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Title
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Multitasking
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Author
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Irwin, Matthew
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Wang, Zheng
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Research Area
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Cognition and Emotions
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Topic
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Cognitive Plasticity
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Abstract
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Multitasking has become increasingly prevalent, especially as we continue to incorporate more and more new media technologies into our daily activities. This essay first identifies trends in the availability and use of media devices in daily life and multitasking behaviors related to such trends. Second, given the general consensus that multitasking impairs performance outcomes, recent multitasking trends call for greater research attention to the subject. We outline the historical perspectives on cognitive structures and processes related to a human's general ability to multitask, culminating with the more recent threaded cognition theory. Third, we present two new research directions on multitasking. One is the exploration of long‐term consequences of multitasking behaviors, such as their impacts on cognitive functions, and dynamic changes in individuals' needs and multitasking behavioral changes over time; the other is a cognitive dimensional framework for defining multitasking, which may offer a means to reconcile findings across various multitasking research paradigms, and also to guide designs of multitasking technologies and environments. Finally, looking to the future, we propose several ways to advance the research on multitasking.
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Identifier
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etrds0230
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extracted text
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Multitasking
MATTHEW IRWIN and ZHENG WANG
Abstract
Multitasking has become increasingly prevalent, especially as we continue to
incorporate more and more new media technologies into our daily activities. This
essay first identifies trends in the availability and use of media devices in daily
life and multitasking behaviors related to such trends. Second, given the general
consensus that multitasking impairs performance outcomes, recent multitasking
trends call for greater research attention to the subject. We outline the historical perspectives on cognitive structures and processes related to a human’s general ability
to multitask, culminating with the more recent threaded cognition theory. Third,
we present two new research directions on multitasking. One is the exploration
of long-term consequences of multitasking behaviors, such as their impacts on
cognitive functions, and dynamic changes in individuals’ needs and multitasking
behavioral changes over time; the other is a cognitive dimensional framework
for defining multitasking, which may offer a means to reconcile findings across
various multitasking research paradigms, and also to guide designs of multitasking
technologies and environments. Finally, looking to the future, we propose several
ways to advance the research on multitasking.
INTRODUCTION
Multitasking refers to doing two things at the same time. More precisely,
it is when a person simultaneously manages more than one goal, attends
to more than one set of stimuli, and generates more than one set of cognitive or behavioral responses. Although even simple activities—such as
in the idiomatic expression “walking and chewing gum”—are instances
of multitasking; multitasking research has focused on situations that place
high cognitive demands on the individual, such as distracted driving (e.g.,
Levy, Pashler, & Boer, 2006) and reading while watching television (e.g.,
Armstrong & Chung, 2000).
Although multitasking research can be traced back to at least two centuries
ago (e.g., Hamilton, 1859), the term multitasking originated relatively recently
in reference to a computer’s ability to run multiple programs concurrently
(e.g., Havender, 1968). Cognitive psychologists interested in finding how
humans process information often use the computer as an analogy for
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
the mind and brain, making the adoption of such a word in the context
of human cognition understandable. Such phrasing hints at the general
perspective taken by many multitasking researchers. Much like with a
computer, the human brain is said to use specific processes and resources
that allow for—and place limits upon—its ability to perform several tasks
at once. Understanding the relationships between cognitive processes and
resources, and the resulting human capabilities and limitations, is central to
multitasking research.
Major technological advances and the increasing prevalence of multitasking in recent years have attracted more research interest in multitasking. In
both personal and professional settings, people are constantly surrounded
by multiple technologies. Such devices allow for, and in fact encourage, multitasking in two ways. On the one hand, improved device mobility makes it
easier to act on-the-go while performing other tasks; on the other hand, the
devices by themselves facilitate multiple (possibly unrelated) tasks at once,
such as writing, talking, viewing images, and searching for information.
In this essay, first, we will review the trends and prevalence of multitasking,
especially media multitasking behaviors. Then we will describe major cognitive theories that have been used to account for performance consequences
of multitasking, followed by a review of a recently developed multitasking
theory, threaded cognition. Finally, new trends in multitasking research will
be reviewed and future research directions will be discussed.
