chang, Akacem, Montgomery-Downs and Buxton 2017 ). This is also reflected in current intervention studies that solely focus on removing digital media from the bedroom altogether (Martin et al., Reference Martin, Bednarz and Aromataris 2020 ). Although this advice is common and accepted by many, there are at least two problems related to this advice.

First, this strategy is in stark contrast to adolescents’ lived experience and developmental needs, and consequently it is unlikely that adolescents will agree to completely ban these devices from their bedrooms. Second, this advice is based on a rather simplistic view on the effects of digital media on sleep that considers the use of the device as universally detrimental. However, how exactly adolescents use digital media before bedtime can vary tremendously, plausibly resulting in differential effects on their sleep quantity and quality. Despite years of research into the effects of digital media on sleep, there are still important shortcomings in the literature that make it difficult to draw final conclusions about the effects of digital media on sleep. Solving these issues in future research is critical to being able to provide adolescents with effective advice on how to use digital media in healthy ways.

Although there is consistent evidence in the literature for a negative relationship between digital media use and sleep, the direction of this relationship is less than clear. The vast majority of the existing studies are based on cross-sectional designs, making it impossible to draw conclusions about the direction of the relationship (Exelmans & van den Bulck, Reference Exelmans and van den Bulck 2019 ). Although it is generally assumed that the use of digital media deteriorates sleep, it could also be that the relationship is reversed in that adolescents who sleep less tend to use more digital media. For example, adolescents who do not sleep well might use digital media as a means to cope with stress and insomnia, or because they are depleted and do not have the capacities for regulating their media use efficiently. For example, university students used more social media on days they had slept less during the previous night (Mark et al., Reference Mark, Wang, Niiya and Reich 2016 ). Similarly, sleep-deprived children watched more TV during the day in an experimental study (Hart et al., Reference Hart, Hawley and Davey 2017 ).

Findings like this cast doubt on the idea that there is a simple cause-and-effect relationship between digital media and sleep. Recent advancement in media effects theories conceptualize media use and effects as reciprocal, evolving dynamically over time (Slater, Reference Slater 2007 ). In the case of media use and sleep this could mean that adolescents suffering from sleep problems are more likely to use more media that in turn may further deteriorate their sleep. This dynamic and reciprocal nature for smartphone use and sleep is understudied as it demands assessing use and effects over longer time periods in the natural environment of adolescents. One two-wave study found some evidence by showing that media use and sleep times were reciprocally related in adolescents over a one-year period (Poulain et al., Reference Poulain, Vogel, Buzek, Genuneit, Hiemisch and Kiess 2019 ). Understanding the nature of the relationship between digital media use and sleep is of key importance for our understanding of the effects of digital media and for intervention and prevention programs.

Individual Responses and Potential Facilitating Effects

Recent theoretical advances in media effects research stress the importance of individual susceptibilities to media effects (Beyens et al., Reference Beyens, Pouwels, van Driel, Keijsers and Valkenburg 2020 ; Valkenburg & Peter, Reference Valkenburg and Peter 2013 ). Also, for the relationship between sleep and digital media, individual differences are likely to be of importance. First, individuals differ in how they use digital media before sleep. For example, Scott and Woods ( Reference Scott and Woods 2018 ) showed that adolescents with higher levels of fear of missing out tended to use social media longer before sleep time and were more cognitively aroused before falling asleep. Thus individualized usage patterns might lead to varying effects. This is also important because not all evening media diets might be problematic. Some adolescents might use their smartphones in a way that benefits their sleep by actually decreasing their aroused state. This assumption builds on established media effects paradigms that argue that media are used to regulate arousal levels and to establish physiological homeostasis (Zillmann, Reference Zillmann 1988 ). For example, people can use apps to seek out social support, relax, and regulate sensory stimulation (Harrison et al., Reference Harrison, Vallina, Couture, Wenhold and Moorman 2019 ). Research has shown that some people report that they use media in bed to wind down from the day (Eggermont & van den Bulck, Reference Eggermont and van den Bulck 2006 ). However, little research has investigated whether digital media can be used in ways that benefit adolescents’ sleep. Understanding such effects could help to educate adolescents to use their smartphones in more beneficial ways.

