Memory and Cognition PSY 324 Topic 8: Knowledge Dr. Ellen Campana Arizona State University

Memory and Memory and CognitionCognition

PSY 324PSY 324

Topic 8: KnowledgeTopic 8: Knowledge

Dr. Ellen CampanaDr. Ellen Campana

Arizona State UniversityArizona State University

Concepts and Concepts and CategoriesCategories

Why do we have Why do we have concepts?concepts?

Last time … memories constructed through Last time … memories constructed through the process of the process of inferenceinference ““War of the ghosts”War of the ghosts” Estimating high school gradesEstimating high school grades Flashbulb memories (and other episodic Flashbulb memories (and other episodic

memories)memories) InferenceInference happens all the time, not just in happens all the time, not just in

memorymemory New store opens, scripts help us know how to New store opens, scripts help us know how to

buybuy Strange hungry (but healthy) cat at the door… Strange hungry (but healthy) cat at the door…

food?food?

ConceptsConcepts

ConceptsConcepts are mental representations that are mental representations that make it possible to do inference, make it possible to do inference, understand language, do reasoning, and understand language, do reasoning, and rememberremember Make up semantic memories from last sectionMake up semantic memories from last section Used for construction and other inferences, so Used for construction and other inferences, so

also part of episodic memory also part of episodic memory Can be used for Can be used for categorizationcategorization

Entities placed into groups called Entities placed into groups called categoriescategories

CategoriesCategories Knowing what category an entity is a member

of gives you a lot of information about it Without concepts all knowledge about each Without concepts all knowledge about each

entity would come from experience entity would come from experience with that with that entityentity Each restaurant, each person, each cat, each class,

etc. Broken pencil replaced – learn to use it for writing all

over Helps explain things that would otherwise be

odd Pittsburgh Steelers fanPittsburgh Steelers fan

CategoriesCategoriesLikes to rub up againstpeople and objects

Likes milk, fish

A feline: related to lions and tigers

Difficult to train

Has a tail

Sleeps a lot, but more active at night

Catches mice

CAT

CategoriesCategories

TEAPOT

Models of CategorizationModels of Categorization Definitional ApproachDefinitional Approach (Aristotle) (Aristotle)

Membership by definition, like a checklistMembership by definition, like a checklist Family ResemblancesFamily Resemblances (Wittgenstein) (Wittgenstein)

Membership by similarityMembership by similarity Prototype ApproachPrototype Approach (Rosch) (Rosch)

Membership by similarity to “average” of Membership by similarity to “average” of categorycategory

Exemplar Approach Exemplar Approach Membership by similarity to examples of Membership by similarity to examples of

membersmembers More specific version of More specific version of family resemblancesfamily resemblances

Definitional ApproachDefinitional Approach In geometry a square can be defined as In geometry a square can be defined as

“a plane figure having four equal sides”“a plane figure having four equal sides” Features of a Features of a squaresquare: planar figure, four : planar figure, four

sides, sides are equalsides, sides are equal If something has all of these, it is a square If something has all of these, it is a square If something is a square it must have all of theseIf something is a square it must have all of these

Definitional approach uses definitions Definitional approach uses definitions like this for everythinglike this for everything Bachelor: Male, unmarried, adult, humanBachelor: Male, unmarried, adult, human

What about the pope?What about the pope?

Definitional ApproachDefinitional Approach

AssumesAssumes Sharp category boundary (in or out)Sharp category boundary (in or out) Equality of membersEquality of members Representation of category is Representation of category is list of necessary list of necessary

and sufficient featuresand sufficient features If an entity meets the conditions it is a memberIf an entity meets the conditions it is a member If an entity is a member we know it meets the If an entity is a member we know it meets the

conditionsconditions

In practice it is difficult to find such In practice it is difficult to find such featuresfeatures Is a bookend furniture?Is a bookend furniture?

Family ResemblanceFamily Resemblance No one feature No one feature

that all have in that all have in common….common….

