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User Technology:

From Pointing to Pondering

Stuart K. Card and Thomas P. Moran

Xerox Palo Alto Research Center

 

technical understanding of the user

himself and of the nature of human-computer interaction

  • the scientific base of user technology, is necessary in order to
  • understand why interaction techniques are (or are not) successful,
  • to help us invent new techniques,
  • and to pave the way for machines that aid humans in performing significant intellectual tasks

Our focus is on what

we have learned about users in our years of studying them

and how we see our findings relating to the original visions

of the personal workstation.

 

  1. The Vision of an Applied User Psychology

1974 PARC

Xerox’s idea was to draw concepts from cognitive psychology and artificial intelligence to create an applied cognitive science of the user

Applied Information-processing Psychology Project (AIP)

Worked with Allen Newell

Substantial payoff to designing systems with detailed understanding of the way the human must process the information attendant thereto

  • Emerging psychology of cognitive behavior
  • Central activities within scope of emerging theory
  • Computer sci is one-sided in treatment of phenomena (no effort in understanding user)

HCI: involving two active agents each capable of initiating an exchange of information involves human cognitive processing

Wanted to create science of the user rooted in cognitive theory and have practical application (conceptual tools to think abt key characteristics of user and calculational tools to take account of user’s behavior)

Abbreviated history

The cognitive interface: The user has certain

characteristics as an information processor,

such as a limited working memory, that

together with the goals he is trying to achieve

determine his behavior.

The conceptual #interface: The computer

system is also a complex information

processing mechanism, and the the user

needs to have some kind of mental model of

it in order to effectively interact with it.

The task interface." Systems are designed to

help their users do tasks, not only small,

routine tasks, but also larger, difficult

intellectual tasks that are the object of the

grand visions of personal workstations.

 

  1. The Physical Interface: Pointing

we wanted to

understand the reasons why the results came out the way

they did. We therefore made mathematical models of each

device and tested them against the data until we found

models that fit. The model for the mouse was particularly

instructive. The mouse was best modeled by a version of

Fitts's Law:

Movement Time = Constant + .1 log2(D/S + .05) sec, where D is the distance the hand moves to the target and S

is the target width

this is the same law that describes movement

time for the hand alone, with about the same constant of

proportionality

 

the limiting factor in moving the mouse is not in the mouse, but in the eye-hand coordinate system itself

 

wanted to build theory:

model precise enough to let designers perform back-of-the-envelooe calculations and identified key constraints in the design space for pointing devices

 

  1. The Cognitive Interface: Cognitive Skill

The Model Human Processor

An interesting result of the mouse study was the way

an evaluation of pointing devices led to a consideration of

human information processing characteristics

1982 Model Human Processor inspired by the processors, memories, switches (PMS) notation of Bell and Newell for describing the architecture of the user

  • three processors
  • four memories
  • 19 parameters of these
  • 10 principles of operation

preserving the illusion of causality

MHP can be used to compute predictions about human performance

Text Editing and Cognitive Skill

MHP also contains Herbert Simon’s bounded rationality principle:

A person acts so as to attain his goals through

rational action, given the structure of the task; his inputs of information, and boutided by limitations

upon his knowledge and processing ability. (Card,

Moran, & Newell, 1983).

 

behavior

could be modeled as a search through a space of states of

the problem, a problem space

assumption: we would find users

searching through problem spaces to accomplish goals,

trying various solutions, backing off and taking other tacks

when they ran into trouble.

Two early decisions:

Although we had come

to PARC initially with the intention of studying computer

programming, we decided once we arrived that there were

strategic advantages to studying text editing. A second,

tactical, decision was to work with expert subjects rather

than novices, in order to have more stable behavior to

analyze.

This wasn't problem solving; we came to call it cognitive

skill and set out to build models to characterize and predict

this mode of behavior.

The result was a class of models in which

the user's cognitive structure consists of four components:

(1) a set of familiar Goals that the user would recognize

when faced with a specific task; (2) a set of primitive

Operators (actions) that the user was skilled at performing

and could deploy whenever necessary; (3) a set of Methods,

consisting of "compiled" sequences of subgoals and

operators, that the user could use to attain his goals; and (4)

a set of Selection rules that enable the user to choose among

competing methods for goals. We call a model specified by

these components a GOMS model

together, these components constitute the user’s cognitive skills for performing tasks (no need for problem-solving strategies)

practical question

related to the applicability of our models was whether the

additional work of constructing a fine-grained model was

worth the effort

We were

surprised to discover that fine-grained modcls did not yield

a worthwhile or even a significant increase in prediction

accuracy

even

the crude models seemed to capture and predict behavior

fairly well. These properties suggested that the GOMS

model could be turned into the kind of engineering tool

that a designer could use to model and predict skilled user

behavior in computer-mediated tasks.

