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.
Evaluation function
The process whereby a plan is created, carried out, and evaluated. This is where the program works out all the possible moves and each is evaluated based on value for completing the goal and acted on accordingly.
Problem space
The representation of a problem, including the goal to be reached and the various ways of transforming the given situation into the solution. Chess has a very complicated problem space.
Search tree
A representation of all the possible moves branching out from the initial state of the problem. Chess has an absolutely massive search tree. It is like making your way through a maze where to make it through you have to make the right decision at each choice point. This can become so large with so many alternatives it leads to the combinatorial explosion and a systematic algorithm can’t handle it.
General Problem Solver (GPS)
A computer program used to perform non-systematic searches. Can be used to solve all kinds of games including Tower of Hanoi.
Toy problems
Problems used to analyze the problem-solving process, like Tower of Hanoi.
Production rules
A production rule consists of a condition and an action (C - ->A). Ex. Problem solved –> halt.
Means-end analysis
The procedure used by General Problem Solver to reduce differences between current and goal states. It is a heuristic procedure and subgoals are used in this analysis of problems.
Goal stack
The final goal to be reached is on the bottom of the stack, with the subgoals piled on top of it in the reverse order in which they are to be attained.
Thinking aloud or concurrent verbalization
The verbalization of information as the participant is attending to it. This gives us a window into how we solve problems.. an example of inner speech being externalized.
Solving problems in science: historical accounts and the cognitive history of science
Combining case studies of historical scientific practices with scientific investigations of how humans reason, judge, represent, and come to understand. Essentially case studies of working scientists are informed by the framework that cognitive science provides.
Zeigarnik effect
The “quasi-need” to finish incomplete tasks. Why scientists sometimes spend their whole lives working on certain problems.
Solving problems in science: Observation of ongoing scientific investigations/laboratory studies
Studying how problem solving in science is happening by observing ongoing scientific investigations, called “in vivo” or “in the living”, while “in vitro” or in “glass” involves laboratory studies of scientific problem solving. Essentially in vivo research is studying ecologically valid or real world research and in vitro is studying lab research. This is important because you can investigate a question in a naturalistic setting and then go back to the lab and conduct controlled experiments on what has been identified. It is time-consuming however. Can lead to unexpected findings and distributed reasoning.
Unexpected findings
Although scientists may initially resist information that disconfirms favoured hypotheses, successful problem-solvers attempt to explain surprising results.
Distributed reasoning
Reasoning done by more than one person. Helps dispel the Einstellung effect.
Solving problems in science: Computational models
Scientific problem-solving can be studied by creating computer programs that simulate well-known discoveries. One example is BACON.
BACON
A computer program that has been able to “discover” several well-known scientific laws by searching for patterns or relationships between information.
Face validity
Methods that clearly measure what they are supposed to measure are said to be “face valid”. Computational models are low in this but allow for rigorous models of problem-solving. Studies of historical records have great face validity and lab studies are not obviously face-valid, but through control of certain variables they can be. And direct observation of scientific work has face validity and may expose new phenomena that other methods may not reveal.
