Chapter 10 Flashcards
Gestalt switch
A sudden change in the way information is organized. Going from seeing an old woman to a young woman looking over her shoulder.
Insight problem
A problem that we must look at from a different angle before we can see how to solve it. Insight is something that happens to us and when we get insight it comes suddenly, in a flash.
Productive thinking (Wertheimer)
Thinking based on a grasp of the general principles that apply to the situation at hand.
Structurally blind/reproductive thinking
The tendency to use familiar or routine procedures, reproducing thinking that was appropriate for other situations, but is not appropriate for the current situation. We can become structurally blind and get set in a certain way of thinking how to solve a problem, and this can interfere with solving it. We need to see it with fresh eyes or from a different perspective.
Analysis of the situation
Determining what functions the objects in the situation have and how they can be used to solve the problem.
Functional fixedness (Duncker)
The inability to see beyond the most common use of a particular object and recognize that it could also perform the function needed to solve a problem; also, the tendency to think about objects based on the function for which they were designed rather than how they could actually be used in certain situations. Older children and adults are much more likely to be functionally fixed than children under the age of 5. In Apollo 13 they had to overcome this to save the astronauts using crazy methods. The nine dot problem (drawing lines outside the box) is another example of functional fixedness… we make an assumption about how something works and get fixed in it. This also happens when we are first shown how something like a tool or object works and then have to use it for some other purpose. We get fixed on what it was first used for (think box and standing on it to reach something).
Hints (Maier’s view)
A hint must be consistent with the direction that the person’s thinking is taking, and cannot be useful unless it responds to a difficulty that the person has already experienced.
Feeling of “warmth”
The feeling that many people have as they approach the solution to a problem (i.e. “getting warm”). This occurs for stepwise solution problems like calculus or arithmetic problems. This is different than insight.
Feeling of knowing
The feeling that you will be able to solve a particular problem. Again this is related to stepwise problems… insight cannot be predicted at all because they are based on the sudden emergence of knowledge. These feelings are examples of metacognition.
Progress monitoring theory
The theory that we monitor our progress on a problem, and when we reach an impasse we are open to an insightful solution. This forces us to look for an insightful solution.
Representational change theory
The theory that insight requires a change in the way participants represent the problem to themselves. This is how we are able to reach an insightful solution. Think about the matchsticks problem with Roman numerals.
Constraint relaxation
An aspect of representational change theory: the removal of assumptions that are blocking problem solution.
Chunk decomposition
An aspect of representational change theory: parts of the problem that are recognized as belonging together are separated into “chunks” and thought about independently.
Insight and the brain; insight and sleep
The anterior cingulate cortex (ACC) is involved in detecting the conflict between the way we are thinking about the problem and the correct way to solve it. The hippocampus also is involved. It strengthens memory traces, encourages insight, and catalyzes mental restructuring. Also sleep helps lead to greater insight (think of the number reduction task with the nine numbers, 1, 4, 9)
Einstellung effect (Luchins)
The tendency to respond inflexibly to a particular type of problem; also called a rigid set. Once we find a solution to a problem, we get stuck in using that solution over and over and we don’t want to find a new and better way of doing things. (Jars full of water example).
Negative transfer
The tendency to respond with previously learned rule sequences even when they are inappropriate. The more practice of one method we have previously, the greater the negative transfer is and the worse we perform later.
Strong but wrong routines
Overlearned response sequences that we follow even when we intend to do something else.
Flexibility-rigidity and the brain
The left dorsolateral prefrontal cortex (DLPFC) plays a big part in selecting between alternative response tendencies. Those with damaged prefrontal lobes struggled to find counterintuitive and alternate solutions to problems.
Mindfulness vs. mindlessness (Langer)
Openness to alternative possibilities is mindfulness. Behaving as if the situation had only one possible interpretation is mindlessness. With the water jars, participants behaved mindfully to find the “B- A - 2C” rule, but then used it mindlessly the rest of the time.
Artificial intelligence
The “intelligence” of computer programs designed to solve problems in ways that resemble human approaches to problem-solving. AI can use heuristics, algorithms, and many other methods of problem-solving that mimic us. Even a very difficult insight problem can be analyzed by AI. The program, after unsuccessfully searching for a solution for a while, it will apply a stop rule, and when it resumes it begins by attending to a previously neglected aspect of the problem. That is a model of insight. So it can show or mimic having insight.
Heuristic
A problem-solving procedure (typically a rule of thumb or shortcut); heuristics can often be useful, but do not guarantee solutions.
Algorithm
An unambiguous solution procedure (e.g. the rules governing long division). There are two types of algorithms: systematic ones are guaranteed to find the solution if one exists and non-systematic ones are not guaranteed to find a solution.
Subgoal
A goal derived from the original goal, the solution of which leads to the solution of the problem as a whole. Computers can stack subgoals in order to solve a complex problem.
Data structure
How a computer understands the problem. In Xs and Os it is the possible states of each position on the board and a representation of the playing board.
