Human-computer interaction
Chapter 3 – Reasoning and problem solving
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Definitions
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Reasoning
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The process by which we use the knowledge we have to draw
conclusions or infer something new about the subject of interest
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Types of reasoning
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Deductive
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Inductive
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Abductive
Problem solving
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Use information we have to find solutions in new situations
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Gestalt (or form) theory
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The problem space (Newell et Simon)
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Analogy
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Deductive reasoning
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Premises => Algorithms => Conclusions
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Example :
We don't work on weekends.
We are saturday
Saturday is the first day of the weekend
Conclusion : I do not work today
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The conclusion always derives from the premises by logical operations
but this does not mean that the premises were correct !
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Deductive reasoning may lead to false conclusions
Certain people are babies
Certain babies cry
conclusion : certain people cry ???
FALSE, since it is not said that ALL babies are people... those who cry may
not be people
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Inductive reasoning (1)
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Inductive reasoning corresponds to the generalization that we
make from cases we know to infer information about case we
don't know. The facts are true (experience) but the
generalizations (making a law) may not be : one counterexample is enough to disprove the « law ».
Example 1 :
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The first line hereafter shows 5 hexagones ; it is asked to add the 6-th
one, selected between those shown in the second line.
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Inductive reasoning (2)
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Inductive reasoning (2)
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Inductive reasoning helps us learn and orient in a changing
environment. But the law established by inductive reasoning
must be constantly verified
Example 2 : Wason cards
7 E 4 K
Each card has a number on one side and a letter on the other side. Find which cards
must be turned on to prouve the following « law » that was « established » by
previous tests, not presented here :
If a card has a vowell on one side, it has an even number on the other side
What happens if we omit saying that each card has a vowell on one side and a
number on the other ?
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Abductive reasoning
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Abduction : it is the type of reasoning used to determine the
causes of a given effect :
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The result is observed
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One knows causes to effect possible relations
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A hypothesis to explain the causes is put forward
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The hypothesis is checked
This tipe of reasoning is common to police inquieries and to
medical diagnostic, but it is also used in everyday life.
Another application are the « expert systems » : in order to
choose the most appropriate hypothesis, one sets up a
mechanism to evaluate the « value » of each one proposed.
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Problem solving : gestalt theory
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Gestalten in german means « to structure, to give form » ; it has
no equivalent in other languages and for this reason the word is
used « as is » in french, english, romanian...
The main ideas are :
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We are pre-programmed to percieve forms that we use constantly to
understand our environment, by grouping and restructuring them to
solve the new problems
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The « whole » is not the sum of the parts : we percieve the whole before
percieving the details
This principles are the basis for the « gestalt » theory and are
also useful guides to HCI design. The theory establishes the
following « laws » :
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The « laws » of the gestalt theory
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The good form : an informal set is first percieved as a simple form, stable
and symmetric
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Continuity : close points are first percieved as forming a line
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Similitude : in a set we try to assemble similar objects
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Common destiny : the objects moving simultanously in the same direction
are percieved as being part of the same form
Closure : it is easier to percieve closed forms surrounding a surface
Conclusion : we combine our perception with our knowledge and thus
we add information which is absent in the stimuli
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Gestaltic perception
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Example :
Although attractive, this theory leaves unexplained many aspects of the
problem solving mechanisms.
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Problem solving : the problem space
Initial state
Search
Desired state
Search
Problem space
(Newell, Simon - 1972)
Problem space :
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Initial state : problem statement and known conditions
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Research state : information space containing all data and procedures that will be used to
solve the problem
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Goal state : target description
Limitations :
● The model works if both the initial and final states are well defined and well known.
Sometimes this is impossible and the state definitions become a part of the problem, like in
the case of functional specificatons in programming.
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Problem solving exercise
Identify the goals and operators involved in the problem
'delete the second paragraph of the document' on a word
processor. Now use a word processor to delete a paragraph
and note your actions, goals and subgoals. How well did
they match your earlier description?
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Problem solving : analogy
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The analogy is used to solve a new problem by looking for
similitudes with known situations and events, for which the
problem was solved.
Example : a doctor has to blast a malignian tumour with very
strong rays. But he knows that the surrounding healthy tissue
will also be destroyed ; however, if he lessens the rays' intensity
the healthy tissue will be preserved but the tumour will remain.
How should the doctor proceed ?
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Problem solving : the following story can help ?
A greek general attacking an ennemy city had to avoid the main
road, since the defenders could easily stop his soldiers. He
decided to split his army in small groups which attacked arriving on
several roads simultaneously. The manoeuvre was successfull.
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Skill acquisition
Understanding the skill acquisition mechanisms became more and
more important with the advent of computer and it's use in computer
assisted training. The help systems integrated to computer
applications are also addressing skill acquisition mechanisms.
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Concept acquisition
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Skill acquisition
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Repetition's role
Experts vs novices behavour (chess ?)
Methods
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Structured acquisition
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Try and error
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Human errors
Understanding human errors' reasons is important for decision taking
processes. Some errors may have catastrophic effects. There are many reasons
why humans make errors ; wrong man / machine interfaces can be a cause (the
case of the Spitfire pilots)
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Bad design
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Response time
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Incompetence
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Lack of attention or concentration
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To many informations arriving simultaneously can divert the user
Context change
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Unexpected (example : a difference between the real behaviour of a device and
it's description in the documentation or explanation during training)
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Loose preparation (example : a behavioural change is asked but the operator
was not informed or incomplete information was provided).
Natural or provoked emotion
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