 

The Keystroke-Level Model

idealized prediction problem:

Given a task (possibly involving several

subtasks), lhe command language of a system,

the motor skill parameters of the user, the

response time parameters of the system, and

the method used for the task, predict how long

an ~xpert user will take to execute the task

using the system, providing he uses the method

without error.

Each action of the method is

described in terms of the operators of the model, then a

time for the method is computed.

 

understanding how well the schemes would work for

expert users (which most users would be for most of their

time on the system) was expensive, because a long time

would have to be spent training the users. The solution was

to run experiments for novices and to use the

Keystroke-Level Model for predicting expert performance.

 

Each think-execute chunk of behavior is a unit task.

The unit task structure of cognitive skill is interesting

because the performance limitations of the user show

through the purely rational organization of his behavior.

limitations in the user's

working memory

If the information in

working memory should reach a level higher than the

working memory capacity, then user performance will

stiffer, usually manifested by the user committing errors.

Unit-task behavior is at present a theoretical notion

based on empirical observation

“cognitive rhythm” important feature of user behavior

 

The Psychology of HCI

Calculational models: relating these to the classical literature in cognitive

psychology and human factors

the Model Human Processor captured the relevant psychological

literature in the terms of a unified, approximative model.

This model also provided a foundation for our other

Models

 

The other big missing piece for us was to understand

the relationship between the cognitive skill we had

discovered and the classical notion of problem solving in

cognitive psychology.

Here we built a theory of the

behavioral continuum between problem solving and

cognitive skill and showed how practice on a task would

gradually chan~ge problem solving behavior into skilled

behavior. This is all active area of research in cognitive

psychology today

 

  1. The Conceptual Interface: Mental Models

expert users: procedural knowledge

the expert

users have some sort of mental model of what is happening

inside the computer Chow-it-works" knowledge). The “user’s model” conceptual model the user can have of the system

user’s conceptual model is distinct from but related to deisgner’s implementation model

The user's model provides an integrated package of

knowledge that allows the user to predict what the system

will do if certain commands are executed, to predict the

state of the system after the commands have been executed,

to plan methods for novel tasks, and to deal with odd error

situations (by characterizing the system's state according to

the model, then choosing operations necessary to leave that

state).

 

Early Encounters with Conceptual Models

It is clear that users attempt to make sense--by

building mental models--of the behavior of a system as they use it. If a simple model is not explicitly or implicitly

provided, users formulate their own myths about how the

system works.

if

the user is to understand the system, the system has to be

designed with an explicit conceptual model that is easy

enough for the user to learn. We call this the intended user's

model

 

User Interface Design Methodology

PARC ,and the Systems Development Division (SDD),

which was created to develop office system products based

on the research at PARC

The committee decided not to try to design an actual

interface, but to propose a methodology

(1) analyze what tasks

the user will want to do and the steps they go through to

accomplish the tasks;

(2) design an intended user's model in

terms of which the tasks may be cast; (

3) design a command

language to make that model work; and

(4) design an

information display to reflect the operations of the system

in terms of the conceptual model.

 

we recommended that the designer should lay

out an intended user's model before designing the

command language and the information display

 

Empirical Studies

The goal was to see whether we could find some

kind of mental models buried in the user's knowledge

no difference

between the two groups in both the routine and complex

problems, but the model group performed much better on

the invention tasks. The most surprising result was that

even some of the no-model group were able to perform

some of the invention tasks

in the

routine and complex problems, the behavior was almost all

skilled method execution: the subjects had been taught

what to do, and they did it; even for the complex problems

it wasn't difficult to knit together the methods they had

learned for solving the problems.

We found that the most critical (although not very time

consuming) problem solving was in the task space, where

the user analyzed the given task into subtasks and delegated

them to the model space or the methods space. The main

difference between the users who had a model and those

who didn't was that they had different problem spaces in

which to work

The model space was an

effective problem space, within which the solution to the

invention problems could be found; the method space was

not particularly effective, but it was sufficient to allow some

non-model users to stumble onto solutions to some of the

invention problems (often, much to their surprise).

Model-based

problem solving appears to be very mentally intensive, so

users avoid it if they can apply cognitive skills. But, if users

don't have appropriate methods available, they will retreat

to some kind of problem solving. In these cases, a good

conceptual model provides an effective problem space in

which to work.

Thus, system designers should think of a conceptual

model of a system as not just a simple view of a complex

system, but as a problem space through which users can

search for solutions to a variety of novel problems

the intended user’s model should be closely related to the kinds of tasks the

users arc likely to do, and the users should be provided with

heuristics for moving through the model space.

