in context
cognitive overload, interruption, multi-tasking, workflow, activity space, work environment
Today's workplace is a complex knowledge environment in which the flow of
information is mediated by an ill understood array of
technologies, at-hand resources, and shifting teams of people. Few of us believe,
any longer, that office work is straightforward
and procedural. We recognize that people engage in many tasks at once, often
in ways that cause interference. People interact
with each other and with their tools in little known ways; they constantly
develop work-arounds to standard operating procedures,
and their primary work space is not confined to the physical region within
arm's reach, but is a distributed cluster of 2D and 3D
spaces near key resources, computers, telephones and bookcases. Indeed modern
workspaces now include virtual spaces --
customized computer `desktops' and applications that have their own worlds
of organizational structure, information space, and
workflow requirements. Given this complexity of tasks and spaces it is no
wonder that workers have trouble effectively managing
their office activities and coping with information. Email, telephone calls,
electronic discussion groups, websites, pushed intranet
news, letters and memos, faxes, stick-ems, calendars, pagers, and, of course,
physical conversations and meetings, are just a few
of the communicative events that bombard today's knowledge worker. The upshot
is a workspace of increased complexity,
saturated with multi-tasking, interruption, and profound information overload.
The effect of this cognitive overload at a social level
is tension with colleagues, loss of job satisfaction, and strained personal
relationships. (IFTF/Gallup [97] study of Fortune 1000
workers.)
To understand how people handle this bewildering matrix of information and
activity spaces typical of modern workspaces
requires close attention to the fine grain of interaction. Given the prevalence
of multi-tasking and interruption: How do we switch
attention from one task to another? How do we maintain control over our multiple
inquiries? What do we find intrusive,
distracting, or annoying? What are the effects of interruption and what sort
of cognitive strategies have people developed to
minimize their consequences? There is a large body of psychological literature
on attention – both single and dual task attention.
But the issues that concern us here, lie as much in the interaction between
agent and environment as in the agent's cognitive
make-up itself – an area experimental psychologists have spent less time exploring.
When people adapt to their environments they
not only adapt internally by altering mental processes and behavior, they
also change the very environment posing the adaptive
challenge. If we are to develop theories of information overload, multi-tasking,
distraction, and interruption – all key components
of a general theory of cognitive overload -- we will have to understand this
co-evolution. We will need to understand how people
dynamically manage their interaction, how they are cognitively coupled to
their environments, and how they structure workflow by
using the environment as a cognitive ally.
In this paper I will take a first look at some issues that arise when we set
out to design real life environments in which
multi-tasking, interruption and cognitive overload are the order of the day.
So many specific areas for research are opened by
these topics there is space to explore just two:
1.What is cognitive overload?
2.How can an understanding of the cognitive workflow in environments lead
us to design better workspaces?
What is cognitive overload?
Many of the consequences of cognitive overload are well described in business
studies. In 'Dying for Information? -- an
investigation into the effects of information overload in the U.K and World-wide',
[Waddington, 96] a 350 page report based on
a survey of 1,313 junior, middle and senior managers in the U.K, U.S, Australia,
Hong Kong and Singapore -- the key findings
were:
two thirds of managers report tension with work colleagues, and loss of job
satisfaction because of stress associated with
information overload.
One third of managers suffer from ill health, as a direct consequence of stress
associated with information overload.This
figure increases to 43% among senior managers.
Almost two thirds (62%) of managers testify their personal relationships suffer
as a direct result of information overload.
43% of managers think important decisions are delayed, and the ability to
make decisions is affected as a result of having
too much information.
44% believe the cost of collating information exceeds its value to business.
People feel information anxiety[1] and suffer. But as this study suggests,
the focus of overload studies, so far, has been on the
consequences of information overload. Typically this term has been left sufficiently
ambiguous that it is not clear whether it
includes other factors such as the increased number of decisions knowledge
workers must make, the increased frequency of
interruptions they confront, or the increased need for time management in
everyday activity – the relentless need to be efficient.
Cognitive overload has something to do with all these concerns. Everyone has
so many tasks and obligations that multi-tasking is
our way of life. Information is relentlessly pushed at us, and no matter how
much we get we feel we need more, and of better
quality and focus. Our workplaces are supposed to help us cope with these
problems. But our tools and resources remain
inadequate. We can turn the ringer off our phones, we can close our doors,
we can auto-filter our email, we can personalize
search engines, ask people to honor privacy, and so forth. But blocking out
sacred time segments or sealing ourselves off from
outside contact and even filtering email is not a serious solution in most
organizations. And where it is acceptable, it still leaves
unaddressed the overload that arises from multi-tasking, interruption and
information overload that we create ourselves in having
to decide how to manage our desks, files, computers, and different projects.
The increase in cognitive overload seems an
inevitable consequence of the complexity of our information intense environments.
Let us look at the components of overload more closely. We consider four systems
of causes: too much information supply, too
much information demand, the need to deal with multi-tasking and interruption,
and the inadequate workplace infrastructure to
help reduce metacognition .
Supply-Related Overload
Following modern conventions we can distinguish two forms of information supply.
Pushed information is information arriving in our workspace over which we
have little short term control – the memos,
letters, newspapers, email, telephone calls, journals, calendars etc. that
land in one of our inboxes.
Pulled or retrievable information is information we can tap into when we want
to find an answer to a question or acquire
background knowledge on a topic.
We have greater control over pulled information in that we intentionally seek
it. But it resides in vast repositories such as
libraries, online journals, filing cabinets, newspapers, archived discussion
groups, our own email and of course the web.] At
a more interactive level, discussions with colleagues and chat requests in
discussion are additional examples.
Both of these types of information are part of the great supply of information that we must decide whether, how and when to use.
Oversupply of Pushed Information
Here is a standard case of pushed oversupply and the activity it spawned.
After 10 days of travel a colleague of mine returned to
his office. He collected his paper mail from the mail room, went to his office,
found on his chair another stack of paper mail far too
large to fit in his mailbox, discovered 290 email messages in his inbox, and
listened to 14 telephone messages on his answering
machine. After scanning the topics of his email, he checked 21 in detail and
quickly answered 6, he returned to his telephone
messages, answered 4 that were still timely, and then he sat on the carpeted
floor, pulled over his garbage can and began tackling
his paper mail. As he worked he started building piles on the floor. Journals
and magazines for his lab went `there', requests for
article reprints he put over `here', he began filling a large manila envelope
with letters and receipts he was going to take home.
Newly arrived software went beside the journals for the lab, and so on. Halfway
through, he stopped to read 30% of a newsletter
before trashing it. When he was largely finished, he had 9 untidy piles in
different parts of the room – mostly on the floor, and then
he left, carrying the pile `designated' for his lab.
No knowledge worker needs reminding that we are bombarded with information.
It comes in all categories of urgency, media,
size, timeliness, complexity and value. To deal with it we have to make decisions.
Is this garbage? Might it be useful? When?
Where should I put it? Must I make a new file or new category for this? Can
my colleague use this? Making these decisions
carries a cost in time, effort and stress. Even if we have a system for making
such decisions we still must scrutinize each piece of
information, categorize it as of value for this or that project and consider
what to do with it. Too often information falls between
the cracks of our classifying scheme and we are forced to go through the challenging
process of creating new indices and
categories, or uncomfortably stretching old ones. Moreover, whenever we create
a new category or stretch an old one, there is
the danger that where we place the information – on our desk, in our filing
cabinet, in a computer folder – will be forgotten the
next time we look for it. All this is stressful. But particularly so, the
less one has a system for dealing with pushed information and
the more one must make ad hoc decisions for each incoming piece. The psychological
effort of making hard decisions about
pushed information is the first cause of cognitive overload.