… … Yet all are in Yet all are in some way some way similar to the similar to the other category other category membersmembers

Variation Variation within within categories OKcategories OK

Family Resemblance Family Resemblance

AssumesAssumes No strict “definition” of what’s in/out based on No strict “definition” of what’s in/out based on

individual featuresindividual features Membership based on similarityMembership based on similarity

Some members can be “better” examples than Some members can be “better” examples than othersothers

Think about it long and it gets confusing…. Think about it long and it gets confusing…. Similarity, but similarity Similarity, but similarity to whatto what?? Next two approaches are more detailed Next two approaches are more detailed

versionsversions

Prototype ApproachPrototype Approach What’s a What’s a

prototypeprototype? An ? An “average” of all “average” of all membersmembers

The guy in the The guy in the center is closest to center is closest to the prototype the prototype ((highest highest prototypicalityprototypicality) ) but he isn’t the but he isn’t the prototypeprototype Prototype isn’t herePrototype isn’t here

Prototype ApproachPrototype Approach

Features:Features: Glasses (yes/no)Glasses (yes/no) Hair (dark/light)Hair (dark/light) Nose (big/small)Nose (big/small) Ears (big/small)Ears (big/small) Mustache (yes/no)Mustache (yes/no)

PrototypePrototype 2/3 glasses, 7/9 light hair, 7/9 big nose, 2/3 glasses, 7/9 light hair, 7/9 big nose,

7/9 big ears, 5/9 mustache7/9 big ears, 5/9 mustache Center guy has the Center guy has the highest highest

prototypicalityprototypicality

Support for PrototypesSupport for Prototypes

Rosch (1975)- Rosch (1975)- prototypicalityprototypicality rating rating Participants got category names (Participants got category names (birdbird) )

and lists of 50 members (and lists of 50 members (robin, canary, robin, canary, ostrich, penguin, sparrow…ostrich, penguin, sparrow…))

Provided rating on how well the item Provided rating on how well the item represented the categoryrepresented the category

Results: Much agreement on ratings Results: Much agreement on ratings between participantsbetween participants

Rosch (1975)Rosch (1975)

Very Good

Poor

CATEGORY: BIRDS

Bat Penguin Owl Sparrow

Very Good

Poor

CATEGORY: FURNITURE

Telephone MirrorChina Closet

Chair,Sofa

Rosch and Mervis (1975)Rosch and Mervis (1975) Participants wrote down as many characteristics Participants wrote down as many characteristics

/ attributes as they could think of for each item/ attributes as they could think of for each item Bicycle: two wheels, you ride them, handlebars, Bicycle: two wheels, you ride them, handlebars,

pedals, don’t use fuel….pedals, don’t use fuel…. Dog: have four legs , bark, have fur…Dog: have four legs , bark, have fur…

When attributes overlap with many other When attributes overlap with many other members’ attributes members’ attributes family resemblancefamily resemblance is is highhigh

Results: Items with high prototypicality ratings Results: Items with high prototypicality ratings also have high family resemblancealso have high family resemblance Chairs and sofas, birds, etc.Chairs and sofas, birds, etc.

Other Studies about Other Studies about PrototypesPrototypes

Smith and Coworkers (1974)- Smith and Coworkers (1974)- Typicality Typicality EffectEffect Used Used sentence verificationsentence verification

T/F: an apple is a fruitT/F: an apple is a fruit T/F: a pomegranate is a fruitT/F: a pomegranate is a fruit

Results: sentences about Results: sentences about highly prototypicalhighly prototypical objects are judged more quickly (objects are judged more quickly (typicality typicality effecteffect))

Mervis and Coworkers (1976)Mervis and Coworkers (1976) When people name objects in a category, the When people name objects in a category, the

most prototypical objects tend to come firstmost prototypical objects tend to come first


Rosch (1975b) – repetition priming Rosch (1975b) – repetition priming Prototypical members of a category are Prototypical members of a category are

affected by a priming stimulus more than affected by a priming stimulus more than nonprototypical onesnonprototypical ones

Task: Ignore words, just say whether the two Task: Ignore words, just say whether the two color circles match or notcolor circles match or not

Hear “Green”

“Same”

“Different”

“Same”

610ms

780ms


Rosch (1975b) – repetition priming Rosch (1975b) – repetition priming Take-away message: people are faster to Take-away message: people are faster to

respond “same” when the colors are more respond “same” when the colors are more highly prototypicalhighly prototypical

