An analysis of auditory alphabet confusions.

The present study, using the nonhierarchical overlapping clustering algorithm MAPCLUS to fit the Shepard-Arabie (1979) ADCLUS model, attempted to derive a set of features that would accurately describe the auditory alphabet confusions present in the data matrices of Conrad (1964) and Hull (1973). Separate nine-cluster solutions accounted for 80% and 89% of the variance in the matrices, respectively. The clusters revealed that the most frequently confused letter names contained common vowels and phonetically similar consonants. Further analyses using INDCLUS, an individual differences extension of the MAPCLUS algorithm and ADCLUS model, indicated that while the patterns of errors in the two matrices were remarkably similar, some differences were also apparent. These differences reflected the differing amounts of background noise present in the two studies.