For much of today's lecture we will complete our discussion of long term memory, with a focus on a model of memory and thinking that provides a comprehensive account of a number of phenomena we all experience and makes a nice bridge to our consideration of higher level cognition in the form of problem solving. The slides are the same as those of the last two classes before we transition to problem solving and the new slides below. In discussing problem solving, we define a problem as a situation where you want to get from some starting place (some initial knowledge state) to some goal and the pathway is not immediately obvious.

For this series of lectures (problem solving & expertise), the slides are here

• Well-defined vs. ill-defined problems: Problems where the goal or solution is recognizable--where there is a right answer (ex. a math or physics problem) vs. problems where there is no "right" answer but a range of more or less acceptable answers (ex. design a building or write good short story).
• Knowledge rich vs. knowledge lean problems: problems whose solution depends on specialized knowledge (ex. What is the weight of a liter of carbon dioxide at 1 atmosphere pressure and 40 degrees C?) vs. problems that do not depend on such knowledge (ex. How many weighings on a two pan balance does it take to decide which of 12 similar appearing balls is heavy or light?).
• Insight vs. non-insight problems (those presumably solved "all of a sudden" vs. those solved more incrementally--in a step by step fashion.

• Functional fixedness (the string problem).
• Problem-solving set (Luchins' work-the water jar problem).
• Memory for interrupted problems (the Zeigarnik effect).

• Problem solving as search and the size of the search space,
• Methodology: Verbal protocols and computer simulation.
• Problem difficulty and the kind of representation the problem-solver uses in solving the problem,
• Cognitive processing limitations and our ability to solve difficult problems.
• Algorithms and heuristics, including some common heuristics or weak (general) methods, such as total enumeration or trial and error, hill climbing, means-ends-analysis, working backwards, fractionation or subgoaling, etc.