Complex Learning Tasks

1. Complex Learning Tasks Chapter 12

2. Concept Learning 2 • Concept • A symbol that represents a class of objects or events with common properties. • For example, think of an airplane: • Have fixed wings • Are heavier than air • Are driven by a screw propeller or high velocity rearward jet • Are supported by the dynamic reactions of the air against the wings • These concepts allow us to easily identify these objects as airplanes.

3. 3 • Concept learning significantly enhances our ability to effectively interact with the environment. • Rather than separately labeling and categorizing each new event or object, we incorporate it into our existing concepts.

4. Well Defined Concepts 4 • Attribute • Any feature of an object or event that varies from one instance to another. • Rule • A rule defines the objects or events that are examples of a particular concept. • Types of Rules • Affirmative rule • A rule that specifies that a particular attribute defines a concept. • Negative rule • A concept is defined by the rule that any object or event having a certain attribute is not a member of the concept.

5. Studying Concept Learning in Humans 5 • Smoke (1933) conducted studies examining concept learning in humans. • He presented subjects with a large number of figures that differed in shape, size, number, and location of their dots. • Subjects’ task was to learn the concept of DAX, which consisted of a circle with one dot inside and another dot outside.

7. 7 • Conjunctive rule • The simultaneous presence of two or more attributes that define a concept. • Disjunctive rule • When the concept is defined by the rule that the concept can possess either or both of the two common attributes. • Not all examples of a concept necessarily have all the attributes characteristic of that concept. • There are degrees to which particular items fit a concept.

8. Fuzzy Concepts 8 • Family resemblance • The degree to which a member of a concept exemplifies the concept. • Prototype • Object with the greatest number of attributes that are characteristic of the concept. • Chair is the most typical prototype of furniture.

9. Who’s sexier?

10. 10 • A German Shepherd may fit the concept of dog more than a poodle. • The more an object or event differs from the prototype, the more difficult it is to identify it as an example of the concept.

11. Multiple Concepts 11 • Two objects or events may share certain attributes but not be examples of the same concept. • Although a robin and a bat both have wings, a robin is a bird and a bat is a mammal. • We do not always know the boundaries that define a concept.

12. Study of Concept Learning in Animals 12 • Concept learning involves identification of the properties that characterize a concept as well as those that do not. • Herrnstein et al. (1976) found that pigeons demonstrated concept learning. • Trick is to use a large number of very different examples • D’Amato and Van Sant (1988) found that monkeys could learn the concept of humans. • Vonk and MacDonald found that a female gorilla could learn to discriminate between primate and non primate animals. • Animals can also learn the concepts of same and different.

13. Cook & Blaisdell (1996) 13

14. Theories of Concept Learning 14 • Hull (1920) envisioned concept learning as a form of discrimination learning. • Said that concepts have both relevant and irrelevant attributes. • As a result of reinforcement, response strength increases to the attributes characteristic of the concept. • Same/Different judgements require an abstract code or rule, so the theory fails here.

15. Abstract Concepts 15 • Bruner, Goodnow, and Austin (1956) suggested that a concept is learned by testing hypotheses about the correct solution. • If the first hypothesis is correct, the individual has learned the concept. • If it is incorrect, another hypothesis will be generated and tested, and this will be repeated until the correct solution is discovered. • Research by Levine (1966) suggests that individuals do engage in hypothesis testing of concepts.

16. Two Ways of Knowing 16 • However, a concept can be learned via associative learning or hypothesis testing, which are not necessarily mutually exclusive. • A concept can be learned using either method, but a concept is learned best when both methods are employed.

17. TOM Concept or Discrimination? 17 • Theory of Mind: Understanding that others have mental processes that may differ from one’s own. Emotions Knowledge Visual Perspective

19. Knowledge Attribution 19 • Povinelli (1991) • Knower – sees food being hidden • Guesser – outside of room • Stage 1: As above • Stage 2: Knower wears hat • Stage 3: Guesser stays in room with a bagged head

20. Chimpanzees (Great Apes) Rhesus Monkeys (New World) 20

21. Alternative 21 • Did chimps discriminate between the two situations based on subtle differences in how the “guesser” and “knower” acted? • Maybe they choose the one with eyes open during hiding?

22. “Begging Experiment” 22 • Povinelli (1999) • Beg from “seeing” vs. “nonseeing” • Front vs. Back – Yes • Pail Beside vs. Over Head - No • Averted Eyes vs. Over Shoulder Look – No • Blindfold Mouth vs. Blindfold Eyes - No

23. “Chimps Fail Begging Experiment” 23

24. “Elephants Pass Begging Experiment” 24 However, this doesn’t imply elephants can “mind-read”

25. Mark Test 25 • Gallup’s Mark Test (Great Apes)

26. The Nature of the Problem 26 • Problem • A situation in which a person is motivated to reach a goal, but some obstacle(s) block the attainment of the goal. • Thorndike (1898) proposed that animals and people solve problems by trial and error.

27. Insight or Trial and error? 27 • Kohler (1925) suggested a different view of problem solving • Says that an animal internally or mentally explores the problem before exhibiting a specific response. • The exploration involves considering and rejecting possible solutions and finally developing insight as to the correct solution. • But…..only Chimps with certain past experiences solved the banana problem

28. Insight: What is it? 28 • Insight • A sudden realization of how to solve a problem. • Kohler found that once the subjects solved the problem, they were able to quickly solve other similar problems. • Initial state • The starting point of a problem. • Goal state • The desired endpoint of a problem. • Two additional processes: • Identify the operations that solve the problem. • Restrictions limit what you can do.

29. Well or Ill? 29 • Well-defined problem • A problem with clear initial and goal states. • Ill-defined problem • A problem with no clear starting or end point. • Creating a set of manageable subproblems provides the structure for converting an ill-defined problem into a well-defined one.

30. A Strategy for Solving Problems 30 • After the problem has been defined, the next step is to develop a plan of attack. • There are two major strategies—algorithms and heuristics—that can be used to solve problems. • Algorithm • A precise set of rules to solve a particular problem. • Heuristic • A “best guess” solution to problem solving.

31. 31 • Working backward heuristic • A technique for finding the solution to a problem by starting with the end point and working back to the start point. • Is often used in mathematical and other formal systems of analysis. • Means-end analysis • Breaking a particular problem into a series of solvable subproblems.

32. Means Ends Analysis? Learning a) allows the bird to solve b)

34. Consequences of Past Experience 34 • Functional fixedness • Difficulty recognizing novel uses for an object. • Prior experience using an object to solve one problem makes it difficult to recognize that the same object can be used in a different manner to solve another problem. • Reflects rigidity that can impair problem solving; however there are ways to overcome functional fixedness.

35. Mental Sets 35 • The tendency to use an established method for solving problems. • Sets may blind people to fresh ways of exploring problems, which is unproductive when other solutions are more efficient.