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Human Computer Interaction

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Human Computer Interaction (CS408)
VU
Lecture 10
Lecture 10. Cognitive Processes - Part II
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 learning
·
Discuss planning, reasoning, decision making
·
Understand problem solving
·
Today is part second of our two parts series lecture on Cognitive Process. As we have
earlier seen that cognition involves following processes:
·  Attention
Memory
·
Perception and recognition
·
Learning
·
Reading, speaking and listening
·
Problem solving, planning, reasoning, decision-making.
·
Today we will learn about learning and thinking. Let us first look at learning.
Learning
10.1
Learning can be consider in two terms:
·  Procedural
Declarative
·
Procedural
According to procedural learning we come to any object with questions like how to
use it? How to do something? For example, how to use a computer-based application?
Declarative
According to declarative learning we try to find the facts about something. For
example, using a computer-based application to understand a given topic.
Jack Carroll and his colleagues have written extensively about how to design
interfaces to help learners develop computer-based skills. A main observation is that
people find it very hard to learn by following sets of instructions in a manual. For
example, when people encounter a computer for the first time their most common
reaction is one of fear and trepidation. In contrast, when we sit behind the steering
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wheel of a car for the first time most of us are highly motivated and very excited with
the prospect of learning to drive. Why, then m is there such a discrepancy between
our attitudes to learning these different skills? One of the main differences between
the two domains is the way they are taught. At the end of the first driving lesson, a
pupil will have usually learned how to drive through actually doing. This includes
performing a number of complex tasks such as clutch control, gear changing, learning
to use the controls and knowing what they are. Furthermore, the instructors are keen
to let their pupils try thing out and get started. Verbal instruction initially is kept to
minimum and usually interjected only when necessary. In contrast, someone who sits
in front of a computer system for the first time may only have a very large manual,
which may be difficult to understand and poorly
presented. Often training and reference materials are written as a series of ordered
explanations together with step by step exercises, which may cause the learner to feel
overloaded with information or frustrated at not being able to find information that
she wants. One of the main developing usable training materials and helps facilities.
There is general assumption that having read something in the manual users can
immediately match it to what is happening at the interface and respond accordingly.
But as you may have experienced, trying to put into action even simple descriptions
can sometimes be difficult.
Experienced users also appear to be reluctant to learn new methods and operations
from manuals. When new situations arise that could be handled more effectively by
new procedures, experienced users are more likely to continue to use the procedures
they already know rather than try to follow the advanced procedures outlined in a
manual, even if the former course takes much longer and is less effective.
So, people prefer to learn through doing. GUI and direct manipulation interface are
good environments for supporting this kind of learning by supporting exploratory
interaction and importantly allowing users to `undo' their actions, i.e., return to a
previous state if they make a mistake by clicking on the wrong option.
Carroll has also suggested that another way of helping learners is by using a `training
wheels' approach. This involves restricting the possible functions that can be carried
out by a novice to the basics and then extending these as the novice becomes more
experienced. The underlying rationale is to make initial learning more tractable,
helping the learner focus on simple operations before moving on to more complex
ones.
There have also been numerous attempts to harness the capabilities of different
technologies, such as web-based, multimedia, and virtual reality, is that they provide
alternative ways of representing and interacting with information that are not possible
with traditional technologies. In so doing, they have the potential of offering learners
the ability to explore ideas and concepts different ways.
People often have problems learning the difficult stuff---by this we mean
mathematical formulae, notations, laws of physics, and other abstract concepts. One
of the main reasons is that they find it difficult to relate their concrete experiences of
the physical world with these higher-level abstractions. Research has shown,
however, that it is possible to facilitate this kind of learning through the use of
interactive multimedia.
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Dynalinking
The process of linking and manipulating multimedia representations at the interface is
called dynalinking. It is helpful in learning. An example where dynalinking have been
found beneficial is in helping children and students learn ecological concepts. During
experiment a simple ecosystem of a pond was built using multimedia. The concrete
simulation showed various organisms swimming and moving around and occasionally
an event where one would eat another. When an organism was clicked on, it would
say what it was and what it ate.
