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| Pregunta | Respuesta |
| Hierarchical network model | -Cognitive economy -Property stored nonredundantly at highest/most general level EVIDENCE: increasing levels of separation of concepts increased RT |
| Semantic memory experiment methods | Sentence verification task DV:RT |
| Problems with hierarchical network model | -RT does not mirror hierarchical relationship eg. dog is animal < dog is a mammal -Doesn't predict typicality effects: Canary is a bird < ostrich is a bird -Predicts faster negative judgements for closer concepts, but opposite is true Canary is salmon < canary is ostrich ==Frequency of association model |
| Spreading activation model | activation of a concept spreads to other concepts linked to it -Non hierarchical -Explains lack of hierarchy effect -Links vary in strength -Explains typicality -Explains semantic priming |
| Feature comparison model | -Knowledge is represented as distributed features in semantic space -Multidimensional scaling -Ss rate how similar pairs of concepts are eg. sheep-goat, sheep-lion - data presented across underlying dimensions (can be numerous dimensions ie. size, yellowness etc) |
| Defining chars Vs Characteristic chars (feature comparison model) | Defining: essential Characteristic: less important |
| Linguistic hedges | Features represented by predicate noun ie. "technically" chicken is bird (defining) "loosely speaking" bad is a bird (characteristic) |
| Feature comparison model: Two-stage decision process | 1) look at characteristic features, if most are true or false we can quickly decide - if not then: 2) Go to 2nd level and compare defining features Explains: Typicality effect (on positive decisions) Similarity effect (on negative decisions) |
| Problems with feature comparison | Clear definition of XX characteristics is lacking |
| Distributed plus hub (Patterson, et al 2007) | Delayed copy drawings, -drew general characteristics of animals minus specific details =four legged duck Coloured pumpkin Summary -There is a hub for each concept/object along with distributed modality-specific information -Why hubs? -Efficient way of integrating knowledge of specific concepts -Simplify detection of semantic similarities across concepts which may differ greatly in modality-specific attributes -Concept hubs are stored in the anterior temporal lobes (ATL) -Degeneration of ATL is "invariably evident" in SD patients |
| Interactive activation model | 1) Feature level 2)Letter level 3)Word level excitory and inhibitory feedback |
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