Recognition confusions among quantifiers

Abstract

Subjects listened to a story containing sentences with five different quantifiers (all, many, some, a few, and none). They were then given a recognition test in which for each sentence they were asked to rate all the quantifiers as to how certain they were that each had appeared in the sentence. The degree of confusion between any two quantifiers (measured by both mean certainty ratings and first choice frequencies) declined monotonically with the separation of the two terms in a linear order. The confusion pattern was identical regardless of whether subjects were given intentional or incidental learning instructions. Successive interval scaling and the Luce biased choice model were both used to derive unidimensional scales of quantifier values that accurately described the obtained confusion matrix. A semantic feature model was also able to provide a fairly good overall account of the data, except that it had difficulty explaining the high frequency of confusions between all and many. The results raise the possibility that a major component of the memory trace for quantifiers is an essentially analog representation of magnitude.