long term memory: semantic kimberley clow kclow2@uwo.ca

Long Term Memory: SemanticLong Term Memory: Semantic

Kimberley ClowKimberley Clow

kclow2@uwo.cakclow2@uwo.ca

http://instruct.uwo.ca/psychology/130/http://instruct.uwo.ca/psychology/130/

OutlineOutline

MethodsMethods– Explicit MeasuresExplicit Measures– Implicit MeasuresImplicit Measures

TheoriesTheories– Defining FeaturesDefining Features– Prototype ModelsPrototype Models– Probabilistic ModelsProbabilistic Models– Network ModelsNetwork Models

TaxonomyTaxonomy

Memory

Long Term Memory Short Term Memory Sensory Memory

Explicit Memory Implicit Memory

Semantic Memory Episodic Memory

Episodic vs. Semantic MemoryEpisodic vs. Semantic Memory

Episodic MemoryEpisodic Memory– Memory for specific events / episodesMemory for specific events / episodes

» Where were you when you first heard about theWhere were you when you first heard about the attack on attack on the the world trade centerworld trade center??

Semantic MemorySemantic Memory– Memory for general world knowledgeMemory for general world knowledge

» What is the date of the attack on the world trade What is the date of the attack on the world trade centercenter??

» In what city was the world trade In what city was the world trade centercenter located? located?

» What does the word “trade” mean?What does the word “trade” mean?

Example of Semantic InformationExample of Semantic Information

Concept of dogsConcept of dogs– CharacteristicsCharacteristics

» Has furHas fur

» Has 4 legsHas 4 legs

» Has a tailHas a tail

» BarksBarks

» Bites postal workersBites postal workers

– Types of dogsTypes of dogs» DalmatiansDalmatians

» PoodlesPoodles

» TerriersTerriers

Implicit vs. ExplicitImplicit vs. Explicit

Measures of explicit Measures of explicit memory are sensitive to memory are sensitive to how the information is how the information is processed / studied.processed / studied.

Measures of implicit Measures of implicit memory usually show memory usually show facilitation regardless of facilitation regardless of how the information was how the information was processed / studiedprocessed / studied

Defining FeaturesDefining Features

An item belongs to a category/concept if that An item belongs to a category/concept if that item incorporates the concept’s defining item incorporates the concept’s defining featuresfeatures– Defining FeaturesDefining Features

» Essential featuresEssential features

» Necessary and jointly sufficientNecessary and jointly sufficient

Boundaries between concepts are clear cutBoundaries between concepts are clear cut All members of a category are equallyAll members of a category are equally

representativerepresentative

ExampleExample

Bachelor

noun(Human) (Animal)

(Male)[lowest academicdegree]

[who has never married]

[young knight serving underthe standard ofanother knight]

[young seal withouta mate during breeding time]

(Male)

CriticismsCriticisms

Many features are not absolutely necessaryMany features are not absolutely necessary– If a dog is hairless or loses a leg, it is still a dogIf a dog is hairless or loses a leg, it is still a dog– Not all apples are sweet or redNot all apples are sweet or red

Not all categories have clearly marked Not all categories have clearly marked boundariesboundaries– What are the defining features of “game”?What are the defining features of “game”?

Research suggests that all members of a Research suggests that all members of a category are NOT represented equallycategory are NOT represented equally

Typicality EffectsTypicality Effects

The typicality of each as fruit (highest to The typicality of each as fruit (highest to lowest):lowest):– AppleApple 1.31.3– PlumPlum 2.32.3– PineapplePineapple 2.32.3– StrawberryStrawberry 2.32.3– FigFig 4.74.7– OliveOlive 6.26.2

Explicit TasksExplicit Tasks

Typicality RatingsTypicality Ratings– On a scale of 1-6, how typical of fruit is a(n) On a scale of 1-6, how typical of fruit is a(n)

» Apple?Apple?» Olive?Olive?» Banana?Banana?» Pineapple?Pineapple?

Similarity RatingsSimilarity Ratings– On a scale of 1 - 6, how similar is a(n) On a scale of 1 - 6, how similar is a(n)

» apple to a plum?apple to a plum?» plum to a lemon?plum to a lemon?» apple to a lemon?apple to a lemon?» olive to a plum?olive to a plum?

From these types of ratings…From these types of ratings…

Multidimensional ScalingMultidimensional Scaling

Typicality vs. SimilarityTypicality vs. Similarity

Typicality ratings seem to reflect similarityTypicality ratings seem to reflect similarity

BirdBird RobinRobin ChickenChickenFliesFlies + + - -SingsSings + + - -Lays eggsLays eggs + + + +Is smallIs small + + - -Nests in treesNests in trees + + - -

And What About These Findings…And What About These Findings…

Some Strange EffectsSome Strange Effects– Minimality ViolationMinimality Violation– Symmetry ViolationSymmetry Violation– Triangle InequalityTriangle Inequality

Prototype TheoryPrototype Theory

A prototype is the best or ideal example of a A prototype is the best or ideal example of a conceptconcept

