Task switchingStephen MonsellSchool of Psychology Univer.docx

Task switching
Stephen Monsell
School of Psychology University of Exeter, Exeter, EX4 4QG,
UK
Everyday life requires frequent shifts between cognitive
tasks. Research reviewed in this article probes the con-
trol processes that reconfigure mental resources for a
change of task by requiring subjects to switch fre-
quently among a small set of simple tasks. Subjects’
responses are substantially slower and, usually, more
error-prone immediately after a task switch. This
‘switch cost’ is reduced, but not eliminated, by an
opportunity for preparation. It seems to result from
both transient and long-term carry-over of ‘task-set’
activation and inhibition as well as time consumed by
task-set reconfiguration processes. Neuroimaging
studies of task switching have revealed extra activation

in numerous brain regions when subjects prepare to
change tasks and when they perform a changed task,
but we cannot yet separate ‘controlling’ from ‘con-
trolled’ regions.
A professor sits at a computer, attempting to write a paper.
The phone rings, he answers. It’s an administrator,
demanding a completed ‘module review form’. The pro-
fessor sighs, thinks for a moment, scans the desk for the
form, locates it, picks it up and walks down the hall to the
administrator’s office, exchanging greetings with a col-
league on the way. Each cognitive task in this quotidian
sequence – sentence-composing, phone-answering, con-
versation, episodic retrieval, visual search, reaching and
grasping, navigation, social exchange – requires an
appropriate configuration of mental resources, a pro-
cedural ‘schema’ [1] or ‘task-set’ [2]. The task performed
at each point is triggered partly by external stimuli (the
phone’s ring and the located form). But each stimulus
affords alternative tasks: the form could also be thrown in
the bin or made into a paper plane. We exercise intentional
‘executive’ control to select and implement the task-set,
or the combination of task-sets, that are appropriate to
our dominant goals [3], resisting temptations to satisfy
other goals.
Goals and tasks can be described at multiple grains or
levels of abstraction [4]: the same action can be described
as both ‘putting a piece of toast in one’s mouth’ and
‘maintaining an adequate supply of nutrients’. I focus here
on the relatively microscopic level, at which a ‘task’
consists of producing an appropriate action (e.g. conveying
to mouth) in response to a stimulus (e.g. toast in a

particular context). One question is: how are appropriate
task-sets selected and implemented? Another is: to what
extent can we enable a changed task-set in advance of the
relevant stimulus – as suggested by the term ‘set’?
Introspection indicates that we can, for example, set
ourselves appropriately to name a pictured object aloud
without knowing what object we are about to see. When an
object then appears, it is identified, its name is retrieved
and speech emerges without a further ‘act of intention’: the
sequence of processes unfolds as a ‘prepared reflex’ [5,6].
Many task-sets, which were initially acquired through
instruction or trial and error, are stored in our memories.
The more we practice a task, or the more recently we have
practised it, the easier it becomes to re-enable that task-
set. At the same time, in the absence of any particular
intention, stimuli we happen to encounter evoke ten-
dencies to perform tasks that are habitually associated
with them: we unintentionally read the text on cereal
packages or retrieve the names of people we pass in the
street. More inconveniently, stimuli evoke the tendency to
perform tasks habitually associated with them despite a
contrary intention. The standard laboratory example of
this is the Stroop effect [7]: we have difficulty suppressing
the reading of a colour name when required to name the
conflicting colour in which it is printed (e.g. ‘RED’ printed
in blue). Brain damage can exacerbate the problem, as in
‘utilization behaviour’, which is a tendency of some
patients with frontal-lobe damage to perform the actions
afforded by everyday instruments, such as matches,
scissors and handles, even when these actions are
contextually inappropriate [8].
Hence the cognitive task we perform at each moment,
and the efficacy with which we perform it, results from

a complex interplay of deliberate intentions that are
governed by goals (‘endogenous’ control) and the avail-
ability, frequency and recency of the alternative tasks
afforded by the stimulus and its context (‘exogenous’
influences). Effective cognition requires a delicate, ‘just-
enough’ calibration of endogenous control [9] that is
sufficient to protect an ongoing task from disruption
(e.g. not looking up at every movement in the visual
field), but does not compromise the flexibility that allows
the rapid execution of other tasks when appropriate
(e.g. when the moving object is a sabre-toothed tiger).
To investigate processes that reconfigure task-set, we
need to induce experimental subjects to switch between
tasks and examine the behavioural and brain correlates of
changing task. Task-switching experiments are not new
(Box 1), but the past decade has seen a surge of interest,
stimulated by the development of some novel techniques
for inducing task switches and getting subjects to prepare
for them (Box 2), and some surprising phenomena revealed
thereby, as well as by the broader growth of interest in
control of cognition (e.g. [10]).Corresponding author: Stephen
Monsell ([email protected]).
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Task switching: basic phenomena

