Introduction

The ambiguity problem takes center stage in models of sentence processing because, to a first approximation, linguistic input is presented sequentially to the processing system in both spoken language and in reading. Thus, even unambiguous sequences of speech or text are briefly ambiguous. A rich empirical database now demonstrates that the processing system makes partial commitments that are closely time-locked to the input as it unfolds over time (for recent reviews see Tanenhaus & Trueswell, 1995, Pickering, Clifton, & Crocker, this volume). Because the syntactic information available to the system at any point in the sentence often underdetermines its structure, other constraints that are correlated with the syntactic alternatives could, in principle, provide information that is useful for syntactic ambiguity resolution.

For more than a decade, researchers in the sentence processing community have been relying on data from increasingly fine-grained measures of processing difficulty, especially eye-movement data, in an attempt to adjudicate among competing models of how correlated constraints are used in ambiguity resolution. But despite a rapidly growing experimental literature, an increasingly rich empirical base, and a plethora of explicit parsing models, the basic architectural issues remain unresolved. We argue that this state of affairs is likely to continue until researchers in the sentence processing community begin to develop explicit quantitative models that (a) identify and quantify the relevant sources of constraint and (b) specify mechanisms that link information integration to processing time.