**A Symbiotic Human-Machine Model**

**for Tracking User Micro-Attributes**

Dr. Diane Nahl

Information and Computer Sciences Department

Library and Information Science Program

University of Hawaii

nahl@hawaii.edu

**Definition of the Model**

A model is presented that specifies the mental procedures that people
perform when interacting with a computer or system. It is a universal
biological model of human agency depicting the micro-behavioral
procedures that are performed by every individual in the sensorimotor,
cognitive, and affective domains of mental behaviors (Nahl, 2001;
Jakobovits & Nahl-Jakobovits, 1987). Figure 1 shows these three domains
of behavior operating in a sequence of six steps that must be performed
by every person when interacting with a computer or system, which is
defined as the relevant information environment \[E\]. The computer
functions in modular integration with the human through satisficing
\[E**^S^**\] and optimizing \[E**^O^**\] affordances that the system
provides for interaction. For instance, user input devices such as a
mouse, keyboard, touch screen or hyperlink are optimizing affordances
\[E**^O^**\] because they provide access to system features that the
person needs to optimize a goal intention. Optimizing affordances give
users control over the system's states, such as use a mouse or an arrow
key to scroll. Satisficing affordances \[E**^S^**\] are output devices
such as displaying file contents, mapping mouse location, or delivering
a result in some calculation.

The computer is therefore integrated with the human in a symbiotic
operation to the extent that satisficing affordances are noticed \[ES\]
{13} and optimizing affordances are manipulated \[SE\] {14} (see Figure
1). When a satisficing affordance is noticed \[ES\] {13} or already
known by a user, a modular connection is established between human and
machine. This occurs through the sensory system of the body. Once
connection is established, a series of orderly mental states are
activated. These behavioral sequences are numbered to permit reference
to specific micro-procedures performed by users while interacting with
the computer or system.

When a system feature is noticed \[ES\] {13}, it is passed to an
appraising procedure \[SC\] {11}. This cognitive \[C\] processing
operation may include interpreting what is seen, heard or noticed, and
constructing a situational context as such as discerning, "These are
links" or "It's a dialog box" or "It's not in this folder." Once the
incoming information has been appraised, it is passed to a satisficing
procedure \[CA\] {8}. This affective \[A\] processing operation may
include attaching some value or priority to the appraised information.
For instance, when noticing that the computer is not responding to
typing \[ES\] {13}, and interpreting this as abnormal activity \[SC\]
{11}, we attach a value to it, e.g., "This is bad." or "Now I am in
trouble." Or "This is pretty annoying." Sometimes the information
evaluation procedure is occasioned directly when noticing something
obvious, so that the appraising process is short-circuited as indicated
by path \[SA\] {9}.

As soon as the information has been satisficed or value-attached, it is
passed to the optimizing phase of doing something about it \[AA\] {1}.
This path is numbered {1} because it is the heart or control center that
integrates human and machine. The function and validity of every
satisficing affordance of the system \[E**^S^**\] is established or
created by the affective value attachment procedure \[ESCA\] {13, 11,
8}, and every optimizing affordance of the system \[E**^O^**\] is made
use of by the affective goal-intention procedure \[ACSE\] {2, 5, 14}.
Goal-planning and problem solving \[C**^O^**\] are cognitive operations
like appraising \[C**^S^**\], but they contrast in directionality.
Appraising procedures are performed when interpreting input \[ESC\] {13,
11} while planning procedures are performed when engaging in output
\[CSE\] {5, 14}. Note that the model provides for \[CC\] {4, 10}
interactions between appraising and planning, short-circuiting or
by-passing local affective procedures. According to the model, these
local circuits are maintained by global goals. Goal-intentions motivate
and direct goal-planning \[AC\] {2}, including how long to continue the
cognitive operation or interrupt it and passing it to the sensorimotor
procedures of manipulating the system's states through its optimizing
affordances \[SE\] {14}.

