SOME HIERARCHICAL SCALING METHODS FOR CONFUSION MATRIX ANALYSIS II. APPLICATIONS TO LARGE MATRICES

Extends the theory presented in Smith et al. (1975) to larger confusion matrices. Stimuli and responses are described by a series of features which take a discrete set of values. Processing models are proposed where each feature is tested independently (Independence model), where the processing of a given feature depends on the responses that have been made in testing for other features (Response Conditional model) or where the processing of a given feature depends on whether other features have been correctly identified or not (Correct Conditional model). Computer programs are described which fit these models to arbitrarily large confusion matrices. Difficulties in interpreting the behaviour of these models with real data are reviewed and solutions are offered. These points are illustrated by detailed analyses of confusion matrices obtained in studies of speech perception, memory for speech and semantic memory.