MULTITASKING BEHAVIORS AND CONTRIBUTING FACTORS
Increases in multitasking behaviors are directly affected by growing media
accessibility and consumption. Rideout, Foehr, and Roberts (2010) surveyed
702 American children and adolescents between 8 and 18 years old as part
of a series of surveys conducted every 5 years. They found that on average,
a home has 3.8 televisions, 2.5 radios, 2.0 computers, and 2.3 video game
consoles. These figures have risen consistently since these studies began in
1999, with the exception of radio. This translates to heavy media consumption, including multitasking. These children reported 7.5 h of media use per
day in 2009 and were multitasking with media sources 29% of that time, averaging 10.75 h of media exposure in a day.
These recent changes in media environments are reflected in generational
differences in multitasking as well. Carrier, Cheever, Rosen, Benitez, and
Chang (2009) measured media use combinations (e.g., television and e-mail)
in Baby Boomers (born 1946–1964), Gen Xers (1965–1978), and Net Geners
(after 1978). Baby Boomers averaged 23.2 combinations out of 66 possibilities, Gen Xers 32.4, and Net Geners 37.5. Research indicates that multitasking
has become a way of life for many children and adolescents. A majority of
Multitasking
3
Americans aged 8–18 multitask “most” or “some” of the time while listening
to music (73% of respondents), using a computer (66%), watching TV (68%),
and reading (53%) (Rideout et al., 2010). Similar multitasking trends are
reported for other countries (e.g., Svoen, 2007).
In addition to increasing media accessibility, a number of other factors have
been found to relate to multitasking. For instance, Jeong and Fishbein (2007)
found that for teenagers aged 14 to 16, media multitasking behaviors are
predicted by having televisions in the bedroom, supporting the argument
that media accessibility is a contributing factor. However, they also found
that high sensation seeking, a psychological construct of a person’s need
for novel or exciting experiences, is a predictor of multitasking behaviors.
Intriguingly, Sanbonmatsu, Strayer, Medeiros-Ward, and Watson (2013)
found that those most likely to multitask—people who are impulsive,
sensation-seekers, and/or have weak executive control—are actually the
worst at multitasking performance. Such alarming findings warrant further
study. Other individual differences that impact multitasking tendencies and
performance include extraversion and neuroticism (Oswald, Hambrick, &
Jones, 2007; Wang & Tchernev, 2012), attentional styles (Hawkins et al., 2005),
and expertise (Lin, Robertson, & Lee, 2009).
Beyond individual differences, the needs and wants that drive multitasking behaviors have been examined. On the basis of survey data, Zhang
and Zhang (2012) described how different types of gratifications (e.g.,
goal-oriented/work and emotional/social) are sought by different types of
computer multitasking. Using longitudinal experience sampling method
over 4 weeks and time series analysis, Wang and Tchernev (2012) further
identified reciprocal causal relationships between media multitasking
behaviors, needs, and gratifications obtained. They found that among the
college students sampled, cognitive needs (e.g., homework, information)
drive their media multitasking behaviors but are not satisfied by the
behaviors. Instead, emotional gratifications (e.g., relaxation, entertainment)
are obtained despite not being actively sought. In addition, habitual gratifications (e.g., daily routines) and the behavioral system’s endogenous
influences (i.e., self-causing or self-maintaining influences) predict media
multitasking behaviors. They suggest that these are partially the reasons
why multitasking behaviors are persistent, and pursued at the cost of
cognitive productivity.
FOUNDATIONAL RESEARCH
MAJOR COGNITIVE THEORIES OF MULTITASKING
Before the term multitasking had come into common use, the presence of
new devices in daily life was noteworthy to scholars. Consider the anecdote
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
that Cantril and Allport (1935) use in speculating about the effect of radios,
and then a relatively new technology, as background sound in homes:
The story is told of a French mathematician who in the war selected a ruined
house within sound of guns at the front because he found that his attention
to his problems became sharper. Inattention to one stimulus always means
attention to some other; inhibition of one response requires concentration
on another. The stronger the potential distraction, the greater is the compensatory attention (p. 25).