An oversupply of Retrievable Information
The information arriving at our doorstep is a fraction of what we need for
our work. We constantly consult our files, the Web, our
colleagues, libraries, online discussion groups, journals, etc. for more.
Another source of cognitive overload stems from the effort
of performing effective search. We always seem to want more and better focused
information. And no matter what we have found
so far, most people harbor a lingering belief that even more relevant information
lies outside, somewhere, and if found will save
having to duplicate effort.
The problem is bizarre. From an economic viewpoint, if information were like
other commodities then beyond an initial threshold
we ought to find both the marginal cost and the marginal value of the next
piece of information falling with quantity. The more that
is produced the less it ought to cost per unit, and the more we accumulate
the less additional pieces ought to be worth. Hence our
life ought to get easier in information rich environments. The missing premise
is quality. In economics it is assumed that it is
possible to increase the quantity of a commodity available, in this case information,
without reducing its quality. This may be
possible in principle, but we all know it is not so in practice. There is
evidence that the ratio of high quality to low quality
information is falling. See figure 1. The increase in low cost information
now readily available with the web has massively outpaced
the increase in quality information available. This drives up the individual
cost of search for quality information. Where before we
turned to trusted information sources, such as refereed journals or quality
magazines, we now do more of our information hunting
ourselves. Yet no search engine seems to return hits with sufficient precision
to save us from having to browse dozens of useless
pages in our effort to berry pick the best items. Substantially the same holds
true for digital and physical library searches now that
these have increased so substantially in size. The result is that we spend
more time searching than we feel we should. Time is
wasted and jobs that must be done are left undone. More stress. The second
source of cognitive overload, then, stems from our
effort to cope with the uncertain quality and relevance of our information
supply. There is more search and less satisfaction.
In the next section we explore a further consequence of increased information
supply: a partially justified belief that there are
always higher quality facts somewhere other than where we are looking.
1995 1996 1997 1998
Figure 1. Here we see that in the last few years the amount of information
has been rising
exponentially while the amount of usable or high quality information has been
rising only
linearly.
Demand Side Overloading
Demand side overloading is the consequence of the complexity of our desire
function for information. Uncertainty about how
much information will be needed, when it will be needed, and how valuable
it will be, leads to a complex demand structure that
interacts in peculiar ways with information supply. If we treat having the
information we think we need at the time we think we
need it as a problem in inventory control then we can understand demand side
overload as the product of computational
complexity. Knowledge inventory control is simply so complex that knowledge
workers cannot optimally solve it. Instead they
rely on reactive methods of coping rather than careful planning.
Every task a knowledge worker is involved in has its informational requirements.Sometimes
these can be satisfied without
knowledge retrieval; the worker already knows what must be known, and so he
or she can complete the task without searching or
soliciting information. More often, though, additional information or knowledge
is required. Economic analyses of the value of
information have shown that if the cost of missing a piece of information
or knowledge is known, it is possible to calculate the time
a rational agent ought to spend in securing it.[2] If all the information
required by the totality of one's projects is specified, and a
value is assigned to each piece, then there is a well defined function determining
the optimal allocation of time to be apportioned to
any particular information search.
Several factors upset such normative analyses, however. First, knowledge workers
rarely know how valuable the information is
which they do not have. The theory of information value cannot offer guidance
if an agent cannot make a reliable estimate of the
value of information. Without a well defined value function, economic analyses
are of little use. This occurs whenever a knowledge
worker does not know what he or she needs to know to do a task well. For instance,
if I wish to write an essay do I need to
know the ins and outs of MS Word? It really depends on what I end up doing
in that essay. This may be hard to know in
advance. How many figures will I have? How many formatting styles? Will I
try to automate making my citation list, a table of
contents and so on? Normally, we cope with these sorts of problems by using
online help, a tool in well designed programs that
allows us to get information just at the moment we need it.
Online help is a nice example of a tool for just-in-time learning that does
not require going very far outside of one's current
environment of activity. It is an excellent example of improved infrastructure.
But even with online help there are occasions when it
would be unwise to interrupt our task to get the information we need. How
deeply into the help system will we have to go? The
deeper we go the more we are apt to lose the thread of our composition. In
such cases we would like to already have the relevant
knowledge.
Moreover, outside of computer environments there is rarely anything analogous
to online help. This raises issues associated with
the setup cost to starting a search. Where will we find the information? By
calling someone? By looking in a manual on our shelf?
In someone else's office? Even in cases where this process has been streamlined
and there is easy access to information it can still
be hard to estimate its value because the value of information is non-monotonic
and non-linear. A little can go a long way, or a
little knowledge can be a dangerous thing. It all depends on the task. Imagine
trying to fix a jammed photocopier. Sometimes you
just need to know a little about photocopiers, sometimes you need to know
a lot. So even in tasks where we have prior
acquaintance with the task it can be hard to know when you have enough information
and when you should continue hunting.
What you need might be just around the corner. On the other hand, it might
be rather far away. In fact, it might not be in the
manual or in anyone's head at all! How are you to know? How long should you
keep looking before you regard your lack of
success to be a sign that the information is not around? It is for such reasons,
that coming to a task with a little knowledge already
can make all the difference. It is extremely valuable to know the lay of the
land in advance. Unfortunately, it is not always obvious
when you know the general layout adequately. See figure 2. How nice it would
be if we had enough infrastructure to know the
complexity of problems and the amount of information required to solve our
problems.
I have been arguing, so far, that imperfect information about the value of
information makes it hard for knowledge workers to
develop a coherent demand function for information, and so to sit down and
plan their information gathering strategies. A second
facet of their workplace that further complicates developing a clean demand
function – and so developing a usable method of
knowledge inventory control -- is information timing. When does the knowledge
worker think that he or she will need the
information? Which tasks do they think they will need information for in the
next weeks? A knowledge worker may know that he
is to deliver a major report in a month, but not yet know whether he will
have dozens of different sized reports, memos and
discussion papers to deal with in the interim. Or whether he will be reviewing
papers, or mostly doing other things. Since most
knowledge work requires information gathering it would be most efficient if
any information that would be useful for both the
interim tasks and the major report would be gathered first. In the absence
of knowledge of what these tasks are, such a
calculation cannot be made and information scheduling becomes ad hoc. It also
complicates the filtering function to be used for
pushed information. For if there is a chance that incoming documents may soon
be useful for new assignments it is rational to keep
them around rather than to trash them. Because most documents have a shelf
life, and lose their value over time trashing is a key
component of information inventory control. But again uncertainty makes it
hard to apply a useful trashing strategy.
It is hard to predict the utility structure of information
Figure 2. For a given task it is hard to predict the utility structure of
information. Here we
see three different ways quantity of information affects performance. Utility
is here
assumed to mean how valuable information is in improving performance. Note
that
initially information may increase, decrease, or have no effect on performance.
Put
differently, more or less knowledge may be required for even minimal performance.
It all
depends on the task and the problem instance.
Observed Information Inventory Control Strategies
Given the resultant uncertainty in the demand function for information and
knowledge, it is not easy to tell knowledge workers the
strategies they ought to use for information inventory control. As a descriptive
matter though it is common to find people following
a few obvious but rather different information gathering and information accumulating
strategies. Well designed environments
would provide infrastructural mechanisms that would improve performance for
each of these different strategies. Here are several
of the most prevalent strategies.
Blind Accumulation: If any piece of information might suddenly become critical,
it is rational to accumulate all information that
might have future value. Naturally, this is rational only if the information
can be found later when it is needed; but false beliefs about
retrieval abound. The result is overstocking. The cost of overstocking depends
on how the information is stored. If it is filed then
such people will spend an inordinate amount of time filing. If they save their
filing jobs to end of week or end of month, then piles
will accumulate in their office.
