Proceedings of the Annual Meeting of the Cognitive Science Society

A primary problem for a child learning her first language is that her ungrammatical utterances are rarely explicitly corrected. It has been argued that this dearth of negative evidence regarding the child's grammatical hypotheses makes it impossible for the child to induce the grammar of the language without substantial innate knowledge of some universal principles common to all natural grammars. However, recent connectionist models of language acquisition have employed a learning technique that circumvents the negative evidence problem. Moreover, this learning strategy is not limited to strictly connectionist architectures. What we call Incremental Distributed Prediction Feedback refers to when the learner simply listens to utterances in its environment and makes internal predictions on-line as to what elements of the grammar are more or less likely to immediately follow the current input. Once that subsequent input is received, those prediction contingencies (essentially, transitional probabilities) are slightly adjusted accordingly. Simulations with artificial grammars demonstrate that this learning strategy is faster and more realistic than depending on infrequent negative feedback to ungrammatical output Incremental Distributed Prediction Feedback allows the learner to produce its own negative evidence from positive examples of the language by comparing incrementally predicted input with actual input.