Distributed Cognition: A New Type of Human Performance Model

+-----------------------------------+-----------------------------------+
| Robert G. Eggleston               | ## Michael J. Young               |
+-----------------------------------+-----------------------------------+
| Air Force Research Laboratory     | Air Force Research Laboratory     |
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| Crew Systems Division             | Deployment and Sustainment        |
|                                   | Division                          |
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| 2255 H Street, Bldg. 248          | 2698 G St., Bldg. 190             |
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| Wright-Patterson AFB, OH          | Wright-Patterson AFB, OH          |
| 45433-7604                        | 45433-7604                        |
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| Robert.Eggleston@he.wpafb.af.mil  | Michael.Young@wpafb.af.mil        |
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# Motivation

What can scientists learn about human cognition from the study of human
work? Cognitive psychologists who have studied work in natural
environments have discovered that individuals who differ in knowledge or
learning employ different types of strategies when performing the same
task, that a given individual will employ different strategies depending
upon environmental circumstances, that individuals who have similar
knowledge but differ in what can be called skill perform a given task
quite differently, and that individuals often employ an associational
mode of inferencing through which they "directly perceive" courses of
action in the environment (Dreyfus and Dreyfus, 1988; Rasmussen, 1986;
Rasmussen, Pejtersen, & Goodstein, 1994; Sperandio, 1978; Vincente,
1999).

Rasmussen (1986) in a study of electronic trouble shooting noticed that
professional electronics technicians and engineers performed the same
task quite differently. To understand these differences, we must first
discuss conceptual knowledge. When contemplating a system one can
conceptualize the system at different levels of abstraction. For
example, one can contemplate the system's physical form, or the physical
processes that it embodies, or the general functions it provides, or its
abstract functionality, or its overall functional purpose. In addition,
one can think about the total system, various sub-systems, functional
units, sub-assemblies, or components. What Rasmussen discovered in his
study of trouble shooting was that individuals rapidly shifted between
these various ways of contemplating a system. In addition, and more
interestingly, he discovered that technicians and engineers shifted
views in characteristically different ways. The different knowledge they
possessed cause them to use different strategies when trouble shooting.

![](media/image1.wmf){width="4.238188976377953in"
height="2.451968503937008in"} Figure 1 depicts the trace of an engineer
trouble shooting a piece of electronic equipment. The trace is derived
from verbal protocol data. Each node in the figure represents one
statement. What is characteristic about engineers in general is that
they use logical reasoning to determine how to diagnose the faulty
equipment They think about which sub-assembly could generate the fault,
based upon the available data. They then thoughtfully set a probe to
test their hypothesis and factor the result into their reasoning
process.

The engineer's strategy can be contrasted with the strategy of an
electronic technician, a trace of which is show in figure 2. The
technician's strategy can be summarized as divide and conquer. They used
measurements to determine in which half of the board the problem was.
They would then use another measurement to localize it further. They
used minimal reasoning about
![](media/image2.wmf){width="3.955511811023622in"
height="2.7590551181102363in"}the functional characteristics of the
electronic board. In sum, individuals with different educational
backgrounds and different job demands have been shown to often use
different strategies when accomplishing the same task.

Sperandio (1978) in a study of air traffic controllers noticed that
controllers regularly adopted different work strategies to keep
cognitive workload manageable as environmental demands changed. In a low
load condition (e.g., controlling three planes), controllers would
calculate the optimal flight path for each plane based upon such
variables as type of aircraft, speed, course and altitude. As task
demands increased (e.g., controlling four to six planes), controllers
would adopted a more cognitive economical strategy where they used
uniform speeds across aircraft and stereotyped flight plans to control
the aircraft. As task demands increased further (e.g., controlling more
than six planes) controllers would create waiting buffers consisting of
a stream of several aircraft. When workload permitted, they would bring
aircraft off the buffer and towards the runway at a uniform speed and
descent path. In summary, in a low workload condition controllers would
try to both control an aircraft an its optimal performance criteria and
meet all safety concerns; as workload increased, the controllers shifted
their focus and cognitive strategy to maintain safety considerations,
while relaxing the concern for optimizing aircraft flying criteria.