The suggestion here is that background distractors, such as a radio, can
actually improve performance on the primary task at hand.
Unfortunately, contemporary studies of multitasking so far generally suggest otherwise. Existing laboratory experimental research has consistently
found that multitasking harms task performance (e.g., Bowman, Levine,
Waite, & Gendron, 2010; Wang et al., 2012), though this depends on, to some
extent, how performance is defined (e.g., “efficiency” versus “accuracy,”
Adler & Benbunan-Fich, 2012). In fact, the negative consequences of multitasking extend beyond inefficiencies and can potentially be life-threatening,
such as the dangerously high cognitive demands placed on pilots and
drivers (e.g., McCartt, Hellinga, & Bratiman, 2006). In recent years, research
in this vein has garnered interest and concern from the mainstream press, as
attested to by articles and books with titles such as The Autumn of the Multitaskers (Kirn, 2007), Distracted (Jackson, 2008), and The Dumbest Generation
(Bauerlein, 2008). For researchers, explaining such worrisome findings with
models and theories has become the goal as a means to better understand,
predict, and perhaps recommend useful policies for multitasking behavior.
A number of cognitive theoretical perspectives have helped in this regard.
CENTRAL BOTTLENECK THEORIES
Central bottleneck theories originate in earlier research on the single-channel
hypothesis (Welford, 1967). As both phrases suggest, these theories posit that
cognition is limited by a “central” or “single-channel” structure that constrains information processing. The information modality of the stimuli does
not matter (e.g., one task could involve audio and the other images), as all
activities are ultimately handled by a singular cognitive structure that cannot operate on two processes at the same time (Marois & Ivanoff, 2005). Such
a structural limitation would account for declining performance while multitasking as it requires a person to process multiple tasks one at a time.
The concept of a central bottleneck provides a parsimonious (i.e., simple;
technically, requiring fewer parameters if in a formal model) way to account
for declines in performance while multitasking. It is often used to explain
findings within the psychological refractory period (PRP) research paradigm,
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in which participants are generally found to take longer to respond to a second task if they are still processing a prior task. Further, its plausibility is
supported by evidence of a neural network in the brain that may act as a
central bottleneck. Researchers using functional magnetic resonance imaging (fMRI) found activity in the posterior lateral prefrontal cortex and the
superior medial frontal cortex after inducing a PRP, suggesting that these
regions may act as the structures predicted by central bottleneck theories
(Dux, Ivanoff, Asplund, & Marois, 2006).
Intriguingly, if the cognitive system is truly only capable of fully processing one set of stimuli at a time as central bottleneck theories predict, this
would suggest that multitasking is in essence a process of sequentially managing tasks (i.e., first this, then that) rather than concurrently processing them
(i.e., both this and that simultaneously). Multitasking would then seem better
described as a process of rapid task switches, rather than doing two things
at once.
Some critics of central bottleneck theories have suggested alternative
cognitive structures. For example, early research by Allport, Antonis, and
Reynolds (1972) found little to no loss in performance efficiency in a series
of “divided attention” experiments. As an alternative to the single channel
hypothesis, they suggest the mind may actually have multiple channels
that allow for such multitasking. In this way, there may still be interference
between two tasks, but only if they share the same cognitive channels.
More recently, Townsend and colleagues have accumulated experimental
evidence showing that certain elementary psychological processes can
be categorized as parallel, instead of serial, processing, and developed
mathematical models and tools to differentiate parallel versus serial (e.g.,
Townsend, 1990; Townsend & Wenger, 2004). It should be instrumental to
apply their approach to test cognitive processing during multitasking.
LIMITED CAPACITY THEORIES
Rather than focusing on the cognitive structure in information processing,
limited capacity theories focus on cognitive resources. A pioneer on this question is philosopher, Sir William Hamilton. As reported by a posthumous book
in 1859, he experimented with visual attention using marbles scattered on
the floor, and concluded that on average, visual attention span is limited to
six to seven items. A century later, similar findings were reported by Miller
(1956) in his well-known article on “the magical number seven, plus or minus
two.” Kahneman (1973) lays out the foundational arguments by suggesting
that cognitive capacity, or “attention,” is limited, divisible, and can be automatically elicited and consciously controlled. Thus, such theories suggest a
person may successfully process two or more tasks simultaneously as long as
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
cognitive demands divided among tasks do not exceed her/his total capacity. That is, multitasking as traditionally conceived—“doing two things at
once”—is indeed possible.
