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The objective is to lay out the rationale for multiple resource theory and the particular 4-D multiple resource model, as well as to show how the model is useful both as a design tool and as a means of predicting multitask workload overload. I describe the discoveries and developments regarding multiple resource theory that have emerged over the past 50 years that contribute to performance and workload prediction. The article presents a history of the multiple resource concept, a computational version of the multiple resource model applied to multitask driving simulation data, and the relation of multiple resources to workload. Research revealed the importance of the four dimensions in accounting for task interference and the association of resources with brain structure. Multiple resource models yielded high correlations between model predictions and data. Lower correlations also identified the existence of additional resources. The model was shown to be partially relevant to the concept of mental workload, with greatest relevance to performance breakdowns related to dual-task overload. Future challenges are identified. The most important application of the multiple resource model is to recommend design changes when conditions of multitask resource overload exist.
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HISTORY AND MULTIPLE RESOURCE
MODEL DEVELOPMENT
Multitasking is prevalent in our society. Issues
such as the dangers of using cell phones while
driving call for understanding the extent to which
such dual-task performance will lead to decreases
in time-sharing ability. Multiple resource theory is
one approach to this understanding. The concept
of multiple resources in attention was spawned,
in my mind, from two seeds. First, Kahneman’s
(1973) influential book on attention inspired me
as a graduate student, with concise theory-based
writing and a model in which human performance
is supported by a general pool of mental “effort”
or undifferentiated resources (although this model
was actually proposed earlier in a short chapter by
Moray, 1967; see also Kalsbeek & Sykes, 1967).
The concept of graduated effort stood in marked
contrast to the then existing all-or-none single-
channel bottleneck view of attention (Broadbent,
1971; Welford, 1967). Kahneman’s model em-
phasized the demand of task for these limited
resources, the lack of availability of resources for
concurrent tasks, and the suffering of performance
of the latter as a consequence. However, in the
final chapter, Kahneman makes note of the other
sources of “structural interference,” which could
not be accounted for by a pure resource demand
or “undifferentiated capacity” model.
Second, there was by this time a growing body
of multitasking studies, some in the experimental
literature (e.g., Bahrick, Noble, & Fitts, 1954;
Bahrick & Shelly, 1958; Briggs, Peters, & Fisher,
1972) and during the 1960s. These contributed to
the creation of the study of “divided attention” in
performance as a discipline. Two such studies
explicitly cast their results within a framework of
multiple resources, postulating that all tasks did
not compete for a single undifferentiated “pool” of
demand-sensitive resources (Kantowitz & Knight,
1976; Wickens, 1976).
Shortly after this, and stimulated by the paral-
lel work of North and Gopher (1976; North, 1977),
I embarked on a series of studies to examine the
costs and benefits of the newly emerging technol-
ogy of voice recognition and synthesis, particu-
larly as applied within the multitask environment
Multiple Resources and Mental Workload
Christopher D. Wickens, University of Illinois, Champaign, Illinois
Objective: The objective is to lay out the rationale for multiple resource theory and
the particular 4-D multiple resource model, as well as to show how the model is use-
ful both as a design tool and as a means of predicting multitask workload overload.
Background: I describe the discoveries and developments regarding multiple resource
theory that have emerged over the past 50 years that contribute to performance and
workload prediction. Method: The article presents a history of the multiple resource
concept, a computational version of the multiple resource model applied to multitask
driving simulation data, and the relation of multiple resources to workload. Results:
Research revealed the importance of the four dimensions in accounting for task inter-
ference and the association of resources with brain structure. Multiple resource mod-
els yielded high correlations between model predictions and data. Lower correlations
also identified the existence of additional resources. Conclusion: The model was shown
to be partially relevant to the concept of mental workload, with greatest relevance to
performance breakdowns related to dual-task overload. Future challenges are identi-
fied. Application: The most important application of the multiple resource model is
to recommend design changes when conditions of multitask resource overload exist.
Address correspondence to Christopher D. Wickens, Alion Science Corporation, Micro Analysis and Design, 4949 Pearl East
Circle, Suite 300, Boulder, CO 80301; cwickens@alionscience.com. HUMAN FACTORS, Vol. 50, No. 3, June 2008, pp. 449–455.
