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Human Computer Interaction

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Human Computer Interaction (CS408)
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Lecture
11
Lecture 11. The Psychology of Actions
Learning Goals
As the aim of this lecture is to introduce you the study of Human Computer
Interaction, so that after studying this you will be able to:
Understand mental models
·
Understand psychology of actions
·
Discuss errors.
·
Mental model
11.1
The concept of mental model has manifested itself in psychology theorizing and HCI
research in a multitude of ways. It is difficult to provide a definitive description,
because different assumption and constraints are brought to bear on the different
phenomena it has been used to explain. A well-known definition, in the context of
HCI, is provided by Donald Norman: `the model people have of themselves, others,
the environment, and the things with which they interact. People form mental models
through experience, training and instruction'.
It should be noted that in fact the term mental model was first developed in the early
1640s by Kenneth Craik. He proposed that thinking `...models, or parallels reality':
`If the organism carries a "small-scale model" of external reality and of its own
possible actions within its head, it is able to try out various alternatives, conclude
which is the best of them, react to future situations before they arise, utilize the
knowledge of past events in dealing with the present and future, and in every way to
react in a much fuller, safer, and more competent manner to emergencies witch face
it.'
Just as an engineer will build scale models of a bridge, in order to test out certain
stresses prior to building the real thing, so, too, do we build mental models of the
world in order to make predictions about an external event before carrying out an
action? Although our construction and use of mental models may not be as extensive
or as complete as Craik's hypothesis suggests, it is likely that most of us can probably
recall using a form of mental simulation at some time or other. An important
observation of these types of mental models is that they are invariably incomplete,
unstable, and easily confusable and are often based on superstition rather than
scientific fact.
Within cognitive psychology the term mental model has since been explicated by
Johnson-Laird (1983, 1988) with respect to its structure and function in human
reasoning and language understanding. In terms of structure of mental models, he
argues that mental models are either analogical representations or a combination of
analogical and prepositional representations. They are distinct from, but related to
images. A mental model represents the relative position of a set of objects in an
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analogical manner that parallels the structure of the state of objects in the world. An
image also does this, but more specifically in terms of view of a particular model.
An important difference between images and mental models is in terms of their
function. Mental models are usually constructed when we are required to make an
inference or a prediction about a particular state of affairs. In constructing the mental
model a conscious mental simulation may be `run' from which conclusions about the
predicted state of affairs can be deduced. An image, on the other hand, is considered
to be a one-off representation. A simplified analogy is to consider an image to be like
a frame in a movie while a mental model is more like a short snippet of a movie.
So, after this discussion we can say that while learning and using a system, people
develop knowledge of how to use the system and, to a lesser extent, how the system
works. These two kinds of knowledge are often referred to as a user's mental model.
Having developed a mental model of an interactive product, it is assumed that people
will use it to make inferences about how to carry out tasks when using the interactive
product. Mental models are also used to fathom what to do when something
unexpected happens with a system and when encountering unfamiliar systems. The
more someone learns about a system and how it functions, the more their mental
model develops. For example, TV engineers have a deep mental model of how TVs
work that allows them to work out how to fix them. In contrast, an average citizen is
likely to have a reasonably good mental model of how to operate a TV but a shallow
mental model of how it worked.
To illustrate how we use mental models in our everyday reasoning, imagine the
following scenario:
·  You arrive home from a holiday on a cold winter's night to a cold house. You
have small baby and you need to get the house warm as quickly as possible.
Your house is centrally heated. Do you set the thermostat as high as possible
or turn it to the desired temperature (e.g., 70F)
Most people when asked the questions imagine the scenario in terms of what they
would do in their own house they choose the first option. When asked why, a typical
explanation that is given is that setting the temperature to be as high as possible
increases the rate at which the room warms up. While many people may believe this,
it is incorrect.