Just-in-case Learning. Some people like to know enough to be prepared for
anything. Just-in-case learning – or just-in-case
research - is the result. It is the gathering counterpart to the blind accumulation
strategy. In just-in-case learning the emphasis is
again on knowledge that might be useful later, particularly if it has a certain
probability of having to be acquired sometime. Again,
though, because it is impossible to predict exactly when it will be needed
there is uncertainty about the current value of
information. Conservative knowledge workers may feel the best plan is to always
`be prepared'. Hence they will spend an
inordinate amount of time reading journals and magazines when they arrive,
and doing ancillary research in the library even when
they could be extremely focused on a narrow task. Our standard school system
is based on this model of learning, coupled with >
the belief that a certain level of background knowledge must be achieved before
any knowledge of a topic can be acquired. E.g.
math is necessary for physics or modern economics.
Surface Clutter: Accumulators try to keep all information somewhere, preferably
in an easy to access region. But if some of the
accumulated information has a shelf life, or if it is expected to be useful
in the near future, it will often be `shelved' nearby, so that it
may be readily found, may be noticed opportunistically, and the costs of `serious'
filing can be saved. As piles accumulate the
occasion for creating combinations defining new `overlap' or ad hoc categories
increases. As this matrix of overlapping
categorization deepens it becomes harder to pay the break up cost, leading
to an ever more daunting task of filing, if it is ever
undertaken at all. Frequent trashing can cull this matrix, but there is also
a cost to stirring up the categorization scheme.
Understanding how clutter arises and exploring what can be done about it is
a rich area for research.
Just-in-Time Information Gathering: A further information strategy it is rational
to adopt when you are uncertain about your
demand for information is to ignore all information needs except those you
know you need for your current task. Rather than
collect information on speculation that it might be valuable later, collect
information just in time, when you can be precise about
what you do need. This is a local maximizing strategy because you guarantee
high average value for each piece of information. The
danger is that you may not be able to find crucial information in time. Since
some information gathering requires advance
preparation, you may not know enough to use the information you can find quickly
unless you mastered enough background
information earlier. Moreover, you risk settling into local optima that are
far short of the global optima because you are not
maximizing with respect to your overall information needs. In focusing so
narrowly you may be myopic.
Trashing strategies: Frequently, knowledge workers temperamentally disposed
to just-in-time gathering strategies also are
effective trashers: they dispose information soon after it has been used.
Just-in-case gatherers are often less effective trashers. It is
an empirical topic of some interest to determine the different strategies
people have for throwing documents out.
Although all these strategies have been described discursively it is a small
matter to restate them formally in terms of standard
value of information theory plus individual differences in risk taking disposition.
Infrastructure Failure
There will never be a completely satisfactory solution to the problem created
by an overabundance of information. But we can
hope to design operational environments which increase our effectiveness,
whatever our strategy, while reducing stress. In addition
to information overload, however, there are two other major causes of cognitive
overload we must design for: interruption and
multi-tasking[3]. They are related.
As a consequence of multi-tasking, agents are constantly interrupted. They
begin one task, the telephone rings, they are
interrupted when they answer it, and then, if they are not drawn off to a
new task, they try returning to the original task.
Analogously, they begin the task of writing a report, discover they need a
reference, or they realize they need to find out what has
already been written on a topic, and off they go in search of new information.
This too is an aspect of cognitive overload. A well
designed workspace would have tools and resources to minimize the cost of
exiting an activity and re-entering it later. It would
store much of the worker's task state in some convenient external way. In
particular, it would make it easy for the worker to
recover his or her place in the task. This means encoding, in some non-overwhelming
manner, knowledge of where they had been
in the task before the interruption, what they had been intending to do next,
and what they had learned that was relevant to
deciding how to proceed. So far, our environments do little to support these
facets of task state. They lack adequate scaffolding.
Hence, we are obliged to remember as much state as we can by ourselves. The
result is more overload.
The topic of how to design environments to reduce interruption is an area
of major theoretical interest beyond our purview here.
One important facet of design to discuss now, however, is task collapsing:
if we restructure the workspace to integrate several of
the different tasks knowledge workers have to perform they needn't be interrupted
quite as frequently.
A word is in order. Interruption occurs when we shift from one environment
– one task context -- to another. Upon exiting we
must store that task's state and upon entering another task context, another
environment, we must recover its task state. If we can
accomplish our tasks in fewer environments we may reduce the number of interruptions
we encounter. At a minimum, we may
hope to reduce their disruptiveness.
To see clear examples of task integration it is constructive to observe the
history of software development. As successful software
products evolve they tend to include more and more features that address functional
requirements of users. This may often seem
like feature bloat – a negative consequence of trying to address the needs
of users at completely different levels of expertise -- but
in virtuous cases a program can be expanded into completely new areas so that
a task which before required running two
programs can now be run within one.
For instance, several graphics programs have now included enough picture manipulation
tools to duplicate the functionality of
dedicated photo manipulation programs, such as PhotoShop. This simplifies
activity because now a user does not have to leave
one program with its features and feel, export a file to a new format, open
it in a new program with a completely different layout
and workspace, and work on it there. It is particularly helpful since we rarely
can anticipate all the illustration tasks we wish to
perform and all the photo manipulation ones. Typically, we try something,
see how it looks, undo it or keep it, and then go on.
Our natural workflow is data driven. Whenever we are forced to move between
different programs, however, we are obliged to
modularize our composition process as much as possible so as to stabilize
it. Since this is not, in general, the way we work we find
ourselves going back and forth between the two programs as we decide to use
a pencil and then an airbrush to touch up our
picture. This is disruptive. The effect is more swapping of memory state,
more cognitive effort and ultimately more stress than
doing the two tasks in a single program.
Enhancing the functionality of environments by absorbing entire activities
is potentially a powerful method of decreasing
interruptions. A second way, is to collapse multi-step operations into single
step ones, so that what before required five steps can
now be done in one step. This too is a form of interruption reduction since
every time we have to plan a lengthy sequence of
actions to accomplish a sub-goal we are being interrupted from our main goal
by the need to enter a planning phase. Reducing the
need for planning sub-goals saves mental effort.
As useful as both these modifications are there comes a design point when
there are so many tools available that our environments
lose their simplicity and the cost in added complexity outweighs the benefits
of convenience. Another form of feature bloat. A case
in point can again be found in the history of modern graphics programs, such
as PhotoShop, Canvas, Micrographics Designer and
Illustrator. With each successive version these packages have added more functionality
to the point, a few years ago, where it
became difficult for the average user to know where to find all the buttons
and tools needed to perform even simple tasks.
Obviously a redesign was required.
This redesign occurred soon after. Software engineers struck on the idea of
modes. In a mode, only certain of the tools present in
a program are visible at any moment. Others can be invoked by pressing an
icon that causes a whole tray of tools to appear.
These tools are context sensitive as well. If we are working on a bit map
then standard illustrating tools are not visible; only bit
map tools are. But shifting back to drawing does not require saving the file
or changing the appearance of the workplace. We just
click on the drawing toolkit icon. Admittedly, a neophyte to such programs
will still not know where to look to find the tools he or
she wants. But for that matter, she will not even know what tools might be
available. It is simply not possible to find effective
designs for both advanced users and beginners. For that reason our focus here
is on intermediate users and beyond. But, we will
not go far wrong in assuming that ordinary people are near expert in their
daily activities. So to design everyday environments,
rather than software environments, we may assume that we are looking for ways
of enhancing the activities of everyday experts.
