Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume (...) that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation. We show how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, how they interweave production and comprehension processes, and how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal. (shrink)

Although the target article emphasizes the important role of prediction in language use, prediction may well also play a key role in the initial formation of linguistic representations, that is, in language development. We outline the role of prediction in three relevant language-learning domains: transitional probabilities, statistical preemption, and construction learning.

The present paper argues that there is ample corpus evidence of statistical preemption for learners to make use of. In the case of argument structure constructions, a verbi is preempted from appearing in a construction A, CxA, if and only if the following probability is high: P(CxB|context that would be suitable for CxA and verbi). For example, the probability of hearing a preemptive construction, given a context that would otherwise be well-suited for the ditransitive is high for verbs like explain (...) that overwhelmingly appear in the dative, and low for verbs like tell that readily appear in the ditransitive. Strength of statistical preemption is determined both by this probability, and by the frequency (ln (F)) of a verb in a preemptive construction when the context is at least as well suited to the preempted construction. The critiques of preemption by Stefanowitsch (Cognitive Linguistics 19: 513–531, 2008, this volume) are countered by arguing that the relevant probabilities were not considered. Moreover, we find evidence that constructions are somewhat less constrained when yoked to non-alternating verbs, as Stefanowitsch (cf. this volume) suggests should be the case. (shrink)

An essential aspect of knowing language is knowing the words of that language. This knowledge is usually thought to reside in the mental lexicon, a kind of dictionary that contains information regarding a word’s meaning, pronunciation, syntactic characteristics, and so on. In this article, a very different view is presented. In this view, words are understood as stimuli that operate directly on mental states. The phonological, syntactic and semantic properties of a word are revealed by the effects it has on (...) those states. (shrink)