1. Emily Batty Spring Session 2008

2. Syllabus Course Website: www.ualberta.ca/~egray/psyc281.html Textbook: Chance, P. (2006). Learning & Behavior: Active Learning Edition (Fifth Edition). USA: Thompson Wadsworth. Prerequisite: Psyco 104 (or equivalent)

3. Instructor Emily Batty BS P-549 492-7886 (office) or 492-7139 (lab) emily.batty@ualberta.ca “Psych 281” in subject Name & ID in message Email is the BEST way to reach me!!! Office hours: Monday, 2-3:30, or by appointment

4. Grading Breakdown 1 midterm: 25% Final: 35% 2 assignments: 15% each 5 quizzes: 2% each

5. Schedule Chapters 1,2,3: May 5, 7 Chapters 4,5: May 12, 14 NO CLASS: May 19 Chapters 6: May 21  Assignment #1 due (21 st) MIDTERM: May 26 Chapters 7,10: May,26, 28 Chapters 8,9: June 2, 4  Assignment #2 due (2 nd ) Chapters 11,12: June 9, 11 FINAL: Thursday, June 12 – 3 pm

6. Grading A+ (4.0) = 95-100% A (4.0) = 90-94% A- (3.7) = 85-89% B+ (3.3) = 80-84% B (3.0) = 75-79% B- (2.7) = 70-74% C+ (2.3) = 65-69% C (2.0) = 60-64% C- (1.7) = 55-59% D+ (1.3)= 50-54% D (1.0) = 45-49% F (0.0) = 0-44% Not graded on a curve Grades will be based on: Quizzes: 10% (5 x 2%) Assignments: 30% (2 x 15%) Midterm exam: 25% Final exam: 35%

7. Quizzes 5 SHORT quizzes worth 2% each Given at the BEGINNING of class 10 minutes to answer Based on readings or previous day’s lecture e.g. What is the definition of ‘learning’?

8. Assignments 2 assignments worth 15% each One-page limit You can work in pairs Deductions for late assignments e.g. Explain gambling behaviour in terms of different schedules of reinforcement. Cheating & Plagarism http://www.uofaweb.ualberta.ca/governance/StudentAppe alsCheatsheet.cfm? http://www.uofaweb.ualberta.ca/governance/StudentAppe alsCheatsheet.cfm

9. Exams Multiple Choice Fill-in-the-blank Short answer

11. What is learning? School kids? Studying for exams? Learning to drive? What else? Cognitive constants across species Cognitive differences across species How to make good rat poison?

12. Definitions Learning is: Some event at Time 1 affects behaviour at Time 2 Problem with this definition… A change in behaviour due to experience A change in behaviour is not sufficient to show learning Not all behaviours are learned Even some complex behaviours are innate Reflexes, fixed action patterns, general behaviour traits

13. Where do these innate behaviours come from?

14. Natural Selection Variation, inheritance, selection, differential reproductive success Acts on the level of the individual Natural selection commonly known to work on physical traits E.g. white & black peppered moths

15. Natural Selection Behaviours, as well as physical characteristics, can be selected for! Survival of the Sneakiest: http://evolution.berkeley.edu/evolibrary/article/0_0_0/ sneakermales_01 http://evolution.berkeley.edu/evolibrary/article/0_0_0/ sneakermales_01 Evolutionary advantages for behaviours like reflexes? More complex behaviours, like altruism and pair bonding?