There is much to be learned about the objectives of redesign by studying the
details of software evolution. My main point, though,
is theoretical. By and large, existing workplaces have failed to keep pace
with the incredibly fast rate of change in work
requirements. The shift to knowledge work and the remarkable rise of the World
Wide Web has created a new set of
informational demands on workers which is leading to ever increased cognitive
overload. I believe it is possible to find cognitive
principles that can inform design change and reduce cognitive overload. But
even in the field of Human Computer Interaction,
where such concerns are foremost, our understanding of these principles is
still in its infancy.
Many theorists agree that, as a first step, however, we require some account
of the cost structure, in cognitive terms, of our
workflow. A fundamental obstacle, however, is that the key concepts of workflow,
particularly cognitive workflow, task context,
environment and activity space have yet to receive adequate elaboration. We
can all agree that the objectives of designers is to
realign the cost-structure of the environment, thereby reducing the stress
and cognitive costs of performing a job. But without a
theory of what occurs when this happens it is not possible to let theory drive
design.
My goal up until now, accordingly, has been to show just how complex the informational
world is in which we live, and to
introduce the argument that to make serious progress in the design of better
workspaces we need a new theoretical understanding
of our activity space and our dynamic relation to our environments. The line
I shall take next is that to design for cognitive
overload we must understand the full set of cognitive relations we have with
our environments. An important step is to clarify what
an environment of activity is and the diversity of ways we connect cognitively
with such environments. It is to that I now turn.
Cognitive workflow and Environments
As can be appreciated from our brief discussion of software adaptation, the
goal of good software designers is to create work
environments which complement and simplify users' workflow. The same applies
to designers of everyday work environments.
But what does that phrase "complement and simplify users' workflow" really
mean?
One thing it means is that a well designed piece of software should provide
a good work `environment' – an integrated workplace
– for users to perform all the tasks and sub-tasks that go into performing
a job. To a first order, workflow is the way users move
through these tasks and sub-tasks. For instance, if their job is to write
an essay then the major workflow tasks involve outlining
possible topics, sketching ideas, rearranging text, copying and editing, erasing,
finding synonyms, formatting, adding citations,
placing pictures, spell checking and the like. A good word processor ought
to create a work environment with the appropriate
tools and resources to facilitate these tasks. Because a digital medium makes
it easier to cut, paste, copy, store, tag, and
manipulate, a word processor ought to provide a better work environment in
which to work than paper and pencil. The workflow
of composition should be simplified and complemented in digital media. Hence
digital media such as word processors constitute a
better environment for the activity of writing essays.
As word processors mature, however, designers are beginning to appreciate
that there is more to the workflow of composition
than is found in the organization of the sub-tasks which modern word processors
support. Word processing is itself a system of
tasks woven into a larger tapestry of activities that make up the more complete
process of composition. Many of these activities
take place outside the computer, on scraps of paper, in dictation devices,
in discussion, in front of whiteboards, in using library
search engines, in skimming abstracts, in reading and annotating articles
and books, in making notes, and even in conversations
taking place through email. The workflow of composition is multifaceted. It
takes place in many environments, involves many
tools, and requires considerable mental effort.[4]
Workflow analyses take a more realistic and a more cognitive turn when they
expose the fine details about how people exploit
their environments to make the cognitive aspect of their activities easier.
All too often workflow analyses have been confined to
studies of how workers move from one aspect of work to another as they complete
a task.As we see in composition this may
involve acting in a single environment, in many environments, or in making
linkages across environments. But a more complete
analysis of workflow will not only identify the principle components of a
task – the sub-tasks and activities that constitute it and the
different environments in which they occur – it will also track the lines
of information – both data lines and control lines – that link
the component tasks and permit the author to keep track of her goals across
environments. What representations do people use
when they compose? What representations do they use to keep track of where
they are? What activities do they perform to bring
different media together? How do they coordinate their activities? These various
activities of planning next moves, clearing off
clutter in their environments to get on with their sub-tasks, marking intentions
to make it easy to pick up where they left off,
tracking what they are doing, highlighting, annotating, talking to themselves
as they work – all these and more, constitute elements
we must study if we are to begin to paint a more complete account of the cognitive
workflow occurring when people compose
documents.
Environment as Activity Space
I have been suggesting that if we closely track the workflow of individuals
we see them operating in several different environments,
attempting to coordinate the key processes in each by a variety of techniques
ranging from talking to themselves, to annotating, to
making notes, to leaving cues and markers and so on. More often than not people
do not complete one task before initiating
another, and they move from job to job, environment to environment, as a
function of plans, interruptions, exigencies and habits.
The complexity of this trajectory and the fact that on certain analyses agents
change task environments without changing physical
environments makes understanding workflow at a significant level of detail
a daunting job.
Lurking at the bottom of this more cognitive way of thinking about workflow
is the concept of an environment as the space in
which work takes place. Since the primary goal of designers is to improve
the structure of an environment to make it easier to
work in, we need to look more deeply into the relation of workflow and environment.
We shall think of an environment as an activity space – originally a physical
space but now virtual spaces qualify as activity spaces
as well – populated with resources, tools and constraints in which an agent
operates. The reason the same physical space can
support multiple environments or activity spaces is that the way an agent
projects meaning onto a space partly constitutes it. A
snapshot of a checkerboard along with its pieces can (with some imagination)
be seen as a moment in either a game of checkers
or a game of chess. How things unfold soon gives the lie to one of these interpretations,
but the simple fact is that there is more to
activity spaces than physical structure. Agents project meaningful structure
onto environments.
This process of constitution depends on having:
1.1. a task to perform and thus seeing the affordances of the environment
through task-colored glasses,
2.2. a history of experience with similar objects and resources, and thus
having a body of associations that enrich the meaning
and understood possibilities of each situation, and
3.3.acknowledging certain norms of work conduct.
Change any of these and you change what the agent thinks he or she may do
in an environment, even what they think is possible in
that environment, and what they see the consequences of actions to be. In
brief, change one of these and you change the activity
space – the environment of action.
At the same time that an environment represents a space of possibility it
also represents a set of constraints. An environment is the
space in which structures are created and actions have consequences. It is
the substrate in which new structural and meaningful
configurations (situations) can be created and the substrate which constrains
the possibilities of creation. Not anything can be
created. The work environment therefore constrains both what it is possible
or acceptable to do, and what happens as a result of
performing actions. It is partly the product of an agent's projections and
partly the product of underlying causal realities.
It is important to understand the parameters of an environment since any effort
at designing environments to minimize interruption,
disruptiveness and to facilitate information management will focus on manipulating
these design parameters. A natural way to
capture the sense of constraint and possibility inherent in an environment
is to enumerate the set of actions it supports. What can
an agent do while there? And what happens as a result of those actions. As
noted by Newell and Simon, [Newell 82,90, Newell
and Simon 72, Simon 73] this characterization, as it stands, is too free;
it lacks focus. We don't want to include in an
environment's activity space all the actions an agent can perform there. In
physical environments, for example, agents can move
their arms, wiggle their noses, jump up and down, push objects at different
speeds, as well as numberless other actions.
Sometimes their actions result in a change in themselves (scratching makes
me less itchy) or in a momentary change in their relation
to their environment (running on the spot, walking to a new place). At other
times, the focus of action is on the objects in the
environment, and change consists in having these objects occupy new positions
or orientations (move the frying pan) or occupy
new states (crack the shell, egg is fried). We need to narrow our notion of
the activity space to the space of actions that are
relevant to the task at hand. Actions that make sense to a purposeful agent.
Newell and Simon restricted their notion of environment to the task environment.
They included in a task environment just those
actions which might make a difference to the task. This worked quite well
for formal problems such as Tower of Hanoi, where the
environment was impoverished. In these stylized environments the actions deemed
essential to the task environment all have to
have a measurable effect on performance – on the time, energy, or number of
actions an agent would need to complete the task.
