Connectionist models describe the human language system as a high-dimensional state space composed of a neural network in which layers of processing units for different aspects of a linguistic signal (visual or acoustic features, orthography, semantics, etc.) interact with one another. Lexical ambiguity emerges when there is conflicting input within or between processing layers. Bilingual connectionist models, such as the bilingual interactive activation (BIA) and bilingual interactive activation plus (BIA+) models, treat bilingualism as the inclusion of new dimensions into this network, resulting in new opportunities for conflict, such as interlingual homonyms and cognates. We outline connectionist accounts of lexical ambiguity resolution in monolinguals and bilinguals, which we visually depict as movement through a multidimensional state space.

Language is not a module. Well, at least, it is not a feedforward encapsulated domain-specific perceptual input system in the way that Fodor (1983) imagined. To be sure, there are regions of cortex that are conspicuously specialized for language-like processes (e.g., Gazzaniga, 2000; Kuperberg, Holcomb, Sitnikova, Greve, Dale, & Caplan, 2003; Ojemann, 1983), but when cognitive neuroscientists refer to these cortical areas as “modules,” they certainly do not imply solely feedforward synaptic projections or encapsulation from neighboring cortical areas. The vast and recurrent interconnectedness between anatomically and functionally segregated cortical areas (e.g., Douglas, Koch, Mahowald, Martin, & Suarez, 1995; Haxby, Gobbini, Furey, Ishai, Schouten, & Pietrini, 2001; Van Orden, Jansen op de Haar, & Bosman, 1997) unavoidably compromises any assumptions of information encapsulation, and can even wind up blurring the distinction between feedback and feedforward signals.

What this means is that we should expect language processes to function in concert with other perceptual, cognitive, and motor processes, not independently of them. For example, McGurk's famous and compelling demonstration of visual perception of mouth shape influencing the immediate percept of a spoken phoneme (McGurk & MacDonald, 1976) is emblematic of the intense degree to which speech perception and visual perception pay close attention to one another. More recently, visual perception has also been shown to play a strong role in spoken word recognition, syntactic processing, and reference resolution (Tanenhaus, Spivey-Knowlton, Eberhard, & Sedivy, 1995).

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.