multitasking experiment examples

Multitasking

Introduction

About this implementation, run the demo, data output file, further reading.

Multitasking can be defined in slightly different ways:

Carrying multiple tasks out at the same time

Real life: Driving while talking on the phone

Laboratory: Drawing a circle with the left hand while drawing a rectangle with the right hand.

Carrying out multiple tasks in rapid sequential order

Preparing a meal. You cut the vegetables. Put the potatoes in the pan. Sort the spices. Check over the cookbook. Check if potatoes boil. Stir fry the vegetables.

Rapidly switching between two computer tasks compared to doing only one task

The reality is that you can argue that people always multi-task in some way. After all, we always monitor our environment to some degree, no matter what. For example, no matter how deeply you concentrate on doing a task, if you hear someone shout "fire", you will process that information and act on it.

In this multitasking experiment, we compare performance when just doing one task compared to when two tasks are mixed. In essence, this is simply a task switching paradigm. It is a cued task switching paradigm, although the cue (the location of the stimulus) and the imperative stimulus are presented at exactly the same time. In that sense, it is a bit mid-way between the alternative run and explicit cued-task paradigm (again, it is a bit a matter of definition).

In this paradigm, we have two different types of comparisons:

Single tasking (doing a block of one task type) compared with multi-tasking (rapidly interleaving two tasks).

Within a multi-tasking block, task-repeat compared with task-switch trials (this is what task-switching paradigms typically study).

reported that women are better in switching between two tasks than men (as compared to doing one task at the time).

This example is very close to the task used in the study by Stoet, Connor, O’Connor, and Laws (2013) .

Note that this code includes a feedback section and that the code keeps track of which trials are task-repeat and task-switch trials. In principle, this is not necessary unless you want to give participants direct feedback about these things. If you wish, this code can be simpler, but then you would need to calculate these variables in the post-processing for your data analysis. PsyToolkit lets you do it the way you want.

In this experiment, you respond with the keys and to diamonds and rectangles (in the ) and circles (in the ). The instructions are all on screen and require some concentrated reading. Altogether, it will take you 10 to 15 minutes.

If you make a mistake, you get a 3 second timeout. That might feel like a long time, but it is a way to make participants concentrate. You do not need to press a button, just need to wait.

This is a difficult task! You need to remember the rules of two different tasks and you need to frequently switch between them. In the cognitive laboratory, this is considered one of the more difficult tasks. Are you up for it?

Click here to run a demo of the task-switching paradigm

In PsyToolkit, the data output file is simply a textfile. The line of the PsyToolkit experiment script determines what is being saved in the data output file. Typically, for each experimental trial, you would have exactly one line in your text file, and each number/word on that line gives you the information you need for your data analysis, such as the condition, response speed, and whether an error was made.

Meaning of the columns in the output datafile. You need this information for your data analysis.

Colum	Meaning
1	blockname
2	tablerow number (1-4)
3	name of task (fill or shape)
4	congruency (congruent or incongruent)
5	response position (left or right )
6	status (1=correct, 2=wrong, 3=slow)
7	response time (milliseconds)
8	mixing (1=in pure block, 2=in mixing block)
9	switching (1=repeat trial, 2=switch trial)

The PsyToolkit code zip file

If you have a PsyToolkit account, you can upload the zipfile directly to your PsyToolkit account. . If you want to upload the zipfile into your PsyToolkit account, make sure the file is automatically uncompressed (some browsers, especially Mac Safari, by default uncompress zip files). .

Stoet, G., O’Connor, D.B., Conner, M., and Laws, K. R. (2013). Are women better than men at multitasking? BMC Psychology , 1:18. You can read this open access paper by clicking here .

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

Gopher, D., Armony, L. & Greenspan, Y. (2000). Switching tasks and attention policies. Journal of Experimental Psychology: General, 129 , 308-229.

Mayr, U. & Kliegl, R. (2000). Task-set switching and long-term memory retrieval. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26 , 1124-1140.

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.

Monsell, S., Yeung, N., & Azuma, R. (2000). Reconfiguration of task-set: Is it easier to switch to the weaker task? Psychological Research, 63 , 250-264.

Monsell, S. & Driver, J., Eds. (2000). Control of cognitive processes: Attention and Performance XVIII. Cambridge, Mass.: MIT Press.

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.

Yeung, N. & Monsell, S. (2003). Switching between tasks of unequal familiarity: The role of stimulus-attribute and response-set selection. Journal of Experimental Psychology-Human Perception and Performance, 29(2) : 455-469.

How good is your multitasking? Try this simple game to find out

Count to 10

Timekeeper asks the player- “Can you count to 10 in a loud, clear voice?”
Timekeeper starts a countdown- 3, 2, 1, and starts the stopwatch
Player counts 1 to 10 as fast as possible
When the player has counted to 10, timekeeper stops the stopwatch and writes the time on a whiteboard

Recite alphabet to J

Timekeeper asks the player- “Can you recite the alphabet from A to J?”
Player recites A to J as fast as possible
When the player is done, timekeeper stops the stopwatch and writes the time on a whiteboard

Alternate counting numbers and reciting the alphabet

Timekeeper explains to the player- alternate letters and numbers while counting to 10 and reciting the alphabet to J. For example, “A, 1, B, 2” and so on. The player cannot use any aids such as fingers or paper.
Player recites the sequence. Here it is for the timekeeper’s reference: “A, 1, B, 2, C, 3, D, 4, E, 5, F, 6, G, 7, H, 8, I, 9, J, 10”. Don’t interrupt even if the player makes a mistake. Let them be confused, muddle around, make a mistake, or give up.
When the player is done correctly, the timekeeper stops the stopwatch and writes the time on a whiteboard. If the player finishes but made a mistake, make them do it again!

Most players won’t be able to recite the sequence correctly on the first try, or even with any number of tries. Even if they do complete it, it will take substantially longer than the combined time of reciting each sequence separately.

Repeat the game with a new player and timekeeper. Enjoy a bit of fun competition!

Compare the time it took for the first two exercises with the time required to correctly do the third exercise. In your team, discuss what that indicates for multitasking in daily work.

What’s the Point?

If you can’t successfully multitask with such a simple exercise, do you really think you’re able to manage switching between complex tasks throughout the day? Don’t bet on it. The human brain has limitations. You and your team will be much more effective if you work within those limitations instead of pretending multitasking isn’t a problem. It’s much better to choose a task, start it, work it to completion, and then find the next task to do.

Unfortunately, PSA tools such as ConnectWise Manage and Datto Autotask PSA don’t make this very easy. Their way of visualizing tickets, tasks, and projects is very basic- usually just a simple report showing all the work and leaving the sorting and filtering up to you. It’s easy to get distracted with multiple tickets at once, to be unclear about what’s the most important work that needs to be prioritized right now, or to miscommunicate in your team about the status and priority of the work.

That’s why TopLeft built Kanban boards for ConnectWise and Autotask. Kanban tools and practices help busy IT teams stay focused. Kanban boards make it easy to see the work you’ve already started, so you can focus on finishing work before starting new work- minimizing multi-tasking and improving your team productivity, improving communication, and identifying neglected work and bottlenecks. Ready to see it in action? Schedule a demo .

The Myth of Multitasking Exercise – Updated

Think you’re the world’s best multitasker? Know someone who thinks they can do many things at once? Here’s your chance to put your skills to this test! In this video, I walk you through the all-new version of my myth of multitasking exercise.

Complete the exercise.
Share your result below.
Then invite at least two people you know to complete it, too! Compare your experiences with each other.

NOTE: This exercise is adapted from my time management keynote speech and workshop. There are some minor changes to clear up confusion that can happen in a short-video format.

Looking for the downloadable PDF? Here it is: The Myth of Multitasking Exercise – Revisited. As promised, here are the definitions of switchtasking and background tasking :

Switchtasking = attempting to do multiple attention-requiring tasks at the same time. Each switch in attention incurs switching cost, which includes a loss of time, decrease in performance, and an increase in stress levels. When most people say they are “multitasking,” they are most often referring to switchtasking.

Background tasking = performing a task while something mindless or mundane occurs in the background. Examples include: delegating tasks to employees while you work on more valuable activities, putting a machine to work on a large job while you answer email, and exercising while you listen to music. Background tasking can improve productivity overall.

Multitasking is neither a good thing nor a bad thing…it simply does not exist! The question is, are you background tasking , which may improve productivity, or are you switchtasking , which always harms productivity? Please help me spread the word!

Complete the Myth of Multitasking exercise.
Then invite at least two people you know to complete it, too!

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Research article
Open access
Published: 24 October 2013

Are women better than men at multi-tasking?

Gijsbert Stoet 1 ,
Daryl B O’Connor 2 ,
Mark Conner 2 &
Keith R Laws 3

BMC Psychology volume 1 , Article number: 18 ( 2013 ) Cite this article

142k Accesses

77 Citations

402 Altmetric

Metrics details

There seems to be a common belief that women are better in multi-tasking than men, but there is practically no scientific research on this topic. Here, we tested whether women have better multi-tasking skills than men.

In Experiment 1, we compared performance of 120 women and 120 men in a computer-based task-switching paradigm. In Experiment 2, we compared a different group of 47 women and 47 men on "paper-and-pencil" multi-tasking tests.

In Experiment 1, both men and women performed more slowly when two tasks were rapidly interleaved than when the two tasks were performed separately. Importantly, this slow down was significantly larger in the male participants (Cohen’s d = 0.27). In an everyday multi-tasking scenario (Experiment 2), men and women did not differ significantly at solving simple arithmetic problems, searching for restaurants on a map, or answering general knowledge questions on the phone, but women were significantly better at devising strategies for locating a lost key (Cohen’s d = 0.49).

Conclusions

Women outperform men in these multi-tasking paradigms, but the near lack of empirical studies on gender differences in multitasking should caution against making strong generalisations. Instead, we hope that other researchers will aim to replicate and elaborate on our findings.

Peer Review reports

In the current study, we address the question whether women are better multi-taskers than men. The idea that women are better multi-taskers than men is commonly held by lay people (for a review see Mäntylä 2013 ). While the empirical evidence for women outperforming men in multi-tasking has been sparse, researchers have shown that women are involved more in multi-tasking than men, for example in house-hold tasks (Offer and Schneider 2011 ; Sayer 2007 ). In this paper we address the question if it is true that women actually outperform men when multi-tasking.

Multi-tasking is a relatively broad concept in psychology, developed over several decades of research (for a review see Salvucci and Taatgen 2010 ); this research has enormous relevance for understanding the risk of multi-tasking in real-life situations, such as driving while using a mobile phone (Watson and Strayer 2010 ).

There are at least two distinct types of multi-tasking abilities. The first type is the skill of being able to deal with multiple task demands without the need to carry out the involved tasks simultaneously. A good example of this type of multi-tasking is carried out by administrative assistants, who answer phone calls, fill in paperwork, sort incoming faxes and mail, and typically do not carry out any of these tasks simultaneously.

A second type of multi-tasking ability is required when two types of information must be processed or carried out simultaneously . An example of the latter category is drawing a circle with one hand while drawing a straight line with the other hand. While humans have no difficulty carrying out each of these tasks individually, drawing a circle with one hand and drawing a straight line with the other simultaneously is nearly impossible (the circle becomes more of an ellipse and the line more of a circle, Franz et al. 1991 ). Another example is the requirement to process different types of sensory information at the same time (Pashler 1984 ), such as different auditory streams on different ears (Broadbent 1952 ). While humans frequently are asked to do such tasks in the psychological laboratory, humans seem to try to avoid these situations in real life, unless they are highly trained (e.g., playing piano, with the left and right hands playing different notes, or having a conversation while driving a car). Arguably, we are not good at doing multiple tasks simultaneously (except when well trained), and that probably explains why this type of multi-tasking is less common than the type in which we serially alternate between two tasks (Burgess 2000 ). It is because of this that we focus on the first type of multi-tasking in this study. Also, it is important to note that the two types of multi-tasking described above are two extreme examples on a continuum of multi-tasking scenarios.

Cognitive scientists and psychiatrists have postulated a special set of cognitive functions that help with the coordination of multiple thought processes, which include the skills necessary for multi-tasking, namely "executive functions" (Royall et al. 2002 ): task planning, postponing tasks depending on urgency and needs (i.e., scheduling), and ignoring task-irrelevant information (also known as "inhibition"). Healthy adults can reasonably well interleave two novel tasks rapidly (Vandierendonck et al. 2010 ). The involved (human) brain areas necessary for multi-tasking have been investigated and we can at the very least make a reasonable estimate of which are involved (Burgess et al. 2000 ). Among primates, humans seem to have a unique way of dealing with task switching (Stoet and Snyder 2003 ), which we hypothesize reflects an evolutionary unique solution for dealing with the advantages and disadvantages of multi-tasking (Stoet and Snyder 2012 ). The specific contributions of individual brain areas to executive control skills in humans have been linked to a number of mental disorders, in particular schizophrenia (Evans et al. 1997 ; Kravariti et al. 2005 ; Royall et al. 2002 ; Semkovska et al. 2004 ; Dibben et al. 2009 ; Hill et al. 2004 ; Laws 1999 ).

Currently, there are few studies on gender and multi-tasking, despite a seemingly confident public opinion that women are better in multi-tasking than men (Ren et al. 2009 ). Ren and colleagues ( 2009 ) extrapolated the hunter-gatherer hypothesis (Silverman and Eals 1992 ) to make predictions about male and female multi-tasking skills. The hunter-gatherer hypothesis proposes that men and women have cognitively adapted to a division of labor between the sexes (i.e., men are optimized for hunting, and women are optimized for gathering). Ren and colleagues speculated that women’s gathering needed to be combined with looking after children, which possibly requires more multi-tasking than doing a task without having to look after your offspring. In their experiment, men and women performed an Eriksen flanker task (Eriksen and Eriksen 1974 ) either on its own (i.e., single task condition) or preceded by an unrelated other cognitive decision making task (i.e., multi-tasking condition). They found that in the multi-tasking condition, women were less affected by the task-irrelevant flankers than men. Thus, the latter study supports the hypothesis that women are better multi-taskers.

We tested whether women outperform men in the first type of multi-tasking. In Experiment 1, we tested whether women perform better than men in a computer-based task-switching paradigm. In Experiment 2 a , we tested whether women outperform men in a task designed to test "planning" in a "real-life" context that included standardized tests of executive control functions. Our prediction was that women would outperform men.

Experiment 1

In this experiment, we used a task-switching paradigm to measure task-switching abilities. Task-switching paradigms are designed to measure the difficulty of rapidly switching attention between two (or more) tasks. Typically, in these types of studies, performing a task consists of a simple response (e.g., button press with left or right hand) to a simple stimulus (e.g., a digit) according to simple rules (e.g., odd digits require left hand response, even digits a right hand response).

In task-switching paradigms, there are usualy two different tasks (e.g., in task A deciding whether digits are odd or even, and in task B deciding whether digits are lower or higher than the value 5). An easy way to think of task-switching paradigms is to call one task "A" and another task "B". A block of just ten trials of task A can be written as "AAAAAAAAAA" and a block of just ten trials of task B can be written as "BBBBBBBBBB". Most adults find carrying out sequences of one task type relatively simple. In contrast, interleaving trials like "AABBAABBAABB" is difficult, as demonstrated for the first time in 1927 by Jersild ( 1927 ). Today, the slowing down associated with carrying out a block of mixed trials compared to a block of pure trials is known as "mixing cost". Further, within mixed blocks, people slow down particularly on trials that immediately follow a task switch (in AA B B A A there are two such trials, here indicated in bold font); the latter effect is known as "switch cost".

Researchers have given switch costs more attention than mixing costs, especially since the mid-1990s(Vandierendonck et al. 2010 ) b . In the current experiment, we measured both types of costs.

Participants

We recruited participants via online advertisements and fliers in West Yorkshire (UK). Our recruitment procedure excluded participants with health problems and disorders that could potentially affect their performance, which included color-vision deficits, as tested with the Ishihara color test (Ishihara 1998 ) before each experimental session. Altogether, we selected 240 participants stratified by gender and age (Figure 1 ).

