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DOD Multidisciplinary Research Program: MURI Operator Performance Under Stress (OPUS).
White Paper
Stress, Human Information Processing, and Performance Mediators: Application of
a Descriptive Framework to Current Modeling Tools
P. Ward, J.L. Szalma, and P.A. Hancock.
Department of Psychology and Institute for Simulation and Training
University of Central Florida
Work performed under the U.S. Army Research Office – MURI: Optimizing Cognitive Readiness Under Combat Conditions
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Stress, Human Information Processing, and Performance Mediators: Application of
a Descriptive Framework to Current Modeling Tools
Abstract
The effects of stress upon performance have typically been researched using either an
input (e.g., environmental stressors), throughput (e.g., adaptive processes) or output (e.g.,
physiological response) approach rather than considering each as part of a single dynamic
process. The extant research has also tended to focus upon the effects of a single
environmental stressor on one specific cognitive process and has often failed to consider
the multidimensionality of stress. To address these issues, Hancock and Warm (1989)
outlined a dynamic model of stress and performance detailing an individual’s
psychological and physiological adaptability to stressful environments. The model
predicts that psychological adaptability (e.g., attentional narrowing) is exhausted in a
similar manner to the way in which stress degrades physiological response capacity (e.g.,
homeostatic regulation). Within this model, task demand is proposed to be one of the
primary sources of stress. According to the model, the effect of stress upon task
performance is intimately related to the nature of the task, type of stressor and moderators
of the stress-performance relationship. However, without a comprehensive evaluation of
the literature, prediction of how task performance will be debilitated or facilitated by
stress, or whether stress effects can be alleviated remains to be fully answered. A three
dimensional matrix is proposed to detail the effects of stress on information processing as
well as moderators of the stress-performance relationship. The aim of this paper is to
present a descriptive framework that will inform current modeling practice in a military
context. The U.S. ARL-HRED presently employ a task network modeling architecture,
IMPRINT, to predict soldier-system performance under stress. Within IMPRINT,
performance shaping functions and stress degradation algorithms are applied to modeled
data to reflect individual differences in task performance under stressful conditions.
Performance on military occupational specialty tasks is currently modeled under
standardized conditions using the ACT-R cognitive architecture. Whilst IMPRINT has
been successfully implemented, it is somewhat limited both by the availability of suitable
stress degradation algorithms and the broad brush approach used to apply the same
function/algorithm to all tasks grouped together by a taxonomic classification system.
The matrix aims to provide resolution, at least in part, to a number of issues in this
regard. Firstly, by clearly outlining the multitude of environmental, physical and taskrelated stressors within the matrix, and considering the transactional relationship between
operator and task, a more complete understanding of stress effects on human performance
can be delineated. Similarly, a comprehensive description and subsequent quantification
of the effects of stressors on a range of information processing variables sub-serving,
perception, cognition, and action will allow both theoretical and practical implementation
issues related to current modeling approaches to be addressed. In addition, the inclusion
of moderator variables within the matrix will provide an added dimension that will
facilitate future modeling endeavors.
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Introduction
A plethora of research exists which documents the effects of stress on human
performance (see Hancock & Desmond, 2001). However, researchers have typically
examined stress via one of three inter-related approaches. Whilst some have considered
stress as a property of the environment (e.g., Hockey, 1983), others have examined the
appraisal or coping mechanisms of stress (e.g., Lazarus, 1966), or the associated
physiological response (Selye, 1956). Researchers have often embarked upon these
approaches independently of each other. In addition, the majority of research has
concentrated upon the effects of a single stressor on ‘isolated’ information processes
(e.g., selective attention, working memory). To date, very few assessments have been
made on a broad range of interactive stressors with the specific intention of detailing both
the variable and predictable effects upon perception, cognition, and action. In one
exception, Hockey and Hamilton (1983) examined the cognitive patterning of stress
states (e.g., anxiety noise, narcotic, etc.) on strategic (i.e., selective attention, decisionmaking) and structural (i.e., short-term memory, alertness) processes. This research
highlighted that while some commonality exists between predominantly environmental
stressors, a multidimensional approach was necessary to explain the lack of response
uniformity. While some of the conclusions made by Hockey and Hamilton were ‘reliable
and substantiated’ the authors acknowledged that on occasion, interpretation was based
upon ‘rather isolated results’ (p.350). The results were also not discriminated by the
magnitude of effect further decreasing the clarity of interpretation. Notwithstanding,
Hockey and Hamilton’s research provides a first step to making a concerted effort in
determining a range of stressor effects on various information processes within a single
investigation.
