Why Not Prototype Models?
This paper presents some problems faced by prototype models. It shows that the way artificial categories areexperimentally generated leads to a statistical advantage to prototypes, and discusses the notion of feature abstraction duringcategory acquisition. Next, the linear separability problem demon...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Brasil |
| Institución: | Universidade de Brasília (UnB) |
| Repositorio: | Psicologia (Universidade de Brasília. Online) |
| Idioma: | portugués |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/17237 |
| Acceso en línea: | https://periodicos.unb.br/index.php/revistaptp/article/view/17237 |
| Access Level: | acceso abierto |
| Palabra clave: | Categorização Protótipos Conceitos Categorization Prototypes Feature models |
| Sumario: | This paper presents some problems faced by prototype models. It shows that the way artificial categories areexperimentally generated leads to a statistical advantage to prototypes, and discusses the notion of feature abstraction duringcategory acquisition. Next, the linear separability problem demonstrates that prototype models fail in predicting ease of learningfor different category types. Problems with complex categories (e.g., leather shoes, shirt with blue stripes) are introduced.Finally, a discussion of context effects shows the inflexibility of prototype models in dealing with conceptual variability acrossdifferent situations. |
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