1

Semantic Memory(pp 202-209)

Jim Clark

j.clark@uwinnipeg.ca

www.uwinnipeg.ca/~clark/teach/2600

1For Test 3, Not Test 2!

2Semantic Memory

Introduction

Sentence Verification

Task

Connectionist

Hierarchical

More SM TasksSM vs.

EpisodicProduction

Distributed Rep’n

Cognitive Economy

Rating & Sorting

Episodic Memory

Spreading Activation

Lexical Decision Task

Applications

Models

Learning

Semantic vs. Episodic Memory

Two kinds of Long-Term Memory(Tulving, 1972)

Semantic MemoryGeneral knowledge

about world

Episodic MemoryPersonal experiences

Time-stamped

• Your friend Kate lives in Winnipeg.

• It’s cold in winter.• Big Bang Theory is

hilarious.

Yesterday afternoon you studied for

Cognition with your friend Kate in the

library.

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Fundamental units of representation

Concept

• Idea about something that helps you understand world

• Mental representation

• Think about cats

Categories

A way to organize

concepts

Pets, Animals, …

4

5

Hierarchical Semantic Network Model• Nodes represent concepts interconnected by associations (connections, links, pointers)

• Hierarchy of concepts/categories (Rosch)

• Cognitive Economy– Properties at highest level to which they apply (F6.6)

6• Testing Principle of Cognitive Economy

– Collins & Quillian (1969): Sentence Verification Task

• Results (F6.6)

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Problems for Hierarchical Model

• Falsification RTs

– A cat meows = A bird meows

• Reverse Hierarchical Effects (Familiarity?)

– Poodle is Dog faster RT than Poodle is Animal

– Dog is Mammal slower RT than Dog is Animal

• Typicality Effects (next slide)

– Robin is Bird faster RT than Penguin is Bird

• Strong version of cognitive economy doubtful, but not all information stored redundantly

– Led to more “liberal” associative or network models

7 8Typicality and Verification Time

9Revised Semantic Network Model

• Spreading Activation Model

– A dominant model in contemporary cognitive psychology

– Nodes + Links (associations, pathways)

• Nodes = Concepts, Categories, Properties, …

• Links = connections, labelled in some models (isa, has): Activation spreads along links exciting or inhibiting related nodes

– But NOT strictly hierarchical

– Multiple kinds of relationships / associations

• Category, Property

• Also less well specified: house – fire, street – ambulance

– Sample Networks and some evidence on following slides

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Semantic Network

• Hierarchical organization eliminated (reduced?)

• Clusters of related

concepts

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F6.7: Spreading activation: when “bread” node excited, activation travels to related nodes

• Measuring Spreading Activation

• Priming Tasks– Prime �Target

– Prime Related or Unrelated to Target

– RT to process Target: Lexical Decision, Color Naming, Word Reading, …

– Next two slides

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Priming Task: Lexical Decision RTs

• Meyer and Schvaneveldt (1971)– Subjects saw pairs of letter strings and decided whether both words or not

– Correct response Yes for rows 1, 2, 4 and No for 3, 5

– 85 ms Faster for associated pairs vs. unassociated pairs

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• Priming Task: Colour Naming (Stroop effect)– Typical Stroop Task: blue, green, yellow, …

– Adapted as measure of word activation

– Warren (1974)

Prime Rel Unr Diff Examples

High 929 834 +95 slow - fast

Medium 906 856 +50 bible - god

Low 858 838 +20 wish – dream

• People slower to name colour with related prime because more interference from word reading

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Connectionist Models• Neural Networks,

Parallel-Distributed Processing (PDP)

• Examples (F6.8, next few slides)

• Distributed representations

• No single “node” represents concept

• Hidden Units

• Experience modifies weights on paths

Network Model

Connectionist Model

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Distributed (top) vs. Local (bottom) Representation•

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Example of Connectionist Model18

Connectionist model

4

Connectionist Model (CM)

Like semantic network models

Modeled on nervous systemNeurons have

Excitatory or Inhibitoryconnections

Links in CM Excitatory or Inhibitory & also vary in strength (weight)

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Demo

Connectionism & Learning

Goal: Specify how network learns correct pattern

Signal changed through excitation & inhibition

Output pattern

Correct pattern

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Connectionism

Initial output compared to correct output

Error signal: difference between initial output & correct

Back propagation: process that tells model to adjust weights based on error signal

Eventually results in Output pattern = Correct pattern

21 22More SM Paradigms (Tasks)