Lazarus and Folkman’s (1984) focus on mechanisms of appraisal and coping has
emphasized the person-environment-stress interaction. This viewpoint suggests that the
transaction between an individual and their appraisal of the environmental task as taxing,
or exceeding available resources, moderates the effect of a stressor. Consistent with
Hancock and Warm (1989), Matthews (2001) indicated that this transaction occurs at
multiple levels (e.g., biological, psychological). The negative effects of stress occur when
individuals appraise an event as threatening and when they consider that available coping
strategies would be ineffective in dealing with the stressor. Consequently, those variables
that moderate the relationship between sources of stress and the nature of task-related
information processing are likely to be influenced by the transaction between operator
and task. Although a wealth of literature exists on coping (e.g., Matthews & Campbell,
1998; Smith & Lazarus, 1993) and effort allocation (e.g., Eysenck & Calvo, 1992),
surprisingly, only limited empirical research has specifically attempted to identify those
variables that moderate stress effects on task-related information processing in a realworld setting.
Hancock and Warm (1989) suggested that the three approaches described as the ‘trinity
of stress’ are, in reality, three aspects of a single dynamic process which could be used to
describe responses at multiple levels of analysis (Hancock & Warm, 1989). More
specifically, these authors suggested that the study of the physical characteristics of
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environmental stressors was a deterministic, input approach to examining the influence of
stress upon performance, whereas coping mechanisms reflected a nomothetic,
compensatory or adaptive approach, and bodily response, an ideographic, output
approach.
The Hancock and Warm (1989) model outlined three modes of operation that are
reflective of adaptational success, ranging from dynamic stability to dynamic instability.
Hancock and Warm predicted that at minor levels of input stress, behavior remains
relatively stable and is not adversely affected. Thus, no output stress would be observed.
These low levels of stress are adequately dealt with by an individual’s adaptive
capability. On a continuum ranging from hypostress to hyperstress, the central point, the
zone of comfort, reflects such adaptive capability to minor stress levels. However, as the
intensity of, or exposure to, stress rises (i.e., as individuals transition towards the region
of maximal adaptability), and adaptive capability is eventually surpassed, output is
affected. The result is increasing discomfort, a reduction in performance efficiency and
then a rapid decrease in adaptive capability. Importantly, the model indicates that input
stressors affect both psychological (i.e., resources allocation) and physiological
adaptability (i.e., homeostatic regulation). Whilst, an input stressor may appear to affect
psychological processes alone, physiological functioning is also typically indirectly
affected. Moreover, the region of maximal adaptability is larger for physiological
functioning. Although attentional resources are depleted when maximal psychological
adaptability is exceeded, a swift decline in physiological adaptability closely follows.
The model is defined by two different constituents of task-related information,
information rate; the temporal flow of environmental information, and information
structure; the perceived meaning of the information’s content. The latter is specifically
influenced by the task, stressor and future expectations. Although many forms more of
input stress can be observed, the primary source is task demand (Hockey, 1986; Hancock
& Warm, 1989). Moreover, Hockey and Hamilton’s research illustrates that variable
effects of stressors are likely to be observed as the nature of the information processing
requirements of the task change. In a normal information-rich environment and under low
levels of stress, multiple solutions to a task may be available, and different individuals are
likely to interpret information in a number of ways to achieve a successful outcome. The
result is an array of functionally operational goal-directed behaviors. However, as stress
increases, the strategies available to an operator that lessen task demand are reduced, and
the number of solution paths with respect to the goal is diminished. Consequently, the
stereotypical nature of behavior is increased as attention narrows to finding the most
appropriate and available solution to the task. During task performance, individuals seek
to maximize their capacity to retain optimum information throughput using such
compensatory strategies as ‘attentional narrowing’ to modify the perceived rate and
structure of information.