DOI 10.1518/001872008X288394. Copyright © 2008, Human Factors and Ergonomics Society. All rights reserved.
GOLDEN ANNIVERSARY SPECIAL ISSUE
450 June 2008 – Human Factors
of the aircraft cockpit. In interpreting the results
of studies in this area, along with the collective
implications of the growing body of multitask
studies referred to earlier, I created, for a chapter
in Attention & Performance VIII, a sort of meta-
analysis. In this analysis, I tried to account for the
variance in time-sharing efficiency revealed across
over 50 different studies by two characteristics:
(a) the extent to which time-shared tasks used the
same versus different processing structures and
(b) the extent to which “difficulty insensitivity”
was expressed when the two tasks used different
structures. Difficulty insensitivity occurs when an
increase in the difficulty of one task fails to de-
grade the performance of a concurrent one.
Out of these two analyses emerged a fairly co-
herent picture that “defined” separate resources
in terms of a set of three dichotomies of infor-
mation processing, now quite familiar to many
readers and expressed (because of their three
dimensions) in the “cube” shown in Figure 1.
The stages of processing dimension indicates that
perceptual and cognitive (e.g., working memory)
tasks use different resources from those underlying
the selection and execution of action (Isreal, Ches-
ney,Wickens, & Donchin, 1980).
The codes of processing dimension indicates that
spatial activity uses different resources than does
verbal/linguistic activity, a dichotomy expressed in
perception, working memory (Baddeley, 1986), and
action (e.g., speech vs. manual control; Liu & Wick-
ens, 1992; Wickens & Liu, 1988).
The modalities dimension (nested within perception
and not manifest within cognition or response) in-
dicates that auditory perception uses different re-
sources than does visual perception.
Thus, to the extent that two tasks use different
levels along each of the three dimensions, time-
sharing will be better. Note that this assertion does
not imply that perfect time-sharing will emerge
whenever different resources are used for two
tasks. For example, time-sharing an auditory and
visual task will still compete for common percep-
tual resources (and may also compete for common
code-defined resources if, say, both are linguistic,
involving speech perception and reading).
•To these three dimensions was later added a fourth:
visual channels, distinguishing between focal and
ambient vision (Leibowitz & Post, 1982; Previc,
1998), a nested dimension within visual resources.
Focal vision, primarily (but not exclusively) foveal,
supports object recognition and, in particular, high
acuity perception such as that involved in reading
text and recognizing symbols. Ambient vision, dis-
tributed across the entire visual field and (unlike
focal vision) preserving its competency in peripheral
vision, is responsible for perception of orientation and
movement, for tasks such as those supporting walk-
ing upright in targeted directions or lane keeping on
the highway (Horrey, Wickens, & Consalus, 2006).
Concurrent with the identification of the first
three dimensions, Navon and Gopher (1979) de-
veloped an elegant mathematical theory of multi-
ple resources that predicts the consequences to
Figure 1. The 4-D multiple resource model.
MULTIPLE RESOURCES AND MENTAL WORKLOAD 451
concurrent task performance of the resource allo-
cation policy between the tasks, their demand for
limited resources (task difficulty), and their de-
mand for common versus distinct resources (e.g.,
resource overlap). After these authors integrated
their theory with my postulation of the identity of
the three (later four) specific resource dimensions,
this one particular version of the multiple resource
model emerged (Wickens, 1980, 1984).
The rationale for defining these four dimensions
is based strongly on the confluence and joint sat-
isfaction of two criteria. First, the four dimensions
defining the model should have neurophysiolog-
ical plausibility, in the sense that the dichotomies
have parallels in brain anatomy. In the 3-D + 1
model, they certainly do: (a) Perceptual-cognitive
activity is associated with neural activity posterior
to the central sulcis of the brain, whereas motor
and action-oriented activity is anterior. (b) A well-
established line of research associates the pro-
cessing of spatial and verbal material, respectively,
with the right and left cerebral hemispheres of
most individuals. Recently work by Just and col-
leagues (Just et al., 2001; Just, Carpenter, & Miyaki,
2003) has directly associated activity in these areas,
as assessed by functional magnetic resonance
imaging (fMRI) analysis, with dual-task process-
ing and resource demand. (c) Auditory and visual
processes are distinctly associated with auditory
and visual cortices, respectively. (d) Focal and am-
bient vision are supported by ventral and dorsal
visual pathways, respectively (Previc, 1998).