There are two commonly held folk theories about thermostats: the timer theory and
the valve theory. The timer theory proposes that the thermostat simply controls the
relative proportion of time that the device stays on. Set the thermostat midway, and
the device is on about half the time; set it all the way up and the device is on all the
time; hence, to heat or cool something most quickly, set the thermostat so that the
device is on all the time. The valve theory proposes that the thermostat controls how
much heat comes out of the device. Turn the thermostat all the way up, and you get
maximum heating or cooling.
Thermostats work by switching on the heat and keeping it going at a constant speed
until the desired temperature set is reached, at which point they cut out. They cannot
control the rate at which heat is given out from a heating system. Left a given setting,
thermostats will turn the heat on an off as necessary to maintain the desired
temperature. It treats the heater, oven, and air conditioner as all-or-nothing devices
that can be either fully on or fully off, with no in-between states. The thermostat turns
the heater, oven, or air conditioner completely on--at full power--until the
temperature setting on the thermostat is reached. Then it turns the unit completely off.
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Setting the thermostat at one extreme cannot affect how long it takes to reach the
desired temperature.
The real point of the example is not that some people have erroneous theories; it is
that everyone forms theories (mental models) to explain what they have observed. In
the case of the thermostat the design gives absolutely no hint as to the correct answer.
In the absence of external information, people are free to let their imaginations run
free as long as the mental models they develop account for the facts as they perceive
them.
Why do people use erroneous mental models?
It seems that in the above scenario, they are running a mental model based on general
valve theory of the way something works. This assumes the underlying principle of
"more is more": the more you turn or push something, the more it causes the desired
effect. This principle holds for a range of physical devices, such as taps and radio
controls, where the more you turn them, the more water or volume is given. However,
it does not hold for thermostats, which instead function based on the principle of an
on-off switch. What seems to happen is that in everyday life people develop a core set
of abstractions about how things work, and apply these to a range of devices,
irrespective of whether they are appropriate.
Using incorrect mental models to guide behavior is surprisingly common. Just watch
people at a pedestrian crossing or waiting for an elevator (lift). How many times do
they press the button? A lot of people will press it at least twice. When asked why, a
common reason given is that they think it will make it lights change faster or ensure
the elevator arrives. This seems to do another example of following the "more is
more" philosophy: it is believed that the more times you press the button, the more
likely it is to result in he desire effect.
Another common example of an erroneous mental model is what people do when the
cursor freeze on their computer screen. Most people will bash away at all manner of
keys in the vain hope that this will make it work again. However, ask them how this
will help and their explanations are rather vague. The same is true when the TV starts
acting up: a typical response is to hit the top of the box repeatedly with a bare hand or
a rolled-up newspaper. Again, as people why and their reasoning about how this
behavior will help solve the problem is rather lacking.
Indeed, research has shown that people's mental models of the way interactive
devices work is poor, often being incomplete, easily confusable, based on
inappropriate analogies, and superstition. Not having appropriate mental models
available to guide their behavior is what caused people to become very frustrate--
often resulting is stereotypical "venting' behavior like those described above.
On the other hand, if people could develop better mental models of interactive
systems, they would be in a better position to know how to carry out their tasks
efficiently and what to do if the system started acting up. Ideally, they should be able
to develop a mental model that matches the conceptual; modal developed by the
designer. But how can you help users to accomplish this? One suggestion is to
educate them better, however, many people are resistant to spending much time
learning about how things work, especially if it involves reading manuals and other
documentation. An alternative proposal is to design systems to be more transparent,
so that they are easier to understand.
People do tend to find causes for events, and just what they assign as the cause varies.
In part people tend to assign a causal relation whenever two things occur in
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succession. If I do some action A just prior to some result R, then I conclude that A
must have caused R, even if, there really was no relationship between the two.
Self-blaming
Suppose I try to use an everyday thing, but I can't: where is the fault, in my action or
in the thing? We are apt to blame ourselves. If we believe that others are able to use
the device and if we believe that it is not very complex, then we conclude that any
difficulties must be our own fault. Suppose the fault really lies in the device, so that
lots of people have the same problems. Because everyone perceives the fault to be his
or own, nobody wants to admit to having trouble. This creates a conspiracy of silence,
maintaining the feeling of guilt and helplessness among users.