Word “green” may be linked to the prototypeWord “green” may be linked to the prototype

Hear “Green”

“Same”

“Different”

“Same”

610ms

780ms

Exemplar ApproachExemplar Approach ExemplarsExemplars are specific examples – provide are specific examples – provide

another account for the effects of we have another account for the effects of we have seenseen Examples of category members are saved in Examples of category members are saved in

memory (typical as well as atypical)memory (typical as well as atypical) Potential members compared to all exemplarsPotential members compared to all exemplars Those with high family resemblance are like more Those with high family resemblance are like more

of the exemplars of the exemplars Remember: Family resemblance correlates w/ Remember: Family resemblance correlates w/

prototypicalityprototypicality May be more useful for smaller categories May be more useful for smaller categories

(US presidents, very tall mountains)(US presidents, very tall mountains)

Exemplar ApproachExemplar Approach

Features:Features: Glasses (yes/no)Glasses (yes/no) Hair (dark/light)Hair (dark/light) Nose (big/small)Nose (big/small) Ears (big/small)Ears (big/small) Mustache (yes/no)Mustache (yes/no)

Why does center guy fit?Why does center guy fit? Glasses like 2/3, light hair like 7/9, big Glasses like 2/3, light hair like 7/9, big

nose like 7/9, big ears like 7/9, mustache nose like 7/9, big ears like 7/9, mustache like 5/9like 5/9

Center guy has high Center guy has high family resemblancefamily resemblance

Models of CategorizationModels of Categorization

Four ApproachesFour Approaches Definitional ApproachDefinitional Approach (Aristotle) (Aristotle) Family ResemblancesFamily Resemblances (Wittgenstein) (Wittgenstein) Prototype ApproachPrototype Approach (Rosch) (Rosch) Exemplar Approach Exemplar Approach

Current consensus – people use both Current consensus – people use both the prototype approach and the the prototype approach and the exemplar approach (perhaps for exemplar approach (perhaps for different types of categories)different types of categories)

Levels of Levels of CategoriesCategories

Levels of CategoriesLevels of Categories

Categories have Categories have hierarchical organizationhierarchical organization Is one level more important or “privileged”? Is one level more important or “privileged”?

Furniture

Chair Table

KitchenDiningroom Kitchen

Diningroom

SuperordinateLevel

Basic Level

SubordinateLevel

Levels of CategoriesLevels of Categories

LEVELLEVEL EXAMPLEEXAMPLE

SuperordiSuperordinatenate

FurnitureFurniture

BasicBasic TableTable

SubordinaSubordinatete

Kitchen Kitchen TableTable

Common Features

3

9

10.3

Lose a lot of information

Gain just a little info

Levels of CategoriesLevels of Categories Rosch, Mervis and Coworkers (1976)Rosch, Mervis and Coworkers (1976)

Rating of common features for each categoryRating of common features for each category BASIC level seems to be “special” (go higher and you BASIC level seems to be “special” (go higher and you

lose a lot of information, go lower and it doesn’t help lose a lot of information, go lower and it doesn’t help much)much)

When given a picture (Levis/Jeans/Clothes) When given a picture (Levis/Jeans/Clothes) people label it with the basic level categorypeople label it with the basic level category

Rosch, Simpson and Coworkers (1976)Rosch, Simpson and Coworkers (1976) Category label followed by picture – is it a Category label followed by picture – is it a

member?member? People were faster for basic level categoriesPeople were faster for basic level categories

Coley and Coworkers (1997) Coley and Coworkers (1997) Students described trees with the word “trees” Students described trees with the word “trees”

rather than “oak tree” or “plant”rather than “oak tree” or “plant”

Experience and Experience and CategoriesCategories

When people become experts at a category, When people become experts at a category, they tend to use more specific categoriesthey tend to use more specific categories Tanaka & Taylor (1991) – bird experts and Tanaka & Taylor (1991) – bird experts and

novicesnovices Experts: “robin, sparrow, jay, cardinal”Experts: “robin, sparrow, jay, cardinal” Nonexperts: “bird”Nonexperts: “bird”

