Proceedings of the Annual Meeting of the Cognitive Science Society

Prelinguistic infants must find a way to isolate meaningful chunks from the continuous streams of speech that they hear. This bootstrapping problem has recently been the focus of several attempts to model the cognitive problem computationally. How can we evaluate whether this kind of simulation is relevant to the cognitive situation, and how can we compare different computational approaches? I discuss my O-B algorithm, a variable-length clustering procedure, and compare it with five other models—three connectionist ones and two statistical programs which use Minimum Description Length as a decision metric. I show that the models differ in their similarity to cognitive processes with respect to: a) the timing of inputs and outputs; b) constraints on the incremental learning process; c) clustering vs. dividing strategy; and d) whether the goal is to find words or to learn word-finding rules.