They cannot be too general or task independent for then they would have nothing
specifically to do with the task. For example,
breathing, blinking and perspiring are behaviors which when not undertaken
may drastically affect performance. But their
contribution is more to the general condition of the agent rather than to
anything task specific. They are background actions which
contribute to the creature's being in its normal state of being, hence in
a task ready state, but are not themselves part of any task in
particular. At the same time, actions cannot be too idiosyncratic; they cannot
be so specific to one agent, that any other agent
also performing similar actions in similar task circumstances would neither
improve nor worsen its position in the state space.
Performing a chant before hitting a tennis ball may be `necessary' for a
certain player, but it is certainly not part of the activity
space of tennis.[5]
By focussing on task-relevant actions, Newell and Simon attempted to operationalise
the idea that one's environment of action –
one's task environment -- is somehow tied to projected meanings as well as
to what potentially works in that environment. The
activity space that matters when performing a task is the one which is somehow
connected to accomplishing the task in an
allowable way. I believe this is substantially correct.
But Newell and Simon sacrificed too much for their formalism. They adopted
a narrow view of both the creative possibility of
humans and what it means for an action to be connected to a task. We know
that people constantly exploit the affordances of
objects to do things they believe may advance their case even though these
behaviors often cannot be found in the canonical
action repertoire. Similarly, we know that people perform a host of actions
that make no sense from a pragmatic as opposed to an
epistemic viewpoint. Not all actions can be seen as potentially bringing one
physically closer to the goal state. Rather they help
bring one epistemically closer. They serve a host of cognitive functions that
are not merely idiosyncratic; they are cognitively useful
given particular strategies, ways of looking at problems, and cognitive styles.
So although these actions again fall outside the
canonical action repertoire they are connected to accomplishing the task.
It is this last collection of actions that makes analysis of environments
especially difficult. In the Tower of Hanoi, for instance, all
the rings are supposed to be resting on the three pegs at every moment. There
are no extra degrees of freedom in where they can
go and what you can do with them (unless you count holding them in your hand
before placing them on a peg as something you
can do). The rules of the game even outlaw using pencil and paper to help
you decide. After all, the Tower of Hanoi is a type of
memory task. But no one prohibits muttering to oneself. Such muttering can
potentially improve performance, since it engages the
phonological loop and so can offer a few bits of extra memory to encode useful
task information. Effective encoding in this loop
can reliably improve performance. So it ought to be part of the activity space.
But by Newell and Simon's narrow definition it is
not an event in the task environment. Since, virtually every task in the natural
world offers agents the possibility of using local
resources to cleverly encode task useful information, it may be difficult
to keep a tidy notion of task environment while viewing
each such resource use as a task relevant event. But is hard to see how we
can deny that encoding helpful knowledge in an
environment is part of task relevant performance and so an element of the
task environment. All potential actions that can reliably
improve performance are by definition part of the task environment.
Analyses of task structure are further complicated because in most tasks people
undertake outside the laboratory, the rules of
engagement are not well defined. The actions and meanings that are projected
onto the environment do not flow from the agent or
the task setup unconstrained by the history of agent environment interactions.
They are somehow "negotiated". I habitually use a
slipper to stop our screen door slamming in the wind, although that is not
its orthodox function. In cooking dinner, I find it
convenient to use the large wrapping paper my fish comes in as an interim
plate to hold the fish once it is floured. Not only does
this save me from washing an extra plate, it gives me a surface that is large
enough to lay out all the fish so that they when I flour
them they do not touch each other. If I were to pile them on a plate they
would stick together. The moral is that people co-opt the
function of everyday items to help them with their tasks. Sometimes their
creativity saves them physical resources (e.g. re-using
wrapping paper and so saving a plate), sometimes it saves them physical effort
(e.g. not having to open the door each time it slams
shut), sometimes it saves memory (e.g. not having to remember the task state
by encoding information in the layout), sometimes it
save mental effort or computation (e.g. preparing an assembly task by laying
out the pieces to be assembled in a geometric pattern
that shows one where to put the next piece.) An adequate definition of environment
or task environment should allow any of these
creative uses of tools and resources to be part of task performance. It should
recognize that people make cognitive use of their
environments whenever they act.
These ideas about the activity space of tasks has clear consequences for design.
Well designed environments ought to take note of
the cognitive needs people have in performing their tasks and build in scaffolding
that simplifies the way people make cognitive
use of their environments to increase task reliability. There is no end to
the variety of methods that can help, so this is not a closed
design task, where there is an optimal solution. Nonetheless, we can start
with a description of the cognitive or epistemic actions
afforded by an environment and work from there to find ways of overcoming
some of the physical and cognitive constraints of the
given environment.
Cognitive Workflow
When we observe the fine grain of activity occurring as a person performs
a task it is evident that much of what they are doing is
not directly concerned with improving their `pragmatic' position. [Kirsh forthcoming,
98, 97,95, Kirsh & Maglio 95] Some of their
actions serve an epistemic function, and some serve a complementary function,
helping agents to coordinate their internal and
external activity [6].
A particularly clear case of an epistemic function is found in the computer
game Tetris. Good players will rotate a piece very soon
after it appears at the very top of the board if by so doing they may more
easily discover what sort of piece it is. Their action
unearths information. Similarly Tetris players will rotate pieces externally
when they are trying to decide what to do next rather
than rotate the mental image of those pieces. Besides saving the mental effort
of image rotation, physical rotation is almost three
times as fast. Players are able to more quickly enter the mental state of
knowing what their piece looks like when rotated.
A particularly clear case of complementary actions that help coordination
is found in young children when they count objects in
story books. Unless they coordinate the rhythm between the way they point
at objects and the way they count out loud their error
rate soars. Internal counting (which is coupled to external counting) must
be coordinated with external pointing (which is coupled
to visual tracking). Disrupt this rhythm and they lose their stride, and falter.
A more sensitive account of workflow ought to describe the cognitive and interactive
processes that are occurring when agents
perform their sub-tasks and move from one aspect of work to another. I call
this type of workflow analysis `cognitive workflow'.
It is the next step in understanding designers must take if they wish to design
scaffolding and other environmental resources that
can reduce the cognitive complexity of tasks. It implies that we must understand
the cognitive environment the agent operates in.
Many of the activities that are meaningful in the cognitive environment are
easy to understand. Returning to the task of composing
an essay, we see that agents perform all sorts of meaningful activities that
help them process information and help them encode
idea fragments. For instance, agents reorganize their references and notes.
If their references are physical articles and books, they
put them on their desk or on their shelves in one or more piles. As they read
them they shift their spatial position, possibly keeping
them open at salient places by putting them face down, or by folding or bookmarking
them, perhaps interleaving pages of two or
more articles to help define an ad hoc category, or possible theme. If the
references are digital then they shift them around their
computer screen, hiding and occluding some, putting others in new directories.
But many of the important interactive activities we perform in a task are
less easy to understand. For instance, in managing the
resources available to us, we may develop routines that are idiosyncratic.
I mentioned several different knowledge inventory
control methods above, but there may be many more such methods we use for
dealing with clutter, or helping us to manage our
time. All these are additional relations we bear to our environment that we
would like support for. And this is before we consider
the complications added by working on tasks with others, or the complications
added by working with complex artifacts and
tools. Since most of our environments include both of these highly complex
systems we are constantly relying on a further
collection of strategies for coordinating our actions.