**Theoretical Context of the Model**

The ecological model was constructed by integrating Nahl's concept of
taxonomic mental states in information behavior (Nahl, 2001, 1998) and
the construct of affordances used by Simon (1956; 1967), Gibson (1979),
Gaver (1996), and Norman (1999) (see also Carroll, 2003, p. 168 and p.
440). The "ecological" perspective is one of the three "scientific
foundations of design rationale" or architecture (Carroll, 2003, p.
439). The empirical taxonomic basis for the \[ACS\] information behavior
theory has been discussed in Nahl (1998; 2001). The historical and
theoretical justification of the model, and its various applications to
design architecture, are presented in Nahl (2005).

The model introduces a new theoretical distinction between satisficing
affordances and optimizing affordances. The mental elements of the model
consist of the three biologically based human agency functions widely
recognized in psychology and education. The directional control of
cognitive operations by affective states \[AC\] {2} has been recognized
throughout HCI (Hudlicka 2002; 2003) and in cognitive psychology (Isen,
2004). Similarly, cognitive control over sensorimotor action \[CS\] {5}
has been researched in the literature on cognitive productivity and
attentional processes (Thelen & Smith, 1994; Leuthold & Sommer, 1999).
The model can be useful for charting the mental micro-procedures that
users perform in common tasks and offers an integrated account of what
user attributes can be monitored and modeled by intelligent systems.
These attributes include what users notice or ignore about an interface
\[ES\] {13}, how they attribute meaning to its features \[SC\] {11}, how
they attach value or utility to it \[CA\] {8}, and then how they make
use of the interface feature by attaching a goal-intention to it \[AA\]
{1}, thinking about how to optimize it \[AC\] {2}, and execute the plan
\[SE\] {14}.

Optimal foraging theory provides support for the ecological model
(Stephens & Krebs, 1986; reviewed in Pirolli, 2003, p. 166). It intends
to account for the ways in which people adapt to the information
environment by orienting to its features analogous to animals surviving
by foraging for food. The term "optimizing" in this context refers to an
individual's motivation to gain as much information as possible "per
unit cost, given the constraints of the task environment" (Pirolli,
2003, p. 167). "Information scent" refers to the limited information
that people have available when they make decisions on clues or snippets
\[ES\] {13}. "A user's information goal \[A**^O^**\] activates a set of
chunks in a user's memory \[ACC\] {2, 10}, and text on the display
screen \[ES\] {13} activates another set of chunks \[SC\] {11}."
(Pirolli, 2003, p. 174; code added). The process of "information
foraging evaluation" \[A**^S^**\] has been studied by Pirolli & Card
(1999) in connection with "scatter/gather designs" (Carroll, 2003, p.
181).

The sequenced operations traced in Figure 1 are performed at different
levels of goal-setting or optimizing. For instance, while typing an
email message, an overall goal-intention \[A**^O^**\] of sending the
email presides \[ACSE\] {2, 5, 14}. Working in parallel, local
goal-intentions are active \[A**^O^**\], e.g., when typing a word, which
is a sub-sub-task that has a beginning and end. The goal intention of
wanting to type a particular word \[A**^O^**\] elicits the planning,
which is the evocation of a known spelling \[AC\] {2} and its motor
execution \[CSE\] {5, 14}. The motor action modifies the appearance of
the file \[SEE\] {14, 15} through the system's optimizing affordances
(e.g., keyboard functions). This modification of the system's state is
shown to the user through its satisficing affordances such as a screen
display of the file's content \[EES\] {15, 13}. Sub-goals, like typing
more words that form a sentence, are also simultaneously operative. Nahl
(1998) has shown that these three distinct types of mental procedures
\[ACS\] occur in the speech or discourse of users talking about what
they are doing and why they are doing it while carrying out a task using
computers. Nahl (2001) has also shown that their procedures are
organized in levels or hierarchies of goal-intentions.