The distribution of participants by gender and age. The average age of women was 27.4 years ( SD = 6.0); the average age of men was 27.8 years ( SD = 6.4).

Research ethics

Research was in accordance with the declaration of Helsinki, and approval of ethical standards for Experiment 1 was given by the ethics committee of the Institute of Psychological Sciences, University of Leeds. All participants gave written or verbal consent to participate.

Apparatus and stimuli

The experiment was controlled by a Linux operated PC using PsyToolkit software (Stoet 2010 ). A 17" color monitor and a Cedrus USB keyboard (model RB-834) were used for stimulus presentation and response registration, respectively. Of the Cedrus keyboard, only two buttons were used. These were the buttons closest to the participant (3.2 × 2.2 cm each, with 4.3 cm between the two buttons), which we will further refer to as the left and right button, respectively.

A rectangular frame (7 × 8 cm) with an upper and lower section (Figure 2 a) was displayed. The words "shape" and "filling" were presented above and below the frame, respectively. Further four imperative stimuli were used in different trials (Figure 2 b). These four were the combination of two shapes (diamond and rectangle) and a filling of two or three circles. The frame and the imperative stimuli were yellow and were presented on a black background. Feedback messages were presented following trials that were not performed correctly ("Time is up" or "That was the wrong key").

Schematic representation of the task-switching paradigm. A : Example trial. During a block of trials, a rectangular frame with the labels "shape" and "filling" was visible. On each trial, a different imperative stimulus (i.e., a stimulus that requires an immediate response) was presented in the top or bottom part of this frame. The location (i.e., in top or bottom part of frame) determined whether the participant had to apply the shape or filling task rules to it. B : There were four different imperative stimuli, which needed to be responded to as follows. In the shape task, a "diamond" required a left-button response, and a rectangle a right-button response. In the filling task, a filling of two circles required a left-button response, and a filling of three circles a right-button response. Congruent stimuli are those that required the same response in both tasks, whereas incongruent stimuli required opposite responses in the two tasks. Thus, the imperative stimulus in panel A is incongruent: It appears in the top of the frame, thus is should be responded to in accordance to the shape task, and because it is a diamond (the filling of three circles is irrelevant in the shape task) it should be responded to with a left-button response (see Additional file 1 for demonstration).

Participants were seated in a quiet and dimly lit room, and received written and verbal instructions from the experimenter. They were instructed to respond to stimuli on the computer screen. There were two different tasks, namely a shape and a filling task. In the shape task, participants had to respond to the shape of imperative stimuli (diamonds and rectangles required a left and right response, respectively). In the filling task, participants had to respond to the number of circles within the shape (two and three circles required a left and right response, respectively). The essential feature of this procedure was that both task dimensions (shape and filling) were always present and that the two dimensions required opposite responses on half the trials (incongruent stimuli). This meant that participants were forced to think of which of the two tasks needed to be carried out and to attend to the relevant stimulus dimension. Participants were informed which task to carry out based on the imperative stimulus location: If the stimulus appeared in the upper half of the frame, labeled "shape", they had to carry out the shape task, and when it appeared in the bottom half of the frame, labeled "filling", they had to carry out the filling task.

Participants first went through 3 training blocks (40 trials), and then performed 3 further blocks (192 trials total) that were used in the data analysis. The first two blocks were blocks with just one of the two tasks (pure blocks), and in the third block the two tasks were randomly interleaved (mixed block). In the mixed block, task-switch trials were those following a trial of the alternative task, and task-repeat trials were those following the same task. The order of blocks was identical for all participants. The computer used a randomisation function to choose which task would occur on a given trial. Further, it is important to note that participants had training in both tasks before the blocks started that were used for data analysis; this means that even in the first pure block of the analyzed data, participants were aware that incongruent stimuli were associated with opposite responses in the alternative task.

In each trial, the frame and its labels (as displayed in Figure 2 a) were visible throughout the blocks. When an imperative stimulus (one of the four shown in Figure 2 b) appeared (they were chosen at random by the software), participants had up to 4 seconds to respond. The imperative stimulus disappeared following a response or following the 4 seconds in case no response was given. Incorrect responses (or failures to respond) were followed by a 5 seconds lasting reminder of the stimulus-response mapping, and then followed by a 500 ms pause. The intertrial interval lasted 800 ms. A demonstration of the task is available in the Additional file 1 .

When we report response times in task switching trials or in pure blocks, we always report the average of both tasks. For example, when reporting the response times in the pure blocks, we will report the average of the pure block of the shape task and pure block of the filling task.

Response time analyses were based on response times in correct trials following at least one other correct trial. Further, we excluded all participants who performed not significantly different from chance level in all conditions. This exclusion is necessary, given that response time analyses in cognitive psychology are based on the assumption that response times reflect decision time. When participants guess, for example because they find the task difficult, the response times are no longer informative of their decision time.

The procedure for testing if participants performed better than chance was carried out as follows. Given that there were only two equally likely response alternatives on each trial, participants had 50% chance to get a response correct. To determine if a participant performed significantly better than chance level, we applied a binomial test to the error rates in each condition. Based on this analysis, we concluded that nine participants (5 men and 4 women, aged 18-36) did not perform better than chance in at least one experimental condition. We found that each of these nine participants worked at chance level in the incongruent task-switching condition (with error rates ranging from 29% to 60%), and for five of them, this was the only condition they failed in. None of these nine failed in the pure task blocks. We excluded these participants from all reported analyses.

The next set of analyses were carried out to confirm that the used paradigm showed the typical effects of task-switching and task-mixing paradigms as described in the introduction (Figure 3 ). Throughout, we only report statistically significant effects ( α criterion of.05).

The response times and error rates + 1 standard error of the mean in the pure, task-switching and task-mixing conditions. Further, data is split up for congruent and incongruent stimuli, and for men and women.

We analyzed task-switch and incongruency costs in response times in the mixed blocks. We carried out a mixed-design ANOVA with the within-subject factors "switching" and "congruency" and between-subject factor "gender". We found a significant effect of switching, F (1,229) = 743.90, p < .001: Participants responded 247 ± 9 ms more slowly in the task-switch (1010 ± 14 ms) than in the task-repeat (763 ± 10) condition c . Further, participants were 35 ± 5 ms slower in incongruent (904 ± 11 ms) than in congruent (869 ± 11 ms) trials, F (1,229) = 52.48, p < .001.

We repeated the same analysis on the error rates. Again, we found a significant effect of switching, F (1,229) = 53.20, p < .001, with people making 1.97 ± 0.27 error percentage points (ppt) more in the task-switch (4.62 ± 0.27%) than in the task-repeat (2.65 ± 0.18%) condition. Further, people made 3.77 ± 0.31 ppt more errors in incongruent (5.52 ± 0.30%) than in congruent (1.75 ± 0.18%) trials, F (1,229) = 143.90, p < .001. Finally, the interaction between switching and congruency was significant, F (1,229) = 14.65, p < .001.

Next, we analyzed task-mixing costs using a similar approach as above. Now, we contrasted trials in the pure blocks with task-repeat trials in mixed block. We observed a slow down of 319 ± 8 ms due to mixing, F (1,229) = 1555.34, p < .001, with an average response time in mixed trials of 763 ± 10 ms and in pure trials of 444 ± 5 ms. This effect interacted significantly with the gender of participants. The slow down due to mixing was 336 ± 11 ms in men and 302 ± 12 ms in women (the effect size of this gender difference expressed as Cohen’s d = 0.27). We also found an effect of congruency, F (1,229) = 24.46, p < .001, with people responding 18 ± 4 ms slower in incongruent (613 ± 7 ms) than congruent (594 ± 7 ms) trials. Finally, there was a significant interaction between mixing and congruency, F (1,229) = 10.37, p = .001.

We carried out the same analysis using error rate as dependent variable, and we found a significant effect of task-mixing again. People made 0.55 ppt more errors in the task mix condition (2.65 ± 0.18%) than in the pure condition (2.10 ± 0.13%), F (1,229) = 9.17, p = .003. People made 1.77 ± 0.20 ppt more mistakes in the incongruent (3.26 ± 0.19%) than in the congruent (1.49 ± 0.13%) condition, F (1,229) = 80.86, p < .001. The factors switching and congruency interacted, F (1,229) = 26.94, p < .001. In the error rates, there were no effects of gender. Even so, it might be of interest to report that women’s mixing cost in error rates was 0.50 ± 0.28 percentage points and that of men 0.60 ± 0.23 percentage points.

Altogether, the ANOVAs of task-switching, task-mixing, and congruency confirmed the well known picture of task-switching data. The novelty is the gender difference in task-mixing costs. Although men and women did not show an overall speed difference, we wanted to ensure that the gender difference was not simply related to overall speed (e.g., people with larger switch costs might also have had a different baseline speed). To do so, we analyzed relative mixing costs as well. Relative mixing costs is the percentage slowing down in mixed compared to pure task blocks. For example, if a person responds on average in 500 ms in mixing blocks and 400 ms in pure blocks the person gets 25% slower due to mixing tasks.

We found that when analyzing the relative slow down due to mixing in relationship to performance in pure blocks, there was a significant effect of gender. Women’s relative slow down (69.1 ± 2.6%) was, in correspondence to the ANOVA of the absolute response time, less than that of men (77.2 ± 2.6%), t (229) = 2.18, p = .030; in other words, both the analysis of absolute and relative mixing costs show the same phenomenon.

Experiment 2

In Experiment 1, we found that men’s and women’s performance differed in a computer-based task measuring the capacity to rapidly switch between different tasks. One of the difficulties with computer-based laboratory tasks is their limited ecological validity. Experiment 2 aimed to create a multi-tasking situation in a "real-life" context that included standardized neurocognitive tests.

The approach of this experiment is based on tasks common in cognitive neuropsychology. From a neuropsychological perspective, Burgess (Burgess et al. 2000 ) described multi-tasking as the ability to manage different tasks with different (sometimes unpredictable) priorities that are initiated and monitored in parallel. Furthermore, goals, time, and other task constraints are seen as self defined and flexible. Shallice and Burgess (Shallice and Burgess 1991 ) devised the Six Elements Test to assess precisely these abilities (later modified by others, Wilson et al. 1998 ). In this task, participants receive instructions to do three tasks (simple picture naming, simple arithmetic and dictation), each of which has two sections, A and B. The subject has 10 minutes to attempt at least part of each of the six sections, with the proviso that they cannot do sections A and B of the same task after each other.

Burgess and colleagues (Burgess 2000 ; Burgess et al. 2000 ) have highlighted various features of multitasking behaviour, including: (1) several discrete tasks to complete; (2) interleaving required for effective dovetailing of task performance; (3) performing only one task at a particular time; (4) unforeseen interruptions; (5) delayed intentions for the individual to return to a task which is already running; (6) tasks that demand different task characteristics (7) self-determining targets with which the individual decides for him/herself; and (8) no minute-by-minute feedback on how well an individual performs. As Burgess and colleagues note, most laboratory-based tasks do not include all of these features when assessing multi-tasking. If this is indeed the case, there is a real advantage in studying multi-tasking using this approach.

We recruited 47 male and 47 female participants, largely undergraduate students of Hertfordshire University. The mean age was 24.2 years ( S D = 8.1, range 18–60) for men, and 22.6 years ( S D = 5.6, range 18–49) for women; there was no significant age difference between these two groups, t (92) = 1.1, p = .28.

Research Ethics

Research was in accordance with the declaration of Helsinki, and approval of ethical standards for Experiment 2 was given by the ethics committee of the School of Life and Medical Sciences, University of Hertfordshire. All participants gave written or verbal consent to participate.

We used three different tasks. The "Key Search task" was taken from the Behavioral Assessment for Dysexecutive Syndrome (BADS, Wilson et al. 1998 ). This is a specific test of planning and strategy, in which participants are required to sketch out how they might route an attempt to search a "field" for a missing set of keys. This task is normally used as a measure of problems in executive function, and low scores are indicative of frontal lobe impairment. In the healthy population, this task reveals no evidence of a gender difference according to test norms and personal communication with Jon Evans (one of the test designers). The test designers reported a high ( r = .99) correlation between raters (Wilson et al. 1998 ).

The Map search task was taken from the "Tests of Everyday Attention" (Robertson et al. 1994 ). The task requires individuals to find restaurant symbols on an unfamiliar color map of Philadelphia (USA) and its surrounding areas. Again, this task reveals no evidence of a gender difference according to the test norms and personal communication with test designer Ian Robertson.

The third task was custom designed and involved solving simple arithmetical questions presented on paper as shown in Figure 4 . We did pilot these mathematics questions (unlike the first two tests, this test is not standardised, and after piloting we moderated these questions to make sure they could be largely successfully attempted while doing the other tasks).

Example of the arithmetic task.

Although there are reports that men outperform women on more complex mathematics problems, this is typically not the case for simple calculations like this (Halpern et al. 2007 ).

A scoring system established within the BADS marks these plans according to set rules such as parallel patterns and corner entry. A panel of 3 scorers agreed on the scores for each test to ensure reliable scoring. Examples of key search strategies are shown in Figure 5 .

Examples of the key search task. The example on the left is from a male participant, the example on the right from a female participant.

Each participant was given 8 minutes to attempt the three tasks described above (Arithmetic, Map, Key Search). The layout of the position of the map task, maths task and key search was counterbalanced to avoid any bias affecting which tasks participants chose to do. They were instructed that each task held equal marks; it was left to participants to decide how they would organize their time between each task. The participants were also informed that they would receive a phone call at some unknown time point (always after 4 minutes) asking them 8 simple general-knowledge questions (e.g., "What is the capital of France"), it was again left to participants to decide whether or not they answered the phone call. Without or with answering the phone call, they were multi-tasking; answering the call just added to that multi-tasking ’burden’ as such. If they attempted to multi-task while answering the phone call, this was recorded. We recorded time spent on each task as well as performance.

We compared test scores (Table 1 ) and response times (Table 2 ) of men and women using t tests. We found that women (10.26 ± 0.58) scored significantly higher than men (8.13 ± 0.68) on the key search task. Importantly, this finding cannot simply be explained as a preference difference for the speed with which the task was carried out, as no response time differences were found (Table 2 ).

No differences emerged in the numbers of men and women who answered the phone (79% of men and 81% of women, χ 2 (1) = 0.06, p = .80). Those who answered the phone heard 8 simple general knowledge questions and the correct answers did not differ between men (3.35 ± 0.35) and women (3.84 ± 0.34), t (73) = 1.0, p = .32; nor did time spent on the phone differ between men (97.68 ± 3.13 seconds) and women (106.87 ± 3.65 seconds), t (73) = 1.91, p = .06. Of those that did answer the phone, we also measured whether they actively multi-tasked while on the phone or concentrated purely on this phone - and there was no significant difference 73% of men and 84% of women multi-tasked, χ 2 (1) = 1.41, p = .24.

Using two very different experimental paradigms, we found that women have an advantage over men in specific aspects of multi-tasking situations. In Experiment 1, we measured response speed of men and women carrying out two different tasks. We found that even though men and women performed the individual tasks with the same speed and accuracy, mixing the two tasks made men slow down more so than women. From this, we conclude that women have an advantage over men in multi-tasking (of about one third of a standard deviation). In Experiment 2, we measured men and women’s multi-tasking performance in a more ecologically valid setting. We found that women performed considerably better in one of the tasks measuring high level cognitive control, in particular planning, monitoring, and inhibition. In both experiments, the findings cannot be explained as a gender difference in a speed-accuracy trade off. Altogether, we conclude that, under certain conditions, women have an advantage over men in multi-tasking.

Relation to other work

As noted in the introduction, there is almost no empirical work addressing gender differences in multi-tasking performance. For example, even though there are numerous task-switching papers, none has focused on gender differences d . In fact, most task-switching studies do not explore individual differences, and accordingly are carried out with small samples.

Because they are typically carried out in psychology undergraduate programmes (with less than 20% male students), there are few male participants. The novelty of our study is not only the relatively large number of participants, but also the good gender balance. Despite the few studies about gender differences in multi-tasking, there has been an interesting discussion very recently about a study by Mäntylä ( 2013 ) which received much attention. Probably the main reason for the attention in the media for this study was the conclusion that men performed better than women in a multi-tasking paradigm. The finding of that study thus not only contrasts with the widely held belief that women are better at task switching, it also contrasts with our current data and the experiment by Ren and colleagues ( 2009 ).