In a task-switching experiment, subjects are first pre-
trained on two or more simple tasks afforded by a set of
stimuli (Figs 1 and 2 provide examples). Each task
requires attention to, and classification of, a different
element or attribute of the stimulus, or retrieval from
memory or computation of a different property of the
stimulus. Then, a stimulus is presented on each of a series
of trials and the subject performs one of the tasks. There
are several methods for telling the subject which task to
perform (Box 2), but in all cases the task sometimes
changes from one trial to the next, and sometimes does not.
Thus, we can examine performance or brain activation on
and following trials when the task changes for evidence of
extra processing demands that are associated with the
need to reconfigure task-set. We can also examine the
effects of localized brain damage, transient magnetic
stimulation (TMS) or pharmacological interventions on
behavioural indices of switching efficiency. Four phenom-
ena of primary interest (of which the first three are
illustrated in Figs 1 and 2) are described below.
Switch cost (task-repetition benefit)
Generally, responses take longer to initiate on a ‘switch trial’
than on a ‘non-switch’ or task-repetition trial, often by a
substantial amount (e.g. 200 ms relative to a baseline of
500 ms). Also, the error rate is often higher after a task switch.
Preparation effect
If advance knowledge is given of the upcoming task and
time allowed to prepare for it, the average switch cost is
usually reduced.
Residual cost

Preparation generally does not eliminate the switch cost.
In the examples shown, the reduction in switch cost seems
to have reached a substantial asymptote, the ‘residual
cost’, after ,600 ms of preparation. Substantial residual
costs have been reported even when 5 s or more is allowed
for preparation (e.g. [11,12]).
Mixing cost
Although performance recovers rapidly after a switch
(Fig. 1), responses remain slower than when just one task
must be performed throughout the block: there is a long-
term as well as a transient cost of task switching.
These phenomena have been demonstrated with a wide
range of different tasks and they are modulated by
numerous other variables. What explains them?
Sources of the switch cost
Time taken by control operations
To change tasks, some process or processes of ‘task-set
reconfiguration’ (TSR) – a sort of mental ‘gear changing’ –
must happen before appropriate task-specific processes
can proceed. TSR can include shifting attention between
stimulus attributes or elements, or between conceptual
criteria, retrieving goal states (what to do) and condition –
action rules (how to do it) into procedural working memory
(or deleting them), enabling a different response set and
adjusting response criteria. TSR may well involve inhi-
bition of elements of the prior task-set as well as activation
of the required task-set.
An account of the switch cost that appeals intuitively is

that it reflects the time consumed by TSR. The preparation
effect then suggests that, if sufficient time is allowed, TSR
can, to some extent, be accomplished under endogenous
control, before the stimulus onset. The residual cost is
more perplexing. Rogers and Monsell [13] suggest that
Box 1. Early research on task-set and task switching
The intentional and contextual control of ‘set’ (‘Einstellung’)
was
discussed in 19th and early 20th century German experimental
psychology. In 1895, von Kries used as examples the way the
clef sign
changes the action performed to play a note on the musical
stave, and
the way the current state of a game changes how one sets
oneself to
respond to an opponent’s behaviour [58]. Exner and the
Wurzburg
school described the ‘prepared reflex’, and, in 1910, Ach
described
experiments on overlearned responses competing with the
acqui-
sition of a novel stimulus – response mapping, see [6]. Until
recently,
in the English-language literature, ideas about control of task-
set have

been stimulated mainly by the observation of impairments of
control,
both in everyday action and as a result of neurological damage,
see
[2], despite some experimentation on normal executive function
in
cognitive laboratories [5].
The invention of the task-switching paradigm is credited to
Jersild
[59] who had students time themselves working through a list of
items, either repeating one task or alternating between two.
Some
task pairs (adding 3 to vs. subtracting 3 from numbers) resulted
in
dramatic alternation costs; others (adding 3 to a number vs.
writing
the antonym of an adjective) did not. Jersild’s paradigm was
revived,
and his results replicated using discrete reaction-time
measurements,
by Biederman and Spector [60]. Despite this work and some
pioneering task-cueing studies (e.g. [61 – 63]) it was only in the
mid

1990s that the present surge of research on task switching
developed.
Box 2. Task switching paradigms
There are several methods of telling a subject which task to do
on each
trial. Jersild’s method (Box 1), which is still sometimes used
(e.g. [39]),
compares the duration of blocks of trial in which the subject
alternates
tasks as rapidly as possible with blocks in which they repeat
just one
task. This contrast of alternated and repeated tasks can also be
used
with discrete reaction-time measurement (e.g. [14]). However,
this
comparison confounds switch costs and mixing costs. Also, the
alternation blocks impose a greater working memory load – to
keep
track of the task sequence and maintain two tasks in a state of
readiness – and might promote greater effort and arousal. These
problems are avoided by the alternating-runs paradigm [13], in
which

the task alternates every N trials, where N is constant and
predictable
(e.g. Fig. 1, predictable condition, and Fig. 2), so that one can
compare
task-switch and task-repetition trials within a block. An
alternative is to
give the subjects short sequences of trials [20,27] with a
prespecified
task sequence (e.g. colour – shape – colour). Either way, one
can
manipulate the available preparation time by varying the
stimulus –
response interval, but this also varies the time available for any
passive dissipation of the previous task-set.
In the task-cueing paradigm [63,64], the task is unpredictable,
and
a task cue appears either with or before the stimulus (e.g. Fig.
1,
random condition). It is now possible to manipulate
independently
the cue – stimulus interval (allowing active preparation) and the
response – cue interval (allowing passive dissipation).
Alternatively,