A number of theoretical predictions can be made both from the model and recent works
elaborating on this theme (Hancock, Szalma & Weaver, 2003; Szalma & Hancock, 2003;
Ward, Szalma & Hancock, 2003). Of particular interest here, however, is the way in
which performance degrades over time under stressful conditions and how performance
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is restored to within a normative range. The model predicts that the drain on attentional
resources (e.g., narrowing) occurs in a similar manner to the way in which stress
degrades physiological response capacity. However, recent predictions derived from the
model indicate that stress may affect tasks differently, depending upon the nature of the
task and/or how well the task is learnt. One hypothesis is that performance may degrade
quicker for those tasks that are more knowledge-driven than those that are more skilloriented. The degree to which a task is automated or proceduralized (see Anderson, 1983;
Fitts & Posner, 1967); a product of learning and skill development, may well determine
the relative effect of stress on performance. In contrast, recent evidence suggests that
stress acts to bring normally automated tasks under conscious control, thus competing for
resources which are either otherwise deployed or already exhausted (see Beilock et al,
2002; Masters, 1992). Lastly, it is unknown whether there is a hysteresis effect with
respect to depletion and recovery of attentional resources dependent upon the nature of,
and experience with, the task. The implication of these hypotheses is that if stress affects
tasks differently depending upon the nature or constituents of the task, then tools used to
model performance effects may need to take these factors into consideration during,
rather than after the modeling process.
Detailing the multidimensional nature of stress and performance is an onerous task.
However, without a comprehensive evaluation of the literature, prediction of how task
performance will be debilitated or even facilitated by stress will be even more arduous.
What is more, this is considerably amplified when one considers the vast range of
potential interactions between different environmental, task-based and social stressors
and the information processing requirements of tasks sub-serving perception, cognition
and action. A recent review by Pew and Mavor (1998) suggested that only limited
information is available on the effects of all known environmental stressors. These
authors summarized known effects for heat, toxic substances, noise, vibration, as well as
factors related to fatigue (e.g., sleep degradation), and cognitive workload. While the
details of the effects are beyond the scope of this paper, Pew and Mavor concluded that
much of the current research has been conducted with the aim of eliciting threshold limits
as opposed to determining the degree of degradation per unit of time. When one includes
those factors that may moderate or mitigate the stress-performance relationship, the
complexity of performance prediction becomes all too evident.
From a practical perspective, one often simply wants to know how stress affects
performance and by how much performance will typically be degraded. Moreover, the
question often arises, is there anything one can do to alleviate these effects? Of related
interest are whether particular individuals are more suited to the task than others? Can we
aid in the selection of best man for the job? Can we train the right person to acquire the
appropriate strategies? In order to answer each of these questions the stressor-taskmoderator relationship outlined above needs to be fully delineated. To fill the current
void, we propose a descriptive framework which details the effects of a broad range of
stressors on information processing and examines the potential variables that may
moderate this interaction. Our aim is to provide both a descriptive database and a series
of meta-analytic reviews that inform current mechanisms for predicting task performance
under stress. Ultimately, the purpose of this venture is to increase the current prediction
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capacity of existing models of human performance that incorporate some form of stress
degradation algorithm, moderation process or workload assessment. To meet these goals,
a three-dimensional matrix has been constructed which encapsulates the variables of
interest on each dimension: sources of stress, information processes, and moderators of
the stress-performance relationship. Before giving significant detail on each dimension,
an overview of the current model of soldier-system and human cognitive performance
prediction is provided.
