1
Expertise and Skilled Performance
Guest Lecturer: Dr. Karol Ross
Lecture Overview

The nature and development of expertise

Expert judgment and decision making in naturalistic settings

Training and systems design to support expertise

Neuroscience and skill acquisition
This lecture surveys a broad area, though by no means all areas, of research and
application in the area of skilled performance and expertise. The guiding assumption of this
lecture is that expertise and skilled performance are domain specific—learned and practiced in
context. The primary focus of this lecture is in the area of naturalistic performance, i.e., what
actual skilled and expert performers do in their domains of practice, as opposed to laboratory
studies of naïve participants.
The Nature of Expertise
Characteristics of Performance
Experts function differently than others. Their abilities, be they cognitive or physical,
“make it look easy” when they perform. The following characteristics have been compiled by
researchers about practitioners as they perform in their areas of expertise (adapted from Phillips,
Klein, & Sieck, 2004):
Perceptual skills – Experts have the ability to make fine discriminations. They see more
in a situation than a novice by noticing cues a novice does not (Klein & Hoffman, 1993).
Mental models – Experts have rich internal representations of how things work in their
domain of practice (Rouse & Morris, 1986). These mental models allow them to learn and to
understand situations more rapidly (Ross, Battaglia, Phillips, Domeshek, & Lussier, 2003).
Sense of typicality and associations – Experts have a large repertoire of patterns. They
recognize what is typical in a situation (Ericsson & Simon, 1993) and they recognize complex
patterns. They also recognize when things are not going as expected, that is, when there is an
anomaly or something is missing.
Routines – Experts know how to get things done (Anderson, 1983). They have a wide
repertoire of tactics. They don’t just know about things; they know how to do things.
7 Dec 2006
Karol G. Ross
Expertise
2
Declarative knowledge – Experts know more facts and details and have more tacit
knowledge than novices do. Tacit knowledge is the operational knowledge inaccessible to
consciousness. Much of expertise operates without conscious effort, and that tacit knowledge
supporting expertise is not verbally encoded, nor easily articulated (Crandall, Klein, Hoffman,
2006).
Mental simulation – Experts run mental simulations to refine their course of action or to
understand how a situation got to the point at which they found it (Klein & Crandall, 1995).
Assessing the situation – Experts spend more time than novices understanding the
dynamics of the situation. Novices spend more time deliberating over the course of action
(Lipshitz & Ben Shaul, 1997).
Finding leverage points – Experts can find leverage points in a situation and capitalize on
them to implement innovative strategies (Klein & Wolf, 1998). Leverage points are opportunities
for making critical changes at relatively low effort.
Managing uncertainty – Experts have a range of strategies for managing uncertainty in
the field (Lipshitz & Strauss, 1997; Schmitt & Klein, 1996).
Understanding one’s own strengths and limitations (metacognition) – Experts are better
self-monitors than novices (Chi, Feltovich, & Glaser, 1981; Larkin, 1983).
Stages of Development
A rule of thumb that is still generally referred to in the field of expertise research is that it
takes 10 years of effective practice to develop expertise. As one develops in the cognitive arena,
there are stages or levels of proficiency. Ross, Phillips, Klein, & Cohn, (2005) defined these
stages as shown in the table below and then further developed the stage model of novice to expert
development. The purpose of training is to move individuals from their current state of skill and
knowledge to a higher state. What, then, are the stages of learning, and what do individuals know
and do at each stage? Without a commonly recognized account of the stages along the learning
continuum, we lack a roadmap with which to pinpoint where we are with a particular individual
and where we’re trying to go. A great deal of research documents the nature of expertise and
contrasts it to novice behavior—the two ends of the performance continuum (e.g., Chi, Glaser, &
Farr, 1988; Ericsson & Smith, 1991; Feltovich, Ford, & Hoffman, 1997). A much smaller body
of research enlightens the nature of performance between these two endpoints. This research has
generated and tested a stage model of proficiency that delineates knowledge and abilities at stages
between novice and expert (Benner, 1984, 2004; Dreyfus & Dreyfus, 1986; Dreyfus & Dreyfus,
1980; Houldsworth, O'Brien, Butler, & Edwards, 1997; McElroy, Greiner, & de Chesnay, 1991).