Interestingly enough, the common tendency to blame ourselves for failures with
everyday objects goes against the normal attributions people make. In general, it has
been found that normal attribute their own problems to the environment, those of
other people to their personalities.
It seems natural for people to blame their own misfortunes on the environment. It
seems equally natural to blame other people's misfortunes on their personalities. Just
the opposite attribution, by the way, is made when things go well. When things go
right, people credit their own forceful personalities and intelligence. The onlookers do
the reverse. When they see things go well for someone else, they credit the
environment.
In all cases, whether a person is inappropriately accepting blame for the inability to
work simple objects or attributing behavior to environment or personality, a faulty
mental model is at work.
Reason for self-blaming
Learned helplessness
The phenomenon called learned helplessness might help explain the self-blame. It
refers to the situation in which people experience failure at a task, often numerous
times. As a result, they decide that the task cannot be done, at least not by them: they
are helpless. They stop trying. If this feeling covers a group of tasks, the result can be
severe difficulties coping with life. In the extreme case, such learned helplessness
leads to depression and to a belief that the person cannot cope with everyday life at
all. Some times all that it takes to get such a feeling of helplessness is a few
experiences that accidentally turn out bad. The phenomenon has been most frequently
studied as a precursor to the clinical problem of depression, but it might easily arise
with a few bad experiences with everyday life.
Taught helplessness
Do the common technology and mathematics phobias results from a kind of learned
helplessness? Could a few instances of failure in what appear to be straightforward
situations generalize to every technological object, every mathematics problem?
Perhaps. In fact, the design of everyday things seems almost guaranteed to cause this.
We could call this phenomenon taught helplessness.
With badly designed objects--constructed so as to lead to misunderstanding--faulty
mental models, and poor feedback, no wonder people feel guilty when they have
trouble using objects, especially when they perceive that nobody else is having the
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same problems. The problem is that once failure starts, it soon generalizes by self-
blame to all technology. The vicious cycle starts: if you fail at something, you think it
is your fault. Therefore you think you can't do that task. As a result, next time you
have to do the task, you believe you can't so you don't even try. The result is that you
can't, just as you thought. You are trapped in a self-fulfilling prophecy.
The nature of human thought and explanation
It isn't always easy to tell just where the blame for problem should be placed. A
number of dramatic accidents have come about, in part, from the false assessment of
blame in a situation. Highly skilled, well-trained people are using complex equipment
when suddenly something goes wrong. They have to figure out what the problem is.
Most industrial equipment is pretty reliable. When the instruments indicate that
something is wrong, one has to consider the possibility that the instruments
themselves are wrong. Often this is the correct assessment. When operators
mistakenly blame the instruments for an actual equipment failure, the situation is ripe
for a major accident.
It is spectacularly easy to find examples of false assessment in industrial accidents.
Analysts come in well after the fact, knowing what actually did happen; with
hindsight, it is almost impossible to understand how the people involved could have
made the mistake. But from the point of view of the person making decisions at time,
the sequence of events is quite natural.
Three Mile Island Nuclear Power Plant
At the Three Mile Island nuclear power plant, operators pushed a button to close a
valve; the valve had been opened (properly) to allow excess water to escape from the
nuclear core. In fact, the valve was deficient, so it didn't close. But a light on the
control panel indicated that the valve position was closed. The light actually didn't
monitor the valve, only the electrical signal to the valve, a fact known by the
operators. Still, why suspect a problem? The operators did look at the temperature in
the pipe leading from the valve: it was high, indicating that fluid was still flowing
through the closed valve. Ah, but the operators knew that the valve had been leaky, so
the leak would explain the high temperature; but the leak was known to be small, and
operators assumed that it wouldn't affect the main operation. They were wrong, and
the water that was able to escape from the core added significantly to the problems of
that nuclear disaster. Norman says that the operators' assessment was perfectly
reasonable: the fault wan is the design of the lights and in the equipment that gave
false evidence of a closed valve.