Expanding on this, information processing is said to occur in subprocesses,
such as encoding, storage, and retrieval (Lang, 2000). From this perspective,
deficiencies in performance while multitasking are caused by a lack of available resources to allocate to one or more subprocesses, rather than a cognitive
structure that always operates in a sequential fashion. Resource allocation
problems may happen to any of the subprocesses. For example, though attention can be consciously controlled, external stimuli (e.g., sound effects from
movies, emotional images) may also elicit orienting responses that automatically allocate attentional resources toward the stimuli. This explains deficient
performance while multitasking, such as why people retain less information
from reading while watching television (e.g., Armstrong & Chung, 2000). As
a person tries to read, her/his resources may be distracted from encoding
and storing textual information to encoding auditory or visual cues from the
television.
MULTIPLE RESOURCES THEORIES
As a variation on the concept of a shared resource pool in limited capacity
theories, multiple resources theories argue that there are distinct resources
available for different cognitive operations. Navon and Gopher (1979) argue
that cognition is divided across multiple channels and that these channels have
their own resource pools to draw upon. Channels can serve different modalities
(e.g., visual processing, auditory processing), process stages (e.g., perceiving,
responding), visual channels (e.g., focal, ambient), and codes (e.g., verbal,
spatial) (Wickens, 2002). In terms of multitasking, the more overlap between
two tasks, the more likely they are to share resource pools and interfere with
one another. Conversely, this suggests that two or more tasks could be successfully performed simultaneously as long as they draw primarily from
separate resource pools.
Going back to the context of television, multiple resources theories help
reveal the complex nature of simply comprehending information while
watching a show. Not only are there separate resources and pathways
required for perceiving the visual and auditory information from television,
but also still more distinct resources are next drawn upon for cognitive
processing, and again for storing such information in memory (Basil, 1994).
Identifying such distinctions makes more refined predictions about possible
multitasking outcomes, such as what task combinations are more harmful
or benign (e.g., Wang et al., 2012). To use an example, reading and watching
television might be seen as a generally more difficult task combination
Multitasking
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than reading and listening to music, because of the higher degree of shared
modality between reading and television.
THREADED COGNITION
Threaded cognition (Salvucci & Taatgen, 2008) is a multiple resources theory
aiming to explain cognitive processes and outcomes of multitasking behaviors. It is based on ACT-R (Adaptive Control of Thought-Rational), a computational model of the mind’s architecture that attempts to identify and
represent fundamental structures and processes (Anderson et al., 2004).
Threaded cognition argues there are different resource pools for different
cognitive processes, including a central pool for procedural memory (i.e.,
how tasks are done) and peripheral pools for perception, motor abilities,
and declarative memory. While multitasking, each task is organized around
the person’s specific goals as an autonomous cognitive “thread” (or multiple
threads for complex tasks). When a goal is established, it triggers a series of
rules—drawing from the central procedural resource pool—that guide cognitive subprocesses and the use of resources from other pools for the achievement of the goal. Consider a simple situation: a person is walking down a
hallway and talking with a friend. There are two distinct goals (walking and
talking) with established cognitive rules for each. The rules are triggered in
procedural memory; and necessary perceptual, motor, or declarative memory processes involved in performing each task are incorporated into cognitive threads (e.g., monitoring the environment, moving legs, generating
utterances).
Most important, only one thread can actively draw upon a given resource
pool at once. This is unlike the general limited capacity or multiple resources
theories described earlier, in which the limiting factor is the amount of
resources available in a pool (e.g., Wickens, 2002). Instead, if a resource pool
is engaging with a process from one thread, then a process from another
thread is delayed. In the example of walking and talking, there may not be a
great degree of overlap in resources between the various subprocesses. This
allows for both goals to be executed concurrently. In more complex tasks,
there may be greater overlap between resources, producing bottlenecks or
interference between tasks.
