COGNITIVE PROCESSES: LEARNING, READING, SPEAKING, LISTENING, PROBLEM SOLVING, PLANNING, REASONING, DECISION-MAKING

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Human Computer Interaction (CS408)

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

Human Computer Interaction (CS408)

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.

Human Computer Interaction (CS408)

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

Human Computer Interaction (CS408)

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.

Human Computer Interaction (CS408)

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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