To return to composition, in today's world, and probably for a some time to
come, many of the resources we use are both
tangible and virtual. Most books currently reside outside the computer, and
we value the affordances this provides, and certainly
our colleagues reside outside our computers and most of discussions with them
take place non-virtually. But use of tangible
resources including people also carries a transaction cost for the modern
writer. For now we must transfer the contents of those
books, and conversations, as well as the contents of any notes and annotations
we wrote down, from outside to inside our
computer. No doubt an effective way to meet this challenge will be worked
on for several years. It is not a simple problem to be
solved by digital scanners or video cameras. For our goal is to capture the
content arising in the activity of composition as it is
distributed over many spaces and many environments . Each of these environments
– virtual and physical – has its own
functionality and each has its own special resources that we have learned
to use. Accordingly, the first step in redesigning the
composition landscape is to create a map of the cognitive workflow. This involves
tracking the trajectory of these highly situated
meanings through these multiple environments. It shows how work is coordinated
over many functional environments and how
agents manage the resources available in each environment to maintain control
of their overall task.
Altering the Cost Structure of an Activity Space
The principle objective of redesigning environments is to improve workflow
– both cognitive and physical. Improved workflow
ought to lead to reduced cognitive overload, since one key component of a
well designed environment is that it minimizes the
cognitive effort agents must exert when performing their task. To make this
idea of improving workflow and minimizing cognitive
effort more operational, however, requires quantifying the cost structure
of an environment. It is here that the idea of treating an
environment as an activity space proves useful. Given the tools and resources
available in an environment we can try to estimate
how easy or difficult it is to perform an action. Once we understand how to
improve the cost structure of particular environments
-- of how to lower the cost of managing activity in that environment -- we
may then ask how to improve the design of the overall
workplace, since our actual workplace is really a superposition of many specific
environments which we slip between. Put in a
slightly different way, there are many distinct interpretative frameworks
we operate with during the course of a day or during the
course of an activity, and these frameworks interact with our workspace to
create a theoretical construct we are calling an activity
space. As designers we must analyze activity spaces separately before trying
to come up with an effective design for their
superposition.
Returning now to the question of operationalizing the cost structure of a
specific activity space we can ask of a specific
environment how hard is it to perform a task -- for instance, to write a
note in it. If we assume a normal physical environment, then
presumably as long as there is pen and paper around and there is a flat surface
to write on, it is easy to write a note. Of course
there is more to it than this, and calling a task easy is a purely qualitative
judgment. But at least we differentiate note-taking in
minimally equipped environments from note-taking where there are no convenient
affordances for writing -- no flat, clean surfaces,
no places to rest. Without the necessary affordances the cost of writing in
E1 is much higher. Not high, perhaps, for making simple
marks on an existing note, but for any extensive amount of writing it may
be extremely difficult or painful to produce legible output.
The cost structure of E1, for writing, then, is non-linear. To write large
notes is far more difficult than writing the same number of
words in several small notes. Add a table and the overall cost structure is
lowered and flattened. Of course, so far we have said
nothing of psychological factors. What is the content or topic of the note?
Jotting down a phone message or writing an essay?
Depending on the task demands different factors increase in importance. How
noisy is it in E1? Is writing a note the only thing
agents must do at the time? What is the mean time before the next interruption?
How demanding will that interruption be?
Or consider the effects of clutter in the environment.. If our goal is to
take a note in response to a telephone message, how hard is
it to find the message pad in E1 or the earlier note to be annotated? It depends
on who is looking. Rooms with stacks of files
everywhere are likely to make the task of finding things hard for anyone who
has not been involved in creating the clutter. Less so
for those who created the mess. The cost structure of retrieval in E1 depends
on what has to be found and where it is located.
Even more important, the cost structure also depends on what the agent knows.
If the author of a mess were to share facts about
his `indexing' or filing system the overall cost structure of retrieval (by
an arbitrary person) might be significantly lowered.
The idea of activity spaces having a cost structure -- a metric which assigns
particular costs to particular actions -- is intuitively
;
attractive but requires clarification and operationalization before it can
be put to constructive use. A first step can be taken by
distinguishing behavioral measures of environmental goodness from cognitive
measures.
On the behavioral side the key questions naturally concern performance related
measures, such as time to complete the task,
number of errors made at a given speed (speed-accuracy trade-off), the probability
of making an error on being interrupted and
the cost of that error, the hardest version of the task that can be performed
in that environment. See Figure 3. Certainly there may
be complex non linear relations between these parameters, but in the simplest
case a given environment E1 is better than
environment E2 if all parameters are better in E1 and none are worse (the
Pareto sense).
Figure 3. Environments or activity spaces differ in several performance related
dimensions. In Fig 3a we see that the same task can be solved faster in E1
than in E2 ,
that fewer errors are made, that E1 is more robust to interruption, and that
harder
problems can be solved. The speed accuracy curves of E1 therefore are better
than in
E2. In Fig 3b we see that E1 continues to be better than E2 as we increase
the hardness
of problems. So E1 is better than E2 in a Pareto sense.
Behavioral measures are important for measuring cost components. But in fact
the underlying factors most directly connected to
cognitive overload and to cognitive workflow have to do with cognition. Obviously
the factor most directly determining overload is
cognitive load. Environments that are better designed than others should allow
the same task or task instance to be solved or
performed with less cognitive load, measured in terms of computation, memory,
amount of sustained concentration, and stress.
See figure 4.
Figure 4. Here we see represented the key cognitive dimensions that determine
the cost
structure of an activity space. For a given environmental setup we would like
to
determine the cost in terms of memory, computation, stress and attention or
concentration required to perform tasks of varying hardness
We have all had the experience that a task may be easier to solve if the right
artifacts are present. It is easier, for instance, to sum
a long column of numbers with a calculator than in the head. It is more reliable
to sum a list if the calculator displays the last few
numbers just added, since then if you are interrupted you can merely look
at the number trail to see where you left off. Similarly, it
is easier to sum a long list of numbers if you use ruled paper with small
lined columns, since then you will find it easier to keep your
numbers more precisely lined up; and that summing numbers that are nicely
aligned in turn makes it easier to focus on the
appropriate next number to sum, and so to reduce the probability of errors.
Over the years innovation and experience has led to
the introduction of graph paper with small squares to guide printing. See
Figure 5.
Figure 5. It is easier to sum numbers when they are lined up. Graph paper
is a cultural
adaptation to help people align their numbers.
Cultures (both national and micro-cultures such as business organizations)
accumulate helpful tricks for reducing the complexity of
tasks and increasing the capabilities of their members to undertake harder
tasks. Sometimes the way culture increase capacity is
by introducing helpful representations, or helpful designs. This is a fundamental
component of activity space since so much of our
activity involves using representations and representational formalisms.
It is well known that if the way information is represented in the environment
is changed it is possible to change the cost structure
of information because some information will be easy to extract or notice
- on the surface - and other information harder to
extract; that is, more computation will be required to extract it.[7] For
instance, there are times when a table of values is a good
way to represent the relation that holds between the different cells in it,
and times when it is a weak or bad way. It all depends on
the information that is to be extracted. A simple example is seen in figure
6. Here the goal is to identify the maximum value. As is
clear from the figure, the perceptual action of extracting the global maximum
is much easier in graphical form than in matrix form.
Global maxima tend to pop out in graphical forms because there are powerful
parallel visual processes for noting certain key
spatial relations in the visual field. This is especially true for maps where
a great deal of Cartesian distance information can be
neatly encapsulated in a 2D map. On the other hand, if one's goal is to determine
the distance of key stops along the way, a matrix
representation which explicitly states the distance in miles between those
stops will be easier to work with. See figure 7. Each
representation, graphic type and matrix, has its strength and weaknesses.
Each makes some pieces of information easy to pick up
and other pieces hard.
234
458
746
436
876
234
436
765
989
265
398
936
456
938
764
632
864
345
876
283
287
932
873
725
528
728
385
284
Figure 7.