The second criterion emerged from my human
factors orientation. I felt it important that the
dimensions of the model coincide with relatively
straightforward decisions that a designer could
make in configuring a task or work space to sup-
port multitask activities: Should one use keyboard
or voice? Spoken words, tones, or text? Graphs or
digits? Can one ask people to control while en-
gaged in visual search or memory rehearsal? These
two criteria, neurophysiological plausibility and
design decisions, appeared to be fairly well sat-
isfied in the proposed cube model. Furthermore,
with a few qualifications described at the end of
this article, the model has appeared to stand the test
of time in its ability to account for three decades
of dual-task research and to support design deci-
sions (see Wickens, 2002, for a recent summary).
Alternative multiple resource models have been
proposed. Two have been grounded most promi-
nently in the hemispheric laterality framework
described earlier. Polson and Friedman (1988)
focused exclusively on this dimension, whereas
Boles (Boles, 2002; Boles, Bursk, Phillips, &
Perdelwitz, 2007; Boles & Law, 1998), using fac-
tor analysis, has further differentiated auditory and
visual processing within each hemisphere into
subprocesses such as spatial positional, spatial
quantitative, auditory linguistic, and auditory
emotional resources. In this manner, 14 separate
perceptual resources emerge (and 17 overall). This
structure also has been evaluated in dual-task par-
adigms, revealing that task pairs with a greater
degree of resource overlap suffer greater dual-
task decrements. With the proliferation of more
resources, it becomes more difficult to precisely
associate each with brain locations (and therefore
gain full neurophysiological plausibility; Wickens,
2007), but fMRI technology offers promise that
such association might be forthcoming.
Both EPIC (Kieras, 2007; Meyer & Kieras,
1997) and a model of threaded cognition (Salvucci
& Taatgen, 2008) invoke multiple resource con-
structs within perceptual modalities to account for
dual-task interference patterns. Finally, single-
channel bottleneck theory (Pashler, 1998) rep-
resents a version of multiple resource theory
(although not cast in those terms), based primar-
ily on the stage dimension. Here response selec-
tion depends on a very limited supply of resources,
forcing essentially on sequential processing with
high time demand tasks, whereas perceptual-
cognitive resources are limited but more available.
As noted by Wickens and Hollands (2000), such
multiple resource assumptions are quite consistent
with data from the double-stimulation paradigm
(Pashler, 1989, 1998).
A COMPUTATIONAL MODEL
We have recently developed a relatively simple
computational model of multiple resources, fol-
lowing a more elaborate version described and val-
idated in Sarno and Wickens (1995). This simple
model (Horrey & Wickens, 2003; Wickens, 2002,
2005; Wickens, Dixon, & Ambinder, 2006) pre-
dicts total interference between a time-shared pair
of tasks to be the sum of two components, a de-
mand component (resource demand) and a multi-
ple resource conflict component (degree to which
overlapping resources are required). Each of these
components can range in ordinal values from 0 to
4, providing a simple, intuitive, and bounded scale.
452 June 2008 – Human Factors
For the demand component, each task is specified
as being automated (D = 0), easy (D = 1), or dif-
ficult (D = 2), independently of which resources
may be demanded (e.g., perception vs. response,
auditory vs. visual). Hence, the total task demand
can range from 0 (two automated tasks) to 4 (two
difficult tasks).
