Proceedings of the Annual Meeting of the Cognitive Science Society

For language comprehension, using an easily specified task instead of a linguistic theoretic structure as the target of training and comprehension ameliorates several problems, and using constraint satisfaction as a processing mechanism ameliorates several more: namely, 1) stipulating an a priori linguistic representation as a target is no longer necessary, 2) meaning is grounding in the task, 3) constraints from lexical, syntactic, and task oriented information is easily learned and combined in terms of constraints, and 4) the dramatically informal, "noisy" grammar of natural speech is easily handled. The task used here is a simple jigsaw puzzle wherein one subject tells another where to place the puzzle blocks. In this paper, only the task of understanding to which block each command refers is considered. Accordingly, the inputs to a recurrent P D P model are the consecutive words of a command presented in turn and the set of blocks yet to be placed on the puzzle. The output is the particular block referred to by the command. In a first simulation, the model is trained on an artificial corpus that captures important characteristics of subjects' language. In a second simulation, the model is trained on the actual language produced by 42 subjects. The model learns the artificial corpus entirely, and the natural corpus fairly well. The benefits of embedding comprehension in a communicative task and the benefits of constraints satisfaction are discussed.