**Integrating Affective, Cognitive, and Sensorimotor Interactions**

The ecological information model in Figure 1 shows the symbiotic
relationship that exists between mental states or functions and system
states or features, as well as the integrated but distinct elements of
information activity and use. It is not possible to type a word, click
on a link, or search for a file name \[SE\] {14}, without performing
noticing and appraising activities \[ESC\] {13, 11}, as well as
goal-intention activities \[AC\] {2}. All information behavior,
processing, evaluation, and use are integrated operations involving the
sensorimotor \[S\], cognitive \[C\], and affective \[A\] domains of
human agency. Hudlicka (2003) reviewed the literature on "affective HCI"
which aims to develop the knowledge that system designers need to
"assess the range of possible affective states the users
may\...experience during interactions with the system, and that they
understand their effects on the user, and thus on task performance. Such
understanding then allows informed decisions regarding which affective
considerations must be addressed, when and how" (p. 7). Affective states
are defined as "a range of conditions, including simple bi-polar
'reactions' such as like and dislike, boredom and excitement, or
approach and avoid; basic emotions such as joy, sadness, frustration,
anger, fear and anxiety; complex emotions such as shame, guilt,
jealousy; and long term moods." (Hudlicka, 2003, p. 7-8).

According to *Augmented Cognition International*

> "The goal of augmented-cognition research is to develop computational
> methods that address information processing bottlenecks inherent in
> the human-computer interaction. These include limitations in
> attention, memory, learning, comprehension, visualization abilities,
> and decision making. Limitations in human cognition are due to
> intrinsic restrictions in the number of mental tasks that a person can
> execute at one time, and this capacity itself may fluctuate from
> moment to moment depending on a host of factors including mental
> fatigue, novelty, boredom and stress." (retrieved July 2005 at
> http://www.augmentedcognition.org)

It is therefore recognitized that cognitive operations \[CC\] {4, 10}
are broadly linked to affective states \[AA\] {1, 7} such as fatigue,
novelty, boredom, stress, inattention, distraction. However, it is not
yet recognized that the interaction between the cognitive \[C\] and
affective \[A\] channels takes place continuously at the level of micro
behavior circuits that actually constitute the broadly termed mental
states such as boredom, stress, alertness, or fun. The model in Figure 1
can help researchers to track how these information micro-procedures are
operationally symbiotic and interlinked.

Ikehara and Crosby (2002) report on their efforts in augmented cognition
research to monitor satisficing attributes of the user during task
performance with a computer. The design intent is for the computer to be
able to optimize the user's task performance under varying mental states
of stress and focus. What is displayed to the user, and at what rate and
density, is adjusted to the user's level of attention, attention span,
fatigue, affectivity and interest. This type of system architecture
integrates the human and the machine into a unified behavior cycle, as
may be seen by referring to Figure 1 in the following description.

The user's phase of the cycle consists of the satisficing behavior
circuits during the adaptation phase of incoming information, which is
shown at the top with arrows pointing from the computer as environment
\[E\] to the user as three channels of mental states \[SCA\]. The first
symbiotic interaction between user and computer takes place as the
behavior of *noticing* the features of the system (e.g., its displays
and functions) \[ES\] {13}. The environment is therefore transformed
from physical signal to mental sensory input called noticing. This
well-practiced visual or auditory categorization routine operates with
acquired filtering mechanisms, allowing automatic identification and
recognition of overt features, or else the activity of ignoring them.

The noticing operation in the sensorimotor channel is passed to the
appraising procedure, which is the cognitive operation of constructing
or attaching meaning to what has been noticed \[SC\] {11}. This
cognitive operation is then passed to an affective operation, which
consists of experiencing an evaluative reaction to the noticed and
appraised information \[CA\] {8}. The entire input sequence \[ESCA\]
{13, 9, 11, 8} is the human phase of satisficing and adapting to the
system environment, and the entire output sequence \[ACSE\] {2, 3, 5,
14} is the human phase of optimizing and performing goal-setting,
planning, execution, and modification operations on the system.

In augmented computing the design intent is to have the machine phase
kick in at some point in order to optimize the user's task performance
and goal intentions \[ACSE\] {2, 5, 14}. Ordinarily this second phase is
left totally to users, to fend for themselves with the equipment. But
with augmented computing, the machine senses and monitors a user during
the adaptation satisficing phase, e.g., a user's attention span \[SSEE\]
{6, 14, 15, 13, 6}, rate of work \[SE\] {14}, errors \[CSE\] {5, 14},
degree of stress \[A**^S^**\], affectivity \[A**^S^**\]. The system uses
this data and responds to these user "attributes" by modifying the
environment \[EE\] {15} to compensate for the user's current behavior
procedures. For example, it could adjust the rate of information
presentation \[E**^S^**\], offer a rest period with a game \[E**^S^**\]
or begin playing some favorite music \[E**^S^**\]. Human and machine are
therefore integrated into a symbiotic satisficing-optimizing cycle of
interaction and performance.