Resolving such bottlenecks between tasks requires that resources are somehow managed and eventually assigned to each thread. By what process does
this happen? Threaded cognition attempts to explain this process without
positing centralized executive structures in the mind. By doing so, it avoids
the problem of a homunculus (“little man”), or using circular reasoning suggesting the mind itself has a mind-like structure that makes decisions. As
stated by Salvucci and Taatgen (2008):
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
We believe that humans have a basic ability to perform multiple concurrent
tasks, and that this ability does not require supervisory or executive processes.
Instead, this ability is best represented by a general, domain-independent, parsimonious mechanism that allows for concurrent processing and provides basic
resource conflict resolution. (p. 102)
This mechanism is fairly straightforward. First, threads are conceptualized
as both “greedy” and “polite” (Salvucci & Taatgen, 2008). “Greedy” implies
that if resources are available for a thread to proceed, the thread will take
immediate advantage of the resources. “Polite” implies that a thread will free
up resources for another thread as soon as possible. In this manner, a process is completed as quickly and efficiently as possible if resources are free.
However, in multitasking situations where more than one thread requires
the same resource, a bottleneck occurs. If there is only one thread waiting
to access this resource pool, it will “greedily” acquire the resources as soon
as the preceding thread “politely” makes it available. If there are multiple
threads waiting for the same resource pool, the cognitive system attempts to
balance processing times by giving privilege to the longest cued thread (i.e.,
on a first-come, first-serve basis).
CUTTING-EDGE RESEARCH
A large body of research has estimated the prevalence of multitasking using
survey methods and assessed performance impairment of multitasking
behaviors using laboratory experiments. In recent times, a couple of new
research directions have emerged. First, given the increasing prevalence of
multitasking, researchers have started to explore the long-term consequences
of multitasking behaviors, such as the impacts of chronic multitasking on
cognitive functions, and dynamic changes in individuals’ needs and multitasking behavioral changes over time. Second, researchers are trying to
understand how people adaptively select certain multitasking behaviors
over others to more “optimally” achieve their multiple goals under the
constraints of the environment and their own cognitive systems. They
hope to understand how multitasking media technologies and task designs
can be improved to help mitigate negative influences of multitasking and
possibly even produce positive outcomes. In order to achieve these goals, a
dimensional approach to multitasking has been proposed.
LONG-TERM EFFECTS OF MULTITASKING
Ophir, Nass, and Wagner (2009) developed the media multitasking index
(MMI) to identify individuals as light versus heavy media multitaskers
Multitasking
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(LMMs vs HMMs) based on the amount of media they reported using
simultaneously. They then performed a series of experiments comparing
the cognitive control exhibited by these two groups. HMMs found it more
difficult to filter out irrelevant stimuli, ignore irrelevant information in
memory, and showed an increase in switch costs as they alternated between
two tasks. The finding that HMMs show greater switching cost is perhaps
the most surprising; one might expect people are better able to switch
between tasks the more they multitask. The authors suggest that these
findings demonstrate that HMMs have breadth-biased cognitive control,
meaning that they are more likely to respond to new and irrelevant stimuli
from the environment at cost to performance on a single task. Interestingly,
a follow-up study by Alzahabi and Becker (2013) found contradictory evidence. In two experiments, they found that HMMs actually performed better
at task switching than LMMs, while there were no differences between the
two groups at dual-tasks (i.e., when they perform two tasks simultaneously).
These discrepant findings suggest there is much to be learned about the
long-term cognitive effects of multitasking. One potential direction is to test
the causal relationship between personal differences (in cognitive functions,
in needs and motives, in personality traits, etc.) and multitasking behaviors. One such exploration is by Wang and Tchernev (2012). They found
evidence of dynamic mutual causal influences between media multitasking
behaviors, and individuals’ various needs and gratifications, which are
further moderated by neuroticism. Their findings suggest that different
multitasking behavioral patterns can be formed as a result of motivational
and cognitive differences in individuals, which in turn, are further cultivated
by these behaviors over time.
