Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
A system making optimal use of available information in incremental language comprehension might be expected to use linguistic knowledge together with current input to revise beliefs about previous input. Under some circumstances, such an error-correction capability might induce comprehenders to adopt grammatical analyses that are inconsistent with the true input. | Integrating surprisal and uncertain-input models in online sentence comprehension formal techniques and empirical results Roger Levy Department of Linguistics University of California at San Diego 9500 Gilman Drive 0108 La Jolla CA 92093-0108 rlevy@ucsd.edu Abstract A system making optimal use of available information in incremental language comprehension might be expected to use linguistic knowledge together with current input to revise beliefs about previous input. Under some circumstances such an error-correction capability might induce comprehenders to adopt grammatical analyses that are inconsistent with the true input. Here we present a formal model of how such input-unfaithful garden paths may be adopted and the difficulty incurred by their subsequent disconfirmation combining a rational noisy-channel model of syntactic comprehension under uncertain input with the surprisal theory of incremental processing difficulty. We also present a behavioral experiment confirming the key empirical predictions of the theory. 1 Introduction In most formal theories of human sentence comprehension input recognition and syntactic analysis are taken to be distinct processes with the only feedback from syntax to recognition being prospective prediction of likely upcoming input Jurafsky 1996 Narayanan and Jurafsky 1998 2002 Hale 2001 2006 Levy 2008a . Yet a system making optimal use of all available information might be expected to perform fully joint inference on sentence identity and structure given perceptual input using linguistic knowledge both prospectively and retrospectively in drawing inferences as to how raw input should be segmented and recognized as a sequence of linguistic tokens and about the degree to which each input 1055 token should be trusted during grammatical analysis. Formal models of such joint inference over uncertain input have been proposed Levy 2008b and corroborative empirical evidence exists that strong coherence of current input with a perceptual .