Members of Itza culture use “oak tree” , not Members of Itza culture use “oak tree” , not “tree”“tree”

Partly because they live in close contact with the Partly because they live in close contact with the natural environmentnatural environment

Basic levels may not be so “special” for Basic levels may not be so “special” for everyone (depends on experience)everyone (depends on experience)

Semantic Semantic NetworksNetworks

Semantic NetworksSemantic Networks

Semantic networksSemantic networks describe a theory for describe a theory for how concepts are organized in the mindhow concepts are organized in the mind Goal: to develop a computer model of memoryGoal: to develop a computer model of memory

Model we’ll discuss is Collins & Quillian Model we’ll discuss is Collins & Quillian (1969)(1969) Network of Network of nodesnodes connected by connected by linkslinks

NodesNodes = individual categories or concepts = individual categories or concepts LinksLinks = relationships between categories or = relationships between categories or

conceptsconcepts Nodes are also associated with Nodes are also associated with properties properties

Semantic NetworkSemantic NetworkCollins & Quillian (1969)Collins & Quillian (1969)

Animal

Bird Fish

Canary Ostrich Shark Salmon

Has skin

Can move aroundEats

Breathes

Has fins

Can swim

Has gills

Swims upstreamto lay eggs

Is pink

Is edible

Can biteIs dangerous

Has wings

Can fly

Has feathers

Has long thin legs

Is tall

Can’t fly

Is yellowCan sing


Information about the properties of an Information about the properties of an individual category is individual category is retrievedretrieved by by accessing a node, then following links until accessing a node, then following links until we find the desired property (or it’s we find the desired property (or it’s opposite)opposite)

Properties are associated with the highest-Properties are associated with the highest-level category that they apply to in generallevel category that they apply to in general saves space in memory saves space in memory (Cognitive economy)(Cognitive economy) ExceptionsExceptions added at lower nodes to deal with added at lower nodes to deal with

unusual casesunusual cases Example: Properties of a canaryExample: Properties of a canary

Properties of a CanaryProperties of a Canary

Animal

Bird

Canary

Has skin

Can move around

Eats

Breathes

Has wings

Can fly

Has feathers

Is yellowCan sing

Can singIs yellowHas wingsCan flyHas feathers

BreathesHas skinCan move aroundEats

List of all properties

Properties of an OstrichProperties of an Ostrich

Animal

Bird

Has skin

Can move around

Eats

Breathes

Has wings

Can fly

Has feathers

Can’t flyHas long thin legsIs tallHas wingsCan flyHas feathers



Ostrich Has long thin legs

Is tall

Can’t fly


Network is a Network is a functional modelfunctional model, not a , not a physiological onephysiological one It is designed to address how concepts It is designed to address how concepts

are organized in the mind, not the brainare organized in the mind, not the brain Nodes DO NOT correspond to specific brain Nodes DO NOT correspond to specific brain

areasareas Links DO NOT correspond to connections Links DO NOT correspond to connections

between neurons or networks of neuronsbetween neurons or networks of neurons

But how well does the model fit with But how well does the model fit with data on how the mind organizes data on how the mind organizes information?information?

Properties of a CanaryProperties of a Canary

Animal

Bird

Canary

Has skin

Can move around

Eats

Breathes

Has wings

Can fly

Has feathers

Is yellowCan sing

Can singIs yellowHas wingsCan flyHas feathers



Levelsaway from“canary”

0

1

2

Collins and Quillian Collins and Quillian (1969)(1969)

Experiment: timed true/false Experiment: timed true/false judgmentsjudgments Compared properties with different Compared properties with different

distances from the original conceptdistances from the original concept Measured reaction time to simple Measured reaction time to simple

statementsstatements


A canary

can sing

A canary can fly

A canary

has skin

A canary is a

canary

A canary is a bird

A canary is an

animal

Reaction time(higher

is slower)

Levels away from “canary”

0 1 2


Experiment: timed true/false judgmentsExperiment: timed true/false judgments Compared properties with different Compared properties with different

distances from the original conceptdistances from the original concept Measured reaction time to simple Measured reaction time to simple

statementsstatements Results: As properties increased in Results: As properties increased in

distance from the concept node, reaction distance from the concept node, reaction times increasedtimes increased Provides support for counter-intuitive claims Provides support for counter-intuitive claims

related to related to cognitive economycognitive economy Provides support for the model in generalProvides support for the model in general

