Chapter 10

Question 1

Gestalt switch

Answer 1

A sudden change in the way information is organized. Going from seeing an old woman to a young woman looking over her shoulder.

Question 2

Insight problem

Answer 2

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.

Question 3

Productive thinking (Wertheimer)

Answer 3

Thinking based on a grasp of the general principles that apply to the situation at hand.

Question 4

Structurally blind/reproductive thinking

Answer 4

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.

Question 5

Analysis of the situation

Answer 5

Determining what functions the objects in the situation have and how they can be used to solve the problem.

Question 6

Functional fixedness (Duncker)

Answer 6

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).

Question 7

Hints (Maier’s view)

Answer 7

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.

Question 8

Feeling of “warmth”

Answer 8

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.

Question 9

Feeling of knowing

Answer 9

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.

Question 10

Progress monitoring theory

Answer 10

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.

Question 11

Representational change theory

Answer 11

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.

Question 12

Constraint relaxation

Answer 12

An aspect of representational change theory: the removal of assumptions that are blocking problem solution.

Question 13

Chunk decomposition

Answer 13

An aspect of representational change theory: parts of the problem that are recognized as belonging together are separated into “chunks” and thought about independently.

Question 14

Insight and the brain; insight and sleep

Answer 14

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)

Question 15

Einstellung effect (Luchins)

Answer 15

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).

Question 16

Negative transfer

Answer 16

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.

Question 17

Strong but wrong routines

Answer 17

Overlearned response sequences that we follow even when we intend to do something else.

Question 18

Flexibility-rigidity and the brain

Answer 18

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.

Question 19

Mindfulness vs. mindlessness (Langer)

Answer 19

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.

Question 20

Artificial intelligence

Answer 20

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.

Question 21

Heuristic

Answer 21

A problem-solving procedure (typically a rule of thumb or shortcut); heuristics can often be useful, but do not guarantee solutions.

Question 22

Algorithm

Answer 22

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.

Question 23

Subgoal

Answer 23

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.

Question 24

Data structure

Answer 24

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.

Question 25

Evaluation function

Answer 25

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.

Question 26

Problem space

Answer 26

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.

Question 27

Search tree

Answer 27

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.

Question 28

General Problem Solver (GPS)

Answer 28

A computer program used to perform non-systematic searches. Can be used to solve all kinds of games including Tower of Hanoi.

Question 29

Toy problems

Answer 29

Problems used to analyze the problem-solving process, like Tower of Hanoi.

Question 30

Production rules

Answer 30

A production rule consists of a condition and an action (C - ->A). Ex. Problem solved –> halt.

Question 31

Means-end analysis

Answer 31

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.

Question 32

Goal stack

Answer 32

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.

Question 33

Thinking aloud or concurrent verbalization

Answer 33

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.

Question 34

Solving problems in science: historical accounts and the cognitive history of science

Answer 34

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.

Question 35

Zeigarnik effect

Answer 35

The “quasi-need” to finish incomplete tasks. Why scientists sometimes spend their whole lives working on certain problems.

Question 36

Solving problems in science: Observation of ongoing scientific investigations/laboratory studies

Answer 36

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.

Question 37

Unexpected findings

Answer 37

Although scientists may initially resist information that disconfirms favoured hypotheses, successful problem-solvers attempt to explain surprising results.

Question 38

Distributed reasoning

Answer 38

Reasoning done by more than one person. Helps dispel the Einstellung effect.

Question 39

Solving problems in science: Computational models

Answer 39

Scientific problem-solving can be studied by creating computer programs that simulate well-known discoveries. One example is BACON.

Question 40

BACON

Answer 40

A computer program that has been able to “discover” several well-known scientific laws by searching for patterns or relationships between information.

Question 41

Face validity

Answer 41

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.