IMPRINT
Improved Performance Research Integration Tool (IMPRINT) is a task network modeling
architecture developed by the U.S. Army Research Laboratory Human Research and
Engineering Directorate (ARL-HRED). It is used to represent soldier-system
performance for the purposes of test and evaluation. Two of the primary aims of
IMPRINT are to determine the soldier performance requirements for the acquisition of a
new system and in particular, to predict soldier-system performance under a myriad of
conditions (Allender, Salvi, & Promisel, 1997; Lebeire, Biefeld, Archer, Archer,
Allender, et al., 2002; O’Brien, Simon, & Swaminathan, 1992). Earlier DOS-based
versions of IMPRINT (e.g., HARDMAN III) were constructed from discrete modules for
a range of operator-, task-, and system-related estimations. IMPRINT is now
implemented using the Microsaint task network modeling environment which subdivides
system missions into functions and tasks, and produces performance estimates and
comparisons to a standard. Given that a multitude of performance changes are likely to
occur under stressful compared to standardized conditions and that some individuals may
be more suited to a particular task than others, of specific interest in these and subsequent
operations are the performance shaping functions (PSFs), stress degradation algorithms
(SDAs) and workload modeling tools used in performance prediction. The PSFs predict
relative changes in performance for a specific set of personnel characteristics and training
regimen (i.e., recency and frequency of practice) whereas the SDAs (or look up tables for
stressors) modify typical/baseline measures to account for negative affects upon
performance. Current methods of workload prediction will be addressed in a later section.
The relationships between predictor and performance for both PSFs and SDAs are
described for categories of tasks (i.e., taxons) rather than specific tasks. That is, different
functions/algorithms were developed for different taxons (see O’Brien et al., 1992).
Similar effects for tasks that fall into the same taxon can be accounted for using this
method. The task taxonomy was largely borrowed from Berliner, Angell, and Shearer
(1964, also see Fleishman & Quantance, 1984). Nine taxons were identified spanning
perceptual, cognitive, psychomotor and communication modalities:
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visual recognition/discrimination
numerical analysis
information processing/problem solving
fine motor (discrete)
fine motor (continuous)
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gross motor (heavy)
gross motor (light)
communication (reading and writing)
communication (oral)
Tasks relevant to each Military Occupation Specialty (MOS) are user-assigned to a
maximum of three taxons. Personnel characteristics such as the Armed Services
Vocational Aptitude Battery (ASVAB) composite scores, reading grade level, complex
perceptual speed/accuracy, simple reaction speed/accuracy, etc. are used to predict task
performance for all tasks that are pre-assigned to each taxon by means of the PSFs
(which translate to a series of regression equations based on aggregated and standardized
test scores). Due to the differential effects of stressors on each task, tasks are weighted
according to the taxon to which they have been assigned so that, when the SDA is applied
(post application of the PSF), the impact of stressors on each task is accordingly weighted
(for a more detailed overview of SDAs and PSFs, see Allender et al., 1997; O’Brien et
al., 1992).
At present, five, predominantly environmental, stressor equations exist within IMPRINT.
These relate to Mission Oriented Protective Posture (MOPP) gear for biological,
chemical and nuclear defense (i.e., protective clothing), heat, cold, noise, and sustained
operation (i.e., sleepless hours). Each of these algorithms are applied individually or in
combination via the use of a power function (see Harris, 1985). The majority of
degradation factors for each stressor were derived from research prior to 1988, some of
which dates back to the 1950’s. Moreover, the SDAs that have been developed are only
capable of modifying tasks within a taxon for a very limited number of stressors (i.e., one
to three). In addition, where degradation algorithms have been written, a comprehensive
evaluation of current stressors has not followed. Allender et al. (1997) indicated that this
is a high priority for research. Even a cursory glance at the literature suggests that there is
much scope for updating SDAs to incorporate ‘state of the art’ knowledge and
consequently bridge the gap between existing stressors and their effects on current
tasks/taxons. Equally important is that this information will increase the number of
stressors and the associated degradation factor that could be applied to each taxon. The
U.S. ARL-HRED and similarly, the U.S. Defense Special Weapons Agency are currently
conducting research in this area to develop a prioritized list stressors and their effects
based upon ‘user need’ and the ‘availability of generalizable data’ (Allender et al., 1997;
see also Anno, Dore & Roth, 1996). Finally, the cognitive mechanisms that underlie
stress effects need to be explored. For performance under standard conditions, the
IMPRINT model utilizes the ACT-R theory of cognition to model performance. It is to
this cognitive architecture that we now turn.