DIMENSIONAL FRAMEWORKS OF MULTITASKING
On the basis of information processing and resource allocation research
described earlier, multiple cognitive dimensions can be identified to help
assess the cognitive demands of a given multitasking combination. This
dimensional framework can help predict the prevalence of certain multitasking behaviors. Indeed, new evidence shows that as expected, people
typically avoid more cognitive demanding task combinations and favor
easier ones (Wang, Irwin, Cooper, & Srivastava, 2014). This suggests that,
instead of being labeled as “maladaptive” (because of performance deterioration), increasing multitasking behaviors in daily life may be adaptive to
our media-saturated environments. In addition, a dimensional framework of
multitasking can help better pinpoint the underlying concepts being tested
across multitasking experiments or surveys to help synthesize findings.
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Two such frameworks have been proposed. Oswald et al. (2007) considered
various dimensions of tasks and individual differences (e.g., ability, personality, motivation). They propose a total of 17 task dimensions related to five
categories: task characteristics, task structure, task timing, task control, and
task outcomes. Alternatively, Wang et al., 2014 propose a dimensional framework specifically for media multitasking (i.e., multitasking with at least one
media device), which could potentially be applied to general multitasking
situations. They identified 11 cognitive dimensions in four categories: task
relations, task inputs, task outputs, and individual differences, which are
summarized in the following text.
The task relations category includes five dimensions regarding how tasks in
a multitasking situation relate to each other. (i) Task hierarchy refers to whether
a task is considered primary and the others secondary, or if tasks are relatively equal in significance to the person. Research has shown that more
cognitive resources are allocated to more important tasks, such that a person
performs better by focusing on a primary task over a peripheral task than if
both are treated equally (Dijksterhuis & Aarts, 2010; Lin et al., 2009). (ii) Task
switch is the degree of control a person has in switching between tasks, that
is, whether task switches are intentional or externally controlled. Some tasks
allow for greater control over when they are engaged, while others may force
attention switches (Wang et al., 2012). (iii) Task relevance refers to the relatedness of the goals in each task—are they ultimately done for the same or
different ends? While multitasking research often examines tasks with unrelated goals (e.g., Adler & Benbunan-Fich, 2012), studies have shown that
task performance may improve if tasks are highly related (e.g., Levy & Pashler, 2008). (iv) Modality variation is the degree to which sensory modalities
engaged by each task overlap or are distinct. A multitasking behavior should
be less demanding when tasks are spread across multiple modalities (Moreno
& Mayer, 1999; Salvucci & Taatgen, 2008; Wang et al., 2012). (v) Finally, task
contiguity assesses the physical proximity of the multiple tasks, including
task stimuli responses. Relevant tasks benefit from physical proximity as task
switch costs are reduced, while there may be greater interference between
irrelevant tasks in close proximity (e.g., Mayer & Moreno, 2002).
The task inputs category includes three dimensions of the format and content of incoming stimuli. (i) Information modality refers to the type and number of modalities engaged by each task. Threaded cognition theory suggests
that the more sensory modalities engaged, the more likely that interference
will occur between tasks that share a common resource pool and a greater
burden on the central procedural pool (Salvucci & Taatgen, 2008). (ii) Informational flow can range from static information (e.g., stories in print newspapers) to transitory, dynamic information that requires immediate attention
(e.g., moving images and sound). The limited capacity research applied to
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media processing (Lang, 2000, 2006) emphasizes that the video and audio
content of media can elicit attention in a dynamic manner distinct from activities such as reading. Although processing of a media task may benefit from
increased attention, too many information changes can overwhelm the cognitive system—especially if cognitive resources are split between two tasks.
(iii) Lastly, emotional content refers to the valence and intensity of stimuli that
can motivate resource allocation. Positive or negative emotional content can
trigger activation of the appetitive and/or aversive systems, allocating cognitive resources toward or away from the content depending on the level of
intensity (Wang, Lang, & Busemeyer, 2011).
The third category is task outputs, consisting of two dimensions. (i) Behavioral responses distinguish tasks that require a person to provide behavioral
responses (e.g., making statements during a conversation) from those that are
primarily cognitive (e.g., processing information while watching television).