In terms of an abstraction-decomposition space discussed previously
(e.g., figure 1 or 2), a specific individual will dynamically change
their path through this space based upon environmental stimuli (i.e.,
the number of aircraft that need to be serviced). These changes in
trajectories are correlated with changes in perceived workload and
denote the fact that an individual can perform a task in several
different ways. Individuals seem to shift between alternative task
strategies as a way to control cognitive workload. That is, individuals
seem to have a preferred level of "arousal" and dynamically adjust work
strategies to maintain this level.

Dreyfus and Dreyfus (1988) propose a model of the stages of expertise.
Their model is derived from many studies of individuals in diverse
career fields. It consists of five stages. In the novice stage, an
individual learns and employs facts, features and rules. Further,
knowledge is initially context free, the individual is not yet sensitive
to situated factors. At this stage information processing is essentially
rule-based. Advanced beginners still uses rules, but they are now more
sophisticated; they take into account some contextual features. At the
third stage, competent performers organize information around a plan of
action. Facts are evaluated according to their relevance to the plan.
Information processing is now selective. In contrast, at the fourth
stage proficient performers begin to process information
associationally. Through perception, they connect current context and
stimuli with plans that have previously been successful. Only in
unfamiliar situations due they revert to an analytic rule-based model of
processing. Finally at the fifth stage, experts automatically associate
environmental cues with a course of action. They effortlessly perceive
context and stimuli, which together provides them a deep understanding
of the situation. They do not process rules to generate this
understanding.

In terms of our previous discussion of work strategies, one's relative
experience in a task strongly influences the way information is
processed. Individuals can process information in multiple ways (e.g.,
following a rule or directly perceiving a situation and the required
course of action) and the mode they use is heavily dependent upon their
familiarity with a situation. In terms of the verbal protocol traces,
this corresponds to using a different form of "inferencing" within one
of the numbered nodes in figures 1 and 2.

The notion that experts can directly perceive possible courses of action
in the environment is also supported by research in ecological
psychology. Gibson (1979) coined the term direct perception to describe
the ability of an agent to perceive an object in terms of its ability to
permit or support specific actions by the agent in the environment. In
direct perception, one perceives "affordances." The original conception
did not differentiate between different types of affordances. In
contrast, our current theorizing leads us to propose four different
modes of direct perception. To help explicate our view of direct
perception we must first summarize some recent findings from
neurophysiology.

In recent years, a new computational metaphor of mind has been proposed
by several neurophysiologists which relies on changes of state to model
brain processes (e.g., Damasio, 1989; Edelman, 1989; Engel, Konig, Gray,
& Singer, 1992; Hebb, 1949; Singer, 1993, 1995; von der Malsburg,1986).
The state-change framework of information processing hypothesizes that
the basic information processing element is not a neuron, but rather a
cell assembly: a collection of neurons that operate collectively as a
local circuit. Neurophysiologically, cell assemblies are collections of
neurons that have local inhibitory connections and distal excitatory
connections. Cell assemblies are connected via both feed forward and
feedback (or reentrant; Edelman, 1989) connections. From a functional
perspective, cell assemblies are feature detectors (Hebb, 1949; Edelman,
1989). They are comprised of sub-populations of cells whose function is
to detect and communicate the presence of specific features.

Damasio (1989) proposes that cell assemblies with similar functional
purposes are grouped together into domains in separate parts of the
cortex. For example, cell assemblies that detect such things as the
location, orientation, spectral composition, and direction of motion of
perceptual objects form a visual domain (located in the striate and
extrastriate cortex). Damasio proposes that domains exist for a wide
variety of processes (e.g., processing music, numbers, faces, social
events, or words) and that these areas work according to uniform
information-processing principles (i.e., cognitive domains process
information using neural substrates and algorithms similar to those
found in sensory areas).

In a similar fashion, Edelman (1989) proposes that the brain consists of
four functionally distinct subsystems. One subsystem processes external
sensory information and consists of the primary and secondary cortex
areas associated with each sensory modality. A second subsystem
processes internal sensory information concerning the state and needs of
the body and consists of the brain stem, hypothalamus and autonomic
centers. A third subsystem correlates information across subsystems and
time; this subsystem consists of the septum, amygdala, cerebellum and
hippocampus. The final subsystem is a special value/category memory
consisting of sections of the frontal, temporal and parietal areas of
the cortex.