The fact that there are different ways of representing the same information,
each of which imposes different demands on the
cognitive faculties of users becomes particularly interesting in the course
of everyday activity as soon as we ask:
how people structure the space around them to encode information which they
expect to need soon;
what representational artifacts and tools they have to help keep track of
useful information
As an example of using space to encode information consider the simple problem
of sorting playing cards while you play a game
such as gin rummy. Cards that are well arranged for gin rummy display their
owner's key sub-goals and plans. For instance, in
Figure 8b, it is easy to see that the player has almost won and is just looking
for two of the following: a 4 spades, a 3 or 6 of
clubs, K of hearts or K of diamonds. In Figure 8b, by contrast, it is much
harder to tell the player's goal structure and therefore
harder to note what cards are being sought after.
(a) (b)
Figure 8.
Representation is one key method agents have of simplifying their work activity.
Another is by learning new practices or algorithms
that make it easier to solve problems. For instance, in gin rummy a useful
practice is to first sort cards in ascending order
regardless of suit, then sort them by suit in ascending order. This practice
has the effect of showing off all potential sub-goals, since
it lowers that chance of noticing cards of the same value. After sub goals
have been chosen agents can re-order their cards as they
wish to highlight the plays they are looking for.
In a more serious vein managers in corporations are often taught how to conduct
better meetings, how to coordinate the activities
of team members so that more is done with less effort and stress. These methods
often become part of the corporate culture and
new employees are sent to training class to learn them. Typically practices
involve use of particular representations or resources.
For instance, day planners are widely used to help structure the workflow
of their users, helping them to organize their activity
throughout the day and record commitments, obligations and comments in a
single easy to find place, thereby reducing the stress
of having to remember certain facts and commitments and offload both memory
and effort. New resources lead to new practices,
usually representational practices, and the net result is that the cost structure
of the activity space is altered.
To sum up then, several key facts emerge in understanding the cost structure of an activity space.
1.the relevant activity space must be defined relative to a task or family
of tasks
2.there is no absolute measure of the cost of different actions or activities
to be had in abstraction of the different algorithms
which agents have. Change the algorithm and the cost structure of the activity
space also may change.
3.the cost structure is also relative to the underlying skills and cognitive
capacities of an agent. People with large working
memories or who are expert in a task may experience a different cost structure
than novices or people with smaller working
memories.
4.The resources available in an environment affect the cost structure only
if users have developed means for using them
effectively in context. Often this is the consequence of cultural learning.
But some of these resources can be analyzed
themselves in terms of their own cost structure.
To do justice to the variety of ways individuals and especially groups have
of reshaping the cost structure of their activity space,
and to understand how activity spaces are superimposed one on the other, is
a topic of great interest. One aspect I have not
discussed is how agents coordinate among themselves, how they create new types
of coordinative structures and practices that
simplify tasks and distribute load among individuals and their environment
in ways that reduce stress and improve performance. To
my knowledge no one has yet discovered how to quantify the impact on cost
structure such coordinative mechanisms have. My
objective here has merely been to introduce the notion and suggest how it
might figure in analyses of activity and cognitive load.
Conclusion
Cognitive overload is a brute fact of modern life. It is not going to disappear.
In almost every facet of our work life, and in more
and more of our domestic life, the jobs we need to do and the activity spaces
we have in which to perform those jobs are
ecologies saturated with overload. As technology increases the omnipresence
of information, both of the pushed and pulled sort,
the consequence for the workplace, so far, is that we are more overwhelmed.
There is little reason to suppose this trend to
change.
On the positive side, though, I have been arguing that designers and participants
both have the capacity to reshape work spaces to
alter the cost structure of the activity that takes place in those spaces.
These efforts to reshape activity space come in three forms.
Change the physical layout,
change the methods, algorithms and practices agents use to perform their tasks,
and
change the resources, particularly the resources associated with cognitive
scaffolding available in the environment.
By changing the physical layout useful affordances of both a physical and
cognitive nature can be brought closer to where agents
need them and at the time they need them. The simple fact that these affordances
are available at the right moment can help agents
notice possibilities they might otherwise overlook. A trivial example is
leaving an easy to use calculator by the desk of someone
doing their taxes, or by substituting a thin computer screen for the large
monitor normally on one's desk. The calculator soon gets
put to good use, and the extra space on one's desk soon becomes filled up
with helpful paper memos, paper products, books etc
that agents find useful for their work, but which before had to be stored
or placed off to the side. Similarly, by relocating people
with complementary knowledge in easy ear or eyeshot from each other, the result
is usually that they consult and help each other
more than before. They may also interrupt each other too, so the resulting
social and work ecology is not unambiguously for the
better for everyone. But the received wisdom of our day is that teams of cooperating
agents distribute cognitive effort and thereby
reduce individual stress.
By changing the methods, algorithms and practices of agents cognitive load
can be reduced because better methods of solving
problems -- methods that allow practitioners to solve problems harder, faster,
or more accurately -- the chief factors we think are
the driving factors of the cost structure of their personal activity space.
Examples of such improvements are easy to find. For
instance, better techniques for conducting meetings, for personal time management,
for recording results, for accessing corporate
memory, for dealing with interruptions, and for coordinating activity both
at an individual and group level can reduce cognitive
load. Often these will require that agents use artifacts they never used before.
Day planners for time management, wall charts or
white boards for meeting management, new search software for indexing and
retrieving knowledge and information (i.e.
knowledge management), or learning to use the hold button on the telephone,
are all examples of artifacts which when effectively
combined with practices can improve efficiency and reduce overload and stress.
These last set of artifacts are examples of the sort of resources often called
cognitive scaffolding that designers are eager to create.
Other examples are new representational formalisms such as Pert and Gannt
charts, better information visualization, better search
engines, new devices for non-intrusively capturing and recording views of
what was said and done, and even contextualized help
in our physical workplace.[8] There is no magic design principle that yields
new scaffolding. I have been arguing, however, that at
a theoretical level it may be possible to analyze in a more rigorous manner
the cost structure of activity spaces and the impact
which alterations might have on that cost structure. It is not clear whether
these analyses will prove useful to designers before they
innovate helpful change or only after their most creative phase and they are
attempting to analyze why their idea works and how it
might be generalized. But as cognitive scientists we need such analytic frameworks
to contextualize our discussion of physical and
cognitive behavior. It has been my goal in this paper to advance this discussion
further by introducing a set of distinctions and
concepts that are consistent with other thinking in the cognitive and biological
sciences.
Footnotes
[1] Information anxiety is the overwhelming feeling one gets from having too
much information or being unable to find or interpret
data. Wurman [90] writes: 'Information anxiety is produced by the ever-widening
gap between what we understand and what we
think we should understand. It is the black hole between data and knowledge,
and it happens when information does not tell us
what we want or need to know.'
[2] For instance, in Minimizing Information Overload: The Ranking of Electronic
Messages Journal of Information Science 15
(3) 1989, 179–189. Losee defines a "formal economic rule for deciding whether
to examine a message: a message should be
selected for examination if the cost of doing so is less than the cost of
not doing so." P. 183
[3] Multi-tasking is a term drawn from computer science to refer to systems
which handle many tasks seemingly at once. An
operating system multi-tasks by rapidly switching between tasks and placing
in an intermediate store all state knowledge required
by each task. It therefore interrupts each task at a stable moment, and then
later swaps back the state it was in before the
interruption and carries on from where it was. Humans need to be able to stabilize
their state knowledge when they are interrupted
or they risk losing their place when they attempt to pick up the task later.
