Learning and morphological change.
An account is offered to change over time in English verb morphology, based on a connectionist approach to how morphological knowledge is acquired and used. A technique is first described that was developed for modeling historical change in connectionist networks, and that technique is applied to model English verb inflection as it developed from the highly complex past tense system of Old English towards that of the modern language, with one predominant "regular" inflection and a small number of irregular forms. The model relies on the fact that certain input-output mappings are easier than others to learn in a connectionist network. Highly frequent patterns, or those that share phonological regularities with a number of others, are learned more quickly and with lower error than low-frequency, highly irregular patterns. A network is taught a data set representative of the verb classes of Old English, but learning is stopped before reaching asymptote, and the output of this network is used as the teacher of a new net. As a result, the errors in the first network were passed on to become part of the data set of the second. Those patterns that are hardest to learn led to the most errors, and over time are "regularized" to fit a more dominant pattern. The results of the networks simulations were highly consistent with the major historical developments. These results are predicted from well-understood aspects of network dynamics, which therefore provide a rationale for the shape of the attested changes.
Hare
M
M
Center for Research in Language, University of California, San Diego, La Jolla 92093-0526, USA.
Elman
J L
JL
D006801:Humans; D016571:Neural Networks, Computer; D011578:Psycholinguistics; D012660:Semantics; D014706:Verbal Learning; D014825:Vocabulary