prob solving

• 4 types of probs: 1. well defined (e.g. step-by-step algorithm) 2. ill defined

  (e.g. social issues) 3. knowledge rich (e.g. math) 4. knowledge lean (IQ)
• Much laboratory research has focused on well-defined, knowledge-lean problems, but many everyday issues we are confronted with are ill-defined, knowledge-rich problems. 
• 3 major aspects of prob solving: 1. purposeful 2. controlled process 3. lack of relevant knowledge
• Monty Hall problem • type of prob: well defined. The great majority of people produce the wrong answer to the Monty Hall problem. Why is it so difficult? There are at least five factors that make us prefer the wrong answer: ambiguous, illusion of control, uniformity fallacy, substantial demands on central executive, hard to think abt causality so mistake for random 
• Problem space model • According to Newell and Simon (1972), solving a problem can be compared to finding a path in a maze. • Two strategies can be used to solve a problem: Either systematic scanning of the problem space by means of an algorithm, or educated guesses that are likely to reduce the distance between the problem and the solution (heuristics). 
• • Because we have limited short-term memory capacity and our complex information processing is serial, we are more likely to make use of heuristics than of algorithms. Heuristics are cognitively less demanding and in daily life usually lead to the solution, even if not always in an optimal way. Heuristics also work for illdefined problems. • Problem solvers often engage in only a modest amount of planning, because such planning is cognitively demanding. They can plan more effectively if encouraged to do so. 
• Means–ends analysis • Means–ends analysis is a heuristic that involves forming a subgoal designed to reduce the difference between the current state and the end-goal. It is used even when counterproductive. 
• Hill climbing • This heuristic is simpler than means–ends analysis, because it only looks for immediate gain. It is generally used when the problem solver has no clear understanding of the problem structure. 
• Progress monitoring • Progress monitoring involves assessing the rate of progress toward the goal. If progress is too slow, the problem solver adopts a different strategy. 
• Availability • We mostly go for the solution with the highest availability to us. We also tend to estimate the frequency and the difficulty of problems on the basis of solution availability. • The availability heuristic makes us sometimes underestimate the difficulty of a problem, because we have a tendency to overestimate our knowledge, in particular when we have read an easy text on the topic (illusion of knowledge). 
• Using analogies • Individuals solving a current problem can use analogies based on previous problems sharing similarities with it. Superficial similarities are easiest to perceive, but often irrelevant. We are more likely to attend to solution-relevant similarities when we are asked to generate analogies ourselves or if we are allowed to interact with the problem. 
• heuristics used in prob solving: 1. means ends analysis 2. hill climbing 3. progress monitoring 4. availability 5. analogy
• Insight involves a sudden transformation of a problem to provide

  the solution. In this transformation, one problem representation
• Insight vs. non-insight problems

  • The Gestalt psychologists argued that insight problems are completely

  different from non-insight (analysis) problems. Several

  findings are in line with this proposal. There is a feeling of a sudden

  increase in closeness to the solution with insight problems but a

  more gradual increase with non-insight ones. Brain regions in the

  temporal and frontal lobes are activated in particular when solutions

  involve insight.
• At the same time, there are many commonalities among insight

  and non-insight problems, and insight is a gradual process rather

  than an abrupt transition. Insight can be made more or less easy by

  providing subtle clues and hints. In addition, there is evidence that

  insight emerges for some time before the participant becomes

  consciously aware of it.
• One way to see insight problems is to think of problems with a

  conspicuous dead-end path in the problem space, which must be

  overcome. According to the representational change theory, this

  involves restructuring of the problem. Non-insight problems do not

  have such a misleading path.
• Incubation

  • Incubation generally enhances problem solving, especially when

  there is a long preparation time and the problem has multiple

  solutions. Incubation seems to work largely via forgetting of

  unsuccessful strategies.
• It is indisputable that extensive practice is essential for the

  development of expertise.

  + The main factors making practice effective have been identified.

  They include using tasks of intermediate difficulty, providing

  performance feedback, and allowing learners to repeat the task

  and to correct their mistakes.
• The notion that natural talent is unimportant is unconvincing. As

  Sternberg and Ben-Zeev (2001, p. 302) argued, “Is one to believe

  that anyone could become a Mozart if only he or she put in the

  time? … or that becoming an Einstein is just a matter of deliberate

  practice?”
• Knowing that practice is important, is not enough. We also need

  to know more about why some individuals decide to devote

  hundreds or thousands of hours to effortful deliberate practice to

  achieve high levels of expertise.
• Expertise

  • Expertise is highly skilled, competent performance in one or more

  tasks. Experts plan more and have more solutions ready in

  memory.
• Chess-playing expertise

  • Chess knowledge is stored in templates, each containing information

  relating to about 15 pieces. Chess experts possess larger

  templates than non-experts. In addition, experts make more

  effective use of slow, strategy-based processes.
• Medical expertise

  • Medical experts make more use than non-experts of fast,

  automatic processes when diagnosing, whereas non-experts rely

  more on slow, deliberate processes. However, experts often crosscheck

  their diagnoses with slow, deliberate processes.
• Deliberate practice

  • It has been claimed that deliberate practice is more effective than

  other forms of practice and that innate talent is irrelevant to the

  development of expertise. The evidence indicates that deliberate

  practice is necessary (but not sufficient) for expertise to develop.

  Natural talent or ability is also needed.
• Functional fixedness and mental set

  • Although experience and expertise in general are good, occasionally

  they lead to problem-solving problems. This is the case when

  experience with objects prevents us from seeing new uses for

  these objects (functional fixedness) or when the repeated use of

  one solution makes us blind to seeing that a new, related problem

  may require a different type of solution. Techniques like the

  generic-parts technique or brainstorming can be used to counteract

  the negative aspects of expertise.
• Hypothesis testing is an important part of problem solving: Possible

  solutions are proposed and subsequently tested.
• Hypothesis testing can be done in two ways: through verification

  and falsification. Verification is the best known test, certainly when we are testing our own proposals. It leads to a confirmation bias,

  however, which may prevent us from discovering the true mechanisms
• A more secure form of hypothesis testing is falsification. Because

  this form is less intuitive, it has to be taught to scientists.
• Humans are prone to motivated reasoning: They are much more

  critical about conclusions they dislike than about conclusions they

  like. They are also more likely to question others’ interpretations

  than their own. Therefore, discussions with disagreeing groups is

  very informative.