title: What do we mean by prediction in language comprehension?
abstract: We consider several key aspects of prediction in language comprehension: its computational nature, the representational level(s) at which we predict, whether we use higher level representations to predictively pre-activate lower level representations, and whether we 'commit' in any way to our predictions, beyond pre-activation. We argue that the bulk of behavioral and neural evidence suggests that we predict probabilistically and at multiple levels and grains of representation. We also argue that we can, in principle, use higher level inferences to predictively pre-activate information at multiple lower representational levels. We also suggest that the degree and level of predictive pre-activation might be a function of the expected utility of prediction, which, in turn, may depend on comprehenders' goals and their estimates of the relative reliability of their prior knowledge and the bottom-up input. Finally, we argue that all these properties of language understanding can be naturally explained and productively explored within a multi-representational hierarchical actively generative architecture whose goal is to infer the message intended by the producer, and in which predictions play a crucial role in explaining the bottom-up input.
authors.affiliation: Department of Psychology and Center for Cognitive Science, Tufts University; Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School.
authors.forename: Gina R
authors.initials: GR
authors.lastname: Kuperberg
authors.affiliation: Department of Brain and Cognitive Sciences, Department of Computer Science, Department of Linguistics, University of Rochester.
authors.forename: T Florian
authors.initials: TF
authors.lastname: Jaeger
keywords: generative model; language comprehension; prediction error; probabilistic; surprisal