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Cards (31)
what is a problem
when it is not clear how to get from the
start
state to
goal
state
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cheap
necklace problem
4
chains with
3
links each
• Need to make
1
chain
•
2
cents to open link
•
3
cents to close link
• You have only
15
cents
This problem is
difficult
for many people because they tend to adopt an
inappropriate
mental set to approach this problem
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types
of problems
well
defined and
ill
defined problems
knowledge
lean and
knowledge
rich problems
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well
defined problems
problems
in which the initial state, the
goal
, and the methods available for solving them are clearly laid out
eg
chess
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ill defined problems
Start
state
or
end state
or the possible strategies may be unknown
eg
exams
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knowledge
rich problems
Problems that require specific knowledge
eg
medicine
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knowledge lean problems
problems that can be solved without the use of much
prior knowledge
eg
puzzle
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behaviourist
approach
trial and error learning
Thorndike's
cat experiment
- unsystematic
-
no knowledge
-
slow
- doesn't work for all
problems
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Thorndike's
cat
experiment (
1898
)
puzzle
boxes from which cats could escape by pulling a lever to open a door and gain access to
food outside
the box
random
behaviours
at start but eventually, through
trial and error
, would perform the correct action
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Gestalt
approach
Success in solving a problem is
influenced
by how it is represented in the
mind
involves
insight
,
aha moment
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problem
restructuring (gestalt)
restructure or reorganize their representation of the problem, notice relevant information not
previously
noticed,
switch
strategies
Koehler's (1925) monkey experiment show problem is solved after an
incubation
period
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functional
fixedness (gestalt)
the tendency to perceive an item only in terms of its most common use
Duncker's (1945) candle problem - fix and light candle on a wall so wax doesn't drip on table, pin box and place candle inside
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problem
solving and insight (gestalt)
maier (1931) two string problem
attach scissors to end of rope and create pendulum to reach second
facilitated by unconscious hints, researcher would move rope by accident
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neuroscience
and insight
activation in
right anterior superior temporal gyrus
fMRI - becomes more active when individuals experience
insight
EEG
- distinctive peak in brain activity just before moment of insight, shows neural signature that precedes conscious realization of solution
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gestalt
approach evaluation
Recognises the role of
insight
Mechanisms
underlying insight are not specified
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representational
change theory
explains processes underlying insight:
- construct problem
representation
-
retrieve
moves (
operators
) by spreading activation from problem rep
- impasses occur when problem rep doesn't cue right
operators
- impasses broken by
restructuring
problem rep
- once impasse is broken an
insight
may occur
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problem representations can be changed by
- changing the
representation
of the problem
- relaxing the
constraints
on what moves are allowed
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changing the
representation
of a
problem
changing the way you think about the problem
board has
62
squares, can you cover whole board with
31
dominos?
(no because there's
two black squares
taken away, need red and
black
)
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relaxing the constraints
assume anything is possible
move one matchstick
so equation is correct
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representational
change theory evaluation
explains some of the mechanisms underlying insight
doesn't explain what leads to
representational
change or why
incubation
helps
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information
processing approach
computational modelling approach -
general problem solver
most problems don't require insight, focus instead on knowledge lean problems
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general
problem solver (info processing approach)
problem
solving is a search through the
problem space
(all possible states of a problem)
we don't have
WM capacity
to think of all possible moves, use
general purpose heuristics
instead
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heuristics (general problem solver in info processing approach)
forward
search (from current state to future)
backward
search (from goal to current state)
avoid loops
(new solutions over previous paths)
hill climbing
(actions that move you closer)
means-end analysis
(sub-goals)
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information
processing approach evaluation
- Works well for well-defined problems
- Good objective measures of how well people perform
- Led to well-specified computer models
- Many everyday problems are ill-defined
- Doesn't work for insight Problems
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analogical
problem solving
we learn from past problems/ experience
negative
transfer - functional fixedness
positive
transfer - near transfer to similar context and far to different context
learn from analogy using surface
features
and structural
features
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analogical
mapping - (Gick and Holyoak 1980)
1. gave the radiation problem with no analogy 10%
2. If you gave the fortress problem and solution
30%
3. Tell them fortress will help you get it right
70%
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analogical
problem solving evaluation
- Retrieving
analogies
is hard unless the problems share
similar
surface features
- In real life, this may be even more difficult because the time and
context
may be more
distant
than in lab studies
- Individual Differences not well
understood
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expertise
focus on problems that depend on
extensive knowledge
and
learning
have a lot of knowledge that can be accessed
quickly
more
automatic
process
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chess
expertise (
DeGroot 1965
)
grand
masters considered more moves and took
less
time to move with move rating of 8.2
experts considered
less
moves, took
more
time, and had move rating of 5.2
chess masters recalled
91
% of 5s board
presentation
, experts
43
%
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medical
experts (Melo et al 2012)
medical experts fast at detecting
tumours
(~
1
sec)
activates same brain areas as
object
recognition
experts use fast and
automatic
processes which develop with
practice
View source
Theories
of Problem-Solving:
Behaviourist
Approach, Gestalt Approach, Problem Restructuring, Information
Processing
, Analogical Problem-Solving
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