ACT-R (/PM)
Adaptive Control of Thought-Rational (ACT-R) (Anderson & Lebiere, 1998) is both a
theory of cognition and a hybrid cognitive architecture that models and predicts human
cognition. It operates at both symbolic (e.g., as a production system) and sub-symbolic
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(e.g., as a neural-like activation-based system) levels. At the symbolic level ACT-R
assumes two types of knowledge. Declarative knowledge is that which we are
consciously aware of and is verbally describable, whereas procedural knowledge
specifies how declarative knowledge is instantiated to solve a problem. Its basic cognitive
operation is a production-rule, often referred to as a condition-action pair or ‘if…
then…do…’ statement. ACT-R is both a parallel-matching and serial firing production
system centered on the current goal or focus of attention. When the left hand side of the
production (i.e., the condition) is matched with the current goal, post conflict resolution
(i.e., selection of a single production from multiple productions that match information
currently held in working memory), the production-rule fires and the right hand side of
the production (i.e., the action) modifies the current, or deposits a new, goal on to the
‘goal stack’ (i.e., in working memory). As an alternative to a cognitive action, a physical
action may also be executed.
Goals are represented as chunks (i.e., encoded structures with a number of slots or
attributes (≤ 6), each of which hold a single piece of information) and stored in
declarative memory along with other information also represented as chunks. New
productions are created from declarative chunks via a process of production compilation.
That is, a new generalizable production cycle is generated from the residual execution
trace of multiple production firings (Lebiere et al., 2002). At the sub-symbolic level,
retrieval of chunks from declarative memory is based upon the chunk with the highest
level of activation. Activation within this framework is derived from the attentional
weight given to the goal based upon factors such as history of use, information contained
within a chunk, and time-based decay. A set of sub-symbolic learning processes can also
modify activation weights. In a similar manner, conflict resolution is determined as a
rational process of both the estimate of a production’s worth and the trade-off between
the probability and cost (e.g., time) of goal achievement (for more details, see Anderson
& Lebiere, 1998).
In an attempt to extend ACT-R’s application to tasks that involve both perception and
action as well as cognition, Byrne and Anderson (1998) first proposed ACT-R/PM as a
mechanism to augment central cognition. The perceptual-motor (PM) component is now
comprised of visual, motor, speech, and auditory modules (see also Anderson, Matessa,
& Lebiere, 1997; Byrne, 2001) and was largely influenced by the EPIC architecture (see
Kieras & Meyer, 1997), particularly at the motor level. ACT-R/PM was developed to
model both the interaction with environmental stimuli and the perceptual-motor demands
of most real world tasks.
ACT-R and its integration with IMPRINT
ACT-R and IMPRINT have recently been integrated for the purposes of modeling and
predicting task performance in situated events. The most developed application to date
has been in a pilot navigation context for taxiing along the runway (Lebiere et al., 2002).
A similar integration of ACT-R and an IMPRINT-related task network modeling
environment (TNME) has also recently been implemented in a shoot-list management
task (e.g., recognition and decision making) by the U.S. Air Force Research Laboratory’s
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Human Performance Model Integration (HMPI) program using the Combat Automations
Requirements Testbed (CART) TNME (see Craig et al., 2002). In both situations, ACT-R
and IMPRINT functionally combine by focusing upon different aspects of performance.