The integrated behavior-environment theory represented in Figure 1 is
thus suitable for understanding information behavior from both the
individual-self perspective and the human-machine perspective. Both
perspectives strive to integrate the satisficing adaptation phase with
the optimizing task performance phase. Monitoring channels that have
been used to investigate sensorimotor satisficing behavior circuits
\[SEESS\] {14, 15, 13, 6} include eye position tracking, pupil dilation,
galvanic skin conductivity, seat movement and pressure, heart rate and
body temperature (Ikehara and Crosby, 2002; Hudlicka, 2003). Ikehara and
Crosby's (2002) research expands this spectrum of "passive" user
monitoring to the optimizing "force applied to the computer mouse while
clicking responses to a task" \[SE\] {14}.

Figure 1 makes it clear that mental states occur in three distinct human
biological channels termed affective \[A\], cognitive \[C\] and
sensorimotor \[S\]. Sometimes the term "cognitive" is used in the
literature quite broadly, like the word "mental," to include all three
channels. The field of HCI generally, and augmented cognition
specifically, has an interest in all three channels of user attributes:

> *affective states* \[A\]\
> stress, impatience, emotionality, affectivity, evaluation,
> value-attachment, judgment, rating, prioritizing, distractibility,
> self-confidence, optimism, fun, affective load, motivation, vigilance,
> alertness, goal-intentions, involvement, engagement, perseverance,
> interest, etc.
>
> *cognitive operations* \[C\]\
> memory, visualization, comprehension, appraising, misconception,
> biased reasoning, bottlenecks, limitations, goal-planning, problem
> solving, etc.
>
> *sensorimotor activity* \[S\]\
> proprioceptive, social interaction, biometrics, interaction,
> perceptual, verbalizing, texting, keyboarding, performing,
> productivity, etc.

**Applications of the Model**

Figure 1 can be used to chart the sequence of sub-operations that
constitute any user attribute. For instance, *alertness* is a user
attribute that involves all three information behavior channels. The
full satisficing-optimizing cycle of procedures for the user attribute
of *alertness* can be charted on Figure 1:

> goal-setting intentions are activated when the user initiates an
> affective state of aversion for keyboard and mouse errors\
> \[AAS\] {1, 3}
>
> goal-setting intentions are activated when the user maintains
> sensorimotor procedures of vigilance for the ensuing minutes\
> \[ASS\] {3, 12, 6}
>
> goal-setting intentions are activated when the user inhibits
> distracting thought sequences\
> \[AC\] {2}
>
> noticing procedures are applied to proprioceptive feedback from hand
> and fingers\
> \[SSS\] {12, 6}
>
> noticing visual feedback from looking at the keyboard and the screen\
> \[ES\] {13}
>
> appraising all noticings to interpret meaning\
> \[SC\] {11}
>
> evaluating the noticing before and after the appraisal, and judging it
> as acceptable/not acceptable, good/bad, etc., using learned frames of
> normative reference\
> \[SA\] {9} and \[CA\] {8}
>
> optimizing the feelings of evaluation through goal-setting procedures
> such as to let stand or modify the keyboard entry or mouse click\
> \[AAS\] {1, 3}
>
> executing hand and eye motor coordination (either continuing or else
> interrupting for corrections or modifications)\
> \[ASE\] {3, 14} and \[ACSE\] {2, 5, 14}
>
> The full descriptive code by assembling the above sequences:\
> \[AASACSSSESCASAASEACSE\]\
> {1, 3, 3, 2, 12, 6, 13, 11, 9, 1, 3, 3, 14, 2, 5, 14}

This coded summary sequence of information procedures represents what
users perform when being alert while carrying out a task with a
computer. The code 'signature' for *alertness* indicates that this user
attribute consists of specific types of affective, cognitive, and
sensorimotor interactions with the computer. This theoretical definition
of alertness may be considered a testable hypothesis, though this still
remains to be done with a variety of user attributes. Nahl (2001) has
shown that information searching skills and errors can be classified in
a taxonomy of affective, cognitive, and sensorimotor elements at three
distinct levels of internalization.