For the resource conflict component, the two
tasks are compared in the extent to which they
share demands on common levels of each of the
four dimensions of the 3-D + 1 multiple resource
model, 0, 1, 2, 3, or 4. From the sum of these two
components, a total interference component can
be produced ranging from 0 to 8. This is a simpli-
fied version of the kind of computation that goes
into predicting multitask workload, in software
packages such as IMPRINT©(Laughery, LeBiere,
& Archer, 2006) or MIDAS (Booher & Minninger,
2003; Gore & Jarvis, 2005). Of course, the simple
prediction of the total interference between tasks
does not inform the modeler as to which task suf-
fers when there is interference, and hence added
use must be made of a third, resource allocation
component of the multiple resource model, which
can express, for example, the extent to which driv-
ing versus cell phone conversation suffers when
the two compete for multiple resources. This issue
is discussed later.
Although the validation of a more elaborate
version of this computational model is described
in Sarno and Wickens (1995) and in Wickens et al.
(2006) in studies demonstrating the importance
of invoking multiple rather than single resource
models, a third validation has been described
(Horrey & Wickens, 2003). The experimental
data, reported in Horrey and Wickens (2004), were
those collected in a high-fidelity driving simula-
tor, as drivers drove on roadways of three differ-
ing demands (varying traffic and curvature) while
engaging in concurrent tasks delivered in differ-
ent modalities (auditory, visual head up, and head
down). A slightly modified (but conceptually
equivalent) version of the multiple resource for-
mula was imposed on the task interference data
created by these nine conditions of the 3 ×3 de-
sign for the three tasks of lane keeping, respond-
ing to unexpected hazards, and performing the
in-vehicle task. For each task, the dual-task in-
terference score was assessed by subtracting the
single-task (baseline) performance measure. The
analysis revealed great success in predicting both
hazard response (98% of variance accounted for
by the model) and in-vehicle task performance
(92%). However, the model did not predict differ-
ences in lane-keeping performance well at all for
two reasons: (a) separate ambient and focal vision
channels which were not included in that version
of the model. Thus, for example, it is the ambient
vision of the upper visual field that allows the
driver to glance downward and still keep the car
headed forward in the lane center (Horrey et al.,
2006). (b) Drivers protected lane keeping from
interference (treated it as the primary task), hence
protecting it from variance in interference caused
by structural differences in task resource competi-
tion. This clearly demonstrates the resource allo-
cation effect.
Before I conclude this section on multiple re-
source model prediction, it is important to empha-
size that the primary value of such a model is
predicting relative differences in multitasking be-
tween different conditions or interfaces. Such a
model is not designed to make absolute predic-
tions of performance – that is, generate a predicted
multitask performance level that can be desig-
nated as acceptable or unacceptable. This issue
as related to workload will be addressed later.
MULTIPLE RESOURCES
AND WORKLOAD
Multiple resource theory and mental workload
are two related concepts that are often confused.
They overlap but are distinct. To distinguish them,
remember that the multiple resource model archi-
tecture consists of three components related to
demand, resource overlap, and allocation policy.
The concept of mental workload relates most
strongly to the first of these, characterizing the
demand imposed by tasks on the human’s limited
mental resources, whether considered as single
or multiple (Moray, 1979).
Importantly, this demand can be conceptually
associated with one of two “regions” of task
demand level (Wickens & Hollands, 2000). The
first is one in which the demand is less than the
capacity of resources available (i.e., there is “resid-
ual capacity” unused in task performance). This
is the ideal state because it means that the worker
will have some resources available should unex-
pected circumstances be imposed. The second
region is one in which the demand exceeds the
capacity, and, as with any economic system (Navon
& Gopher, 1979), performance will break down.
Sometimes the distinction between these regions
MULTIPLE RESOURCES AND MENTAL WORKLOAD 453
is referred to as a “red line” of workload (Grier,
2008).
Task demand, varying along this continuum,
can result from either single-task demand (e.g.,
driving progressively faster on a winding road im-
poses progressively more resource demand, until
lane keeping begins to fail) or dual-task demand.
In characterizing single-task demand, the “multi-
ple” aspect of multiple resource theory has little
relevance. Furthermore, whether one is doing one
or two tasks in the “residual capacity” region, mul-
tiple resource theory is not fully germane. Only in
the region where overload is imposed by multi-
ple tasks does multiple resource theory make an
important contribution to mental workload by pre-
dicting how much performance will fail once over-
load has been reached (i.e., the size of dual-task
decrements). Here multiple resource theory can of
course also provide guidance as to how redesign
can restore performance to the residual capacity
region.