Whilst IMPRINT is predominantly concerned with the macro-components of real world
tasks, ACT-R contends with a task’s cognitive, perceptual, and motor components.
To direct ACT-R’s goal driven processes, task-related information is sent from IMPRINT
to ACT-R via a component object model (COM) link which specifies a goal in ACT-R.
This in turn creates a goal set that corresponds to the task description. Each sub-goal
specified within ACT-R results in data regarding how the goal was accomplished, as well
as the latency and accuracy (i.e., number of errors) of each goal’s completion. Latencies
for retrieval are determined as a function of a chunk’s activation whereas the time taken
to apply a production is determined by a production’s action component. Rudimentary
internal actions take approximately 50 ms, while external actions typically take longer
and are determined by the ACT-R/PM module.
Errors occur due to the stochastic nature of the decision process and its interaction with a
dynamic environment. As opposed to errors being a problem of ‘faulty symbolic logic’
(Lebiere et al., 2002, p.3), the inherent variability is more likely to reflect an accurate
representation of human operators’ performance under changeable conditions than that of
a purely deterministic system. Errors may originate from a number of sources within
ACT-R such as retrieving the wrong information or failure to retrieve necessary
information from memory (i.e., memory failures), use of an ineffective production rule
(i.e., choice or selection errors), or failure of the perceptual modules to detect important
information (i.e., attentional errors).
One of the benefits of using this type of integration is the reduced need for endless data.
Moreover, ACT-R can generate distributions of time and error data for each task within
IMPRINT that appropriately reflect the actual perceptual, cognitive, and motor
components of tasks experienced in the real world, albeit under standardized conditions
(for detailed summary, see Lebiere et al., 2002).
In addition to modeling cognitive performance, associated workload is currently being
modeled in ACT-R for use within IMPRINT (see Lebiere, 2001; Lebiere et al., 2001)
such that the level of effort required to perform each MOS task could be used to moderate
the ‘attributes of the memory model’ (Lebiere et al., 2002, p.6). As an adjunct to the
IMPRINT/ACT-R integration model, recent developments have been made in modeling
related factors such as emotion (e.g., Belavkin, 2001), behavior moderators (e.g., Ritter et
al., 2002), and fatigue (e.g., Jongman & Taatgen, 1999) within ACT-R. There is no
current initiative to replace IMPRINT’s performance shaping functions and stress
degradation algorithms with an ACT-R (/PM) model. Regardless, the potential exists to
model the effects of stress in this manner. Accordingly, when determining the effects of
stressors and moderators on perceptual, cognitive and motor mechanisms current and
potential modeling developments need to be considered.
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The Matrix
In addition to gaining greater understanding of the stress-performance relationship, the
impetus for this research is to increase current prediction capacity under stressful
conditions by informing the construction of performance shaping functions and
degradation algorithms for existing and future stressors within IMPRINT. The matrix is a
three-dimensional database detailing the qualitative (and quantitative) effects of stress
upon performance, and where applicable, the effects of specific moderators upon that
relationship. Our purpose is to detail the psychological and physiological adaptability
with respect to different information processing tasks, stressors, and moderators. Such a
system will allow the trinity of stress (e.g., input, adaptive, and output processes) to be
fully examined and the associated effects to be evaluated. The first dimension on the
matrix will catalog a range of performance stressors, including environmental (e.g.,
social), physical (e.g., fatigue, hunger) and task-based stress. The second dimension will
detail the range of human information processes underlying perception, cognition, and
action that could potentially be affected by these stressors. Moderators of the stressperformance relationship, such as personality traits, skill level and training will reside on
the third dimension.
