Year: 2006
Type: article
Abstract: Learning to categorize objects involves learning which sources of variability are meaningful and which should be ignored or generalized.In this light, theories and models of category learning can be viewed as accounts of how people capture and represent meaningful variation.Similarity-based models, such as prototype and exemplar models, cannot correctly predict that humans classify a stimulus halfway between the nearest members of a low-variability and high-variability category into the high-variability category.Distributional accounts, descending from the unequal variance signal detection model, can accommodate the result.We present a simple extension to similarity-based models that allows them to display the sensitivity to category variability that humans display.We conclude by discussing what constitutes similarity-based representations and processes and noting the points of convergence between similarity-based and distributional approaches.
Authors: Matt Jones, Braley C. Love, Yasuaki Sakamoto
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2767512750