An activation--verification model for letter and word recognition: the word-superiority effect.

An activation-verification model for letter and word recognition yielded predictions of two-alternative forced-choice performance for 864 individual stimuli that were either words, orthographically regular nonwords, or orthographically irregular nonwords. The encoding algorithm (programmed in APL) uses empirically determined confusion matrices to activate units in both an alphabetum and a lexicon. In general, predicted performance is enhanced when decisions are based on lexical information, because activity in the lexicon tends to constrain the identity of test letters more than the activity in the alphabetum. Thus, the model predicts large advantages of words over irregular nonwords, and smaller advantages of words over regular nonwords. The predicted differences are close to those obtained in a number of experiments and clearly demonstrate that the effects of manipulating lexicality and orthography can be predicted on the basis of lexical constraint alone. Furthermore, within each class (word, regular nonword, irregular nonword) there are significant correlations between the simulated and obtained performance on individual items. Our activation-verification model is contrasted with McClelland and Rumelhart's (1981) interactive activation model.

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