7 Dec 2006
Karol G. Ross
Expertise
3
These two bodies of literature strongly influence our description of the levels of proficiency for
learners in cognitively complex domains.
To understand proficiency we begin by describing the original stage model of
performance developed by Dreyfus and Dreyfus (1980; 1986). The five-stage model describes
performance at levels during the transition from novice to expert in ill-structured, cognitively
complex domains. The model has been applied to training and instruction within domains such as
combat aviation, nursing, industrial accounting, psychotherapy, and chess (Benner, 1984, 2004;
Dreyfus & Dreyfus, 1986; Houldsworth et al., 1997; McElroy et al., 1991). Like tactical thinking,
these domains demand that decisions be made quickly in environments that are complex,
ambiguous, and dynamic. Further, skill can be acquired only through firsthand experience doing
the task. Table 1 below presents an overview of our general stage model of cognitive skill
acquisition.
Expert Judgment and Decision Making in Naturalistic Settings
Tacit Knowledge and Expert Cognitive Processes
Experts and skilled performers use recognition-based cognitive processes to perform
adroitly. These cognitive processes are based on tacit knowledge, i.e., the knowledge is not
verbally encoded or consciously accessed during performance. It is therefore very difficult for the
very skilled or expert performer to explain how and what they are doing. The RecognitionPrimed Decision model (RPD) (Klein, 1998) was derived from field research. It is descriptive of
what experts actually do in situations that are dynamic, uncertain, high-stakes, and timepressured. The RPD Model states that when it comes to high-stakes, time-pressured decisions,
people do not use “rational choice” methodologies; instead, they rely on their experience. An
expert confronted with a situation is able to recognize that this situation is typical, an instantiation
of a “prototype.” This prototype is a cognitive package that includes the type of situation this is,
what to expect from the situation (expectancies), suitable goals, typical courses of action (COAs),
and relevant cues. Once the expert has this prototype in mind, he knows what he’s facing and
what to do next without going through elaborate analyses. Recognition of a situation prototype
can and often does lead directly to action that involves no comparison of options, because the
situation prototype is linked to a COA that the expert already knows will work. RPD is the type
7 Dec 2006
Karol G. Ross
Expertise
4
Table 1. The Stage Model of Cognitive Skill Acquisition
Stage
Novice
Advanced
Beginner
Competent
Proficient
Expert
Characteristics
Rigid adherence to taught rules or plans
Little situational perception
No discretionary judgment
Guidelines for action based on attributes or
aspects
Situational perception is still limited
All attributes and aspects are treated
separately and given equal importance
Sees action at least partially in terms of
longer-term goals
Conscious, deliberate planning
Standardized and routinized procedures
Plan guides performance as situation
evolves
Sees situation holistically rather than in
terms of aspects
Sees what is most important in a situation
Perceives deviations from the normal
pattern
Uses maxims, whose meanings vary
according to the situation, for guidance
Situational factors guide performance as
situation evolves
No longer relies on rules, guidelines, or
maxims
Intuitive grasp of situations based on deep
tacit understanding
Intuitive recognition of appropriate decision
or action
Analytic approaches used only in novel
situations or when problems occur
How
knowledge is
treated
Without
reference to
context
Recognition of
relevance
How context
is assessed
Decision
making
None
Analytically
Rational
In context
Present
Holistically
Intuitive
of cognitive process most often used by experts in the field. The experts’ knowledge base, training,
and experience generally render them able to satisfactorily assess a situation, even if it is not
exactly the same as previous situations encountered. Simply put, when the typical aspects of a
situation are recognized, a plausible COA usually comes to mind. That initial COA is based on the
expert’s recognition of aspects or patterns in the situation, and at times, the pattern of the entire
situation. That recognition brings to mind associated, typical actions that are likely to work in that
circumstance. Experienced decision makers then assess and refine that course of action by
mentally simulating that solution.