16. Artificial Selection Animal breeders Pet domestication Genetic engineering

17. Sexual Selection Mate choice based on non-adaptive traits i.e. traits that don’t increase survivability may evolve if they help an organism compete for mates Peacocks

18. Limits of Natural Selection SLOW! Generational lag Not very helpful within a lifetime e.g. new predators

19. Reflexes, Fixed Action Patterns & General Behaviour Traits

20. Reflexes Response to an environmental stimuli (i.e. an event) Relationship between a specific event and a simple response Not learned, innate responses Survival mechanism Primitive reflexes

21. Sensory neuron Inter-neuron Motor neuron BRAIN

22. changes in reflexes Reflexes are generally very stereotypic i.e. they don’t change much in terms of form, strength However, they can vary between people & time Sensitization & Habituation Simple forms of learning Changes in reflexive behaviour patterns Different from sensory adaptation and fatique Discreet stimulus

23. sensitization Intensity and repetition of the event can affect habituation & sensitization Sensitization: an INCREASE in the intensity or probability of response to stimuli Sensitization example: Light touch: no response Painful shock: flinch Light touch: flinch

24. habituation Habituation: a DECREASE in the intensity or probability of response to stimuli Stimulus specific Habituation example: Loud noise: startle Loud noise: less startle

25. Fixed Action Patterns Similar to reflexes: innate and very stereotypic Involve more complex actions, or a series of actions Set off by a ‘sign stimulus’ or ‘releaser’ Start-to-finish

26. fixed action patterns MAIL

27. Other examples of FAPs Greylag Goose rolling eggs Gulls Yawning? Westermarck effect

28. General Behaviour Traits Behavioural traits strongly influenced by genes Not the same as FAPs More plastic, flexible No single sign stimulus Species specific defense reactions Rats: freeze

29. Evolved modifiability Natural selection is slow, so innate behaviours are not enough… Ability to learn Nature vs. Nurture

31. Learning & Behaviour Remember: Learning is a change in behaviour due to experience Why behaviour?? Hard Line Behaviourism? Watson Skinner

32. The Science of Learning Avoiding circular explanations Q: Why did the chicken cross the road? Q: How do we know the chicken wanted to get to the other side? A: To get to the other side.A: Because it crossed the road

33. Operational Definitions A precise way of defining events Multiple observers can agree on occurrence of event Or, quantify an event Inter-rater reliability How to define choice? -When bird lands on perch? -When bird pulls off Velcro? -When beak touches Velcro?

34. Measuring Learning Reduction in errors Change in speed Change in topography (form) Change in intensity Change in latency Change in rate or frequency

35. How to study learning Anecdotal evidence First- or second-hand reports of personal experiences Good for ideas, but not very scientific

36. How to study learning Anecdotal evidence Naturalistic Observation Observe subjects in their natural setting Limited controls Little to no interaction

37. How to study learning Anecdotal evidence Naturalistic Observation Case Studies More detail than an anecdote Study an individual/event/small group in detail Time consuming Hard to generalize Doesn’t answer all questions (e.g. causation)

38. How to study learning Anecdotal evidence Naturalistic Observation Case Studies Descriptive Studies Questionnaires, statistical analyses More information than case studies, but less detail Correlations, not causation

39. How to study learning Anecdotal evidence Naturalistic Observation Case Studies Descriptive Studies Experimental Studies Manipulate variables Different designs High control Measures effect of specific variables on behaviour

40. Types of Variables Independent variables Manipulated Dependent variables Measured Controlled variables Things to keep constant

41. Experimental Designs Between Subjects design Two or more groups of participants/subjects Experimental & Control groups Manipulate independent variable between groups One group gets it, and one doesn’t Measure dependent variable between groups Assignment into groups can be random or matched

42. Between Subjects

43. Experimental Designs Within Subjects design One group of participants/subjects, compare across time points

44. Experimental Designs Within Subjects Designs ABA reversals

45. Evaluation Research Validity How well a study, a procedure, or a measure does what it is supposed to do Reliability How well a measure can be reproduced Replicability

46. Evaluating Research Sampling bias Sample: collection of subjects selected for a study Population: much larger collection of animals or people from which the sample was drawn Distortions in self-reports Self-reports: subjects give a verbal/written account of their own performance

47. Evaluation Research Placebo effects Demand characteristics Experimental bias Intentional and unintentional Single- and double-blind procedures

48. Animal Research How useful to human learning? Control over variables Ethics Cognitive constants, differences Comparative Cognition Animal Rights Computer simulations