 

Theoretical Studies: Task Mapping

theoretical analysis of conceptual

models to show where they fit into the overall structure of

the user interface

The Command Language Grammar

formalism (Moran) shows how models relate to

the task domain, the command language, and the detailed

user-computer interactions.

conceptual model provides the user with a link between his

task domain and the syntax of the interactive dialogue

called a surrogate model

 

task-action mappings

The properties of radically different calculator

designs, such as algebraic versus stack calculators, could be

best understood by an analysis of how well calculation tasks

could be directly mapped into the actions available on the

calculators. bypassed surrogate models

 

we have proposed a

calculus, called ETIT analysis, for task mapping:

The "fit" of a system to a task domain can be

assessed by enumerating rules for reformulating

system-independent task descriptions ("external tasks") into

system-specific task descriptions C'internal tasks").

Rule-based system description techniques, look promising as a way provide system designers with

calculational techniques for predicting the learnability and

"guessability" of systems.

 

  1. The Task Interface: Pondering Ideas

the role of a science of the user in

the future development of the personal workstation

 

The challenge is to create systems that, through

intimate cognitive interaction with users, aid them in

structuring and manipulating their ideas.

The key to building such systems is to find ways by

which a user can act on his ideas as objects, just as current

text editors allow him to act on words as objects. externalize the ideas

two problems:

  1. representation (exploit AI and cognitive sci)
  2. manipulation (exploit interactive computer graphics)

 

a shift in our research

strategy from studying users of existing systems to studying

users of new systems that we ourselves build, which enables

us to understand the nature of the tasks and the limits of

users and systems for dealing with them

 

Representation: Idea Structuring

MT: example is Scrivener

The key research issue here is to help the users develop

explicit mental models of idea structures, so they can see

them, play with them, and evaluate them. This requires the

invention of representations for externalizing ideas and idea

structures.

NoteCards

Open system

we are now observing a population of

idiosyncratic, exploratory users

 

Manipulation: Idea Browsing

how a computer

system can compensate for human cognitive limitations

The display can be used as an external memory to augment

the user's internal working memory

But the problem is that these techniques, which work

well with with a few dozen objects, do not scale well up to

the several hundred or even thousands of objects

Window Working Set Model analyzes access to screen objects in a manner analogous

to the analysis of virtual memory operating systems.

Informally, this model suggests that screen space itself is the

key constraint and that at some point as the number of, say,

overlapped windows required increases, user performance

will decrease in a sudden and non-linear way, sending the

user into the window version of thrashing.

Advanced graphics systems, however, open up a new

set of possibilities.

The visual movement techniques is coupled with other

retrieval techniques for allowing the user to focus on a

limited number of items at a time (as required by our

understanding of user's processing capacity) while retaining

rapid access to a large number of items (as required by our

understanding of the requirements of complex intellectual

tasks).

 

  1. Conclusions

At the physical interface level, we have discovered that

user performance with pointing devices is constrained by

the information-processing capacity of the user. We have

learned the quantitative law describing this constraint and

have determined that certain devices, such as the mouse, are

at the performance limits allowed by this law.

At the cognitive level, we have learned that routine

human-computer interaction, such as text-editing, does not

involve problem solving, but rather cognitive skills based on

the execution of known methods. We can see the

information-processing constraints of the user show

through this skilled performance in the unit task of users.

We have characterized cognitive skill to the extent of

developing an engineering model for use by user-interface

designers.

At the conceptual level, we have learned that usersoften have mental models of the systems they use, and that

such models enable performance of novel tasks. System

conceptual models provide the basis for users to acquire

mental models, and thus are an important basis for system

design. But mental models are cognitively intensive, and

users will avoid them by attempting to map directly from

their tasks to the actions required in a system. Theories of

task mapping are just beginning to emerge.

 

We believe that the most

interesting problems are at the task level: understanding the

nature of complex intellectual tasks and finding ways to

build idea-structuring tools, both representation tools for

structuring ideas and display tools for browsing ideas

 

Computer as Rorschach

Sherry Turkle

looking at the computer as a

metaphor and as a projective medium, and suggesting that

this subjective side of the computer presence is highly

relevent to understanding issues concerning computation

and public life

ethnographic investigation of the relationships that people

form with computers and with each other in the social

worlds that grow up around the machines.

ideas about computers

carry feelings about political and personal issues

But in

addition, the expert enters into relationships with computers

which can give concreteness and coherence to political

and private concerns far removed from the world of computation.

In particular, the act of programming can be an

expressive activity for working through personal issues

relating to control and mastery

there is a subjective side to people's

relationships with technology

the

elusiveness of computational process and of simple descriptions

of the computer's essential nature undermine

such consensus and make the computer an exemplary

"constructed object," a cultural object which different

people and groups of people can apprehend with very

different descriptions and invest with very different attributes.

 

The Subjective Computer

The fact

that the computer touches on a sphere--intelligence--that

man has long thought to be uniquely his, means that even

popular discourse about computers can raise tense questions

about what is man and what is machine.