7 Dec 2006
Karol G. Ross
Expertise
5
In situations in which RPD is applicable there is often no time to seek the optimal
solution; all that is required is one that will work. This principle of accepting what will work
rather than continuing to look for what is ideal is called “satisficing” (a term coined by Herbert A.
Simon, 1957). Rather than comparing and contrasting several options simultaneously on a set of
abstract evaluation dimensions, experts consider a single workable solution and examine potential
flaws through the mental simulation of the situation. These findings have been generalized to a
wide variety of tasks and specialties and have been replicated a number of times (Fallesen &
Pounds, 2001; Klein, 1998; Pascual & Henderson, 1997). Forcing the consideration of several
options can actually reduce effective decision making. Klein et al. (1995) showed that skilled
decision makers generated a good COA as the first one they considered. Johnson and Raab (2003)
replicated this finding and extended it, showing that when skilled decision makers abandoned
their initial COA in favor of one they generated subsequently, the quality of that subsequent COA
was significantly lower than their initial COA. Johnston, Driskell, and Salas (1997) showed that
recognitional processes, now sometimes referred to as intuitive decision making, resulted in
higher performance than analytical processes. These findings call into question the very rationale
of the MDMP, which attempts to ensure good decision making by having planners generate
multiple COAs and evaluate them analytically in order to find the best one possible. In reality, the
commander and staff naturally produce a good basic COA after an initial assessment and then
work to make sure it is satisfactory for the purpose.
Cognitive Task Analysis
Because it is very difficult for experts to articulate what they are doing and how, methods
have been developed to help the expert “unpack” what is happening during performance. There
are a number Cognitive Task Analysis (CTA) approaches in use in the field. (See for example
Crandall, Klein, & Hoffman, 2006; Schraagen, Chipman, & Shalin, 2000). A type of CTA
associated with naturalistic decision making research is the Critical Decision Method (CDM) as
our primary interview methodology (Hoffman, Crandall, & Shadbolt, 1998). CDM is an incidentbased technique for eliciting knowledge from specific, non-routine events that challenge a
person’s expertise. Using recollection of a specific incident as its starting point, CDM employs a
semi-structured interview format with focused probes to elicit particular types of information
from the interviewee. The types of information sought include the following:

goals that were considered during the incident

options that were generated, evaluated, and eventually chosen
7 Dec 2006
Karol G. Ross
Expertise
6

perceptual cues that influence sensemaking and judgments

contextual elements critical to the decisions and judgments

situation assessment factors and background knowledge relevant to particular decisions.
Training and Systems Design to Support Expertise
Developing Expert Cognition: Fidelity and Feedback
Cognitively authentic scenarios and facilitation of training (coaching and feedback) are
key aspects of training that supports the development of cognitively-based expertise. Structured
deliberate practice is motor skills. Practice alone without attention to reflection and form will not
yield the same results. Simply playing a sport or engaging in other types of performance will not
lead to expertise in a domain. Design of training and training systems must be based on the
specific challenges and known performance attributes of domain experts. (See attached article on
expertise in mine detection.)
Making People Stupid
Technology, when poorly designed, can actually make people stupid and interrupt the
process of developing expertise (Klein, 1998). When skilled and expert performers use
information in a situation, they are looking for patterns, connecting information to existing mental
models, and creating understanding of current situations. When system designs interrupt this
process experts cannot perform and emerging experts have no chance to practice putting together
information in their domain. This unintended effect can happen when too much data is provided,
when people are disconnected from data and given “smoothed over” interpretations, or limit the
practitioner’s ability to dig into the data when she chooses. New technology can make
performance worse by taking the expert’s ability “out of the loop.” (See for example, Hoffman,
Trafton, and Roebber, 2006).
Neuroscience and Skill Acquisition
An emerging area of research is the blending of neuroscience and the study of expertise
and skilled performance. Studies of athletes have provided major insight into how the brain
performs skilled motor activities (Milton, Small, & Solodkin, 2004). Recent neuroscience
research indicates that as a skill is being learned, the number of neurons dedicated and associated
with its execution gradually decreases, adding a dimension of efficiency to the neural system.