Another known user attribute, *stress*, is an adaptation procedure that
users maintain when the appraisal of incoming information \[ESC\] {13,
11} occasions negative emotions and feelings of evaluation \[C A\] {8}.
The negative satisficing procedure of *stress* or *stressing* triggers
ineffective optimizing procedures \[AASE\] {1, 3, 14} such as
unnecessarily increasing the pressure on the mouse, along with other
disruptive motor activity like trembling of the hand, contraction of the
pupil, tightening of the jaw, the "green light reflex" that causes
swayback posture, or the "red light reflex" that causes tightening of
the abdominal muscles. These motor activities are ineffective optimizing
output procedures in response to the negative satisficing of the input
\[AASE\] {1, 3, 14}. Negative affectivity such as stressing and feeling
anxious or frustrated \[A**^S^**\] has been observed to disrupt
cognitive problem solving operations \[AAC\] {1, 2} (Ortony, Clore &
Collins, 1988).

According to Pausch and Proffitt (2005) "Augmented cognition is a means
for optimizing the flow of information to a user by accommodating this
flow to the user\'s current cognitive workload, which is assessed via
neurophysiological sensors detecting arousal and brain activations."
Again, it is evident in Figure 1 that the user's cognitive workload is
centered in the \[C\] channel {4, 10} which operates the behavior
circuits involved in appraising and planning, that is, in remembering
\[C**^S^**\], interpreting \[C**^S^**\], justifying \[C**^S^**\],
planning \[C**^O^**\], analyzing \[C**^O^**\], and problem solving
\[C**^O^**\]. The model indicates how the computer or system environment
\[E\] is symbiotically integrated with the user through the human
sensorimotor channel for *noticing satisficing affordances* provided by
the system \[ES\] {13}, and for *manipulating optimizing affordances*
that also provided by the system \[SE\] {14}.

At the micro analysis level of user behavior, cognitive operations
continuously interact with the affective channel of the user. The
cognitive channel \[C\] takes the information input from the
sensorimotor channel \[S\] and passes it to the affective channel \[A\]
for value-attaching and satisficing. For example, suddenly noticing that
our typing is not registering on screen \[ES\] {13}. If we don't react
by evaluating or satisficing \[CA\] {8}, the noticing dissipates. The
affective feeling of satisficing or evaluating information \[A**^S^**\]
gives us the capacity to care about it, to arouse our motivation to do
something about it \[A**^O^**\] {1}. The satisficing evaluation feeling
triggers an optimizing feeling that is different in quality \[AA\] {1}.
This affective interaction procedure is the beginning of all human
action, hence it is marked as path 1 on the diagram.

*Satisficing feelings* \[A**^S^**\] have to do with evaluating, with
accepting it as good enough to be satisfied, given the constraints of
the situation \[ESCA\] {13, 9, 11, 8}. *Optimizing feelings*
\[A**^O^**\] have to do with what one wants or desires to do about
something, in other words, goal-setting, goal intentions, goal
motivations, and goal-regulating \[ACSE\] {3, 2, 5, 14}. For example, in
order to continue scrolling down a list of items, a person must initiate
and maintain a goal-intention of "keep scrolling" \[A**^O^**\], and this
is maintained until it is interrupted by another goal-intention (e.g.,
"Stop. Go back. Let me see that again." \[SAAS\] {9, 1, 3}.

It is widely accepted that cognitive processes could not continue to
operate unless they are repeatedly satisficed and optimized as the
thinking process proceeds (Simon, 1956, 1967; Bandura, 1996, 1997). For
example, after noticing that typing is not registering on screen \[ES\]
{13}, we immediately experience a negative satisficing reaction \[SA\]
{9}. We don't have to appraise that noticing \[SC\] {11} because it is
common, automatic and can bypass the appraisal circuit. The negative
satisficing "Something is wrong here." triggers an optimizing strategy
\[AAC\] {1, 2}, which is to engage in goal-directed, purposeful problem
solving \[C**^O^**\]. This cognitive effort would not occur without the
optimizing feeling of wanting to find a way of getting the cursor back
on the screen. This motivated and goal directed cognitive solution
\[AC\] {2} is passed to the sensorimotor channel for execution \[CSE\]
{5, 14} (e.g., clicking anywhere on the screen returns the cursor).