Categorization is based on similarity between a Categorization is based on similarity between a specific instance (exemplar) and prototypespecific instance (exemplar) and prototype

Feature List ModelsFeature List Models

Membership in a category is based on Membership in a category is based on characteristic and defining propertiescharacteristic and defining properties– Some members have more characteristic properties Some members have more characteristic properties

than othersthan others– Defining properties are not necessarily singularly Defining properties are not necessarily singularly

necessary and jointly sufficientnecessary and jointly sufficient Something belongs to a category if it is similar Something belongs to a category if it is similar

to members of that categoryto members of that category Category boundaries are fuzzyCategory boundaries are fuzzy

Smith’s Feature Overlap ModelSmith’s Feature Overlap Model

How It WorksHow It Works

Implicit TasksImplicit Tasks

Sentence Sentence VerificationVerification TTaskask – Shown subjectShown subject-predicate-predicate sentences sentences

» A canary is a birdA canary is a bird

– Tested different sentenceTested different sentence types types» Set inclusionSet inclusion

A canary is a bird (true)A canary is a bird (true) A whale is a fruit (false)A whale is a fruit (false)

» Property-attributeProperty-attribute A canary has feathers (true)A canary has feathers (true) A whale has seeds (false)A whale has seeds (false)

In a sentence verification task…In a sentence verification task…

Feature Verification Task Feature Verification Task – Shown a concept and attribute (feature)Shown a concept and attribute (feature)

» LEMON – yellowLEMON – yellow

– Need to indicate whether the feature is ever true Need to indicate whether the feature is ever true of the conceptof the concept

» LEMON – sourLEMON – sour

» LEMON – fruityLEMON – fruity

» LEMON – hardLEMON – hard

– Differences in speed indicate how semantic Differences in speed indicate how semantic information is organizedinformation is organized

PrimingPriming – Present two wordsPresent two words

» First word called the primeFirst word called the prime

» Second word called the targetSecond word called the target

Repetition PrimingRepetition Priming– can be long-lasting (hours)can be long-lasting (hours)

» Study: TRUCKStudy: TRUCK

» Test: TRU__Test: TRU__

Semantic PrimingSemantic Priming– short-lived (seconds)short-lived (seconds)

ExampleExample

Priming ResultsPriming Results

Tversky’s Contrast ModelTversky’s Contrast Model

LemonLemon OrangeOrange

yellowyellow orangeorange

ovaloval roundround

soursour sweetsweet

treestrees treestrees

citruscitrus citruscitrus

-ade-ade -ade-ade

navelnavel

Similarity Similarity = a(3) - b(3) - c(4)= a(3) - b(3) - c(4)

Similarity (I,J) Similarity (I,J) = a(shared) - b(I but not J) - c(J but not I)= a(shared) - b(I but not J) - c(J but not I)

CriticismsCriticisms What are characteristic vs. defining features is What are characteristic vs. defining features is

not well definednot well defined– Not all concepts have defining characteristicsNot all concepts have defining characteristics

» Problem for Overlap ModelProblem for Overlap Model

Doesn’t work too well for property Doesn’t work too well for property comparisonscomparisons– ROBIN – has wings (feature verification)ROBIN – has wings (feature verification)

» Problem for Overlap ModelProblem for Overlap Model

Cannot account for effects of frequency and Cannot account for effects of frequency and associative strengthassociative strength

» Problem for Overlap and Contrast ModelsProblem for Overlap and Contrast Models

Collins & QuillianCollins & QuillianAssociative Network ModelAssociative Network Model

To Visualize Another Way…To Visualize Another Way…

SUPERORDINATE

SUBORDINATE

Conrad (1972)Conrad (1972)

People respond faster to People respond faster to high frequency high frequency associatesassociates

Distance in hierarchical Distance in hierarchical structure not as structure not as important as frequency important as frequency of associationof association

Collins & QuillianCollins & Quillian

Collins & Loftus (1975)Collins & Loftus (1975)

ModificationsModifications– Concepts are Concepts are NOT NOT

organizedorganized as a hierarchyas a hierarchy» Explains lack of Explains lack of

hierarchical findingshierarchical findings

– Links vary in associativeLinks vary in associative strengthstrength // accessibilityaccessibility

» Nodes that are closer Nodes that are closer together are higher in together are higher in associative strengthassociative strength

» ExplainsExplains typicality effect typicality effectss

Connectionist NetworksConnectionist Networks

Built upon the associative networksBuilt upon the associative networks Distributed processing assumptionDistributed processing assumption

– Concept is represented Concept is represented as a pattern of as a pattern of distributed distributed featuresfeatures

» Many units rather than one nodeMany units rather than one node» These units are similar to neurons (or groups of neurons)These units are similar to neurons (or groups of neurons)

If a unit is detected, it becomes activated and If a unit is detected, it becomes activated and “fires” to connected units“fires” to connected units– Connections between units have weights based on Connections between units have weights based on

associative strength (and vary with experience)associative strength (and vary with experience)» Positive weights increase activation of linked unitsPositive weights increase activation of linked units» Negative weights decrease activation of linked unitsNegative weights decrease activation of linked units