Information is processed in this framework as a series of state-changes
occurring among cell assemblies. Information entering the system through
a sensory modality initially triggers state-changes in cell assemblies
in the appropriate sensory domain; these state-changes correspond to the
detection of features in the environment. The constellation of cell
assemblies thus formed, in turn, triggers additional state-changes in
cell assemblies in cognitive, motor, and other domains. Eventually,
state-changes among cell assemblies in the motor domain may produce
observable behavior.

From a phenomenological perspective, synchronized state changes among
cell assemblies in the brain corresponds to the recognition of a
percept, the recollection of a memory, or the formation of a plan,
depending upon in which domain (i.e., part of the brain) the
constellation of cell assemblies occurs. Phenomenologically, as
additional state-changes occur, the individual experiences new thoughts,
memories, plans, or produces observable motor output, once again based
upon where in the brain the constellation of cell assemblies are
occurring.

From a computational perspective, there are a couple of things that are
important about this new model of information processing. First, this
new model suggests that there is not a central location in the brain
that processes all information. Rather, information flows to and is
processed in domains, specialized areas of the brain that processes
particular types of information. Second, this model suggests that much
information processing is associational in nature. The recognition of
specific sets of features by one area of the brain can directly trigger
activity in other areas.

We propose that direct perception correspond to an associational mode of
information processing. Further, we have enfolded the affordance
construct into an information-processing model that differentiates four
different types of stimuli: signal, sign, symbol, and affordance.

A *signal* is a simple associational trigger. It corresponds to the
activation of a reflexive behavior. A *sign*, in contrast, is a learned
association. The perception of a "sign" produces a mapping from a
sensory stimulus to a phenomenal object. A *symbol* is a learned
conceptualization. The perception of a symbol simultaneously produces a
mapping from a sensory stimulus to symbolic language and it activates
(what can be called) understanding. An *affordance* is also a learned
conceptualization. The perception of an affordance produces a mapping
from a sensory stimulus to the goal state of the actor and to the
functional capabilities provided by the percept (with respect to the
actor's goal state).

Signs, signal, symbols, and affordances differ from one another in both
the domains they activate within the brain and the "fan" of activation.
Signals produce tightly coupled associations. Signs produce a mapping
from a stimulus to a domain; they produce more activation that a signal
(i.e., they produce a larger constellation of cell assemblies). Symbols
produce a mapping from a stimulus to multiple domains. They activate
both language areas and declarative memory (i.e., they produce an even
larger constellation of cell assemblies). Affordances also produce a
mapping from a stimulus to multiple domains. They activate motor domains
and declarative memory. Affordances, as well as symbols, produce more
activation than signs. At the ecological scale of the actor the
affordance mapping is experienced as a 'direct' association of a percept
with action needed to satisfy work within the prevailing situation.

In short, our motivation for creating the Distributed Cognition (DCOG)
model is to create a computational model that exhibits the same type of
flexible and adapt behavior that human's exhibit. Our goal is not to
build a normative model or even a model that summarizes analytically the
result of an experiment (or several experiments), but rather to create a
model that can readily display the effects of differences in educational
background and skill on information processing and behavior.

**Overview of the Distributed Cognition Model**

The DCOG model is a bounded simulation of the brain's functionality. For
the initial implementation we modeled the functionality of four
relatively well defined brain regions, or domains. The choice of these
domains was determined by the behavior required to perform an Air
Traffic Control (ATC) task. This task is a limited simulation of an ATC
sector controller transferring aircraft from one sector to another and
responding to speed increases. We chose this task because there was a
task simulation available and there was human performance data available
to compare to our model. The four modules we emulate are: 1) a cognitive
module which sets goals and prioritizes tasks; 2) a procedural memory
module which contains information on how to accomplish the task; 3) a
visual sampling module which controls eye movements and provides for
perceptual recognition; and, 4) a motor module through which the model
operates the display console.

The cognitive domain consists of goals and a mechanism to prioritize
actions. A goal is often characterized as a hierarchical knowledge
structure that specifies an action an actor wishes to achieve and the
conditions that enable and criteria that define its satisfactory
achievement (Deutsch, Adams, Abrett, Cramer, and Feehrer, 1993). The
nodes or leafs in the mid level of the hierarchy represent strategies,
sets of plans to accomplish a goal. Strategies differ in terms of their
processing demands (e.g., load on working memory) and their riskiness.
The leafs at the lower level of a goal hierarchy are specific plans,
sets of potential actions that can accomplish a goal.