Tasks that require behavioral responses demand not only motor resources,
but also additional cognitive resources to make decisions. (ii) Time pressure
refers to the context when such behavioral responses are required. Does a
person need to act within a set time period?
Finally, a number of individual differences have been identified that can
influence multitasking performance, such as sensation seeking (Sanbonmatsu et al., 2013), neuroticism (Wang & Tchernev, 2012), and expertise
(Lin et al., 2009). These variables can help predict both the likelihood that a
person will opt to multitask and performance outcomes.
KEY ISSUES FOR FUTURE RESEARCH
Much more effort is still needed to explore the two new research directions
described above. First, it is important to further understand the long-term
impacts of chronic multitasking behavior. We have seen evidence of the
persistence of multitasking behaviors and intricate dynamic reciprocal
impacts between the behaviors and individuals’ needs and gratifications
(Wang & Tchernev, 2012) and their potential influences on cognitive functions (Alzahabi & Becker, 2013; Ophir et al., 2009). These studies need to
be replicated, especially when contrastive evidence is presented. Meanwhile, new measures and methods should be used to further explore these
questions. For example, multitasking behaviors potentially can shape the
underlying neurocognitive architecture that controls future behavior (La
Cerra & Bingham, 1998). On the basis of extensive neuroscientific evidence,
particularly the extraordinary functional plasticity of the neocortex of
mammals, including humans, it is argued that flexible cortical representational networks are modified and constructed through adaptive interactions
with the environment (La Cerra & Bingham, 1998), including a mediated
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
environment. Considering the incessant expansion of our information
environment facilitated by media technologies, it is critical to carefully
examine the long-term mutual influences between media multitasking,
cognitive functions, neural substrates, and personal traits from a dynamic
and developmental perspective.
Second, the dimensional framework of multitasking can be used to compare and synthesize existing research on multitasking. In addition, it can
be used to guide designs of more efficient and friendly multitasking technologies and environments. For example, as threaded cognition theory and
multiple resources theories indicate, we can employ different modalities for
different tasks to reduce competition between threads for the same modality
resource.
Third, related to the dimension of individual differences, we may design
training programs to develop better multitasking capabilities. For example,
as threaded cognition theory suggests, as tasks become more routinized, they
shift from depending on declarative (i.e., memorized, informational) to procedural (i.e., how to perform an action) memory (Salvucci & Taatgen, 2008).
As this process occurs, tasks become less cognitively demanding and multitasking becomes easier. In addition, media multitasking literacy should be
integrated into media literacy education to help mitigate negative influences
of multitasking, such as potentially dangerous overestimation of one’s own
multitasking capabilities (Wang et al., 2012).
Finally, more advanced approaches, such as dynamic methods and analysis, and computational models, will help advance this research agenda.
Prominently, these include the formal computational models of threaded
cognition and Townsend’s response time models to identify series versus
parallel cognitive processes in the context of complex multitasking.
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FURTHER READING
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MATTHEW IRWIN SHORT BIOGRAPHY
Matthew Irwin (BS in Psychology & Film Studies, The Ohio State UniversityColumbus, 2010) is a PhD student in the School of Communication at the
Ohio State University-Columbus, and a member of the Communication and
Psychophysiology research laboratory. He is interested in real-time cognitive
processes during media use and choices, and their immediate and long-term
effects on memory, attitude, and pro-social/health behaviors.
ZHENG WANG SHORT BIOGRAPHY
Zheng Wang (PhD in Communications & Cognitive Science, Indiana
University-Bloomington, 2007) is an Associate Professor in the School of
Communication and the Center for Cognitive and Brain Sciences at the
Ohio State University, Columbus. She directs the Communication and
Psychophysiology research laboratory. One of her research foci is to study
how people process and use media. In particular, she is interested in the
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
dynamic reciprocal influences between media choice/use behavior and
media information processing over time. Another research focus is to
understand contextual influences on decision and cognition by building
new probabilistic and dynamic systems based upon quantum rather than
classical probability theory. Her research is supported by the National
Science Foundation and the Air Force Office of Scientific Research. She is
co-editing the Oxford Handbook of Computational and Mathematical Psychology
(forthcoming in 2014).
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