Spreading ActivationSpreading Activation We have been talking about retrieval as We have been talking about retrieval as

traveling through the semantic network, traveling through the semantic network, but the model actually claims that but the model actually claims that retrieval happens through the process of retrieval happens through the process of spreading activationspreading activation Whenever a node becomes “active”, some Whenever a node becomes “active”, some

activity travels through links to activity travels through links to closely closely associatedassociated nodes nodes

Spreading activation leads to further Spreading activation leads to further predictions in the modelpredictions in the model PrimingPriming of associated concepts of associated concepts

Spreading ActivationSpreading Activation

Network as a Network as a person searches person searches from canary to from canary to birdbird

Activation spreads Activation spreads from bird to all from bird to all linked nodeslinked nodes

Nodes that get Nodes that get activation are activation are primed primed (faster to (faster to recognize)recognize)

Animal

Bird

Canary

Robin

Ostrich

PrimingPriming

PrimingPriming leads to faster recognition of leads to faster recognition of conceptsconcepts One type is One type is repetition primingrepetition priming, which you know, which you know Myer and Schvaneveldt (1971) looked at Myer and Schvaneveldt (1971) looked at priming priming

of associates of associates using ausing a lexical decision lexical decision methodmethod Presented pairs of words, participants decided whether Presented pairs of words, participants decided whether

or not both words in the pair were real wordsor not both words in the pair were real words YES: Chair/Money or Bread/WheatYES: Chair/Money or Bread/Wheat NO: Fundt/Glurb or Bleem/DressNO: Fundt/Glurb or Bleem/Dress

Critical comparison: pairs of real words, close Critical comparison: pairs of real words, close associates vs. distantly related conceptsassociates vs. distantly related concepts

Myer & Schvaneveldt Myer & Schvaneveldt (1971)(1971)

Reaction Time

Words Associat

ed

Words Not Associated

Problems with C & Q Problems with C & Q modelmodel

Didn’t account for Didn’t account for typicality effecttypicality effect A canary is a birdA canary is a bird verified faster than verified faster than an an

ostrich is a birdostrich is a bird Model predicts equal reaction times for bothModel predicts equal reaction times for both

Evidence against Evidence against cognitive economycognitive economy idea idea People store some properties at concept node People store some properties at concept node

Evidence against Evidence against hierarchical structurehierarchical structure A pig is an animalA pig is an animal verified faster than verified faster than a pig is a pig is

a mammala mammal Model predicts the opposite because mammal Model predicts the opposite because mammal

is supposed to be between is supposed to be between pigpig and and animalanimal

Collins and Lofus VersionCollins and Lofus Version

Abandon hierarchy structure in favor of a Abandon hierarchy structure in favor of a structure based on individual experiencestructure based on individual experience

Allowed multiple links between conceptsAllowed multiple links between concepts Pig to animal AND pig to mammal to animalPig to animal AND pig to mammal to animal

Link distances could affect spread of Link distances could affect spread of activationactivation Shorter links for closely related conceptsShorter links for closely related concepts Longer links for more distantly related Longer links for more distantly related

conceptsconcepts This accounts for This accounts for typicality effecttypicality effect

Collins and Loftus Collins and Loftus VersionVersion

StreetVehicle

Car

Truck Bus

Ambulance

Fire EngineToo powerful!

A Theory that Explains Too A Theory that Explains Too Much?Much?

Collins & Loftus’s model rejected for Collins & Loftus’s model rejected for being too powerful (Johnson-Laird & being too powerful (Johnson-Laird & Coworkers, 1984)Coworkers, 1984) Too difficult to Too difficult to falsifyfalsify Model can “explain” any pattern of data by Model can “explain” any pattern of data by

adjusting link lengthadjusting link length No definite methods for determining lengthNo definite methods for determining length Can vary from person to personCan vary from person to person

No constraints on how long activation No constraints on how long activation hangs around, or how much information is hangs around, or how much information is needed to trigger a node needed to trigger a node