Our model treats goals, strategies, and action procedures
associationally. Links are formed on the basis of context and prior
experience that enables different forms of representation (related to
different levels of the Abstraction-Decomposition Space) to serve as the
activated goal expression . The active goal expression, along with an
activated strategy serves to implement unique levels of expertise.

The model currently has two high-level, abstract goals: avoid
preventable delays and prevent accidents. The first goal addresses the
way a sector controller transfers aircraft into and out of his or her
sector. For each type of transfer there are decisions that have to be
made and sequences of actions that have to occur by specified distances
from the sector boundaries or an aircraft will go into a holding pattern
(i.e., its flight will be delayed). The sector controller's goal is to
avoid delays whenever possible. The second goal addresses safety
concerns. A speed request is only granted when there is not an another
aircraft in front of the requesting aircraft.

Figure 3 depicts a simplified goal representation as part of the
abstraction-decomposition space structure contained in the DCOG model.
It is a simplified depiction because not all of the possible leaves, or
path relations, are shown. More specifically, it does not depict the
different strategies a controller could use. For example, a controller
could process one transfer or speed request completely before going onto
the next activity (e.g., servicing the next aircraft). Alternatively,
the controller could chose a strategy to interweave actions relating to
different aircraft. This path would require the controller to remember
where he or she is at in an activity sequence for each aircraft, and
consequently place a greater demand on working memory. This strategy
increases the risk that the controller will forget which action is
required next for one or more aircraft, which is the same as increasing
the chance that the aircraft will be required to go into a holding
pattern. Further, this simplified depiction does not specify the
enabling criteria for an action and its success criteria. While this
information must be specified, it was omitted from the diagram for
simplicity sake.

The DCOG memory domain consists of procedural knowledge represented as
event or action schema (Deutsch, et al., 1993). A schema is a data
structure where the framework (or schema structure) specifies spatial,
temporal, or conceptual relations among the nodes, while the nodes
represent declarative information that designates a specific instance or
occurrence of the schema relationship. Example event schema in the DCOG
model are: accept-aircraft, transfer-aircraft, and
respond-to-speed-request. The relations represented for accept-aircraft,
for example, include aircraft name, direction headed, location, and
distance to boundary. When instantiated, or activated, accept-aircraft
nodes contain data on a specific aircraft (i.e., its name, heading,
location, etc.) Example action schema are: activate-welcome-button,
activate-transfer-aircraft-button, and accept-speed-request. Action
schema represent action sequences such as pushing the welcome button,
clicking on the appropriate button, and clicking on the send button. The
actions are bound together in the schema by a temporal relationship
(i.e., there is a correct order to follow when executing the behavior).
The nodes of an action schema contain information on where a button is
located, for example, and data on a specific aircraft (e.g., the number
of the aircraft that requested a speed increase).

The visual sampling domain consists of mechanisms which control eye
movements and which recognize perceptual situations (e.g., aircraft
approaching the border). Two types of eye movements are modeled: visual
scanning and directed gaze. While research has shown that these two eye
movements differ across several dimensions (e.g., field of view,
fixation pause, velocity of movement, etc.), in DCOG we only model
differences in the pattern of saccadic movement. In visual scanning, the
saccadic pattern of movement is to primarily examine "areas of tension"
on the display. Research on Air Traffic Controllers has discovered that
controllers scan select parts of their displays much more frequently
than other (Means, Mumaw, Roth, Schlager, Williams, Gagne, Rice,
Rosenthal, & Heon, 1988). These so-called areas of tension are areas
where the controller anticipates a requirement to control an aircraft.
In the DCOG model, we mimic this finding by having the model scan the
areas around the sector boundaries much more frequently than other
areas. In directed gaze, the saccadic pattern is to primarily follow a
target. Directed gaze is employed when the model is actively controlling
an aircraft (i.e., when the model handing off an aircraft to another
sector).

Both visual scanning and directed gaze can be interrupted by a novel
stimulus. When the DCOG model is engaged in either form of visual
sampling, saccadic eye shifts cause it to sample other areas, albeit
less frequently. If, for example, an aircraft icon has changed color,
this will capture visual attention if it falls with the visual field of
view and consequentially cause the visual stimulus to be processed by
the cognitive domain.

