The ostensive goal of this paper is to construct a general complexity metric for the processing of natural language sentences, focusing on syntactic determinants of complexity in sentence comprehension. The ultimate goal, however, is to determine how the grammars of natural languages respond to different types of syntactic processing complexity.

A complexity metric that accurately predicts the relative complexity of processing different syntactic structures is not, in itself, of much theoretical interest. There does not seem to be any compelling reason for linguistic theory or psycholinguistic theory to incorporate such a metric. Rather, ultimately the correct complexity metric should follow directly as a theorem or consequence of an adequate theory of sentence comprehension.

Different theories of sentence comprehension typically lead to distinct predictions concerning the relative perceptual difficulty of sentences. Hence, one reason for developing a complexity metric is simply to help pinpoint inadequacies of current theories of sentence comprehension and to aid in the evaluation and refinement of those theories. An explicit complexity metric should also help to reveal the relation between the human sentence processor and the grammars of natural languages. In particular, developing a well-motivated complexity metric is a crucial prerequisite for evaluating the hypothesis that the grammars of natural languages are shaped in some respect by the properties of the human sentence processor since the most common form of this hypothesis claims that grammars tend to avoid generating sentences that are extremely difficult to process.