1. Lindsay et al. (2004) Participants heard true and false stories about their childhood Group 1: saw a classroom photo from 2 nd grade  more likely to think false memories are true Group 2: no photo Cues enhance false memory!

2. Eyewitness testimony Jury believes a confident witness  Confidence-accuracy correlation only 0.29  200 people per day become accused based on eyewitness testimony Wells et al. (2000) - 40 cases where DNA evidence exonerated someone 36 involved witness ID of innocent people People served average of 8.5 years 5 sentenced to death

3. Pick the gunman (Wells & Bradfield, 1983): Participants watched a videotape Gunman in view for 8 seconds Then picked gunman out of a lineup Each participant picked someone The gunman was not even IN the lineup

4. Stanny & Johnson (2000) ERRORS DUE TO ATTENTION -during crime emotions are high -attention narrows as arousal increases (Easterbrook, 1959) Fired weapon decreases memory for perpetrator, victim, etc.

5. Ross el al. (1994)ERRORS DUE TO FAMILIARITY Proxy: Go with the teacher who resembles robber

6. Wells & Bradfield (1998): ‘Good, you identified the suspect…’ [perpetrator not included] ERRORS DUE TO SUGGESTION

7. Confidence rating Items correctly identified Asked questions Not asked questions Items not correctly identified Shaw (1996) CONFIDENCE (AND ERRORS) DUE TO POSTEVENT QUESTIONING -saw items in room -recognition test GROUP#1: no follow-up questions GROUP#2: follow-up questions refer to answers on recognition test

8. What Is Being Done? 1.Don’t tell criminal is in this lineup  this caused 42% decrease in false ID (Malpass & Devine, 1981) 2. Increase similarity among lineup people  may decrease correct ID a bit, but will decrease errors as well! CHOSE INNOCENT CHOSE GUILTY low high SIMILARITY low high SIMILARITY PERPETRATOR IN LINEUP PERPETRATOR NOT IN LINEUP Lindsay & Wells (1980)

9. What Is Being Done? 3. In lineup, use sequential presentation, not simultanoeus -avoid making a relative judgment of comparing people when suspect not in lineup: % of falsely identified is… … … … … 17% 43%

10. 10am

11. concept : mental representation used for a variety of cognitive functions categorization is the process by which concepts are organized in some systematic way Concepts are building blocks of knowledge

12. Why Categorize?  understand new cases  make inferences about items in the category  understand behaviors  not to mention, organize our knowledge!

13. Definitions  insufficient to place things in categories  variability within a category  functional considerations family resemblance : members of a category resemble one another in number of ways

14. Prototypes  averaging the category members prototypicality -high - member closely resembles prototype (sparrow) -low - member does not resemble typical (penguin) “average” cat

15. Demo 1 (Rosch & Mervis, 1975) write as many characteristics or attributes that you feel are common to each object chair sofa mirror telephone prototypical objects have high family resemblance  a lot of overlap with other items in the category

16. Demo 2 (Smith et. al., 1974) an apple is a fruit a tomato is a fruit a pomegranate is a fruit a watermelon is a fruit typicality effect  statements about prototypical items verified rapidly

17. Demo 3 list as many objects as you can for each category office furniture transportation colors  prototypical objects named first office furniture - desk, chair, lamp, couch... bookshelf transportation - car, bus, truck, train, bike... pogo stick colors - red, blue, yellow, green, orange, purple, black, white, teal... tan

18. prototypical objects affected more by priming priming - presentation of one stimulus affects response to another Rosch (1975)

19. SUMMARY

20. Exemplars  comparing to examples of members within a category  atypical cases  no ‘averaging’  variable categories which are harder to form a prototype of Exemplars & Prototypes  complementary  initially try to form a prototypical member of category and later include exceptions (exemplars) that also fit category smaller categories = exemplars larger categories = prototypes

21. 11am

22. Levels  categorization is organization of information  categories themselves are organized  hierarchical

23. Basic Level Categories easiest to access and use How many common features can you name? 3 9 10.3

24. Lose a lot of information Gain just a little information

25. Basic Level Categories  name items at the basic level category  faster at deciding membership at the basic level  individual differences [experts don’t rely as much on this ‘basic-level’]

26. Semantic Networks  concepts arranged in networks ~ the way concepts organized in the mind  model of knowledge representation

27. Semantic Networks Collins & Quillian (1969)

28. Semantic Networks

29. Predictions  time it takes a person to retrieve information is determined by distance traveled through network

31. Spreading Activation  activity spreads out along any link connected to activated node PRIMING!

32. Priming Effects Meyer & Schvaneveldt (1971) lexical decision task - is it a word or not?

33. Priming Effects Meyer & Schvaneveldt (1971)

34. Criticisms of Collins & Quillian  did not explain typicality effect (faster response for more typical members of a category): “Canary is a bird.” “Ostrich is a bird.” Collins et al. prediction: should be the same RT

35. Collins & Loftus Model  not hierarchical  link length is how ‘related’ the two items are  based on person’s experience  explains too much!  model that explains everything, explains nothing  adjusting length of connections fits any result!

