New beginnings and happy endings: Psychological plausibility in computational models of language acquisition

Language acquisition may be one of the most difficult tasks that children face during development. They have to segment words from fluent speech, figure out the meanings of these words, and discover the syntactic constraints for joining them together into meaningful sentences. Over the past couple of decades computational modeling has emerged as a new paradigm for gaining insights into the mechanisms by which children may accomplish these feats. Unfortunately, many of these models assume linguistic knowledge likely to be beyond the abilities of developing young children. In this paper, we argue that for computational models to be theoretically viable they must be psychologically plausible. Consequently, the computational principles have to be relatively simple, and ideally empirically attested in the behavior of children. To demonstrate the usefulness of simple computational mechanisms in language acquisition, we present results from a series of corpus analyses involving a simple model for discovering lexical categories using word beginnings and endings