Lockheed L-1011
Similarly many airline accidents happened just due to misinterpretations. Consider
flight crew of the Lockheed L-1011 flying from Miami, Florida, to Nassau, Bahamas.
The plane was over the Atlantic Ocean, about 110 miles from Miami, when the low
oil pressure light for one of the three engines went on. The crew turned off the engine
and turned around to go back to Miami. Eight minutes later, the low-pressure lights
for the remaining two engines also went on, and the instruments showed zero oil
pressure and quantity in all three engines. What did the crew do now? They didn't
believe it! After all, the pilot correctly said later, the likelihood of simultaneous oil
exhaustion in all three engines was "one in millions I would think." At the time,
sitting in the airplane, simultaneous failure did seem most unlikely. Even the National
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Transportation Safety Board declared, "The analysis of the situation by the flight crew
was logical, and was what most pilots probably would have done if confronted by the
same situation."
What happened? The second and third engines were indeed out of oil, and they failed.
So there were no operating engines: one had been turned off when its gauge registered
low, the other two had failed. The pilots prepared the plane for an emergency landing
on the water. The pilots were too busy to instruct the flight crew properly, so the
passengers were not prepared. There was semi-hysteria in the passenger cabin. At the
last minute, just as the plane was about to ditch in the ocean, the pilots managed to
restart the first engine and land safely to Miami. Then that engine failed at the end of
the runway.
Why did all three engine fail? Three missing O-rings, one missing from each of three
oil plugs, allowed all the oil to seep out. The O-rings were put in by two different
people who worked on the three engines (one for the two plugs on the wings, the
other of the plug on the tail). How did both workers make the same mistake? Because
the normal method by which they got the oil plugs had been changed that day. The
whole tale is very instructive, for there were four major failures of different sorts,
from the omission of the O-rings, to the inadequacy of the maintenance procedures, to
the false assessment of the problem, to the poor handling of the passengers.
Fortunately nobody was injured. The analysts of the National Transportation Safety
Board got to write a fascinating report.
Find an explanation, and we are happy. But our explanations are based on analogy
with past experience, experience that may not apply in the current situation. In the
Three Mile Island incident, past experience with the leaky valve explained away the
discrepant temperature reading; on the flight from Miami to Nassau, the pilots' lack of
experience with simultaneous oil pressure failure triggered their belief that the
instruments must be faulty. Once we have an explanation--correct or incorrect--for
otherwise discrepant or puzzling events, there is no more puzzle, no more
discrepancy. As a result, we are complacent, at least for a while.
How people do things
To get something done, you have to start with some notion of what is wanted--the
goal that is to be achieved. Then, you have to do some thing to the world , that is, take
action to move yourself or manipulate someone or something. Finally, you check to
see that your goal was made. So there are four different things to consider: the goal,
what is done to the world, the world itself, and the check of the world. The action
itself has two major aspects: doing something and checking. Call these execution and
evaluation Goals do not state precisely what to do--where and how to move, what to
pick up. To lead to actions goals must be transformed into specific statements of what
is to be done, statements that are called intentions. A goal is some thing to be
achieved, often vaguely stated. An intention is specific action taken to get to the goal.
Yet even intentions are not specific enough to control actions.
Suppose I am sitting in my armchair, reading a book. It is dust, and the light has
gotten dimmer and dimmer. I decide to need more light (that is the goal: get more
light). My goal has to be translated into the intention that states the appropriate action
in the world: push the switch button on the lamp. There's more: I need to specify how
to move my body, how to stretch to reach the light switch, how to extend my finger to
push the button (without knocking over the lamp). The goal has to be translated into
an intention, which in turn has to make into a specific action sequence, one that can
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control my muscles. Note that I could satisfy my goal with other action sequences,
other intentions. If some one walked into the room and passed by the lamp, I might
alter my intention form pushing the switch button to asking the other person to do it
for me. The goal hasn't changed, but the intention and resulting action sequence have.