in the intermittent-instruction paradigm, the series of trials is
interrupted occasionally by an instruction that indicates which
task
to perform on the trials following the instruction [65]. Even
when the
instruction specifies continuing with the same task, there is a
‘restart’
cost after the instruction [29], but this is larger when the task
changes;
the difference yields a measure of switch cost.
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part of TSR cannot be done until exogenously triggered
by stimulus attributes that are associated with the
task; Rubinstein et al. [14] characterize this part as
retrieval of stimulus – response rules into working
memory. An alternative account, from De Jong [15],
makes no distinction between endogenous and exogen-
ously-triggered TSR. It proposes that, although sub-
jects attempt TSR before stimulus onset (given the
opportunity), they succeed on only a proportion of
switch trials. If they succeed they are as ready for the
changed task as on a task-repetition trial. If they ‘fail

to engage’, the whole TSR process must be performed
after stimulus onset. This idea of TSR as a probabil-
istic all-or-none state change is supported by the fit of
a discrete-state mixture model to the distribution of
reaction times (RTs) on prepared switch trials [15,16].
But why should TSR be all-or-none? One rationale is
that TSR includes an attempt to retrieve either the
goal or the task rules from memory; retrieval attempts
either succeed or fail [17,18].
Fig. 1. Predictable and unpredictable task switching. In this
experiment (Ref. [42], Exp. 2), the tasks were to classify the
digit as either odd/even or high/low, with a left or
right key-press. (a) For some subjects, the task was cued by the
background colour (as illustrated) and for others by the
background shape; the colour or shape changed at
the beginning of every trial. The response – stimulus interval in
different blocks was 50 ms, 650 ms and 1250 ms, during which
subjects could prepare for the next stimulus.
In some blocks, the task changed predictably every four trials
(with a ‘clock hand’ rotating to help keep track of the
sequence): the ‘switch cost’ was limited to the first trial
of the changed task (b). In other blocks, the task varied
randomly from trial to trial and recovery from a task switch was
more gradual. In both cases, the switch cost was
reduced by ,50% by extending the time available for preparation
to 650 ms (the ‘preparation effect’); a further increase had little
effect (the ‘residual cost’). These data
demonstrate that, at least in normal, young adults, even with
complete foreknowledge about the task sequence, switch costs
are large, and that recovery from a task switch

is characteristically complete after one trial. When the task is
unpredictable, recovery might be more gradual, but a few
repetitions of a task results in asymptotic readiness
for it. (Data redrawn with permission from Ref. [42].)
TRENDS in Cognitive Sciences
(a)
Predictable task sequence
Random task sequence
Trial
Cue (50, 650,
or 1250 ms)
Stimulus
(until response)
8
6 8 1 3 8 4
2 7 9 1 8 2
(b)
500
600
700

800
900
1000
50
650
1250
Predictable Random
0.0
2.0
4.0
6.0
1 2 3 4
Position in run
0.0
2.0
4.0
6.0
1 2 3 4
500

600
700
800
900
1000
E
rr
o
rs
(
%
)
M
e
a
n
c
o
rr
e
ct
R

T
(
m
s)
Fig. 2. Preparation effect and residual cost. (a) In this
experiment (Ref. [13], Exp. 3), the stimulus is a character pair
that contains a digit and/or a letter. The tasks were to clas-
sify the digit as odd/even, or the letter as consonant/vowel. The
task changed predictably every two trials and was also cued
consistently by location on the screen (rotated
between subjects). (b) The time available for preparation
(response – stimulus interval) varied between blocks. Increasing
it to ,600 ms reduced switch cost (the ‘prep-
aration effect’), but compared with non-switch trials there was
little benefit of any further increase, which illustrates the
‘residual cost’ of switching. (Data redrawn with per-
mission from Ref. [13].)
TRENDS in Cognitive Sciences
600
650
700
750
800

850
900
0 500 1000 1500
Response–stimulus interval (ms)
Switch trial
Non-switch trial
M
e
a
n
c
o
rr
e
ct
R
T
(
m
s)
(a) (b)
G7 #E
4A L9

Letter task
(switch)
Letter task
(non-switch)
Digit task
(switch)
Digit task
(non-switch)
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Transient task-set inertia
Consider Stroop stimuli. It is well-known that incongru-
ence between the colour in which the word is displayed and
the colour it names interferes much more with naming the
display colour than with naming the word, an asymmetry
of interference that is attributable to word naming being
the more practised, and hence ‘stronger’, task-set [19].
Surprisingly, if subjects must switch between this pair of
tasks, switching to the stronger task results in the larger
switch cost [20 – 22]. In another striking example, bilingual
subjects named digits more slowly in their second langu-
age on non-switch trials, but on switch trials named more
slowly in their first language [23]. This surprising
asymmetry of switch costs eludes explanation in terms of

the duration of TSR. How could it take longer to
reconfigure for the more familiar task? Allport et al. [20]
propose that one must apply extra inhibition to the
stronger task-set to enable performance of the weaker.
This inhibition then carries over to the next trial;
overcoming the inhibition prolongs response selection.
Subsequent work reveals some problems with this
account. For example, the surprising asymmetry of switch
costs can be reversed by manipulations that produce only a
modest reduction in the asymmetry of Stroop-like inter-
ference between the tasks [22,24]. However, this pattern
can be accommodated by a model that combines transient
persistence of task-set activation (or inhibition) with the
assumption that executive processes apply the minimum
endogenous-control input that enables the appropriate
task, given the anticipated interference [22]. The detection
of cross-task interference during a trial might also prompt
the ramping-up of endogenous control input, which would
lead to greater TSI on a switch trial following an
incongruent stimulus [9].
Other observations support the transient carry-over of
task-set activation from trial to trial. Several researchers
[25,26] report evidence that, with preparation held
constant, a longer delay after the last performance of the
previous task improves performance on the switch trial.
This suggests dissipating activation of the competing task-
set. Sohn and Anderson [18] fit data on the interaction
between preparation interval and foreknowledge with a
model that assumes exponential decay of task-set acti-
vation following a trial, and an endogenous preparation
process whose probability of success increases throughout
the preparation interval. There is also evidence for
persistence of inhibition applied to a task-set in order to
disengage from it: so, for example, responses are slower on

