Long Term Memory: Semantic Kimberley Clow

Outline Methods Methods –Explicit Measures –Implicit Measures Theories Theories –Defining Features –Prototype Models –Probabilistic Models –Network Models

Taxonomy Memory Long Term Memory Short Term Memory Sensory Memory Explicit Memory Implicit Memory Semantic Memory Episodic Memory

Episodic vs. Semantic Memory Episodic Memory Episodic Memory –Memory for specific events / episodes »Where were you when you first heard about the attack on the world trade center? Semantic Memory Semantic Memory –Memory for general world knowledge »What is the date of the attack on the world trade center? »In what city was the world trade center located? »What does the word “trade” mean?

Example of Semantic Information Concept of dogs Concept of dogs –Characteristics »Has fur »Has 4 legs »Has a tail »Barks »Bites postal workers –Types of dogs »Dalmatians »Poodles »Terriers

Implicit vs. Explicit Measures of explicit memory are sensitive to how the information is processed / studied. Measures of explicit memory are sensitive to how the information is processed / studied. Measures of implicit memory usually show facilitation regardless of how the information was processed / studied Measures of implicit memory usually show facilitation regardless of how the information was processed / studied

Defining Features An item belongs to a category/concept if that item incorporates the concept’s defining features An item belongs to a category/concept if that item incorporates the concept’s defining features –Defining Features »Essential features »Necessary and jointly sufficient Boundaries between concepts are clear cut Boundaries between concepts are clear cut All members of a category are equally representative All members of a category are equally representative

Example Bachelor noun (Human)(Animal) (Male)[lowest academic degree] [who has never married] [young knight serving under the standard of another knight] [young seal without a mate during breeding time] (Male)

Criticisms Many features are not absolutely necessary Many features are not absolutely necessary –If a dog is hairless or loses a leg, it is still a dog –Not all apples are sweet or red Not all categories have clearly marked boundaries Not all categories have clearly marked boundaries –What are the defining features of “game”? Research suggests that all members of a category are NOT represented equally Research suggests that all members of a category are NOT represented equally

Typicality Effects The typicality of each as fruit (highest to lowest): The typicality of each as fruit (highest to lowest): –Apple1.3 –Plum2.3 –Pineapple2.3 –Strawberry2.3 –Fig4.7 –Olive6.2

Explicit Tasks Typicality Ratings Typicality Ratings –On a scale of 1-6, how typical of fruit is a(n) »Apple? »Olive? »Banana? »Pineapple? Similarity Ratings Similarity Ratings –On a scale of 1 - 6, how similar is a(n) »apple to a plum? »plum to a lemon? »apple to a lemon? »olive to a plum?

From these types of ratings…

Multidimensional Scaling

Typicality vs. Similarity Typicality ratings seem to reflect similarity BirdRobinChicken Flies + - Sings + - Lays eggs + + Is small + - Nests in trees + -

And What About These Findings… Some Strange Effects –Minimality Violation –Symmetry Violation –Triangle Inequality

Prototype Theory A prototype is the best or ideal example of a concept A prototype is the best or ideal example of a concept Categorization is based on similarity between a specific instance (exemplar) and prototype Categorization is based on similarity between a specific instance (exemplar) and prototype

Feature List Models Membership in a category is based on characteristic and defining properties Membership in a category is based on characteristic and defining properties –Some members have more characteristic properties than others –Defining properties are not necessarily singularly necessary and jointly sufficient Something belongs to a category if it is similar to members of that category Something belongs to a category if it is similar to members of that category Category boundaries are fuzzy Category boundaries are fuzzy

Smith’s Feature Overlap Model

How It Works

Implicit Tasks Sentence Verification Task Sentence Verification Task –Shown subject-predicate sentences »A canary is a bird –Tested different sentence types »Set 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-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…

Feature Verification Task Feature Verification Task –Shown a concept and attribute (feature) »LEMON – yellow –Need to indicate whether the feature is ever true of the concept »LEMON – sour »LEMON – fruity »LEMON – hard –Differences in speed indicate how semantic information is organized

Priming Priming –Present two words »First word called the prime »Second word called the target Repetition Priming Repetition Priming –can be long-lasting (hours) »Study: TRUCK »Test: TRU__ Semantic Priming Semantic Priming –short-lived (seconds)

Example

Priming Results

Tversky’s Contrast Model LemonOrange yelloworange ovalround soursweet treestrees citruscitrus -ade-ade navel Similarity = a(3) - b(3) - c(4) Similarity (I,J) = a(shared) - b(I but not J) - c(J but not I)

Criticisms What are characteristic vs. defining features is not well defined What are characteristic vs. defining features is not well defined –Not all concepts have defining characteristics »Problem for Overlap Model Doesn’t work too well for property comparisons Doesn’t work too well for property comparisons –ROBIN – has wings (feature verification) »Problem for Overlap Model Cannot account for effects of frequency and associative strength Cannot account for effects of frequency and associative strength »Problem for Overlap and Contrast Models

Collins & Quillian Associative Network Model

To Visualize Another Way… SUPERORDINATE SUBORDINATE

Conrad (1972) People respond faster to high frequency associates People respond faster to high frequency associates Distance in hierarchical structure not as important as frequency of association Distance in hierarchical structure not as important as frequency of association

Collins & Quillian

Collins & Loftus (1975) Modifications Modifications –Concepts are NOT organized as a hierarchy »Explains lack of hierarchical findings –Links vary in associative strength / accessibility »Nodes that are closer together are higher in associative strength »Explains typicality effects

Connectionist Networks Built upon the associative networks Built upon the associative networks Distributed processing assumption Distributed processing assumption –Concept is represented as a pattern of distributed features »Many units rather than one node »These units are similar to neurons (or groups of neurons) If a unit is detected, it becomes activated and “fires” to connected units If a unit is detected, it becomes activated and “fires” to connected units –Connections between units have weights based on associative strength (and vary with experience) »Positive weights increase activation of linked units »Negative weights decrease activation of linked units