This distinction between regions is critical be-
cause most measures of workload are designed as
much for the residual capacity region as for the
overload region, if not more so (Gawron, 2008;
Tsang & Vidulich, 2006). Secondary tasks are ex-
plicitly designed to probe “residual capacity” not
used for a primary task; physiological and sub-
jective measures operate across the entire range
of both regions (and do not generally distinguish
which resources, within the multiple resource
framework, are used). Thus, the greatest value of
such measures is to ensure that task demands can
remain within the residual capacity region. Impor-
tantly, some models, such as IMPRINT©(Laughery
et al., 2006) or MIDAS (Gore & Jarvis, 2005) do
have a mental workload component, with task
competition based on multiple resource theory and
with workload channels defined to correspond to
the dimensions in Figure 1. However, this com-
ponent is really intended to predict performance
decrements in the overload region as a result of
multitask requirements, which, as we saw, repre-
sents only one portion of the workload concept.
Such models have been employed in important de-
sign decisions regarding workload and crew size
(Booher & Minninger, 2003).
FUTURE CHALLENGES FOR
MULTIPLE RESOURCES
Although the multiple resource model has been
able to account for a good deal of variance in
time-sharing efficiency when heterogeneous
tasks are imposed (Horrey & Wickens, 2003; Liu
& Wickens, 1992; Sarno & Wickens, 1995; Wick-
ens & Colcombe, 2007; Wickens, Goh, Helleberg,
Horrey, & Talleur, 2003; Wickens, Sandry, &
Vidulich, 1983), both the theory and the model
remain challenged by a number of issues. These
include the following:
Adding another level to the modalities dimension,
related to tactile input (Boles et al., 2007)
The fact that other mechanisms, unrelated to re-
sources, also account for variance in dual-task per-
formance. In particular, these relate to confusion
between task elements (e.g., the problems of time-
sharing a perception and working memory task both
using digits; Hirst & Kalmar, 1987) and cooperation
between elements (e.g., dual-task processing sup-
ported by different dimensions of a single object;
Duncan, 1979; Wickens & Hollands, 2000).
The inability to characterize resource demand on a
single scale, on which the “red line” distinguishing
the reserve capacity from the overload region can be
placed (Hart & Wickens, 1990; Grier, 2008)
Understanding what drives the allocation policy. In
the laboratory, this often can be driven by primary
and secondary task instructions (Tsang, 2006), but in
the real world, phenomena such as unwanted diver-
sion of attention to interruptions (Trafton & Monk,
2008), “cognitive tunneling” (Wickens & Alexan-
der, in press), and auditory preemption (Horrey
& Wickens, 2004; Wickens & Colcombe, 2007;
Wickens, Dixon, & Seppelt, 2005) often operate in
ways that are clearly at odds with optimal allocation,
as witnessed by the safety compromise of driving
while using a cell phone (Strayer & Drews, 2007).
Such findings are consistent with recent develop-
ments that allocation policy is a function closely re-
lated to that of the central executive, a construct well
established to account for differences in time-sharing
efficiency (Engle, 2002; Monsell, 2003; Wickens &
McCarley, 2008), as well as to identifiable prefrontal
brain regions (Kramer & Parasuraman, 2007).
Such challenges await the allocation of research
resources to the interested human factors investi-
gator.
ACKNOWLEDGMENTS
I thank Dr. Bill Horrey and Dr. David Boles for
their comments on this manuscript.
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Christopher D. Wickens is a senior scientist at Alion
Science Corporation, Micro Analysis & Design Opera-
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University of Illinois at Urbana-Champaign. He re-
ceived his Ph.D. in psychology from the University of
Michigan in 1974.
Date received: September 7, 2007
Date accepted: February 26, 2008
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BackgroundHSI System Success FactorsHSI Factors: Examples from Army SystemsCase Studies of System BenefitsHSI Factors and Future Weapons Systems AcquisitionSummary and Conclusions
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