The simulation was dynalinked with other abstract representations of the pond
ecosystem. One of these was a food web diagram. People were encouraged to interact
with the interlinked diagrams in various ways and to observe what happened in the
concrete simulation when something was changed n the diagram and vice versa.
Dynalinking is a powerful form of interaction and can be used in a range of domains
to explicitly show relationships among multiple dimensions, especially when the
information to be understood or learned is complex.
Reading, Speaking and Listening
10.2
These three forms of language processing have both similar and different properties.
One similarity is that the meaning of sentences or phrases is the same regardless of
the mode in which it is conveyed. For example, the sentence "Computer are a
wonderful invention" essentially has the same meaning whether one reads it, speaks
it, or hears it. However, the ease with which people can read, listen, or speak differs
depending on the person, task, and context. For example, many people find listening
much easier than reading. Specific differences between the three modes include:
·  Written language is permanent while listening is transient. It is possible to
reread information if not understood the first time round. This is not possible
with spoken information that is being broadcast.
Reading can be quicker than speaking or listening, as written text can be
·
rapidly scanned in ways not possible when listening to serially presented
spoken works.
Listening require less cognitive effort than reading or speaking. Children,
·
especially, often prefer to listen to narratives provided in multimedia or web-
based learning material than to read the equivalent text online.
Written language tends to be grammatical while spoken language is often
·
ungrammatical. For example, people often start and stop in mid-sentence,
letting someone also start speaking.
There are marked differences between people in their ability to use language.
·
Some people prefer reading to listening, while others prefer listening.
Likewise, some people prefer speaking to writing and vice versa.
Dyslexics have difficulties understanding and recognizing written words,
·
making it hard for them to write grammatical sentences and spell correctly.
People who are hard of hearing or hart of seeing are also restricted in the way
·
they can process language.
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Incorporating Language processing in applications
Many applications have been developed either to capitalize on people's reading
writing and listening skills, or to support or replace them where they lack or have
difficulty with them. These include:
Interactive books and web-based material that help people to read or learning
·
foreign languages.
Speech-recognition systems that allow users to provide instructions via spoken
·
commands.
Speech-output systems that use artificially generated speech
·
Natural-language systems that enable users to type in questions and give text-
·
based responses.
Cognitive aids that help people who find it difficult to read, write, and speak.
·
A number of special interfaces have been developed for people who have
problems with reading, writing, and speaking.
Various input and output devices that allow people with various disabilities to
·
have access to the web and use word processors and other software packages.
Design Implications
Keep the length of speech-based menus and instructions to a minimum.
·
Research has shown that people find it hard to follow spoken menu with more
than three or four options. Likewise, they are bad at remembering sets of
instructions and directions that have more than a few parts.
Accentuate the intonation of artificially generated speech voices, as they are
·
harder to understand than human voices.
Provide opportunities for making text large on a screen, without affecting the
·
formatting, for people who find it hard to read small text.
Problem  Solving,
Planning,
Reasoning
and
10.3
Decision-making
Problem solving, planning, reasoning and decision-making are all cognitive processes
involving reflective cognition. They include thinking about what to do, what the
options are, and what the consequences might be of carrying out a given action. They
often involve conscious processing (being aware of what one is thinking about),
discussion with others, and the use of various kinds of artifacts, (e.g., maps, books,
and pen and paper). For example, when planning the best route to get somewhere, say
a foreign city, we may ask others use a map, get instructions from the web, or a
combination of these.
Reasoning also involves working through different scenarios and deciding which is
the best option or solution to a given problem. In the route-planning activity we may
be aware of alternative routes and reason through the advantages and disadvantages of
each route before deciding on the best one. Many family arguments have come about
because one member thinks he or she knows the best route while another thinks
otherwise.
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Comparing different sources of information is also common practice when seeking
information on the web. For example, just as people will phone around for a range of
quotes, so too, will they use different search engines to find sites that give the best
deal or best information. If people have knowledge of the pros and cons of different
search engines, they may also select different ones for different kinds of queries. For
example, a student may use a more academically oriented one when looking for
information for writing an essay, and a more commercially based one when trying to
find out what's happening in town.
