Cascaded versus noncascaded models of lexical and semantic processing: Theturple effect
Autor: | J. O. Hector, Kenneth I. Forster |
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Rok vydání: | 2002 |
Předmět: |
business.industry
Decision Making Experimental and Cognitive Psychology Cognition Semantic property Semantics computer.software_genre Vocabulary Linguistics Task (project management) Random Allocation Neuropsychology and Physiological Psychology Variation (linguistics) Arts and Humanities (miscellaneous) Categorization Reaction Time Lexical decision task Humans Semantic memory Artificial intelligence Psychology business computer Natural language processing |
Zdroj: | Memory & Cognition. 30:1106-1117 |
ISSN: | 1532-5946 0090-502X |
DOI: | 10.3758/bf03194328 |
Popis: | The density of the orthographic neighborhood surrounding an item has been shown to have an inhibitory effect for nonwords in a lexical decision experiment. Four experiments are reported investigating whether a similar pattern holds for a semantic categorization task (animal vs. non-animal). In the first experiment, no effects of neighborhood density were found for nonexemplars, whether they were words or nonwords. The absence of any inhibitory effect for nonwords implies that close orthographic neighbors are ignored. However, the second experiment showed that if the nonword has a neighbor that is an animal name (eg., turple), an interference effect is observed, implying that neighbors do exert an effect if they have the right semantic properties. The same items showed no additional interference in lexical decision. These results suggest the involvement of semantic properties very early in the processing cycle. A cascaded processing system monitoring activation in semantic features can explain these results, but cannot explain the frequency effect observed for nonexemplar words or the fact that variation in density is irrelevant when one of the neighbors is an exemplar. It is argued that existing models of semantic categorization must be extended to accommodate these results. |
Databáze: | OpenAIRE |
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