A second reason why it is important to study individual differences is that, while uniform effects of some content are possible, adolescents likely differ in their individual responses to digital media content. For example, one study found that adolescents who used social media more frequently slept less well than those who used social media less frequently. However, this effect disappeared when social media stress was taken into account, showing that only those respondents who experienced high levels of stress from their social media use suffered from sleep problems (van der Schuur et al., Reference van der Schuur, Baumgartner and Sumter 2019 ). Moreover, this study showed that social media use was more problematic for the sleep of girls and early adolescents. Similarly, others found that only those who were more emotionally invested in their social media use slept less well (Woods & Scott, Reference Woods and Scott 2016 ), and that physiological reactions to violent game play differed depending on previous game experience (Ivarsson et al., Reference Ivarsson, Anderson, Åkerstedt and Lindblad 2013 ). Investigating these individual responses to smartphone use is crucial to understand why specific content is problematic for some adolescents but not for others.

Improved Measurement

The vast majority of existing studies relied on self-reports of media use and/or sleep. Self-reports for media use and sleep have been shown to be unreliable and it is thus likely that existing studies suffer from substantial measurement errors. Luckily recent developments in digital media and sleep tracking make it easier to assess digital media use as well as sleep unobtrusively and objectively. For example, there is a multitude of commercially available sleep trackers available with some studies showing promising results using them. We therefore hope that future research will try to combine self-reports with more objective measures for both digital media use and sleep. Assessing the complexity of digital media use objectively will be a crucial step to move beyond investigating screen time toward understanding differential effects of specific content (Carter et al., Reference Carter, Rees, Hale, Bhattacharjee and Paradkar 2016 ; Hale et al., Reference Hale, Li, Hartstein and LeBourgeois 2019 ; Scott & Woods, Reference Scott and Woods 2019 ) .

Overall Conclusion

Parents, educators, and researchers alike are interested in the effects that our digitalized society has on adolescents. Whether digital media impairs attention and sleep has been investigated in a large amount of studies. Yet, the conclusions that we can draw are still limited. Overall, there is compelling evidence that adolescents who use social media more frequently and who are engaging in media multitasking more frequently are more likely to show attention problems in their everyday lives. Moreover, using digital media before bedtime is related to less sleep and more sleep problems. However, the key question of whether digital media causally impairs attention and sleep cannot yet be conclusively answered. To answer this question, it is crucial for the field to advance the theoretical as well as methodological approaches that we currently employ.

Concerning theory development, it is of key importance to identify content characteristics and affordances of digital media that drive such effects. Extracting these factors is crucial to understand not only the effects of today’s digital media landscape but also the effects of future media technologies that will emerge (see also Orben, Reference Orben 2020 ). Moreover, identifying content characteristics will allow us to differentiate potential detrimental from facilitating digital media use. For some adolescents, specific types of media use might have beneficial effects, for example, when they use relaxing smartphone content before they go to bed. Such beneficial effects are oftentimes neglected in current research.

Once we have a clearer theoretical understanding of the content characteristics that drive effects, we need to employ methodological techniques that are able to empirically test those effects in more precise ways. For this, it is important to move beyond cross-sectional studies relying on self-reports of general “screen time” toward assessing digital media in its complexity. Current technological developments facilitate the tracking of digital media use and sleep unobtrusively, objectively, and continuously. Moreover, current advancements in computational methods allow us to integrate, extract and analyze these types of complex data in more efficient ways. This will pave the way toward more advanced studies that examine the dynamic nature of digital media use, sleep and attention in unprecedented ways, and that will accelerate our knowledge of the effects of digital media on youth.

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• The Effects of Digital Media and Media Multitasking on Attention Problems and Sleep
• By Susanne E. Baumgartner
• Edited by Jacqueline Nesi , Brown University, Rhode Island , Eva H. Telzer , University of North Carolina, Chapel Hill , Mitchell J. Prinstein , University of North Carolina, Chapel Hill
• Book: Handbook of Adolescent Digital Media Use and Mental Health
• Online publication: 30 June 2022
• Chapter DOI: https://doi.org/10.1017/9781108976237.017

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