General Knowledge Dr. Claudia J. Stanny EXP 4507 Memory & Cognition Spring 2009.
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Transcript of General Knowledge Dr. Claudia J. Stanny EXP 4507 Memory & Cognition Spring 2009.
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General Knowledge
Dr. Claudia J. StannyEXP 4507
Memory & Cognition
Spring 2009
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Overview• How is knowledge represented in semantic
memory?• Models of the structure of semantic memory• Feature comparison model• Prototypes and family resemblances• Exemplars• Network models
• Schemas and scripts• Influence on memory storage and retrieval
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Semantic & Episodic MemorySemantic Memory
General knowledgeFacts, ideas, concepts,
categoriesGeneric informationConceptually organizedNo temporal coding
Subjective experience of retrieval: “KNOW”
Episodic MemorySpecific event knowledgeEvents, episodesMay include specific
information about self (Autobiographical Memory)
Temporally organized
Subjective experience of retrieval: “REMEMBER”
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Types of concepts represented in semantic memory
Logical categories and concepts• Clear definitions• Clear category membership
Natural categories and concepts• Things that occur naturally in the environment• Tend to be thought about in terms of essential
elements or features, but specific examples do not always have these features
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How are concepts represented in semantic memory?
Defining sets of features (Feature comparison model)
Prototypes
Family resemblances
Exemplars
Network models
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How to study these models?
Sentence verification tasks• Present a sentence• Measure RT to respond that the sentence is true
or false• Use patterns in the RT to make decisions about
organization and retrieval of semantic information
Example of trials in a sentence verification task
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Sentence Verification Task Findings
Some sentences take longer to verify than others (semantic difference effect)
Typicality Effect: RT is faster for sentences about typical examples of a concept or categoryA canary is a bird.
A penguin is a bird.
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Feature Comparison Model
Concepts are defined in terms of features
Defining features (necessary / required features)• Features must be present for meaning of a
concept or category membership
Characteristic features (features that are descriptive but not required)• Anything with all of the necessary features is
automatically included in the concept
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How well does this model work?Logical categories • Easily described with a list of defining features• Membership is clear and unambiguous• All members of the concept are equally good as
examples of the concept
Natural categories• Not all members have all the “defining” features• Features are correlated with one another• Members vary in how well they fit the concept
(typicality effects, graded category membership)
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Prototypes
Abstract, idealized representations of a concept
The prototype stored need not correspond to any specific example
Features of the prototype are highly typical of the concept
What might the prototype be for dog ?What might the prototype be for animal ?
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PrototypesEvidence for prototypes• Typicality effects (graded structure of categories)• Ease of access as an example of a category:
Name a type of fruit
• Prototypes benefit more from semantic priming than non-prototypes
Problem: prototypes do not address how we represent our knowledge of the variability of members of a category
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Family Resemblance
Category membership is not determined by a common set of defining features
Games
Instead, category members share an overlapping set of common traits that create a family resemblance for the category
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catch
tennis
bridge
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Levels of Categorization (Rosch)Superordinate level categories• General categories
furniture, food, animals
Basic level categories• Specific enough to identify objects clearly
chair, tomato, cat
Subordinate level categories• More specific, more detail than needed for some
purposesChippendale arm chair, beefsteak tomato, Siamese cat
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Evidence related to category levels
Basic level categories are our “default” category levels • We use basic level category names to identify and
talk about objects• We access basic category names faster than other
levels of category names• Memory for category information migrates
toward basic level names (errors in recall will be basic level substitutions)
Bigger priming effects for basic level namesExperts develop more category levels in their domain
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ExemplarsConcept is represented by the set of specific
representations for members of the category we have previously encountered and classified
Variability of category members is represented directly (in a set of examples)
Typical members and prototypes are created from existing representations
No economy in storage: all examples are stored
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Network Models
Characteristics are derived from the pattern of associations or linkages among concepts stored in semantic memory
Collins & Loftus Model (knowledge)
Anderson’s ACT-R Theory (knowledge and procedures)
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Collins & Loftus
Knowledge is stored in a network of connected nodes and links
Retrieval and sentence verification task entail activation of relevant information in the network
Spreading activation moved from node to node though links, RT depends on number of links
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Based on Collins & Quillian (1969) Semantic Network Model
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Testing Semantic Network ModelsAssume that activation of a node takes timeQuestions that require activating nodes at greater
distances in the network will require more time than questions that activate nodes close together in the network
Property Questions Category QuestionsA canary can sing A canary is a canaryA canary can fly A canary is a birdA canary has skin A canary is an animal
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Spreading Activation Model (Collins & Loftus)
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ACT-R Model (Anderson)Adaptive Control of ThoughtDeclarative memory• Information represented in networks of
interconnected nodesProcedural memory• Knowledge represented as production rules• Goal → Required Conditions → Actions• Model for acquisition of skilled behavior
motor programs as production rules• Application to skilled cognition
problem solving algorithms as production rules
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Neural Network Models
Parallel Distributed Processing (PDP) approach
Connectionistic, neural network model• Networks of neuron-like units or nodes• Highly interconnected – multiple connections
between units• System learns by adjusting connection weights
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Knowledge represented as a pattern of connections
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Stimulus input
Response output
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Knowledge represented as a pattern of connections
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apple
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Knowledge represented as a pattern of connections
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pear
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Knowledge represented as a pattern of connections
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cat
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Characteristics of distributed network models
Network knowledge is built up by encoding specific experiences (exemplars)
Spontaneous generation of categories emerges from patterns of connectivity & shared units
Fill in missing information in new examples (default assignment)
Protection from damage to part of the network • Graceful degradation (partial retrievals)
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Schemas & ScriptsHeuristics or organizational structures• Categorical information (schemas)• Event information (scripts)
Facilitates comprehension
Organizes information in memory
Provides retrieval cues to facilitate recollection
Potential explanation for errors in recollection• Use of schematic information to “fill in blanks”
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How schemas and scripts are usedDirect attention to relevant details during
encoding
Fill in partial recollections with details from relevant schema or script
Schemas represent the gist or general meaning of an experience or event
Use schemas to make inferences about ambiguous information presented in a story
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