From rote learning to system building: acquiring verb morphology in children and connectionist nets.
The traditional account of the acquisition of English verb morphology supposes that a dual architecture underlies the transition from early rote-learning processes (in which past tense forms of verbs are correctly produced) to the systematic treatment of verbs (in which irregular verbs are prone to error). A connectionist account supposes that this transition can occur in a single mechanism (in the form of a neural network) driven by gradual quantitative changes in the size of the training set to which the network is exposed. In this paper, a series of simulations is reported in which a multi-layered perceptron learns to map verb stems to past tense forms analogous to the mappings found in the English past tense system. By expanding the training set in a gradual, incremental fashion and evaluating network performance on both trained and novel verbs at successive points in learning, it is demonstrated that the network undergoes reorganizations that result in a shift from a mode of rote learning to a systematic treatment of verbs. Furthermore, we show that this reorganizational transition is dependent upon the number of regular and irregular verbs in the training set and is sensitive to the phonological sub-regularities characterizing the irregular verbs. The pattern of errors observed is compared to that of children acquiring the English past tense, as well as children's performance on experimental studies with nonsense verbs. It is concluded that a connectionist approach offers a viable alternative account of the acquisition of English verb morphology, given the current state of empirical evidence relating to processes of acquisition in young children.
Plunkett
K
K
Department of Experimental Psychology, University of Oxford, UK.
Marchman
V
V
D002648:Child; D002675:Child, Preschool; D005793:Generalization, Psychological; D006801:Humans; D007223:Infant; D007804:Language Development; D011939:Mental Recall; D016571:Neural Networks, Computer; D011578:Psycholinguistics; D012660:Semantics; D014825:Vocabulary