1. Cognitive Psychology Chapter 7

2. Cognitive Psychology: Overview  Cognitive psychology is the study of perception, learning, memory, and thought  The study of how people attend to, acquire, transform, store, and retrieve knowledge

3. Cognitive Psychology: Overview  The word cognition comes from the Latin “cognoscere” meaning “to know”  Cognitive psychologists are interested in the ability to reason  In the late 1950s, the brain began to be compared to a computer

4. Concept Formation  Concepts are mental categories people use to classify events and objects according to common properties  Concept formation is how people organize and classify events, usually to solve problems

5. Concept Formation  Much of a child’s learning involves classification or separating dissimilar events, finding commonalities, and then grouping similar items together  Unclear concepts are sometimes called “fuzzy concepts”  People define “fuzzy concepts” by using prototypes

6. Problem Solving  Problem solving is confronting and resolving situations that require insight or determination of some unknown elements

7. Figure 7.2 Stages in Problem Solving

8. Problem Solving  An algorithm is a means for solving a problem by using a set of rules over and over again until a solution is found  Algorithms will always lead to a correct answer if followed properly

9. Problem Solving  The time and effort to perform algorithms sometimes makes them impractical  Rules-of-the-thumb allow less rigid problem solving than algorithms  Heuristics are flexible guidelines for solving problems  Heuristics may lead to a quick solution

10. Problem Solving  Functional fixedness is an inability to see that an object can have a function other than its stated or usual one  A mental set is limited ways of thinking about possibilities

11. Creative Problem Solving  Creativity is a feature of thought that generates or recognizes ideas that are original, novel, and appropriate  An original response does not copy or imitate another response  A novel response is new or has no precedent  An appropriate response is reasonable in terms of the situation

12. Creative Problem Solving  Divergent thinking lessens the likelihood of mental sets and functional fixedness  The opposite of divergent thinking is convergent thinking or the narrowing of choices and alternatives

13. Reasoning and Decision Making  Reasoning is the purposeful process by which a person generates logical and coherent ideas, evaluates situations, and reaches conclusions  Logic is the system of reasoning used to reach valid conclusions or make inferences  Decision making means assessing and choosing among alternatives

14. Uncertainty: Estimating Probabilities  Decisions can be based on formal logic, hypothesis, testing, or an educated guess  An educated guess is one based on knowledge gained from past experience  Because of their mood or lack of attention, people may act irrationally, ignore key data, and make bad decisions

15. Barriers to Decision Making  The gambler’s fallacy is the belief that an event is more likely to occur if it has not recently occurred  The availability heuristic is to judge the probability of an event based on how easy it is to think of examples of it

16. Barriers to Decision Making  Overconfident people become so committed to their ideas that they are often more confident than correct  In the confirmation bias, people cling to beliefs despite contradictory evidence

17. Neural Networks  Various bits of information are stored in different parts of the brain  A convergence zone is needed to mediate and organize the information located in various areas of the brain

18. Neural Networks  This may occur through parallel distributed processing (PDP)  PDP involves many operations taking place at the same time in various parts of the brain  Researchers have devised “artificial neural networks” to study PDP

19. Neural Networks  Electronic neural networks have pattern-recognition abilities and can be taught to recognize single letters  Recognizing letters shows that the network has learned a prototype  Neural networks learn by noting changes in the weight or values associated with various connections