Finally, the motor domain is fairly simplistic. It is the domain that
operates the display switchology by controlling a mouse. In the DCOG
model, we employ an average time for the model to move the mouse to a
button and click. The only limitation we place on the model is that it
can only be engaged in one movement at a time.

**DCOG Implementation**

One of our goals in creating DCOG was to develop a symbolic
implementation of the state-change model of information processing
described above. We chose to model cell assemblies as procedures and
domains/modules as collections of procedures. In the brain, when cell
assemblies reach a certain level of activation, they fire, signaling the
formation of a thought, percept, or plan depending upon where in the
brain the firing occurs. Corresponding, we created procedures that need
to reach a certain level of activation before they can execute.

We created the DCOG model using the Distributed Operator Model
Architecture (DOMAR) integrative architecture (Deutsch, 1997). DOMAR is
a set of languages, analysis tools, and a simulator created to support
the development of knowledge-based simulations of human performance,
with a focus on the cognitive skills of the human operator. DOMAR
provides three languages to represent the different facets of human
performance. The SCORE Procedure Definition Language (SCORE) provides a
set of sixty computational primitives for modeling the parallel
execution of goals, plans, and tasks. The Rule Definition Language (RDL)
provides computational primitives for representing rules (i.e.,
condition action pairs of the form If_Then) and for binding sets of
rules into packets which specify when and how the rules are to be
applied to a specific situation. The Simple Frame Language (SFL)
provides computational primitives to hierarchically represent knowledge
about objects and their attributes, and the relationships among objects.

The basic behavioral element in a DOMAR simulation is an agent. An agent
is a special class of SFL object which, when it has a procedure bound to
it at run-time, can run in the SCORE simulator. The DOMAR simulator is a
discrete event simulator which contains mechanisms to simulate the
parallel execution of agent behavior. We use this capability in DCOG to
simulate the brain as a set of agents which operate in parallel and
asynchronously; and whose collective interactions gives rise to the
model's behavior.

> The SCORE procedural language is distinctive in that it provides two
> independent mechanisms for controlling the execution of computer code
> within a simulation. The first mechanism is the standard procedure
> call technique used in traditional computer languages. When a program
> is executed via procedure calls, the active procedure first performs
> some logic or computational function (i.e., it executes a test); based
> upon the result of this computation the procedure directly calls, and
> consequently passes control to, another procedure. Most local agent
> behavior in the model described below is controlled through sets of
> procedures that are activated via such procedure calls.
>
> In addition, the SCORE language provides a second way to activate
> procedures: the *signal-event/with-signal* utility. A signal-event is
> analogous to an attention grabber; it is a user-defined occurrence
> that is broadcast throughout the simulation, "grabbing the attention"
> of (i.e., activating) all agents/procedures which contain the
> appropriate with-signal key. The signal-event is implemented as a list
> containing two parts: the signal key and some associated data. When a
> signal activates a procedure, the associated data is typically used by
> the procedure to effect some operation.
>
> Signal events can arise from within the DOMAR simulation environment
> or from the external environment. DOMAR's SCORE simulator is a
> discrete event simulator that operates on a time-sorted queue of
> future events when the simulation is operating is stand alone mode,
> and asynchronously when the simulation is linked to an external
> environment. A simulation begins when the scenario agent signals the
> initial event. The scenario agent is a special agent that controls
> both the queue of future events and which can receive input from the
> external environment.
>
> The DOMAR system comes with interface code which enables it to
> participate in Common Object Request Broker Architecture (CORBA)
> distributing computing environments and High Level Architecture (HLA)
> federations, or be linked directly to a wide variety of systems via a
> socket interface. In stand alone model, the scenario agent maintains a
> list of stimuli that are to occur at specific times within the
> simulation. When linked to the external environment, the scenario
> agent packages incoming data as signal events and broadcasts the
> events throughout the simulation.

The signaling of an event begins a cascade of activity in other agents.
As these (user defined) agents or procedures execute, they also place
future procedures (i.e., signals) on the future-event queue as closures,
which is a name for a function or procedure which has had variables
bound to it. At the appropriate time interval, the simulator pops all
the procedures to be executed at that time off the queue and executes
them. This execution often adds additional closures to the queue for
future execution. In addition, it can result in the generation of a
signal-event-external which is both broadcast internally to DOMAR and
externally. When a signal-event-external occurs, the scenario agent
generates an appropriate message to be sent to the external environment
(e.g., either a CORBA object, a HLA interaction, or a serialized list).

