We present a processing model that integrates same important psychological claims about the human sentence‐parsing mechanism: namely, that processing is influenced by limitations an working memory and by various syntactic preferences. The model uses time‐constraint information to resolve conflicting preferences in a psychologically plausible way. The starting paint far this proposal is the Sausage Machine model (Fodor & Frazier, 1980: Frazier & Fodor, 1978). From there, we attempt to overcome the original model's dependence an ad hoc aspects of its grammar, (...) and its omission of verb‐frame preferences. We also add mechanisms far lexical disambiguation and semantic processing in parallel with syntactic processing. (shrink)

We present a processing model that integrates same important psychological claims about the human sentence‐parsing mechanism: namely, that processing is influenced by limitations an working memory and by various syntactic preferences. The model uses time‐constraint information to resolve conflicting preferences in a psychologically plausible way. The starting paint far this proposal is the Sausage Machine model (Fodor & Frazier, 1980: Frazier & Fodor, 1978). From there, we attempt to overcome the original model's dependence an ad hoc aspects of its grammar, (...) and its omission of verb‐frame preferences. We also add mechanisms far lexical disambiguation and semantic processing in parallel with syntactic processing. (shrink)

The problems of access—retrieving linguistic structure from some mental grammar —and disambiguation—choosing among these structures to correctly parse ambiguous linguistic input—are fundamental to language understanding. The literature abounds with psychological results on lexical access, the access of idioms, syntactic rule access, parsing preferences, syntactic disambiguation, and the processing of garden‐path sentences. Unfortunately, it has been difficult to combine models which account for these results to build a general, uniform model of access and disambiguation at the lexical, idiomatic, and syntactic levels. (...) For example, psycholinguistic theories of lexical access and idiom access and parsing theories of syntactic rule access have almost no commonality in methodology or coverage of psycholinguistic data. This article presents a single probabilistic algorithm which models both the access and disambiguation of linguistic knowledge. The algorithm is based on a parallel parser which ranks constructions for access, and interpretations for disambiguation, by their conditional probability. Low‐ranked constructions and interpretations are pruned through beam‐search; this pruning accounts, among other things, for the garden‐path effect. I show that this motivated probabilistic treatment accounts for a wide variety of psycholinguistic results, arguing for a more uniform representation of linguistic knowledge and for the use of probabilistically‐enriched grammars and interpreters as models of human knowledge of and processing of language. (shrink)

There is no doubt that the model presented here is incomplete. Many important categories, particularly negation and the adverbials, have been entirely ignored, and the treatment of Tense and the affixes is certainly inadequate. It also remains to be seen how the many constructions that have been ignored here are to be accommodated within the framework that has been outlined. However, the fact that a standard categorial lexicon, plus the four rule schemata, seems to come close to exhaustively specifying the (...) main clause constructions of English, and also seems to explain a number of major constraints on transformations, encourages us to compare the theory with certain alternatives, and to examine its broader implications. (shrink)

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. (shrink)