Prediction in the Processing of Repair Disfluencies

Imagine a speaker who says "Turn left, uh I mean…" Before hearing the repair, the listener is likely to anticipate the word "right" based on the context, including the reparandum "left." Thus, even though the reparandum is not intended as part of the utterance, the listener uses it as information to predict the repair. The issue we explore in this article is how prediction operates in disfluency contexts. We begin by describing the Overlay model of disfluency comprehension, which assumes that the listener identifies a reparandum as such only after a repair is encountered which creates a local ungrammaticality. The Overlay model also allows the reparandum to influence subsequent processing, because the reparandum is not deleted from the final representation of the sentence. A somewhat different model can be developed which assumes a more active, anticipatory process for resolving repair disfluencies. On this model, the listener might predict the likely repair when the speaker becomes disfluent, or even identify a reparandum if the word or word string seems inconsistent with the speaker's intention. Our proposal is that the prediction can be made using the same mechanism involved in the processing of contrast, in which a listener uses contrastive prominence to generate likely alternates (the contrast set). We suggest that these two approaches to disfluency processing are not inconsistent: Successful repair processing requires listeners to use statistical and linguistic evidence to identify a reparandum and to integrate the repair, and the lingering of the reparandum is due to the coexistence in working memory of the reparandum, the repair, and unselected members of the contrast set.