**Example Simulation Run**

We now provide an example of a notional simulation run to further
illustrate the DCOG model. A simulation trial begins when the air
traffic control simulation sends data describing the initial condition
of the radar display. The DCOG model takes this data and stores it into
a radar-display object. This object is not part of the psychological
model per se, but rather a data structure the model uses when engaged in
visual search behavior. As the simulation continues, aircraft-update
information is continually loaded into this structure. In addition,
there are other data structures that store textual information that is
found on other parts of the radar display.

When the model is initialized, there are two active goals in the
cognitive domain: prevent-accidents and avoid-preventable-delays,
expressed in a specific form to model individual differences/expertise.
The activation of these goals cause a signal-event to be sent to the
visual domain, which it turn, causes the visual domain to begin scanning
the ATC display using the visual scanning algorithm. The model scans all
the areas of tensions, to include the speed request portion of the
display and the sector boundaries. The presence of aircraft near a
sector border, for example, triggers the activation of an appropriate
goal within the cognitive domain. This activation is achieved through
the visual sampling domain sending an appropriate signal-event to the
cognitive domain. This signal-event contains declarative information
(e.g., name, heading, etc. of aircraft) in its body, which is mapped
into the appropriate goal schema node positions. One scan may result in
the detection of various aircraft that need to be processed. For each
aircraft, one signal-event is sent, which, in turn, instantiates a
separate goal within the cognitive domain.

The inducement of active avoid-preventable-delays goals within the
cognitive domain also triggers the formation of a goal-setting schema
(again via the signal-event mechanism). This schema signals the visual
sampling domain to use the directed gaze algorithm to determine the
distance of each aircraft from the relevant boundary, which results in
another signal being return to the cognitive domain. This signal sets a
level of activation variable for each avoid-preventable-delays goal
schema (which is active). The specific level of activation is dependent
upon how close (in a temporal sense) the aircraft is to requiring
service. The sooner it will need service, the higher the level of
activation.

If the sum of the total activation received by the goal setting schema
is below a given threshold (which corresponds to the model having time
to service each aircraft in turn with out triggering a delay) then a
signal is sent to the serial-processing-strategy schema. This
signal-event contains in its body data regarding the aircraft that needs
to be serviced first (which correspond to the aircraft with the highest
activation level).

The serial-processing-strategy schema then activates the appropriate
schema in the procedural memory domain. If, for example, the aircraft
that needs servicing first is an aircraft entering the sector, then an
accept-aircraft schema is activated. This schema sequentially activates
procedures in the motor domain, which operate the appropriate
switchology on the display (which is the DCOG simulation corresponds to
forming and sending the appropriate signal-event-external to the
simulation of the task domain).

As this procedure completes its task, control is handed back to the
serial-processing-strategy schema, which signals the visual domain to
conduct another scan of the areas of tension. This, in turn, starts the
process over. If, however, the sum total of activation resulting from
the new scan of the displays is above the set threshold, then a
parallel-processing-strategy schema becomes activate.

The parallel processing strategy differs significantly from the serial
processing strategy. Whereas the serial-processing-schema attempts to
completely process one aircraft before going on to the next aircraft,
the parallel-processing-strategy schema attempts to partially process
several aircraft at a time. The complete processing of an individual
aircraft typically requires multiple steps. For example, accepting an
aircraft requires a controller to first respond to a request from a
controller in another sector to accept the aircraft and then later to
respond to a message from the aircraft entering their airspace. While
the serial-processing-schema attempts to accomplish both actions before
going on to the next aircraft, the parallel-processing-schema only
attempts to accomplish the first action before processing another
aircraft.