• Paradigms

– Standard tasks used to study psychological phenomena

– Priming Paradigm is one example

• Diverse Paradigms implicate SM

– SM fundamental for many cognitive processes,

including perception, attention, episodic memory, thinking, …

– Production Tasks

– Rating & Sorting Tasks

– Episodic Memory Tasks

23SM Paradigms: Production or Association

• Subjects presented with concept(s), often word(s)

• Respond with other word or words that stimulus brings to mind: e.g., list animals, generate properties, first word that comes to mind (Free Association), …

• Measures of Strength of Semantic Connection

– Commonality: # subjects giving response

– Order of output: strongest first

– Stability/Reliability: stronger responses repeated

– RT: latency to respond

• Various measures tend to converge (i.e., agree), as in next slide

24Order of Response and % Repetitions

# # %

Order Resp Reps Stble

1 629 381 61

2 613 292 48

3 599 242 40

4 589 199 34

5 528 172 32

6 465 140 30

7 359 91 25

8 268 55 21

9 187 29 16

10 124 14 11

11 48 6 13

5

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SM Paradigms: Rating & Sorting Tasks

• Present words or pairs of words to Rate on some semantic attribute or to Sort into categories

– Rated Typicality of category members (next slide)

– Rate Similarity of category members

– Sort Category members into groups

– …

• Ratings & Sorting input to statistical analysis

– Multidimensional Scaling: Use Ratings or Sorting to produce spatial representation of SM (+2 slides)

– Hierarchical Cluster Analysis: Determine hierarchy that best fits the rated similarity or sorting groups (+3 slides)

26Prototypicality

• Furniture: chair, sofa, table,… piano, cushion, mirror, …, closet, vase, telephone

• Vehicle: car, truck, bus, … tractor, cart, wheelchair, … skates, wheelbarrow, elevator

• Fruit: orange, apple, banana, … grapefruit, pineapple, blueberry, … tomato, olive

• Weapon: gun, knife, sword, … club, tank, teargas, … words, foot, screwdriver

• Vegetable: peas, carrots, string-beans, … lettuce, beets, tomato, … pumpkin, rice

• Clothing: pants, shirt, dress, … socks, pajamas, bathing-suit, … purse, wristwatch, necklace

27Multi-Dimensional Scaling (MDS)

• MDS results

• Researcher then tries to label the underlying dimensions

that are thought to reflect organization of SM

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Hierarchical Cluster Analysis

# Ss 11 10 9 8 7

camel ----------------------|

|

bull -------| |

|---------| |

horse -------| | |

|----|

wolf -------------| |

| |

tiger --| |---|

|----------|

lion --|

# subjects grouping words togethertiger horse bull wolf camel

lion 11 4 2 8 3tiger 5 3 9 4horse 10 8 3bull 6 1wolf 7

• Hierarchical Cluster Analysis determines hierarchy (taxonomy) that best fits the rating or sorting data

• Again thought to reflect organization of SM

29SM Paradigms: Episodic Memory

• Many Episodic Memory phenomena reflect SM

• Free Recall

– Lists of words from superordinate categories

• e.g., dog cow horse shirt jacket pants ...

– Measures

• Category Clustering: words recalled by category even if presented randomly

• Recall for categorical better than for unrelated words

• Recall better for Blocked vs Random presentation

• People who organize items recall more

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• False Memory

– Lists of related words: e.g., pillow, snore, night, tired, bed, …

– People often wrongly recall or rate as old related words

that were not presented, called Lures (e.g., sleep)

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31Applications of SM

• Clinical Psychology

– Schizophrenia: bizarre speech patterns (word salad) may reflect dysfunctional associative networks

– Dissociative Disorders (Multiple Personality)

• Osgood: Semantic Differential for “Three Faces of Eve”

– Associative Models of Disorders

• Phobia: Lang

• Depression: Gotlib

• Neuropsychology

– Aphasia: word finding difficulties

• Education

– Associative networks reflect learning

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Multiple Personality

Eve Black I

Eve White II

Me

Me

Sex

Sex

Father

Father

33Lang (1979) Network of Phobia

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Semantic Memory and Depression

SAD

TABLE

35Aphasia and Name Retrieval– Brain insults can lead to word retrieval difficulties (left)

– Sometimes limited to specific categories: e.g., living things

• Ashcraft (1993): personal anecdote

– Growth in left temporal lobe "stole" blood

– 45 min disruption in semantic retrieval

– Conscious: Knew meanings for objects, but not exact word

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Applications of SM: Education• Knowledge

– Study how semantic memory structures change and become organized during learning: structures become more organized with expertise (right)

– McKeachie (1986): University students taking course on aging• Semantic Structures for terms: senile dementia, attachment, encoding, ...

• Degree of semantic structure increased from beginning to end of course

• Similarity between student/instructor cognitive maps r=.51 with final grade