Nahl (2005) shows how the ecological behavior-environment model can be
applied to reconstruct information behavior circuits through qualitative
analyses of text or discourse produced by users in self-reports,
electronic messages, and e-commerce communications with customers. An
example from an email message by a customer:

> *I'm thinking about buying a new xx*\
> customer is asking for support \[A**^O^**\] by announcing \[S**^O^**\]
> the optimizing intention of planning to buy a new xx \[E**^O^**\]\
> \[ASE\] {3, 14}
>
> *I can't decide between a brand X model 100 and a Brand Z model 727*.\
> customer's specific satisficing need \[A**^s^**\] is occasioned while
> appraising two brands \[C**^S^**\]\
> \[CA\] {8}
>
> *Can anyone help?*\
> customer appeals for help \[A**^O^**\] by voicing \[S**^O^**\] a
> direct request\
> \[ASE\] {3, 14}
>
> Overall coded sequence:\
> \[ASECAASE\]\
> {3, 14, 8, 3, 14}

This is an example of how the model can be used to code interpretive
discourse constructed by users while engaging in tasks with computers
and interacting with systems. Romano *et al*. (2003) review the
literature and conclude "Internet conversation text can yield meaningful
information about consumers' wants, needs, and attitudes toward
products." (p. 218). Companies have found it useful to rely on this
qualitative content analysis of customer feedback or inquiry to make
needed design changes in products. Romano *et al.* show how this type of
content analysis can be automated for large volume processing of Web
text.

Additional evidence for the validity of the model comes from the
analysis of user self-reports written by college students carrying out
an assigned search task, which included the instruction to describe the
steps that were followed to carry out the search task (Nahl, 2005).
Below is one brief sample showing how user discourse is coded using the
schema in Figure 1:

> *I opened up my web browser\
> *user optimizes her intention by performing a motor routine\
> \[ASE\] {3, 14}
>
> *I went to my favorite search engine on the web,
> [google.com](http://www.google.com/)\
> *user's goal intention of going to Google \[A**^O^**\] is satisficed
> as her favorite engine \[A**^S^**\] and goes there \[SE\]\
> \[AASE\] {7, 1, 3, 14}
>
> *In the search bar I put in the following "mailbox copper finish"\
> *user appraises the location of the query window or "search bar"
> \[ESC\] and types the query \[CSE\]\
> \[ESCCSE\] {13, 11, 4, 5, 14}
>
> *I got 33,300 results but I didn't find a right one until I got to the
> second page of results when I came across the furnitureontheweb.com
> site.\
> *user appraises the number of hits \[ESC\], which occasions negative
> satisficing \[A**^S^**\] at first ("not the right one"), followed
> after some browsing \[ACSE\] by positive satisficing \[ESA\]\
> \[ESCAACSEESA\] {13, 11, 8, 1, 2, 5, 14, 15, 13, 9}
>
> *There I found that they had a beautifully decorated mailbox with a
> copper verde finish that was pre-mounted\
> *user browses, notices, and appraises until a satisficing feeling is
> reached\
> \[ACSEESCA\] {2, 5, 14, 15, 13, 11, 8}
>
> *I copied the page with all the information I needed and pasted it
> into my report\
> *user copies appropriate information \[ACSSCCS\] and pastes it
> \[ACS\]\
> \[ACSSCCSACS\] {2, 5, 12, 11, 4, 5, 12, 11, 4, 5}

These analyses of the micro procedures that users routinely perform are
indicative of the actual level of attention and focus at which users
operate while performing familiar tasks with computers. It is the level
at which users make the task comprehensible to themselves, thus enabling
them to use the system to attain their goals. The analysis reflects how
users continuously integrate the elements of the task into their
thoughts and feelings, to generate behavior circuits that in the end
lead to successful performance.

