Feature lists and confusion matrices

This report compares three feature list sets for capital letters, previously proposed by different investigators, on the ability of each to predict empirical confusion matrices. Toward this end, several variants of assumed information processes in recognition were also compared. The best model incorporated: (1) variable feature retrieval probabilities, (2) a goodness-of-match lower threshold below which guessing determines response, and (3) response bias on guessing trials. This model, when combined with one particular proposed feature list set, produced stress values of less than 9% in comparisons to empirical matrices for each of three different Ss. The feature retrieval probability vectors associated with these minimum-stress predictions were highly correlated ( (bar r = .83) ), suggesting considerable generality of process and feature sets between Ss.