the last trial of the sequence Task A, Task B, Task A, than
the sequence Task C, Task B, Task A [27,28].
Associative retrieval
Even when performing only one task (e.g. word naming),
responses are slower if subjects have performed another
task afforded by the same stimuli (e.g. colour naming) in
the previous few minutes [20,21,29]. This long-term
priming has been attributed to associative retrieval of
task-sets that are associated with the current stimulus
[29,30], and seems likely to be the source of the mixing
cost. Allport and colleagues found this priming to be
magnified on a switch trial or when performance was
merely resumed after a brief pause, which suggests that
associative interference may contribute also to switch
costs [21,29]. Further experiments [30] demonstrated that
this priming can be quite stimulus-specific. In these
experiments, each stimulus was a line drawing of one
object with the name of another superimposed (e.g. a lion
with the word APPLE). In the first block, subjects named
the object, ignoring the word. Later, they showed larger
switch costs for naming the word in stimuli for which they
had previously named the picture, even if only once and
several minutes before.
All of the above?
Initial theorising tended to try to explain switch costs in
terms of just one mechanism (e.g. [13,20]). Although
single-factor models of task switching continue to be
proposed [31] most authors now acknowledge a plurality of
causes, while continuing to argue over the exact blend. For
example, although long-term effects of task priming imply
associative activation of competing task-sets by the

stimulus, the contribution this makes to the transient
switch cost observed with small sets of stimuli, all recently
experienced in both tasks, is uncertain. Moreover, residual
switch costs occur even with ‘univalent’ stimuli (i.e. those
associated with only one task) for which there should be no
associative competition [13,26], and switch costs some-
times do not occur for bivalent stimuli where there should
be massive associative competition, such as switching
between prosaccades and antisaccades to peripheral
targets [32]. Transient carry-over of task-set activation
or inhibition is now well established as an important
contributor to switch costs, especially the residual cost, but
it remains unclear whether the effect is to slow task-
specific processes (e.g. response selection) or to trigger
extra control processes (ramping up of control input when
response conflict is detected). A combination of both
mechanisms is likely. Something of a consensus has
developed around the idea that the preparation effect, at
least, reflects a time-consuming, endogenous, task-set-
reconfiguration process, which, if not carried out before the
stimulus onset, must be done after it.
Issues for further research
Unfortunately, the foregoing consensual account of the
preparation effect is not without problems. First, there are
studies in which the opportunity for preparation with
either full [33] or partial [34] foreknowledge of the
upcoming task does not reduce the switch cost, even
though it improves overall performance. Second, in task-
switching experiments, to know whether TSR is necessary,
a subject must discriminate and interpret an external cue
(with unpredictable switching), retrieve the identity of the
next task from memory (with predictable switching), or
both (many predictable switching experiments provide
external cues as well). The contribution of these processes

to switch costs has been neglected. Koch [35] has reported
that, with predictable switching, a preparation interval
reduces the switch cost only when there is an external cue
to help subjects remember which task is next. Logan and
Bundesen [36] found that changing the cue when repeat-
ing the task produced nearly as much of a preparation
137
effect as changing both cue and task. Hence, processes of
interpreting the cue and/or determining whether TSR is
required might contribute much of the preparation effect.
It is even possible that, in some cases, these processes are
so demanding that they constitute a separate task, thus
vitiating the distinction between ‘switch’ and ‘non-switch’
trials.
Another intriguing issue is the role of language.
Introspection indicates that in both everyday life and
task-switching experiments people to some extent verbal-
ize what they intend to do next (‘er…colour’) and how (‘if
red, this key’). Goschke [9] found that requiring subjects to
say an irrelevant word during a 1.5 s preparation interval
abolished the reduction in switch cost observed when the
subject either named the task (‘colour’ and ‘letter’) or said
nothing. He attributed this to interference with verbal
self-instruction, extending to TSR the Vygotskian view
[37] that self-instruction using language is fundamental to
self-regulation. Others have found that irrelevant con-
current articulation (e.g. saying ‘one – two – one – two…’) –

which is known to interfere with phonological working
memory – impairs performance disproportionately in task
alternation compared to single task blocks [38,39]. It is
also suggested that the association claimed between
damage to the left prefrontal cortex and switching deficits
(see below) reflects impaired verbal mediation caused by
left hemisphere damage, rather than a more general
control deficit [40]. However, subjects in these studies were
relatively unpractised. Traditional theories of skill acqui-
sition [41] assign language a relatively transitory role in
task-set learning. A task-set, especially if acquired via the
verbal instructions of another person, may be represented
initially via verbal self-instruction, but after sufficient
practice, control shifts from declarative (including verbal)
representations to a learned, procedural representation.
Standard examples are learning to shift gear or tie a knot.
Hence, we might expect that any cost or benefit of verbal
self-instruction in reconfiguring a task-set would vanish
with practice.
Experiments on task switching have thrown up
numerous other puzzling observations. Why does an
opportunity for preparation often reduce switch costs
without reducing Stroop-like interference from the other
task [13,25,42]? Why are switch costs larger when the
response is the same as the previous trial [13]? We are
unlikely to make sense of the increasingly complex set of
variables that are known to influence switch costs without
either computational simulation [43,44] or mathematical
modelling [18,22,45,46] of their interactions. Progress in
disentangling the complex causation of switch costs is
necessary to interpret the effects of ageing [47 – 49] and
brain damage [50,51] on, and individual differences [52] in,
task-switching costs, and their association and dis-
sociation with behavioural indices of other control func-
tions. Even without a full understanding of their

