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Cue validity is the conditional probability that an object falls in a particular category given a particular feature or ''cue''. The term was popularized by , and especially by Eleanor Rosch in her investigations of the acquisition of so-called ''basic categories'' (;). ==Definition of cue validity== Formally, the cue validity of a feature with respect to category has been defined in the following ways: * As the conditional probability ; see , , . * As the deviation of the conditional probability from the category base rate, ; see , . * As a function of the linear correlation; see , , , . * Other definitions; see , . For the definitions based on probability, a high cue validity for a given feature means that the feature or attribute is more diagnostic of the class membership than a feature with low cue validity. Thus, a high-cue validity feature is one which conveys more information about the category or class variable, and may thus be considered as more useful for identifying objects as belonging to that category. Thus, high cue validity expresses high feature ''informativeness''. For the definitions based on linear correlation, the expression of "informativeness" captured by the cue validity measure is not the full expression of the feature's informativeness (as in mutual information, for example), but only that portion of its informativeness that is expressed in a linear relationship. For some purposes, a bilateral measure such as the mutual information or category utility is more appropriate than the cue validity. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「cue validity」の詳細全文を読む スポンサード リンク
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