The parallel-processing-schema maintains a ranked ordered list of the
aircraft that need to be processed. This lists may contain up to seven
items. When it becomes active, the parallel-processing-strategy schema
will execute the initial action (i.e., if there is more than one) that
is required to process the first aircraft in the list by activating the
appropriate schema and procedures in the procedural memory domain, which
in turn activates a procedure in the motor domain. Once this first
action is complete (e.g., the controller has accepted the aircraft),
control is passed back to the parallel-processing-schema in the
cognitive domain. This is in contrast to the serial-processing-schema
which would have maintained control and attempted to execute the next
appropriate action (e.g., welcome the aircraft). The
parallel-processing-strategy schema then updates the status of the first
aircraft and activates the appropriate schema in the procedural memory
domain to begin processing the second aircraft in the list. This newly
active schema will execute the applicable action for the second aircraft
and then return control, once again, to the cognitive domain. This
activity continues until the list is fully processed or the ongoing
activity is disrupted.

Execution activity can be disrupted in one of two ways. First there is a
risk that the information contained in the list will decay before all
that actions are completed. That is, activation of a schema corresponds
to putting items in short term memory. The number of items in the list
correspond to the number of elements in short-term memory. These items
slowly decay as time moves on. The schema will continue to execute
elements off the list until the remaining items decay below a threshold.
Decay is modeled in the DCOG model as a Gamma distribution.

Second, there is also a risk that a saccade will fixate on another
higher priority aircraft that needs to be processed before the
parallel-processing-strategy schema has finished processing the list .
The DCOG model simulates parallel processing among the various emulated
domains. While the cognitive domain is setting and achieving goals, the
visual domain is still, in parallel, sampling the ATC display.
Interspersed among the direct gazes that are conducted to support goal
achievement are saccades whose purpose (within an actual brain) is to
continually refresh visual awareness. If during a saccade the visual
gaze falls upon an aircraft which has turned red, for example, this will
immediately trigger a signal being sent to the cognitive domain which
activates an avoid-preventable-delays goal. The activation of this high
priority goal immediately triggers the re-setting, or re-instantiation,
of the goal-setting schema, which in turn, quite frequently results in a
reprioritization of goals (e. g., process the delayed aircraft now!).

**Conclusion**

The DCOG model emulates the state-change model of information processing
through its dynamic linkage of procedures and the way it perceives
affordances. Further, this model of information processing, at a
symbolic level, seems to correspond closely to the processes that
scientists have perceived in the study of work in natural environments.

Within the brain, cell assemblies in different domains synchronize their
firing when executing behavior. Which specific cell assemblies become
coupled depends to a large extent upon the context of the situation,
with contextual cues being directly provided through the perception of
affordances. Within the DCOG model, procedures within different modules
synchronized their activity in a similar fashion as cell assemblies
through the exchange of signal-event messages. Further, which procedures
exchange messages depends heavily upon the context of the situation. As
the visual sampling domain continuously scans the environment it
identifies aircraft that need servicing. Each perception of a needy
aircraft directly activates knowledge. The specific knowledge activated
across domains depends upon the type of problem perceived (e.g., speed
request, transfer, etc.), the number of aircraft the need servicing at a
given time, and the expertise level of the actor.

The coupling of procedures also changes dynamically in a context
dependent fashion as other information is picked-up from the
environment. The perception of an aircraft entering a holding pattern
(i.e., turning red), for example, will immediately trigger a
reevaluation of the model's goal. A change in goal typically causes one
set of procedures to uncouple and another to couple. This change in
coupling corresponds to disparate cell assemblies changing their
patterns of synchronized firing.

The current version of the model only demonstrates how one type of
stimuli can generate an affordance. However, it should be easy to
perceive how this mechanism could be extended to other types of stimuli
and other domains. For example, an association could be set-up which
corresponds to a reflexive behavior. In this case an environmental
stimuli, once perceived, would directly activate a procedure in the
motor domain.

The current model changes strategies as workload increase in a similar
manner to that seen in studies of individuals working in a natural
environment. This change in strategy can be graphed in an
abstraction-decomposition space (e.g., figure 1 or 2) as a different
trajectory through the space. While the current model does not fully
implement alternative strategies resulting from the possession of
different knowledge, it does demonstrate different strategies in action.

In summary, the DCOG model described herein is our first attempt to
create a model that emulates the way individuals actually work, as
determined from process trace studies conducted in actual work settings.
It is based upon a neurophysiologically plausible model of
information-processing. We have designed and implemented several of the
fundamental properties of the DCOG architecture. In the future, we plan
to incrementally improve the computational modeling methodology as we
continue to investigate the state-change approach as a means to better
understand complex human behavior.

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