In the study by Mäntylä ( 2013 ), men and women’s accuracy in a visual detection task was measured. Participants had to detect specific numerical patterns in three different counters presented on a computer screen. Simultaneously, participants had to carry out an N-back task (stimuli appeared above the aforementioned counters). Men had a higher accuracy score of detecting the correct numerical patterns than women. The latter study is of great interest, because it addresses gender differences in multi-tasking of the second type, namely when tasks need to be carried out simultanously. Of interest is that for this specific type of multi-tasking, men had an advantage over women, and the degree of the advantage was directly related to men’s advantage in spatial skills. But as argued in the introduction, this type of multi-tasking is potentially of less relevance to daily life contexts in which people often carry out tasks sequentially. In a comment on the study by Mäntylä ( 2013 ), Strayer and colleagues ( 2013 ) argue that gender is a poor predictor of multi-tasking. They present data to back this up from their own work on multi-tasking when driving. Arguably, studies showing no gender differences might simply have received less attention due to a publication bias for positive effects. We think that Strayer et al.’s comments are valuable to the discussion, although their findings seem to primarily apply to the concurrent multi-tasking situations. That said, we found only one study that reported no gender differences in a task-switching paradigm in which people switched between two tasks. Buser and Peter (Buser and Peter 2012 ) had three groups of participants solving two different types of puzzles (sudoku and word-search). The group that did the two puzzles without switching between them solved the puzzles best, while switching between the puzzles while solving them impaired performance. The degree of impairment was similar for men and women, irrespective of whether the switching was voluntary or imposed. This situation is somewhat similar to Experiment 2, and thus, especially gender differences in this type of task-switching need further study to draw strong conclusions.

Finally, our finding that men and women did not differ in the effect of phone calls might be linked to a study by Law and colleagues ( 2004 ). They stated that the effects of interruptions are "quite subtle" and that more research on their effect on multi-tasking is necessary.

Limitations

We would like to consider a number of limitations of our current study that have implications for the interpretation of our results. First, as already mentioned above, there are many different ways to test multi-tasking performance. Because this is an emerging field with a small extant knowledge base we cannot exclude the possibility that our findings only hold true for the two specific paradigms we employed. Given the aforementioned work by Mäntylä ( 2013 ) and others that did not find the effect, and the general sparsity of the reports on the effect, this is a possibility that must be seriously considered.

A second limitation is that we did not formally record levels of education or control for general cognitive ability. Although we think it is not very likely, we appreciate the comment of one of the reviewers that if their were different levels of education this could potentially affect cognitive performance. The only way to exclude this possibility is to formally record the highest level of education of all participants.

A third limitation is that the power of the Experiment 2 may be low. Again, it is difficult to say although evidently powerful enough to detect moderate differences on the key search task - so it may be a task-related issue and further work needs to investigate task-based constraints in multi-tasking. For example, we did not conclude that there was a gender difference in arithmetic performance or time spent on the phone, but this could potentially be due to a lack of statistical power. In the case of the arithmetic task, there are good reasons not to expect a gender difference on simple arithmetic problems, even though we acknowledge the complexity of the study of gender differences in mathematical ability (c.f., Halpern et al. 2007 ).

A final limitation is that although we checked that no gender differences emerged on the Key Search with both the test authors and with the published norms, we cannot eliminate the possibility that a difference may have emerged tested alone. We could have retested the individual tasks with another sample of participants. Also, we could have run a repeated measures design (same participants on the individual tasks), although this would defeat the novelty aspect of the task. The best way to address this issue is for another research group to replicate the finding.

Our findings support the notion that woman are better than men in some types of multi-tasking (namely when the tasks involved do not need to be carried out simultaneously). More research on this question is urgently needed, before we can draw stronger conclusions and before we can differentiate between different explanations.

a The two experiments were carried out by independent groups of researchers. We only realised the similarity between the two experiments and their findings afterwards. We believe that the two experiments complement each other: While Experiment 1 uses a laboratory based reaction time experiment, Experiment 2 uses a much more ecologically valid approach.

b This is likely because of the availability of computers to measure response times. In the 1920s, it would have been hard, if not impossible, to accurately measure task-switching costs, while measuring mixing costs could be done with the paper-and-pensil tests used by Jersild ( 1927 ).

c Throughout the results section, we report means ±1 standard error of the mean.

d To the best of our knowledge.

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Acknowledgements

Experiment 1 was made possible with a grant from the British Academy to Stoet, O’Connor, and Conner and with the assistance of Weili Dai, Caroline Allen, and Tansi Warrilow.

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GS, DO, and MC carried out Experiment 1. KL carried out Experiment 2. The four authors wrote the article together. All authors read and approved the final manuscript.

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Stoet, G., O’Connor, D.B., Conner, M. et al. Are women better than men at multi-tasking?. BMC Psychol 1 , 18 (2013). https://doi.org/10.1186/2050-7283-1-18

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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.

Jeong S-H, Hwang Y. Media multitasking effects on cognitive vs. attitudinal outcomes: A meta-analysis . Hum Commun Res . 2016;42(4):599-618. doi:10.1111/hcre.12089

Madore KP, Wagner AD. Multicosts of multitasking . Cerebrum . 2019;2019:cer-04-19.

Moisala M, Salmela V, Hietajärvi L, et al. Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults . NeuroImage . 2016;134:113-121. doi:10.1016/j.neuroimage.2016.04.011

Wiradhany W, Koerts J. Everyday functioning-related cognitive correlates of media multitasking: A mini meta-analysis . Media Psychol . 2021;24(2):276-303. doi:10.1080/15213269.2019.1685393

Rubinstein JS, Meyer DE, Evans, JE. Executive control of cognitive processes in task switching . J Exp Psychol Human. 2001;27(4):763-797. doi:10.1037/0096-1523.27.4.763

Bellur S, Nowak KL, Hull KS. Make it our time: In class multitaskers have lower academic performance . Comput Hum Behav . 2015;53:63-70. doi:10.1016/j.chb.2015.06.027

Wechsler K, Drescher U, Janouch C, Haeger M, Voelcker-Rehage C, Bock O. Multitasking during simulated car driving: A comparison of young and older persons . Front Psychol . 2018;0. doi:10.3389/fpsyg.2018.00910

Sanbonmatsu DM, Strayer DL, Medeiros-Ward N, Watson JM. Who multi-tasks and why? Multi-tasking ability, perceived multi-tasking ability, impulsivity, and sensation seeking . PLOS ONE . 2013;8(1):e54402. doi:10.1371/journal.pone.0054402

Uncapher MR, Lin L, Rosen LD, et al. Media multitasking and cognitive, psychological, neural, and learning differences . Pediatrics . 2017;140(Supplement 2):S62-S66. doi:10.1542/peds.2016-1758D

Lui KFH, Wong AC-N. Does media multitasking always hurt? A positive correlation between multitasking and multisensory integration . Psychon Bull Rev . 2012;19(4):647-653. doi:10.3758/s13423-012-0245-7

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

What’s one of the keys to mastering multitasking? Feedback

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Some 2.5 percent of people are thought to be supertaskers — people who excel at multitasking. And we can use some of their strategies to boost our own skills, says psychiatrist Srini Pillay.

When you need to respond to an email while you’re on the telephone, you have to read, write and listen at the same time. Frequently, however, your brain can do only one task at a time properly. It’s as if there’s one checkpoint, and tasks that need to be done have to arrange themselves in a single file in your brain.

When many tasks try to squeeze through a bottleneck, the results can be disastrous. Think of driving from an on-ramp onto a jam-packed highway. That’s what every new thought faces. It slows down, and eventually the brain becomes a parking lot for thoughts. That’s when you drop the multitasking, when instead you should see this as a signal to switch to the mode that I call “supertasking.”

Supertasking is like switching over to a juggler’s mindset. A juggler does not have to think hard about making each catch and throw — her unconscious plays a greater role in her thinking, allowing her to be more flexible when needed. In 2015, UCSF neuroscientist Omar Al-Hashimi and his colleagues examined how certain people’s brains overcome bottlenecks — how they deftly switch brain lanes and somehow find a way through. They used the video game NeuroRacer, in which players perform single tasks and then work up to multi-component tasks. For example, players have to keep a car within a target box while also responding to various road signals; the signals increase in number as the game gets harder. As the number of things players have to pay attention to increases, an information bottleneck is created in their brains.

Some of the players had superior multitasking performance. They had faster response times, made fewer errors, and were more accurate. The researchers noted that the superior parietal lobule (SPL) was key, helping them quickly switch between tasks by loosening the grip of focus. The SPL also efficiently manages brain resources by keeping things for a longer time in short-term memory so that the person can, effectively, pick up where they left off more easily.

Unfocusing on your separate goals and tinkering with them to see which ones can be combined will decongest your bottleneck in the short term.

One way to reduce bottlenecks is through redundancy reduction — combining one or more tasks to save time. If your day’s tasks include picking up a friend and going to a supermarket near that friend’s house, you focus and notice their commonality — both require you to drive through the same neighborhood. Even though stopping to consciously think through ways to reduce redundancies might take time at first, taking the step of unfocusing on your separate goals and tinkering with them to see which ones can be combined will decongest your bottleneck in the short term. With practice, it becomes much more automatic.

You can also integrate the tasks on your to-do list and manage bottlenecks. I witnessed this kind of cognitive flexibility in action while having dinner at a friend’s home. As I stood with her in the kitchen, I watched her put a casserole in the oven, put leftover roast chicken and vegetables in a frying pan on the stovetop, fry some bacon, and warm up leftover mashed potatoes in the microwave. She did all this while talking to me and attending to her daughter, who came in intermittently to ask some oddly (but nonetheless charming) existential questions.

Cool, calm and collected, my friend got into the rhythm of each action, not doing one thing at a time but starting and stopping at will. I could see her cognitive wheels turning. The casserole went in first. Intermittently, she checked on it and changed the oven temperature when she needed to. Halfway there, she put the chicken and vegetables in a pan over medium heat. She left them for about ten minutes, turning them every now and then. A few minutes before everything was ready, she put the bacon on.

When I finally saw the roast chicken, bacon, mashed potatoes, vegetables and casserole on my plate, I realized that she was the bottleneck master. In and out, back and forth, tinker and wait — cognitive rhythm saved the day. She smoothly switched between tasks in a way made possible not just by practice but by being willing to leave things halfway in the process and then return to them.

Without feedback, your brain loses track of its own results. That makes multitasking more difficult.

You need to be flexible and go back and forth between multiple things without obsessing about completing each thing first. Although my friend’s dinner came together swiftly, she would not have cooked the food so well had she not continuously sought feedback — prodding the chicken, checking on the casserole. Without feedback, your brain loses track of its own results. That makes multitasking more difficult.

But it turns out that the scope of feedback that you allow yourself to consider is meaningful. Cognitive science researcher Hansjörg Neth of University of Konstanz, Germany, and his colleagues compared local and global feedback in the context of multitasking. They used a computer program called Tardast, aptly named after the Persian term for “juggler,” to investigate multitasking behavior, complex system management, and constant supervision.

During the experiment, the researchers presented the participants with 10 trials via a computer screen. For each five-minute trial, the participants had to manage six tasks. Performing a “task” meant pressing a button to fill up a vertical white bar with black. Pressing the button raised the level, while releasing the button caused the level to fall. The participants’ goal was to get the black to rise to the highest level within each vertical bar. They had to press the button quickly to get the level to rise, and they could press buttons only one at a time and in quick sequence. But some bars were more difficult to fill than others, and all the bars increased and decreased at different speeds. After each five-minute trial, participants received feedback on how they had done.

The researchers found any feedback improved multitasking, but local feedback (how the person did on the last trial) was superior to global feedback (how they had done all day). In the kitchen, as my friend cooked dinner and prodded the chicken to check for doneness, she was getting feedback that helped her determine how much longer to cook it. Had she prodded it and then considered all her previous prods, she would have gotten caught up in feedback analysis. As it happened, her quest for feedback was not deep or far-reaching. She kept her mind on the present texture of the chicken and moved on. And that served her (and me) well.

Don’t just take it for granted that your brain is updating information as you go — stop and think about what you just did and how it relates to what you have to do next.

Without feedback, your brain gets overwhelmed. If you feel like you have a billion things to do in the course of a day, dipping into conscious feedback is a way of taking stock. Don’t just take it for granted that your brain is updating information as you go along. Give it local feedback — stop and think about what you just did and how it relates to what you have to do next. This momentary period of unfocus will allow you to tinker with your approach to make it better.

But asking the right questions — ones that bring local feedback to bear — is key. The ER physician who is inundated with trauma-related procedures might say to herself, “Three down, seven to go.” That is global feedback, an emphasis on the full day’s work. Or she might say, “That last one went well,” which is local feedback, emphasizing just the last task. The ER doctor who gets a little more specific with her local feedback — “That last one went well, but next time make sure all the dried blood has been cleared so there’s not even a speck before you suture” — allows her work to improve. Taking time to give this feedback may stop the flow of her work in the short term, but it allows her to tinker with the lineup of sutures so that each successive one is better yet requires that much less conscious brainpower the next time.

When you practice thinking this way, it trains your brain for supertasking. And when you’re in supertasking mode, your brain helps you remember half-completed tasks while you move on so that you can return to them. It helps you remember not to leave something on the stove when the phone rings. It also helps you re-strategize about pending goals as you go along.

Excerpted with permission from the new book Tinker, Dabble, Doodle, Try: Unlocking the Power of the Unfocused Mind by Srini Pillay. Published by Ballantine Books, an imprint and division of Penguin Random House LLC, New York. Copyright © 2017 by Srini Pillay. All rights reserved.

Watch the TEDxRockCreekPark from Srini Pillay here:

About the author

Srini Pillay is a psychiatrist, brain-imaging researcher and a brain-based technology innovator. Currently an assistant professor at Harvard Medical School, he is also an invited faculty member in the executive education programs at Harvard Business School and Duke University Business School. He is the founder and CEO of NeuroBusiness Group, an executive coaching, consulting, and technology firm. His previous book, “Life Unlocked: 7 Revolutionary Lessons to Overcome Fear,” won a Books for a Better Life Award.

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The Curious Science of When Multitasking Works

by Walter Frick

Trying to do two things at once is usually a recipe for doing both badly , according to a long line of research. We’re slower and less accurate when we try to juggle two things. Experts came to believe that there wasn’t much that could be done about this, so most of the advice in HBR has been to avoid multitasking as much as possible .

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25 Multitasking Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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multitasking examples and definition, explained below

Multitasking refers to the art of doing two tasks simultaneously. It is believed to be a desirable skill in a fast-paced world, but has also been critiqued for causing high cognitive load and decreasing task performance (Calderwood et al., 2014; Rosen, 2008; Salvucci & Taatgen, 2010).

The term originates from computer processing, referring to the ability of a machine to execute more than one task at the same time.

Today, however, this concept tends to describe human behavior, where an individual engages in multiple tasks simultaneously or alternates rapidly between tasks.

Rresearch has shown that humans don’t actually conduct multiple cognitive tasks at the same time, despite the perception (Rosen, 2008). Instead, we switch our attention from one task to another very rapidly, giving the illusion of simultaneous multitasking. This act of rapidly shifting attention tends to cause high cognitive load and lead to exhaustion and errors.

Types of Multitasking

Multitasking encompasses several different methods of task management, falling into four general categories: concurrent, serial, background, and cognitive multitasking.

Concurrent multitasking refers to carrying out multiple tasks simultaneously. You might see a chef preparing multiple dishes at a time, carefully monitoring each process (Calderwood et al., 2014).
Serial multitasking involves switching between tasks quickly. An example is a driver changing the radio station while keeping an eye on the road and handling the steering wheel (Carrier et al., 2015).
Background multitasking allows one task to run in the “background” while a person focuses on another task. An instance is listening to music while writing a report, where the music serves as background stimulus but does not require active attention.
Cognitive multitasking is different as it involves handling or thinking about multiple cognitive tasks simultaneously, which is usually discouraged (Carrier et al., 2015). An example might be a student trying to work on a math problem while writing an English essay (which would likely result in errors in one or both tasks).