7 Dec 2006
Karol G. Ross
Expertise
7
Research using imaging studies show that when elite athletes perform, their brains use fewer but
more appropriate neurons (Hatfield & Hillman, 2001). Experts tend to exhibit activation in the
visual and spatial portions of the cortex, whereas beginners display more diffuse and global
activation. Other researchers have also observed that neural networks of elite performers may be
structurally different from those just beginning to learn a skill (e.g., Milton, Solodkin, Hlustik,
Crews & Small, 2003; Milton et al., 2004; Tracy et al., 2003). Milton et al. observed that
professional golfers had significantly more activation in the parietal lobe, occipital cortex and
dorsal lateral premotor cortex. Generally speaking, these data suggest that neuroelectric activation
in the expert brain is more specific as compared to the beginner’s brain.
This notion is consistent with research by Elbert et al., (1995) who illustrated that finger
representations in the motor cortex become highly organized as skilled musical string expertise
develops. Using magnetoencephalography (MEG), Elbert and his colleagues showed that the
amount of cortical reorganization in the size of the somatosensory representation of each finger in
the cortex positively correlated with number of years playing the instrument. Activation
specificity has also been illustrated by the work of Deeny, Hillman, Janelle, and Hatfield (2003)
that examined coherence1 between multiple brain areas and the motor planning region (Fz).
Across the scalp and between the two groups, EEG coherence measures were almost identical,
except between the T3 (verbal-analytic) and Fz (motor planning) electrodes in the alpha and beta
bandwidths. Superior competitors exhibited a reduction in T3-Fz coherence, implying a reduction
in task-irrelevant communication (i.e., a more streamlined network).
When we compare these findings to the traditional three stage motor learning theory
(cognitive, associative, and autonomous), we find correlates and explanations for the
physiological findings. Fitts and Posner (1967) viewed acquisition of motor skill proficiency as
concomitant with the individual no longer being consciously aware of the control of movement.
Directing conscious attention to motor tasks is usually detrimental to performance once skill has
been obtained both in motor and cognitive skills (e.g., Csikszentmihalyi, 1990; Klein, Wolf,
Militello, & Zsambok, 1995). Neural correlates indicate activity is taken away from conscious
cortical control and allocated to non-verbal automaticity for peak performance.
1
In order to assess the refinement of neural networks, one can employ measures of coherence via
electrophysiological recordings. Coherence is a statistical measure of the degree of repeated linear
correlation between the power spectral densities (frequency domain) of two separate electrodes taken from
the same time series. Coherence values indicate the correlation of the amount of power present (or the
regularity of the phase relationship) in a specific bandwidth between two different electrode cites. High
coherence implies communication between areas, while low coherence indicates independence.
7 Dec 2006
Karol G. Ross
Expertise
8
Much of the literature on the acquisition of expertise addresses the concept of practice
and specifically deliberate practice (Ericsson & Charness, 1994). Deliberate practice brings one to
a stage of automaticity, characterized by minimal cognitive investment, fewer errors and
development of a non-verbal internal model (Fitts, & Posner, 1967; Graybiel, 2000; Cavaco et al.,
2004). “Thus the motor programs are stored in memory are not specific records of the movements
to be performed, but a set of general rules, concepts, and relationships that can be called upon to
solve situations as they arise” (Milton, 2005, p. 12). These findings lead us to believe that motor
skills require “mental models”—a construct reputedly underlying complex, higher-level cognitive
performance in much of the literature.
Research utilizing electrophysiological recordings of brain activity provides an objective
measure of cortical dynamics and further support this model of motor skill acquisition in terms of
reductions in activity within the verbal-associative areas of the brain and decreased networking
between task-irrelevant and task-relevant regions (Hatfield et al., 2004). Expertise in visuo-motor
performance is not verbally encoded. With attainment of automaticity, the verbal-associative
sections of the left temporal region appear to become task-irrelevant, while the visuo-spatial
integration associated with right hemispheric activation becomes refined. Our observations and
the findings associated with the RPD model lead us to conclude a very similar process is in
operation when tactical thinking experts are engaged in performance—non-verbally encoded,
task-relevant, and highly visually-based brain activity.