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The characteristics of experiments examining the stress-performance relationship are
currently being descriptively coded and entered into the searchable database. Details
include sample characteristics, task description, dependent measures and experimental
design. To date, over 120 independent experiments from 100 articles have been entered
into the matrix across a range of stress and performance indices and mediators of that
relationship. Each study descriptively coded within the matrix is graphically represented
as a specific x, y, z co-ordinate within the three-dimensional space. Figure 1 illustrates the
potential ability of the matrix database and subsequent meta-analyses to update
IMPRINT and its SDAs. Findings from the first meta-analysis of noise intensity and
memory recall can be found in Ward, Szalma and Hancock (2003). Collectively, the
matrix provides an integrative database that will facilitate future modeling efforts, drive
theoretical development and allow existing models of stress and performance (e.g.,
Hancock & Warm, 1989) and their associated predictions to be tested.
Issues for Concern within IMPRINT.
As previously noted, only five stress degradation algorithms have been derived within
IMPRINT, and in the worst case scenario, only one algorithm is available for a task that
falls within a particular taxon. Clearly, the current implementation is not exhaustive and
other environmental, physical and task-based stressors need to be incorporated within this
dimension to make it comprehensive. More specifically, how these stressors affect a wide
range of information processes sub-serving perception, cognition and action is of
particular concern. One of the goals of our research was to build upon the seminal work
of Hockey and Hamilton (1983) to determine both the dynamic and stochastic nature of
the cognitive patterning of some stressors while potentially identifying the deterministic,
or at least invariant, performance effects for others. Hockey and Hamilton’s approach fits
well within the IMPRINT model in that it mirrors the taxon system (e.g., particular stress
effects for certain types of tasks) devised within IMPRINT to which SDAs are applied.
However, given the current delineation of taxons within IMPRINT, it is likely to prove
impossible to derive a ‘common’ stress degradation algorithm for each taxon within the
existing categorization scheme. That is, other than at the perceptual, cognitive, and
psychomotor level only modest parity exists between taxons and the vast array of
information processes upon which research has been conducted. For instance, in the
current taxonomy, cognitive tasks are broken down into either those that require
numerical analysis or those that require some form of information processing and
problem solving. Accordingly, tasks that pose differential information processing
demands such as vigilance and memory recall tasks will, for all intents and purposes, be
modeled similarly by the current implementation of IMPRINT. Using the current
taxonomy as a template for dimension division is likely to result in a disproportionate and
asymmetrical structure that conceptualizes the majority of processes as essentially
equivalent and consequently, has little explanatory power. Although some of the original
research which lead to the current instantiation suggested that the existing task taxonomy
was developed with Wickens’ (1980) processing structure in mind (see O’Brien et al.,
1992), there is little which resembles this model and that distinguishes it from Berliner et
al.’s (1964) original classification scheme. The parsimony achieved by devolving human
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information processing into the existing taxonomic classification system is likely to prove
costly when attempting to accurately model the effects of stress on performance.
Moreover, there is concern regarding the reliability of user assignment of tasks to taxons.
Christensen and Mills (1967) highlighted a median inter-rater reliability of .78 (Rho
range = .29 to 1.00). This has enormous implications for applying SDAs to tasks, where
differences between users in task assignment could lead to different SDAs being applied
and contradictory predictions on performance for the same task.
On a theoretical level, very little ‘human’ research has been conducted on the effects of
stressors on procedural and declarative memory or on any of the sub-processes defined
within the ACT framework. Even less research exists that has successfully modeled
human stress and emotion in ACT-R (for exceptions, see Belavkin, 2001). Considering
the questions raised by Szalma and Hancock (2002) regarding the effects of stress on
Rasmussen’s (1983) trichotomy of skill, rules, and knowledge, the application of stress
degradation algorithms post modeling of performance via both procedural and declarative
mechanisms may be a contentious issue. This issue is complicated further when one
considers the potential shift from automatic to controlled processing of previously
automated skills under stressful conditions (see Beilock et al, 2002; Masters, 1992).