Action Cycle
Human action has two aspects, execution
Goals
and evaluation. Execution involves doing
What we want to
something. Evaluation is the comparison of
happen
what happened in the world with what we
wanted to happen
Evaluation
Execution to
Stages of Execution
What we do
Comparing what
the world
Happened with what we
Start at the top with the goal, the state that is
wanted to happen
to be achieved. The goal is translated into an
intention to do some action. The intention
must be translated into a set of internal
commands, an action sequence that can be
THE
WORLD
performed to satisfy the intention. The
action sequence is still a mental event:
noting happens until it is executed, performed upon the world.
Stages of Evaluation
Evaluation starts with our perception of the world. This perception must then be
interpreted according to our expectations and then compared with respect to both our
intentions and our goals
Seven stages of action
Goals
Intention to act
Evaluation of the
Interpretations
The stages of execution (intentions, action
sequence, and execution) are coupled with
the  stages  of  evaluation  (perception,
sequence of
Interpreting the
interpretation, and evaluation), with goals
actions
perception
common to both stages.
Errors
11.2
execution of
Perceiving the state
The action sequence
of the world
Human capability for interpreting and
manipulating
information
is
quite
impressive. However, we do make mistake.
Whenever we try to learn a new skill, be it
skiing, typing, cooking or playing chess, we
are bound to make mistakes. Some are
THE WORLD
trivial, resulting in no more than temporary
inconvenience or annoyance. Other may be more serious, requiring substantial effort
to correct. In most situations it is not such a bad thing because the feedback from
making errors can help us to learn and understand an activity. When learning to use a
computer system, however, learners are often frightened of making errors because, as
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well as making them feel stupid, they think it can result in catastrophe. Hence, the
anticipation of making an error and its consequences can hinder a user's interaction
with a system.
Why do we make mistakes and can we avoid them? In order to answer the latter part
of the question we must first look at what is going on when we make an error. There
are several different types of errors. Some errors result from changes in the context of
skilled behavior. If a pattern of behavior has become automatic and we change some
aspect of it, the more familiar pattern may break through and cause an error. A
familiar example of this is where we intend to stop at the shop on the way home from
work but in fact drive past. Here, the activity of driving home is the more familiar and
overrides the less familiar intention.
Other errors result from an incorrect understanding, or model, of a situation or system.
People build their own theories to understand the casual behavior of systems. These
have been termed mental models. They have a number of characteristics. Mental
models are often partial: the person does not have a full understanding of the working
of the whole system. They are unstable and are subject to change. They can be
internally inconsistent, since the person may not have worked through the logical
consequences of their beliefs. They are often unscientific and may be based on
superstition rather than evidence. Often they are based on an incorrect interpretation
of the evidence.
A classification of errors
There are various types of errors. Norman has categorized them into two main types,
slips and mistakes:
Mistakes
Mistakes occur through conscious deliberation. An incorrect action is taken based on
an incorrect decision. For example, trying to throw the icon of the hard disk into the
wastebasket, in the desktop metaphor, as a way of removing all existing files from the
disk is a mistake. A menu option to erase the disk is appropriate action.
Slips
Slips are unintentional. They happen by accident, such as making typos by pressing
the wrong key or selecting wrong menu item by overshooting. The most frequent
errors are slips, especially in well-learned behavior.
