Abstract not found

Word learning is a ‘‘chicken and egg’’ problem. If a child could understand speakers ’ utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers’ intended meanings. To the beginning learner, however, both individual word meanings and speakers ’ intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers’ intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.

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For more than three decades, research into the psycholinguistics of pronoun interpretation has argued that hearers use various interpretation ‘preferences ’ or ‘strategies’ that are associated with specific linguistic properties of antecedent expressions. This focus is a departure from the type of approach outlined in Hobbs (1979), who argues that the mechanisms supporting pronoun interpretation are driven predominantly by semantics, world knowledge and inference, with particular attention to how these are used to establish the coherence of a discourse. On the basis of three new experimental studies, we evaluate a coherence-driven analysis with respect to four previously proposed interpretation biases—based on grammatical role parallelism, thematic roles, implicit causality, and subjecthood—and argue that the coherence-driven analysis can explain the underlying source of the biases and predict in what contexts evidence for each will surface. The results further suggest that pronoun interpretation is incrementally influenced by probabilistic expectations that hearers have regarding what coherence relations are likely to ensue, together with their expectations about what entities will be mentioned next, which, crucially, are conditioned on those coherence relations.

An incremental dependency parser’s probability model is entered as a predictor in a linear mixed-effects model of German readers’ eye-fixation durations. This dependencybased predictor improves a baseline that takes into account word length, n-gram probability, and Cloze predictability that are typically applied in models of human reading. This improvement obtains even when the dependency parser explores a tiny fraction of its search space, as suggested by narrow-beam accounts of human sentence processing such as Garden Path theory.

In line with recent studies we propose a model of human sentence processing that is based on Optimality Theory (OT). Rather than explaining parsing preferences through extralinguistically motivated parsing strategies or frequencies in the hearer’s linguistic environment, our model explains these preferences as the intermediate results of the incremental application of our OT grammar. In contrast to most other current OT-approaches to language processing, we use constraints from OT semantics rather than from OT syntax to explain on-line comprehension. We illustrate the workings of our model by investigating the comprehension of coordination, a phenomenon which is ill-understood from a competence perspective and sparsely investigated from a processing perspective. The psycholinguistic evidence that is currently available strongly suggests that the on-line comprehension of coordinate structures is influenced by constraints from many different information sources: pragmatics, discourse semantics, lexical semantics, and syntax. The competence / performance model we propose is able to formalize this cross-modular constraint interaction, and to yield concrete predictions with respect to both intermediate parsing preferences and ultimate interpretations.

Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference.

Abstract not found

This article examines cognitive process models of human sentence comprehension based on the idea of informed search. These models are rational in the sense that they strive to quickly find a good syntactic analysis. Informed search derives a new account of garden pathing that handles traditional counterexamples. It supports a symbolic explanation for local coherence as well as an algorithmic account of entropy reduction. The models are expressed in a broad framework for theories of human sentence comprehension. 1

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