Familiar idiom comprehension

Sentences containing idioms used either literally or figuratively were presented repeatedly for 100 msec per presentation. Half of the sentences were preceded by a paragraph. The number of presentations required to read the sentences and cued recall of the sentences were recorded. The serial brief presentations method provided predictable and meaningful results. Sentences containing idioms used literally required more presentations than those containing idioms used figuratively. Cued recall was better for idioms used literally than for those used figuratively. These results are interpreted as support for the Idiomatic Processing Model of idiom comprehension, which suggests that the figurative meaning is processed first; only if that one is inappropriate is the literal meaning processed.     

Hierarchical classes: Model and data analysis

A discrete, categorical model and a corresponding data-analysis method are presented for two-way two-mode (objects × attributes) data arrays with 0, 1 entries. The model contains the following two basic components: a set-theoretical formulation of the relations among objects and attributes; a Boolean decomposition of the matrix. The set-theoretical formulation defines a subset of the possible decompositions as consistent with it. A general method for graphically representing the set-theoretical decomposition is described. The data-analysis algorithm, dubbed HICLAS, aims at recovering the underlying structure in a data matrix by minimizing the discrepancies between the data and the recovered structure. HICLAS is evaluated with a simulation study and two empirical applications.This research was supported in part by a grant from the Belgian NSF (NFWO) to Paul De Boeck and in part by NSF Grant BNS-83-01027 to Seymour Rosenberg. We thank Iven Van Mechelen for clarifying several aspects of the Boolean algebraic formulation of the model and Phipps Arabie for his comments on an earlier draft.