id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_jeaea5yzlnbyvnymnnngmzvjti	Eva Ceulemans	Detecting intra- and inter-categorical structure in semantic concepts using HICLAS	2010	9	.pdf	application/pdf	8832	713	60	cognitive capacity of detecting feature co-occurence in large data bases of features characterizing exemplars, succeeds rather well in predicting interand intra-categorical structure. The intra-categorical structure is reflected extensionally in the extent to which a particular exemplar of a category is typical of the Since semantic concepts are, as mentioned above, usually hierarchically structured, intra-categorical structure (i.e., which features apply to which exemplars) and inter-categorical structure semantic concepts from the correlational structure of psychologically salient features in the entities in the world (Storms & De cognitive implications of the use of data analytic tools such as HICLAS to study interand intra-categorical structure. R, HICLAS approximates the data by a binary I exemplars by J features model matrix M, such that the following loss function is to the corresponding exemplar and feature bundles, form a rectangle of 0s in the data matrix. whether HICLAS retrieves this intra-categorical structure, the Jaccard goodness-of-fit indices of the animals within a category were	./cache/work_jeaea5yzlnbyvnymnnngmzvjti.pdf	./txt/work_jeaea5yzlnbyvnymnnngmzvjti.txt