causation, the substantial magnitude of switch costs
should also be an important consideration in the design
of human – machine interfaces that require operators to
monitor multiple information sources and switch between
different activities under time pressure, such as in air-
traffic control.
Brain correlates of task switching
At first glance, task switching lends itself well to the
subtractive methodology of neuroimaging and electro-
physiology. We can compare event-related activation in
trials that differ only in whether they do or do not follow
another of the same task. Numerous brain regions, usually
in medial and lateral regions of the prefrontal cortex, but
sometime in parietal lobes, cerebellum and other sub-
cortical regions, are reported to be more active on switch
than on non-switch trials. As one example, left dorso-
lateral prefrontal cortex has been reported to be more
active when subjects switch the attribute attended to
[53,54], and this appears consistent with evidence that
patients with left frontal damage have behavioural
abnormalities in switching between attributes [50,51].
Regrettably, as we have learned from behavioural
studies, task switch and repeat trials are likely to differ
in ways other than the occurrence of TSR. There may be
extra interference at the levels of both task-set and
stimulus – response mapping. The greater difficulty of
switch trials is likely to elicit general arousal and extra
error-monitoring. Moreover, even if region X contains an
executive ‘module’ that reconfigures the behaviour of
regions A, B and C, we would expect to see differential
activation, not only of the controlling region X, but also of
areas A, B and C, much as we see modulation of activation
in striate and extrastriate cortex when visual attention is

shifted endogenously [55]. Differential activation evoked
by stimuli on switch and repeat trials does not differentiate
between the ‘source’ and the ‘target’ of the control.
One approach is to try to isolate the brain activity that is
associated with preparing for a task switch. By stretching
out the preparation interval to 5 s [11], 8 s [12] and 12.5 s
[54], one can try to separate modulations of the blood-
oxygen-level-dependent (BOLD) signal that are linked to
preparatory activity from changes associated with process-
ing of the stimulus on switch trials. Some have reported
that preparation for a switch evokes extra activation in
regions that are different from those that undergo extra
activation to a switch-trial stimulus [11,54] whereas
others have not [12]. However, long preparation intervals
might either require extra processing to maintain prepar-
ation, or encourage subjects to postpone preparation. To
deal with this, Brass and von Cramon [56] compared
activation in trials with a task cue followed by a stimulus
1.2 s later, trials in which the stimulus was omitted, trials
in which the cue was delayed until the stimulus onset, and
null trials. Cue-only trials caused activation in the left
inferior frontal junction and the pre-SMA region that
correlated with the behavioural cueing benefit in cue-
stimulus trials. When the cue was delayed, this activation
was also delayed. Hence this activity seems to be cue-
related, but it is unclear (as in behavioural studies)
whether it is associated with interpreting the cue or the
consequent TSR.
In a study focusing on the medial frontal cortex,
Rushworth et al. [57] interrupted a series of stimuli
every 9 – 11 trials with a ‘stay/shift’ cue. When the cue
indicated whether to maintain or reverse the left/right
response rule in the following trials, a larger BOLD signal
was evoked in the pre-SMA region by ‘shift’ than by ‘stay’

2003138
cues. When the cue specified whether to maintain or
switch the stimulus dimension (colour versus shape) used
to direct attention for a perceptual detection task, a
more posterior ‘hot-spot’ was seen. To determine
whether these activations were functionally essential,
brief trains of TMS pulses were applied to these
regions. TMS following a shift, but not a stay, cue
substantially prolonged RT to the upcoming stimulus,
but only for the response-rule reversal. Hence activity
in the pre-SMA region is, apparently, needed to reverse
a stimulus – response assignment. We do not know
whether this activity reflects the source or the target of
an ‘act of control’, or both.
Acknowledgements
Thanks to Hal Pashler, Nachshon Meiran, Ulrich Mayr and an
anonymous
reviewer for their comments on an earlier draft of this article.
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Research Focus
Within the, Update section of TICS, Research Focus articles
highlight important and interesting
recent research developments in cognitive science. The authors
of the original research papers
discussed may be invited to write a Response to the Research
Focus article
If you know of any research just published that you think should
be discussed, please contact
the Editor ([email protected]) with your suggested article and
details of why you think
the article deserves to be highlighted.

2003140
http://www.trends.comTask switchingTask switching: basic
phenomenaSwitch cost (task-repetition benefit)Preparation
effectResidual costMixing costSources of the switch costTime
taken by control operationsTransient task-set inertiaAssociative
retrievalAll of the above&quest;Issues for further researchBrain
correlates of task switchingAcknowledgementsReferences
Stoet et al. BMC Psychology 2013, 1:18
http://www.biomedcentral.com/2050-7283/1/18
RESEARCH ARTICLE Open Access
Are women better than men at multi-tasking?
Gijsbert Stoet1*, Daryl B O’Connor2, Mark Conner2 and Keith
R Laws3
Abstract
Background: 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.
Methods: 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.