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Table of Contents:
  1. RIDDLES FOR THE INFORMATION AGE, ROLE OF HCI
  2. DEFINITION OF HCI, REASONS OF NON-BRIGHT ASPECTS, SOFTWARE APARTHEID
  3. AN INDUSTRY IN DENIAL, SUCCESS CRITERIA IN THE NEW ECONOMY
  4. GOALS & EVOLUTION OF HUMAN COMPUTER INTERACTION
  5. DISCIPLINE OF HUMAN COMPUTER INTERACTION
  6. COGNITIVE FRAMEWORKS: MODES OF COGNITION, HUMAN PROCESSOR MODEL, GOMS
  7. HUMAN INPUT-OUTPUT CHANNELS, VISUAL PERCEPTION
  8. COLOR THEORY, STEREOPSIS, READING, HEARING, TOUCH, MOVEMENT
  9. COGNITIVE PROCESS: ATTENTION, MEMORY, REVISED MEMORY MODEL
  10. COGNITIVE PROCESSES: LEARNING, READING, SPEAKING, LISTENING, PROBLEM SOLVING, PLANNING, REASONING, DECISION-MAKING
  11. THE PSYCHOLOGY OF ACTIONS: MENTAL MODEL, ERRORS
  12. DESIGN PRINCIPLES:
  13. THE COMPUTER: INPUT DEVICES, TEXT ENTRY DEVICES, POSITIONING, POINTING AND DRAWING
  14. INTERACTION: THE TERMS OF INTERACTION, DONALD NORMAN’S MODEL
  15. INTERACTION PARADIGMS: THE WIMP INTERFACES, INTERACTION PARADIGMS
  16. HCI PROCESS AND MODELS
  17. HCI PROCESS AND METHODOLOGIES: LIFECYCLE MODELS IN HCI
  18. GOAL-DIRECTED DESIGN METHODOLOGIES: A PROCESS OVERVIEW, TYPES OF USERS
  19. USER RESEARCH: TYPES OF QUALITATIVE RESEARCH, ETHNOGRAPHIC INTERVIEWS
  20. USER-CENTERED APPROACH, ETHNOGRAPHY FRAMEWORK
  21. USER RESEARCH IN DEPTH
  22. USER MODELING: PERSONAS, GOALS, CONSTRUCTING PERSONAS
  23. REQUIREMENTS: NARRATIVE AS A DESIGN TOOL, ENVISIONING SOLUTIONS WITH PERSONA-BASED DESIGN
  24. FRAMEWORK AND REFINEMENTS: DEFINING THE INTERACTION FRAMEWORK, PROTOTYPING
  25. DESIGN SYNTHESIS: INTERACTION DESIGN PRINCIPLES, PATTERNS, IMPERATIVES
  26. BEHAVIOR & FORM: SOFTWARE POSTURE, POSTURES FOR THE DESKTOP
  27. POSTURES FOR THE WEB, WEB PORTALS, POSTURES FOR OTHER PLATFORMS, FLOW AND TRANSPARENCY, ORCHESTRATION
  28. BEHAVIOR & FORM: ELIMINATING EXCISE, NAVIGATION AND INFLECTION
  29. EVALUATION PARADIGMS AND TECHNIQUES
  30. DECIDE: A FRAMEWORK TO GUIDE EVALUATION
  31. EVALUATION
  32. EVALUATION: SCENE FROM A MALL, WEB NAVIGATION
  33. EVALUATION: TRY THE TRUNK TEST
  34. EVALUATION – PART VI
  35. THE RELATIONSHIP BETWEEN EVALUATION AND USABILITY
  36. BEHAVIOR & FORM: UNDERSTANDING UNDO, TYPES AND VARIANTS, INCREMENTAL AND PROCEDURAL ACTIONS
  37. UNIFIED DOCUMENT MANAGEMENT, CREATING A MILESTONE COPY OF THE DOCUMENT
  38. DESIGNING LOOK AND FEEL, PRINCIPLES OF VISUAL INTERFACE DESIGN
  39. PRINCIPLES OF VISUAL INFORMATION DESIGN, USE OF TEXT AND COLOR IN VISUAL INTERFACES
  40. OBSERVING USER: WHAT AND WHEN HOW TO OBSERVE, DATA COLLECTION
  41. ASKING USERS: INTERVIEWS, QUESTIONNAIRES, WALKTHROUGHS
  42. COMMUNICATING USERS: ELIMINATING ERRORS, POSITIVE FEEDBACK, NOTIFYING AND CONFIRMING
  43. INFORMATION RETRIEVAL: AUDIBLE FEEDBACK, OTHER COMMUNICATION WITH USERS, IMPROVING DATA RETRIEVAL
  44. EMERGING PARADIGMS, ACCESSIBILITY
  45. WEARABLE COMPUTING, TANGIBLE BITS, ATTENTIVE ENVIRONMENTS