Each of these forms of multitasking presents its own challenges and benefits, but they all require substantial cognitive effort. They also all carry the potential for divided attention and mistakes, especially when the tasks require high levels of cognitive engagement.

Multitasking Examples

1. Cooking a Full Meal (Type – Concurrent): Preparing multiple dishes simultaneously is a common example of concurrent multitasking. This could involve chopping vegetables, monitoring pots on the stove, and checking the oven, all at the same time. This method maximizes kitchen productivity, but the downside is the high potential for mistakes due to divided attention. One can forget a key ingredient or overcook a dish while attending to another.

2. Online Meeting and Email Response (Type – Serial): Switching between listening to an online work meeting and responding to professional emails is a form of serial multitasking. It may seem efficient as you appear to get two tasks done at once, but the quality of work may suffer. Attention to both tasks is split, making it difficult to contribute effectively to the meeting or respond comprehensively to emails.

3. Driving and Listening to an Audiobook (Type – Background): While driving, some people listen to audiobooks. Here, the primary task is operating the vehicle safely, while the audiobook runs in the background. It has the advantage of enhancing the driving experience or making it more enjoyable without subtracting from the primary focus (driving). However, in intense traffic or complex driving conditions, the audiobook can become a distraction.

4. Studying while Watching Television (Type – Cognitive): It’s not uncommon for students to engage in cognitive multitasking like studying for an exam while watching TV. This is typically not advisable as both tasks demand high cognitive engagement. The act of studying necessitates attention to detail and comprehension, which TV-watching undermines with constant stimulus and calls for processing new information. The result could be poor understanding and retention of studied materials (Calderwood et al., 2014).

5. Conference Call and Sketch Noting (Type – Concurrent): Business professionals often engage in concurrent multitasking during conference calls by making sketch notes. The visual act of sketching information can support cognitive processing and assist in memory retention. However, the risk is that complex information could be missed or misinterpreted if the attention is significantly divided between the call and the sketching process.

6. Social Media Scrolling and Online Shopping (Type – Serial): Serial multitasking is common online, such as when one alternates between scrolling through a social media feed and browsing in an online store. While it might offer a sort of entertainment variety, constantly switching between the two tasks could lead to an oversight like missing an essential post or wrongly ordering an item.

7. Listening to Background Music while Writing an Essay (Type – Background): It’s quite typical for students to listen to music while writing essays, thinking it aids concentration. As a form of background multitasking, it can indeed provide a pleasant background that doesn’t require active engagement. However, if the music is too loud, contains lyrics, or differs significantly from the individual’s usual taste, it can become a distraction and reduce the quality of the writing.

8. Planning a Vacation while Working on a Budget (Type – Cognitive): Attempting to plan a vacation while concurrently working on a home budget falls under cognitive multitasking. This is generally not recommended as both tasks require significant mental focus and calculating abilities. Mistakes are likely to happen in either or both activities, such as overlooking a cost item in the budget, or making wrong travel bookings.

9. Juggling Multiple Patients (Type – Concurrent): Healthcare professionals often engage in concurrent multitasking, such as when a nurse monitors multiple patients at once. This practice can enhance efficiency , allowing the nurse to attend to more patients in a shorter time frame. However, the risk is high, as divided attention could lead to mistakes or oversights with serious health consequences.

10. Writing a Report and Checking Stock Market (Type – Serial): An investment professional may switch between writing a financial report and checking the stock market. This serial multitasking may seem efficient, enabling the individual to stay updated while also producing work. However, rapidly switching focus can lead to reduced accuracy in both the report and the interpretation of market trends.

11. Running on a Treadmill and Watching the News (Type – Background): People often watch television while doing physical exercises like walking or running on a treadmill. As an example of background multitasking, this act provides entertainment without detracting from the primary task. The only risk is that intensive news topics can stimulate emotional reactions, which might impact the workout rhythm.

12. Math Homework and Composing a Poem (Type – Cognitive): A student who tries to solve math problems while composing a poem is engaged in cognitive multitasking, which requires splitting one’s cognitive attention between two complex, unrelated tasks. Such multitasking typically hinders productivity and quality of work, potentially leading to both incorrect calculations and a fragmented poem.

13. Gardening and Supervising Kids (Type – Concurrent): An adult could be pulling weeds or planting while keeping an eye on children playing nearby. As a form of concurrent multitasking, it allows for efficiency in completing household chores while ensuring the safety of the kids. The downside is that serious accidents can happen in a split second if the supervising adult is too absorbed in the task at hand and gets momentarily distracted.

14. Program Coding and Software Debugging (Type – Serial): A software developer may switch between writing new program codes and debugging existing programs. This kind of serial multitasking can lead to productivity in a time-pressured environment. However, it can also result in overlooked coding errors and insufficient debugging due to the rapidly alternating focus.

15. Listening to Podcasts while Doing Laundry (Type – Background): Many people listen to informational podcasts as they perform routine tasks like doing laundry. As a form of background multitasking, it can make the chore more enjoyable and educational. However, if the podcast contains complex discussions, it might distract from properly sorting, washing, or folding the clothes, or vice versa.

16. Revising An Article while Brainstorming for Another (Type – Cognitive): An author might try to revise one article while brainstorming ideas for a new one. This type of cognitive multitasking usually hinders both the revision process and the generation of quality ideas. Insufficient attention to detail in the revision could lead to overlooked errors, while distraction from brainstorming could result in limited or superficial ideas for the new article.

17. Sales Event and Customer Interaction (Type – Concurrent): A retail salesperson often manages multiple customer interactions while ensuring the smooth operation of a promotional event. This type of multitasking can be challenging, especially during peak shopping hours when customer demands intensify, but can also stimulate a dynamic sales environment and potentially drive up revenue.

18. Online Discussion and Document Review (Type – Serial): Switching between an online discussion and reviewing a related document is a common practice in digital workspaces. While this type of serial multitasking enables quick information sharing and feedback, constant attention shift can reduce comprehension and feedback quality.

19. Reading a Book and Listening to Instrumental Music (Type – Background): Many people love to supplement reading with ambient or instrumental music. As a form of background multitasking, this combination often enriches the reading experience and supports concentration, unless the music becomes too intrusive or the book too demanding, which could mean only one can be effectively engaged with.

20. Studying Two Different Subjects simultaneously (Type – Cognitive): Trying to study for a history exam while solving chemistry equations is an instance of cognitive multitasking. The task switches between unrelated contexts and cognitive demands, likely resulting in both poor historical understanding and incorrect chemical calculations.

21. Tutoring and Lesson Planning (Type – Concurrent): A private tutor might be teaching one student while planning the next session for another. A concurrent form of multitasking, it can utilize time effectively allowing for immediate implementation of planned lessons. However, divided attention could result in overlooking a student’s difficulty in learning or a poorly-constructed plan for the next session.

22. Navigating Traffic and Changing the Radio Station (Type – Serial): It’s common for drivers to adjust music or other devices while focusing on traffic, a form of serial multitasking. It keeps the driving experience enjoyable but can be dangerous if the secondary task draws too much attention away from the primary task of safe driving.

23. Working Out and Monitoring Heart Rate (Type – Background): Many fitness enthusiasts monitor their heart rate while engaging in workouts. It’s a form of background multitasking allowing them to optimize their exercise intensity. But if the exercise is particularly demanding, they may neglect proper monitoring, leading to potential health risks.

24. Preparing a Speech while Checking Emails (Type – Cognitive): This example of cognitive multitasking might seem efficient on the surface but usually results in reduced quality in both tasks. Important nuances for the speech can be missed or poorly constructed, and emails might be misunderstood or improperly responded to due to divided attention.

25. Managing Employee Performance and Organizing Company Events (Type – Concurrent): An HR manager might oversee employee productivity while planning corporate events. This concurrent multitasking can ensure efficient operations but could risk neglecting details in performance reports or event planning, meaning both aspects might suffer.

Pros and Cons of Multitasking

The benefits and drawbacks of multitasking vary. On the positive side, multitasking can increase stimulation and reduce boredom, particularly when performing routine tasks. However, the downsides may include reduced concentration, higher levels of stress and mistakes due to divided attention.

Multitasking skills are often required in today’s fast-paced environment. However, it’s important to recognize when multitasking is beneficial and when it’s actually detrimental to the task at hand. For instance, while it’s possible to listen to a podcast whilst doing house chores, trying to respond to emails while attending a meeting could lead to mistakes and misunderstanding.


	– Can handle multiple tasks at once when they don’t require full focus.	– Often leads to reduced efficiency for complex tasks (Rosen, 2008).
	– Can save time when tasks are simple and routine (Schuch et al., 2019).	– Can waste time due to the constant task-switching.
	– Can train the brain to process information more rapidly.	– Can decrease focus, leading to decreased comprehension (Schuch et al., 2019).
	– Some jobs require simultaneous handling of tasks (Watson & Strayer, 2010).	– Quality might be compromised in jobs requiring deep concentration.
	– Encourages adaptability and flexibility (Mark, 2022).	– Might reduce deep work skills or focused attention span.
	– Some people feel productive when juggling tasks.	– Can lead to increased stress and decreased job satisfaction.
	– May improve handling of distractions for some people (Watson & Strayer, 2010).	– Divides attention, leading to potential mistakes (Carrier et al., 2015).
	– In some scenarios, can enhance skill diversification.	– Reduces the depth of learning and understanding.
	– Quick identification of mistakes in parallel processes.	– Increases the likelihood of errors due to divided attention.
	– Some individuals thrive under the pressure of handling multiple tasks.	– Can increase mental fatigue and stress (Rosen, 2008).

Criticisms of Multitasking Theory

The efficacy of multitasking is a topic of debate. While it can sometimes increase productivity, studies indicate that switching rapidly between tasks can decrease accuracy and efficiency (Rosen, 2008).

This can be understood through the example of a student attempting to study for an exam while continually checking their social media feed (Calderwood et al., 2014). The cognitive shift from deep learning to the superficial engagement of social media can result in inadequate comprehension and retention of study materials.

Multitasking is a common practice that has both positive and negative aspects. It’s crucial to assess individual capabilities and the specific demands of each task to determine the best approach, whether it be targeted focus or a multitasking method.

Calderwood, C., Ackerman, P. L., & Conklin, E. M. (2014). What else do college students “do” while studying? An investigation of multitasking. Computers & Education , 75 , 19-29.

Carrier, L. M., Rosen, L. D., Cheever, N. A., & Lim, A. F. (2015). Causes, effects, and practicalities of everyday multitasking. Developmental Review , 35 , 64-78. doi: https://doi.org/10.1016/j.dr.2014.12.005

Mark, G. (2022). Multitasking in the digital age . New York: Springer Nature.

Rosen, C. (2008). The myth of multitasking. The New Atlantis , (20), 105-110. Doi: https://www.jstor.org/stable/43152412

Salvucci, D. D., & Taatgen, N. A. (2010). The multitasking mind . Oxford: Oxford University Press.

Schuch, S., Dignath, D., Steinhauser, M., & Janczyk, M. (2019). Monitoring and control in multitasking. Psychonomic Bulletin & Review , 26 , 222-240. doi: https://doi.org/10.3758/s13423-018-1512-z

Watson, J. M., & Strayer, D. L. (2010). Supertaskers: Profiles in extraordinary multitasking ability. Psychonomic bulletin & review , 17 , 479-485. Doi: https://doi.org/10.3758/PBR.17.4.479

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Selectively Distracted: Divided Attention and Memory for Important Information

Author Contributions: C. D. Middlebrooks and A. D. Castel developed the study concept. All of the authors contributed to the study design, which was programmed by T. Kerr. C. D. Middlebrooks and T. Kerr supervised data collection. C. D. Middlebrooks analyzed and interpreted the data and drafted the manuscript. All of the authors approved the final version of the manuscript for submission.

Associated Data

Distractions and multitasking are generally detrimental to learning and memory. Nevertheless, people often study while listening to music, sitting in noisy coffee shops, or intermittently checking their e-mail. The current experiments examined how distractions and divided attention influence one’s ability to selectively remember valuable information. Participants studied lists of words that ranged in value from 1 to 10 points while completing a digit-detection task, while listening to music, or without distractions. Though participants recalled fewer words following digit detection than in the other conditions, there were no significant differences between conditions in terms of selectively remembering the most valuable words. Similar results were obtained across a variety of divided-attention tasks that stressed attention and working memory to different degrees, which suggests that people may compensate for divided-attention costs by selectively attending to the most valuable items and that factors that worsen memory do not necessarily impair the ability to selectively remember important information.

The threat of distraction to learning and memory causes students to fill campus libraries to capacity at exam time, with many eschewing home comforts to maintain undivided attention while studying. Permanent sequestration in a hushed library is, however, plainly impossible, and even coveted study cubicles are breached by sounds of typing and whispered conversations. Moreover, there are many situations in which learners actively multitask despite the importance of later remembering presented information ( Calderwood, Ackerman, & Conklin, 2014 ). The ubiquity of mobile devices has even led professors to dissuade or ban their use during lectures, citing the detrimental effects of multitasking—and the visibility of peers’ laptop screens—on learning and comprehension ( Fried, 2008 ; Sana, Weston, & Cepeda, 2013 ).

Costs of divided attention during encoding to memory are manifold ( Castel & Craik, 2003 ; Craik, Govoni, Naveh-Benjamin, & Anderson, 1996 ; Naveh-Benjamin, Craik, Perretta, & Tonev, 2000 ), but the effect of divided attention on memory for important or valuable information, specifically, remains unclear. Does a student’s exam performance hinge on a neighbor’s radio preferences or the insatiable pull of a messaging app during studying? Or can learners mitigate divided-attention effects by selectively focusing on the most important information, even if some of the less important is lost? The cognitive demands of strategically allocating one’s attention may be better met in settings conducive to devoted focus, such as a quiet library. On the other hand, distractions may be less perilous if the learner is cognizant of the potential cost of distraction.

Prior work demonstrates that selective attention to, and memory for, the most critical of to-be-remembered information can be maintained in spite of circumstances that otherwise result in memory impairments, such as insufficient study time ( Middlebrooks, Murayama, & Castel, 2016 ) and advanced age ( Castel, McGillivray, & Friedman, 2012 ; Castel, Murayama, Friedman, McGillivray, & Link, 2013 ; Middlebrooks, McGillivray, Murayama, & Castel, 2016 ). Maintaining prioritization of high-value information at the expense of less-essential information ( Castel et al., 2012 ), despite memory declines, requires an important dissociation between memory itself and the strategizing in which learners engage during encoding. Selective study signifies an awareness of the limitations of one’s study conditions ( Castel et al., 2012 ; Dunlosky, Ariel, & Thiede, 2011 ; Winne & Hadwin, 1998 )—that remembering everything is implausible.

People seem broadly aware that memory suffers when attention is divided ( Barnes & Dougherty, 2007 ; Junco & Cotten, 2011 ), at times even overestimating the degree to which their performance will diminish ( Finley, Benjamin, & McCarley, 2014 ), but this basic knowledge may be insufficient for motivating selective study. Despite anticipating decreased global performance when multitasking, people often fail to apply this knowledge when making item-by-item judgments of encoding quality and retrieval accuracy ( Beaman, Hanczakowski, & Jones, 2014 ; Kelley & Sahakyan, 2003 ; Sacher, Taconnat, Souchay, & Isingrini, 2009 ). So despite acknowledging that memory will likely suffer when attention is divided, learners tend not to account for this possibility when evaluating their own performance, which potentially decreases the likelihood of their adopting a selective study strategy.

Relatedly, distracted learners may be less able to execute a value-based study agenda—even if recognizing the fitness of such an approach—owing to reduced cognitive resources ( Dunlosky et al., 2011 ). Divided attention also seems to have a more pronounced impact when learners encode on a deeper, semantic level ( Anderson et al., 2000 ; Craik, 1982 ), which is precisely the processing in which learners are most likely to engage when studying selectively ( Cohen, Rissman, Suthana, Castel, & Knowlton, 2014 ). Therefore, the very method by which selectivity may be best achieved also seems to be the method most affected by divided attention. Good intentions notwithstanding, divided attention may render selective study relatively unattainable.