Results: 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.
Background
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 psychol-
ogy, 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).

*Correspondence: [email protected]
1School of Education, University of Glasgow, Glasgow,
Scotland, UK
Full list of author information is available at the end of the
article
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 diffi-
culty 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,
© 2013 Stoet et al.; licensee BioMed Central Ltd. This is an
Open Access article distributed under the terms of the Creative
Commons Attribution License
(http://creativecommons.org/licenses/by/2.0), which permits
unrestricted use, distribution, and

reproduction in any medium, provided the original work is
properly cited.
http://creativecommons.org/licenses/by/2.0
Stoet et al. BMC Psychology 2013, 1:18 Page 2 of 10
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 hav-
ing 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 inter-
leave 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 evolution-
ary 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 hunt-
ing, 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 experi-
ment, 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 cogni-
tive 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 2a, we tested
whether women outperform men in a task designed to test
“planning” in a “real-life” context that included standard-
ized 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 dif-
ferent 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 car-
rying out sequences of one task type relatively simple. In
contrast, interleaving trials like “AABBAABBAABB” is dif-
ficult, 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 imme-
diately follow a task switch (in AABBAA there are two
such trials, here indicated in bold font); the latter effect is

known as “switch cost”.
Researchers have given switch costs more atten-
tion than mixing costs, especially since the mid-1990s
(Vandierendonck et al. 2010)b. In the current experiment,
we measured both types of costs.
Methods
Participants
We recruited participants via online advertisements and
fliers in West Yorkshire (UK). Our recruitment procedure
excluded participants with health problems and disor-
ders that could potentially affect their performance, which
included color-vision deficits, as tested with the Ishi-
hara color test (Ishihara 1998) before each experimental
session. Altogether, we selected 240 participants stratified
by gender and age (Figure 1).
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 partici-
pants 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 mon-
itor 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 partic-
ipant (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 2a) 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 2b). These four were the combina-
tion 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”).
Procedure
Participants were seated in a quiet and dimly lit room, and
received written and verbal instructions from the experi-
menter. 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, respec-
tively). 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 par-
ticipants 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 loca-
tion: 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 tri-
als total) that were used in the data analysis. The first
7 6 5 4 3 2 1 0 1 2 3 4 5 6 7
20
25
30
35
40
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).
B
A

Figure 2 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).
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 alter-
native task, and task-repeat trials were those following the
same task. The order of blocks was identical for all par-
ticipants. The computer used a randomisation function
to choose which task would occur on a given trial. Fur-
ther, 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 incon-
gruent stimuli were associated with opposite responses in
the alternative task.
In each trial, the frame and its labels (as displayed in
Figure 2a) were visible throughout the blocks. When an
imperative stimulus (one of the four shown in Figure 2b)
appeared (they were chosen at random by the software),
participants had up to 4 seconds to respond. The impera-
tive stimulus disappeared following a response or follow-
ing the 4 seconds in case no response was given. Incorrect
responses (or failures to respond) were followed by a 5 sec-
onds lasting reminder of the stimulus-response mapping,
and then followed by a 500 ms pause. The intertrial inter-
val 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.
Results
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 anal-
yses in cognitive psychology are based on the assumption
that response times reflect decision time. When partici-
pants guess, for example because they find the task diffi-
cult, the response times are no longer informative of their
decision time.
The procedure for testing if participants performed bet-
ter 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 signifi-
cantly 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 incon-
gruent 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).
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 fac-
tor “gender”. We found a significant effect of switching,
F(1, 229) = 743.90, p < .001: Participants responded
400
500
600
700
800
900
1000
0
2
4
6
8
Figure 3 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.
247 ± 9 ms more slowly in the task-switch (1010 ± 14
ms) than in the task-repeat (763 ± 10) conditionc. Fur-
ther, 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 per-
centage 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 incon-
gruent (3.26 ± 0.19%) than in the congruent (1.49 ±
0.13%) condition, F(1, 229) = 80.86, p < .001. The fac-
tors 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 per-
centage 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 pic-
ture of task-switching data. The novelty is the gender dif-
ference 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 mea-
suring the capacity to rapidly switch between different
tasks. One of the difficulties with computer-based lab-
oratory tasks is their limited ecological validity. Exper-
iment 2 aimed to create a multi-tasking situation in
a “real-life” context that included standardized neuro-
cognitive tests.
The approach of this experiment is based on tasks com-
mon in cognitive neuropsychology. From a neuropsycho-
logical 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 com-
plete; (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 assess-
ing multi-tasking. If this is indeed the case, there is
a real advantage in studying multi-tasking using this
approach.
Methods
Participants
We recruited 47 male and 47 female participants, largely
undergraduate students of Hertfordshire University. The
mean age was 24.2 years (SD = 8.1, range 18–60) for
men, and 22.6 years (SD = 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 Experi-
ment 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.
Material

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 nor-
mally 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 Every-
day Attention” (Robertson et al. 1994). The task requires
individuals to find restaurant symbols on an unfamil-
iar 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 com-
munication with test designer Ian Robertson.
The third task was custom designed and involved solv-
ing simple arithmetical questions presented on paper as
shown in Figure 4. We did pilot these mathematics ques-
tions (unlike the first two tests, this test is not standard-
ised, and after piloting we moderated these questions to
make sure they could be largely successfully attempted
while doing the other tasks).
Although there are reports that men outperform women
on more complex mathematics problems, this is typically
Figure 4 Example of the arithmetic task.

