PSY 101 1st Edition Lecture 14Outline of Last Lecture I. Memory part 2II. Categories and Concepts part 1Outline of Current Lecture  Categories and Concepts part 2o What are prototypes good for?o How to function in a complex world  Definition of Exemplar Problem solving and Decision part 1 o Step 2 o We solve lots of problems every day: Most require little Effort o For some problems faster solutions occur with new representations of these states.o How do we solve problems? Definition of Random search strategies  Definition of unsystematic random search  Definition of systematic random searcho These search strategies are both algorithms Definition of algorithms o By a bottom-up view there are two main ways to solve problems These notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute. Definition of rule of thumb o In contrast to bottom-up approaches, Kohler(Gestalt) thought problems are solved by sudden insight Definition of Preparation  Definition of Incubation  Definition of Illumination  Definition of verification o Consider two basic kinds of decisions:o Compensatory Strategieso But do people really do this?!?o The Representativeness Heuristico The Availability HeuristicCurrent Lecture Categories and Concepts part 2o What are prototypes good for? Prototypes serve as cognitive reference points  Categories based on prototypes are learned quickly  Rosch (1973) studied color perception/ categorization in the Dani tribe of New Guinea - Dani participation learned new color categories - Categories were built around Western prototype or noto How to function in a complex world  Exemplar: a specific example of a category member  Typicality Effect: slower verification times for a typical items  Problem solving and Decision part 1 o Step 2:Develop “rules of thumb” that can be used to solve many similar problemsand guide decision-making – heuristics.o We solve lots of problems every day: Most require little Effort  A problem may be defined as a mismatch of an initial state and a goal state. In this approach, most problems can eventually be solved by “brute force.”o For some problems faster solutions occur with new representations of these states. We’re (usually) impressed by people who solve problems by innovation. They see old things in a new way…o How do we solve problems? From work on operant conditioning, Thorndike(1911) thought that problem solving consisted largely of trial and error (bottom-up approach). By a bottom-up view, there are two main ways to solve problems- Random search strategies o Trial-and-error sequence is maintained until answer is found. Like cats in puzzle boxes. In a unsystematic random search no logical order of attempts is used, andno record is kept of prior attempts systematic random search we are more orderly and keep tracko These search strategies are both algorithms Algorithms try all possible options in a problem space until the answer is found  On average, time to solution increases steadily with size of the problem space o By a bottom-up view there are two main ways to solve problems  Random search strategies and heuristic search strategies - Rule of thumb used to reduce problem space to reasonable size. - Heuristics do not guarantee a solution, but if they find one, it will be fast.o In contrast to bottom-up approaches, Kohler(Gestalt) thought problems are solved by sudden insight Preparation:You recognize that a problem exists and make some preliminary attempts at solving it Incubation:If preliminary attempts fail, you give up for a while. You do notconsciously think about the problem during this stage. Illumination: A sudden flash of insight occurs and the solution becomes conscious Verification: you confirm that the insight solution works o Consider two basic kinds of decisions: Those made under conditions of certainty.- You must select 1 option from a list of several known options, like a menu.- There is a “correct” answer, with known consequences. Those made under conditions of uncertainty.- Not all options are known.- Not all consequences can be realized.o Compensatory Strategies List all options and their attributes.- Most options have both positive & negative attributes. In a compensatory strategy, the positive qualities must compensate for negative qualities. You determine the balance of all attributes o But do people really do this?!? Tversky (1972) figured that we probably don’t use this strategy often…Toomuch effort. We are more likely to use non-compensatory strategies, treating different attributes in a more absolute manner. Tversky believed an elimination by aspects strategy is more common: You consider attributes one by one. If an attribute fails to meet some minimum criterion, the option is dropped.o The Representativeness Heuristic Both birth sequences are equally likely. However, the two sequences do not appear equally representative. (5 boys and 1 girl does not reflect the proportion of boys and girls in the population.) Median response was 30 in Kahneman and Tversky’s experiment o The Availability Heuristic It is a lot easier to think of words which start with the letter K than of words where K is in the third position. However, a typical selection of text contains twice as many words in which K is in the third position than words which start with K. Slovic et al. (1976)- Participants estimated death rates from publicized events to be higher than those for more common