Some theoretical and experimental observerations on naive discriminative learning

Abstract

Natural language use is full of choices among multiple possible alternatives, whether phones, words, or constructions, which are influenced by a large number of contextual factors, and which rather exhibit asymptotic, imperfect tendencies favoring one or more of the alternatives, instead of single, categorical, perfect choices. This contrasts with item-by-item learning in simple controlled experiments which typically have been modelled by the Rescorla-Wagner equations. We find the former "messy" types of problems as a key area of interest in modeling and understanding language use, and consequently consider the application of the Rescorla-Wagner equations in the form of a Naive Discriminative Learning classifier to such complex phenomena of considerable utility in linguistic research.

There is an updated version of this paper ("http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-677573"). Please use the updated version for further reference.