Elements of Good Elements of Good Psychological TheoriesPsychological Theories

Explanatory powerExplanatory power – the theory can tell – the theory can tell us what caused a specific behaviorus what caused a specific behavior

Predictive powerPredictive power – the theory can make – the theory can make predictions about future experimentspredictions about future experiments

FalsifiabilityFalsifiability – the theory can be shown to – the theory can be shown to be wrong through specific experimental be wrong through specific experimental outcomesoutcomes

Generation of experimentsGeneration of experiments – the theory – the theory stimulates a lot of research to test the stimulates a lot of research to test the theory, improve it, use methods suggested theory, improve it, use methods suggested by it, and/or study new questions that it by it, and/or study new questions that it raisesraises

The The Connectionist Connectionist

ApproachApproach

Connectionist NetworksConnectionist Networks

Research in semantic networks dropped Research in semantic networks dropped by 80’s but came back with rise of by 80’s but came back with rise of connectionismconnectionism Book series: Book series: Parallel Distributed Processing… Parallel Distributed Processing…

(McClelland & Rumelhart, 1986)(McClelland & Rumelhart, 1986) Connectionist models contain structures Connectionist models contain structures

like nodes and links, but they operate like nodes and links, but they operate much differently from semantic networksmuch differently from semantic networks Inspired by the biology of the nervous Inspired by the biology of the nervous

system, specifically neurons in the brainsystem, specifically neurons in the brain

Connectionist NetworksConnectionist Networks UnitsUnits – “neuron-like”, connect to form – “neuron-like”, connect to form

circuits, can be activated, can inhibit/excite circuits, can be activated, can inhibit/excite other unitsother units Input unitsInput units – activated by stimulation from – activated by stimulation from

environment (like receptors)environment (like receptors) Hidden unitsHidden units – get input from input units, – get input from input units,

connect to output units connect to output units Output unitsOutput units – get input only from hidden – get input only from hidden

unitsunits Concept = pattern of activation across Concept = pattern of activation across

unitsunits distributed coding distributed coding (unlike semantic nets!)(unlike semantic nets!)

Connectionist NetworksConnectionist Networks Processing is achieved by Processing is achieved by weightsweights at each at each

connection between neuronsconnection between neurons Positive weightsPositive weights = excitation in neural system = excitation in neural system Negative weightsNegative weights = inhibition in neural = inhibition in neural

systemsystem Can vary in connection strength, tooCan vary in connection strength, too

Connectionist approach also called Connectionist approach also called Parallel Distributed Processing (PDP) Parallel Distributed Processing (PDP) approachapproach Representation is distributed across neuronsRepresentation is distributed across neurons Processing happens in parallelProcessing happens in parallel

A Connectionist NetworkA Connectionist Network

Output Units

Hidden Units

Input Units

+5 +3 +10 +4

Supervised LearningSupervised Learning

Weights modified through Weights modified through supervised learningsupervised learning Like a child, making mistakes and being Like a child, making mistakes and being

correctedcorrected In the beginning all weights are randomIn the beginning all weights are random This is a computer program (not a This is a computer program (not a

person)person)

Steps in Supevised Steps in Supevised LearningLearning

Step 1: input presented, activation Step 1: input presented, activation propagates through the layers to the propagates through the layers to the output layeroutput layer

Output Units

Hidden Units

Input Units

+10 +6 +5 +2

-1 +1 -1 +1

CANARY



Step 2: output compared to correct Step 2: output compared to correct output, difference is an output, difference is an error signalerror signal

+5

Output Units

Hidden Units

Input Units

+10 +6 +5 +2

-1 +1 -1 +1

CANARY

+3 +10 +4 CorrectPattern

-5 -3 +5 +2Error Signal




Step 3: error signal is used to adjust Step 3: error signal is used to adjust weights using a process called weights using a process called back back propagationpropagation Connections that contributed most error are Connections that contributed most error are

adjusted the mostadjusted the most

+5

Weights are all

Adjusted

+10 +6 +5 +2

-1 +1 -1 +1

CANARY

+3 +10 +4 CorrectPattern

-5 -3 +5 +2Error Signal




Step 3: error signal is used to adjust Step 3: error signal is used to adjust weights using a process called weights using a process called back back propagationpropagation Connections that contributed most error are Connections that contributed most error are

adjusted the mostadjusted the most Repeat the whole thing many timesRepeat the whole thing many times