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.
Procedure
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 sim-
ple 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 ’bur-
den’ as such. If they attempted to multi-task while answer-
ing the phone call, this was recorded. We recorded time
spent on each task as well as performance.
Results
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 differ-
ence for the speed with which the task was carried out, as
no response time differences were found (Table 2).
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.
Table 1 Scores of men and women in Experiment 2
Task Men Women t test p value Cohen’s d
Arithmetic correct 19.68 (1.07) 17.29 (1.08) 1.57 .12 0.33
Map task (% correct) 75 (3.82) 72.00 (3.72) 0.52 .60 0.11
Key search score 8.13 (0.68) 10.26 (0.58) 5.6 .02 0.49
Standard errors in parentheses.
No differences emerged in the numbers of men and
women who answered the phone (79% of men and 81%
of women, χ2(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 sec-
onds), 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, χ2(1) = 1.41,
p = .24.
Discussion
Using two very different experimental paradigms, we
found that women have an advantage over men in spe-
cific 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 perfor-
mance 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 plan-
ning, 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.
Table 2 Response times (RT, seconds) of men and women
in Experiment 2
Task Men Women t test p value Cohen’s d
Arithmetic 312 (13) 341 (17) 1.33 .19 0.28
Map task 160 (16) 180 (14) 0.91 .37 0.19
Key search 36 (4) 36 (5) 0.03 .98 0.01
Standard errors in parentheses. The sum of the three individual

tasks exceeds
the 480 allocated seconds, because sometimes the participants
carried these
tasks out concurrently and so were double scored on time.
Relation to other work
As noted in the introduction, there is almost no empir-
ical work addressing gender differences in multi-tasking
performance. For example, even though there are numer-
ous task-switching papers, none has focused on gender
differencesd. 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 stu-
dents), there are few male participants. The novelty of our
study is not only the relatively large number of partici-
pants, 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 bet-
ter 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 con-
trasts with our current data and the experiment by Ren
and colleagues (2009).
In the study by Mäntylä (2013), men and women’s accu-

racy 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 car-
ried 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 dif-
ferences 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 con-
current 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 interpre-
tation of our results. First, as already mentioned above,
there are many different ways to test multi-tasking per-
formance. Because this is an emerging field with a small
extant knowledge base we cannot exclude the possibil-
ity 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 abil-
ity. 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 pos-
sibility 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 fur-

ther 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 acknowl-
edge 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 can-
not eliminate the possibility that a difference may have
emerged tested alone. We could have retested the indi-
vidual 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.
Conclusions
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 simultane-
ously). More research on this question is urgently needed,
before we can draw stronger conclusions and before we
can differentiate between different explanations.

Endnotes
aThe 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.
bThis 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).
cThroughout the results section, we report means ±1
standard error of the mean.
dTo the best of our knowledge.
Additional file
Additional file 1: Demonstration of task-switching paradigm
(Java
application which runs on all desktop computers with Java
installed).
Competing interests
The authors declare that they had no competing interests.
Authors’ contributions
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.
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.
Author details
1School of Education, University of Glasgow, Glasgow,
Scotland, UK. 2Institute
of Psychological Sciences, University of Leeds, Leeds, West
Yorkshire, UK.
3School of Life and Medical Sciences, University of
Hertfordshire, Hatfield,
Hertfordshire, UK.
Received: 3 January 2013 Accepted: 28 August 2013
Published: 24 October 2013
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Running head: SHORTENED VERSION OF TITLE
1
Title of Your Research Study
Author(s) First, Middle Initial (if applicable) and Last Name(s)
in Starting with the

Individual who Made the Biggest Contribution (not
alphabetical)
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is the same one that appears on the cover page.
The first paragraph should contain a description of the
phenomena that you are studying. Make a general statement
about the phenomenon and how it is typically measured. Also,
talk about how one might manipulate or influence the outcome
(i.e, what variables could potentially influence the results).
Subsequent research should describe previous research that
examined the phenomena. These studies serve to provide the
rationale for your study. What did the researchers do? What
did they find? What did they conclude?
Do this for each study cited. Typically, one or more paragraphs
are necessary to explain each study. Try to make the transition
smooth from one paragraph to the next. Use transition words
(see SIGNAL WORDS handout). For example, similarly, Jones
et al. found that…or, in contrast, Smith reported that…
Describe studies that used similar experimental procedures to
the ones that you are using and mention the findings.
Describe the present (your) experiment. Define your
experimental question. Describe what you are doing differently

from other studies. Describe your experimental hypothesis (i.e.,
what do you expect to find?).
Method
Participants
This section immediately follows the Introduction. DO NOT
leave extra lines. The only time you start on a new page is if
the heading is by itself at the bottom of the page!!!
Only information related to subjects is presented here. That is,
how many subjects, ages, gender, nature of participation (i.e.,
paid for participation, fulfillment of an academic requirement,
etc.). If you are working with a special population or there
were other criteria for selection, this should also be included.
Materials
Only information related to the stimuli used in the experiment is
presented here. Remember that the stimuli that are described
are for the entire experiment, not just one subject. If you are
using a complex piece of equipment (such as EEG or fMRI) to
perform your study, then you would include an additional
section under the header Apparatus where you would describe
the technical details of the equipment.
Experimental Design
If a complex design is used, information about the experimental
design is presented here. If the design is simple, it may be
incorporated into the procedure section. You must describe the
design, within or between-subjects (i.e., how the independent
variable was manipulated with respect to subjects). You must
define the independent variable (note: DO NOT say the
independent variable was…Rather, name the variable) and
describe the levels of the independent variable. You must
describe any control procedures that were used. For example,

