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!

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

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

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.

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

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

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

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)

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%

10am

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

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

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

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

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

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

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

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

SUMMARY

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

11am

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

Basic Level Categories easiest to access and use How many common features can you name?

Lose a lot of information Gain just a little information

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’]

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

Semantic Networks Collins & Quillian (1969)

Semantic Networks

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

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

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

Priming Effects Meyer & Schvaneveldt (1971)

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

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!

1pm

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

McClelland & Rumelhart, 1986

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

TRIAL#1 TRIAL#2

Contains knowledge of canary. Where? In the pattern!

 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

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

2pm

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)

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

IMAGERY is SPATIAL Mental scanning (Kosslyn, 1978):

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

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

…just like Semantic Networks

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?

3pm

Does the bunny have whiskers? Kosslyn (1978)

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

Priming again! Farah (1985)

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

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

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

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

4pm

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

UNILATERAL NEGLECT Bisiach & Luzzatti (1978)

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

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

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

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