**Future Directions**

The ecological behavior-environment model is general enough that it can
help make explicit what the sub-components of human agency are in a
variety of information settings, such as performing tasks with a
computer, online shopping, modeling user attributes and designing
symbiotic human-machine satisficing-optimizing systems (Hudlicka, 2002;
Picard & Klein, 2002). Future research will explore how the coding
procedure based on Figure 1 might be computationally implemented, which
would allow the modeling of user attributes on a micro level of
representation and monitoring. It can add another relevant dimension to
the understanding of the channels of symbiosis in human interaction with
agents, robots and sensors. In particular, the model can be helpful to
these issues of interest to systems sciences:

1.  User interfaces developed from knowledge of human behavior and human
    interaction with agents, robots, and sensors

2.  Case studies of user interfaces/interactions with intelligent
    systems

3.  Affective user interfaces

4.  Development of models of computers for users, and of models of users
    for computers

5.  Determining and monitoring cognitive status of users

6.  Issues that arise with intelligent systems and modeling expertise

7.  Theories of expertise and applications of expert performance
    research

8.  Skill acquisition in technological and applied domains

9.  Mechanisms and models of expert cognition

10. Adaptive and routine expert performance

11. Expert-novice differences in complex real world tasks

12. User and system error

Figure 1 helps orient investigators to overlapping areas of research
that are of interest in user modeling and the design of intelligent
architectures that support symbiotic interaction:

\(i\) \[ESA\] {13, 9} Identifying user procedures that lead to *biased
noticings* of the display features, where some critical features are
ignored or not noticed

\(ii\) \[SC\] {11} Identifying user procedures that lead to *inadequate
or incomplete appraisal* of incoming information, where wrong
conclusions are arrived at

\(iii\) \[CA\] {8} Identifying user procedures that lead to *resistive
satisficing states*, where rejection is premature vs. enthusiasm and
loyalty

\(iv\) \[AA\] {1, 7} Identifying user procedures that *enhance*
*optimizing* with appropriate goal-setting and regulating vs. wrong
headed and ineffective

\(v\) \[AC\] {2} Identifying user procedures that lead to *goal-directed
planning* and problem solving vs. inconsistent reasoning

\(vi\) \[ACSE\] {3, 2, 5, 14} Identifying user procedures that optimize
performance and develop automaticity with less errors

The first three areas focus on satisficing issues while the other three
focus on optimizing issues. User procedures refer to the behavior
circuits that an individual performs when satisficing input from the
computer and optimizing goal-intentions through planned action or
response. Making errors is not a random activity. Users must follow
particular behavior circuits to accomplish making those errors. Every
error is an "accomplished fact," meaning that a certain step-by-step
procedure had to be followed to commit that error. Similarly with other
user attributes such as:

> being distracted by insufficient goal regulation in problem solving
> sequences\
> \[AC\] (2}
>
> forgetting things, by not optimizing goal-setting and not satisficing
> high standards of accuracy and precision\
> \[ACCAA\] {2, 10, 8, 1}
>
> being impatient and missing available solutions\
> \[AAS\] {1, 3}
>
> feeling suspicious of the system, resisting following instructions\
> \[ESAACSS\] {13, 9, 1, 2, 5. 12}
>
> feeling enthusiasm and single minded focus\
> \[AAASS\] {7, 1, 3, 12}

Research is needed to map out the network of behavioral circuits and how
they become established by an individual to produce characteristic
satisficing and optimizing procedures in information settings. With a
focus on optimizing, it is useful to classify the optimizing stream of
behavior into three levels corresponding to the three memories: long
term, short term, and sudden. Long term behavior circuits involve
goal-setting plans that are optimized longitudinally, such as career
goals or lifestyle strivings. Short term goals use behavior circuits
that run task performance, while sudden goals are micro circuits that
tend to be automated. The three levels of goals and the three channels
of operation define a matrix of nine zones that can be used to map,
monitor, and influence how users behave in information settings. Nahl
(2001) has described several applications of the ennead matrix to
information seeking behavior.

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