Experiment 1

A primary goal of the current research was to examine the effect of divided attention during encoding on the study of, and memory for, valuable information. An additional goal was to investigate whether selectivity is affected by the degree to which the learner is engaged with the distractor—is the learner studying while actively engaged in a concurrent activity or while more passively distracted? In Experiment 1, participants studied to-be-remembered items while completing a digit-detection task or while listening to background music with which they were either familiar or unfamiliar. The costs of a less involving distraction may be less pronounced relative to an attention-dividing activity and, thus, less of an impediment to strategizing. Alternatively, multitasking may be more blatantly injurious to memory, in which case learners may be more likely to prioritize valuable information when multitasking than when merely exposed to a distractor, which would result in better memory for the most important information.

Participants

Participants consisted of 192 undergraduate students at the University of California, Los Angeles (129 female, 62 male, 1 unreported), ranging in age from 18 to 30 years ( M = 20.50, SD = 1.75). Participants received partial credit toward a course requirement for completing the experiment. The current experiment was based on a pooled set of original data ( N = 96) and replication data ( N = 96). The sample size per condition for each period of collection was based on prior research investigating value effects on memory and selectivity ( Castel et al., 2013 ; Hayes, Kelly, & Smith, 2013 ; Middlebrooks, McGillivray, et al., 2016 ; Middlebrooks, Murayama, & Castel, 2016 ); value-directed remembering and selectivity effects have been repeatedly and robustly found with this conventional sample size.

The experiment was designed and presented to participants using the Collector program ( Garcia & Kornell, 2015 ). Stimuli consisted of six lists, each containing 20 words. Word length ranged from four to seven letters and averaged 8.81 ( SD = 1.57, range = 5.48–12.65) on the log-transformed Hyperspace Analogue to Language (HAL) frequency scale ( Balota et al., 2007 ). To avoid potential item effects ( Murayama, Sakaki, Yan, & Smith, 2014 ), we randomly selected the studied words in each list without replacement for each participant from a larger word bank of 280 random nouns and verbs (e.g., twig, button, taste ). Each selected word was then randomly assigned a value from 1 to 10 points, with two words assigned to each point value per list. Lists varied per participant, so one participant might study twig in List 1, while another participant studied twig in List 3 or not at all. Furthermore, twig might be a 3-point word for one participant but a 10-point word for another participant.

Music distractors

An exploratory point of interest was whether or not familiarity with the background music would affect memory and selective study. It may be easier to ignore background music with which you are very familiar, and, thus, perhaps somewhat habituated to, than to ignore unfamiliar background music ( Kang & Lakshmanan, 2017 ; Röer, Bell, & Buchner, 2014 ). On the other hand, familiar music has been shown to be more enjoyable than unfamiliar music, leading to greater activation in limbic and reward-based neural structures ( Pereira et al., 2011 ). If familiar music heightens dopaminergic, reward-based neural activity, irrespective of the to-be-remembered item’s value, then the potentially greater enjoyment resulting from listening to familiar music relative to unfamiliar music could disrupt the selective role that reward-based regions can serve with respect to remembering valuable information specifically ( Cohen et al., 2014 ). Familiar music may also be more likely to activate related memories and thoughts (e.g., remembering other friends that like this song, remembering the last time you heard the song; Janata, 2009 ) than unfamiliar music, which could also make familiar music more distracting than unfamiliar music during study.

A pilot study ( N = 48) was first conducted to select the songs that would serve as background music. Pilot participants were presented with 30-s clips of different lyrical songs, along with the song’s title and the name of the artist. Participants rated each song on a number of dimensions, including their familiarity with and liking of the song. Participants could replay the song clips as desired while making their judgments. The 12 chosen songs—6 familiar and 6 unfamiliar—were consistently rated as being well-liked, upbeat, and mood improving. The chosen familiar songs had an average of 126.6 beats per minute (BPM; ranging from 120–129) and the unfamiliar songs an average BPM of 124.5 (ranging from 113–139). A full list of the songs presented during the pilot task, and the 12 songs ultimately selected for the task, is available from the corresponding author.

In the main experiment, the six songs—familiar or unfamiliar as per the study condition—were randomly assigned without replacement to the to-be-learned lists for each participant. So a participant assigned to listen to familiar music might study List 1 while listening to Katy Perry’s “Roar,” but another participant in the same condition might not hear “Roar” until studying List 4.

Each participant was randomly assigned to one of four different study conditions: a full-attention condition, a divided-attention condition, a familiar-music condition, and an unfamiliar-music condition. All of the participants were told that they would be shown a series of word lists, each containing 20 different words, and that each word would be paired with a value ranging from 1 to 10 points, with 2 words per point value in each list. Participants were instructed to remember as many of the presented words as possible while also aiming to maximize their score, a sum of the points associated with each subsequently recalled word. They were told that they would be asked to recall the words from each list at the conclusion of its presentation, after which they would be told their score (out of 110 possible points). The words were presented at a rate of 3 s per word.

Participants in the divided-attention condition were further told that a series of digits would be read aloud while they studied and that they were to press the space bar every time they heard a sequence of three odd digits. The digits (numbers 1–9) were randomly generated with constraints at a rate of 1 per second: unbeknownst to participants, there were exactly eight instances of three-odd-digit sequences per list, and there was never a sequence of four odd digits in a row, though there could be one or two odd digits in a row (following which the space bar should not have been pressed).

Participants in the familiar-music and unfamiliar-music conditions were told that background music would be playing while they studied the to-be-remembered words. It was explained that they did not need to do anything with the music or remember it—it would simply be playing in the background—and that their task was to memorize the items while maximizing their score. Each of the songs played for the full 60-s duration of each list presentation. At the conclusion of the task, participants were also asked to indicate whether they were familiar or unfamiliar with the songs that were played: All participants in the familiar-music condition reported being familiar with the music, and all participants in the unfamiliar-music condition reported being unfamiliar with the music, consistent with the responses from the pilot study initially used to select the songs.

Participants in the replication experiment also completed a modified operation span task ( Oswald, McAbee, Redick, & Hambrick, 2015 ) to determine whether the impact of the digit-detection task or the background music on selectivity would differ as a function of individual differences in working memory capacity (WMC). It was thought that participants with greater WMC might be better able to inhibit the distractors during study and so devote more of their attention toward the valuable information. There were, however, no evident differences in selectivity as a consequence of individual operation-span scores within or between study conditions, consistent with prior research that has also failed to find differences in selectivity based on WMC in healthy younger adults ( Castel, Balota, & McCabe, 2009 ; Cohen et al., 2014 ; but see Hayes et al., 2013 ). The results of these analyses are available from the corresponding author.

As mentioned, the current experiment was based on a pooled set of original data ( N = 96) and replication data ( N = 96). The results were consistent between data sets; results specific to each data set are provided in the Supplemental Material available online.

Digit-detection performance

Responses on the digit-detection task by participants in the divided-attention condition were scored as correct when made between 50 and 1,200 ms after the third odd digit in a sequence was played. (Responses made within the 50 ms following the third odd digit were not recorded as correct because the initiation of any such presses would have been made prior to the third digit being played and were thus presumptive.) Participants correctly identified an average of 1.87 out of 8 sequences ( SD = 0.42) throughout the experiment. There were also an average of 1.26 incorrect detections ( SD = 0.18), wherein participants pressed the space bar to indicate that three odd digits had been played when they had not. All participants identified at least one sequence (correctly or incorrectly) during each studied list.

Overall recall performance

The proportion of items recalled as a function of study condition and list are provided in Table 1 . Table S1 in the Supplemental Material separately presents recall performance for the original data collection and the replication.

Proportion of Recalled Items as a Function of Study Condition and List in Experiment 1

Condition	List						Average
Condition	1	2	3	4	5	6	Average
Full attention	.34 (.14)	.38 (.13)	.40 (.15)	.40 (.13)	.41 (.14)	.40 (.13)	.39 (.10)
Divided attention	.18 (.10)	.24 (.09)	.27 (.12)	.29 (.10)	.29 (.11)	.30 (.10)	.26 (.08)
Familiar music	.33 (.14)	.35 (.10)	.36 (.17)	.35 (.15)	.38 (.18)	.34 (.16)	.35 (.11)
Unfamiliar music	.31 (.14)	.37 (.17)	.38 (.13)	.42 (.16)	.37 (.15)	.38 (.18)	.37 (.11)

Note: Standard deviations are presented in parentheses.

Initial analyses were conducted to determine whether there was an effect of divided attention via digit detection or music distractions on overall recall performance, irrespective of item value. Bonferroni adjustments were made in all cases of multiple comparisons during post hoc testing, and Greenhouse-Geisser adjustments were made in the case of sphericity violations. A 4 (condition: full attention, divided attention, familiar music, unfamiliar music) × 6 (list: 1–6) repeated measures analysis of variance (ANOVA) revealed a significant effect of list, F (4.56, 857.92) = 14.26, MSE = 0.01, p < .001, generalized η 2 (η 2 G ) = .04, with the total number of items recalled, on average, significantly lower in List 1 than in each of Lists 2 through 6, adjusted p s < .001. Critically, there was also a significant effect of condition, F (3, 188) = 15.22, MSE = 0.06, p < .001, η 2 G = .11; participants in the divided-attention condition recalled significantly fewer items overall than did participants in the other conditions (adjusted p s < .001).

There were no other significant differences between conditions, nor was there a significant interaction between list and condition. These results confirm that the digit-detection task completed by participants in the divided-attention condition diminished participants’ ability to remember the items relative to participants’ ability in the full-attention condition, consistent with prior research ( Castel & Craik, 2003 ; Craik et al., 1996 ; Naveh-Benjamin et al., 2000 ). Background music in the familiar- and unfamiliar-music conditions did not, however, similarly affect general recall; while it is certainly possible that the music was distracting during study, it was evidently not distracting enough to actually impair recall.

Value-directed remembering and selectivity

Recall performance as a function of item value and study condition is presented in Figure 1 . To account for potential within- and between-subjects differences in value-based study and recall, we used hierarchical linear modeling (HLM) to analyze recall as a function of list and item value among the four study conditions ( Castel et al., 2013 ; Middlebrooks, McGillivray, et al., 2016 ; Middlebrooks, Murayama, & Castel, 2016 ; Raudenbush & Bryk, 2002 ). Given the continuous nature of the value scale used in the current task, as opposed to explicit and distinct value categories (e.g., low-, medium-, and high-value items), participants likely differed in terms of how they attended to value during study. A participant who expected to remember many items, for instance, may have intentionally studied all items worth 6 or more points; a less-confident participant may have constrained study to only those items worth 8 to 10 points. Both examples exemplify value-directed study; a mean-based analytic technique (e.g., ANOVA), however, would be unable to detect any direct relationships between item value and recall probability, only whether there were differences, on average, in the recall of particular value points, which would mask variation in strategy implementation. In contrast to mean-based techniques, HLM first clusters the recall data within each participant, which thereby accounted in the current experiment for individual differences in any value-based study strategies, and only then considers differences between conditions, such as in the present value-recall relationship across study conditions (see Middlebrooks, McGillivray, et al., 2016 , and Middlebrooks, Murayama, & Castel, 2016 , for further explanations regarding the use of HLM in analyzing selectivity and value-directed remembering).

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Results from Experiment 1: mean proportion of items recalled across the six lists as a function of item value and study condition. Error bars show ±1 SE .

Item-level recall performance (based on a Bernoulli distribution; 0 = not recalled, 1 = recalled; Level 1 = items, Level 2 = participants) was modeled as a function of each item’s value, the list in which it was presented, and the interaction between value and list. Value and list were entered as group-mean-centered variables, such that value was anchored on the mean value point (5.5) and list was anchored on the mean list (3.5). The model also included the study conditions as Level 2 predictors of those Level 1 effects via three dummy-coded variables, with the full-attention condition as the reference group. Although the full-attention condition served as the control against which effects of distraction and divided attention on recall and selectivity could be compared, the following results were consistent regardless of the reference group.

For the tested model, Table 2 reports the estimated regression coefficients for the fixed effects, and Table 3 reports the variance for the random effects. Table S2 in the Supplemental Material presents the estimated regression coefficients from the same model separately for the original data collection and the replication. Because the models are essentially logistic regression models with a dichotomous outcome, the regression coefficients can be interpreted via their exponential function ( Raudenbush & Bryk, 2002 ). Specifically, exponential beta, Exp(β), is interpreted as the effect of the respective independent variable on the odds ratio of successful recall (i.e., the probability of recalling items divided by the probability of forgetting them; Murayama, Sakaki, et al., 2014 ). An Exp(β) of more than 1.0 indicates a positive effect of the predictor, while an Exp(β) of less than 1.0 indicates a negative (or diminished) effect of the predictor.

Fixed Effects From the Two-Level Hierarchical Generalized Linear Model Predicting Recall Performance From Item Value, List, and Study Condition in Experiment 1

Predictor	β
Intercept (β )	−0.52
Divided attention vs. full attention (β )	−0.62
Familiar music vs. full attention (β )	−0.20
Unfamiliar music vs. full attention (β )	−0.07
Value (β )	0.16
Divided attention vs. full attention (β )	0.01
Familiar music vs. full attention (β )	0.02
Unfamiliar music vs. full attention (β )	−0.02
List (β )	0.04
Divided attention vs. full attention (β )	0.05
Familiar music vs. full attention (β )	−0.03
Unfamiliar music vs. full attention (β )	0.01
List × Value (β )	0.03
Divided attention vs. full attention (β )	0.01
Familiar music vs. full attention (β )	−0.01
Unfamiliar music vs. full attention (β )	−0.01

Note: The logit link function was used to address the binary dependent variable.

Random Effects From the Two-Level Hierarchical Generalized Linear Model Predicting Recall Performance From Item Value, List, and Study Condition in Experiment 1

Random effect	Variance
Intercept (person-level; )	0.21
Value ( )	0.01
List ( )	0.03
List × Value ( )	0.001

Value was a significantly positive predictor of recall performance in the full-attention condition (β 10 = 0.16, p < .001), and this relationship was not significantly different across conditions, p s > .250. Thus, participants across all study conditions were 1.17 times (e 0.16 ) more likely to recall a studied word for each 1-unit increase in its value. The odds of recalling a 10-point item, for example, were 4.88 times (e 0.16×10 ) greater than the odds of recalling a 1-point item, which demonstrates a clear effect of item importance or value on subsequent memory. There was not a significant effect of list on recall for participants in the full-attention condition (β 20 = 0.04, p = .077), nor was there an evident Condition × List interaction, p s > .076. (Note that the use of effect coding in the HLM, rather than dummy coding, complements the main effect of list reflected by the previous ANOVA.)

There was, however, a significant List × Value interaction in the full-attention condition (β 30 = 0.03, p = .001)—which did not differ across the other conditions, p s > .250; the relationship between an item’s value and the probability of it being later recalled increased with continued task experience. As Figure 2 shows, participants were more likely to consider item importance while studying and adjust their strategies to compensate for their inability to remember all of the presented items as the experiment progressed, regardless of the presence (or extent) of distraction that they experienced during study.

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Results from Experiment 1: mean proportion of items recalled as a function of item value and study condition, separately for List 1 and List 6 (the final studied list). Error bars show ±1 SE .

Bayesian analysis

In the HLM analyses, the nonsignificant effect of study condition on the relationship between item value and recall probability suggests that selectivity and value-directed remembering were in no way affected by the music distractors or the digit-detection task during study. Because these results are based on null-hypothesis testing, though, it is truthfully impossible to claim the absence of such condition effects (despite the large sample size; N = 192). Additionally, the reported analyses are based on an aggregate of the original sample and the replication sample, on which interim analyses were conducted. There was no intention to stop data collection contingent on the obtained results, but interim analyses can make the interpretation of obtained p values ambiguous ( Murayama, Pekrun, & Fiedler, 2014 ). Accordingly, a Bayesian analysis was also performed in order to surmount the potential complications of having conducted interim analyses on the pooled data set and to confirm the null effect of condition suggested by the HLM analysis ( Middlebrooks, Murayama, & Castel, 2016 ). Bayes factors as computed in Bayesian analysis make it possible to directly compare the probability of obtaining the stated results under the null hypothesis (i.e., no between-conditions differences in the effect of value on recall) with the probability of the results under the alternative hypothesis (i.e., between-conditions differences; Jarosz & Wiley, 2014 ).

