problem solving
- problem exists when someone lacks relevant knowledge to produce immediate solution
- purposeful, goal-directed
- involves controlled, rather than automatic processes
types of problems
- well-defined problems = all aspects of the problem are clearly specified (= initial state, goal, methods available) (e.g. maze or chess)
- used in research: there’s an optimal strategy and errors/deficiencies can easily be identified - ill-defined problems = definition of problem is underspecified, initial state, goal state & methods unclear (e.g. keys locked in car)
- most everyday problems - knowledge-rich problems = can only be solved with prior knowledge
- knowledge-lean problems = can be solved without prior knowledge, necessary info is provided by problem statement
cynefin framework (LECTURE)
A. predictable problems:
- simple problems: easy to find solution; recipe for cooking
- complicated problems: need expertise to solve
B. unpredictable problems:
- complex problems: experimenting to find solution
- chaotic problems: no idea how to solve problems; if not convert into complex problem ‘worst case’
–> problems can develop back and forth (one step at a time)
factors influencing problem solving
- past experience
- generally increases ability to solve problems
BUT - functional fixedness = inability to detach from usual function of an object; assume that any given object has a limited number of uses (e.g. candle + box of nails)
- mental set/einstellung = using strategy previously useful, but in current situation not helpful; use a familiar strategy even where there is a simpler/more effective alternative
factors influencing problem solving
- incubation
- problem is solved more easily when it is put aside for some time (Wallas)
- stronger effect for creative problems with multiple solutions & when long preparation time prior to
hypotheses: - Wallas: subconscious keeps processing
- Simon: forget control information which makes it easier to adopt a new approach
factors influencing problem solving
- expertise
- chunking theory: memory chunks contain more information & more chunks are stored
- template theory: chunks that are used frequently develop into more complex data structures –> few large templates (= more general) rather than large number of chunks
- template = core (= similar to fixed info stored in chunks) + slots (=contain variable info) thus are more flexible - routine expertise = using acquired knowledge to solve familiar problems efficiently (focus of template theory)
- adaptive expertise = using acquired knowledge to develop strategies for dealing with novel problems
- BEHAVIOURISM
- Thorndike
- trial-and-error learning
x reproductive thinking
- GESTALT
a. main hypothesis
- problem solving requires productive thinking
2.
b. prior history
- Wolfgang Köhler
insight
= experience of suddenly realising how to solve a problem “ahaaaa experience”; productive thinking - replacing one way of thinking about a problem with a new & more efficient way (cognitive conflict)
- non-insight problems: “warmth” (closeness to solution) gradually rises
- insight problems: warmth stays rather low until it suddenly rises before solution
2.
c. concepts
(1) reproductive vs. productive reproductive thinking = re-use knowledge productive thinking = understand underlying structure, restructuring of problem situation to find solution; gain new knowledge (2) barriers to problem solving - mental set - functional fixedness
2.
d. strengths/weaknesses
√ interesting, novel views √ some ideas still used x no mechanisms x only artificial problems x disregard intuitive problem solving
- PROBLEM SPACE HYPOTHESIS
a. main hypothesis
- problem situation (initial, goal, intermediate states, mental operations) is represented in problem space
- humans have limited processing capacity (use heuristics)
3.
b. prior history
- Newell & Simon
- information processing approach = human mind works like a computer
- general problem solver: computer program to solve problems
3.
c. concepts
heuristics
(1) hill climbing: do everything that gets you closer to solution; step-by-step
- works for ill-defined:
(2) means-end-analysis: more elaborate; creating a sub-goal to reduce the difference between the current & goal state
- works for well-defined: need to know structure to set subgoal first
(3) progress monitoring: 1. monitor/check progress 2. if process too slow: change of strategy
3.
d. strengths/weaknesses
√ detailed explanations
√ many applications
x cannot explain insight problem-solving
x everyday problems are not well-defined
- REPRESENTATIONAL CHANGE THEORY
- combination of Gestalt and problem space; emphasising insight
model:
1. current problem representation serves as a memory probe to retrieve related knowledge from LTM (= operators/ possible actions)
2. retrieval process is based on spreading activation among concepts in LTM
3. impasse: problem representation does not permit retrieval of the necessary/possible actions
4. impasse is broken when problem representation is changed (= INSIGHT!) - elaboration/ additional info
- constraint relaxation: inhibitions on what is regarded as permissible are removed
- re-encoding: some aspect of the problem is reinterpreted
5. new mental representation acts as new memory probe
4.
- limitations
x difficult to predict when/how the representation of a problem will change
x single-factor theory: constraint relaxation as single solution to insight problems
x de-emphasised individual differences
analogical problem-solving
= solving problems by using analogies (= comparison between two objects/between current and previous problem that emphasises similarities between them)
- important in everyday life: relating novel situations to previous situations
types of similarities (analogical problem-solving)
similarities between problems: we must detect similarities to solve our problem
- superficial similarity: solution-irrelevant details are common to the two problems
- structural similarity: causal relations among some of the main components are shared by both problems
- procedural similarity: procedures for turning the solutions principle into concrete operations are common to both problems
transfer (analogical problem-solving)
= effects of previous learning and problem solving on current problem
positive vs negative transfers
- positive transfer = past experience helps
–> far transfer: positive transfer to dissimilar context
–> near transfer: positive transfer to similar context
- negative transfer = past experience disrupts ability to solve current problem
other heuristics/techniques
- generate and test
- backward: analyse goal to determine last step needed to achieve it
- backtracking: keeping track of when/which assumptions were made
- introspection: observing one’s own thoughts
different kinds of thinking
- focused vs. unfocused
- productive vs. reproductive
- divergent vs. convergent
- system I (fast) vs. system II (slow)
brain parts
- dorsolateral PFC
- right DLPFC: associated with working memory system and attention
- highly active during strategy planning
- response inhibition (familiar responses)
brain parts
- adaptive control of thought
= about the activation of all brain areas involved
- posterior parietal/occipital cortex: visual, spatial processing; imaginal module
- anterior cingulate cortex: monitors conflict and errors; goal module
- (inferior ventrolateral) prefrontal cortex: planning, cognitive control; retrieval module
- basal ganglia/caudate nucleus: decision making; procedural module
- anterior superior temporal gyrus (right hemisphere): INSIGHT