[4] It is not easy to predict the details of how word processors will change
to accommodate this more complete reality of the
composition workflow. But we can be confident that current word processors
are just a moment in the design evolution of
composition tools. The considerations that are driving them to evolve have
to do with understanding what people do when they
compose, and how they move from one aspect of the composition process to another.
For this reason it is a good bet that
tomorrow's word processors will have to provide better facilities for taking
notes on digital library materials, and better facilities
for displaying these notes and reference materials so that writers can cut
and paste between them.
[5] This tripartite description of the conditions determining the actions
to be included in the action repertoire linked to a task
environment is drawn from Kirsh, D., Adapting the environment instead of oneself,
Adaptive Behavior, vol. 4:3/4, pp. 415-452,
1996.
[6] Several nice examples of complementary actions are found in the way we
use our hands to help us think and see. To count
closely packed items, especially if they are identical in appearance, we often
need to point because our visual systems easily skip
items. To compare items we like to pick them up and put them beside each other.
[7] For an extended discussion of the differences between information that
is one the surface -- explicit - and information that is
buried -- implicit -- see When is information explicitly represented? [Kirsh
90].
[8] So far, we have not begun treating our work spaces as domains that can
be enriched with context sensitive help. We rely on
co-workers to help us, and occasionally we consult manuals, but to date, we
have not harnessed the possibilities of computers to
give us contextual help outside of the computers themselves. As our walls
become information visualizers we can expect this to
change.
Acknowledgements
The ideas in this paper owe much to helpful conversations with Saadi Lahlou, Aaron Cicourel, Ed Hutchins and Jim Hollan.
References
Berghel Hal; Cyberspace 2000 dealing with information overload. Commun. ACM 40, 2 (Feb. 1997), Pages 19 – 24
Chase, W.G., & Simon, H.A. (1973). The mind's eye in chess. In W.G. Chase
(Ed.), Visual information processing (Chap. 5).
New York, NY: Academic Press.
Institute of the Future; Gallup Organization. Managing Corporate Commiuniation in the Information Age. 1997. http://www.iftf.org
Kirsh, D; Adaptable Rooms and Cognitive Workflow, in Streitz, N, et al. (Eds.)
Cooperative Buildings. Integrating Information,
Organization, and Architecture. Lecture Notes in Computer Science. Springer:
Heidelberg, 1998.
Kirsh, D. Interactivity and MultiMedia Interfaces. Instructional Sciences. 1997.
Kirsh, D. Are all Actions Exploratory or Performatory? Ecological Psychology. (forthcoming).
Kirsh, D. Adapting the Environment instead of Oneself. Adaptive Behavior. (1996).
Kirsh, D. The Intelligent Use of Space. Artificial Intelligence. (1995).
Kirsh, D. and P. Maglio. On Distinguishing Epistemic from Pragmatic Actions. Cognitive Science. (1995).
Kirsh, D. When is Information Explicitly Represented? The Vancouver Studies in Cognitive Science. (1990).
Losee, Minimizing Information Overload: The Ranking of Electronic Messages
Journal of Information Science 15 (3) 1989,
179–189.
Nelson Mark R; We have the information you want, but getting it will cost
you!: held hostage by information overload.
Crossroads 1, 1 (Sep. 1994), Pages 16 - 21
Newell, Allen: The Knowledge Level. Artificial Intelligence 18(1): 87-127 (1982).
Newell, Allen. Unified theories of cognition. Cambridge, Mass. : Harvard University Press, 1990.
Newell, A., & Simon, H.A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall.
Simon, H.A. (1973). The structure of ill-structured problems. Artificial Intelligence, 4, 181-202.
Waddington, P. Dying for information: an investigation of information overload
in the UK and world-wide.1996. London: Reuters
Business Information.
Wurman,Richard Saul. Information Anxiety : What to Do When Information Doesn't
Tell You What You Need to Know.
Bantam. 1990
Other Articles
Kirsh, D. (2000). A Few Thoughts on Cognitive Overload, Intellectica,
Kirsh, D. (1999). Distributed Cognition, Coordination and Environment Design,
Proceedings of the European conference on
Cognitive Science
Maglio, P. P., Matlock, T., Raphaely, D., Chernicky, B., & Kirsh D. (1999).
Interactive skill in Scrabble. In Proceedings of
Twenty-first Annual Conference of the Cognitive Science Society. Mahwah, NJ:
Lawrence Erlbaum.
Knoche, H., De Meer, H., Kirsh, D. (1999). Utility Curves: Mean opinion scores
considered biased. Proceedings of the Seventh
International Workshop on Quality of Service
Kirsh, D. (1998). Adaptive Rooms, Virtual Collaboration, and Cognitive Workflow.In
Streitz, N., et al. (Eds.), Cooperative
Buildings - Integrating Information, Organization, and Architecture. Lecture
Notes in Computer Science. Springer:
Heidelberg.
Elvins, T, Nadeau, D., Schul, R., Kirsh, D. (1998).Worldlets: 3D Thumbnails
for 3D Browsing. Proceedings of the Computer
Human Interaction Society.
Kirsh, D. (1997). Interactivity and MultiMedia Interfaces. Instructional Sciences.
Elvins, T, Nadeau, D., Schul, R., Kirsh, D. Worldlets: 3D Thumbnails for Wayfinding in Virtual Environments UIST97 1997.
Kirsh, D. (1996). Adapting the Environment Instead of Oneself. Adaptive Behavior, Vol 4, No. 3/4, 415-452.
Kirsh D. (1995). Complementary Strategies: Why we use our hands when we think.
In Proceedings of the Seventeenth Annual
Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum.
Kirsh, D. (1995). The Intelligent Use of Space. Artificial Intelligence. 73: 31-68
Kirsh, D., & Maglio, P. (1994). On distinguishing epistemic from pragmatic
action. Cognitive Science. 18, 513-549. [ps file,
1000K]
Kirsh, D., & Maglio, P. (1992, March). Perceptive actions in Tetris. In
R. Simmons AAAI Spring Symposium on Selective
Perception. [ps file, 191K]
Kirsh, D., & Maglio, P. (1992). Some epistemic benefits of action: Tetris,
a case study. In Proceedings of the Fourteenth Annual
Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum.
[ps file, 875K]
Kirsh, D., & Maglio, P. (1992). Reaction and reflection in Tetris. In
J. Hendler (Ed.), Artificial intelligence planning systems:
Proceedings of the First Annual International Conference (AIPS92). San Mateo,
CA: Morgan Kaufman.
Kirsh, D. et al. (1992). Architectures of Intelligent Systems, in Exploring
Brain Functions: Models in Neuroscience. John
Wiley.
Kirsh, D. (1992). PDP Learnability and Innate Knowledge of Language. In S.
Davis (Ed.), Connectionism: Theory and practice
(Volume III of The Vancouver Studies in Cognitive Science, 297-322). NY: Oxford
University Press.
Kirsh, D. (1991). Foundations of artificial intelligence: The big issues. Artificial Intelligence , 47, 3-30.
Kirsh, D. (1991). Today the earwig, tomorrow man. Artificial Intelligence,
47, 161-184. Reprinted in M. Boden (ed)
Philosophy of Artificial Life. Oxford University Press (in press)
Kirsh, D. (1990). When is information explicitly represented? In P. Hanson
(Ed.), Information, language, and cognition.
(Volume I of The Vancouver Studies in Cognitive Science, 340-365) Vancouver,
BC: University of British Columbia Press.
Kirsh, D. (1987). Putting a price on cognition. The Southern Journal of Philosophy,
26 (suppl.),119-135. Reprinted in T.
Horgan & J. Tienson (Eds.), 1991, Connectionism and the philosophy of
mind. Dordrecht, ND: Kluwer.
David Kirsh
Dept. of Cognitive Science
Univ. California, San Diego