A two-step approach was used to allow for simpler Bayesian analysis with hierarchical data owing to the difficulty in directly comparing Bayes factors using HLM (see Lorch & Myers, 1990 ; Murayama, Sakaki, et al., 2014 ). Specifically, item recall was regressed on item value within each list for each participant using logistic regression. A 4 (condition) × 6 (list) repeated measures Bayesian ANOVA was then conducted on these value slopes using JASP software with default priors ( Love et al., 2015 ). For condition, the resultant Bayes factor 10 (BF 10 ), which reflects the probability of the data under the alternative hypotheses (1) relative to the null hypothesis (0), was 0.015. In other words, the present data are 66.67 times (1/0.015) more likely to be consistent with the null model than with the alternative, which provides strong evidence for a null effect of study condition on the value-recall relationship ( Kass & Raftery, 1995 ). These results confirm that selectivity during study and value-directed remembering was comparable across the study conditions.

The results of Experiment 1 indicate that participants who were either distracted by music (regardless of their familiarity with it) or whose attention was divided by the digit-detection task studied the valuable information as selec-tively as participants in the full-attention control condition. Memory overall was not impaired by the music distractors relative to memory in the full-attention condition, so the fact that selectivity remained could reflect comparable availability of attentional resources during study. Memory was, however, impaired by the digit-detection task, yet selectivity was maintained.

It is possible, however, that the digit-detection task was simply too difficult for participants and so was largely neglected; although this task is a common method of dividing attention, performance in Experiment 1 was notably lower in this condition than has been reported in other studies (e.g., Castel & Craik, 2003 ; Jacoby, 1991 ). The nature of the primary task—not only to remember presented items, but also to consider their values, contrast performance with earlier feedback, evaluate and execute strategies, etc.—may have amplified the difficulty of the digit-detection task. In light of this possibility, it is unclear as to whether selectivity was maintained in spite of divided attention or because attention was not actually divided.

Experiment 2

Experiment 2 was designed in part to address the concern that low digit-detection performance in Experiment 1 reflected a failure to properly divide participants’ attention. Experiment 2 also examined the extent to which participants’ attending to the divided-attention task may have deviated as a consequence of the studied material’s value.

Instead of a digit-detection task, participants’ attention in Experiment 2 was divided using three different tone-detection tasks, across which the difficulty, and the extent to which working memory may be required to complete the concurrent task, was increased to determine whether selectivity and value-directed remembering would be differentially affected. (Tone detection was used in place of digits in an effort to reduce the potential conflict between the numbers in the divided-attention task and the values of the to-be-remembered items, which may have contributed to the low digit-detection performance in Experiment 1.) Responses to these tone-detection tasks were made during each item’s presentation, which enabled us to execute a more detailed analysis than was possible in Experiment 1 of the potential costs and shifts of participants’ attention between the studied material and the divided-attention task.

Participants consisted of 96 undergraduate students at the University of California, Los Angeles (75 female, 20 male, 1 unreported), ranging in age from 18 to 27 years ( M = 20.61, SD = 1.44). Participants received partial credit toward a course requirement for completing the experiment.

Materials and procedure

The to-be-remembered stimuli in Experiment 2 were the same as in Experiment 1. Participants were randomly assigned to one of four study conditions: a full-attention condition, a tone-monitoring con-dition, a paired-tones condition, and a 1-back condition. As in Experiment 1, all participants were told that they would be shown a series of words lists and that each word would be paired with a value ranging from 1 to 10 points, the goal of the task being to recall as many words as possible at test while also maximizing one’s recall score. The words were presented for 3 s at a time. Participants in all but the full-attention condition were further told that they would hear a series of low-pitched (400 Hz) and high-pitched (900 Hz) tones played in the background during study. These tones were played continuously throughout the study of each list, and each tone was played for 1 s with a 750-ms intertone interval, which resulted in exactly two tones being played during each to-be-remembered item’s presentation. The exact tone sequence was generated randomly for each participant, the only constraints being that the same pitch could not play more than three times in a row.

Participants in the tone-monitoring condition were instructed to indicate via keyboard whether each pitch they heard was low or high. Participants in the paired-tones condition were to indicate via keyboard whether the two tones played during a word’s presentation were the same pitch (i.e., both low pitched or both high pitched) or of different pitches. Participants in the 1-back condition were to indicate via keyboard whether the current tone was the same pitch as the previous tone or a different pitch. (Across conditions, the keys were labeled to increase ease of responding.) Participants in the tone-monitoring and 1-back conditions thus provided two tone-related responses for each word, and participants in the paired-tones condition provided one response after the second tone was played. A prompt to attend to the tone-monitoring task was presented to participants who failed to respond correctly or did not respond to more than three detections in a row. An example of how the tone-related responses differed across conditions is provided in Figure 3 .

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Illustration of the design of Experiment 2. As participants saw each to-be-remembered word (along with its point value), they heard two consecutive tones (top rows). Each tone was pseudorandomly chosen to be low or high pitched. In the three experimental conditions (bottom rows), participants had to identify each tone as low or high (tone-monitoring condition), identify the two tones as the same or a different pitch (paired-tones condition), or identify whether each tone was the same as or different from the previous tone (1-back condition). In a fourth condition (the full-attention condition), participants completed the primary task, but no tones were played.

In the tone-monitoring condition, participants were not required to keep track of the tones playing or remember anything about them, but were only to report the pitch of the tone in the moment. Contrastingly, participants in the paired-tones condition had to determine and remember the pitch of the first tone played during a word’s presentation and then compare it with the second tone played before providing a response, which should have required more working memory resources than in the tone-monitoring condition. Working memory demand was presumed to be the most stressed in the 1-back condition because participants had to continuously monitor and compare tones across studied items, repeatedly updating the tone against which they were to compare the currently playing tone.

The proportion of items recalled as a function of study condition and list are provided in Table 4 . As in Experiment 1, initial analyses were conducted to determine whether there was an effect of divided attention on overall recall performance across the three tone conditions, irrespective of item value. Bonferroni adjustments were made in all cases of multiple comparisons during post hoc testing, and Greenhouse-Geisser adjustments were made in the case of sphericity violations. A 4 (condition: full attention, tone monitoring, paired tones, 1-back) × 6 (list: 1–6) repeated measures ANOVA revealed a significant effect of condition, F (3, 92) = 17.20, MSE = 0.05, p < .001, η 2 G = .25, with participants in the full-attention condition recalling significantly more items overall than participants in the three other conditions (adjusted p s < .001). There was also a significant List × Condition interaction, F (13.34, 409.07) = 2.00, MSE = 0.01, p = .019, η 2 G = .03. Although total recall did not change significantly across lists in the full-attention condition ( p > .250), there was a significant effect of list in the other conditions ( p s < .029); the total number of items recalled increased with continued task experience. Finally, there was a significant effect of list, F (4.45, 409.07) = 11.50, MSE = 0.01, p < .001, η 2 G = .05; total recall in the first three lists was significantly lower than in the last three lists.

Proportion of Recalled Items as a Function of Study Condition and List in Experiment 2

Condition	List						Average
Condition	1	2	3	4	5	6	Average
Full attention	.39 (.16)	.37 (.18)	.38 (.18)	.39 (.14)	.41 (.16)	.41 (.15)	.39 (.14)
Tone monitoring	.19 (.10)	.24 (.10)	.30 (.16)	.27 (.12)	.25 (.10)	.26 (.09)	.25 (.07)
Paired tones	.19 (.12)	.26 (.14)	.25 (.11)	.26 (.11)	.30 (.13)	.30 (.12)	.26 (.10)
1-back	.14 (.06)	.17 (.07)	.21 (.06)	.24 (.09)	.23 (.08)	.25 (.09)	.21 (.05)

These results confirm that the tone-detection task diminished participants’ ability to remember the presented items relative to full-attention study, consistent with prior research ( Craik et al., 1996 ; Gardiner & Parkin, 1990 ). Notably, there were no significant differences in recall among the three tone-detection conditions, despite differences in the demands of the tone task.

Recall performance as a function of item value and study condition is presented in Figure 4 . As in Experiment 1, HLM was used to analyze recall as a function of list and item value among the four study conditions. The model used was identical to that in Experiment 1, save for the differences in the actual conditions. Table 5 reports the estimated regression coefficients for the fixed effects, and Table 6 reports the variance for the random effects.

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Results from Experiment 2: mean proportion of items recalled across the six lists as a function of item value and study condition. Error bars show ±1 SE .

Fixed Effects From the Two-Level Hierarchical Generalized Linear Model Predicting Recall Performance From Item Value, List, and Study Condition in Experiment 2

Predictor	β
Intercept (β )	−0.52
Tone monitoring vs. full attention (β )	−0.72
Paired tones vs. full attention (β )	−0.67
1-back vs. full attention (β )	−0.98
Value (β )	0.21
Tone monitoring vs. full attention (β )	−0.02
Paired tones vs. full attention (β )	−0.05
1-back vs. full attention (β )	−0.05
List (β )	0.01
Tone monitoring vs. full attention (β )	−0.01
Paired tones vs. full attention (β )	0.06
1-back vs. full attention (β )	0.09
List × Value (β )	0.03
Tone monitoring vs. full attention (β )	0.02
Paired tones vs. full attention (β )	0.03
1-back vs. full attention (β )	0.0003

Note: The logit link function was used to address the binary nature of the recall outcome.

Random Effects From the Two-Level Hierarchical Generalized Linear Model Predicting Recall Performance From Item Value, List, and Study Condition in Experiment 2

Random effect	Variance
Intercept (person-level; )	0.22
Value ( )	0.03
List ( )	0.002
List × Value ( )	0.003

Value was a significantly positive predictor of recall performance in the full-attention condition (β 10 = 0.21, p < .001), and this relationship between item value and recall likelihood was not significantly different across conditions, p s > .250. There was also a significant List × Value interaction in the full-attention condition (β 30 = 0.03, p = .008), which, again, did not differ across conditions, p s > .117; selectivity increased with continued task experience, as Figure 5 shows. These results are consistent with those of Experiment 1: Despite impairing overall recall, the tone-detection tasks did not result in significant changes to selectivity relative to the full-attention condition.

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Object name is 10.1177_0956797617702502-fig5.jpg

Results from Experiment 2: mean proportion of items recalled as a function of item value and study condition, separately for List 1 and List 6 (the final studied list). Error bars show ±1 SE .

Tone-detection performance

Responses to the tone-detection task across conditions were scored as correct when made between 50 and 1,750 ms of the respective tone’s onset. Accuracy for responses to tones within a list was based on the possible number of responses: 40 (i.e., two responses per word) in the tone-monitoring and 1-back conditions and 20 (i.e., one response per word) in the paired-tones condition.

A 3 (condition: tone monitoring, paired tones, 1-back) × 6 (list: 1–6) repeated measures ANOVA was conducted in order to assess whether overall tone-detection accuracy differed as a consequence of the task demands—namely, the extent to which previously heard tones had to be remembered in order to provide an accurate response. There was a significant effect of list, F (2.87, 198.18) = 10.44, MSE = 0.03, p < .001, η 2 G = .05; specifically, detection accuracy was significantly lower in List 1 than in Lists 2 through 6, adjusted p s < .006. There was also a significant effect of condition, F (2, 69) = 4.01, MSE = 0.17, p = .023, η 2 G = .07. Participants in the tone-monitoring condition accurately responded to a significantly greater proportion of the tone events ( M = .78, SD = .19) than did participants in the 1-back condition ( M = .66, SD = .17), adjusted p = .037. Tone performance in the paired-tones condition ( M = .77, SD = .13) was also marginally greater than in the 1-back condition, adjusted p = .071, but did not significantly differ from performance in the tone-monitoring condition. So participants were less able to successfully complete the 1-back tone-detection task than the other tone tasks, consistent with the predicted difference in task difficulty owing to an increase in task demands. That performance did not differ between the tone-monitoring and paired-tones condition suggests that the difference in the two tasks’ demands may not have differentially affected their level of difficulty. Regardless, average performance indicates that participants were actively engaged in the tone tasks, which assuaged our concerns in Experiment 1 about the extent to which digit-detection performance actually divided attention.

Two HLM analyses were also conducted to determine whether tone-detection accuracy and the time (in seconds) that it took participants to make their tone-related responses in the three tone-detection conditions differed owing to item value or the list in which it appeared, or whether the effect of value on tone accuracy changed across lists. (Such an analysis was not possible in Experiment 1 because of the low digit-detection performance, in terms of both response rates and response accuracy.)

The tested models and their estimated regression coefficients are provided in Table S3 in the Supplemental Material . Although there were no evident effects of value or list on tone-response accuracy, there was a significant effect of list on reaction time: Participants made their tone responses significantly faster with continued task experience (β 20 = −0.02, p = .001; see Table S3 ). There was also a small but significant List × Value interaction with respect to reaction time; item value became slightly more predictive of reaction time across lists (β 30 = 0.003, p = .001), with participants responding slightly more slowly when concurrently studying a high-value item than a low-value item. In general, however, item value was not predictive of reaction time (β 10 = 0.002, p > .250).

The results of these analyses indicate that participants were not only engaged with the tone-detection tasks, as evidenced by their overall response accuracy, but also that participants did not strategically neglect the tone task when presented with more valuable materials. Rather, participants were engaged throughout study with the concurrent tone task and consistently so across items, regardless of their values.

Although participants in the tone-detection conditions recalled fewer items than those in the full-attention condition, recall of the most important items did not differ relative to the full-attention condition. In all but the full-attention condition, participants may have adjusted to this general memory impairment by selectively allocating their attention to the high-value items and refining their strategy with continued task experience, as suggested by performance in later lists (see Fig. 5 ). Overall, these results provide a more detailed analysis of attention during encoding of high- and low-value items, and they support the main findings from Experiment 1.

General Discussion

Distractions are often unavoidable, and despite a global awareness of consequent impairments ( Barnes & Dougherty, 2007 ; Finley et al., 2014 ), learners frequently partake in distracting activities that lead to poorer comprehension of and memory for to-be-learned information ( Fried, 2008 ; Sana et al., 2013 ). The current experiments examined whether divided attention during encoding similarly diminishes selective attendance to valuable information when remembering everything is unachievable, and whether the extent to which learners engage with the distraction during encoding affects selectivity.

In Experiment 1, participants studied the to-be-remembered items while completing a digit-detection task or while listening to familiar or unfamiliar background music. Participants in the digit-detection condition remembered fewer items overall than participants in the other conditions, but there were no significant differences in memory for the higher-valued items across conditions. These results were confirmed in an exact replication of Experiment 1 and upheld in Experiment 2 using a range of tone-detection tasks: Despite the fact that participants’ attention was divided during study to varying degrees, selectivity was consistently maintained.

That participants were able to study selectively in spite of the concurrent tasks, and resultant memory impairments, is surprising and warrants further investigation. Divided attention appears most detrimental to elaborative, semantic processing ( Anderson et al., 2000 ; Craik, 1982 )— by which value-directed remembering is thought to be best enacted ( Cohen et al., 2014 )—and so should have compromised the execution of a selective strategy. Moreover, a task designed to decrease available resources should reduce one’s ability to study strategically if selecting and executing an optimal strategy depends on working memory availability ( Dunlosky et al., 2011 ). Even if participants decided on a selective strategy in advance of study (though prior work indicates the need for task experience; Castel et al., 2012 ), limits to cognitive resources have nevertheless been shown to impair execution of that strategy, even if it had been previously implemented successfully ( Dunlosky & Thiede, 2004 ). The 1-back tone condition in Experiment 2 was specially intended to place additional demands on working memory relative to the other conditions, yet selectivity was preserved.