Learning Objectives
At the end of this lecture you should be knowledgeable of the characteristics of expert
performance and how expertise develops, particularly in the cognitive domains. You should
understand the cognitive basis for expert judgment and decision making in natural performance
settings. You will develop an initial framework for understanding how neuroscience is
investigating the development of skilled motor performance and the role of neuroscience. You
should be aware of the processes and advantages of understanding expertise in the development
of training and systems. You will have key references to further your own study of any of these
objectives.
7 Dec 2006
Karol G. Ross
Expertise
9
Recommended Reading/Additions to your professional library
Crandall, B., Klein, G., & Hoffman, R. R. (2006). Working minds: A practitioner’s guide to cognitive task
analysis. Cambridge, MA: MIT Press.
Ericsson, K. A, Charness, N., Feltovich, P. J., & Hoffman, R. R. (2006). The Cambridge Handbook of
Expertise and Expert Performance. Cambridge, MA: Cambridge University Press.
Gladwell, M. (2005). Blink. New York: Little, Brown and Company.
Hatfield, B. D., & Hillman, C. H. (2001). The psychophysiology of sport: A mechanistic understanding of
the psychology of superior performance. In The Handbook of Sport Psychology, 2nd Ed., R. N.
Singer, H. A. Hausenblas, and C. M. Janelle (Eds.). New York: John Wiley, pp. 362-386.
Klein, G. (1998). Sources of Power. Cambridge, MA: MIT Press.
Klein, G. A., & Hoffman, R. (1993). Seeing the invisible: Perceptual/cognitive aspects of expertise. In M.
Rabinowitz (Ed.), Cognitive science foundations of instruction (pp. 203-226). Mahwah, NJ:
Lawrence Erlbaum Associates.
Williams, A. M., & Hodges, N. J. (Eds.). (2004). Skill Acquisition in Sport—Research, Theory and
Practice. New York: Routledge, Taylor & Francis Group.
References
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
Benner, P. (1984). From novice to expert: Excellence and power in clinical nursing practice. Menlo Park,
CA: Addison-Wesley Publishing Company Nursing Division.
Benner, P. (2004). Using the Dreyfus model of skill acquisition to describe and interpret skill acquisition
and clinical judgment in nursing practice and education. Bulletin of Science, Technology &
Society, 24(3), 189-199.
Cavaco, S., Anderson, S. W., Allen, J. S. Castrol-Caldas, A., & Damasio, H. (2004). The scope of
preserved procedural memory in amnesia. Brain, 12, 1853-1867.
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems
by experts and novices. Cognitive Science, 5, 121-152.
Chi, M. T. H., Glaser, R., & Farr, M. J. (1988). The Nature of Expertise. Mahwah, NJ: Lawrence Erlbaum
& Associates.
Crandall, B., Klein, G., & Hoffman, R. R. (2006). Working minds: A practitioner’s guide to cognitive task
analysis. Cambridge, MA: MIT Press.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal performance. New York: Harper & Row.
Deeny, S.P., Hillman, C.H., Janelle, C.M., & Hatfield, B.D. (2003). Cortico-cortical communication and
superior performance in skilled marksmen: An EEG coherence analysis. Journal of Sport and
Exercise Psychology. 25, 188-204.
7 Dec 2006
Karol G. Ross
Expertise
10
Dreyfus, H. L., & Dreyfus, S. E. (1986). Mind over machine: The power of human intuitive expertise in the
era of the computer. New York: The Free Press.
Dreyfus, S. E., & Dreyfus, H. L. (1980). A five stage model of the mental activities involved in directed skill
acquisition (Unpublished report supported by the Air Force Office of Scientific Research (AFSC),
USAF Contract #F49620-79-C-0063). Berkeley, CA: University of California at Berkeley.