The extent to which people engage in the various forms of reflective cognition
depends on their level of experience with a domain; applications about what to do
using other knowledge about similar situations. They tend to act by trial and error,
exploring and experimenting with ways of doing things. As a result they may start off
being slow, making errors and generally being inefficient. They may also act
irrationally, following their superstitions and not thinking ahead to the consequences
of their actions. In contrast experts have much more knowledge and experience and
are able to select optimal strategies for carrying out their tasks. They are likely to able
to think ahead more, considering what the consequences might be of opting for a
particular move or solution.
Reasoning
Reasoning is the process by which we use the knowledge we have to draw
conclusions or infer something new about the domain of interest. There are a number
of different types of reasoning:
·  Deductive reasoning
Inductive reasoning
·
Abductive reasoning
·
Deductive reasoning
Deductive reasoning derives the logically necessary conclusion from the given
premises. For example,
It is Friday then she will go to work
It is Friday
Therefore she will go to work
It is important to note that this is the logical conclusion from the premises; it does not
necessarily have to correspond to our notion of truth. So, for example,
If it is raining then the ground is dry
It is raining
Therefore the ground is dry.
Is a perfectly valid deduction, even though it conflicts with our knowledge of what is
true in the world?
Inductive reasoning
Induction is generalizing from cases we have seen to infer information about cases we
have not seen. For example, if every elephant we have ever seen has a trunk, we infer
that all elephants have trunks. Of course, this inference is unreliable and cannot be
proved to be true; it can only be proved to be false. We can disprove the inference
simply by producing an elephant without a trunk. However, we can never prove it true
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because, no matter how many elephants with trunks we have seen or are known to
exist, the next one we see may be trunkless. The best that we can do is gather
evidence to support our inductive inference.
In spite of its unreliability, induction is a useful process, which we use constantly in
learning about our environment. We can never see all the elephants that have ever
lived or will ever live, but we have certain knowledge about elephants, which we are
prepared to trust for all practical purposes, which has largely been inferred by
induction. Even if we saw an elephant without a trunk, we would be unlikely to move
from our position that `All elephants have trunk', since we are better at using positive
than negative evidence.
Abductive reasoning
The third type of reasoning is abduction. Abduction reasons from a fact to the action
or state that caused it. This is the method we use to derive explanations for the events
we observe. For example, suppose we know that Sam always drives too fast when she
has been drinking. If we see Sam driving too fast we may infer that she has been
drinking. Of course, this too is unreliable since there may be another reason why she
is driving fast: she may have been called to an emergency, for example.
In spite of its unreliability, it is clear that people do infer explanations in this way and
hold onto them until they have evidence to support an alternative theory or
explanation. This can lead to problems in using interactive systems. If an event
always follows an action, the user will infer that the event is caused by the action
unless evidence to the contrary is made available. If, in fact, the event and the action
are unrelated, confusion and even error often result.
Problem solving
If reasoning is a means of inferring new information from what is already known,
problem solving is the process of finding a solution to an unfamiliar task, using the
knowledge we have. Human problem solving is characterized by the ability to adapt
the information we have to deal with new situations. However, often solutions seen to
be original and creative. There are a number of different views of how people solve
problems. Te earliest, dating back to the first half of the twentieth century, is the
Gestalt view that problem solving involves both reuse of knowledge and insight. This
has been largely superseded but the questions it was trying to address remain and its
influence can
be seen in later research. A second major theory, proposed in the 1970s by Newell
and Simon, was the problem space theory, which takes the view that the mind is a
likited information processor. Later variations on this drew on the earlier thory and
attempted to reinterpret Gestalt theory in terms of information-processing theories.
Let us look at these theories.
Gestalt theory
Gestalt psychologists were answering the claim, made by behaviorists, that problem
solving is a matter of reproducing known responses or trial and error. This
explanation was considered by the Gestalt school to be insufficient to account for
human problem-solving behavior. Instead, they claimed, problem solving is both
productive and reproductive. Reproductive problem solving draws on previous
experience as the behaviorist claimed, but productive problem solving involves
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insight and restructuring of the problem. Indeed, reproductive problem solving could
be hindrance to finding a solution, since a person may `fixate' on the known aspects
of the problem and so be unable to see novel interpretations that might lead to a
solution.
Although Gestalt theory is attractive in terms of its description of human problem
solving, it does not provide sufficient evidence or structure to support its theories. It
does not explain when restructuring occurs or what insight is, for example.