the order of conditions (i.e., counterbalanced, Latin Square
design, randomly ordered, etc.) and the assignment of subjects
to conditions (important in between-subjects designs).
Following the description of the control procedures for the
presentation of conditions to subjects (within subject designs)
and/or the assignment of subjects to conditions (between
subjects designs) describe any other control procedures related
to the presentation of stimuli or the order of trials within each
condition. If you do not use an experimental design subheading,
you must provide this information at or near the beginning of
the procedure section.
Procedure
A concise description of the experimental procedures. That is,
what the subject experienced. Organize this section around the
events in each trial. This includes the order and the timing of
different stimuli that were presented. When you get to critical
stimulus events, give the specific details about its/their nature
(how stimuli were presented etc.) Then describe the nature of
the subject's response and the instructions to the subject
regarding task performance. Next, describe how the specific
responses are measured (i.e., response time, reaction time,
number of errors, etc.) This includes a definition of the
dependent variable and how the variable was measured. For
example, if the dependent measure was response time,
operationally define response time.
In the next paragraph, describe the remaining important details
of the testing situation and conditions (i.e., the number of trials
of each type, the length of the practice and experimental
portions of the session--were they time-based or performance
based). If practice sessions were performance based, you must
provide the performance criteria.

The last part of this subsection ends with a statement regarding
the treatment of the data including data reduction (means for
each subject, and/or means across subjects), transformation,
statistical tests employed and alpha level. Data reduction and
transformation information is required in psych 213/advanced
experimental courses for instructional purposes. This
information is not always required when simple designs are
employed.
Results
(immediately follows Method – don’t leave extra lines!!!)
Present a statement about the overall results of the manipulation
(i.e., there was an effect or not). For example, “Group-mean
response times varied as a function of the number of
alternatives in a card-sorting task.” Then describe the data
under each condition. Present the descriptive statistics first. If
tables or figures were used, point the reader to a Figure or
Table. For each table or figure, provide a structure statement
(tell the reader how to read the figure or table). For example,
Table 1 displays both the group mean response times and the
mean sort time for each subject under each condition. Then
present a content statement that describes the message that the
data reveals. For example, "The data show that the group-mean
response time under the 2-alternative condition was less than
the group mean response time under the 4-alternative
condition." Do not repeat the information provided in the table
or figure in the text. That is, if the table presents the group
mean response times under each condition, do not present the
mean response times in the text. Once the data have been
described, present the results of inferential statistical tests. Tell
the reader what tests were applied and what measures were
subjected to the test. For example "The difference between
group means was found to be significant, t(df)=t value, p<.05.”
Do not provide information about the meaning of the null
hypothesis or the meaning of the alpha level and what chance

factors have to do with the findings. Do not use the word
"prove." You may use the word "significant." Do not use the
word "insignificant." You may say "not significant."
This basic format should be followed for all variables, tables,
figures, and statistical tests. Report the results, but do not
interpret them except with simple statements such as “the data
(the findings, the analyses) suggest that the number of
alternatives affects response time.” The results section should
be used for stating what was found. The discussion section is
used for explaining why you think you found what you did.
Discussion
The discussion immediately follows the results section. Do not
skip spaces following the results.
Restate your experimental question. Describe your findings.
Did you find what you predicted?
Go back to the other research that you cited in the introduction.
Are your findings similar to or different from these studies? If
different, do you have any idea why? What information do you
have to support this?
Talk about any procedural differences between your study and
others. How might they have affected the outcome of your
study?
Reiterate your conclusions. Talk about any shortcomings or
limitations to the present study. Suggest ideas for improving

the study and for future research.
References
(The reference page always begins on a new page. Below is a
sample of the formatting)
American Psychological Association (2001). Publication
Manual of the American
Psychological Association (5th ed.). Washington, DC: Author.
Berntsen, D. (1996). Involuntary autobiographical memory.
Applied Cognitive Psychology, 10, 435-454.
Brown, S.W., Newcomb, D.C. & Kahrl, K.G. (1995).
Temporal-signal detection and individual
differences in timing. Perception, 24, 525-538.
Eisler, H. (1996). Time perception from a psychophysicist’s
perspective. In: H. Helfrich (Ed.),
Time and mind (pp.65-86). Seattle: Hogrefe & Huber
Publishers.
Hicks, R.E. & Miller, G.W. (1976). Transfer of time judgments
as a function of feedback.
American Journal of Psychology, 89, 303-310.
Hogarth, R.M., Gibbs, B.J., McKenzie, C.R.M. & Marquis, M.
A. (1991). Learning from feedback: Exactingness and
incentives. Journal of Experimental Psychology: Learning,
Memory & Cognition, 17(4), 734-752.

Task switchingStephen MonsellSchool of Psychology Univer.docx

Task switchingStephen MonsellSchool of Psychology Univer.docx

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