36. 1pm

37. knowledge = distributed activity of many units parallel distributed processing (PDP) nodes links (weights) ‘neuron-like’: excitation/inhibition CONNECTIONISM

38. McClelland & Rumelhart, 1986

39. These patterns are learned, not hardwired supervised learning model makes mistakes and gets corrected

40. TRIAL#1 TRIAL#2

41. Contains knowledge of canary. Where? In the pattern!

42.  graceful degradation damage to part of the system does not disrupt all  generalizeability similar concepts have similar patterns of activation  computer models simulate it well language processing  train on multiple concepts  each concept is ‘encoded’ in the network (weights)  learns to respond to various inputs  slow learning - so changes to weights don’t disrupt previous knowledge Properties

43. Criticisms  how many units?  how many levels?  how much training?  who trains?

44. 2pm

45. VISUAL IMAGERY How is the furniture arranged in your bedroom? Is the gas tank on the left or right side of your car? No sensory input  sensory impression History of science: Aristotle: “thought impossible without an image” Watson (behaviorism): images “unproven, mythological” Paivio (1963): easier to remember concrete vs abstract nouns Shepard & Metzler (1971): mental rotation Same or different? RT=f(angle)

46. IMAGERY is like PERCEPTION Mental scanning (Kosslyn, 1973): Participants memorize image Move from one part of image to another Mental scan time is proportional to spatial distance

47. IMAGERY is SPATIAL Mental scanning (Kosslyn, 1978):

48. IMAGERY is like LANGUAGE Propositional, not spatial (Pylyshyn, 1973): Spatial representation may be epiphenomenal abstract & symbolic

49. IMAGERY is PROPOSITIONAL RT=f (conceptual “distance”) : how many nodes away?

50. …just like Semantic Networks

51. Tacit Knowledge? Imagining = mental simulation...in the real world it takes longer to move from A to B...this fact is incorporated into imagining what Kosslyn considers spatial is simply based on experiential knowledge about the world – not necessarily image-like Evidence against Tacit knowledge: Finke & Pinker (1982) [2 sec delay] Distance (dot, arrow) Reaction time Was the arrow pointing at the dot?

52. 3pm

53. Does the bunny have whiskers? Kosslyn (1978)

54. Interactions of imagery and perception “Imagine a banana on the screen, and describe it.” Perky (1910)

55. Priming again! Farah (1985)

56. Imagery and the brain Krieman et al (2000): Neurons in the Temporal Lobe

57. Imagery and the brain LeBihan et al (1993): Neurons in the Visual Cortex fMRI

58. Transcranial Magnetic Stimulation (TMS):  knock out parts of brain for few minutes  fMRI not causing imagery Pylyshyn: fMRI may be epiphenomenon

59. Transcranial Magnetic Stimulation (TMS):  knock out parts of brain for few minutes  Perception & Imagery conditions  which stripes are longer? RESULTS:  TMS caused a slowdown in response time  slowdown for both perception and imagery

60. 4pm

61. Removing part of the visual cortex decreases image size Farah (1992) NEUROPHYSIOLOGICAL EVIDENCE FOR VISUAL IMAGERY

62. UNILATERAL NEGLECT Bisiach & Luzzatti (1978)

63. R.M.: Can visually identify objects in front of him, but can’t accurately describe imagery from memory. “A grapefruit is larger than an orange.” C.K.: Cannot visually identify, but can draw vivid and accurate pictures based on imagery NEUROPHYSIOLOGICAL EVIDENCE FOR VISUAL IMAGERY: DOUBLE DISSOCIATION

65. Using imagery to improve memory  Visualizing interacting images enhances memory  Organizational effect of imagery enhances memory Method of Loci

66. Folk psychology & memory self-help books: bizarre imagery helps memory? INTERACTING > NONINTERACTING BIZARRE == NONBIZARRE

67. Mechanical problems: Hegarty (2004) Schwarz & Black (1999) Rule-based approach Mental simulation Which cup flows over earlier? 0 delay: @ same angle After imagining: wide cup