Incrementality and Prediction in Human Sentence Processing

Abstract

We identify a number of principles with respect to prediction that, we argue, underpin adult language comprehension: (a) comprehension consists in realizing a mapping between the unfolding sentence and the event representation corresponding to the real-world event being described; (b) the realization of this mapping manifests as the ability to predict both how the language will unfold, and how the real-world event would unfold if it were being experienced directly; (c) concurrent linguistic and nonlinguistic inputs, and the prior internal states of the system, each drive the predictive process; (d) the representation of prior internal states across a representational substrate common to the linguistic and nonlinguistic domains enables the predictive process to operate over variable time frames and variable levels of representational abstraction. We review empirical data exemplifying the operation of these principles and discuss the relationship between prediction, event structure, thematic role assignment, and incrementality.

Fig. 1

(A) Elman's (1990) simple recurrent network and (B) the modified version from Dienes et al. (1999). The large arrows in (B) illustrate the two training “routes” described in the main text.