There is a dearth of research investigating metamemory judgments made while participants’ attention is divided ( Barnes & Dougherty, 2007 ; see Beaman et al., 2014 ; Kelley & Sahakyan, 2003 ; and Sacher et al., 2009 , for work concerning postencoding judgments), but the current results intimate that divided attention did not incapacitate metacognitive mechanisms in either of these experiments, which left participants capable of judging their memory capacity, performance, and methods by which they might compensate for additional demands on attention. Accordingly, divided attention may not affect metamemory like it does memory.

The present results do not imply that selectivity will always be impervious to distraction, but they suggest that attentional stressors that impair memory will not necessarily impair study strategizing. In examining the influence of distractions on strategy application, future research should consider situations in which the learner must first determine importance (i.e., when value is not explicitly denoted). The detriment of divided attention to comprehension ( Craik, 1982 ; Sana et al., 2013 ) may mean that learners inaccurately judge importance; if the learner fails to recognize the value in something when distracted, then the appropriate strategy will not be applied, even if it could have been executed.

Future research should also consider the effect of divided attention on self-regulated study choices. Participants in the current study were unable to control what or when they studied; in real-world situations, however, learners often decide when to engage with a distractor (e.g., deciding when to check one’s e-mail during a lecture) or control the pacing of their primary task (e.g., if background music in a café is distracting, a learner could choose to reread a passage). Pashler, Kang, and Ip (2013) reported divided-attention effects on memory when study time was experimenter-paced; when study was self-paced, however, participants compensated for distractions by studying longer. Given the opportunity to self-pace, participants might believe that they can compensate for distractions by slowing their study, which makes them less likely to study selectively and, thus, potentially more likely to forget important information.

The current study examined whether distraction, consistently shown to diminish memory, similarly impairs the strategic study of valuable information. Though dividing participants’ attention reduced recall in general, neither active multitasking nor passive exposure to background music prevented their prioritizing high-value items during study. Participants compensated for limitations owing to divided attention by devoting their remaining resources to the most important items, which provides further evidence that factors that worsen memory do not necessarily similarly affect study strategizing.

Supplementary Material

Acknowledgments.

We thank Kou Murayama for helpful comments and Brenna McCarty, Cassia Ng, Kelly Patapoff, Sukhman Bassi, and Alexis Baird for help with data collection.

Action Editor: Kathleen McDermott served as action editor for this article.

Declaration of Conflicting Interests: The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.

Funding: This work was supported by the National Institutes of Health National Institute on Aging (Award No. R01AG044335 to A. D. Castel).

Supplemental Material: Additional supporting information can be found at http://journals.sagepub.com/doi/suppl/10.1177/0956797617702502

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Self-improvement
Productivity

7 Myths and Truths about Multitasking that you should know

July 2, 2020

6 Comments

This is the first post in my new ‘Productivity Hacks Series – Things I wish someone had told me a decade ago’. Hindsight is 20-20. There are many things I wish my younger self knew. For instance, I wish someone had told me about the myths and truths about Multitasking.

“ Learn from the mistakes of others . You can’t live long enough to make them all yourself.” Eleanor Roosevelt.

I have spent countless hours of my life on well-intentioned but misguided productivity experiments. Attempting to multitask – a lot – was one of them. Not surprisingly, these trials weren’t yielding the expected results.

To find out where I went wrong, I turned to scientific literature and studies on multitasking. What I learnt changed my life. Here are some key myths and truths about multitasking and lessons I learnt.

Start with an experiment – Seeing is believing

Indulge me for a few minutes. Get a pen, a few blank papers and a phone with a stopwatch/timer app and find a quiet place. I have a little experiment for you. Perform the tasks below at a comfortable pace. No need to rush!

Tasks A and B

Task A: Start your stopwatch and do the following: On a blank sheet of paper, write down your favorite song lyrics or directions to your favorite restaurant from your home. (This experiment works best if you pick something that takes you at least 5 minutes to do). Stop the watch and note the time taken.
Task B : Take a second blank sheet of paper. Reset the stopwatch and start again. This time write down numbers 200 to 1 in descending order. When you’re done, stop the watch and note the time taken.

Add up A + B to get the total time taken. Feel free to take a break, if needed.

When ready to resume, take another blank sheet of paper. I prefer to use the Timer feature on the phone instead of the stopwatch for this part. Set the timer to 30 seconds. Start writing down the lyrics to the same song or directions to the same restaurant that you did in (A) above. When the timer ends, restart it right away for another 30 seconds, but this time write down the number sequence in descending order starting from 200 (B). Keep alternating every 30 seconds between the lyrics/directions and number sequence until you complete both the lyrics/directions and the descending number series. Add up the total time taken.

Compare the time for A+B to time taken for C (assuming you finished Task C and didn’t tear up all your papers already). And…Drum roll!

You just learnt really valuable lessons on the Myths and Truths about Multitasking. I’ll explain what these mean soon. First, some definitions.

Definitions

Unitasking (Serial processing): processing one task at a time (Tasks A and B above)
Multitasking (Parallel processing): processing more than one task simultaneously.
Task Switching : processing more than one task not simultaneously but alternating rapidly between tasks. Task C in the example above. Task Switching is often mistaken as multitasking .

Myths and Truths about Multitasking – Cognitive multitasking

Both tasks in the experiment above (writing out lyrics and writing down the number sequence) involve some degree of thinking (cognition), so we’ll refer to these as cognitive tasks . We’ll discuss other sorts of tasks later.

Myth 1 – Cognitive multitasking is possible

Read the definition of multitasking above. It is impossible to think about the lyrics to a song and a number sequence at EXACTLY the same time.

Cognitive task switching is possible. While cognitive multitasking is impossible, cognitive task switching is possible, albeit at a significant cost. Some of these costs/drawbacks to task switching are below:

Switching from one task to another takes time . Our brain exercises ‘executive control’. It determines which resources to allocate to each task and for how long. You make the brain work harder by asking it to switch between tasks. This act of determining which task to perform, in itself, takes up time.
By the time you start thinking about the number sequence, a little part of your brain is still thinking about song lyrics. This is called residual attention. On the flip side, knowing what you need to do next provides you a stimulus preview causing a part of your brain to start thinking prematurely about the number sequence even while you are still writing out the song lyrics.
It is more stressful to switch tasks than to stay focused on one. If you did this experiment, you’d have felt it. Also, forced task switching (under a deadline) is much harder than voluntary/rewarding task switching (scrolling through an Insta feed)!
Leaving a task midway means you need to remember where you left off. Although your brain has the capacity to ‘bookmark’, it will still take some effort to retrieve the bookmark so you can resume at that point.
The example in the above experiment was of two rather simple tasks. The degree of task switching difficulty increases exponentially when the task complexity or rules increase. For instance, instead of simply writing a decreasing number sequence, if I had asked you to write a decreasing number sequence and also to multiply each even number in the sequence by 2. This would take longer both when unitasking and task switching, but it would be much harder to do when task switching. I dare you to try it!
The rate of errors is much higher when task switching rather than unitasking.

Are you convinced now that it’s better to focus on one cognitive task at a time? It is far easier and less of a strain to perform cognitive tasks in serial mode aka Unitask because Cognitive task switching comes at a significant cost.

Myths and Truths about Multitasking – making cognitive multitasking efficient

Still on the subject of cognitive tasks, is there any way at all to make cognitive multitasking or task switching as efficient as unitasking?

Myth 2 – Cognitive multitasking/ Cognitive task switching can be trained to be as efficient as unitasking

Well, not really. Cue sad face emoji!

If you’re reading this, I assume you have, like me, no more than one brain (although yours may well be much better equipped).

There are, unfortunately, inherent structural limitations to how our brains are wired and the amount of glucose/glycogen that the brain can process at any given amount of time. These play a role in how efficient task switching can be.

It is prudent to accept these constraints. Though, science is trying really hard to find a way out.

Though task switching cannot be as efficient as unitasking, it may be possible to make cognitive task switching less annoying.

With practice, task switching does get easier. But this means always pairing the same two tasks in the same order. Even, in the example above, since Task C is really a repetition of Tasks A and B, C is still easier to do because you have had some practice. It would take even longer if you did Task C first before A and B.

Age and working memory do play a part but not as much as millennials and Gen Zers would like to think!

Take intentional breaks but avoid switching back and forth between two cognitive tasks.

When working on hard problems, it can get tiresome to the brain to stay focused continuously over long periods of time. The recommendation then is to focus for a set duration, say 30 – 40 minutes, take a break and then go back to the hard problem. Remember, there is a world of difference between taking an intentional break versus constant task switching.

Myths and Truths about Multitasking – Multimodal multitasking

Learning the stuff above was enough to puncture my happy multitasking bubble, but I still had one burning question. Are we consigned to a life of simply doing one task at a time? Please, please, say it ain’t so. The Type A in me really wanted to know.

Cognitive multitasking is, as yet, unattainable. What about other types?

I scavenged around a whole bunch of research articles hoping to find some information that supports multitasking. And I did find something. Not quite the winner’s trophy but a consolation prize. I’ll take that. Beggars can’t be choosers.

This may not be news to you, but in addition to the thinking (cognition) brain, we have other parts of the brain that direct our other human parts to pick up sensory stimuli or perform motor functions. I’m referring to our eyes (visual), ears (audio), skin (tactile), arms and legs (motor functions).

Myth 3: It’s possible to do multiple non-cognitive tasks simultaneously.

Unfortunately, no. As you’ll see in the examples below.

Yes, it’s possible to simultaneously perform tasks that are controlled by different resource pools in the brain. This is called Multi-modal multitasking or Cross-modal multitasking. Of course, every bit of good news has a BUT attached to it. There are limitations to multimodal multitasking.

You notice so many examples in daily life where you can seemingly multitask.

You CAN walk (motor function) and listen to music (auditory) at the same time. Bear in mind again, I’m talking about pure natural movement and pure auditory impulses. However, if you’re trying to emulate Monty Python’s silly walk (google it, if you don’t know what I mean) while also trying to match Beyonce’s pitch and tone, you’ll end up falling flat while squealing like a cat. Because you broke the rule . You tried to cognitive multitask by trying to think through two things at once instead of simply sensory multitasking.
You can drive and listen to music but cannot watch TV and drive at the same time. (Though, that hasn’t stopped some adventurous (desperate?) folks from trying). That’s because the visual field is engaged in watching the road and is not free to watch TV.
You can nod yes when listening to a person talking, but you cannot actively formulate a clever response. Alas, we seem to prefer the response formulation part over active listening. This may be the #1 reason for a lot of marital disputes. Just sayin’!
In recent years, wearables such as smart watches have Haptic-functionality built in that take advantage of the multimodal concept. If you’re running hard, you can have your heart rate monitor convey information to you easily in the form of haptic alerts when you hit a threshold. Much less intrusive than you having to stop and visually check your device.

You can pair certain tasks, especially when they are rote or routine, so long as they have different sensory stimuli . You can dip into each sensory bucket just once. Also, know that you may not be as effective as when doing them one at a time.

Scientific consensus is still evolving in the area and effectiveness of multimodal tasks. Research shows that some pairs of tasks are ‘compatible’ and enhance the primary task experience whereas others interrupt it.

For instance, if you’re intensely focused on a cognitive task, such as writing or solving a math problem, a cellphone ping (though auditory) will interrupt your primary thought. However, a white noise machine, in some circumstances, is said to enhance creativity.

We’ll let scientists duke it out on this one.

Myths and Truths about Multitasking – Other common myths

I’d be remiss if I did not address a few other common myths around multitasking. Some are folklore. Some are just overly optimistic expectations.

Myth 4: Women are better multitaskers than men.

I have used this popular stereotype to a lot of dramatic effect in my own life. Turns out it’s not true. Devastating.

Here is the role gender plays in the ability to multitask – NONE. NADA. ZIP. ZILCH. In reality, multitasking causes a decline in performance across the board – for both men and women. Take that!

This one is a general life lesson rather than a lesson on multitasking. Don’t believe gender stereotypes. About multitasking or anything else. So profound yet so true!

Myth 5: The more you try to multitask the better you’ll get

Oh, yes! The power of practice. Haven’t we been told ‘If you don’t succeed, try, try again’. My advice. Don’t. Because of what is said below.

The more you try to multitask the worse you’ll get .

In fact, constantly multitasking, says Clifford Nass, a pioneer research scientist in this field, makes you unable to filter out key information. You lose the ability to determine what’s relevant and what’s irrelevant anymore. Ouch!

Don’t poke the bear. Trust science. Find some other challenges in life if you’re bored.

Myth 6: Multitasking is an important productivity hack

This one stems from the assumption that one of the core principles of successful folks is their ability to multitask. Or the fear that you won’t be able to master it all if you didn’t multitask. Because, c’mon who really has the time to focus on one task at a time?

Multitasking is a productivity killer.

Studies have shown multitasking reduces productivity by anywhere between 30 to 40%. Regular multitaskers who continue to multitask notice severe decline in their performance. Another study in London estimated that a person’s IQ drops by 10 to 15 points after constant cognitive multitasking.

Need I say more?

Not to state the obvious, productivity by definition means increasing efficiency. Study after study has proven cognitive multitasking does not help with improved productivity.

Myth 7: Multitasking can be taught

If you believe you’ve been doing it all wrong and simply needed to learn the right techniques, then I have some bad news for you.

The multitasking world is clearly divided into the have-nots and the haves.

98% of us are the have-nots! Truth. Unfortunately, the more people think they are good at it, the more likely they are not. Only 2% of the world’s population has a predisposition to multitask efficiently. They are called supertaskers . It is quite likely you are not one of them, because you probably would have known by this time. If in doubt, you are welcome to take the online test Are you a supertasker? here at our own peril! This is a legit test, not a Buzzfeed quiz to waste your time. If you end up with the world’s worst headache after taking the test, you only have your ego to blame!

The twain shall never meet! So don’t waste time trying.

Media multitasking is a reality and is the subject of rapidly evolving research. Gen Zers and even some millennials are a generation of ‘digital natives’. They claim to have inherent multitasking skills. They’d benefit from knowing the Myths and Truths about multitasking. If you find yourself or your teen constantly switching between devices, stop and ponder. Because. Science. And a 15 point drop in IQ may just be too precipitous.

References:

https://link.springer.com/article/10.1007/s00426-018-0970-2

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5818435/#:~:text=While%20dual%2Dtask%20performance%20requires,with%20sequentially%20processed%20component%20tasks.

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0220150

https://www.pnas.org/content/106/37/15583

https://www.talentsmart.com/articles/Multitasking-Damages-Your-Brain-and-Your-Career,-New-Studies-Suggest-2102500909-p-1.html

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Wow… Loved it…. Female are not better at multitasking broke my heart… 🙂 The first quote you mentioned about learning from other mistakes….. Was stated by Chanakya…. that too so long ago.. 300 BC.. Yes at home we are multitasking…. Your blog reminds us to be more mindful of what we r doing and it’s productivity for good and for bad.. Lol on yelling at kids during cooking which we all do…. A very interesting read…

Dolly. Thank you for reading! It was definitely a long post, so thanks for the patience :). Interesting about Chanakya, I didn’t know that.

Regarding the research on women not being good at multitasking, I think that bummed everyone out, including me!

Thanks for your support as always.

We have had these discussions at work, mind you only at work 🙂 because at home there is no way for me to think I can do one thing at a time …..

Cooking while doing dishes and scolding kids 🙂

So what I wanted to acknowledge was that in name of multitasking, we have compromised on quality and people just don’t have focus to stay on one problem It is easy way out And with the smart devices in our hands, only thing smart that is left are the devices 🙂 🙂

Thankyou for yet another fun read !

Jaya! I know, ironic that we try to do the best at work. At home, you’re right – maybe it’s an easy way out to not focus on one thing! Imagine how stressful the kids can get, if we did really focus on them 🙂 🙂

Nice way to pop the mythical balloons, the gender one especially skewered mine nicely. On other hand it is such a joy to do just one thing at a time, like reading a book. Music still makes cooking easier for me. Good read, this one.

Div! Thank you again so much for reading and the great feedback. Yes, writing this one was like writing a note to myself. I needed all these reminders.

