EXPERIMENTAL PSYCHOLOGY TASK SWITCHING EXPERIMENT ONE PARAGRAPH ANSWERING BOTH QUESTIONS 1. Define and discuss the idea of a task set with an example (ref

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ONE PARAGRAPH ANSWERING BOTH QUESTIONS

1. Define and discuss the idea of a task set with an example (refer

to research that has been done by others)

2. Define and discuss the idea of task set reconfiguration, and

explain how this idea explains the task switching cost. (also

define switch cost)

[FIRST READING (INTRO MONSELL) IS BEST, BUT IF YOU NEED MORE INFO THE SECOND ONE CAN HELP. PLEASE IGNORE THE HIGHLIGHTED AREAS]

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 (s.monsell@ex.ac.uk).

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1. Pt 2

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,theerrorrateisoftenhigherafterataskswitch.

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.

Review TRENDS in Cognitive Sciences Vol.7 No.3 March 2003 135

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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

or
s

(%
)

M
ea

n
co

rr
ec

t 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].)

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600

650

700

750

800

850

900

0 500 1000 1500
Response–stimulus interval (ms)

Switch trial
Non-switch trial

M
ea

n
co

rr
ec

t 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

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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 …

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