Elbert, T., Pantev, C., Wienbruch, C., Rockstroh, B., Taub, E. (1995). Increased use of the left hand in
string players associated with increased cortical representation of the fingers. Science, 270, 305307
Ericsson, K. A., & Charness, N. (1994). Expert performance: Its structure and acquisition. American
Psychologist, 49(8), 725-747.
Ericsson, K. A, Charness, N., Feltovich, P. J., & Hoffman, R. R. (2006). The Cambridge Handbook of
Expertise and Expert Performance. Cambridge, MA: Cambridge University Press.
Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data 2nd ed. Cambridge, MA:
Cambridge University Press.
Ericsson, K. A., & Smith, J. (Eds.). (1991). Toward a general theory of expertise. Cambridge, MA:
Cambridge University Press.
Fallesen, J., & Pounds, J. (2001). Identifying and testing a naturalistic approach for cognitive skills
training, in E. Salas and G. Klein, (Eds.), Linking Expertise and Naturalistic Decisionmaking.
Mahwah, NJ: Lawrence Erlbaum Associates.
Feltovich, P. J., Ford, K. M., & Hoffman, R. R. (1997). Expertise in context. Menlo Park, CA: AAAI
Press/The MIT Press.
Fitts, P. M., & Posner, M. I. (1967). Human Performance. Belmont, CA: Brooks/Cole Publishing
Company.
Graybiel, A. M. (2000). The basal ganglia. Current Biology, 10, pp. 509-511.
Hatfield, B. D., Haufler, A. J., Hung, T., & Spalding, T. W. (2004). Electroencephalographic studies of
skilled psychomotor performance. Journal of Clinical Neurophysiology, 21 (6), 1-13.
Hatfield, B. D., & Hillman, C. H. (2001). The psychophysiology of sport: A mechanistic understanding of
the psychology of superior performance. In The Handbook of Sport Psychology, 2nd Ed., R. N.
Singer, H. A. Hausenblas, and C. M. Janelle (Eds.). New York: John Wiley, pp. 362-386.
Hoffman, R. R., Crandall, B. W., & Shadbolt, N. R. (1998). Use of the critical decision method to elicit
expert knowledge: A case study in cognitive task analysis methodology. Human Factors, 40(2), 254276.
Hoffman, R., Trafton, G., & Roebber, P. (2006). Minding the weather: How expert forecasters reason.
Cambridge, MA: MIT Press.
Houldsworth, B., O'Brien, J., Butler, J., & Edwards, J. (1997). Learning in the restructured workplace: A
case study. Education and Training, 39(6), 211-218.
Johnson, J. G., & Raab M. (2003). Take the First: Option Generation and Resulting Choices,
Organizational Behavior and Human Decision Processes, 91(2), 215-229.
Johnston, J. G., Driskell, J. E., & Salas, E. (1997). Vigilant and Hypervigilant Decisionmaking. Journal of
Applied Psychology, 82.
7 Dec 2006
Karol G. Ross
Expertise
11
Klein, G. (1998). Sources of Power. Cambridge, MA: MIT Press.
Klein, G. A., & Crandall, B. W. (1995). The role of mental simulation in naturalistic decision
making. In P. Hancock, J. Flach, J. Caird, & K.Vicente (Eds.), Local applications of the ecological
approach to human-machine systems (Vol.2, pp. 324–358). Mahwah, NJ: Lawrence Erlbaum
Associates.
Klein, G. A., & Hoffman, R. (1993). Seeing the invisible: Perceptual/cognitive aspects of
expertise. In M. Rabinowitz (Ed.), Cognitive science foundations of instruction (pp. 203-226).
Mahwah, NJ: Lawrence Erlbaum Associates.
Klein, G., & Wolf, S. (1998). The role of leverage points in option generation. IEEE Transactions on
Systems, Man and Cybernetics: Applications and Reviews, 28(1), 157-160.
Klein, G., Wolf, S., Militello, L., & Zsambok, C. (1995). Characteristics of skilled option
generation in chess. Organizational Behavior and Human Decision Processes, 62(1), 6369.