Problem space theory
Newell and Simon proposed that problem solving centers on the problem space. The
problem space comprises problem states, and problem solving involves generating
these states using legal state transition operators. The problem has an initial state and
a goal state and people use the operator to move from the former to the latter. Such
problem spaces may be huge, and so heuristics are employed to select appropriate
operators to reach the goal. One such heuristic is means-ends analysis. In means-ends
analysis the initial state is compared with the goal state and an operator chosen to
reduce the difference between the two. For example, imagine you are recognizing
your office and you want to move your desk from the north wall of the room to the
window. Your initial state is that the desk is at the north wall. The goal state is that the
desk is by the window. The main difference between these two is the location of your
desk. You have a number of operators, which you can apply to moving things: you
can carry them or push them or drag them, etc. however, you know that to carry
something it must be light and that your desk is heavy. You therefore have a new sub-
goal: to make the desk light. Your operators for this may involve removing drawers,
and so on.
An important feature of Newell and Simon's model is that it operates within the
constraints of the human processing system, and so searching the problem space is
limited by capacity of short-term memory, and the speed at which information can be
retrieved. Within the problem space framework, experience allows us to solve
problems more easily since we can structure the problem space appropriately and
choose operators efficiently.
Analogy in problem solving
A third element of problem solving is the use of analogy. Here we are interested in
how people solve novel problems. One suggestion is that this is done by mapping
knowledge relating to a similar known domain to the new problem-called analogical
mapping. Similarities between the known domain and the new one are noted and
operators from the known domain are transferred to the new one.
This process has been investigated using analogous stories. Gick and Holyoak gave
subjects the following problem:
A doctor is treating a malignant tumor. In order to destroy it he needs to blast it with
high-intensity rays. However, these will also destroy the healthy tissue, surrounding
tumor. If he lessens the ray's intensity the tumor will remain. How does he destroy the
tumor?
The solution to this problem is to fire low-intensity rays from different directions
converging on the tumor. That way, the healthy tissue receives harmless low-intensity
rays while the tumor receives the rays combined, making a high- intensity does. The
investigators found that only 10% of subjects reached this solution without help.
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However, this rose to 80% when they were given this analogous story and told that it
may help them:
A general is attacking a fortress. He can't send all his men in together as the roads are
mined to explode if large numbers of men cross them. He therefore splits his men into
small groups and sends them in on separate roads.
In spite of this, it seems that people often miss analogous information, unless it is
semantically close to the problem domain.
Skill acquisition
All of the problem solving that we have considered so far has concentrated on
handling unfamiliar problems. However, for much of the time, the problems that we
face are not completely new. Instead, we gradually acquire skill in a particular domain
area. But how is such skill acquired and what difference does it make to our problem-
solving performance? We can gain insight into how skilled behavior works, and how
skills are acquired, by considering the difference between novice and expert behavior
in given domains.
A commonly studied domain is chess playing. It is particularly suitable since it lends
itself easily to representation in terms of problem space theory. The initial state is the
opening board position; the goal state is one player checkmating the other; operators
to move states are legal moves of chess. It is therefore possible to examine skilled
behavior within the context of the problem space theory of problem solving.
In all experiments the behavior of chess masters was compared with less experienced
chess players. The first observation was that players did not consider large number of
moves in choosing their move, nor did they look ahead more than six moves. Maters
onsidered no mire alternatives than the less experienced, but they took less time to
make decision and produced better moves.
It appears that chess masters remember board configurations and good moves
associated with them. When given actual board positions to remember, masters are
much better at reconstructing the board than the less experienced. However, when
given random configurations, the groups of players were equally bad at reconstructing
the positions. It seems therefore that expert players `chunk' the board configuration in
order to hold it in short-term memory. Expert player use larger chunks than the less
experienced and can therefore remember more detail.
Another observed difference between skilled and less skilled problem solving is in the
way that different problems are grouped. Novices tend to group problems according
to superficial characteristics such as the objects or features common to both. Experts,
on the other hand, demonstrate a deeper understanding of the problems and group
them according to underlying conceptual similarities, which may not be at all obvious
from the problem descriptions.
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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