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Individual differences in everyday multitasking behavior and its relation to cognition and personality

Published: 04 July 2022
Volume 87 , pages 655–685, ( 2023 )

Cite this article

Samsad Afrin Himi ORCID: orcid.org/0000-0001-6955-1757 1 , 3 ,
Gregor Volberg ORCID: orcid.org/0000-0002-4427-2409 2 ,
Markus Bühner ORCID: orcid.org/0000-0002-0597-8708 3 &
Sven Hilbert ORCID: orcid.org/0000-0001-5808-8357 4

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Our ability to multitask—focus on multiple tasks simultaneously—is one of the most critical functions of our cognitive system. This capability has shown to have relations to cognition and personality in empirical studies, which have received much attention recently. This review article integrates the available findings to examine how individual differences in multitasking behavior are linked with different cognitive constructs and personality traits to conceptualize what multitasking behavior represents. In this review, we highlight the methodological differences and theoretical conceptions. Cognitive constructs including executive functions (i.e., shifting, updating, and inhibition), working memory, relational integration, divided attention, reasoning, and prospective memory were investigated. Concerning personality, the traits of polychronicity, impulsivity, and the five-factor model were considered. A total of 43 studies met the inclusion criteria and entered the review. The research synthesis directs us to propose two new conceptual models to explain multitasking behavior as a psychometric construct. The first model demonstrates that individual differences in multitasking behavior can be explained by cognitive abilities. The second model proposes that personality traits constitute a moderating effect on the relation between multitasking behavior and cognition. Finally, we provide possible future directions for the line of research.

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Multiple processing limitations underlie multitasking costs

Multitasking as a choice: a perspective.

Executive Functions

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References marked with an asterisk (*) indicate a study included in the review

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The first author gratefully acknowledges the financial support given by the German Academic Exchange Service (DAAD) for carrying out her doctoral program in Germany (Grant 57129429).

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Himi, S.A., Volberg, G., Bühner, M. et al. Individual differences in everyday multitasking behavior and its relation to cognition and personality. Psychological Research 87 , 655–685 (2023). https://doi.org/10.1007/s00426-022-01700-z

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Why multitasking does more harm than good

If you’ve ever opened another tab and made the grocery order during a Zoom meeting, folded laundry while helping your kids with their homework, or listened to a podcast while working out, you’ve been guilty of multitasking. Why guilty? Isn’t this just being super efficient? Not really. A growing body of research has found that it’s far less efficient to try to do two (or more!) things at once than to focus on just one task at a time. Multitasking can interfere with working memory , cause students to do worse in school, and could possibly even create potentially long-term memory problems .

Your Brain While Multitasking

When we take on a task, several brain networks dealing with attention and cognitive control are involved. These are the frontoparietal control network, the dorsal attention network, and the ventral attention network. Attempts to multitask can create interference among these networks, and this can lead to slower processing as well as mistakes, explains Kevin Paul Madore, a neuroscientist at Stanford University. “One way we can examine the effects of multitasking on behavior and the demands it places on relevant brain networks is by analyzing ‘task switch costs,'" he says.

A switch cost is a loss of accuracy or speed that comes when you shift between tasks. Though some of the costs of multitasking are subtle, they are by no means trivial. Too much multitasking can interfere with both working memory and long-term memory. Research by Madore and colleagues found that heavier media multitasking is associated with attention lapses and forgetfulness. However, it’s still not clear what’s causing what. “ Some research has indicated that chronic everyday media multitasking is related to errors in our ability to hold and use information in mind (working memory) and our ability to retrieve information (long-term memory),” says Madore, but he adds that more research is needed to determine the direction of causality.

Still, whether you’re less efficient because you’re multitasking or you’re multitasking because you’re less efficient (why is that to-do list so long?), multitasking doesn’t really solve anything.

Multitasking Light

It might seem that some types of multitasking are easier to pull off than others. Sure, texting while driving is a no-go , but surely folding the laundry while helping the kids with their homework is easy enough. But no, that doesn’t work either. You’re not risking life and limb — yours or anyone else’s — when you combine laundry and school work, but you’re still not going to be able to do your best at either task when trying to do both at once. “When you have competing sources of attention, your task performance is often going to be reduced,” says Madore. “You’re probably slower at folding laundry or maybe you drop some things on the floor when you’re helping a child with homework versus folding laundry alone.”

Dropping a few socks is not a big deal, and certainly worth the cost of having some time with your kids (though being with your kids while not giving them your full attention might have its own costs). On the other hand, some of the consequences of trying to do two things at once, even if those things seem simple, can be horrific — having a car accident, for example. Even eating a sandwich or fiddling with the CD player while driving can increase your risk of an accident.

There’s at least one situation, though, where multitasking can be your friend. Some studies have shown that taking a walk while trying to sort out a thorny problem improves creativity and can help you come up with better solutions. So perhaps when we’re trying to figure out how to get through that seemingly endless to-do list, we should forget about trying to do two or three of those things at once and go out for a walk. That might just lead to a creative solution to the time crunch problem.

mother and son in the Netherlands having dinner with father in the background.

Women may multitask better, but focusing on one task is still better for productivity.

Who Multitasks Best? Women, Of Course

But a new study says focusing on one task at a time is more productive.

For better or worse, multitasking is part of our lives, whether it's talking on the phone while driving (not a good idea), browsing the Internet while working (proceed at your own risk), or text messaging friends while in class (ditto).

Gijsbert Stoet , a psychologist at the University of Glasgow in Scotland, wanted to know who were the better multitaskers—men or women? A study carried out by Stoet and his colleagues and recently published in the journal BMC Psychology hints that women prevail. We asked him to share his thoughts on the subject.

How do you define multitasking in your study?

Good question, because it is important to realize that there are different types of multitasking. We focused on the type that demands a rapid switch between multiple tasks. For example, imagine you are making a meal. You might be cutting vegetables and at the same time preparing an oven dish. You rapidly switch back and forth between the vegetables and the oven.

Another type of task switching we didn't focus on is performing two tasks simultaneously. For example, you play the piano and play different things with each hand. This is not what we focused on, though, because in daily life when we multitask, we typically switch rapidly between two tasks [as opposed to doing two simultaneously]—something humans find easier and more convenient to do.

At what point does multitasking affect productivity?

Once you start doing more than one task at one time, it affects productivity. In another study, we found that even with training, you never become as efficient as when you do one task at a time. We are just not good at multitasking. Multitasking is part of life; we have to do it. But to be really efficient, do one thing at a time. It's much better to focus half an hour on one task, then do half an hour on another task, than to switch back and forth.

In modern life we are forced to switch between tasks even though we don't want to because external events drive us to do so. For example, the phone rings, or your colleague comes in to chat, and you say, "Okay, I'll do this for five minutes, then go back to the other thing."

Why do you think women are better at multitasking?

It might go back to the division of labor between men and women. We were essentially adapted to surviving the dangers of a Stone Age environment. In that environment women did more than look after their children. Like men, they worked on tasks necessary for survival. They could not just focus on making clothes or finding food. At the same time, they had to keep an eye on their children; if not, the children would have been eaten by wild animals and the race would have died out. We are the result of that successful behavior.

Do you have any advice for multitaskers?

You are more efficient when you focus on one task. Whenever we make a switch, it takes time and we lose efficiency. If you are, say, writing an article, you should get rid of all distractions—Internet, email, phone.

That advice is not just for adults, but for children as well. If I ran a school, I would not allow children to have mobile phones. Mobile phones take away a child's ability to focus on one thing. Think about children doing their homework while texting and being on Facebook. We need to think about what this does to a younger generation. I think there is not enough research on the costs for society as a whole.

Will you continue your research on this topic?

Yes. I was surprised at the attention the study got, which tells me people are really interested. What we hope with this paper is to get the attention of the research community and say, "Look, we just don't know enough about this topic."

Follow Ashleigh N. DeLuca on Twitter .

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Guest Essay

An Experiment in Lust, Regret and Kissing

By Curtis Sittenfeld

Ms. Sittenfeld is the author of the forthcoming story collection “Show Don’t Tell” and several novels.

This summer, I agreed to a literary experiment with Times Opinion: What is the difference between a story written by a human and a story written by artificial intelligence?

We decided to hold a contest between ChatGPT and me, to see who could write — or “write” — a better beach read. I thought going head-to-head with the machine would give us real answers about what A.I. is and isn’t currently capable of and, of course, how big a threat it is to human writers. And if you’ve wondered, as I have, what exactly makes something a beach read — frothy themes or sand under your feet? — we set out to get to the bottom of that, too.

First, we asked readers to vote on which themes they wanted in their ideal beach read. We also included some options that are staples of my fiction, including privilege, self-consciousness and ambivalence. ChatGPT and I would then work using the top vote-getters.

Lust, regret and kissing won, in that order. Readers also wrote in suggestions. They wanted beach reads about naps and redemption and tattoos gone wrong; puppies and sharks and secrets and white linen caftans; margaritas and roller coasters and mosquitoes; yearning and bonfires and women serious about their vocations. At least 10 readers suggested variations on making the characters middle-aged. One reader wrote, “We tend to equate summer with kids,” and suggested I explore “Why does summer still feel special for older people?”

So I added middle-age and another write-in, flip-flops — because it seemed fun, easy and, yes, summery — to the list and got to work on a 1,000-word story.

My editor fed ChatGPT the same prompts I was writing from and asked it to write a story of the same length “in the style of Curtis Sittenfeld.” ( I’m one of the many fiction writers whose novels were used, without my permission and without my being compensated, to train ChatGPT. Groups of fiction writers, including people I’m friends with, have sued OpenAI, which developed ChatGPT, for copyright infringement. The New York Times has sued Microsoft and OpenAI over the use of copyrighted work.)

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COMMENTS

Multitasking
In this multitasking experiment, we compare performance when just doing one task compared to when two tasks are mixed. In essence, this is simply a task switching paradigm. It is a cued task switching paradigm, although the cue (the location of the stimulus) and the imperative stimulus are presented at exactly the same time. ... This example is ...
Multitasking: Switching costs
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 ...
How good is your multitasking? Try this simple game to find out
In reality, multitasking has a huge negative impact on your productivity. To demonstrate the problem, get your team to join you in this simple game. ... For example, "A, 1, B, 2" and so on. The player cannot use any aids such as fingers or paper. Timekeeper starts a countdown- 3, 2, 1, and starts the stopwatch;
Multitasking: Brain Drain or Boost in Efficiency?
You might consider adding an "unexpected" event to your experiment like the example in the video while the volunteers are already busy multitasking. Task switching. Do a "task switching" experiment instead of (or in addition to) a multitasking experiment. Have your volunteers start another math test, and then interrupt them every 30-60 seconds ...
The Myth of Multitasking Exercise- Dave Crenshaw
Here it is: The Myth of Multitasking Exercise - Revisited. As promised, here are the definitions of switchtasking and background tasking: Switchtasking = attempting to do multiple attention-requiring tasks at the same time. Each switch in attention incurs switching cost, which includes a loss of time, decrease in performance, and an increase ...
Are women better than men at multi-tasking?
There seems to be a common belief that women are better in multi-tasking than men, but there is practically no scientific research on this topic. Here, we tested whether women have better multi-tasking skills than men. In Experiment 1, we compared performance of 120 women and 120 men in a computer-based task-switching paradigm. In Experiment 2, we compared a different group of 47 women and 47 ...
Multitasking, Productivity, and Brain Health
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.
What's one of the keys to mastering multitasking? Feedback
For example, players have to keep a car within a target box while also responding to various road signals; the signals increase in number as the game gets harder. As the number of things players have to pay attention to increases, an information bottleneck is created in their brains. Some of the players had superior multitasking performance.
Multitasking: Definition, Examples, & Research
Each of us can probably generate many examples of multitasking from a given day in our lives. For example, today I packed for a trip at the same time that I put away some clean laundry, talked to a friend while running an errand, and tried to make progress on two different work projects at the same time. The first pair was the easiest, while ...
Multitasking Splits the Brain
15 Apr 2010. By Gisela Telis. Share: When the brain tries to do two things at once, it divides and conquers, dedicating one-half of our gray matter to each task, new research shows. But forget about adding another mentally taxing task: The work also reveals that the brain can't effectively handle more than two complex, related activities at once.
The Curious Science of When Multitasking Works
The Curious Science of When Multitasking Works. Your ability to juggle may depend on how you're trained. Trying to do two things at once is usually a recipe for doing both badly, according to a ...
25 Multitasking Examples (2024)
Serial multitasking involves switching between tasks quickly. An example is a driver changing the radio station while keeping an eye on the road and handling the steering wheel (Carrier et al., 2015). Background multitasking allows one task to run in the "background" while a person focuses on another task.
Selectively Distracted: Divided Attention and Memory for Important
Abstract. Distractions and multitasking are generally detrimental to learning and memory. Nevertheless, people often study while listening to music, sitting in noisy coffee shops, or intermittently checking their e-mail. The current experiments examined how distractions and divided attention influence one's ability to selectively remember ...
Multitasking, working memory and remembering intentions.
Multitasking refers to the performance of a range of tasks that have to be completed within a limited time period. it differs from dual task paradigms in that tasks are performed not in parallel, but by interleaving, switching from one to the other. it differs also from task switching paradigms in that the time scale is very much longer, multiple different tasks are involved, and most tasks ...
The Myth of Multitasking Test (2014 Version
There is a NEW VERSION OF THIS VIDEO: https://youtu.be/5eQyfirx2HAPlease use the NEW version, as it clears up common misconceptions._____FREE STUFF — http://...
7 Myths and Truths about Multitasking that you should know
The example in the above experiment was of two rather simple tasks. The degree of task switching difficulty increases exponentially when the task complexity or rules increase. For instance, instead of simply writing a decreasing number sequence, if I had asked you to write a decreasing number sequence and also to multiply each even number in ...
The Eye-Opening Truth About Multitasking
Discover the surprising truth about multitasking vs. single-tasking in our in-depth guide. Learn how multitasking can hinder productivity and focus, and explore practical strategies for efficient ...
Individual differences in everyday multitasking behavior and its
Multitasking behavior—the ability to perform numerous activities simultaneously—is an amazing capability of our cognitive system. Multitasking has been a subject in experimental psychology for decades (Hommel, 2020).Typical laboratory experiments addressed reaction speed and accuracy in single- versus multiple-task situations, either during simultaneous ("dual task") or alternating ...
How multitasking fuels original thinking
According to a compelling new study, busy periods of multitasking - just the kind of activities I try to avoid - can actually fuel our subsequent creativity. Thanks to a "spillover effect ...
Why multitasking does more harm than good
Too much multitasking can interfere with both working memory and long-term memory. Research by Madore and colleagues found that heavier media multitasking is associated with attention lapses and forgetfulness. However, it's still not clear what's causing what. " Some research has indicated that chronic everyday media multitasking is ...
Who Multitasks Best? Women, Of Course
Women, Of Course. Women may multitask better, but focusing on one task is still better for productivity. Who Multitasks Best? Women, Of Course. But a new study says focusing on one task at a time ...
The Art of Multitasking: Definition + 25 Examples
25 examples of multitasking. Here are the most common examples of multitasking in personal and professional settings: Responding to emails while listening to a podcast. Taking notes during a lecture. Completing paperwork while reading the fine print. Driving a vehicle while talking to someone. Talking on the phone while greeting someone.
Opinion
A version of this article appears in print on , Section SR, Page 8 of the New York edition with the headline: An Experiment In Lust, Regret And Kissing. Order Reprints ...

Multitasking

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Multitasking: Switching costs

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How good is your multitasking? Try this simple game to find out

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Are women better than men at multi-tasking?

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BMC Psychology

How Multitasking Affects Productivity and Brain Health

Brain Function in Multitaskers

Frequently Asked Questions

Examples of Multitasking

How Multitasking Hampers Productivity

Multitasking Is Distracting

Multitasking Slows You Down

Multitasking Impairs Executive Function

Multitaskers Make Mistakes

Teens and Multitasking

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Break the Multitasking Habit

What’s one of the keys to mastering multitasking? Feedback

Some 2.5 percent of people are thought to be supertaskers — people who excel at multitasking. And we can use some of their strategies to boost our own skills, says psychiatrist Srini Pillay.

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Selectively Distracted: Divided Attention and Memory for Important Information

Associated Data

Experiment 1

Participants

Music distractors

Digit-detection performance