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Cognitive Psychology, 2 nd Ed. Chapter 8 Semantic Memory. Representing Concepts. Concepts are general ideas that enable the categorization of unique stimuli as related to one another. Concepts are characterized by dimensions of variation among exemplars.
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Cognitive Psychology, 2nd Ed. Chapter 8 Semantic Memory
Representing Concepts • Concepts are general ideas that enable the categorization of unique stimuli as related to one another. • Concepts are characterized by dimensions of variation among exemplars.
Rule governed concepts specify the features and relations that define category membership on an all or none basis. Classical view holds assumes defining features are related by a conjunctive rule. Object concepts refer to natural kinds and artifacts that violate the classical view. Characteristic features are disjunctively related, creating a family resemblance structure and a fuzzy boundary. Contrasting Types of Concepts
Prototype • The best or most typical example of a category that serves in the mental representation of a concept. • The range of feature variation on a stimulus dimension and feature frequency of occurrence define in part the gradient of category membership. • The gradient creates typicality effects in categorization speed, acquisition order, and priming.
Schema • A schema is a cognitive structure that organizes related concepts and integrates past events. • Frames organize the physical environment (e.g., an office frame). • Scripts represent routine activities (e.g., a restaurant script). Cumulative recall of script events is linear whereas object exemplars follow a negatively accelerated curve.
Meta-Representation • Defined as a mental representation of another mental representation. Thinking about thinking requires meta-representation. • Pretending a banana is a telephone requires a meta-representation linking the two object concepts. Meta-representation thus affords flexible and creative cognition. • Between ages 2-4 the use of meta-representation develops.
Theory of Mind • Theory of mind refers to the human ability to infer that others, like ourselves, have mental states. It helps account for why we are not all adherents of solipsism. • By age 4 children can not only pretend but can predict the consequences of another having false beliefs. • Mindblindness is an inability to understand that others possess mental representations and is characteristic of autism. An autistic child is socially isolated and treats others as robots without feelings and thoughts.
Abstract means of mental representation. Schematic and verbal. Each proposition is an assertion that may be true or false. Coded as a relation and arguments (e.g., Fred is tall). Perceptual means of mental representation. Concrete and nonverbal. One image conveys Represent multiple features and relations. Can images be decomposed into propositions? Propositions vs. Images
Functional Equivalence Hypothesis • Visual imagery, while not identical to perception, is mentally represented and functions the same as perception. • An image is isomorphic to the referent object (second-order), meaning spatial relations are analogous. • An image is an analog representation of the object, as shown by mental rotation and image scanning.
The Nature of Propositions • “Fred is tall” is a single proposition coded as a relation with two arguments (is, Fred, tall). • “The ants ate the sweet jelly that was on the table” expresses four propositions. • Latent Semantic Analysis is a mathematical procedure for extracting and representing the meanings of propositions expressed by a text. It represents the co-occurrence of words and their contexts. Using a database of co-occurrence relations, it can compute the similarity in meaning of two words or texts.
Hierarchical network of concepts . Cognitive economy stipulates features are represented only once in the hierarchy. Used in WordNet to represent word meanings. Feature vector defines each concept for each level (e.g., robin, bird, animal). Stages of feature search (characteristic vs. defining) explains typicality effects. Semantic Network vs. Feature Comparison Models
Network model assumes that feature search must proceed from level 0 to level 1to confirm dog. Must proceed to level 2 to confirm animal, taking more time. Feature comparison assumes search of characteristic features is sufficient to confirm dog. Must proceed to Stage 2 search of defining features to confirm animal. Category Size Effect“All collies are dogs” faster than “All collies are animals.”