Larkin, J. H. (1983). The role of problem representation in physics. In D. Gentner & A. L. Stevens (Eds.),
Mental models. Mahwah, NJ: Lawrence Erlbaum Associates.
Lipshitz, R., & Ben Shaul, O. (1997). Schemata and mental models in recognition-primed decision making.
In C. Zsambok & G. Klein (Eds.), Naturalistic decision making (pp. 293-304). Mahwah, NJ:
Lawrence Erlbaum & Associates.
Lipshitz, R., & Strauss, O. (1997). Coping with uncertainty: A naturalistic decision making analysis.
Organizational Behavior and Human Decision Processes, 66, 149–163.
McElroy, E., Greiner, D., & de Chesnay, M. (1991). Application of the skill acquisition model to the
teaching of psychotherapy. Archives of Psychiatric Nursing, 5(2), 113-117.
Milton, J. G. (2005). The brain and the brawn of athletic performance.
http://faculty.jsd.claremont.edu/jmilton/reprints/chicago_05.pdf
Milton, J. G., Small, S. S., & Solodkin, A. (2004). On the road to automatic: dynamic aspects in the
development of expertise. Journal of Clinical Neurophysiology, 21(3), 134-143.
Milton, J. G., Solodkin, Hlustik, P., Crews, D., & Small, S. (2003). Expert motor performance: Limbic
activation is inversely related to skill level. Neurology, 60 (Suppl 1), A345.
Pascual, R., & Henderson, S. (1997). Evidence of naturalistic decision making in military command and
control. In C. E. Zsambok &G. Klein (Eds.), Naturalistic decision making (pp. 217– 226).
Mahwah, NJ: Lawrence Erlbaum Associates.
Phillips, J. K., Klein, G., & Sieck, W. R. (2004).Expertise in judgment and decision making: A case for
training intuitive decision skills. In D. J. Koehler & N. Harvey (Eds.), Blackwell handbook of
judgment & decision making (pp. 297–315). Victoria, Australia: Blackwell Publishing.
Ross, K. G., Battaglia, D. A., Phillips, J. K., Domeshek, E. A., & Lussier, J. W. (2003). Mental models
underlying tactical thinking skills (submitted). In Proceedings of the Interservice/Industry
Training, Simulation, and Education Conference 2003.
Ross, K. G., Phillips, J. K., Klein, G., & Cohn, J. (2005). Creating expertise: A framework to guide
technology-based training (Final Technical Report for Contract #M67854-04-C-8035 for the
7 Dec 2006
Karol G. Ross
Expertise
12
Marine Corps Systems Command Program Manager for Training Systems, Orlando, FL).
Fairborn, OH: Klein Associates.
Rouse, W. B., & Morris, N. M. (1986). On looking into the black box: Prospects and limits on the search
for mental models. Psychological Bulletin, 100(3), 349-363.
Schraagen, J. M., Chipman, S. F., & Shalin, V. L. (Eds.).(2000). Cognitive Task Analysis. Mahwah,
NJ:Lawrence Erlbaum Associates.
Schmitt, J. F., & Klein, G. (1996). Fighting in the fog: Dealing with battlefield uncertainty. Marine Corps
Gazette, 80, 62-69.
Simon, H. A. (1957). Models of man: Social and rational. New York: John Wiley & Sons, Inc.
Tracey, J. Flanders, A., Madi, S., Laskas, J., Stoddard, E., Pyroos, A., Natale, P. & DelVecchio, N. (2003.
Refional brain activation associated with different performance aptterns during learning of a
complex motor skill. Cerebral Cortex, 13, pp. 904-910.
Williams, A. M., & Hodges, N. J. (Eds.). (2004). Skill Acquisition in Sport—Research, Theory and
Practice. New York: Routledge, Taylor & Francis Group.
Notes
This lecture will be supplemented by a PowerPoint presentation available upon request.
Send requests for a copy to kross@ist.ucf.edu
7 Dec 2006