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Psycholinguistic research spanning a number of decades has produced diverging results with regard to the nature of constraint integration in online sentence processing. For example, evidence that language users anticipatorily fixate likely upcoming referents in advance of evidence in the speech signal supports rapid context integration. By contrast, evidence that language users activate representations that conflict with contextual constraints, or only indirectly satisfy them, supports nonintegration or late integration. Here we report on a self-organizing neural network framework that addresses 1 aspect of constraint integration: the integration of incoming lexical information (i.e., an incoming word) with sentence context information (i.e., from preceding words in an unfolding utterance). In 2 simulations, we show that the framework predicts both classic results concerned with lexical ambiguity resolution (Swinney, 1979; Tanenhaus, Leiman, & Seidenberg, 1979), which suggest late context integration, and results demonstrating anticipatory eye movements (e.g., Altmann & Kamide, 1999), which support rapid context integration. We also report 2 experiments using the visual world paradigm that confirm a new prediction of the framework. Listeners heard sentences like "The boy will eat the white …" while viewing visual displays with objects like a "white cake" (i.e., a predictable direct object of "eat"), "white car" (i.e., an object not predicted by "eat," but consistent with "white"), and distractors. In line with our simulation predictions, we found that while listeners fixated "white cake" most, they also fixated "white car" more than unrelated distractors in this highly constraining sentence (and visual) context.

Dynamical, self-organizing models of sentence processing predict "digging-in" effects: The more committed the parser becomes to a wrong syntactic choice, the harder it is to reanalyze. Experiment 1 replicates previous grammaticality judgment studies (F. Ferreira & J. M. Henderson, 1991b, 1993), revealing a deleterious effect of lengthening the ambiguous region of a garden-path sentence. The authors interpret this result as a digging-in effect. Experiment 2 finds a corresponding effect on reading times. Experiment 3 finds that making 2 wrong attachments is worse than making 1. Non-self-organizing models require multiple stipulations to predict both kinds of effects. The authors show that, under an appropriately formulated self-organizing account, both results stem from self-reinforcement of node and link activations, a feature that is needed independently. An implemented model is given.