Stop when Stop when error signalerror signal is 0 is 0

Output Units

Hidden Units

Input Units

+5 +3 +10 +4

-1 +1 -1 +1

CANARY

+5 +3 +10 +4 CorrectPattern

0 0 0 0Error Signal

Supervised LearningSupervised Learning Supervised learning may seem Supervised learning may seem

straightforward, but the trick is training straightforward, but the trick is training the network to represent many concepts at the network to represent many concepts at the same timethe same time It can do it if learning all concepts at the same It can do it if learning all concepts at the same

timetime Making only small changes to the weights helpsMaking only small changes to the weights helps

Hidden units free to respond in any patternHidden units free to respond in any pattern Over time (0-2500 trials), hidden unit patterns Over time (0-2500 trials), hidden unit patterns

differ for different conceptsdiffer for different concepts Similar concepts have similar hidden unit Similar concepts have similar hidden unit

activation activation

Advantages of Advantages of Connectionist ModelsConnectionist Models

Goals of the approachGoals of the approach Slow learning process creates network that can Slow learning process creates network that can

handle many different inputshandle many different inputs Representation is distributed (as in the brain)Representation is distributed (as in the brain)

Graceful degradationGraceful degradation: Damage and/or : Damage and/or incomplete information does not completely incomplete information does not completely disrupt a trained network disrupt a trained network

Learning can be Learning can be generalizedgeneralized: because : because similar concepts have similar patterns, similar concepts have similar patterns, network can make predictions about concepts network can make predictions about concepts it has never seenit has never seen

Computer modelsComputer models have been developed that have been developed that match some aspects of human performance match some aspects of human performance and deficits related to brain damageand deficits related to brain damage

Connectionism TodayConnectionism Today

Opinions are divided Opinions are divided Some like the connection with biology, while Some like the connection with biology, while

some think the connection is tenuous at bestsome think the connection is tenuous at best Recent trend in cognitive science to return Recent trend in cognitive science to return

to ideas about semantic networks and to ideas about semantic networks and combine them with connectionist approachescombine them with connectionist approaches

Those who are really interested in Those who are really interested in connectionist models and what they can connectionist models and what they can express have begun to call their work express have begun to call their work connection scienceconnection science (not cognitive science (not cognitive science or cognitive psychology)or cognitive psychology)

Categories in the BrainCategories in the Brain Warrington and Shallice (1984) – patients with Warrington and Shallice (1984) – patients with

damage to the inferior temporal lobe (IT)damage to the inferior temporal lobe (IT) Visual agnosiaVisual agnosia – patients can see, but not name – patients can see, but not name Double dissociationDouble dissociation for living / nonliving things for living / nonliving things

Hills and Caramazza (1991) – IT damage as Hills and Caramazza (1991) – IT damage as wellwell Patient could not name nonliving things or some Patient could not name nonliving things or some

living things (fruits & veggies), could name other living things (fruits & veggies), could name other living things (animals)living things (animals)

Chao and Coworkers (1999) – fMRI shows Chao and Coworkers (1999) – fMRI shows specialized areas for categories… with overlapspecialized areas for categories… with overlap

Categories in the BrainCategories in the Brain

It looks like there are areas in the brain It looks like there are areas in the brain that are specialized for different categoriesthat are specialized for different categories Distributed coding with overlapDistributed coding with overlap Categories with similar features, similar Categories with similar features, similar

activationactivation Category-specific neuronsCategory-specific neurons respond to respond to

individual categories like “house” or individual categories like “house” or “snake”“snake” Result from single-cell recordingResult from single-cell recording Specific example may cause a specialized Specific example may cause a specialized

pattern of activation across many category-pattern of activation across many category-specific neuronsspecific neurons

Example: faces from chapter 2Example: faces from chapter 2

The EndThe End

Reminder: Midterm 2 is next Reminder: Midterm 2 is next week!week!

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Memory and Cognition PSY 324 Topic 8: Knowledge Dr. Ellen Campana Arizona State University