Stress may actually change or restrict the nature of processing and so to accurately model
the effects of stress upon human information processing, algorithms may have to be
included as part of the cognitive modeling process, not attached a posteriori. Further
empirical and modeling research is needed to resolve these issues.
At present only limited research exists on the effect of stress moderators. As mentioned
earlier, the person-environment interaction is likely to play an active role in moderating
the effects of stress between operator and task. Consequently, coping and appraisal are
likely to make up a significant proportion of this dimension. However, related factors, for
instance, perceived control (see Bowers et al., 1996), resource allocation policy in terms
of compensatory effort (e.g., Eysenck & Calvo, 1992; Kahneman, 1973; Matthews &
Campbell, 1998; Sanders, 1983), training (e.g., Hall-Johnston & Cannon-Bowers, 1996),
personnel selection (e.g., Hogan & Lesser, 1996), and system design itself (e.g., Wickens,
1996) may also moderate the effects of this interaction. Personnel characteristics and
training data in the form of performance shaping function, and workload estimates are
currently modeled within IMPRINT. However, the interaction between PSFs and stress is
not directly considered. The individual application of PSF and SDAs is unlikely to
provide the same result as the inclusion of the interaction between these factors.
Concluding Remarks
The primary purpose of this paper and the development of the matrix is to improve the
capacity to predict soldier-system performance. The matrix and subsequent meta-analyses
will provide much needed information on the effects of stressors on information
processing and those variables that can be used to moderate the process. Empirical
verification of modeled cognitive processes will facilitate integration of stress-related
effects into future modeling ventures. The integration of new stressors, validation and
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modification of old stressor effects, and revisions to the existing taxon scheme, once
integrated, are hypothesized to all provide an immediate effect upon performance
prediction. However, data can only inform where there is data to inform. The glaring
truth of the matrix’s construction is likely to be that there are as many gaps in the current
knowledge base as there is understanding of these complex effects. These gaps need to be
filled through further empirical research. Where knowledge is lacking however, system
design can still be enhanced by the matrix. Where data does not exist for the effects of
particular stressors on certain processes then design emphasis can be placed on taxing
those systems where the stress effects are known.
Identified Research Questions
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







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
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Can existing models of stress and human performance account for effects of stress
identified by the matrix?
How well do current stress degradation algorithms used within the IMPRINT
model predict real-world performance under the stressful conditions to which they
apply?
To what extent can current stress degradation algorithms be modified by the
matrix to predict known stress effects?
Can stress effects on information processing be delineated in a meaningful
fashion given the abundance of theoretical models of performance and cognition?
As the information processing dimension of the matrix is specified, how will this
information fit with the existing taxon classification scheme?
How does the effect of stress on controlled and automatic (and skill oriented)
processing impact the current method of modeling cognitive, perceptual and
motor performance before stress degradation algorithms are applied?
Can a consensus of the known stress effects on information processing tasks be
reached to fit within the current taxonomic classification system?
Will the differential stress effects on information processing tasks necessitate a
modification of the existing taxon scheme? If so, can a series of appropriate stress
degradation algorithms be derived to fit a new scheme?
Can the effects of stress be modeled within the IMPRINT/ACT-R integration
model?
What are the main variables moderating the effects of stress on task performance?
Can the effects of moderating variables be accounted for in the IMPRINT/ACT-R
integration model, as per the current workload implementation?
What is the commonality of moderator effects across information processes, tasks,
and taxons?
What gaps exist in the literature regarding the stress-performance relationship and
the effects of moderator variables?
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Anticipated Outcomes
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A series of meta-analytic reviews of stress effects upon performance, including
the moderating effects of several variables (e.g., training, operator traits, etc.) will
inform and enhance modeling capacity and theory development.
The descriptive framework can be directly applied to the mechanisms used by
IMPRINT resulting in stronger prediction capacity of this model of performance
under stressful conditions.
Preliminary steps toward an integrative theoretical and modeled account of stress
effects on MOS tasks/taxons.
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